Identify_Customer_Segments.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />

<title>Identify_Customer_Segments</title>

<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>


<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 */
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
  }
}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>
<style type="text/css">
    body[data-notebook-name] > #header #header-container:not(.show-panel), /* body has data-notebook-name attribute when 
a notebook is open. this rule won't be applied when the notebook is not open (in 404 view for example) */
body[data-notebook-name] #menubar-container #maintoolbar:not(.show-panel),
body[data-notebook-name] #menubar-container ul.navbar-nav:not(.show-panel) {
  display: none;
}

#menubar.only-panel #menus.navbar-default {
    background: #fff;
    border-color: #fff;
}

.toolbar.with-statusbar{
    margin-top: 3px;
    margin-bottom: 0;
}

    </style>


<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

div#notebook {
  overflow: visible;
  border-top: none;
}@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Project:-Identify-Customer-Segments">Project: Identify Customer Segments<a class="anchor-link" href="#Project:-Identify-Customer-Segments">&#182;</a></h1><p>In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.</p>
<p>This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. <strong>Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.</strong> The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.</p>
<p>It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. <strong>There will be times in the project where you will need to make and justify your own decisions on how to treat the data.</strong> These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.</p>
<p>At the end of most sections, there will be a Markdown cell labeled <strong>Discussion</strong>. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. <strong>Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.</strong></p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import libraries here; add more as necessary</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">matplotlib.ticker</span> <span class="k">as</span> <span class="nn">mtick</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">Imputer</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="k">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="k">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">explained_variance_score</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>
<span class="kn">import</span> <span class="nn">time</span>

<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">&#39;display.max_columns&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>  <span class="c1"># or 1000</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">&#39;display.max_rows&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>  <span class="c1"># or 1000</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">&#39;display.max_colwidth&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># magic word for producing visualizations in notebook</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="sd">&#39;&#39;&#39;</span>
<span class="sd">Import note: The classroom currently uses sklearn version 0.19.</span>
<span class="sd">If you need to use an imputer, it is available in sklearn.preprocessing.Imputer,</span>
<span class="sd">instead of sklearn.impute as in newer versions of sklearn.</span>
<span class="sd">&#39;&#39;&#39;</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>C:\Users\abduy\Anaconda3\lib\site-packages\ipykernel_launcher.py:17: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[1]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&#39;\nImport note: The classroom currently uses sklearn version 0.19.\nIf you need to use an imputer, it is available in sklearn.preprocessing.Imputer,\ninstead of sklearn.impute as in newer versions of sklearn.\n&#39;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-0:-Load-the-Data">Step 0: Load the Data<a class="anchor-link" href="#Step-0:-Load-the-Data">&#182;</a></h3><p>There are four files associated with this project (not including this one):</p>
<ul>
<li><code>Udacity_AZDIAS_Subset.csv</code>: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).</li>
<li><code>Udacity_CUSTOMERS_Subset.csv</code>: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).</li>
<li><code>Data_Dictionary.md</code>: Detailed information file about the features in the provided datasets.</li>
<li><code>AZDIAS_Feature_Summary.csv</code>: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns</li>
</ul>
<p>Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.</p>
<p>To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the <code>.csv</code> data files in this project: they're semicolon (<code>;</code>) delimited, so you'll need an additional argument in your <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html"><code>read_csv()</code></a> call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.</p>
<p>Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[2]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load in the general demographics data.</span>
<span class="n">azdias</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Udacity_AZDIAS_Subset.csv&#39;</span><span class="p">,</span><span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span>

<span class="c1"># Load in the feature summary file.</span>
<span class="n">feat_info</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;AZDIAS_Feature_Summary.csv&#39;</span><span class="p">,</span><span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check the structure of the data after it&#39;s loaded (e.g. print the number of</span>
<span class="c1"># rows and columns, print the first few rows).</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Demographics data for the general data&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">azdias</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Demographics data for the general data

            AGER_TYP  ALTERSKATEGORIE_GROB      ANREDE_KZ  CJT_GESAMTTYP  \
count  891221.000000  891221.000000         891221.000000  886367.000000   
mean  -0.358435       2.777398              1.522098       3.632838        
std    1.198724       1.068775              0.499512       1.595021        
min   -1.000000       1.000000              1.000000       1.000000        
25%   -1.000000       2.000000              1.000000       2.000000        
50%   -1.000000       3.000000              2.000000       4.000000        
75%   -1.000000       4.000000              2.000000       5.000000        
max    3.000000       9.000000              2.000000       6.000000        

       FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \
count  891221.000000      891221.000000  891221.000000     891221.000000    
mean   3.074528           2.821039       3.401106          3.033328         
std    1.321055           1.464749       1.322134          1.529603         
min    1.000000           1.000000       1.000000          1.000000         
25%    2.000000           1.000000       3.000000          2.000000         
50%    3.000000           3.000000       3.000000          3.000000         
75%    4.000000           4.000000       5.000000          5.000000         
max    5.000000           5.000000       5.000000          5.000000         

       FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER      FINANZTYP    GEBURTSJAHR  \
count  891221.000000          891221.000000     891221.000000  891221.000000   
mean   2.874167               3.075121          3.790586       1101.178533     
std    1.486731               1.353248          1.987876       976.583551      
min    1.000000               1.000000          1.000000       0.000000        
25%    2.000000               2.000000          2.000000       0.000000        
50%    3.000000               3.000000          4.000000       1943.000000     
75%    4.000000               4.000000          6.000000       1970.000000     
max    5.000000               5.000000          6.000000       2017.000000     

       GFK_URLAUBERTYP  GREEN_AVANTGARDE     HEALTH_TYP  LP_LEBENSPHASE_FEIN  \
count  886367.000000    891221.000000     891221.000000  886367.000000         
mean   7.350304         0.196612          1.792102       14.622637             
std    3.525723         0.397437          1.269062       12.616883             
min    1.000000         0.000000         -1.000000       0.000000              
25%    5.000000         0.000000          1.000000       4.000000              
50%    8.000000         0.000000          2.000000       11.000000             
75%    10.000000        0.000000          3.000000       27.000000             
max    12.000000        1.000000          3.000000       40.000000             

       LP_LEBENSPHASE_GROB  LP_FAMILIE_FEIN  LP_FAMILIE_GROB  LP_STATUS_FEIN  \
count  886367.000000        886367.000000    886367.000000    886367.000000    
mean   4.453621             3.599574         2.185966         4.791151         
std    3.855639             3.926486         1.756537         3.425305         
min    0.000000             0.000000         0.000000         1.000000         
25%    1.000000             1.000000         1.000000         2.000000         
50%    3.000000             1.000000         1.000000         4.000000         
75%    8.000000             8.000000         4.000000         9.000000         
max    12.000000            11.000000        5.000000         10.000000        

       LP_STATUS_GROB  NATIONALITAET_KZ  PRAEGENDE_JUGENDJAHRE  \
count  886367.000000   891221.000000     891221.000000           
mean   2.432575        1.026827          8.154346                
std    1.474315        0.586634          4.844532                
min    1.000000        0.000000          0.000000                
25%    1.000000        1.000000          5.000000                
50%    2.000000        1.000000          8.000000                
75%    4.000000        1.000000          14.000000               
max    5.000000        3.000000          15.000000               

       RETOURTYP_BK_S      SEMIO_SOZ      SEMIO_FAM      SEMIO_REL  \
count  886367.000000   891221.000000  891221.000000  891221.000000   
mean   3.419630        3.945860       4.272729       4.240609        
std    1.417741        1.946564       1.915885       2.007373        
min    1.000000        1.000000       1.000000       1.000000        
25%    2.000000        2.000000       3.000000       3.000000        
50%    3.000000        4.000000       4.000000       4.000000        
75%    5.000000        6.000000       6.000000       6.000000        
max    5.000000        7.000000       7.000000       7.000000        

           SEMIO_MAT     SEMIO_VERT     SEMIO_LUST      SEMIO_ERL  \
count  891221.000000  891221.000000  891221.000000  891221.000000   
mean   4.001597       4.023709       4.359086       4.481405        
std    1.857540       2.077746       2.022829       1.807552        
min    1.000000       1.000000       1.000000       1.000000        
25%    2.000000       2.000000       2.000000       3.000000        
50%    4.000000       4.000000       5.000000       4.000000        
75%    5.000000       6.000000       6.000000       6.000000        
max    7.000000       7.000000       7.000000       7.000000        

          SEMIO_KULT      SEMIO_RAT     SEMIO_KRIT      SEMIO_DOM  \
count  891221.000000  891221.000000  891221.000000  891221.000000   
mean   4.025014       3.910139       4.763223       4.667550        
std    1.903816       1.580306       1.830789       1.795712        
min    1.000000       1.000000       1.000000       1.000000        
25%    3.000000       3.000000       3.000000       3.000000        
50%    4.000000       4.000000       5.000000       5.000000        
75%    5.000000       5.000000       6.000000       6.000000        
max    7.000000       7.000000       7.000000       7.000000        

          SEMIO_KAEM  SEMIO_PFLICHT    SEMIO_TRADV    SHOPPER_TYP  \
count  891221.000000  891221.000000  891221.000000  891221.000000   
mean   4.445007       4.256076       3.661784       1.266967        
std    1.852412       1.770137       1.707637       1.287435        
min    1.000000       1.000000       1.000000      -1.000000        
25%    3.000000       3.000000       2.000000       0.000000        
50%    5.000000       4.000000       3.000000       1.000000        
75%    6.000000       6.000000       5.000000       2.000000        
max    7.000000       7.000000       7.000000       3.000000        

             SOHO_KZ       TITEL_KZ       VERS_TYP       ZABEOTYP  \
count  817722.000000  817722.000000  891221.000000  891221.000000   
mean   0.008423       0.003483       1.197852       3.362438        
std    0.091392       0.084957       0.952532       1.352704        
min    0.000000       0.000000      -1.000000       1.000000        
25%    0.000000       0.000000       1.000000       3.000000        
50%    0.000000       0.000000       1.000000       3.000000        
75%    0.000000       0.000000       2.000000       4.000000        
max    1.000000       5.000000       2.000000       6.000000        

            ALTER_HH   ANZ_PERSONEN      ANZ_TITEL  HH_EINKOMMEN_SCORE  \
count  817722.000000  817722.000000  817722.000000  872873.000000        
mean   10.864126      1.727637       0.004162       4.207243             
std    7.639683       1.155849       0.068855       1.624057             
min    0.000000       0.000000       0.000000       1.000000             
25%    0.000000       1.000000       0.000000       3.000000             
50%    13.000000      1.000000       0.000000       5.000000             
75%    17.000000      2.000000       0.000000       6.000000             
max    21.000000      45.000000      6.000000       6.000000             

        KK_KUNDENTYP  W_KEIT_KIND_HH  WOHNDAUER_2008  ANZ_HAUSHALTE_AKTIV  \
count  306609.000000  783619.000000   817722.000000   798073.000000         
mean   3.410640       3.933406        7.908791        8.287263              
std    1.628844       1.964701        1.923137        15.628087             
min    1.000000       0.000000        1.000000        0.000000              
25%    2.000000       2.000000        8.000000        1.000000              
50%    3.000000       4.000000        9.000000        4.000000              
75%    5.000000       6.000000        9.000000        9.000000              
max    6.000000       6.000000        9.000000        595.000000            

        ANZ_HH_TITEL    GEBAEUDETYP    KONSUMNAEHE  MIN_GEBAEUDEJAHR  \
count  794213.000000  798073.000000  817252.000000  798073.000000      
mean   0.040647       2.798641       3.018452       1993.277011        
std    0.324028       2.656713       1.550312       3.332739           
min    0.000000       1.000000       1.000000       1985.000000        
25%    0.000000       1.000000       2.000000       1992.000000        
50%    0.000000       1.000000       3.000000       1992.000000        
75%    0.000000       3.000000       4.000000       1993.000000        
max    23.000000      8.000000       7.000000       2016.000000        

            WOHNLAGE    KBA05_ANTG1    KBA05_ANTG2    KBA05_ANTG3  \
count  798073.000000  757897.000000  757897.000000  757897.000000   
mean   4.052836       1.494277       1.265584       0.624525        
std    1.949539       1.403961       1.245178       1.013443        
min    0.000000       0.000000       0.000000       0.000000        
25%    3.000000       0.000000       0.000000       0.000000        
50%    3.000000       1.000000       1.000000       0.000000        
75%    5.000000       3.000000       2.000000       1.000000        
max    8.000000       4.000000       4.000000       3.000000        

         KBA05_ANTG4   KBA05_BAUMAX      KBA05_GBZ       BALLRAUM  \
count  757897.000000  757897.000000  757897.000000  797481.000000   
mean   0.305927       1.389552       3.158580       4.153043        
std    0.638725       1.779483       1.329537       2.183710        
min    0.000000       0.000000       1.000000       1.000000        
25%    0.000000       0.000000       2.000000       2.000000        
50%    0.000000       1.000000       3.000000       5.000000        
75%    0.000000       3.000000       4.000000       6.000000        
max    2.000000       5.000000       5.000000       7.000000        

            EWDICHTE     INNENSTADT  GEBAEUDETYP_RASTER            KKK  \
count  797481.000000  797481.000000  798066.000000       770025.000000   
mean   3.939172       4.549491       3.738306            2.592991        
std    1.718996       2.028919       0.923193            1.119052        
min    1.000000       1.000000       1.000000            0.000000        
25%    2.000000       3.000000       3.000000            2.000000        
50%    4.000000       5.000000       4.000000            3.000000        
75%    6.000000       6.000000       4.000000            3.000000        
max    6.000000       8.000000       5.000000            4.000000        

          MOBI_REGIO  ONLINE_AFFINITAET       REGIOTYP  KBA13_ANZAHL_PKW  \
count  757897.000000  886367.000000      770025.000000  785421.000000      
mean   2.963540       2.698691           4.257967       619.701439         
std    1.428882       1.521524           2.030385       340.034318         
min    1.000000       0.000000           0.000000       0.000000           
25%    2.000000       1.000000           3.000000       384.000000         
50%    3.000000       3.000000           5.000000       549.000000         
75%    4.000000       4.000000           6.000000       778.000000         
max    6.000000       5.000000           7.000000       2300.000000        

          PLZ8_ANTG1     PLZ8_ANTG2     PLZ8_ANTG3     PLZ8_ANTG4  \
count  774706.000000  774706.000000  774706.000000  774706.000000   
mean   2.253330       2.801858       1.595426       0.699166        
std    0.972008       0.920309       0.986736       0.727137        
min    0.000000       0.000000       0.000000       0.000000        
25%    1.000000       2.000000       1.000000       0.000000        
50%    2.000000       3.000000       2.000000       1.000000        
75%    3.000000       3.000000       2.000000       1.000000        
max    4.000000       4.000000       3.000000       2.000000        

         PLZ8_BAUMAX       PLZ8_HHZ       PLZ8_GBZ         ARBEIT  \
count  774706.000000  774706.000000  774706.000000  794005.000000   
mean   1.943913       3.612821       3.381087       3.167854        
std    1.459654       0.973967       1.111598       1.002376        
min    1.000000       1.000000       1.000000       1.000000        
25%    1.000000       3.000000       3.000000       3.000000        
50%    1.000000       4.000000       3.000000       3.000000        
75%    3.000000       4.000000       4.000000       4.000000        
max    5.000000       5.000000       5.000000       9.000000        

         ORTSGR_KLS9      RELAT_AB  
count  794005.000000  794005.00000  
mean   5.293002       3.07222       
std    2.303739       1.36298       
min    0.000000       1.00000       
25%    4.000000       2.00000       
50%    5.000000       3.00000       
75%    7.000000       4.00000       
max    9.000000       9.00000       
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[4]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AGER_TYP</th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>FINANZTYP</th>
      <th>GEBURTSJAHR</th>
      <th>GFK_URLAUBERTYP</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>LP_LEBENSPHASE_FEIN</th>
      <th>LP_LEBENSPHASE_GROB</th>
      <th>LP_FAMILIE_FEIN</th>
      <th>LP_FAMILIE_GROB</th>
      <th>LP_STATUS_FEIN</th>
      <th>LP_STATUS_GROB</th>
      <th>NATIONALITAET_KZ</th>
      <th>PRAEGENDE_JUGENDJAHRE</th>
      <th>RETOURTYP_BK_S</th>
      <th>SEMIO_SOZ</th>
      <th>SEMIO_FAM</th>
      <th>SEMIO_REL</th>
      <th>SEMIO_MAT</th>
      <th>SEMIO_VERT</th>
      <th>SEMIO_LUST</th>
      <th>SEMIO_ERL</th>
      <th>SEMIO_KULT</th>
      <th>SEMIO_RAT</th>
      <th>SEMIO_KRIT</th>
      <th>SEMIO_DOM</th>
      <th>SEMIO_KAEM</th>
      <th>SEMIO_PFLICHT</th>
      <th>SEMIO_TRADV</th>
      <th>SHOPPER_TYP</th>
      <th>SOHO_KZ</th>
      <th>TITEL_KZ</th>
      <th>VERS_TYP</th>
      <th>ZABEOTYP</th>
      <th>ALTER_HH</th>
      <th>ANZ_PERSONEN</th>
      <th>ANZ_TITEL</th>
      <th>HH_EINKOMMEN_SCORE</th>
      <th>KK_KUNDENTYP</th>
      <th>W_KEIT_KIND_HH</th>
      <th>WOHNDAUER_2008</th>
      <th>ANZ_HAUSHALTE_AKTIV</th>
      <th>ANZ_HH_TITEL</th>
      <th>GEBAEUDETYP</th>
      <th>KONSUMNAEHE</th>
      <th>MIN_GEBAEUDEJAHR</th>
      <th>OST_WEST_KZ</th>
      <th>WOHNLAGE</th>
      <th>CAMEO_DEUG_2015</th>
      <th>CAMEO_DEU_2015</th>
      <th>CAMEO_INTL_2015</th>
      <th>KBA05_ANTG1</th>
      <th>KBA05_ANTG2</th>
      <th>KBA05_ANTG3</th>
      <th>KBA05_ANTG4</th>
      <th>KBA05_BAUMAX</th>
      <th>KBA05_GBZ</th>
      <th>BALLRAUM</th>
      <th>EWDICHTE</th>
      <th>INNENSTADT</th>
      <th>GEBAEUDETYP_RASTER</th>
      <th>KKK</th>
      <th>MOBI_REGIO</th>
      <th>ONLINE_AFFINITAET</th>
      <th>REGIOTYP</th>
      <th>KBA13_ANZAHL_PKW</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-1</td>
      <td>2</td>
      <td>1</td>
      <td>2.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>4</td>
      <td>0</td>
      <td>10.0</td>
      <td>0</td>
      <td>-1</td>
      <td>15.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0</td>
      <td>0</td>
      <td>5.0</td>
      <td>2</td>
      <td>6</td>
      <td>7</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>7</td>
      <td>6</td>
      <td>6</td>
      <td>5</td>
      <td>3</td>
      <td>-1</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>-1</td>
      <td>3</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>2.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-1</td>
      <td>1</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>1</td>
      <td>1996</td>
      <td>10.0</td>
      <td>0</td>
      <td>3</td>
      <td>21.0</td>
      <td>6.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1</td>
      <td>14</td>
      <td>1.0</td>
      <td>5</td>
      <td>4</td>
      <td>4</td>
      <td>3</td>
      <td>1</td>
      <td>2</td>
      <td>2</td>
      <td>3</td>
      <td>6</td>
      <td>4</td>
      <td>7</td>
      <td>4</td>
      <td>7</td>
      <td>6</td>
      <td>3</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>2</td>
      <td>5</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>6.0</td>
      <td>NaN</td>
      <td>3.0</td>
      <td>9.0</td>
      <td>11.0</td>
      <td>0.0</td>
      <td>8.0</td>
      <td>1.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>4.0</td>
      <td>8</td>
      <td>8A</td>
      <td>51</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>6.0</td>
      <td>3.0</td>
      <td>8.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>963.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-1</td>
      <td>3</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>1979</td>
      <td>10.0</td>
      <td>1</td>
      <td>3</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1</td>
      <td>15</td>
      <td>3.0</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>6</td>
      <td>3</td>
      <td>4</td>
      <td>7</td>
      <td>7</td>
      <td>7</td>
      <td>3</td>
      <td>3</td>
      <td>2</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1</td>
      <td>5</td>
      <td>17.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>NaN</td>
      <td>3.0</td>
      <td>9.0</td>
      <td>10.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>2.0</td>
      <td>4</td>
      <td>4C</td>
      <td>24</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>712.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>6</td>
      <td>1957</td>
      <td>1.0</td>
      <td>0</td>
      <td>2</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>9.0</td>
      <td>4.0</td>
      <td>1</td>
      <td>8</td>
      <td>2.0</td>
      <td>5</td>
      <td>1</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
      <td>7</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>5</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1</td>
      <td>3</td>
      <td>13.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>9.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1997.0</td>
      <td>W</td>
      <td>7.0</td>
      <td>2</td>
      <td>2A</td>
      <td>12</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>6.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>596.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>-1</td>
      <td>3</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>5</td>
      <td>1963</td>
      <td>5.0</td>
      <td>0</td>
      <td>3</td>
      <td>32.0</td>
      <td>10.0</td>
      <td>10.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1</td>
      <td>8</td>
      <td>5.0</td>
      <td>6</td>
      <td>4</td>
      <td>4</td>
      <td>2</td>
      <td>7</td>
      <td>4</td>
      <td>4</td>
      <td>6</td>
      <td>2</td>
      <td>3</td>
      <td>2</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>2</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>2</td>
      <td>4</td>
      <td>20.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>9.0</td>
      <td>3.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>3.0</td>
      <td>6</td>
      <td>6B</td>
      <td>43</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>5.0</td>
      <td>435.0</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>5.0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[5]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>attribute</th>
      <th>information_level</th>
      <th>type</th>
      <th>missing_or_unknown</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>AGER_TYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>1</th>
      <td>ALTERSKATEGORIE_GROB</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1,0,9]</td>
    </tr>
    <tr>
      <th>2</th>
      <td>ANREDE_KZ</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>3</th>
      <td>CJT_GESAMTTYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[0]</td>
    </tr>
    <tr>
      <th>4</th>
      <td>FINANZ_MINIMALIST</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1]</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[6]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>attribute</th>
      <th>information_level</th>
      <th>type</th>
      <th>missing_or_unknown</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>80</th>
      <td>PLZ8_HHZ</td>
      <td>macrocell_plz8</td>
      <td>ordinal</td>
      <td>[-1]</td>
    </tr>
    <tr>
      <th>81</th>
      <td>PLZ8_GBZ</td>
      <td>macrocell_plz8</td>
      <td>ordinal</td>
      <td>[-1]</td>
    </tr>
    <tr>
      <th>82</th>
      <td>ARBEIT</td>
      <td>community</td>
      <td>ordinal</td>
      <td>[-1,9]</td>
    </tr>
    <tr>
      <th>83</th>
      <td>ORTSGR_KLS9</td>
      <td>community</td>
      <td>ordinal</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>84</th>
      <td>RELAT_AB</td>
      <td>community</td>
      <td>ordinal</td>
      <td>[-1,9]</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p><strong>Tip</strong>: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut <code>esc --&gt; a</code> (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and <code>esc --&gt; b</code> adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use <code>esc --&gt; m</code> and to convert to a code cell, use <code>esc --&gt; y</code>.</p>
</blockquote>
<h2 id="Step-1:-Preprocessing">Step 1: Preprocessing<a class="anchor-link" href="#Step-1:-Preprocessing">&#182;</a></h2><h3 id="Step-1.1:-Assess-Missing-Data">Step 1.1: Assess Missing Data<a class="anchor-link" href="#Step-1.1:-Assess-Missing-Data">&#182;</a></h3><p>The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the <strong>Discussion</strong> cell with your findings and decisions at the end of each step that has one!</p>
<h4 id="Step-1.1.1:-Convert-Missing-Value-Codes-to-NaNs">Step 1.1.1: Convert Missing Value Codes to NaNs<a class="anchor-link" href="#Step-1.1.1:-Convert-Missing-Value-Codes-to-NaNs">&#182;</a></h4><p>The fourth column of the feature attributes summary (loaded in above as <code>feat_info</code>) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. <code>[-1,0]</code>), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.</p>
<p><strong>As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.</strong></p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Identify missing or unknown data values and convert them to NaNs.</span>
<span class="n">starttime</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Null values before converted: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">azdias</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Converting...&quot;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">feat_info</span><span class="p">)):</span>
    <span class="n">row</span> <span class="o">=</span> <span class="n">feat_info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">missing</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
    <span class="n">missing</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="n">x</span> <span class="o">!=</span> <span class="s1">&#39;X&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">x</span> <span class="o">!=</span> <span class="s1">&#39;XX&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">x</span><span class="o">!=</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">missing</span><span class="p">]</span>
    <span class="k">if</span><span class="p">(</span><span class="n">missing</span> <span class="o">!=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
        <span class="n">azdias</span> <span class="o">=</span> <span class="n">azdias</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">]:</span><span class="n">missing</span><span class="p">},</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
    
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Null values after converted: </span><span class="si">{}</span><span class="s2"> in </span><span class="si">{}</span><span class="s2"> Minutes&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">azdias</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(((</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">starttime</span><span class="p">)</span><span class="o">/</span><span class="mi">60</span><span class="p">),</span><span class="mi">2</span><span class="p">)))</span>
        
        
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Null values before converted: 4896838
Converting...
Null values after converted: 8373929 in 0.49 Minutes
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.1.2:-Assess-Missing-Data-in-Each-Column">Step 1.1.2: Assess Missing Data in Each Column<a class="anchor-link" href="#Step-1.1.2:-Assess-Missing-Data-in-Each-Column">&#182;</a></h4><p>How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html"><code>hist()</code></a> function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)</p>
<p>For the remaining features, are there any patterns in which columns have, or share, missing data?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Perform an assessment of how much missing data there is in each column of the</span>
<span class="c1"># dataset.</span>
<span class="k">def</span> <span class="nf">describe_null</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Describe Columns Which is have null values and return percentages with columns </span>
<span class="sd">    </span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">null_percentages</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">/</span><span class="n">dataset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;# Of Columns with Null: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">null_percentages</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">)))</span>
    <span class="k">return</span> <span class="n">null_percentages</span>

<span class="n">null_before</span> <span class="o">=</span> <span class="n">describe_null</span><span class="p">(</span><span class="n">azdias</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre># Of Columns with Null: 61
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate patterns in the amount of missing data in each column.</span>
<span class="n">null_before</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[9]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>TITEL_KZ                 99.757636
AGER_TYP                 76.955435
KK_KUNDENTYP             65.596749
KBA05_BAUMAX             53.468668
GEBURTSJAHR              44.020282
ALTER_HH                 34.813699
REGIOTYP                 17.735668
KKK                      17.735668
W_KEIT_KIND_HH           16.605084
KBA05_ANTG4              14.959701
KBA05_GBZ                14.959701
MOBI_REGIO               14.959701
KBA05_ANTG1              14.959701
KBA05_ANTG2              14.959701
KBA05_ANTG3              14.959701
PLZ8_GBZ                 13.073637
PLZ8_HHZ                 13.073637
PLZ8_BAUMAX              13.073637
PLZ8_ANTG4               13.073637
PLZ8_ANTG1               13.073637
PLZ8_ANTG3               13.073637
PLZ8_ANTG2               13.073637
SHOPPER_TYP              12.476816
VERS_TYP                 12.476816
HEALTH_TYP               12.476816
NATIONALITAET_KZ         12.153551
PRAEGENDE_JUGENDJAHRE    12.136608
KBA13_ANZAHL_PKW         11.871354
ANZ_HAUSHALTE_AKTIV      11.176913
CAMEO_INTL_2015          11.147852
CAMEO_DEU_2015           11.147852
CAMEO_DEUG_2015          11.147852
LP_LEBENSPHASE_FEIN      10.954859
RELAT_AB                 10.926022
ARBEIT                   10.926022
ORTSGR_KLS9              10.914689
ANZ_HH_TITEL             10.884842
LP_LEBENSPHASE_GROB      10.611509
INNENSTADT               10.518154
EWDICHTE                 10.518154
BALLRAUM                 10.518154
GEBAEUDETYP_RASTER       10.452514
MIN_GEBAEUDEJAHR         10.451729
GEBAEUDETYP              10.451729
WOHNLAGE                 10.451729
OST_WEST_KZ              10.451729
LP_FAMILIE_FEIN          8.728699 
LP_FAMILIE_GROB          8.728699 
KONSUMNAEHE              8.299737 
ANZ_PERSONEN             8.247000 
SOHO_KZ                  8.247000 
ANZ_TITEL                8.247000 
WOHNDAUER_2008           8.247000 
HH_EINKOMMEN_SCORE       2.058749 
CJT_GESAMTTYP            0.544646 
RETOURTYP_BK_S           0.544646 
ONLINE_AFFINITAET        0.544646 
LP_STATUS_FEIN           0.544646 
LP_STATUS_GROB           0.544646 
GFK_URLAUBERTYP          0.544646 
ALTERSKATEGORIE_GROB     0.323264 
FINANZTYP                0.000000 
GREEN_AVANTGARDE         0.000000 
FINANZ_HAUSBAUER         0.000000 
FINANZ_UNAUFFAELLIGER    0.000000 
FINANZ_ANLEGER           0.000000 
FINANZ_SPARER            0.000000 
FINANZ_MINIMALIST        0.000000 
ANREDE_KZ                0.000000 
FINANZ_VORSORGER         0.000000 
SEMIO_FAM                0.000000 
SEMIO_SOZ                0.000000 
SEMIO_REL                0.000000 
SEMIO_MAT                0.000000 
SEMIO_VERT               0.000000 
SEMIO_LUST               0.000000 
SEMIO_ERL                0.000000 
SEMIO_KULT               0.000000 
SEMIO_RAT                0.000000 
SEMIO_KRIT               0.000000 
SEMIO_DOM                0.000000 
SEMIO_KAEM               0.000000 
SEMIO_PFLICHT            0.000000 
SEMIO_TRADV              0.000000 
ZABEOTYP                 0.000000 
dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Remove the outlier columns from the dataset. (You&#39;ll perform other data</span>
<span class="c1"># engineering tasks such as re-encoding and imputation later.)</span>

<span class="n">outliers</span> <span class="o">=</span> <span class="n">null_before</span><span class="p">[</span><span class="n">null_before</span><span class="o">&gt;</span><span class="mi">25</span><span class="p">]</span>
<span class="n">outliers_columns</span> <span class="o">=</span> <span class="n">outliers</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;More than 25 Percent of values are null in&quot;</span><span class="p">,</span> <span class="n">outliers_columns</span><span class="p">)</span>
<span class="n">azdias</span> <span class="o">=</span> <span class="n">azdias</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">outliers_columns</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>More than 25 Percent of values are null in [&#39;TITEL_KZ&#39;, &#39;AGER_TYP&#39;, &#39;KK_KUNDENTYP&#39;, &#39;KBA05_BAUMAX&#39;, &#39;GEBURTSJAHR&#39;, &#39;ALTER_HH&#39;]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[11]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>FINANZTYP</th>
      <th>GFK_URLAUBERTYP</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>LP_LEBENSPHASE_FEIN</th>
      <th>LP_LEBENSPHASE_GROB</th>
      <th>LP_FAMILIE_FEIN</th>
      <th>LP_FAMILIE_GROB</th>
      <th>LP_STATUS_FEIN</th>
      <th>LP_STATUS_GROB</th>
      <th>NATIONALITAET_KZ</th>
      <th>PRAEGENDE_JUGENDJAHRE</th>
      <th>RETOURTYP_BK_S</th>
      <th>SEMIO_SOZ</th>
      <th>SEMIO_FAM</th>
      <th>SEMIO_REL</th>
      <th>SEMIO_MAT</th>
      <th>SEMIO_VERT</th>
      <th>SEMIO_LUST</th>
      <th>SEMIO_ERL</th>
      <th>SEMIO_KULT</th>
      <th>SEMIO_RAT</th>
      <th>SEMIO_KRIT</th>
      <th>SEMIO_DOM</th>
      <th>SEMIO_KAEM</th>
      <th>SEMIO_PFLICHT</th>
      <th>SEMIO_TRADV</th>
      <th>SHOPPER_TYP</th>
      <th>SOHO_KZ</th>
      <th>VERS_TYP</th>
      <th>ZABEOTYP</th>
      <th>ANZ_PERSONEN</th>
      <th>ANZ_TITEL</th>
      <th>HH_EINKOMMEN_SCORE</th>
      <th>W_KEIT_KIND_HH</th>
      <th>WOHNDAUER_2008</th>
      <th>ANZ_HAUSHALTE_AKTIV</th>
      <th>ANZ_HH_TITEL</th>
      <th>GEBAEUDETYP</th>
      <th>KONSUMNAEHE</th>
      <th>MIN_GEBAEUDEJAHR</th>
      <th>OST_WEST_KZ</th>
      <th>WOHNLAGE</th>
      <th>CAMEO_DEUG_2015</th>
      <th>CAMEO_DEU_2015</th>
      <th>CAMEO_INTL_2015</th>
      <th>KBA05_ANTG1</th>
      <th>KBA05_ANTG2</th>
      <th>KBA05_ANTG3</th>
      <th>KBA05_ANTG4</th>
      <th>KBA05_GBZ</th>
      <th>BALLRAUM</th>
      <th>EWDICHTE</th>
      <th>INNENSTADT</th>
      <th>GEBAEUDETYP_RASTER</th>
      <th>KKK</th>
      <th>MOBI_REGIO</th>
      <th>ONLINE_AFFINITAET</th>
      <th>REGIOTYP</th>
      <th>KBA13_ANZAHL_PKW</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2.0</td>
      <td>1</td>
      <td>2.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>4</td>
      <td>10.0</td>
      <td>0</td>
      <td>NaN</td>
      <td>15.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>5.0</td>
      <td>2</td>
      <td>6</td>
      <td>7</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>7</td>
      <td>6</td>
      <td>6</td>
      <td>5</td>
      <td>3</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>3</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>2.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1.0</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>1</td>
      <td>10.0</td>
      <td>0</td>
      <td>3.0</td>
      <td>21.0</td>
      <td>6.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>14.0</td>
      <td>1.0</td>
      <td>5</td>
      <td>4</td>
      <td>4</td>
      <td>3</td>
      <td>1</td>
      <td>2</td>
      <td>2</td>
      <td>3</td>
      <td>6</td>
      <td>4</td>
      <td>7</td>
      <td>4</td>
      <td>7</td>
      <td>6</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>5</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>6.0</td>
      <td>3.0</td>
      <td>9.0</td>
      <td>11.0</td>
      <td>0.0</td>
      <td>8.0</td>
      <td>1.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>4.0</td>
      <td>8</td>
      <td>8A</td>
      <td>51</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>6.0</td>
      <td>3.0</td>
      <td>8.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>963.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>10.0</td>
      <td>1</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>15.0</td>
      <td>3.0</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>6</td>
      <td>3</td>
      <td>4</td>
      <td>7</td>
      <td>7</td>
      <td>7</td>
      <td>3</td>
      <td>3</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>5</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>9.0</td>
      <td>10.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>2.0</td>
      <td>4</td>
      <td>4C</td>
      <td>24</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>712.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>6</td>
      <td>1.0</td>
      <td>0</td>
      <td>2.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>9.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>8.0</td>
      <td>2.0</td>
      <td>5</td>
      <td>1</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
      <td>7</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>5</td>
      <td>4</td>
      <td>4</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>9.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1997.0</td>
      <td>W</td>
      <td>7.0</td>
      <td>2</td>
      <td>2A</td>
      <td>12</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>6.0</td>
      <td>4.0</td>
      <td>NaN</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>596.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>5</td>
      <td>5.0</td>
      <td>0</td>
      <td>3.0</td>
      <td>32.0</td>
      <td>10.0</td>
      <td>10.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>8.0</td>
      <td>5.0</td>
      <td>6</td>
      <td>4</td>
      <td>4</td>
      <td>2</td>
      <td>7</td>
      <td>4</td>
      <td>4</td>
      <td>6</td>
      <td>2</td>
      <td>3</td>
      <td>2</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>4</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>9.0</td>
      <td>3.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>3.0</td>
      <td>6</td>
      <td>6B</td>
      <td>43</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>5.0</td>
      <td>435.0</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>5.0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>3</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1.0</td>
      <td>0</td>
      <td>3.0</td>
      <td>8.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>2</td>
      <td>4</td>
      <td>7</td>
      <td>4</td>
      <td>2</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>7</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>7</td>
      <td>6</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>4</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>5.0</td>
      <td>6.0</td>
      <td>9.0</td>
      <td>5.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>7.0</td>
      <td>8</td>
      <td>8C</td>
      <td>54</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>2.0</td>
      <td>7.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>1300.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>6</th>
      <td>2.0</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>12.0</td>
      <td>0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>10.0</td>
      <td>4.0</td>
      <td>2</td>
      <td>5</td>
      <td>5</td>
      <td>7</td>
      <td>2</td>
      <td>6</td>
      <td>5</td>
      <td>5</td>
      <td>7</td>
      <td>7</td>
      <td>4</td>
      <td>7</td>
      <td>7</td>
      <td>7</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>6.0</td>
      <td>3.0</td>
      <td>9.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>5.0</td>
      <td>4</td>
      <td>4A</td>
      <td>22</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>6.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>867.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>7</th>
      <td>1.0</td>
      <td>1</td>
      <td>3.0</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>5</td>
      <td>9.0</td>
      <td>0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>8.0</td>
      <td>5.0</td>
      <td>7</td>
      <td>7</td>
      <td>7</td>
      <td>5</td>
      <td>6</td>
      <td>2</td>
      <td>2</td>
      <td>7</td>
      <td>5</td>
      <td>1</td>
      <td>1</td>
      <td>2</td>
      <td>5</td>
      <td>5</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>1</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>5.0</td>
      <td>9.0</td>
      <td>6.0</td>
      <td>0.0</td>
      <td>8.0</td>
      <td>3.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>1.0</td>
      <td>2</td>
      <td>2D</td>
      <td>14</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>758.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>8</th>
      <td>3.0</td>
      <td>1</td>
      <td>3.0</td>
      <td>4</td>
      <td>4</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>2</td>
      <td>6</td>
      <td>3.0</td>
      <td>1</td>
      <td>3.0</td>
      <td>10.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>10.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>11.0</td>
      <td>4.0</td>
      <td>4</td>
      <td>5</td>
      <td>4</td>
      <td>1</td>
      <td>5</td>
      <td>6</td>
      <td>4</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>1</td>
      <td>4</td>
      <td>3.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>6</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>8.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>1.0</td>
      <td>1</td>
      <td>1A</td>
      <td>13</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>511.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>9</th>
      <td>3.0</td>
      <td>2</td>
      <td>4.0</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>4</td>
      <td>1</td>
      <td>12.0</td>
      <td>1</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>15.0</td>
      <td>4.0</td>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>6</td>
      <td>6</td>
      <td>3</td>
      <td>4</td>
      <td>7</td>
      <td>6</td>
      <td>7</td>
      <td>1</td>
      <td>3</td>
      <td>3.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>4</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>9.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>1992.0</td>
      <td>W</td>
      <td>7.0</td>
      <td>1</td>
      <td>1E</td>
      <td>15</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>6.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>530.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>1.0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Discussion-1.1.2:-Assess-Missing-Data-in-Each-Column">Discussion 1.1.2: Assess Missing Data in Each Column<a class="anchor-link" href="#Discussion-1.1.2:-Assess-Missing-Data-in-Each-Column">&#182;</a></h4><ul>
<li>I selected columns which have to be removed by having more than 25 Percentage Null values or not.</li>
<li>I dropped 'TITEL_KZ', 'AGER_TYP', 'KK_KUNDENTYP', 'KBA05_BAUMAX', 'GEBURTSJAHR', 'ALTER_HH' columns</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.1.3:-Assess-Missing-Data-in-Each-Row">Step 1.1.3: Assess Missing Data in Each Row<a class="anchor-link" href="#Step-1.1.3:-Assess-Missing-Data-in-Each-Row">&#182;</a></h4><p>Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.</p>
<p>In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.</p>
<ul>
<li>You can use seaborn's <a href="https://seaborn.pydata.org/generated/seaborn.countplot.html"><code>countplot()</code></a> function to create a bar chart of code frequencies and matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html"><code>subplot()</code></a> function to put bar charts for the two subplots side by side.</li>
<li>To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.</li>
</ul>
<p>Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. <strong>Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.</strong></p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># How much data is missing in each row of the dataset?</span>

<span class="n">null_after</span> <span class="o">=</span> <span class="n">describe_null</span><span class="p">(</span><span class="n">azdias</span><span class="p">)</span>
<span class="n">null_after</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre># Of Columns with Null: 55
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[12]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>KKK                      17.735668
REGIOTYP                 17.735668
W_KEIT_KIND_HH           16.605084
MOBI_REGIO               14.959701
KBA05_ANTG4              14.959701
KBA05_ANTG3              14.959701
KBA05_ANTG2              14.959701
KBA05_ANTG1              14.959701
KBA05_GBZ                14.959701
PLZ8_ANTG2               13.073637
PLZ8_ANTG1               13.073637
PLZ8_ANTG3               13.073637
PLZ8_ANTG4               13.073637
PLZ8_BAUMAX              13.073637
PLZ8_HHZ                 13.073637
PLZ8_GBZ                 13.073637
HEALTH_TYP               12.476816
SHOPPER_TYP              12.476816
VERS_TYP                 12.476816
NATIONALITAET_KZ         12.153551
PRAEGENDE_JUGENDJAHRE    12.136608
KBA13_ANZAHL_PKW         11.871354
ANZ_HAUSHALTE_AKTIV      11.176913
CAMEO_INTL_2015          11.147852
CAMEO_DEU_2015           11.147852
CAMEO_DEUG_2015          11.147852
LP_LEBENSPHASE_FEIN      10.954859
RELAT_AB                 10.926022
ARBEIT                   10.926022
ORTSGR_KLS9              10.914689
ANZ_HH_TITEL             10.884842
LP_LEBENSPHASE_GROB      10.611509
INNENSTADT               10.518154
BALLRAUM                 10.518154
EWDICHTE                 10.518154
GEBAEUDETYP_RASTER       10.452514
OST_WEST_KZ              10.451729
MIN_GEBAEUDEJAHR         10.451729
WOHNLAGE                 10.451729
GEBAEUDETYP              10.451729
LP_FAMILIE_FEIN          8.728699 
LP_FAMILIE_GROB          8.728699 
KONSUMNAEHE              8.299737 
ANZ_PERSONEN             8.247000 
ANZ_TITEL                8.247000 
WOHNDAUER_2008           8.247000 
SOHO_KZ                  8.247000 
HH_EINKOMMEN_SCORE       2.058749 
CJT_GESAMTTYP            0.544646 
GFK_URLAUBERTYP          0.544646 
RETOURTYP_BK_S           0.544646 
ONLINE_AFFINITAET        0.544646 
LP_STATUS_FEIN           0.544646 
LP_STATUS_GROB           0.544646 
ALTERSKATEGORIE_GROB     0.323264 
SEMIO_FAM                0.000000 
SEMIO_SOZ                0.000000 
ANREDE_KZ                0.000000 
SEMIO_TRADV              0.000000 
FINANZ_MINIMALIST        0.000000 
FINANZ_SPARER            0.000000 
FINANZ_VORSORGER         0.000000 
FINANZ_ANLEGER           0.000000 
FINANZ_UNAUFFAELLIGER    0.000000 
FINANZ_HAUSBAUER         0.000000 
FINANZTYP                0.000000 
SEMIO_PFLICHT            0.000000 
GREEN_AVANTGARDE         0.000000 
SEMIO_KAEM               0.000000 
SEMIO_DOM                0.000000 
SEMIO_KRIT               0.000000 
SEMIO_RAT                0.000000 
SEMIO_KULT               0.000000 
SEMIO_ERL                0.000000 
SEMIO_LUST               0.000000 
SEMIO_VERT               0.000000 
SEMIO_MAT                0.000000 
SEMIO_REL                0.000000 
ZABEOTYP                 0.000000 
dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Write code to divide the data into two subsets based on the number of missing</span>
<span class="c1"># values in each row.</span>
<span class="n">more_than_5</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span><span class="o">&gt;=</span><span class="mi">4</span><span class="p">]</span>
<span class="n">less_than_5</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span><span class="o">&lt;</span><span class="mi">4</span><span class="p">]</span>

<span class="n">zero_missing</span> <span class="o">=</span> <span class="n">null_before</span><span class="p">[</span><span class="n">null_before</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare the distribution of values for at least five columns where there are</span>
<span class="c1"># no or few missing values, between the two subsets.</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span><span class="n">nrows</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span><span class="n">ncols</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">more_than_5</span><span class="p">[</span><span class="n">zero_missing</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">less_than_5</span><span class="p">[</span><span class="n">zero_missing</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;More Missing&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Less Missing&quot;</span><span class="p">)</span>
    

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Discussion-1.1.3:-Assess-Missing-Data-in-Each-Row">Discussion 1.1.3: Assess Missing Data in Each Row<a class="anchor-link" href="#Discussion-1.1.3:-Assess-Missing-Data-in-Each-Row">&#182;</a></h4><p>In the chart above there is no significant difference in Green Avangart Column but columns related to Finance have huge differences. For example in the Finanz Unauffaelliger column has mostly 5 in the more missing chart but in the less Missing chart this column has mostly 1 value</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-1.2:-Select-and-Re-Encode-Features">Step 1.2: Select and Re-Encode Features<a class="anchor-link" href="#Step-1.2:-Select-and-Re-Encode-Features">&#182;</a></h3><p>Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (<code>feat_info</code>) for a summary of types of measurement.</p>
<ul>
<li>For numeric and interval data, these features can be kept without changes.</li>
<li>Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).</li>
<li>Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.</li>
</ul>
<p>In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.</p>
<p>Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[15]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>attribute</th>
      <th>information_level</th>
      <th>type</th>
      <th>missing_or_unknown</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>AGER_TYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>1</th>
      <td>ALTERSKATEGORIE_GROB</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1,0,9]</td>
    </tr>
    <tr>
      <th>2</th>
      <td>ANREDE_KZ</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>3</th>
      <td>CJT_GESAMTTYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[0]</td>
    </tr>
    <tr>
      <th>4</th>
      <td>FINANZ_MINIMALIST</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1]</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[16]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># How many features are there of each data type?</span>

<span class="n">columns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">less_than_5</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>

<span class="n">feats</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">columns</span><span class="p">)]</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">feats</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of each category:&quot;</span><span class="p">)</span>
<span class="n">counts</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Number of each category:
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[16]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>ordinal        49
categorical    18
mixed          6 
numeric        6 
Name: type, dtype: int64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.2.1:-Re-Encode-Categorical-Features">Step 1.2.1: Re-Encode Categorical Features<a class="anchor-link" href="#Step-1.2.1:-Re-Encode-Categorical-Features">&#182;</a></h4><p>For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:</p>
<ul>
<li>For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.</li>
<li>There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.</li>
<li>For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html">OneHotEncoder</a>), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[17]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Assess categorical variables: which are binary, which are multi-level, and</span>
<span class="c1"># which one needs to be re-encoded?</span>
<span class="n">categorical</span> <span class="o">=</span> <span class="n">feats</span><span class="p">[</span><span class="n">feats</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;categorical&#39;</span><span class="p">][</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span>


<span class="n">multi</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">binary</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">categorical</span><span class="p">:</span>
    <span class="n">multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">less_than_5</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">)</span> <span class="k">else</span> <span class="n">binary</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Multi Level Categorical Features: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">multi</span><span class="p">))</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Binary Categorical Features: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">binary</span><span class="p">))</span>
<span class="nb">print</span><span class="p">()</span>
<span class="k">for</span> <span class="n">attribute</span> <span class="ow">in</span> <span class="n">binary</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">attribute</span><span class="p">,</span> <span class="n">less_than_5</span><span class="p">[</span><span class="n">attribute</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span>

<span class="c1"># OST_WEST_KZ [&#39;W&#39; &#39;O&#39;]</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Multi Level Categorical Features: [&#39;CJT_GESAMTTYP&#39;, &#39;FINANZTYP&#39;, &#39;GFK_URLAUBERTYP&#39;, &#39;LP_FAMILIE_FEIN&#39;, &#39;LP_FAMILIE_GROB&#39;, &#39;LP_STATUS_FEIN&#39;, &#39;LP_STATUS_GROB&#39;, &#39;NATIONALITAET_KZ&#39;, &#39;SHOPPER_TYP&#39;, &#39;ZABEOTYP&#39;, &#39;GEBAEUDETYP&#39;, &#39;CAMEO_DEUG_2015&#39;, &#39;CAMEO_DEU_2015&#39;]

Binary Categorical Features: [&#39;ANREDE_KZ&#39;, &#39;GREEN_AVANTGARDE&#39;, &#39;SOHO_KZ&#39;, &#39;VERS_TYP&#39;, &#39;OST_WEST_KZ&#39;]

ANREDE_KZ [2 1]
GREEN_AVANTGARDE [0 1]
SOHO_KZ [1. 0.]
VERS_TYP [2. 1.]
OST_WEST_KZ [&#39;W&#39; &#39;O&#39;]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[18]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-encode categorical variable(s) to be kept in the analysis.</span>
<span class="n">encoding</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;W&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s1">&#39;O&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
<span class="n">less_than_5_encoded</span> <span class="o">=</span> <span class="n">less_than_5</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;OST_WEST_KZ&#39;</span><span class="p">:{</span><span class="s1">&#39;W&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s1">&#39;O&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">}})</span>

<span class="c1">#Drop multi-level categorical features</span>
<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">multi</span><span class="p">:</span>
    <span class="n">less_than_5_encoded</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">column</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[19]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">less_than_5</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">less_than_5_encoded</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[19]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>66</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Discussion-1.2.1:-Re-Encode-Categorical-Features">Discussion 1.2.1: Re-Encode Categorical Features<a class="anchor-link" href="#Discussion-1.2.1:-Re-Encode-Categorical-Features">&#182;</a></h4><p>I seperated Categorical columns into 2 category:</p>

<pre><code>1 - Multi-level Categorical 
2 - Binary Categorical

</code></pre>
<p>Then Re-encoded 'OST_WEST_KZ' column which is have 2 unique value ['W','O'] I encoded that values into 0,1</p>
<p>After that process I dropped all multi level categorical features</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.2.2:-Engineer-Mixed-Type-Features">Step 1.2.2: Engineer Mixed-Type Features<a class="anchor-link" href="#Step-1.2.2:-Engineer-Mixed-Type-Features">&#182;</a></h4><p>There are a handful of features that are marked as "mixed" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:</p>
<ul>
<li>"PRAEGENDE_JUGENDJAHRE" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.</li>
<li>"CAMEO_INTL_2015" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).</li>
<li>If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.</li>
</ul>
<p>Be sure to check <code>Data_Dictionary.md</code> for the details needed to finish these tasks.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[20]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate &quot;PRAEGENDE_JUGENDJAHRE&quot; and engineer two new variables.</span>
<span class="n">decade</span> <span class="o">=</span>  <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span>
           <span class="mi">10</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">11</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">12</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">13</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">14</span><span class="p">:</span><span class="mi">6</span><span class="p">,</span><span class="mi">15</span><span class="p">:</span><span class="mi">6</span><span class="p">}</span>
<span class="n">movement</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">9</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span>
            <span class="mi">10</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">11</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">12</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">13</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">14</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">15</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
<span class="c1">#Decade:</span>
<span class="c1"># 0-&gt; 40s</span>
<span class="c1"># 6-&gt; 90s</span>
<span class="c1">#</span>
<span class="c1">#Movement: </span>
<span class="c1"># 1-&gt; Mainstream</span>
<span class="c1"># 2-&gt; Avantgarde</span>

<span class="n">less_than_5_encoded</span><span class="p">[</span><span class="s1">&#39;DECADE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">less_than_5_encoded</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">decade</span><span class="p">)</span>
<span class="n">less_than_5_encoded</span><span class="p">[</span><span class="s1">&#39;MOVEMENT&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">less_than_5_encoded</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">movement</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[21]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">less_than_5_encoded</span><span class="p">[[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">,</span><span class="s1">&#39;DECADE&#39;</span><span class="p">,</span><span class="s1">&#39;MOVEMENT&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[21]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PRAEGENDE_JUGENDJAHRE</th>
      <th>DECADE</th>
      <th>MOVEMENT</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>14.0</td>
      <td>6.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>15.0</td>
      <td>6.0</td>
      <td>1.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>8.0</td>
      <td>4.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>3.0</td>
      <td>2.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>6</th>
      <td>10.0</td>
      <td>5.0</td>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[22]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate &quot;CAMEO_INTL_2015&quot; and engineer two new variables.</span>
<span class="n">wealth</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;11&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;12&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;13&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;14&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;15&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span>
          <span class="s1">&#39;21&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;22&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;23&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;24&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;25&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span>
          <span class="s1">&#39;31&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s1">&#39;32&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s1">&#39;33&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s1">&#39;34&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s1">&#39;35&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span>
          <span class="s1">&#39;41&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;42&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;43&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;44&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;45&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span>
          <span class="s1">&#39;51&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;52&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;53&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;54&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;55&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">}</span>

<span class="n">dict_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wealth</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">life_stage</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">dict_keys</span><span class="p">:</span>
    <span class="n">life_stage</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

<span class="c1">#wealth:</span>
<span class="c1"># 1-&gt; Wealthy</span>
<span class="c1"># 5-&gt; Poorer</span>
<span class="c1">#</span>
<span class="c1">#life_stage: </span>
<span class="c1"># 1-&gt; Pre-Family Couples</span>
<span class="c1"># 5-&gt; Elders In Retirement</span>

<span class="n">less_than_5_encoded</span><span class="p">[</span><span class="s1">&#39;WEALTH&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">less_than_5_encoded</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">wealth</span><span class="p">)</span>
<span class="n">less_than_5_encoded</span><span class="p">[</span><span class="s1">&#39;LIFE_STAGE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">less_than_5_encoded</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">life_stage</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">less_than_5_encoded</span><span class="p">[[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">,</span><span class="s1">&#39;WEALTH&#39;</span><span class="p">,</span><span class="s1">&#39;LIFE_STAGE&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[23]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>CAMEO_INTL_2015</th>
      <th>WEALTH</th>
      <th>LIFE_STAGE</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>51</td>
      <td>5.0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>2</th>
      <td>24</td>
      <td>2.0</td>
      <td>4</td>
    </tr>
    <tr>
      <th>4</th>
      <td>43</td>
      <td>4.0</td>
      <td>3</td>
    </tr>
    <tr>
      <th>5</th>
      <td>54</td>
      <td>5.0</td>
      <td>4</td>
    </tr>
    <tr>
      <th>6</th>
      <td>22</td>
      <td>2.0</td>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Discussion-1.2.2:-Engineer-Mixed-Type-Features">Discussion 1.2.2: Engineer Mixed-Type Features<a class="anchor-link" href="#Discussion-1.2.2:-Engineer-Mixed-Type-Features">&#182;</a></h4><p>In this stage i have encoded "PRAEGENDE_JUGENDJAHRE","CAMEO_INTL_2015" columns.</p>
<p>Firstly I created 2 extra columns for each.</p>
<ul>
<li>"PRAEGENDE_JUGENDJAHRE" --&gt; ['decade','movement']</li>
<li>"CAMEO_INTL_2015" --&gt; ['wealth','life_stage']</li>
</ul>
<p>I kept original columns in the prepared dataset in this step. In the next step i'm going to remove original columns</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.2.3:-Complete-Feature-Selection">Step 1.2.3: Complete Feature Selection<a class="anchor-link" href="#Step-1.2.3:-Complete-Feature-Selection">&#182;</a></h4><p>In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:</p>
<ul>
<li>All numeric, interval, and ordinal type columns from the original dataset.</li>
<li>Binary categorical features (all numerically-encoded).</li>
<li>Engineered features from other multi-level categorical features and mixed features.</li>
</ul>
<p>Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep "PRAEGENDE_JUGENDJAHRE", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from <strong>the subset with few or no missing values</strong>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If there are other re-engineering tasks you need to perform, make sure you</span>
<span class="c1"># take care of them here. (Dealing with missing data will come in step 2.1.)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[25]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Do whatever you need to in order to ensure that the dataframe only contains</span>
<span class="c1"># the columns that should be passed to the algorithm functions.</span>
<span class="n">mixed</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">feats</span><span class="p">[</span><span class="n">feats</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;mixed&#39;</span><span class="p">][</span><span class="s1">&#39;attribute&#39;</span><span class="p">])</span>

<span class="k">for</span> <span class="n">attr</span> <span class="ow">in</span> <span class="n">mixed</span><span class="p">:</span>
    <span class="n">less_than_5_encoded</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">attr</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[26]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_ready</span> <span class="o">=</span> <span class="n">less_than_5_encoded</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-1.3:-Create-a-Cleaning-Function">Step 1.3: Create a Cleaning Function<a class="anchor-link" href="#Step-1.3:-Create-a-Cleaning-Function">&#182;</a></h3><p>Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[27]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">clean_data</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Perform feature trimming, re-encoding, and engineering for demographics</span>
<span class="sd">    data</span>
<span class="sd">    </span>
<span class="sd">    INPUT: Demographics DataFrame</span>
<span class="sd">    OUTPUT: Trimmed and cleaned demographics DataFrame</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="c1"># Put in code here to execute all main cleaning steps:</span>
    <span class="c1"># convert missing value codes into NaNs, ...</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Null values before converted: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Converting...&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">feat_info</span><span class="p">)):</span>
        <span class="n">row</span> <span class="o">=</span> <span class="n">feat_info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="n">missing</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
        <span class="n">missing</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="n">x</span> <span class="o">!=</span> <span class="s1">&#39;X&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">x</span> <span class="o">!=</span> <span class="s1">&#39;XX&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">x</span><span class="o">!=</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">missing</span><span class="p">]</span>
        <span class="k">if</span><span class="p">(</span><span class="n">missing</span> <span class="o">!=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
            <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="n">row</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">]:</span><span class="n">missing</span><span class="p">},</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Converted!&quot;</span><span class="p">)</span>    
    <span class="n">null_before</span> <span class="o">=</span> <span class="n">describe_null</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
    
    <span class="c1"># remove selected columns and rows, ...</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dropping outlier columns...&quot;</span><span class="p">)</span>
    <span class="n">outliers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;TITEL_KZ&#39;</span><span class="p">,</span> <span class="s1">&#39;AGER_TYP&#39;</span><span class="p">,</span> <span class="s1">&#39;KK_KUNDENTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;KBA05_BAUMAX&#39;</span><span class="p">,</span> <span class="s1">&#39;GEBURTSJAHR&#39;</span><span class="p">,</span> <span class="s1">&#39;ALTER_HH&#39;</span><span class="p">]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;More than 25 Percent of values are null in the trained set&quot;</span><span class="p">,</span> <span class="n">outliers</span><span class="p">)</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">outliers</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Outlier columns have dropped!&quot;</span><span class="p">)</span>
    
    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span><span class="o">&lt;</span><span class="mi">4</span><span class="p">]</span>

    
    <span class="c1"># select, re-encode, and engineer column values.</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Processing Categorical Attributes...&quot;</span><span class="p">)</span>
    <span class="n">categorical</span> <span class="o">=</span> <span class="n">feats</span><span class="p">[</span><span class="n">feats</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;categorical&#39;</span><span class="p">][</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span>
    <span class="n">multi</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">binary</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">categorical</span><span class="p">:</span>
        <span class="n">multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">)</span> <span class="k">else</span> <span class="n">binary</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>

    <span class="n">encoding</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;W&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s1">&#39;O&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;OST_WEST_KZ&#39;</span><span class="p">:{</span><span class="s1">&#39;W&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s1">&#39;O&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">}})</span>

    <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">multi</span><span class="p">:</span>
        <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">column</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Success!&quot;</span><span class="p">)</span>    
        
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Re-Encoding Features...&quot;</span><span class="p">)</span>    
    <span class="n">decade</span> <span class="o">=</span>  <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span>
           <span class="mi">10</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">11</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">12</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">13</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="mi">14</span><span class="p">:</span><span class="mi">6</span><span class="p">,</span><span class="mi">15</span><span class="p">:</span><span class="mi">6</span><span class="p">}</span>
    <span class="n">movement</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">9</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span>
            <span class="mi">10</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">11</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">12</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">13</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">14</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">15</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
<span class="c1">#Decade:</span>
<span class="c1"># 0-&gt; 40s</span>
<span class="c1"># 6-&gt; 90s</span>
<span class="c1">#</span>
<span class="c1">#Movement: </span>
<span class="c1"># 1-&gt; Mainstream</span>
<span class="c1"># 2-&gt; Avantgarde</span>

    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;DECADE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">decade</span><span class="p">)</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;MOVEMENT&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">movement</span><span class="p">)</span>
        
        
    <span class="n">wealth</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;11&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;12&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;13&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;14&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;15&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span>
          <span class="s1">&#39;21&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;22&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;23&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;24&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span><span class="s1">&#39;25&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span>
          <span class="s1">&#39;31&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s1">&#39;32&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s1">&#39;33&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s1">&#39;34&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span><span class="s1">&#39;35&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span>
          <span class="s1">&#39;41&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;42&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;43&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;44&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span><span class="s1">&#39;45&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span>
          <span class="s1">&#39;51&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;52&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;53&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;54&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;55&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">}</span>

    <span class="n">dict_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wealth</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
    <span class="n">life_stage</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">dict_keys</span><span class="p">:</span>
        <span class="n">life_stage</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

<span class="c1">#wealth:</span>
<span class="c1"># 1-&gt; Wealthy</span>
<span class="c1"># 5-&gt; Poorer</span>
<span class="c1">#</span>
<span class="c1">#life_stage: </span>
<span class="c1"># 1-&gt; Pre-Family Couples</span>
<span class="c1"># 5-&gt; Elders In Retirement</span>

    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;WEALTH&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">wealth</span><span class="p">)</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;LIFE_STAGE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">life_stage</span><span class="p">)</span>
    
    <span class="n">mixed</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">feats</span><span class="p">[</span><span class="n">feats</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;mixed&#39;</span><span class="p">][</span><span class="s1">&#39;attribute&#39;</span><span class="p">])</span>

    <span class="k">for</span> <span class="n">attr</span> <span class="ow">in</span> <span class="n">mixed</span><span class="p">:</span>
        <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">attr</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Success! Clean Dataframe has Created!&quot;</span><span class="p">)</span>
    
    
    <span class="c1"># Return the cleaned dataframe.</span>
    <span class="k">return</span> <span class="n">df</span>
    
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Step-2:-Feature-Transformation">Step 2: Feature Transformation<a class="anchor-link" href="#Step-2:-Feature-Transformation">&#182;</a></h2><h3 id="Step-2.1:-Apply-Feature-Scaling">Step 2.1: Apply Feature Scaling<a class="anchor-link" href="#Step-2.1:-Apply-Feature-Scaling">&#182;</a></h3><p>Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the <a href="http://scikit-learn.org/stable/modules/classes.html">API reference page for sklearn</a> to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:</p>
<ul>
<li>sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html">Imputer</a> to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.</li>
<li>For the actual scaling function, a <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">StandardScaler</a> instance is suggested, scaling each feature to mean 0 and standard deviation 1.</li>
<li>For these classes, you can make use of the <code>.fit_transform()</code> method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[28]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If you&#39;ve not yet cleaned the dataset of all NaN values, then investigate and</span>
<span class="c1"># do that now.</span>
<span class="c1"># Apply feature scaling to the general population demographics data.</span>


<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Before imputation:&quot;</span><span class="p">)</span>
<span class="n">describe_null</span><span class="p">(</span><span class="n">df_ready</span><span class="p">)</span>

<span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([(</span><span class="s1">&#39;imputer&#39;</span><span class="p">,</span><span class="n">Imputer</span><span class="p">(</span><span class="n">strategy</span> <span class="o">=</span> <span class="s1">&#39;most_frequent&#39;</span><span class="p">)),</span>
                 <span class="p">(</span><span class="s1">&#39;scaler&#39;</span><span class="p">,</span><span class="n">StandardScaler</span><span class="p">())])</span>
<span class="n">df_filled</span> <span class="o">=</span> <span class="n">pipe</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df_ready</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Before imputation:
# Of Columns with Null: 18
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>C:\Users\abduy\Anaconda3\lib\site-packages\sklearn\utils\deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.
  warnings.warn(msg, category=DeprecationWarning)
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[29]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply feature scaling to the general population demographics data.</span>

<span class="c1"># Did it on previous cell</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-2.1:-Apply-Feature-Scaling">Discussion 2.1: Apply Feature Scaling<a class="anchor-link" href="#Discussion-2.1:-Apply-Feature-Scaling">&#182;</a></h3><p>I have imputed with most frequent values in the columns and I scaled my data with StandardScaler using pipe. Mean imputation can be effect database so i thought that most_frequent imputation can be more useful in this case</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-2.2:-Perform-Dimensionality-Reduction">Step 2.2: Perform Dimensionality Reduction<a class="anchor-link" href="#Step-2.2:-Perform-Dimensionality-Reduction">&#182;</a></h3><p>On your scaled data, you are now ready to apply dimensionality reduction techniques.</p>
<ul>
<li>Use sklearn's <a href="http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html">PCA</a> class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).</li>
<li>Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html"><code>plot()</code></a> function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.</li>
<li>Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[30]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply PCA to the data.</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span>
<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df_filled</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[30]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>PCA(copy=True, iterated_power=&#39;auto&#39;, n_components=None, random_state=None,
  svd_solver=&#39;auto&#39;, tol=0.0, whiten=False)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[31]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate the variance accounted for by each principal component.</span>
<span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>

<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;# of Components&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Explained Variance Ratio&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">length</span><span class="p">),</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mtick</span><span class="o">.</span><span class="n">PercentFormatter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>

<span class="n">ax2</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Cumulative EVR&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mtick</span><span class="o">.</span><span class="n">PercentFormatter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">length</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">),</span><span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
<span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Explainde Variance Ratio With # Of Components&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[32]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-apply PCA to the data while selecting for number of components to retain.</span>
<span class="n">pca_re</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span>
<span class="n">pca_25</span> <span class="o">=</span> <span class="n">pca_re</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df_filled</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[33]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_filled</span><span class="o">.</span><span class="n">shape</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[33]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(684502, 64)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-2.2:-Perform-Dimensionality-Reduction">Discussion 2.2: Perform Dimensionality Reduction<a class="anchor-link" href="#Discussion-2.2:-Perform-Dimensionality-Reduction">&#182;</a></h3><p>In the Principal Component Analysis Stage nearly at 20 components we have reached 80% of explained variance. For more confidence I selected 25 Components (84% Explained Variance Ratio at cumulative)</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-2.3:-Interpret-Principal-Components">Step 2.3: Interpret Principal Components<a class="anchor-link" href="#Step-2.3:-Interpret-Principal-Components">&#182;</a></h3><p>Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.</p>
<p>As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.</p>
<ul>
<li>To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.</li>
<li>You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the <em>i</em>-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[34]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the first principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="c1"># HINT: Try defining a function here or in a new cell that you can reuse in the</span>
<span class="c1"># other cells.</span>
<span class="k">def</span> <span class="nf">mapping_pca</span><span class="p">(</span><span class="n">pca</span><span class="p">,</span> <span class="n">component</span><span class="p">):</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">df_ready</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
    <span class="n">w</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">component</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">w</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span> <span class="kc">False</span><span class="p">)</span>

<span class="n">mapping_pca</span><span class="p">(</span><span class="n">pca_re</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
    
    
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[34]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>PLZ8_ANTG3               0.220711
PLZ8_ANTG4               0.214165
WEALTH                   0.201747
HH_EINKOMMEN_SCORE       0.199923
ORTSGR_KLS9              0.190927
EWDICHTE                 0.188370
FINANZ_SPARER            0.154826
FINANZ_HAUSBAUER         0.153379
KBA05_ANTG4              0.150497
PLZ8_ANTG2               0.148882
ARBEIT                   0.139579
KBA05_ANTG3              0.135383
ANZ_HAUSHALTE_AKTIV      0.134509
RELAT_AB                 0.129916
SEMIO_PFLICHT            0.122400
SEMIO_REL                0.120072
DECADE                   0.115818
SEMIO_TRADV              0.106040
SEMIO_RAT                0.101987
SEMIO_MAT                0.089735
SEMIO_FAM                0.086258
FINANZ_UNAUFFAELLIGER    0.085244
SEMIO_KULT               0.082415
FINANZ_ANLEGER           0.073740
REGIOTYP                 0.061374
OST_WEST_KZ              0.056272
SEMIO_SOZ                0.049426
PLZ8_HHZ                 0.040628
KKK                      0.040502
ANZ_HH_TITEL             0.032766
HEALTH_TYP               0.031291
SEMIO_KAEM               0.029089
W_KEIT_KIND_HH           0.029003
VERS_TYP                 0.021459
SEMIO_DOM                0.017313
KBA05_ANTG2              0.010936
ANREDE_KZ               -0.001228
SEMIO_KRIT              -0.001638
SOHO_KZ                 -0.001933
ANZ_TITEL               -0.005200
SEMIO_VERT              -0.028651
RETOURTYP_BK_S          -0.029776
ONLINE_AFFINITAET       -0.033356
MIN_GEBAEUDEJAHR        -0.051081
WOHNDAUER_2008          -0.067494
KBA13_ANZAHL_PKW        -0.076114
ANZ_PERSONEN            -0.080256
SEMIO_LUST              -0.094487
SEMIO_ERL               -0.095340
MOVEMENT                -0.111461
GREEN_AVANTGARDE        -0.111461
GEBAEUDETYP_RASTER      -0.113546
BALLRAUM                -0.122375
LIFE_STAGE              -0.123520
FINANZ_VORSORGER        -0.124331
ALTERSKATEGORIE_GROB    -0.135227
INNENSTADT              -0.159287
KONSUMNAEHE             -0.162624
PLZ8_GBZ                -0.163857
KBA05_GBZ               -0.214052
PLZ8_ANTG1              -0.221102
FINANZ_MINIMALIST       -0.221245
KBA05_ANTG1             -0.222708
MOBI_REGIO              -0.238902
Name: 0, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[35]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the second principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>

<span class="n">mapping_pca</span><span class="p">(</span><span class="n">pca_re</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[35]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>ALTERSKATEGORIE_GROB     0.252939
SEMIO_ERL                0.228755
FINANZ_VORSORGER         0.221934
SEMIO_LUST               0.177210
RETOURTYP_BK_S           0.162699
FINANZ_HAUSBAUER         0.124830
SEMIO_KRIT               0.121044
W_KEIT_KIND_HH           0.115419
SEMIO_KAEM               0.113679
PLZ8_ANTG3               0.105631
PLZ8_ANTG4               0.101174
EWDICHTE                 0.100779
ORTSGR_KLS9              0.099729
ANREDE_KZ                0.091042
WEALTH                   0.083804
KBA05_ANTG4              0.078008
ARBEIT                   0.073660
PLZ8_ANTG2               0.071626
RELAT_AB                 0.070655
SEMIO_DOM                0.068838
ANZ_HAUSHALTE_AKTIV      0.068454
HH_EINKOMMEN_SCORE       0.063433
WOHNDAUER_2008           0.054839
KBA05_ANTG3              0.053660
FINANZ_MINIMALIST        0.052308
ANZ_HH_TITEL             0.032066
VERS_TYP                 0.031368
OST_WEST_KZ              0.030104
REGIOTYP                 0.018014
PLZ8_HHZ                 0.015597
ANZ_TITEL                0.007247
KKK                      0.004117
SOHO_KZ                 -0.002265
LIFE_STAGE              -0.006989
KBA05_ANTG2             -0.007224
MOVEMENT                -0.023189
GREEN_AVANTGARDE        -0.023189
KBA13_ANZAHL_PKW        -0.039042
MIN_GEBAEUDEJAHR        -0.042984
GEBAEUDETYP_RASTER      -0.048364
HEALTH_TYP              -0.054563
BALLRAUM                -0.066186
ANZ_PERSONEN            -0.066397
SEMIO_VERT              -0.072376
KONSUMNAEHE             -0.076633
PLZ8_GBZ                -0.080638
INNENSTADT              -0.082353
KBA05_ANTG1             -0.093779
KBA05_GBZ               -0.102256
SEMIO_SOZ               -0.102863
PLZ8_ANTG1              -0.103476
MOBI_REGIO              -0.104431
SEMIO_MAT               -0.153998
ONLINE_AFFINITAET       -0.164028
SEMIO_RAT               -0.166629
SEMIO_FAM               -0.177330
FINANZ_ANLEGER          -0.200528
SEMIO_KULT              -0.217151
FINANZ_UNAUFFAELLIGER   -0.220778
SEMIO_TRADV             -0.224854
FINANZ_SPARER           -0.226786
SEMIO_PFLICHT           -0.227848
DECADE                  -0.234656
SEMIO_REL               -0.258086
Name: 1, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[36]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the third principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="n">mapping_pca</span><span class="p">(</span><span class="n">pca_re</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[36]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>SEMIO_VERT               0.347771
SEMIO_SOZ                0.263247
SEMIO_FAM                0.250498
SEMIO_KULT               0.231953
FINANZ_MINIMALIST        0.157938
RETOURTYP_BK_S           0.116842
FINANZ_VORSORGER         0.100528
W_KEIT_KIND_HH           0.090749
ALTERSKATEGORIE_GROB     0.084443
SEMIO_REL                0.078693
SEMIO_LUST               0.075135
SEMIO_MAT                0.051723
GREEN_AVANTGARDE         0.049770
MOVEMENT                 0.049770
EWDICHTE                 0.046941
ORTSGR_KLS9              0.046833
PLZ8_ANTG4               0.044876
PLZ8_ANTG3               0.044330
WOHNDAUER_2008           0.035592
ARBEIT                   0.031726
RELAT_AB                 0.030602
PLZ8_ANTG2               0.029972
KBA05_ANTG4              0.027933
ANZ_HAUSHALTE_AKTIV      0.025824
WEALTH                   0.024789
VERS_TYP                 0.021825
ANZ_HH_TITEL             0.014213
OST_WEST_KZ              0.011511
ANZ_TITEL                0.010495
KBA05_ANTG3              0.006882
PLZ8_HHZ                 0.006031
SOHO_KZ                  0.000277
ANZ_PERSONEN            -0.005922
REGIOTYP                -0.008129
LIFE_STAGE              -0.008326
KBA05_ANTG2             -0.010749
HEALTH_TYP              -0.013749
MIN_GEBAEUDEJAHR        -0.014119
KKK                     -0.017450
KBA13_ANZAHL_PKW        -0.021148
KBA05_ANTG1             -0.022671
HH_EINKOMMEN_SCORE      -0.023407
KBA05_GBZ               -0.026829
MOBI_REGIO              -0.029614
GEBAEUDETYP_RASTER      -0.030395
BALLRAUM                -0.035567
PLZ8_GBZ                -0.036451
KONSUMNAEHE             -0.038344
INNENSTADT              -0.043077
PLZ8_ANTG1              -0.044711
FINANZ_HAUSBAUER        -0.045769
ONLINE_AFFINITAET       -0.056799
SEMIO_PFLICHT           -0.077715
SEMIO_TRADV             -0.088932
FINANZ_UNAUFFAELLIGER   -0.094609
FINANZ_SPARER           -0.103610
DECADE                  -0.108221
SEMIO_ERL               -0.169458
FINANZ_ANLEGER          -0.189672
SEMIO_RAT               -0.217304
SEMIO_KRIT              -0.268380
SEMIO_DOM               -0.312446
SEMIO_KAEM              -0.336687
ANREDE_KZ               -0.368244
Name: 2, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-2.3:-Interpret-Principal-Components">Discussion 2.3: Interpret Principal Components<a class="anchor-link" href="#Discussion-2.3:-Interpret-Principal-Components">&#182;</a></h3><h4 id="For-the-first-Component:">For the first Component:<a class="anchor-link" href="#For-the-first-Component:">&#182;</a></h4>
<pre><code>PLZ8_ANTG3  --&gt;  0.220711
PLZ8_ANTG4  --&gt;  0.214165
WEALTH      --&gt;  0.201747
KBA05_ANTG1 --&gt; -0.222708
MOBI_REGIO  --&gt; -0.238902
</code></pre>
<ul>
<li><p>PLZ8_ANTG3,PLZ8_ANTG4 and WEALTH have positive correlation with First component.</p>
<ul>
<li>PLZ8_ANTG3 and PLZ8_ANTG4 means #of 6-10 and 10+ Family ( 1- Low Share, 2-High Share) houses have positive correlation with component 1. </li>
<li>Wealth is having positive correlation (High value means low wealth)</li>
</ul>
</li>
<li><p>KBA05_ANTG1, MOBI_REGIO have negative correlation with First component.</p>
<ul>
<li>KBA05_ANTG1 means number of 1-2 family houses in the microcell has negative correlation (1- Lower Share, 4- Very High Share)</li>
<li>MOBI_REGIO means movement patterns (1- Very High Movement, 5- Very Low Movement)</li>
</ul>
</li>
</ul>
<h5 id="Conclusion-for-Component-1:-Related-to-number-of-family,-wealth,-movement">Conclusion for Component 1: Related to number of family, wealth, movement<a class="anchor-link" href="#Conclusion-for-Component-1:-Related-to-number-of-family,-wealth,-movement">&#182;</a></h5><h4 id="In-the-second-Component:">In the second Component:<a class="anchor-link" href="#In-the-second-Component:">&#182;</a></h4>
<pre><code>ALTERSKATEGORIE_GROB --&gt;    0.252939
SEMIO_ERL            --&gt;    0.228755
FINANZ_VORSORGER     --&gt;    0.221934
DECADE               --&gt;   -0.234656
SEMIO_REL            --&gt;   -0.258086

</code></pre>
<ul>
<li><p>ALTERSKATEGORIE_GROB, SEMIO_ERL, FINANZ_VORSORGER  Columns have positive correlation.</p>
<ul>
<li>ALTERSKATEGORIE_GROB means age analysis based on name (1-Young, 4- Old)</li>
<li>SEMIO_ERL means event oriented personality typology (1- Highest affinity, 7- Lowest affinity)</li>
<li>FINANZ_VORSORGER means Be prepared financial typology (1- Very High, 5- Very Low)</li>
</ul>
</li>
<li><p>DECADE and SEMIO_REL have negative correlation with Second component</p>
<ul>
<li>DECADE means Dominating Movement of person's youth years (0- 40s, 6- 90s)</li>
<li>SEMIO_REL means Religious personality typology (1- Highest affinity, 7- Lowest affinity)</li>
</ul>
</li>
</ul>
<h5 id="Conclusion-for-Component-2:-Related-to-Age,-Dominating-Movement,-Personality-and-Financial-Typologies">Conclusion for Component 2: Related to Age, Dominating Movement, Personality and Financial Typologies<a class="anchor-link" href="#Conclusion-for-Component-2:-Related-to-Age,-Dominating-Movement,-Personality-and-Financial-Typologies">&#182;</a></h5><h4 id="Third-Component:">Third Component:<a class="anchor-link" href="#Third-Component:">&#182;</a></h4>
<pre><code>SEMIO_VERT --&gt;  0.347771
SEMIO_SOZ  --&gt;  0.263247
SEMIO_FAM  --&gt;  0.250498
SEMIO_KAEM --&gt; -0.336687
ANREDE_KZ  --&gt; -0.368244


</code></pre>
<ul>
<li><p>SEMIO_VERT, SEMIO_SOZ and SEMIO_FAM Have Positive correlation with the Third Component</p>
<ul>
<li>SEMIO_VERT means dreamful Personality Typology (1- Highest affinity, 7- Lowest affinity)</li>
<li>SEMIO_SOZ means socially minded Personality Typology (1- Highest affinity, 7- Lowest affinity)</li>
<li>SEMIO_FAM family minded Personality Typology (1- Highest affinity, 7- Lowest affinity)</li>
</ul>
</li>
<li><p>SEMIO_KAEM, ANREDE_KZ have negative correlation with Third component</p>
<ul>
<li>SEMIO_KAEM means combative attitude Personality Typology (1- Highest affinity, 7- Lowest affinity)</li>
<li>ANREDE_KZ means gender (1- Male, 2- Female)</li>
</ul>
</li>
</ul>
<h5 id="Conclusion-for-Component-3:-Related-to-Age-and-Personality-Typology">Conclusion for Component 3: Related to Age and Personality Typology<a class="anchor-link" href="#Conclusion-for-Component-3:-Related-to-Age-and-Personality-Typology">&#182;</a></h5>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Step-3:-Clustering">Step 3: Clustering<a class="anchor-link" href="#Step-3:-Clustering">&#182;</a></h2><h3 id="Step-3.1:-Apply-Clustering-to-General-Population">Step 3.1: Apply Clustering to General Population<a class="anchor-link" href="#Step-3.1:-Apply-Clustering-to-General-Population">&#182;</a></h3><p>You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.</p>
<ul>
<li>Use sklearn's <a href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans">KMeans</a> class to perform k-means clustering on the PCA-transformed data.</li>
<li>Then, compute the average difference from each point to its assigned cluster's center. <strong>Hint</strong>: The KMeans object's <code>.score()</code> method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.</li>
<li>Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. <strong>Warning</strong>: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.</li>
<li>Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[37]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">kmeans_prediction</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span><span class="n">cluster_list</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    INPUT: dataset, list of cluster numbers</span>
<span class="sd">    Creating K-Means Clustering Models and fitting dataset</span>
<span class="sd">    OUTPUT: Models and score</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">starttime</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Clustering has began...&quot;</span><span class="p">)</span>
    <span class="n">score</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">models</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">num</span> <span class="ow">in</span> <span class="n">cluster_list</span><span class="p">:</span>
        <span class="n">model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">num</span><span class="p">)</span>
        <span class="n">fitted</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
        <span class="n">models</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fitted</span><span class="p">)</span>
        <span class="n">score</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">fitted</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">dataset</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicted </span><span class="si">{}</span><span class="s2"> to </span><span class="si">{}</span><span class="s2"> Clusters in </span><span class="si">{}</span><span class="s2"> Minutes&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cluster_list</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">cluster_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
                                                             <span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(((</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">starttime</span><span class="p">)</span><span class="o">/</span><span class="mi">60</span><span class="p">),</span><span class="mi">2</span><span class="p">)))</span>
    <span class="k">return</span> <span class="n">models</span><span class="p">,</span><span class="n">score</span>


<span class="c1"># Over a number of different cluster counts...    </span>
<span class="c1"># run k-means clustering on the data and...</span>
<span class="c1"># compute the average within-cluster distances.</span>
<span class="n">cluster_nums</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">30</span><span class="p">)</span>

<span class="n">fitted_list</span><span class="p">,</span><span class="n">score_list</span> <span class="o">=</span> <span class="n">kmeans_prediction</span><span class="p">(</span><span class="n">pca_25</span><span class="p">,</span><span class="n">cluster_nums</span><span class="p">)</span>


</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Clustering has began...
Predicted 1 to 29 Clusters in 81.95 Minutes
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[38]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate the change in within-cluster distance across number of clusters.</span>
<span class="c1"># HINT: Use matplotlib&#39;s plot function to visualize this relationship.</span>

<span class="n">score_diff</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">score_list</span><span class="p">)):</span>
    <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">!=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">score_list</span><span class="p">))))</span> <span class="ow">and</span> <span class="p">((</span><span class="n">score_list</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">score_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="p">)</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">)</span> <span class="p">:</span> <span class="c1">#Find the values which is not decreasing in the next iteration</span>
        <span class="n">score_diff</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Index of scores which is not decreasing: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">score_diff</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">cluster_nums</span><span class="p">,</span><span class="n">score_list</span><span class="p">,</span><span class="n">marker</span> <span class="o">=</span> <span class="s1">&#39;o&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">score_diff</span><span class="p">:</span>
    <span class="n">_</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">cluster_nums</span><span class="p">)[</span><span class="n">i</span><span class="p">],</span><span class="n">score_list</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="n">marker</span> <span class="o">=</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span><span class="n">markersize</span> <span class="o">=</span> <span class="mi">10</span><span class="p">)</span>

    
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Index of scores which is not decreasing: [13]
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXwAAAEDCAYAAAA2k7/eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de3zU9Z3v8dcnF5KQQMIlKAQQvCEKSDSiKx4vVEVtq2jtWW21trXLtqu72vZ4Vttd29rTVktbe7q7tqVqj9bbtoqX1UWkivcKhovcUS6KJGiCECCQkNvn/DG/0CHMTCYwydzez8djHpn5/r6/mc/vMfqZH9+ruTsiIpL5cpIdgIiI9A0lfBGRLKGELyKSJZTwRUSyhBK+iEiWUMIXEckSKZvwzex+M6szs5Vx1L3bzJYFj3fNrKEvYhQRSSeWquPwzexsoBF40N0n9OC8fwQq3f2rvRaciEgaStk7fHd/FdgeXmZmx5jZ82a22MxeM7MTIpx6NfBonwQpIpJG8pIdQA/NBr7u7u+Z2enAPcC0zoNmdhQwFngpSfGJiKSstEn4ZlYCnAn8ycw6iwu6VLsKeNzd2/syNhGRdJA2CZ9Q81ODu0+OUecq4IY+ikdEJK2kbBt+V+6+C9hkZp8HsJCTO4+b2ThgEPCXJIUoIpLSUjbhm9mjhJL3ODPbYmbXA18Erjezd4BVwGVhp1wNPOapOuxIRCTJUnZYpoiIJFbK3uGLiEhipWSn7dChQ33MmDHJDkNEJG0sXrx4m7uXx6qTkgl/zJgxVFdXJzsMEZG0YWYfdFdHTToiIlmi2zt8MysEXiU0ySmP0MSm73WpczdwXvCyPzDM3cuCY+3AiuDYZne/NEGxi4hID8TTpLMPmObujWaWD7xuZnPd/a3OCu7+zc7nnYuXhZ3f1M1kKRER6QPdNul4SGPwMj94xBrLqcXLRERSUFxt+GaWa2bLgDpgvrsvjFIv0uJlhWZWbWZvmdmMGJ8xM6hXXV9f34NLEBGReMQ1SidYjGyymZUBT5rZBHePtDFJpMXLRrt7rZkdDbxkZivcfUOEz5hNaDVMqqqqejwb7KmlNcyat47ahiZGlBVxy/RxzKis6OnbiIhkrB6N0nH3BuBl4KIoVa6iS3OOu9cGfzcG51YefNrheWppDbfNWUFNQxMO1DQ0cducFTy1tCbRHyUikra6TfhmVh7c2WNmRcD5wNoI9Q5avMzMBplZQfB8KDAVWJ2Y0P9q1rx1NLUeuCJyU2s7s+atS/RHiYikrXiadIYDD5hZLqEfiD+6+7NmdgdQ7e7PBPUiLV42HvitmXUE597p7glP+LUNTT0qFxHJRt0mfHdfToRmGHe/vcvr70eo8yYw8TDii8uIsiJqIiT3EWVFvf3RIiJpIyNm2t4yfRxF+bkHlBXl53LL9HFJikhEJPWk5Fo6PdU5GueO/1rF9r2tlA8o4LuXjNcoHRGRMBlxhw+hpP/UDWcBcPP5xynZi4h0kTEJH2DU4CJKi/JZWbMz2aGIiKScjEr4ZsaEioGsUMIXETlIRiV8gAkVpaz7aDctbR3JDkVEJKVkXMKfWFFKa7vz7se7kx2KiEhKyciED6hZR0Ski4xL+KMH92dAYZ4SvohIFxmX8M2MCSNKNVJHRKSLjEv4ABNHlrJ2625a29VxKyLSKSMT/oSKUlraO9RxKyISJiMTfmfHrZp1RET+KiMT/lGD+1NSoI5bEZFwGZnwc3KMk0YMZEXNrmSHIiKSMuLZ8arQzBaZ2TtmtsrMfhChzpfNrN7MlgWPr4Udu87M3gse1yX6AqKZWFHKmq271HErIhKIZ3nkfcA0d280s3zgdTOb6+5vdan3n+5+Y3iBmQ0GvgdUAQ4sNrNn3H1HIoKPZeLIUlraOlhf18j44QN7++NERFJet3f4HtIYvMwPHh7jlHDTgfnuvj1I8vOJvgF6Qk3QjFsRkQPE1YZvZrlmtgyoI5TAF0ao9jkzW25mj5vZqKCsAvgwrM6WoCzSZ8w0s2ozq66vr+/BJUQ2dkgxxf1yNVJHRCQQV8J393Z3nwyMBKaY2YQuVf4LGOPuk4A/Aw8E5Rbp7aJ8xmx3r3L3qvLy8viijyHUcVuqO3wRkUCPRum4ewPwMl2aZdz9E3ffF7z8HXBq8HwLMCqs6kig9pAiPQQTgo7bNnXciojENUqn3MzKgudFwPnA2i51hoe9vBRYEzyfB1xoZoPMbBBwYVDWJyaOHEhzawcb6vf01UeKiKSseEbpDAceMLNcQj8Qf3T3Z83sDqDa3Z8B/snMLgXagO3AlwHcfbuZ/RB4O3ivO9x9e6IvIprwpZLHHTmgrz5WRCQldZvw3X05UBmh/Paw57cBt0U5/37g/sOI8ZCNHVpC/6Dj9spTRyYjBBGRlJGRM2075eYYJw7XHrciIpDhCR9CHbera3fR3hHv1AERkcyU8Ql/YkUpTa3tbKhv7L6yiEgGy/yEP1JLJYuIQBYk/GPKSyjKz1U7vohkvYxP+Lk5xokjBuoOX0SyXsYnfIAJIwaySh23IpLlsiPhV5Syt6WdTdvUcSsi2SsrEv5fO261A5aIZK+sSPjHlpdQmJ+jjlsRyWpZkfDzcnMYrxm3IpLlsiLhA0wYEZpx26GOWxHJUlmT8CdWlNK4r41Nn2ipZBHJTlmT8Dv3uNV4fBHJVlmT8I87ooR+eTlK+CKSteLZ8arQzBaZ2TtmtsrMfhChzrfMbHWwifmLZnZU2LF2M1sWPJ5J9AXEK18dtyKS5eK5w98HTHP3k4HJwEVmdkaXOkuBqmAT88eBn4Yda3L3ycHj0oREfYgmVgxkVY06bkUkO3Wb8D2kc4pqfvDwLnUWuPve4OVbhDYrTzkTRpSye18bH2zf231lEZEME1cbvpnlmtkyoA6Y7+4LY1S/Hpgb9rrQzKrN7C0zmxHjM2YG9arr6+vjCr6nJoTtcSsikm3iSvju3u7ukwnduU8xswmR6pnZNUAVMCuseLS7VwFfAH5pZsdE+YzZ7l7l7lXl5eU9uoh4HX/EAPrl5rBKCV9EslCPRum4ewPwMnBR12Nmdj7wXeBSd98Xdk5t8HdjcO5BG6L3lX55OZwwfIDu8EUkK8UzSqfczMqC50XA+cDaLnUqgd8SSvZ1YeWDzKwgeD4UmAqsTlz4PTehopSVNTtxV8etiGSXeO7whwMLzGw58DahNvxnzewOM+scdTMLKAH+1GX45Xig2szeARYAd7p7chP+iFJ2NbexWR23IpJl8rqr4O7LidAM4+63hz0/P8q5bwITDyfARJsY1nF71JDiJEcjItJ3smambafjjywhP9fUji8iWSfrEn5BXi7jjhzAKm2GIiJZJusSPsCAgjze2LCNsbc+x9Q7X+KppTXJDklEpNd124afaZ5aWkP1BzvoHKRT09DEbXNWADCjsiKJkYmI9K6su8OfNW8dre0HDslsam1n1rx1SYpIRKRvZF3Cr21o6lG5iEimyLqEP6KsqEflIiKZIusS/i3Tx1GUn3tAWVF+LrdMH5ekiERE+kbWddp2dszOmreWmoZmCvJy+MkVE9VhKyIZL+vu8CGU9N+49VP806eOo6W9gzOOHpLskEREel1WJvxOl1dW4A5PL9M4fBHJfFmd8McOLWbyqDKe1MQrEckCWZ3wAa44pYK1H+1mda2WWhCRzJb1Cf8zk0aQl2M8uXRLskMREelVWZ/wBxf349xxw3h6WS3tHdoURUQyV9YnfAg169Tt3scb67clOxQRkV4TzxaHhWa2yMzeMbNVZvaDCHUKzOw/zWy9mS00szFhx24LyteZ2fTEhp8Y004YxoDCPHXeikhGi+cOfx8wzd1PBiYDF5nZGV3qXA/scPdjgbuBuwDM7ETgKuAkQhuf32NmuaSYwvxcPjNpOM+v/Ig9+9qSHY6ISK/oNuF7SGPwMj94dG3svgx4IHj+OPApM7Og/DF33+fum4D1wJSERJ5gl1eOpKm1nRdWf5TsUEREekVcbfhmlmtmy4A6QpuYL+xSpQL4EMDd24CdwJDw8sCWoCzSZ8w0s2ozq66vr+/ZVSRA1VGDGDmoiDlL1KwjIpkproTv7u3uPhkYCUwxswldqlik02KUR/qM2e5e5e5V5eXl8YSVUDk5xuWVFbyxfht1u5r7/PNFRHpbj0bpuHsD8DKh9vhwW4BRAGaWB5QC28PLAyOB2kOMtdddXllBh8PTy1I2RBGRQxbPKJ1yMysLnhcB5wNru1R7BrgueH4l8JK7e1B+VTCKZyxwHLAoUcEn2tHlJZw8qow5Gq0jIhkonjv84cACM1sOvE2oDf9ZM7vDzC4N6twHDDGz9cC3gFsB3H0V8EdgNfA8cIO7tyf6IhLpisoK1mzdxdqPtNSCiGQWc0+92aVVVVVeXV2dlM/+pHEfp//4Ra4/ayy3XTI+KTGIiPSUmS1296pYdTTTtoshJQWcc3w5Ty2r0VILIpJRlPAjuPyUCj7etY+/bPgk2aGIiCSMEn4E548/ggEFeczRCpoikkGU8CMozM/lkomhpRb2tmipBRHJDEr4UVx+SgV7W9p5YdXHyQ5FRCQhlPCjmDJmMBVlRRqTLyIZQwk/ipwcY0blCF5/r15LLYhIRlDCj+HyypF0OFxw9yuMvfU5pt75Ek/pjl9E0lResgNIZStrdmIGO5tCHbc1DU3cNmcFADMqIy76KSKSsnSHH8OseevoOhG5qbWdWfPWJScgEZHDoIQfQ21DU4/KRURSmRJ+DCPKinpULiKSypTwY7hl+jiK8g/cgrcwL4dbpo9LUkQiIodOnbYxdHbMzpq3jtqGJhw4dlgJl00ekdzAREQOgRJ+N2ZUVuxP/L95ZQN3zl3L8ys/4uKJw5McmYhIz8Sz49UoM1tgZmvMbJWZ3RShzi1mtix4rDSzdjMbHBx738xWBMeSs8h9gnztrLGcOHwgtz+zip1NrckOR0SkR+Jpw28Dvu3u44EzgBvM7MTwCu4+y90nBxud3wa84u7bw6qcFxyPuTh/qsvLzeGuz03ik8Z93Dm36y6PIiKprduE7+5b3X1J8Hw3sAaINevoauDRxISXeiaOLOX6s8by6KLNLNyo9fJFJH30aJSOmY0BKoGFUY73By4CnggrduAFM1tsZjNjvPdMM6s2s+r6+vqehNXnvnnB8YwcVMRtT66guTWlt+gVEdkv7oRvZiWEEvnN7h5th+/PAm90ac6Z6u6nABcTag46O9KJ7j7b3avcvaq8vDzesJKif788fnz5RDbW7+GeBeuTHY6ISFziSvhmlk8o2T/s7nNiVL2KLs057l4b/K0DngSmHFqoqeXs48u5vLKCX7+ygXc/3p3scEREuhXPKB0D7gPWuPsvYtQrBc4Bng4rKzazAZ3PgQuBlYcbdKr4l0+Pp6Qgj1ufWE6HNjwXkRQXzx3+VOBaYFrY0MtLzOzrZvb1sHqXAy+4+56wsiOA183sHWAR8Jy7P5+w6JNsSEkB//qZE1myuYGHFn6Q7HBERGIy77ocZAqoqqry6ur0GLLv7nzp/kUs+WAH8791jtbZEZGkMLPF3Q1911o6h8nM+NGMiexra2faz17WRikikrK0tEICLNm8AzCa2zoAbZQiIqlJd/gJMGveOtq6dNpqoxQRSTVK+AmgjVJEJB0o4SdAtI7a4WWFfRyJiEh0SvgJEGmjFICJIwYmIRoRkciU8BNgRmUFP7liIhVlRRhQUVbIGWMHM291HU8s3pLs8EREAI3SSZjwjVIAWto6+PLvF3HrnOWMHFTE6UcPSWJ0IiK6w+81/fJy+PUXT2XU4P78/UOL2bRtT/cniYj0IiX8XlTaP5/ff/k0DPjq/3ubhr0tyQ5JRLKYEn4vO2pIMbO/VEXNjib+/g+LaQkmZ4mI9DUl/D5w2pjB/PTKSSzctJ3b5qwgFdcvEpHMp07bPjKjsoL3P9nDL//8HkeXF3PDeccmOyQRyTJK+H3opk8dx6Zte5g1bx33vraRhr2tjCgr4pbp47Tmjoj0OjXp9CEz46xjh5JjsGNvK85fF1rT6poi0tvi2fFqlJktMLM1ZrbKzG6KUOdcM9sZtkHK7WHHLjKzdWa23sxuTfQFpJtf/vk9um6OpYXWRKQvxNOk0wZ8292XBNsVLjaz+e6+uku919z9M+EFZpYL/AdwAbAFeNvMnolwbtbQQmsikizd3uG7+1Z3XxI83w2sAeJtcJ4CrHf3je7eAjwGXHaowWaCaAutDS0p6ONIRCTb9KgN38zGAJXAwgiH/8bM3jGzuWZ2UlBWAXwYVmcLUX4szGymmVWbWXV9fX1PwkorkRZaM2BXUwvV729PTlAikhXiTvhmVgI8Adzs7ru6HF4CHOXuJwP/BjzVeVqEt4o4CN3dZ7t7lbtXlZeXxxtW2jl4obUivnfpiVQM6s+X7l/Emxu2JTtEEclQcQ3LNLN8Qsn+YXef0/V4+A+Au/+3md1jZkMJ3dGPCqs6Eqg9vJDTX9eF1gAumTica+5dyFd+/zazv1TFOcdn7o+eiCRHPKN0DLgPWOPuv4hS58igHmY2JXjfT4C3gePMbKyZ9QOuAp5JVPCZZNiAQh6b+TccU17C3z1QzZ9Xf5zskEQkw8TTpDMVuBaYFjbs8hIz+7qZfT2ocyWw0szeAX4FXOUhbcCNwDxCnb1/dPdVvXAdGWFwcT8e+bvTGT98AF9/aDFzV2xNdkgikkEsFdd1qaqq8urq6mSHkTS7mlv5yu/fZtmHDXxhyiheWltPbUOTZuWKSFRmttjdq2LV0UzbFDSwMJ8HvzqFMUP684e3NlPT0KRZuSJy2JTwU1RxQR5NLe0HlWtWrogcKiX8FLZ1Z3PEcs3KFZFDoYSfwqLNyh1eVtjHkYhIJlDCT2GRZuUCDCjMp3FfWxIiEpF0poSfwiLNyv3b00ayvq6Rv/3tX6jbFbnJR0QkEg3LTEML1tVxw8NLGNS/Hw98dQrHDitJdkgikmQalpmhzhs3jMdmnsG+tnau/M2bWnRNROKihJ+mJo0sY843pjKofz++cO9Cnl+pWbkiEpv2tE1jo4f054lvnMnXHnibbzy8hMsnj2Dhph2alSsiEekOP80NLu7Hw187g5OGD2TO0lrNyhWRqJTwM0BRv1y27205qFyzckUknBJ+htjaoFm5IhKbEn6GiDYrtyA/h22N+/o4GhFJRUr4GSLSrNy8HKO1rYML736VZ5fXkopzLkSk78Sz49UoM1tgZmvMbJWZ3RShzhfNbHnweNPMTg479r6ZrQg2TtFsql4SaVbuzz5/MnNvPptRg4q48ZGl/MPDS3S3L5LFup1pa2bDgeHuvsTMBgCLgRnuvjqszpmEtkDcYWYXA99399ODY+8DVe4e9+7cmmmbWG3tHfzutU3cPf9digtyueOyCbS1d/CzF97VEE6RDBHPTNtux+G7+1Zga/B8t5mtASqA1WF13gw75S1Cm5VLisjLzeEb5x7D+eOH8b/+9A7/+OhScgw6gt/6ziGcgJK+SAbrURu+mY0BKoGFMapdD8wNe+3AC2a22Mxm9jRASZzjjhjAE984k4GFefuTfScN4RTJfHHPtDWzEuAJ4GZ33xWlznmEEv5ZYcVT3b3WzIYB881srbu/GuHcmcBMgNGjR/fgEqQn8nJz2N0ceWllDeEUyWxx3eGbWT6hZP+wu8+JUmcScC9wmbt/0lnu7rXB3zrgSWBKpPPdfba7V7l7VXl5ec+uQnok2hBOgJ8+v5b63erYFclE8YzSMeA+Qp2yv4hSZzQwB7jW3d8NKy8OOnoxs2LgQmBlIgKXQxdpCGdBXg6TRg7k169sYOpdL/HdJ1fwwSd7AHhqaQ1T73yJsbc+x9Q7X9JyDSJpKp4mnanAtcAKM1sWlH0HGA3g7r8BbgeGAPeEfh9oC3qLjwCeDMrygEfc/fmEXoH0WGfH7Kx56w4apbOxvpHfvbaRP1Vv4dFFmzl5ZBmrt+5iX1sHoA5ekXSmDVAkorpdzdz3xiZmv7KRSP+FVJQV8cat0/o8LhGJTBugyCEbNrCQ2y4eH/W4OnhF0o8SvsQUrYM3x4z7X9/EHm2mLpI2lPAlpkgdvP1ycxg9pIg7nl3NmXe+xKx5a6nbHVqtUx28IqlLO15JTLE6eJds3sHsVzZyz8sb+N2rmzhldBlLP2xQB69IilKnrRy2Tdv2cN/rG3norc0Rj6uDV6T3qdNW+sTYocX8nxkTsSjHaxqaaG5t79OYRORgatKRhBlRVkRNlNE7p/xwPtNOGMYlE4dz3rhhzFv1UcRmIhHpPUr4kjC3TB/HbXNW0BR2N1+Yn8NXzhzLzuZW5q38iGeXbyU/x+hwp12rdYr0KSV8SZhYHbwAd1x6Eos2bedrD1azt+XAJp7O1TqV8EV6jxK+JNSMyoqoSTsvN4czjx1KU0vk9vyahia272lhcHG/3gxRJGup01b6XKzVOv/HXaFx/Q17W/owIpHsoIQvfS7SZK6i/FxuvWgc550wjHte3sBZdy3gFy+sY+feVk3mEkkQNelIn+uurf8fP9rN/33xXX710np+++oG2jugLdiiSx28IodOE68kZa2u3cUV97xBczBzN5wmc4kcKCGbmIsky4kjBu5fpqGrmoYm/mPBeqaMHcykkaUU5IWaiJ5aWqPx/SJRKOFLSos2mSsvx/Zvul6Ql0Pl6DJKC/NZsK6elnat5SMSSTxbHI4yswVmtsbMVpnZTRHqmJn9yszWm9lyMzsl7Nh1ZvZe8Lgu0RcgmS1aB+/PPn8yS//1AmZfeyrXnHEUe/a1M2/1x/uTfafO8f0iEt8dfhvwbXdfEuxPu9jM5rv76rA6FwPHBY/TgV8Dp5vZYOB7QBXgwbnPuPuOhF6FZKzuOngvPOlILjzpSADG3vpcxN25tFmLSEi3Cd/dtwJbg+e7zWwNUAGEJ/zLgAc91AP8lpmVmdlw4FxgvrtvBzCz+cBFwKMJvQrJaLEmc4WL1vxjBo8s3MzfnjaK3JxgibcNG+DnP4eHHoLGRigpgWuugW9/G445JtGXIJISejQO38zGAJXAwi6HKoAPw15vCcqilUd675lmVm1m1fX19T0JSwSI3PxTkJfD2CHFfOfJFcz4jzdYsnkHzJ0LkybBvffC7t3gHvp7772h8rlzk3QFIr0r7oRvZiXAE8DN7r6r6+EIp3iM8oML3We7e5W7V5WXl8cblsh+Myor+MkVE6koK8IIDd2863OT+PO3z+FXV1dSt7uZm3/0BC2XXwF790Jr64Fv0NoaKr/yytC/AMJo8pdkgrhG6ZhZPqFk/7C7z4lQZQswKuz1SKA2KD+3S/nLhxKoSDyiNf9cevIIPnXCMNZ+7tqDE31Xra1w993w7/8OhJJ9+CqgGv0j6arbiVdmZsADwHZ3vzlKnU8DNwKXEOq0/ZW7Twk6bRcDnaN2lgCndrbpR6OJV9JrBg4MNd90o6momG/NfoXGfW28tfETWtsP/v9Ek78klSRq4tVU4FpghZktC8q+A4wGcPffAP9NKNmvB/YCXwmObTezHwJvB+fd0V2yF+lVjY1xVSto3sv6ukaKC/IiJnsI3em7O6F7IpHUp6UVJLvEeYfPwIGwcycAU+98KepOXhMqBnLDuccy/aQjyclR4pfk0Z62Il1dcw3k58euk58P1167/2Wk0T+F+TlcddooGpvb+MbDS7jwl68yZ8kW2to71MErKUt3+JJdNmwIDb3cuzd6nf79YfnyA8bjR1ujp73DeW7FVu5ZsJ61H+1mcHE+u5vbDmgGKsrP5SdXTFQHr/SqeO7wlfAl+8ydGxp62dp64Iid/PzQ4/HH4eKLe/SWHR3Oi2vr+IeHF6uDV5JCq2WKRHLxxaE7+Lvvhj/84a8zba+9Fr75zUOaaZuTY1xw4hG0xejg/f4zqzhtzGBOGzuIYQMKgZ6t7qmVQOVw6Q5fJIGidfAW5OWQm2P7N28/emgxwwYUsHjzjriaf7rOBYhVV7KTmnRE+lisxPzpScNZVbuLRZs+YdGm7by4pi7itPO8HGPyqDIGFuUzoDCPgYX5PLm0hsZ9bQfVVVORdFKTjkgf6251z8mjypg8qoyZZx/D2Fufi/gebR1Ov7wc6nY3s6G+jV1NrRGTPYSait5Yv43Jo8ooLgj976ymH4lGCV8kwQ53dc+KsiIe+bszDiibeueL1DQ0R3yfL967kNwc46QRAxlUlM9fNn5CS7v2AJaDaRy+SJJE29zllunjItQ9IWLdn1wxgQe+OoV/OPcYivvl8cp72/Yn+05Nre3c9fzaxF+ApB3d4YskSXfNPz2pe87xoRVmo20Cs3VnM5/9t9c567ihnHXsUE49ahCF+blq/sky6rQVySDRRgkNKMxj/JEDWbJ5B20dTkFeDmOG9GdD/R7aOuKbJKYfh9SmTluRLHPL9HERRwn98LIJzKisYM++NhZu+oTX3/uEB//y/gHJHkLNP7c/vZJ+eTkcXV7MmCHF+/8l0JMlovXjkJp0hy+SYeJNttGaf8KZwYjSIrY17mNfW8dBx4eXFvLGP087YOE4zRlIDo3DF5GoojX/DC8t5HdfqmLjtj1srG9k07Y9PL2sNur7mEFpUT6D+vejtCifNVt3Rfxx0JyB3qUmHRGJKlrzzz9fdAITKkqZUFG6v7z6/R0RfxxKi/K47m/G0NDUyo69rTTsbYmY7CHUDFS3u3n/shLS97pN+GZ2P/AZoM7dJ0Q4fgvwxbD3Gw+UB5ufvA/sBtqBtu5+fUSk7/RklFC0H4cfXDrhoPqx9g84/ccvMmXMYD4zaTjTJxzJsAGFau/vQ/FscXg20Ag8GCnhd6n7WeCb7j4teP0+UOXu23oSlJp0RFJPvIk5Whv+P33qWJpbO/jvFVt5r64RMzh6SDGbd+yNezlp/ThEl5AmHXd/1czGxPmZVwOPxllXRNJIvDOIu/uXwzcvOJ73Pt7Ncyu28u8vrY84UuhfnlrJzqZWjhhYyJGlhRw5sJA319fz3adWaaTQYYir0zZI+M/GusM3s/7AFuDYzn1rzWwTsANw4LfuPjvG+TOBmQCjR48+9YMPPoj/KkQkLcUzUqg7g4v78dD1p1MxqIjSotBuZtk4UqivO20/C7zRZZPyqe5ea2bDgPlmttbdXxrpb3gAAAZySURBVI10cvBjMBtCTToJjEtEUlS09YRGlBXyzI1n8dHOZj7e1cxHu5r57pMrI77H9j0tXPKr14DQBLOKsiLe37aH5i6dx02t7cyaty6r/zWQyIR/FV2ac9y9NvhbZ2ZPAlOAiAlfRLJPtM7g/z39BIaWFDC0pGD/aKF7FmyI+ONQXlLADy47iZodTWzZsZeahibWfhR5o/qahiZufGQJxw4r4dhhJRxTXsLKmp3c/nT8TUXpLCEJ38xKgXOAa8LKioEcd98dPL8QuCMRnycimSERI4W+++nxXDJx+AF1Y21Es3zLTp5bsZVYrdlNre3cOXctl00egdmBk8rS+V8C8YzSeRQ4FxgKfAx8D8gHcPffBHW+DFzk7leFnXc08GTwMg94xN1/FE9QGqUjIpEc7kihzjb85tZ2NtQ3sr6ukZseWxb184r75TK2vJixQ0toaW3npXV1KTuiSDNtRSRrxZtwo/1roLQon8srK9i4bQ+btjXy4fbIcwvyc41zxw1jaEkB5SX9KB9QwMb6Rh5e9CEtYf0Ivf3joIQvItKNeEf0xBpRdMKRA6jfvY/te1tiNhUV5OXwuVNHMqK0kCNLixhRWsiq2l38fP46mlvj+3GIRksriIh0I95+hFg7lD1/89kAtLV3sH1vC6f/6MWIPw772jp4fuVHbN/TEjOmWCOKDocSvohkvXgmlUXrNA7foSwvN4dhAwpj/ji8ces0mlvb2bqzma0NTXzh3oURP682yvIUh0NbHIqIxGFGZQU/uWIiFWVFGKHkHa3ZpbvtKwvzcxk7tJgzjx1KRVlRxM8bEaX8cOgOX0QkTolaXiJcPP9ySBQlfBGRXtAbPw6HSwlfRCTJ4v1xOFxqwxcRyRJK+CIiWUIJX0QkSyjhi4hkCSV8EZEskZJr6ZhZPRC+5dVQoEf74qaJTL0uyNxr03Wln0y9tq7XdZS7l8c6ISUTfldmVt3dokDpKFOvCzL32nRd6SdTr+1QrktNOiIiWUIJX0QkS6RLwp+d7AB6SaZeF2Tutem60k+mXluPryst2vBFROTwpcsdvoiIHCYlfBGRLJHSCd/MLjKzdWa23sxuTXY8iWRm75vZCjNbZmZpu4Gvmd1vZnVmtjKsbLCZzTez94K/g5IZ46GKcm3fN7Oa4HtbZmaXJDPGQ2Fmo8xsgZmtMbNVZnZTUJ7W31uM68qE76zQzBaZ2TvBtf0gKB9rZguD7+w/zaxfzPdJ1TZ8M8sF3gUuALYAbwNXu/vqpAaWIGb2PlDl7mk9IcTMzgYagQfdfUJQ9lNgu7vfGfxQD3L3f05mnIciyrV9H2h0958lM7bDYWbDgeHuvsTMBgCLgRnAl0nj7y3Gdf1P0v87M6DY3RvNLB94HbgJ+BYwx90fM7PfAO+4+6+jvU8q3+FPAda7+0Z3bwEeAy5LckzShbu/CmzvUnwZ8EDw/AFC/9OlnSjXlvbcfau7Lwme7wbWABWk+fcW47rSnoc0Bi/zg4cD04DHg/Juv7NUTvgVwIdhr7eQIV9ewIEXzGyxmc1MdjAJdoS7b4XQ/4TAsCTHk2g3mtnyoMknrZo9ujKzMUAlsJAM+t66XBdkwHdmZrlmtgyoA+YDG4AGd28LqnSbI1M54VuEstRsfzo0U939FOBi4Iag+UBS36+BY4DJwFbg58kN59CZWQnwBHCzu+9KdjyJEuG6MuI7c/d2d58MjCTUAjI+UrVY75HKCX8LMCrs9UigNkmxJJy71wZ/64AnCX2BmeLjoD21s121LsnxJIy7fxz8j9cB/I40/d6CduAngIfdfU5QnPbfW6TrypTvrJO7NwAvA2cAZWbWuVVttzkylRP+28BxQS90P+Aq4Jkkx5QQZlYcdCphZsXAhcDK2GellWeA64Ln1wFPJzGWhOpMiIHLScPvLegAvA9Y4+6/CDuU1t9btOvKkO+s3MzKgudFwPmE+igWAFcG1br9zlJ2lA5AMHzql0AucL+7/yjJISWEmR1N6K4eQhvJP5Ku12ZmjwLnElqq9WPge8BTwB+B0cBm4PPunnadn1Gu7VxCTQMOvA/8fWe7d7ows7OA14AVQEdQ/B1C7d1p+73FuK6rSf/vbBKhTtlcQjfqf3T3O4Jc8hgwGFgKXOPu+6K+TyonfBERSZxUbtIREZEEUsIXEckSSvgiIllCCV9EJEso4YuIZAklfBGRLKGELyKSJf4/rHFAoUMKJIsAAAAASUVORK5CYII=
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[39]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-fit the k-means model with the selected number of clusters and obtain</span>
<span class="c1"># cluster predictions for the general population demographics data.</span>

<span class="n">after_fitted_list</span><span class="p">,</span><span class="n">after_score_list</span> <span class="o">=</span> <span class="n">kmeans_prediction</span><span class="p">(</span><span class="n">pca_25</span><span class="p">,[</span><span class="mi">16</span><span class="p">])</span>
<span class="n">azdias_pred</span> <span class="o">=</span> <span class="n">after_fitted_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pca_25</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Clustering has began...
Predicted 16 to 16 Clusters in 3.02 Minutes
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-3.1:-Apply-Clustering-to-General-Population">Discussion 3.1: Apply Clustering to General Population<a class="anchor-link" href="#Discussion-3.1:-Apply-Clustering-to-General-Population">&#182;</a></h3><p>I created a function which is taking dataset and cluster number list as an input and returning list of fitted kmeans models and corresponding scores. After that i fit my pca(n_components = 25) into Kmeans with 1 to 29 Clusters. It takes nearly 82 Minutes so this is very long process. Then I plotted the cluster numbers and Kmeans scores for finding elbow curl.</p>
<p>In the plot there is no obvious Elbow curl so that I Take differences between scores for ith and (i+1)th clusters for detecting when the cluster number increas but scores don't decrease.It can be useful information for selecting cluster ,information. And I saw that 14th point  there is no decreasing (Red dots in the plot). I selected n_cluster = 16. It can be more confident</p>
<h5 id="16-Clusters-selection-is-a-good-choice">16 Clusters selection is a good choice<a class="anchor-link" href="#16-Clusters-selection-is-a-good-choice">&#182;</a></h5>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-3.2:-Apply-All-Steps-to-the-Customer-Data">Step 3.2: Apply All Steps to the Customer Data<a class="anchor-link" href="#Step-3.2:-Apply-All-Steps-to-the-Customer-Data">&#182;</a></h3><p>Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.</p>
<ul>
<li>Don't forget when loading in the customers data, that it is semicolon (<code>;</code>) delimited.</li>
<li>Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the <code>clean_data()</code> function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)</li>
<li>Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a <code>.fit()</code> or <code>.fit_transform()</code> method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[40]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load in the customer demographics data.</span>
<span class="n">customers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Udacity_CUSTOMERS_Subset.csv&#39;</span><span class="p">,</span><span class="n">delimiter</span> <span class="o">=</span> <span class="s1">&#39;;&#39;</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[41]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply preprocessing, feature transformation, and clustering from the general</span>
<span class="c1"># demographics onto the customer data, obtaining cluster predictions for the</span>
<span class="c1"># customer demographics data.</span>

<span class="c1">#Preprocess Data</span>
<span class="n">customers_cleaned</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">(</span><span class="n">customers</span><span class="p">)</span>

<span class="c1">#Imputation</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Before imputation:&quot;</span><span class="p">)</span>
<span class="n">describe_null</span><span class="p">(</span><span class="n">df_ready</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Imputation Completed!&quot;</span><span class="p">)</span>

<span class="n">pipe_test</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([(</span><span class="s1">&#39;imputer&#39;</span><span class="p">,</span><span class="n">Imputer</span><span class="p">(</span><span class="n">strategy</span> <span class="o">=</span> <span class="s1">&#39;most_frequent&#39;</span><span class="p">)),</span>
                 <span class="p">(</span><span class="s1">&#39;scaler&#39;</span><span class="p">,</span><span class="n">StandardScaler</span><span class="p">())])</span>
<span class="n">customer_filled</span> <span class="o">=</span> <span class="n">pipe_test</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">customers_cleaned</span><span class="p">)</span>

<span class="c1">#Transform with pca</span>
<span class="n">customer_pca</span> <span class="o">=</span> <span class="n">pca_re</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">customer_filled</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Making Prediction...&quot;</span><span class="p">)</span>
<span class="c1">#Prediction</span>
<span class="n">algorithm</span> <span class="o">=</span> <span class="n">after_fitted_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">customers_pred</span> <span class="o">=</span> <span class="n">algorithm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">customer_pca</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Success&quot;</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Null values before converted: 2252274
Converting...
Converted!
# Of Columns with Null: 61
Dropping outlier columns...
More than 25 Percent of values are null in the trained set [&#39;TITEL_KZ&#39;, &#39;AGER_TYP&#39;, &#39;KK_KUNDENTYP&#39;, &#39;KBA05_BAUMAX&#39;, &#39;GEBURTSJAHR&#39;, &#39;ALTER_HH&#39;]
Outlier columns have dropped!
Processing Categorical Attributes...
Success!
Re-Encoding Features...
Success! Clean Dataframe has Created!
Before imputation:
# Of Columns with Null: 18
Imputation Completed!
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>C:\Users\abduy\Anaconda3\lib\site-packages\sklearn\utils\deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.
  warnings.warn(msg, category=DeprecationWarning)
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Making Prediction...
Success
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-3.3:-Compare-Customer-Data-to-Demographics-Data">Step 3.3: Compare Customer Data to Demographics Data<a class="anchor-link" href="#Step-3.3:-Compare-Customer-Data-to-Demographics-Data">&#182;</a></h3><p>At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.</p>
<p>Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.</p>
<p>Take a look at the following points in this step:</p>
<ul>
<li>Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's <a href="https://seaborn.pydata.org/generated/seaborn.countplot.html"><code>countplot()</code></a> or <a href="https://seaborn.pydata.org/generated/seaborn.barplot.html"><code>barplot()</code></a> function could be handy.<ul>
<li>Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!</li>
</ul>
</li>
<li>Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the <code>.inverse_transform()</code> method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.</li>
<li>Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[42]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare the proportion of data in each cluster for the customer data to the</span>
<span class="c1"># proportion of data in each cluster for the general population.</span>

<span class="n">azdias_props</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">azdias_pred</span><span class="p">,</span><span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">azdias_pred</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span>
<span class="n">customer_props</span> <span class="o">=</span>  <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">customers_pred</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">customers_pred</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">6</span><span class="p">),</span><span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;CLUSTER NUMBER&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;PROPORTION&#39;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span><span class="n">y</span> <span class="o">=</span> <span class="n">azdias_props</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;AZDIAS PROPORTIONS&#39;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span><span class="n">y</span> <span class="o">=</span> <span class="n">customer_props</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;b&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;CUSTOMER PROPORTIONS&#39;</span><span class="p">)</span>

<span class="p">[</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;CLUSTER NUMBER&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)]</span>
<span class="p">[</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;PROPORTIONS&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA3UAAAGDCAYAAACMWjkNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deZxkdX3v/9dbBmRRBGXcQOzWKC4kKo5eDMYNScAouCQKcQGXzM3iGjeMxiU/9bpvMT99zFUCVxEXxCVGDbjgkgvosMkyLmiPMMjSiAsYIiCf+8c5bcqmu2ump7bT83o+HvXoOkt9zqequuvTnzrfc06qCkmSJElSN91i3AlIkiRJkpbPpk6SJEmSOsymTpIkSZI6zKZOkiRJkjrMpk6SJEmSOsymTpIkSZI6zKZOkiRJkjrMpk4TK8mpSX6W5JY98/ZOcu0CtxuTfKVd57VJbkhyTXv7fpL3JrlTT5xHJNm0wDaPbWPded783ZIck+TynpgvXyTvqSTVk9vGJEf3LK8kv2qXXZrkHUm2mxfjsUm+1a730yTHJ9mrZ/lRSX7TxvhlknOSPHZevu9r8/3PJOcleea8bWxMcl0b4/L2ud+qXfaFnvxvSHJ9z/T7F3r9NjPnSvLSeY/blOQRW/o6S9JKkeQvkqxvP2Mvaz+DH9ouOzbJ6+etP1dnVrXTD03yf5P8IsnVSf4jyYOS/H3PZ/d/9dSNa5Nc0D42SV6a5AdtTbg4yZvm1d5j2+0dOi+Pd7Xzj2qnj5q3jbnbndvli9adRV6XU9u8r01yVZKT0tby9rFztenqJKckude8x+/V1qKftrXpW721sl1nyZrcPqfz2lp6eVtbd+tZPvc/x7VJft6+Dw9plz215zW4LslNva9Lz2vy6GXkfF6SW/TMe32SY3umn53ku20tvSLJvyW59WKvtbrPpk4TKckU8EdAAb8tIlV1cVXdqvcGPAS4DnhjT4iPVdWtgdsCTwDuCJyZnsZugW3uAjwJ+AXw1HmL3wncCrg3cJs2px/2eRq7tfkdAbw6ycE9y+7XLns48BTgWT15/BnwEeDdwB7AfYFfA99MsntPjNPaGLsBHwQ+nuS2SXYAvgTctX1tbgO8FHhTkr+bl+Pj2hj3Bx4AvAKgqg7peX2PB97S85r/1QKv3ebmfDXw8iS7LvKaLed1lqTOaj+X30VTw+4A7A38/8Bhm/n4XYHPAf9EU/P2BF4H/Lqq3tjzWf5XtHWjvd23DfEeYC3wDODWwCHAo4CPz9vU94Eje7a7Cvhzbv4Zfdr8Ol1VP+lZvmDdWcJz2/XvSVPv3tmz7C3tsj2BS2lq4Vx+twW+CVxPU5P2aB/7kbZm9ZqryQcCfwH8ZRvjxcCbaWrobYD9aWrrKW2tnfOx9vF7AF8FPgFQVcf3vP6HAD+Z9//L79jCnO8MHL7QC5bk4TS/T0e0/wvdm5u/n1phbOo0qZ4BnA4cS08Rma8tZp8E3lxVX5q/vKpuqKoLaBqnWeDFS2zzScDPgX9cYJsPAj5SVT+rqpuq6rtVdeLmPJGqOg24ANh3gWUXAf9BU9xIEuDtwOvbYnBdVV0OPAe4FnjRAjFuAo4BdgLuBjyd5p+CP6+qmfY1+CLwfOAfF2qo2m38+1weW2ILc94AnLbQ82gt+3WWpK5JchuamvO3VXVSVf2q/cz+16p6ab/Ht+4JUFUnVNVv2s/gk6vqO5ux/XsAfwM8tapOq6ob25r5JODgJI/qWf1fgQN6vqg7GPgOcPlm5vk7trTuVNXVNPV+oVp6HU3T0hvrRTQ16NlVdXn7upwAvAF4e1u75sf5LvANYN+2Vr4OeF5VfbF9XzYCT6Zp7J62wONvpPkidM8kqzfnec2zJTm/BXhd21zP9yCa5vrsNq+rq+q4qrpmGTmpI2zqNKmeQfPBeDzwJ0nusMh6/wJcRPOBt6iq+g3wGZq9f4s5EjgB+ChwryT79Sw7HXhDkme2RXCzpHEAzTduZy+w/F5tThe1s/ahacg+MS//m2iK2UELxFjFfzdQP2jX+UJV/Wreqp8EdqTZezc/xl403yJeNH/ZZtjSnP8BeFH7jeR8y3qdJamjHkLzufyprYjxfeA3SY5Lcsi80RH9HAhsqqpv9c6sqktoPo97P7//C/gs/7136BnA/1lu0ltad5LsQdNsLlRLd6EZFdMb6yDgk20t6vVxmpp1zwXi3IemJp8N/CHNe3NS7zpVdS3wBRauxzvQvC4/BX62Oc9rni3J+STgl8BRC8Q5g+Z/p9clOSA9Q2m1ctnUaeKkOY7grsDHq+pMmqEdf7HAei8GHgg8rapqM0L/hGZoykLb3Bt4JM1eoiuAL/O7e+ueR9NgPhe4MMlFSQ7ps72raIYbfgA4uqq+3LPsrCS/otlzdSrNUBtohloAXLZAvMt6lgPsn+TnNN+SHgE8oap+0a5zs8e33yBeNS/Gp5NcA1wCXAm8ps9zWsiW5ExVnQOcDCx0rNxyXmdJ6qrbAVe1n8/LUlW/BB5Kc7jC/wZmk3x2iS9Dey1YL1o3+/ymaeKe0e5hfDjw6QUet397bNncbf7wzC2tO+9pa925bU69hxG8pF12Dc1r8PTNeG6X9Syfc1aSn9HsjfwAzRfGe7D4ezP/tXlym8d1NEM3/2yZ7+mW5Fw0X5K+en7TVlXfAJ4I7Af8G/DTLHD8vlYWmzpNoiOBk6vqqnb6I8wbDtk2fq+j+eC8ejPj7knTZC3k6cCGtuGAprH4iyTbQzO0oz024YE0RfjjwCcW2ds0Z4+q2r2q7l1V75m3bD+aY8eeAvwPYJd2/txzXujYvzv1LAc4vap2q6o9qmr/nuGnVy30+HaP3h7zYjy+HW//COBe3LyAb44tyXnOq4G/TnLH3pnLfJ0lqat+CuyxyBC6OTcC28+btz1wU3ujqjZU1VFVtRfN8MQ70xyn18+C9aJ1s8/vqvomsBp4FfC5dtjjfHO1ae5293nLt7TuPL+Ns2dVPbWqZnuWva2qdgOmaBqqfTbjud2pZ/mc/dp6ffeqelW7p+wqFn9v5r82H2/zuANwPs0XzsuxJTlTVZ8HLqY5JpJ5y75QVY+j+TL7MJo9es9ZZl7qAJs6TZQkO9GMV394mrNMXU4zxvx+Se7XrnMH4GPAS6pq/WbGvQXwOJqx8gt5BnC3nm2+g6bQ3GwvUfut6BtpGrHpLXl+8+JUVX2c5hizV7ezvwdsojn4fH7+T6LZg9jPl4BD2uEovZ5Ec/KS0xfI5Ws0xy++bQuewpwtzrk9buEk4O8XCzqo11mSJthpNMMaH7/EOhfTNC29poFLFhimN/f5eiwLHHu2gK8Ad0ny4N6ZSe5Cc1KQhWrOh2mOT1/20Ms2z62pO/NjXQy8AHh3+38ENLXwSb1niGw9mWYv4ff7hD2NpmY+sXdmW1sPYeHadhXwP4HXLnVitiUsJ+dXAa8Edl4oYHt8+pdp3uvN+Z1QR9nUadI8HvgNcB+aA57vT3PWpm/QDPnYjua4t69U1fv7BUuyfZJ7t4+5I02zNn+dhwB3Bx7cs8196dlDmOQf0pweeockO9IUj5/TNDRb603A2iR3bIeRvgR4VZpTXO/U7s36ALArv3vWr8V8iKbJ+kSa015vn+RPaM5w9tp2iOZC3gUclGSLTpayFTm/DngmzdnMgKG/zpI0UdrP41cD/5zk8Ul2bj+zD0nylna1TwJ/muSPk2yX5vIAr6I5/psk90ry4vYYtbmG7AgW+AJvge1/H3g/cHyS/dv49223+aVa4ARkNLXkIODrW/XkG8uqOwupqlNoDrOY22v1Tpoa9MEkd0yyY5IjaBqgl/Y7bKN9b14H/FOSg9v3ZYrm+PFNNLV2ocd9l+YEMC9bxtPY4pyr6lTgPH73zKSHJTk8ye7tsf0Pphku2/d3Qt1lU6dJcyTwL9VcuuDyuRvwXprLDBxAc+zbk3Lz6+Bc0BPnKWmuAfNzmgO7fwo8sH73tMq92/xMVZ03b5vvBh7bDv0rmjH2V9EUjYOAP20PmN4qVXUe8DWaUyZTVR+jGQ76onZ7F9Kc2fKAqvrpZsT7NfBomm/1zqA5kPodwCur6q1LPG6W5pvXf1jGc9jinKtqhqYo9u5RHNrrLEmTqKreQXOc2KtoztJ8Cc1xxZ9ul19A06T9L5pDCE6j+Wx/XRviGpph/Ge0x2qfTjMEcKmzPfd6Ls2XcB+mOeHWF2mO9X7SIvleXVVfXqIpesgC9flBi8Radt1ZxFuBlyW5ZVt7HkpzspMLaf4P+Dvg6W3N6quq3kIzouRtNLX0DJr358C21i6Vx9okt9+S5Lci51fxu+cM+BnNsX0/aPP+MPDWqjp+S/JRt2Tzzi8hSZIkSZpE7qmTJEmSpA6zqZMkSZKkDrOpkyRJkqQOs6mTJEmSpA6zqZMkSZKkDls17gQ2xx577FFTU1PjTkOSNGRnnnnmVVW1etx5dIX1UZK2HUvVyE40dVNTU6xfv37caUiShizJj8edQ5dYHyVp27FUjXT4pSRJkiR1mE2dJEmSJHWYTZ0kSZIkdZhNnSRJkiR1mE2dJEmSJHWYTZ0kSZIkdZhNnSRJkiR1mE2dJEmSJHWYTZ0kSZIkddjQmrokxyS5Msn5PfNum+SUJD9of+4+rO1LkiRJ0rZgmHvqjgUOnjfvaODLVXUP4MvttCRJkiRpmYbW1FXV14Gr580+DDiuvX8c8PhhbV+SJEmStgWjPqbuDlV1GUD78/aLrZhkbZL1SdbPzs6OLEFJkiRJ6pJV405gMVW1DlgHsGbNmhpzOtu8jdPTA4s1NTMzsFiSpO6bnt44sFgzM1MDiyVJXTHqPXVXJLkTQPvzyhFvX5IkSZJWlFE3dZ8FjmzvHwl8ZsTblyRJkqQVZZiXNDgBOA3YJ8mmJM8G3gQclOQHwEHttCRJkiRpmYZ2TF1VHbHIogOHtU1JkiRJ2taMevilJEmSJGmAbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSRqxJMckuTLJ+fPmPy/J95JckOQt48pPktQtNnWSJI3escDBvTOSPBI4DPiDqrov8LYx5CVJ6iCbOkmSRqyqvg5cPW/2XwNvqqpft+tcOfLEJEmdZFMnSdJkuCfwR0nOSPK1JA9aaKUka5OsT7J+dnZ2xClKkiaRTZ0kSZNhFbA7sD/wUuDjSTJ/papaV1VrqmrN6tWrR52jJGkC2dRJkjQZNgEnVeNbwE3AHmPOSZLUATZ1kiRNhk8DjwJIck9gB+CqsWYkSeqEVeNOQJKkbU2SE4BHAHsk2QS8BjgGOKa9zMH1wJFVVePLUpLUFTZ1kiSNWFUdsciip400EUnSiuDwS0mSJEnqMJs6SZIkSeowh19KE27j9PTAYk3NzAwsliRJkiaDe+okSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDVo07AUmSNPmmpzcOLNbMzNTAYkmS3FMnSZIkSZ1mUydJkiRJHWZTJ0mSJEkdZlMnSZIkSR1mUydJkiRJHWZTJ0mSJEkdZlMnSZIkSR1mUydJkiRJHWZTJ0mSJEkdZlMnSdKIJTkmyZVJzl9g2UuSVJI9xpGbJKl7bOokSRq9Y4GD589MchfgIODiUSckSeoumzpJkkasqr4OXL3AoncCLwNqtBlJkrrMpk6SpAmQ5FDg0qo6t896a5OsT7J+dnZ2RNlJkiaZTZ0kSWOWZGfglcCr+61bVeuqak1VrVm9evXwk5MkTTybOkmSxu/uwDRwbpKNwF7AWUnuONasJEmdsGrcCUiStK2rqvOA289Nt43dmqq6amxJSZI6wz11kiSNWJITgNOAfZJsSvLsceckSeou99RJkjRiVXVEn+VTI0pFkrQCjGVPXZIXJbkgyflJTkiy4zjykCRJkqSuG3lTl2RP4Pk0xwrsC2wHHD7qPCRJkiRpJRjXMXWrgJ2SrAJ2Bn4ypjwkSZIkqdNG3tRV1aXA24CLgcuAX1TVyaPOQ5IkSZJWgnEMv9wdOIzmejx3BnZJ8rQF1lubZH2S9bOzs6NOU5IkSZI6YRzDLx8NzFTVbFXdAJwE/OH8lapqXVWtqao1q1evHnmSkiRJktQF47ikwcXA/kl2Bq4DDgTWb84DN05PDyyJqZmZgcWSJEmSpHEZxzF1ZwAnAmcB57U5rBt1HpIkSZK0Eozl4uNV9RrgNePYtiRJkiStJOO6pIEkSZIkaQDGsqdOkiRJ2lrT0xsHFmtmZmpgsaRRc0+dJEmSJHWYTZ0kSZIkdZhNnSRJkiR1mMfUrSBex0+SJEna9rinTpIkSZI6zKZOkiRJkjrMpk6SJEmSOsymTpIkSZI6zKZOkiRJkjrMpk6SJEmSOsymTpIkSZI6zKZOkiRJkjrMpk6SJEmSOsymTpIkSZI6zKZOkiRJkjrMpk6SJEmSOmzVuBPYlmycnh5YrKmZmYHFkiRJktRd7qmTJGnEkhyT5Mok5/fMe2uS7yb5TpJPJdltnDlKkrrDpk6SpNE7Fjh43rxTgH2r6g+A7wOvGHVSkqRusqmTJGnEqurrwNXz5p1cVTe2k6cDe408MUlSJ3lMnSSNicfZagnPAj620IIka4G1AHvvvfcoc5IkTSj31EmSNEGSvBK4ETh+oeVVta6q1lTVmtWrV482OUnSRHJPnSRJEyLJkcBjgQOrqsadjySpG2zqejgUSsvl746krZXkYODlwMOr6j/HnY8kqTscfilJ0oglOQE4DdgnyaYkzwbeC9waOCXJOUneP9YkJUmd4Z46SZJGrKqOWGD2B0eeiCRpRXBPnSRJkiR1mE2dJEmSJHWYTZ0kSZIkdZjH1EnSCuVZWSVJ2ja4p06SJEmSOsymTpIkSZI6zKZOkiRJkjrMY+q0TfDYIkmSJK1U7qmTJEmSpA6zqZMkSZKkDrOpkyRJkqQOs6mTJEmSpA6zqZMkSZKkDrOpkyRJkqQOs6mTJEmSpA6zqZMkSZKkDrOpkyRJkqQOs6mTJEmSpA6zqZMkSZKkDtvipi7J7kkyjGQkSeoya6QkaRyWbOqSvDrJvdr7t0zyVeCHwBVJHj2KBCVJmkTWSEnSpOi3p+4pwPfa+0e2P1cDDwfeOKykJEnqAGukJGkirOqz/Pqqqvb+nwAfrarfABuS9HustNk2Tk8PLNbUzMzAYknSEqyRkqSJ0G9P3a+T7JtkNfBI4OSeZTsPLy1JkiaeNVKSNBH6fZP4QuBEmuEk76yqGYAkjwHOHnJukiRNMmukJGkiLNnUVdXpwL0WmP954PPDSkqSpElnjZQkTYolm7okz1hicVXVh5az0SS7AR8A9gUKeFZVnbacWJIkjcOwaqQkSVuq3/DLBy0wL8DjgD2B5RasdwNfrKo/S7IDHnsgSeqeYdVISZK2SL/hl8+bu99eTPWpwMuB04E3LGeDSXYFHgYc1W7jeuD65cSSJGlctqZGJjkGeCxwZVXt2867LfAxYArYCDy5qn42jNwlSStLv7NfkmRVkucAFwKPBv6sqp5SVd9Z5jbvBswC/5Lk7CQfSLLLAttdm2R9kvWzs7PL3JQkScOzFTXyWODgefOOBr5cVfcAvtxOS5LU15JNXZK/pSlUDwQOrqqjqup7Sz1mM6wC9gPeV1UPAH7FAoWrqtZV1ZqqWrN69eqt3KQkSYO1NTWyqr4OXD1v9mHAce3944DHDypXSdLK1u+Yun8CrgQeCvxrM7oEaI4ZqKr6g2VscxOwqarOaKdPxG8jJUndM+gaeYequozmwZcluf1CKyVZC6wF2HvvvZeTtyRphenX1E0PeoNVdXmSS5Ls036jeSDNN52SJHXJwGvk5qiqdcA6gDVr1tQ4cpAkTZZ+J0r5MUCSaeC+NJcf2FBVP9rK7T4POL498+WPgGduZTxJkkZqCDXyiiR3avfS3YlmL6AkSX31u07drjTXk1sDnEMzpOR+Sc4Enl1Vv1zORqvqnDamJEmdNIQa+VngSOBN7c/PDDBdSdIK1u/sl++hGRr5e1X1xKp6AnB34DzgvcNOTpKkCbbsGpnkBOA0YJ8km5I8m6aZOyjJD4CD2mlJkvrqd0zdAVV1VO+MqirgH9uiI0nStmrZNbKqjlhk0YEDyk2StA3pt6cufZZLkrStskZKkiZCv6buP5K8Oj3naQZI8g/A6cNLS5KkiWeNlCRNhH7DL58HfBC4KMk5NGf2egBwNvCcIecmSWO3cXpwZ62fmpkZWCxNBGukJGki9LukwS+BP09yd+A+NENNXl5VPxxFcpIkTSprpCRpUvS7pMF+PZOXtj9vMze/qs4aVmKSJE0ya6QkaVL0G3759iWWFfCoAeYiSVKXWCMlSROhX1P391V12kgykSSpW6yRkqSJ0O/sl/88kiwkSeoea6QkaSJ4nTpJkpbHGilJmgj9hl9OJ/nsYgur6tAB5yNJUldYIyVJE6FfUzfL0geCS5K0rbJGSpImQr+m7pqq+tpIMpEkqVuskZKkidCvqds4iiQkjc/G6emBxZqamRlYLKkDNo47AUmSoM+JUqrqiQvNT3JQklOGk5IkSZPPGilJmhRLNnVJHpnk+0muTfLhJPdJsh54E/C+0aQoSdLksUZKkiZFv0savANYC9wOOBE4HfhQVT2wqk4adnKSJE0wa6QkaSL0O6auqurU9v6nk8xW1buHnJMkSV1gjZQkTYR+Td1uSXqPGUjvtN9ESpK2YdZISdJE6NfUfQ143CLTBViwJEnbKmukJGkiLNnUVdUzR5WIJEldYo2UJE2KfidKIcm+SY5Lsj7Jt9v7vz+K5CRJmmTWSEnSJOh3SYPDgE/RDCl5FvCc9v5J7TJJkrZJ1khJ0qTod0zdPwIHVdXGnnnnJvkK8Jn2JknStsgaKUmaCP2GX24/r1gB0M7bfhgJSZLUEdZISdJE6NfU3ZBk7/kzk9wVuHE4KUmS1AnWSEnSROg3/PI1wJeSvBE4k+YUzQ8CjgZePuTcJEmaZNZISdJE6HdJg08nmQFeDDwPCHA+8OSqOncE+UmSNJGskZKkSdFvTx3AT4D3ABdV1c+HnI8kSV1ijZQkjV2/Sxo8B7gA+Cfgu0kOHUlWkiRNuGHVyCQvSnJBkvOTnJBkx0HElSStXP1OlPJC4L5V9RDgD4FXDD8lSZI6YeA1MsmewPOBNVW1L7AdcPjWxpUkrWz9hl9eX1WzAFX1oyS3HEFOkiR1wbBq5CpgpyQ3ADvTDPGUNAbT0xsHFmtmZmpgsaT5+jV1eyV5z2LTVfX84aQlSdLEG3iNrKpLk7wNuBi4Dji5qk7uXSfJWmAtwN573+yKCpKkbVC/pu6l86bPHFYikqRu2Tg9PbBYUzMzA4s1QgOvkUl2Bw4DpoGfA59I8rSq+vDcOlW1DlgHsGbNmtrabUqSuq/fJQ2Om7uf5FbNrPrV0LOSJGnCDalGPhqYmRvWmeQkmuP1PrzkoyRJ27R+J0ohyV8nuRj4MXBxkh8n+ZvhpyZJ0mQbQo28GNg/yc5JAhwIbBhErpKklavfJQ1eBTwOeERV3a6qbgc8EjikXSZJ0jZpGDWyqs4ATgTOAs6jqdPrBpSyJGmF6ndM3dOB+1XVf83NaM/w9WTgXOD1w0xOkqQJNpQaWVWvAV4zmBQlSduCvsMve4tVz7zrgJuGkpEkSR1hjZQkTYJ+Td2mJAfOn5nkUcBlw0lJkqROsEZKkiZCv+GXzwc+k+SbNKdqLuBBwAE0p1yWJGlbZY2UJE2EJffUVdUFwL7A14Ep4G7t/X3bZZIkbZOskZKkSdFvT93c8QLH9M5Lsl2Sp1bV8UPLTJKkCWeNlCRNgn6XNNg1ySuSvDfJQWk8F/gR8OTRpChJ0uSxRkqSJkW/PXUfAn4GnAb8JfAyYAfgsKo6Z8i5SZI0yayRkqSJ0K+pu1tV/T5Akg8AVwF7V9U1Q89MkqTJZo2UJE2Efpc0uGHuTlX9BpixWEmSBFgjJUkTot+euvsl+SWQdnqnnumqql2Hmp0kSZPLGilJmghLNnVVtd2oEpEkqUuskZKkSbFkU5dkR+CvgN8DvgMcU1U3jiIxSZImmTVSkjQp+h1TdxywBjgPeAzw9qFnJElSN1gjJUkTod8xdffpObPXB4FvDT8lSZI6wRopSZoIW3L2y4EOKUmyXZKzk3xukHElSRqRodVISZK2xOae/RKas3kN8sxeLwA2AJ4dTJLURcOskZIkbbaxnP0yyV7AnwJvAP5uGNuQJGmYPPulJGlS9Bt+OSzvAl4G3LTYCknWJlmfZP3s7OzoMpMkSZKkDuk3/HLgkjwWuLKqzkzyiMXWq6p1wDqANWvW1IjSkyRJ2iLT0xsHFmtmZmpgsSRtO8axp+4A4NAkG4GPAo9K8uEx5CFJkiRJnTfypq6qXlFVe1XVFHA48JWqetqo85AkSZKklWBcx9RJkiRJkgZg5MfU9aqqU4FTx5mDJEmShsPjDaXRcE+dJEmSJHWYTZ0kSZIkdZhNnSRJkiR1mE2dJEmSJHWYTZ0kSRMkyW5JTkzy3SQbkjxk3DlJkibbWM9+KUmSbubdwBer6s+S7ADsPO6EJEmTzaZOkqQJkWRX4GHAUQBVdT1w/ThzkiRNPodfSpI0Oe4GzAL/kuTsJB9Issu4k5IkTTabOkmSJscqYD/gfVX1AOBXwNG9KyRZm2R9kvWzs7PjyFGSNGFs6iRJmhybgE1VdUY7fSJNk/dbVbWuqtZU1ZrVq1ePPEFJ0uTxmDpJkiZEVV2e5JIk+1TV94ADgQvHnZckjdr09MaBxZqZmRpYrEllUydJ0mR5HnB8e+bLHwHPHHM+kqQJZ1MnSdIEqapzgDXjzkOS1B02dZIkaUVzGJeklc4TpUiSJElSh9nUSZIkSVKH2dRJkiRJUofZ1EmSJElSh9nUSZIkSVKH2dRJkiRJUofZ1EmSJElSh9nUSZIkSVKH2dRJkiRJUofZ1EmSJElSh9nUSZIkSVKH2dRJkiRJUofZ1EmSJElSh9nUSZIkSVKH2XgTuvAAABEaSURBVNRJkiRJUofZ1EmSJElSh9nUSZIkSVKH2dRJkiRJUofZ1EmSJElSh60adwKSJEkaj+npjQOLNTMzNbBY0rCttN9999RJkiRJUofZ1EmSJElSh9nUSZIkSVKH2dRJkiRJUofZ1EmSJElSh9nUSZIkSVKH2dRJkiRJUofZ1EmSNGGSbJfk7CSfG3cukqTJZ1MnSdLkeQGwYdxJSJK6waZOkqQJkmQv4E+BD4w7F0lSN9jUSZI0Wd4FvAy4adyJSJK6waZOkqQJkeSxwJVVdeYS66xNsj7J+tnZ2RFmJ0maVKvGnYAkSfqtA4BDkzwG2BHYNcmHq+ppcytU1TpgHcCaNWtqPGlKkhYzPb1xYLFmZqY2az331EmSNCGq6hVVtVdVTQGHA1/pbegkSVqITZ0kSZIkdZjDLyVJmkBVdSpw6pjTkCR1gHvqJEmSJKnDbOokSZIkqcNs6iRJkiSpw0be1CW5S5KvJtmQ5IIkLxh1DpIkSZK0UozjRCk3Ai+uqrOS3Bo4M8kpVXXhGHKRJGlFGMd1kSRJk2Hke+qq6rKqOqu9fw2wAdhz1HlIkiRJ0kow1mPqkkwBDwDOGGcekiRJktRVY2vqktwK+CTwwqr65QLL1yZZn2T97Ozs6BOUJEmSpA4YS1OXZHuahu74qjppoXWqal1VramqNatXrx5tgpIkSZLUEeM4+2WADwIbquodo96+JEmSJK0k49hTdwDwdOBRSc5pb48ZQx6SJEmS1Hkjv6RBVX0TyKi3K0mSJEkr0VjPfilJkiRJ2jo2dZIkSZLUYTZ1kiRJktRhNnWSJEmS1GE2dZIkSZLUYTZ1kiRJktRhNnWSJEmS1GEjv06dJEmSNt/09MaBxZqZmRpYLEmTwz11kiRJktRhNnWSJEmS1GE2dZIkSZLUYTZ1kiRJktRhNnWSJEmS1GE2dZIkSZLUYTZ1kiRJktRhNnWSJEmS1GE2dZIkTYgkd0ny1SQbklyQ5AXjzkmSNPlWjTsBSZL0WzcCL66qs5LcGjgzySlVdeG4E5MkTS731EmSNCGq6rKqOqu9fw2wAdhzvFlJkiadTZ0kSRMoyRTwAOCM8WYiSZp0NnWSJE2YJLcCPgm8sKp+OW/Z2iTrk6yfnZ0dT4KSpIniMXWSJE2QJNvTNHTHV9VJ85dX1TpgHcCaNWtqxOlJEgDT0xsHFmtmZmpgsbZV7qmTJGlCJAnwQWBDVb1j3PlIkrrBpk6SpMlxAPB04FFJzmlvjxl3UpKkyebwS0mSJkRVfRPIuPOQJHWLe+okSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDvKSBpKHaOD09sFhTMzMDiyVJkrRSuKdOkiRJkjrMpk6SJEmSOsymTpIkSZI6zKZOkiRJkjrMpk6SJEmSOsymTpIkSZI6zKZOkiRJkjrM69RJkiRJYzA9vXFgsWZmpgYWS93jnjpJkiRJ6jCbOkmSJEnqMJs6SZIkSeowmzpJkiRJ6jBPlCKp0zZOTw8s1tTMzMBiSZIkjYp76iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDxtLUJTk4yfeSXJTk6HHkIEnSJLJGSpK21KpRbzDJdsA/AwcBm4BvJ/lsVV046lwkSZok1khJgzI9vXFgsWZmpgYWS8Mxjj11DwYuqqofVdX1wEeBw8aQhyRJk8YaKUnaYuNo6vYELumZ3tTOkyRpW2eNlCRtsZEPvwSywLy62UrJWmBtO3ltku9twTb2AK5aOouF0hhQbOMP67UfdvzxvzbDju9rv3Ljr5z39q7L3dAK0bdGDr0+slW/TmOPv3V/CkONP/bXZtjxfe1Xbnzf2/HFnxd70Ro5jqZuE3CXnum9gJ/MX6mq1gHrlrOBJOuras3y0htfbOOPN36Xc+96/C7nbvzxxV6h+tbISa2Pxh9fbOOPL7bxxxu/y7kPOv44hl9+G7hHkukkOwCHA58dQx6SJE0aa6QkaYuNfE9dVd2Y5LnAvwPbAcdU1QWjzkOSpEljjZQkLcc4hl9SVZ8HPj/ETSxrWMoExDb+eON3Ofeux+9y7sYfX+wVacg1ssu/S12P3+Xcux6/y7kbf3yxOxU/VTc7R4kkSZIkqSPGcUydJEmSJGlAVlRTl+TgJN9LclGSowcc+5gkVyY5f5Bxe+LfJclXk2xIckGSFww4/o5JvpXk3Db+6wYZv93GdknOTvK5IcTemOS8JOckWT+E+LslOTHJd9v34CEDjL1Pm/fc7ZdJXjjA+C9q39Pzk5yQZMdBxW7jv6CNfcEg8l7obynJbZOckuQH7c/dBxz/z9v8b0qyVWeZWiT+W9vfne8k+VSS3QYc//9rY5+T5OQkdx5U7J5lL0lSSfYYcO6vTXJpz+//Y5YbX1unqzVyJdTHdjudrJFdro/tNqyR/eMPpEZ2uT4uFr9n2eTXyKpaETeaA8p/CNwN2AE4F7jPAOM/DNgPOH9I+d8J2K+9f2vg+wPOP8Ct2vvbA2cA+w/4Ofwd8BHgc0N4fTYCewzx9+c44Dnt/R2A3Ya0ne2Ay4G7DijensAMsFM7/XHgqAHmuy9wPrAzzTG4XwLusZUxb/a3BLwFOLq9fzTw5gHHvzewD3AqsGYI+f8xsKq9/+Yh5L9rz/3nA+8fVOx2/l1oTszx4635O1sk99cCLxnU76S3Zb83na2RK6E+trE7WSO7Wh/bmNbIzYs/kBrZ5fq4WPx2fidq5EraU/dg4KKq+lFVXQ98FDhsUMGr6uvA1YOKt0D8y6rqrPb+NcAGmg+jQcWvqrq2ndy+vQ3sgMokewF/CnxgUDFHJcmuNH9oHwSoquur6udD2tyBwA+r6scDjLkK2CnJKprCcrPrPm6FewOnV9V/VtWNwNeAJ2xNwEX+lg6j+ceB9ufjBxm/qjZU1ZZcoHlL45/cvj4Ap9NcW2yQ8X/ZM7kLy/zbXeJz7J3Ay5YbdzPia/w6WyO7Xh+huzVyBdRHsEb2jT+oGtnl+rhY/FYnauRKaur2BC7pmd7EAD/0RynJFPAAmm8LBxl3uyTnAFcCp1TVIOO/i+YX/qYBxuxVwMlJzkyydsCx7wbMAv/SDo35QJJdBryNOYcDJwwqWFVdCrwNuBi4DPhFVZ08qPg030A+LMntkuwMPIbfvTDyoNyhqi6D5h844PZD2MaoPAv4wqCDJnlDkkuApwKvHmDcQ4FLq+rcQcVcwHPb4THHbM2wIW2VFVEjO1ofobs1srP1EayRE6hT9bGN3ZkauZKauiwwr3On9kxyK+CTwAvnffuw1arqN1V1f5pvSR6cZN9BxE3yWODKqjpzEPEWcUBV7QccAvxtkocNMPYqmt3h76uqBwC/ohneMFBpLiR8KPCJAcbcneYbvGngzsAuSZ42qPhVtYFmuMQpwBdphmzduOSDtmFJXknz+hw/6NhV9cqquksb+7mDiNn+E/JKBlwE53kfcHfg/jT/VL19iNvS4jpfI7tYH6HzNbKz9bGNa42cEF2rj9C9GrmSmrpN/O63I3sx2F3sQ5dke5qCdXxVnTSs7bRDJ04FDh5QyAOAQ5NspBnS86gkHx5QbACq6iftzyuBT9EMJRqUTcCmnm9mT6QpYoN2CHBWVV0xwJiPBmaqaraqbgBOAv5wgPGpqg9W1X5V9TCaYQM/GGT81hVJ7gTQ/rxyCNsYqiRHAo8FnlrtQPkh+QjwpAHFujvNPzvntn+/ewFnJbnjgOJTVVe0/zDfBPxvBvu3q83X6RrZ4foI3a6RXa6PYI2cCB2tj9CxGrmSmrpvA/dIMt1+43M48Nkx57TZkoRmzPqGqnrHEOKvnjvjUJKdaD7ovjuI2FX1iqraq6qmaF73r1TVwL4JS7JLklvP3ac56HZgZ1irqsuBS5Ls0846ELhwUPF7HMGAh5bQDCnZP8nO7e/QgTTHmwxMktu3P/cGnsjgnwM0f6tHtvePBD4zhG0MTZKDgZcDh1bVfw4h/j16Jg9lcH+751XV7atqqv373URzQorLBxEffvsPyJwnMMC/XW2RztbILtdH6HaN7Hh9BGvk2HW1PkIHa2QN4Gwrk3KjGcv8fZozfL1ywLFPoNktegPNm/rsAcd/KM1QmO8A57S3xwww/h8AZ7fxzwdePaT34BEM+MxeNGP6z21vFwz6vW23cX9gffv6fBrYfcDxdwZ+CtxmCLm/juZD7HzgQ8AtBxz/GzRF/FzgwAHEu9nfEnA74Ms033B+GbjtgOM/ob3/a+AK4N8HHP8imuOV5v52t+bsWwvF/2T7/n4H+Fdgz0HFnrd8I1t3Zq+Fcv8QcF6b+2eBOw36b8DbZr8/nayRK6U+ttvqXI3scn1s41sj+8cfSI3scn1cLP685RNdI9NuRJIkSZLUQStp+KUkSZIkbXNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqtE1IcsckH03ywyQXJvl8knsmmUpys2uCJDk1yZqe6d+u117v5vgk5yU5P8k3k9w1yTnt7fIkl/ZM75DkNz3T5yQ5umc730tybpJvJ7n/IvmfmmR9z/SaJKe2949K8t7F8k+yMck35i0/p+f5PCLJL9p530nypZ7r7hyVZHZe7vdpX4/r2ukLk/yf9uLAkqSOsUZaI9V9q8adgDRsSQJ8Cjiuqg5v590fuAPNtVO21AuAK6rq99tY+wCXV9X92+nXAtdW1dt6crhubvkCnlpV65M8E3grcNAi690+ySFV9YVl5HzrJHepqkuS3HuB5d+oqse2uf4v4G+B17TLPlZVz+1dOckU8MOqun+S7YBTgCcDxy8jN0nSmFgjAWukVgD31Glb8Ejghqp6/9yMqjqnqr6xxGOWcifg0p5Y36uqX29ljgCnAXsusfytwKuWGfvjwFPa+0fQXADzZtrifmvgZ5sbuKp+A3yLpXOXJE0ma6Q1UiuATZ22BfsCZw4w3jHAy5OcluT1Se6xGY/Zad7wjKcssM7BwKeXiHEa8Oskj1xGzicCT2zvPw7413nL/yjJOcDFwKNpnuOcp8zLfafeBybZEfgfwBeXkZckabyskdZIrQAOv5QWVovNq6pzktwN+GOaD/dvJ3lIVW1YIt5SQ0uOT7ILsB2wX5+8Xk/zTeTL++Q6f/7VwM+SHA5sAP5z3rq9Q0teDrwF+Kt22UJDSwDu3ha5ewAnVtV3+uQuSVoZrJHWSE0Y99RpW3AB8MAtfMxPgd17pm8LXDU3UVXXVtVJVfU3wIeBx2xFfk8FpoGPAP+81IpV9RVgR2D/JXK9Wb6tj7XxFxxW0uOzwMP6rAPt8QLA7wH7Jzl0Mx4jSZos1siGNVKdZlOnbcFXgFsm+cu5GUkelOThSzzmVOBp7fh5gCOBr7aPPSDJ7u39HYD7AD/emgSr6gaabxf3X+Qg7V5vAF7WM/1t4IAkd2xzWgPckpsf4P4pmm8X/71P/IcCP9zM1Kmqy4CjgVds7mMkSRPDGtmwRqrTHH6pFa+qKskTgHelOU3yfwEbgRe2q+yTZFPPQ14ErAPuBZybpID1/PcH8t2B97XF7BbAvwGf7JPGTu0wjDlfrKqj5+V5XZK3Ay8Bnr3E8/l8ktme6SuSvAD4fJJbANcCR1TVTfMedw3wZvjt0JBec8cLBPgF8JyeZU9J8tCe6b8BfjLv8Z8GXpvkj7bi4HpJ0ohZI3+7njVSnZaqxYYaS5IkSZImncMvJUmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcNs6iRJkiSpw2zqJEmSJKnDbOokSZIkqcP+H2Pvh1xefTwoAAAAAElFTkSuQmCC
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[53]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># What kinds of people are part of a cluster that is overrepresented in the</span>
<span class="c1"># customer data compared to the general population?</span>
<span class="n">cluster_0</span> <span class="o">=</span> <span class="n">pipe_test</span><span class="o">.</span><span class="n">named_steps</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">pca_re</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">cluster_0_series</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">cluster_0</span><span class="p">,</span><span class="n">customers_cleaned</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[54]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cluster_0_series</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[54]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>ALTERSKATEGORIE_GROB     2.843175   
ANREDE_KZ                0.790523   
FINANZ_MINIMALIST        3.417449   
FINANZ_SPARER            2.341832   
FINANZ_VORSORGER         3.764486   
FINANZ_ANLEGER           2.111802   
FINANZ_UNAUFFAELLIGER    2.619809   
FINANZ_HAUSBAUER         3.171171   
GREEN_AVANTGARDE         0.392789   
HEALTH_TYP               2.111947   
RETOURTYP_BK_S           3.307007   
SEMIO_SOZ                6.300438   
SEMIO_FAM                6.190377   
SEMIO_REL                4.989134   
SEMIO_MAT                4.766152   
SEMIO_VERT               6.802560   
SEMIO_LUST               4.375640   
SEMIO_ERL                2.414309   
SEMIO_KULT               6.444187   
SEMIO_RAT                3.071487   
SEMIO_KRIT               2.001556   
SEMIO_DOM                2.477506   
SEMIO_KAEM               1.711387   
SEMIO_PFLICHT            4.116456   
SEMIO_TRADV              3.819914   
SOHO_KZ                  0.000866   
VERS_TYP                 1.488140   
ANZ_PERSONEN             2.011113   
ANZ_TITEL                0.012881   
HH_EINKOMMEN_SCORE       4.393703   
W_KEIT_KIND_HH           4.563005   
WOHNDAUER_2008           8.241934   
ANZ_HAUSHALTE_AKTIV      9.987494   
ANZ_HH_TITEL             0.063527   
KONSUMNAEHE              2.298795   
MIN_GEBAEUDEJAHR         1992.302221
OST_WEST_KZ              0.141385   
KBA05_ANTG1              1.112102   
KBA05_ANTG2              1.248333   
KBA05_ANTG3              0.770312   
KBA05_ANTG4              0.353500   
KBA05_GBZ                2.776795   
BALLRAUM                 3.383428   
EWDICHTE                 4.936405   
INNENSTADT               3.679923   
GEBAEUDETYP_RASTER       3.548155   
KKK                      2.632194   
MOBI_REGIO               2.560069   
ONLINE_AFFINITAET        3.241365   
REGIOTYP                 4.531704   
KBA13_ANZAHL_PKW         567.671068 
PLZ8_ANTG1               1.788275   
PLZ8_ANTG2               3.206747   
PLZ8_ANTG3               2.136306   
PLZ8_ANTG4               1.051494   
PLZ8_HHZ                 3.770452   
PLZ8_GBZ                 3.001148   
ARBEIT                   3.345499   
ORTSGR_KLS9              6.622829   
RELAT_AB                 3.569241   
DECADE                   4.152352   
MOVEMENT                 0.395726   
WEALTH                   3.644199   
LIFE_STAGE               2.593499   
dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[55]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># What kinds of people are part of a cluster that is underrepresented in the</span>
<span class="c1"># customer data compared to the general population?</span>

<span class="n">cluster_4</span> <span class="o">=</span> <span class="n">pipe_test</span><span class="o">.</span><span class="n">named_steps</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">pca_re</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">algorithm</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">[</span><span class="mi">4</span><span class="p">]))</span>
<span class="n">cluster_4_series</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">cluster_4</span><span class="p">,</span><span class="n">customers_cleaned</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[56]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cluster_4_series</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[56]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>ALTERSKATEGORIE_GROB     3.997503   
ANREDE_KZ                1.703739   
FINANZ_MINIMALIST        4.701583   
FINANZ_SPARER            0.941139   
FINANZ_VORSORGER         4.987737   
FINANZ_ANLEGER           1.631687   
FINANZ_UNAUFFAELLIGER    1.352151   
FINANZ_HAUSBAUER         2.547676   
GREEN_AVANTGARDE         0.250389   
HEALTH_TYP               1.863348   
RETOURTYP_BK_S           3.815765   
SEMIO_SOZ                3.024352   
SEMIO_FAM                2.174701   
SEMIO_REL                1.836490   
SEMIO_MAT                1.972170   
SEMIO_VERT               4.041929   
SEMIO_LUST               6.041878   
SEMIO_ERL                6.588486   
SEMIO_KULT               2.183581   
SEMIO_RAT                2.484181   
SEMIO_KRIT               4.985431   
SEMIO_DOM                4.769736   
SEMIO_KAEM               4.655821   
SEMIO_PFLICHT            2.074964   
SEMIO_TRADV              2.035848   
SOHO_KZ                  0.000834   
VERS_TYP                 1.336672   
ANZ_PERSONEN             2.453161   
ANZ_TITEL                0.011832   
HH_EINKOMMEN_SCORE       2.935038   
W_KEIT_KIND_HH           4.192634   
WOHNDAUER_2008           8.902579   
ANZ_HAUSHALTE_AKTIV     -0.170451   
ANZ_HH_TITEL             0.011006   
KONSUMNAEHE              4.031250   
MIN_GEBAEUDEJAHR         1992.948168
OST_WEST_KZ              0.078542   
KBA05_ANTG1              3.127018   
KBA05_ANTG2              1.150541   
KBA05_ANTG3             -0.041343   
KBA05_ANTG4             -0.055967   
KBA05_GBZ                4.450570   
BALLRAUM                 5.539215   
EWDICHTE                 2.397425   
INNENSTADT               6.221165   
GEBAEUDETYP_RASTER       4.151940   
KKK                      2.640632   
MOBI_REGIO               4.602056   
ONLINE_AFFINITAET        2.841845   
REGIOTYP                 4.162717   
KBA13_ANZAHL_PKW         724.164958 
PLZ8_ANTG1               3.197195   
PLZ8_ANTG2               2.214225   
PLZ8_ANTG3               0.668713   
PLZ8_ANTG4               0.066990   
PLZ8_HHZ                 3.348049   
PLZ8_GBZ                 4.060700   
ARBEIT                   2.286706   
ORTSGR_KLS9              3.184234   
RELAT_AB                 2.107520   
DECADE                   2.224666   
MOVEMENT                 0.253358   
WEALTH                   1.915062   
LIFE_STAGE               3.746428   
dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[58]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Merge Cluster 3 and 13 into 1 DataFrame</span>

<span class="n">clustervs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">cluster_0_series</span><span class="p">,</span><span class="n">cluster_4_series</span><span class="p">])</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
<span class="n">clustervs</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Cluster0_Centroid&#39;</span><span class="p">,</span><span class="s1">&#39;Cluster4_Centroid&#39;</span><span class="p">]</span>
<span class="n">clustervs</span><span class="p">[</span><span class="s1">&#39;Difference&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">clustervs</span><span class="p">[</span><span class="s1">&#39;Cluster0_Centroid&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">clustervs</span><span class="p">[</span><span class="s1">&#39;Cluster4_Centroid&#39;</span><span class="p">]</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[59]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">clustervs</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span> <span class="o">=</span> <span class="s1">&#39;Difference&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[59]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Cluster0_Centroid</th>
      <th>Cluster4_Centroid</th>
      <th>Difference</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>ANZ_HAUSHALTE_AKTIV</th>
      <td>9.987494</td>
      <td>-0.170451</td>
      <td>10.157945</td>
    </tr>
    <tr>
      <th>SEMIO_KULT</th>
      <td>6.444187</td>
      <td>2.183581</td>
      <td>4.260607</td>
    </tr>
    <tr>
      <th>SEMIO_FAM</th>
      <td>6.190377</td>
      <td>2.174701</td>
      <td>4.015676</td>
    </tr>
    <tr>
      <th>ORTSGR_KLS9</th>
      <td>6.622829</td>
      <td>3.184234</td>
      <td>3.438595</td>
    </tr>
    <tr>
      <th>SEMIO_SOZ</th>
      <td>6.300438</td>
      <td>3.024352</td>
      <td>3.276087</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[60]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">clustervs</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span> <span class="o">=</span> <span class="s1">&#39;Difference&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[60]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Cluster0_Centroid</th>
      <th>Cluster4_Centroid</th>
      <th>Difference</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>INNENSTADT</th>
      <td>3.679923</td>
      <td>6.221165</td>
      <td>-2.541242</td>
    </tr>
    <tr>
      <th>SEMIO_KAEM</th>
      <td>1.711387</td>
      <td>4.655821</td>
      <td>-2.944434</td>
    </tr>
    <tr>
      <th>SEMIO_KRIT</th>
      <td>2.001556</td>
      <td>4.985431</td>
      <td>-2.983875</td>
    </tr>
    <tr>
      <th>SEMIO_ERL</th>
      <td>2.414309</td>
      <td>6.588486</td>
      <td>-4.174177</td>
    </tr>
    <tr>
      <th>KBA13_ANZAHL_PKW</th>
      <td>567.671068</td>
      <td>724.164958</td>
      <td>-156.493889</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-3.3:-Compare-Customer-Data-to-Demographics-Data">Discussion 3.3: Compare Customer Data to Demographics Data<a class="anchor-link" href="#Discussion-3.3:-Compare-Customer-Data-to-Demographics-Data">&#182;</a></h3><p>In this section i plotted proportion of clusters seperately for customer dataset and azdias dataset. Cluster[0] is overrepresenting in the customer data compared to the general data and Cluster[4] is underrepresenting in the customer data compared to the general data.</p>
<p>After that i compared cluster[0] and cluster[4] centroids and merge into one dataframe with difference for easily interpret the features.</p>
<p>When we compared clusters (For top 5 and bottom 5 differences):</p>
<ul>
<li>ANZ_HAUSHALTE_AKTIV represents Number of households in the building and Cluster 0 having more Number of households than Cluster 4</li>
<li>SEMIO KULT represents cultural minded Personality and Cluster 0 is having less cultural minded personality</li>
<li>SEMIO FAM represents family minded and Cluster 0 is having less family minded personality</li>
<li>ORTSGR_KLS9 represents size of community and Cluster 0 is having more community size</li>
<li>SEMIO_SOZ represents socially minded and Cluster 0 is having less socially minded personality</li>
<li>INNENSTADT represents distance to the city center and Cluster 0 is having more distance to the city center</li>
<li>SEMIO_KAEM represents combative attitude and Cluster 0 is having more combative attitude</li>
<li>SEMIO_KRIT represents critical minded and Cluster 0 is having more critical minded personality</li>
<li>SEMIO_ERL represents Event Oriented Personality Typology and Cluster 4 is having less event oriented affinity</li>
<li>KBA13_ANZAHL_PKW represents Number of cars in the PLZ8 region so that Cluster 4 having more car in PLZ8 region than Cluster 0</li>
</ul>
<p>Finally we can decide relatively popular with the mailorder company.</p>
<ul>
<li>more Number of households, more community size, more distance to the city center, more combative attitude, more critical minded, more event oriented personality </li>
</ul>
<p>also we can decide relatively unpopular with the mail order company</p>
<ul>
<li>More cultural minded, family minded, socially minded, having more car in PLZ8</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p>Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in <strong>Discussion</strong> cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to <strong>File -&gt; Download as -&gt; HTML (.html)</strong>. You will submit both that document and this notebook for your project submission.</p>
</blockquote>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span> 
</pre></div>

    </div>
</div>
</div>

</div>
    </div>
  </div>
</body>

 
</html>