diff --git a/cookbook/A quick tour of IPython Notebook.ipynb b/cookbook/A quick tour of IPython Notebook.ipynb
index 0df81d766..ad72d3716 100644
--- a/cookbook/A quick tour of IPython Notebook.ipynb
+++ b/cookbook/A quick tour of IPython Notebook.ipynb
@@ -1 +1,313 @@
-{"nbformat": 4, "metadata": {"orig_nbformat": 3}, "cells": [{"source": "# A quick tour of IPython Notebook", "cell_type": "markdown", "metadata": {}}, {"source": "This tour will work a little better in interactive mode, so it'll be better if you get IPython notebook installed and running. You can start it from a terminal by running\n\n`ipython notebook`", "cell_type": "markdown", "metadata": {}}, {"source": "First, we need to explain how to run cells. Try to run the cell below!", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": null, "outputs": [], "source": "import pandas as pd\n\nprint \"Hi! This is a cell. Press the \u25b6 button above to run it\"", "metadata": {"collapsed": false, "trusted": false}}, {"source": "You can also run a cell with Ctrl+Enter or Shift+Enter. Experiment a bit with that.", "cell_type": "markdown", "metadata": {}}, {"source": "One of the most useful things about IPython notebook is its tab completion. \n\nTry this: click just after read_csv( in the cell below and press Shift+Tab (or Tab if you're using IPython 1.x) 4 times, slowly", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": null, "outputs": [], "source": "pd.read_csv(", "metadata": {"collapsed": false, "trusted": false}}, {"source": "After the first time, you should see this:\n
\n\n
\n\nAfter the second time:\n
\n\n
\n\nAfter the fourth time, a big help box should pop up at the bottom of the screen, with the full documentation for the `read_csv` function:\n
\n\n
", "cell_type": "markdown", "metadata": {}}, {"source": "I find this amazingly useful. I think of this as \"the more confused I am, the more times I should press Shift+Tab\". Nothing bad will happen if you tab complete 12 times.\n\nOkay, let's try tab completion for function names!", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": null, "outputs": [], "source": "pd.r", "metadata": {"collapsed": false, "trusted": false}}, {"source": "You should see this:\n\n
\n\n
", "cell_type": "markdown", "metadata": {}}, {"source": "# Writing code", "cell_type": "markdown", "metadata": {}}, {"source": "Writing code in the notebook is pretty normal.", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 1, "outputs": [], "source": "def print_10_nums():\n for i in range(10):\n print i,", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": 2, "outputs": [{"output_type": "stream", "name": "stdout", "text": "0 1 2 3 4 5 6 7 8 9\n"}], "source": "print_10_nums()", "metadata": {"collapsed": false, "trusted": false}}, {"source": "# Saving", "cell_type": "markdown", "metadata": {}}, {"source": "As of the latest stable version, the notebook autosaves. You should use the latest stable version. Really.", "cell_type": "markdown", "metadata": {}}, {"source": "# Magic functions", "cell_type": "markdown", "metadata": {}}, {"source": "IPython has all kinds of magic functions. Here's an example of comparing `sum()` with a list comprehension to a generator comprehension using the `%time` magic.", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 3, "outputs": [{"output_type": "stream", "name": "stdout", "text": "CPU times: user 24 ms, sys: 4 ms, total: 28 ms\nWall time: 27.4 ms\n"}, {"execution_count": 3, "output_type": "execute_result", "data": {"text/plain": "4999950000"}, "metadata": {}}], "source": "%time sum([x for x in range(100000)])", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": 4, "outputs": [{"output_type": "stream", "name": "stdout", "text": "CPU times: user 8 ms, sys: 0 ns, total: 8 ms\nWall time: 8.11 ms\n"}, {"execution_count": 4, "output_type": "execute_result", "data": {"text/plain": "4999950000"}, "metadata": {}}], "source": "%time sum(x for x in range(100000))", "metadata": {"collapsed": false, "trusted": false}}, {"source": "The magics I use most are `%time` and `%prun` for profiling. You can run `%magic` to get a list of all of them, and `%quickref` for a reference sheet.", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 5, "outputs": [], "source": "%quickref", "metadata": {"collapsed": false, "trusted": false}}, {"source": "You can also do nutty things like run Perl code in the notebook with cell magics. This is especially cool for things like Cython code, where you can try out Cython really fast with the `%%cython` magic (you'll need to install it).", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 6, "outputs": [{"output_type": "stream", "name": "stdout", "text": "whoa, perl!"}], "source": "%%perl\n\n$_ = \"whoa, python!\";\ns/python/perl/;\nprint", "metadata": {"collapsed": false, "trusted": false}}, {"source": "That's it for now!", "cell_type": "markdown", "metadata": {}}, {"source": "\n",
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " Unique Key Created Date Closed Date Agency \\\n",
+ "0 26589651 10/31/2013 02:08:41 AM NaN NYPD \n",
+ "1 26593698 10/31/2013 02:01:04 AM NaN NYPD \n",
+ "2 26594139 10/31/2013 02:00:24 AM 10/31/2013 02:40:32 AM NYPD \n",
+ "3 26595721 10/31/2013 01:56:23 AM 10/31/2013 02:21:48 AM NYPD \n",
+ "4 26590930 10/31/2013 01:53:44 AM NaN DOHMH \n",
+ "5 26592370 10/31/2013 01:46:52 AM NaN NYPD \n",
+ "6 26595682 10/31/2013 01:46:40 AM NaN NYPD \n",
+ "7 26595195 10/31/2013 01:44:19 AM 10/31/2013 01:58:49 AM NYPD \n",
+ "8 26590540 10/31/2013 01:44:14 AM 10/31/2013 02:28:04 AM NYPD \n",
+ "9 26594392 10/31/2013 01:34:41 AM 10/31/2013 02:23:51 AM NYPD \n",
+ "10 26595176 10/31/2013 01:25:12 AM NaN NYPD \n",
+ "11 26591982 10/31/2013 01:24:14 AM 10/31/2013 01:54:39 AM NYPD \n",
+ "12 26594169 10/31/2013 01:20:57 AM 10/31/2013 02:12:31 AM NYPD \n",
+ "13 26594391 10/31/2013 01:20:13 AM NaN NYPD \n",
+ "14 26590917 10/31/2013 01:19:54 AM NaN DOHMH \n",
+ "15 26591458 10/31/2013 01:14:02 AM 10/31/2013 01:30:34 AM NYPD \n",
+ "16 26594086 10/31/2013 12:54:03 AM 10/31/2013 02:16:39 AM NYPD \n",
+ "17 26595117 10/31/2013 12:52:46 AM NaN NYPD \n",
+ "18 26590389 10/31/2013 12:51:00 AM NaN DOT \n",
+ "19 26594210 10/31/2013 12:46:27 AM NaN NYPD \n",
+ "20 26592932 10/31/2013 12:43:47 AM 10/31/2013 12:56:20 AM NYPD \n",
+ "21 26594152 10/31/2013 12:41:17 AM 10/31/2013 01:04:37 AM NYPD \n",
+ "22 26589678 10/31/2013 12:39:55 AM NaN NYPD \n",
+ "23 26592304 10/31/2013 12:38:00 AM NaN NYPD \n",
+ "24 26591892 10/31/2013 12:37:16 AM NaN NYPD \n",
+ "25 26591573 10/31/2013 12:35:18 AM 10/31/2013 02:41:35 AM NYPD \n",
+ "26 26590509 10/31/2013 12:33:00 AM NaN DOT \n",
+ "27 26591379 10/31/2013 12:32:44 AM NaN DOHMH \n",
+ "28 26594085 10/31/2013 12:32:08 AM NaN NYPD \n",
+ "29 26589201 10/31/2013 12:32:00 AM NaN DOT \n",
+ "... ... ... ... ... \n",
+ "111039 26428764 10/04/2013 12:17:03 AM 10/04/2013 12:38:37 AM NYPD \n",
+ "111040 26426166 10/04/2013 12:16:22 AM 10/04/2013 05:50:49 AM NYPD \n",
+ "111041 26438565 10/04/2013 12:16:00 AM NaN DEP \n",
+ "111042 26428990 10/04/2013 12:15:52 AM 10/04/2013 12:44:52 AM NYPD \n",
+ "111043 26432659 10/04/2013 12:15:46 AM 10/04/2013 04:18:45 AM NYPD \n",
+ "111044 26426096 10/04/2013 12:14:09 AM 10/04/2013 01:03:46 AM NYPD \n",
+ "111045 26437764 10/04/2013 12:14:00 AM 10/04/2013 12:14:00 AM DEP \n",
+ "111046 26436286 10/04/2013 12:14:00 AM NaN DEP \n",
+ "111047 26428989 10/04/2013 12:13:08 AM 10/04/2013 02:12:47 AM NYPD \n",
+ "111048 26430030 10/04/2013 12:12:07 AM 10/04/2013 02:45:24 AM NYPD \n",
+ "111049 26429663 10/04/2013 12:12:07 AM 10/04/2013 01:03:44 AM NYPD \n",
+ "111050 26437763 10/04/2013 12:11:00 AM NaN DEP \n",
+ "111051 26432955 10/04/2013 12:08:15 AM 10/04/2013 12:48:02 AM NYPD \n",
+ "111052 26437035 10/04/2013 12:08:00 AM 10/04/2013 12:13:00 AM DEP \n",
+ "111053 26433197 10/04/2013 12:08:00 AM 10/04/2013 12:00:00 PM DSNY \n",
+ "111054 26426060 10/04/2013 12:06:39 AM 10/04/2013 12:31:16 AM NYPD \n",
+ "111055 26430628 10/04/2013 12:06:28 AM 10/04/2013 12:21:39 AM NYPD \n",
+ "111056 26431648 10/04/2013 12:06:26 AM 10/23/2013 08:14:52 AM DOT \n",
+ "111057 26437034 10/04/2013 12:06:00 AM NaN DEP \n",
+ "111058 26426094 10/04/2013 12:05:12 AM 10/04/2013 01:08:29 AM NYPD \n",
+ "111059 26429040 10/04/2013 12:04:52 AM 10/04/2013 03:01:04 AM NYPD \n",
+ "111060 26434084 10/04/2013 12:04:00 AM NaN DEP \n",
+ "111061 26426164 10/04/2013 12:03:00 AM 10/04/2013 02:14:57 AM NYPD \n",
+ "111062 26439710 10/04/2013 12:03:00 AM 10/04/2013 12:03:00 AM DEP \n",
+ "111063 26435569 10/04/2013 12:02:00 AM 10/04/2013 01:10:00 AM DEP \n",
+ "111064 26426013 10/04/2013 12:01:13 AM 10/07/2013 04:07:16 PM DPR \n",
+ "111065 26428083 10/04/2013 12:01:05 AM 10/04/2013 02:13:50 AM NYPD \n",
+ "111066 26428987 10/04/2013 12:00:45 AM 10/04/2013 01:25:01 AM NYPD \n",
+ "111067 26426115 10/04/2013 12:00:28 AM 10/04/2013 04:17:32 AM NYPD \n",
+ "111068 26428033 10/04/2013 12:00:10 AM 10/04/2013 01:20:52 AM NYPD \n",
+ "\n",
+ " Agency Name Complaint Type \\\n",
+ "0 New York City Police Department Noise - Street/Sidewalk \n",
+ "1 New York City Police Department Illegal Parking \n",
+ "2 New York City Police Department Noise - Commercial \n",
+ "3 New York City Police Department Noise - Vehicle \n",
+ "4 Department of Health and Mental Hygiene Rodent \n",
+ "5 New York City Police Department Noise - Commercial \n",
+ "6 New York City Police Department Blocked Driveway \n",
+ "7 New York City Police Department Noise - Commercial \n",
+ "8 New York City Police Department Noise - Commercial \n",
+ "9 New York City Police Department Noise - Commercial \n",
+ "10 New York City Police Department Noise - House of Worship \n",
+ "11 New York City Police Department Noise - Commercial \n",
+ "12 New York City Police Department Illegal Parking \n",
+ "13 New York City Police Department Noise - Vehicle \n",
+ "14 Department of Health and Mental Hygiene Rodent \n",
+ "15 New York City Police Department Noise - House of Worship \n",
+ "16 New York City Police Department Noise - Street/Sidewalk \n",
+ "17 New York City Police Department Illegal Parking \n",
+ "18 Department of Transportation Street Light Condition \n",
+ "19 New York City Police Department Noise - Commercial \n",
+ "20 New York City Police Department Noise - House of Worship \n",
+ "21 New York City Police Department Noise - Commercial \n",
+ "22 New York City Police Department Noise - Vehicle \n",
+ "23 New York City Police Department Noise - Commercial \n",
+ "24 New York City Police Department Blocked Driveway \n",
+ "25 New York City Police Department Noise - Street/Sidewalk \n",
+ "26 Department of Transportation Street Light Condition \n",
+ "27 Department of Health and Mental Hygiene Harboring Bees/Wasps \n",
+ "28 New York City Police Department Noise - Street/Sidewalk \n",
+ "29 Department of Transportation Street Light Condition \n",
+ "... ... ... \n",
+ "111039 New York City Police Department Noise - Commercial \n",
+ "111040 New York City Police Department Noise - Commercial \n",
+ "111041 Department of Environmental Protection Noise \n",
+ "111042 New York City Police Department Noise - Street/Sidewalk \n",
+ "111043 New York City Police Department Noise - Commercial \n",
+ "111044 New York City Police Department Noise - Street/Sidewalk \n",
+ "111045 Department of Environmental Protection Water System \n",
+ "111046 Department of Environmental Protection Noise \n",
+ "111047 New York City Police Department Illegal Parking \n",
+ "111048 New York City Police Department Noise - Street/Sidewalk \n",
+ "111049 New York City Police Department Noise - Commercial \n",
+ "111050 Department of Environmental Protection Noise \n",
+ "111051 New York City Police Department Noise - Commercial \n",
+ "111052 Department of Environmental Protection Water System \n",
+ "111053 BCC - Queens East Derelict Vehicles \n",
+ "111054 New York City Police Department Noise - Street/Sidewalk \n",
+ "111055 New York City Police Department Noise - Commercial \n",
+ "111056 Department of Transportation Street Sign - Missing \n",
+ "111057 Department of Environmental Protection Noise \n",
+ "111058 New York City Police Department Noise - Commercial \n",
+ "111059 New York City Police Department Noise - Street/Sidewalk \n",
+ "111060 Department of Environmental Protection Noise \n",
+ "111061 New York City Police Department Noise - Commercial \n",
+ "111062 Department of Environmental Protection Water System \n",
+ "111063 Department of Environmental Protection Water System \n",
+ "111064 Department of Parks and Recreation Maintenance or Facility \n",
+ "111065 New York City Police Department Illegal Parking \n",
+ "111066 New York City Police Department Noise - Street/Sidewalk \n",
+ "111067 New York City Police Department Noise - Commercial \n",
+ "111068 New York City Police Department Blocked Driveway \n",
+ "\n",
+ " Descriptor \\\n",
+ "0 Loud Talking \n",
+ "1 Commercial Overnight Parking \n",
+ "2 Loud Music/Party \n",
+ "3 Car/Truck Horn \n",
+ "4 Condition Attracting Rodents \n",
+ "5 Banging/Pounding \n",
+ "6 No Access \n",
+ "7 Loud Music/Party \n",
+ "8 Loud Talking \n",
+ "9 Loud Music/Party \n",
+ "10 Loud Music/Party \n",
+ "11 Loud Music/Party \n",
+ "12 Double Parked Blocking Vehicle \n",
+ "13 Engine Idling \n",
+ "14 Rat Sighting \n",
+ "15 Loud Music/Party \n",
+ "16 Loud Music/Party \n",
+ "17 Posted Parking Sign Violation \n",
+ "18 Street Light Out \n",
+ "19 Loud Music/Party \n",
+ "20 Loud Music/Party \n",
+ "21 Banging/Pounding \n",
+ "22 Car/Truck Music \n",
+ "23 Loud Music/Party \n",
+ "24 Partial Access \n",
+ "25 Loud Talking \n",
+ "26 Street Light Out \n",
+ "27 Bees/Wasps - Not a beekeper \n",
+ "28 Loud Talking \n",
+ "29 Street Light Out \n",
+ "... ... \n",
+ "111039 Loud Music/Party \n",
+ "111040 Loud Music/Party \n",
+ "111041 Noise: Construction Before/After Hours (NM1) \n",
+ "111042 Loud Talking \n",
+ "111043 Loud Music/Party \n",
+ "111044 Loud Talking \n",
+ "111045 Dirty Water (WE) \n",
+ "111046 Noise: Construction Before/After Hours (NM1) \n",
+ "111047 Posted Parking Sign Violation \n",
+ "111048 Loud Talking \n",
+ "111049 Loud Music/Party \n",
+ "111050 Noise: Construction Before/After Hours (NM1) \n",
+ "111051 Loud Music/Party \n",
+ "111052 Dirty Water (WE) \n",
+ "111053 14 Derelict Vehicles \n",
+ "111054 Loud Talking \n",
+ "111055 Loud Talking \n",
+ "111056 Bus Stop \n",
+ "111057 Noise: Jack Hammering (NC2) \n",
+ "111058 Loud Music/Party \n",
+ "111059 Loud Talking \n",
+ "111060 Noise: Construction Before/After Hours (NM1) \n",
+ "111061 Loud Music/Party \n",
+ "111062 Dirty Water (WE) \n",
+ "111063 Dirty Water (WE) \n",
+ "111064 Structure - Outdoors \n",
+ "111065 Posted Parking Sign Violation \n",
+ "111066 Loud Talking \n",
+ "111067 Loud Talking \n",
+ "111068 Partial Access \n",
+ "\n",
+ " Location Type Incident Zip Incident Address \\\n",
+ "0 Street/Sidewalk 11432 90-03 169 STREET \n",
+ "1 Street/Sidewalk 11378 58 AVENUE \n",
+ "2 Club/Bar/Restaurant 10032 4060 BROADWAY \n",
+ "3 Street/Sidewalk 10023 WEST 72 STREET \n",
+ "4 Vacant Lot 10027 WEST 124 STREET \n",
+ "5 Club/Bar/Restaurant 11372 37 AVENUE \n",
+ "6 Street/Sidewalk 11419 107-50 109 STREET \n",
+ "7 Club/Bar/Restaurant 11417 137-09 CROSSBAY BOULEVARD \n",
+ "8 Club/Bar/Restaurant 10011 258 WEST 15 STREET \n",
+ "9 Club/Bar/Restaurant 11225 835 NOSTRAND AVENUE \n",
+ "10 House of Worship 11218 3775 18 AVENUE \n",
+ "11 Club/Bar/Restaurant 10003 187 2 AVENUE \n",
+ "12 Street/Sidewalk 10029 65 EAST 99 STREET \n",
+ "13 Street/Sidewalk 10466 NaN \n",
+ "14 1-2 Family Mixed Use Building 11219 63 STREET \n",
+ "15 House of Worship 10025 NaN \n",
+ "16 Street/Sidewalk 10310 173 CAMPBELL AVENUE \n",
+ "17 Street/Sidewalk 11236 NaN \n",
+ "18 NaN NaN 226 42 ST E \n",
+ "19 Club/Bar/Restaurant 10033 NaN \n",
+ "20 House of Worship 11216 778 PARK PLACE \n",
+ "21 Store/Commercial 10016 155 E 34TH ST \n",
+ "22 Street/Sidewalk 11419 NaN \n",
+ "23 Club/Bar/Restaurant 11216 371 TOMPKINS AVENUE \n",
+ "24 Street/Sidewalk 10305 1496 BAY STREET \n",
+ "25 Street/Sidewalk 10312 24 PRINCETON LANE \n",
+ "26 NaN NaN 38 ST E \n",
+ "27 3+ Family Mixed Use Building 10025 501 WEST 110 STREET \n",
+ "28 Street/Sidewalk 10026 121 WEST 116 STREET \n",
+ "29 NaN 10309 295 BAYVIEW AVENUE \n",
+ "... ... ... ... \n",
+ "111039 Club/Bar/Restaurant 10022 249 EAST 53 STREET \n",
+ "111040 Store/Commercial 10029 252 EAST 110 STREET \n",
+ "111041 NaN 11231 480 VAN BRUNT STREET \n",
+ "111042 Street/Sidewalk 10014 733 WASHINGTON STREET \n",
+ "111043 Club/Bar/Restaurant 11218 1213 CORTELYOU ROAD \n",
+ "111044 Street/Sidewalk 10032 539 WEST 162 STREET \n",
+ "111045 NaN 10022 251 EAST 51 STREET \n",
+ "111046 NaN 11231 480 VAN BRUNT STREET \n",
+ "111047 Street/Sidewalk 11434 NaN \n",
+ "111048 Street/Sidewalk 10027 215 WEST 131ST STREET \n",
+ "111049 Club/Bar/Restaurant 11209 8915 5 AVENUE \n",
+ "111050 NaN 10028 NaN \n",
+ "111051 Club/Bar/Restaurant 10009 506 EAST 13 STREET \n",
+ "111052 NaN 10022 325 EAST 54 STREET \n",
+ "111053 Street 11413 220-11 145 AVENUE \n",
+ "111054 Street/Sidewalk 11224 4823 BEACH 48 STREET \n",
+ "111055 Club/Bar/Restaurant 11209 7915 3 AVENUE \n",
+ "111056 Street 11378 NaN \n",
+ "111057 NaN 10036 NaN \n",
+ "111058 Club/Bar/Restaurant 11237 211 KNICKERBOCKER AVENUE \n",
+ "111059 Street/Sidewalk 10003 99 2 AVENUE \n",
+ "111060 NaN 10036 NaN \n",
+ "111061 Club/Bar/Restaurant 11106 30-09 BROADWAY \n",
+ "111062 NaN 10022 325 EAST 54 STREET \n",
+ "111063 NaN 10022 311 EAST 50 STREET \n",
+ "111064 Park 11213 NaN \n",
+ "111065 Street/Sidewalk 11434 NaN \n",
+ "111066 Street/Sidewalk 10016 344 EAST 28 STREET \n",
+ "111067 Club/Bar/Restaurant 11226 1233 FLATBUSH AVENUE \n",
+ "111068 Street/Sidewalk 11236 1259 EAST 94 STREET \n",
+ "\n",
+ " ... Bridge Highway Name \\\n",
+ "0 ... NaN \n",
+ "1 ... NaN \n",
+ "2 ... NaN \n",
+ "3 ... NaN \n",
+ "4 ... NaN \n",
+ "5 ... NaN \n",
+ "6 ... NaN \n",
+ "7 ... NaN \n",
+ "8 ... NaN \n",
+ "9 ... NaN \n",
+ "10 ... NaN \n",
+ "11 ... NaN \n",
+ "12 ... NaN \n",
+ "13 ... NaN \n",
+ "14 ... NaN \n",
+ "15 ... NaN \n",
+ "16 ... NaN \n",
+ "17 ... NaN \n",
+ "18 ... NaN \n",
+ "19 ... NaN \n",
+ "20 ... NaN \n",
+ "21 ... NaN \n",
+ "22 ... NaN \n",
+ "23 ... NaN \n",
+ "24 ... NaN \n",
+ "25 ... NaN \n",
+ "26 ... NaN \n",
+ "27 ... NaN \n",
+ "28 ... NaN \n",
+ "29 ... NaN \n",
+ "... ... ... \n",
+ "111039 ... NaN \n",
+ "111040 ... NaN \n",
+ "111041 ... NaN \n",
+ "111042 ... NaN \n",
+ "111043 ... NaN \n",
+ "111044 ... NaN \n",
+ "111045 ... NaN \n",
+ "111046 ... NaN \n",
+ "111047 ... NaN \n",
+ "111048 ... NaN \n",
+ "111049 ... NaN \n",
+ "111050 ... NaN \n",
+ "111051 ... NaN \n",
+ "111052 ... NaN \n",
+ "111053 ... NaN \n",
+ "111054 ... NaN \n",
+ "111055 ... NaN \n",
+ "111056 ... NaN \n",
+ "111057 ... NaN \n",
+ "111058 ... NaN \n",
+ "111059 ... NaN \n",
+ "111060 ... NaN \n",
+ "111061 ... NaN \n",
+ "111062 ... NaN \n",
+ "111063 ... NaN \n",
+ "111064 ... NaN \n",
+ "111065 ... NaN \n",
+ "111066 ... NaN \n",
+ "111067 ... NaN \n",
+ "111068 ... NaN \n",
+ "\n",
+ " Bridge Highway Direction Road Ramp Bridge Highway Segment \\\n",
+ "0 NaN NaN NaN \n",
+ "1 NaN NaN NaN \n",
+ "2 NaN NaN NaN \n",
+ "3 NaN NaN NaN \n",
+ "4 NaN NaN NaN \n",
+ "5 NaN NaN NaN \n",
+ "6 NaN NaN NaN \n",
+ "7 NaN NaN NaN \n",
+ "8 NaN NaN NaN \n",
+ "9 NaN NaN NaN \n",
+ "10 NaN NaN NaN \n",
+ "11 NaN NaN NaN \n",
+ "12 NaN NaN NaN \n",
+ "13 NaN NaN NaN \n",
+ "14 NaN NaN NaN \n",
+ "15 NaN NaN NaN \n",
+ "16 NaN NaN NaN \n",
+ "17 NaN NaN NaN \n",
+ "18 NaN NaN NaN \n",
+ "19 NaN NaN NaN \n",
+ "20 NaN NaN NaN \n",
+ "21 NaN NaN NaN \n",
+ "22 NaN NaN NaN \n",
+ "23 NaN NaN NaN \n",
+ "24 NaN NaN NaN \n",
+ "25 NaN NaN NaN \n",
+ "26 NaN NaN NaN \n",
+ "27 NaN NaN NaN \n",
+ "28 NaN NaN NaN \n",
+ "29 NaN NaN NaN \n",
+ "... ... ... ... \n",
+ "111039 NaN NaN NaN \n",
+ "111040 NaN NaN NaN \n",
+ "111041 NaN NaN NaN \n",
+ "111042 NaN NaN NaN \n",
+ "111043 NaN NaN NaN \n",
+ "111044 NaN NaN NaN \n",
+ "111045 NaN NaN NaN \n",
+ "111046 NaN NaN NaN \n",
+ "111047 NaN NaN NaN \n",
+ "111048 NaN NaN NaN \n",
+ "111049 NaN NaN NaN \n",
+ "111050 NaN NaN NaN \n",
+ "111051 NaN NaN NaN \n",
+ "111052 NaN NaN NaN \n",
+ "111053 NaN NaN NaN \n",
+ "111054 NaN NaN NaN \n",
+ "111055 NaN NaN NaN \n",
+ "111056 NaN NaN NaN \n",
+ "111057 NaN NaN NaN \n",
+ "111058 NaN NaN NaN \n",
+ "111059 NaN NaN NaN \n",
+ "111060 NaN NaN NaN \n",
+ "111061 NaN NaN NaN \n",
+ "111062 NaN NaN NaN \n",
+ "111063 NaN NaN NaN \n",
+ "111064 NaN NaN NaN \n",
+ "111065 NaN NaN NaN \n",
+ "111066 NaN NaN NaN \n",
+ "111067 NaN NaN NaN \n",
+ "111068 NaN NaN NaN \n",
+ "\n",
+ " Garage Lot Name Ferry Direction Ferry Terminal Name Latitude \\\n",
+ "0 NaN NaN NaN 40.708275 \n",
+ "1 NaN NaN NaN 40.721041 \n",
+ "2 NaN NaN NaN 40.843330 \n",
+ "3 NaN NaN NaN 40.778009 \n",
+ "4 NaN NaN NaN 40.807691 \n",
+ "5 NaN NaN NaN 40.749989 \n",
+ "6 NaN NaN NaN 40.681533 \n",
+ "7 NaN NaN NaN 40.671816 \n",
+ "8 NaN NaN NaN 40.739913 \n",
+ "9 NaN NaN NaN 40.668204 \n",
+ "10 NaN NaN NaN 40.634378 \n",
+ "11 NaN NaN NaN 40.730816 \n",
+ "12 NaN NaN NaN 40.788974 \n",
+ "13 NaN NaN NaN 40.891517 \n",
+ "14 NaN NaN NaN 40.626477 \n",
+ "15 NaN NaN NaN 40.796597 \n",
+ "16 NaN NaN NaN 40.636182 \n",
+ "17 NaN NaN NaN 40.632437 \n",
+ "18 NaN NaN NaN NaN \n",
+ "19 NaN NaN NaN 40.852058 \n",
+ "20 NaN NaN NaN 40.673505 \n",
+ "21 NaN NaN NaN 40.746194 \n",
+ "22 NaN NaN NaN 40.692394 \n",
+ "23 NaN NaN NaN 40.684944 \n",
+ "24 NaN NaN NaN 40.607245 \n",
+ "25 NaN NaN NaN 40.553421 \n",
+ "26 NaN NaN NaN NaN \n",
+ "27 NaN NaN NaN 40.803149 \n",
+ "28 NaN NaN NaN 40.802390 \n",
+ "29 NaN NaN NaN 40.517378 \n",
+ "... ... ... ... ... \n",
+ "111039 NaN NaN NaN 40.757248 \n",
+ "111040 NaN NaN NaN 40.793635 \n",
+ "111041 NaN NaN NaN 40.674249 \n",
+ "111042 NaN NaN NaN 40.736304 \n",
+ "111043 NaN NaN NaN 40.640139 \n",
+ "111044 NaN NaN NaN 40.836532 \n",
+ "111045 NaN NaN NaN 40.755980 \n",
+ "111046 NaN NaN NaN 40.674249 \n",
+ "111047 NaN NaN NaN 40.656160 \n",
+ "111048 NaN NaN NaN 40.813339 \n",
+ "111049 NaN NaN NaN 40.619601 \n",
+ "111050 NaN NaN NaN 40.774612 \n",
+ "111051 NaN NaN NaN 40.729531 \n",
+ "111052 NaN NaN NaN 40.757069 \n",
+ "111053 NaN NaN NaN 40.664353 \n",
+ "111054 NaN NaN NaN 40.577515 \n",
+ "111055 NaN NaN NaN 40.628381 \n",
+ "111056 NaN NaN NaN 40.732250 \n",
+ "111057 NaN NaN NaN 40.760116 \n",
+ "111058 NaN NaN NaN 40.703365 \n",
+ "111059 NaN NaN NaN 40.727251 \n",
+ "111060 NaN NaN NaN 40.760405 \n",
+ "111061 NaN NaN NaN 40.762279 \n",
+ "111062 NaN NaN NaN 40.757069 \n",
+ "111063 NaN NaN NaN 40.754662 \n",
+ "111064 NaN NaN NaN NaN \n",
+ "111065 NaN NaN NaN 40.656160 \n",
+ "111066 NaN NaN NaN 40.740295 \n",
+ "111067 NaN NaN NaN 40.640182 \n",
+ "111068 NaN NaN NaN 40.640024 \n",
+ "\n",
+ " Longitude Location \n",
+ "0 -73.791604 (40.70827532593202, -73.79160395779721) \n",
+ "1 -73.909453 (40.721040535628305, -73.90945306791765) \n",
+ "2 -73.939144 (40.84332975466513, -73.93914371913482) \n",
+ "3 -73.980213 (40.7780087446372, -73.98021349023975) \n",
+ "4 -73.947387 (40.80769092704951, -73.94738703491433) \n",
+ "5 -73.881988 (40.7499893014072, -73.88198770727831) \n",
+ "6 -73.831737 (40.68153278675525, -73.83173699701601) \n",
+ "7 -73.843092 (40.67181584567338, -73.84309181950769) \n",
+ "8 -74.000790 (40.73991339303542, -74.00079028612932) \n",
+ "9 -73.950648 (40.66820406598287, -73.95064760056546) \n",
+ "10 -73.969462 (40.63437840816299, -73.96946177104543) \n",
+ "11 -73.986073 (40.73081644089586, -73.98607265739876) \n",
+ "12 -73.952259 (40.78897400211689, -73.95225898702977) \n",
+ "13 -73.836457 (40.89151738488846, -73.83645714593568) \n",
+ "14 -73.999218 (40.6264774690411, -73.99921826202639) \n",
+ "15 -73.970370 (40.7965967075252, -73.97036973473399) \n",
+ "16 -74.116150 (40.63618202176914, -74.1161500428337) \n",
+ "17 -73.888173 (40.63243692394328, -73.88817263437012) \n",
+ "18 NaN NaN \n",
+ "19 -73.934776 (40.85205827756883, -73.93477640780834) \n",
+ "20 -73.951844 (40.67350473678714, -73.95184414979961) \n",
+ "21 -73.978769 (40.74619417253121, -73.97876853124392) \n",
+ "22 -73.833891 (40.69239424979043, -73.8338912453996) \n",
+ "23 -73.944221 (40.6849442562592, -73.94422078036632) \n",
+ "24 -74.061106 (40.60724493456944, -74.06110566015863) \n",
+ "25 -74.196743 (40.55342078716953, -74.19674315017886) \n",
+ "26 NaN NaN \n",
+ "27 -73.964266 (40.80314938553783, -73.96426608076082) \n",
+ "28 -73.950526 (40.80238950799943, -73.95052644123253) \n",
+ "29 -74.203435 (40.517377871705676, -74.20343466779575) \n",
+ "... ... ... \n",
+ "111039 -73.968286 (40.757247604963055, -73.96828647941395) \n",
+ "111040 -73.941649 (40.79363516179995, -73.94164859515777) \n",
+ "111041 -74.016558 (40.6742492231181, -74.01655803856313) \n",
+ "111042 -74.008299 (40.736303747410034, -74.00829935904578) \n",
+ "111043 -73.966847 (40.64013899178716, -73.96684680242933) \n",
+ "111044 -73.941018 (40.836532082987944, -73.9410182995914) \n",
+ "111045 -73.969171 (40.75597977288785, -73.96917140638074) \n",
+ "111046 -74.016558 (40.6742492231181, -74.01655803856313) \n",
+ "111047 -73.767353 (40.656160351546845, -73.76735262738222) \n",
+ "111048 -73.946328 (40.81333907832113, -73.94632769228208) \n",
+ "111049 -74.027826 (40.619601147364584, -74.02782628492785) \n",
+ "111050 -73.948085 (40.77461174278734, -73.94808472177321) \n",
+ "111051 -73.980416 (40.72953107218499, -73.98041550317102) \n",
+ "111052 -73.965933 (40.75706852462872, -73.96593314322774) \n",
+ "111053 -73.757556 (40.6643525308449, -73.75755575708348) \n",
+ "111054 -74.012207 (40.57751513866536, -74.01220705163807) \n",
+ "111055 -74.029040 (40.62838143294541, -74.02904041333245) \n",
+ "111056 -73.924513 (40.73225000573841, -73.92451289173367) \n",
+ "111057 -73.984836 (40.76011648520396, -73.98483562074706) \n",
+ "111058 -73.926345 (40.703365329011284, -73.92634531031759) \n",
+ "111059 -73.988660 (40.727251287038676, -73.98866028603422) \n",
+ "111060 -73.987474 (40.76040500039782, -73.98747426235285) \n",
+ "111061 -73.926013 (40.762278541098084, -73.92601303458156) \n",
+ "111062 -73.965933 (40.75706852462872, -73.96593314322774) \n",
+ "111063 -73.967992 (40.75466197318078, -73.96799173864807) \n",
+ "111064 NaN NaN \n",
+ "111065 -73.767353 (40.656160351546845, -73.76735262738222) \n",
+ "111066 -73.976952 (40.740295354643706, -73.97695165980414) \n",
+ "111067 -73.955306 (40.64018174662485, -73.95530566958138) \n",
+ "111068 -73.900717 (40.640024057399216, -73.90071711703163) \n",
+ "\n",
+ "[111069 rows x 52 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "complaints"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2.2 Selecting columns and rows"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To select a column, we index with the name of the column, like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Noise - Street/Sidewalk\n",
+ "1 Illegal Parking\n",
+ "2 Noise - Commercial\n",
+ "3 Noise - Vehicle\n",
+ "4 Rodent\n",
+ "5 Noise - Commercial\n",
+ "6 Blocked Driveway\n",
+ "7 Noise - Commercial\n",
+ "8 Noise - Commercial\n",
+ "9 Noise - Commercial\n",
+ "10 Noise - House of Worship\n",
+ "11 Noise - Commercial\n",
+ "12 Illegal Parking\n",
+ "13 Noise - Vehicle\n",
+ "14 Rodent\n",
+ "15 Noise - House of Worship\n",
+ "16 Noise - Street/Sidewalk\n",
+ "17 Illegal Parking\n",
+ "18 Street Light Condition\n",
+ "19 Noise - Commercial\n",
+ "20 Noise - House of Worship\n",
+ "21 Noise - Commercial\n",
+ "22 Noise - Vehicle\n",
+ "23 Noise - Commercial\n",
+ "24 Blocked Driveway\n",
+ "25 Noise - Street/Sidewalk\n",
+ "26 Street Light Condition\n",
+ "27 Harboring Bees/Wasps\n",
+ "28 Noise - Street/Sidewalk\n",
+ "29 Street Light Condition\n",
+ " ... \n",
+ "111039 Noise - Commercial\n",
+ "111040 Noise - Commercial\n",
+ "111041 Noise\n",
+ "111042 Noise - Street/Sidewalk\n",
+ "111043 Noise - Commercial\n",
+ "111044 Noise - Street/Sidewalk\n",
+ "111045 Water System\n",
+ "111046 Noise\n",
+ "111047 Illegal Parking\n",
+ "111048 Noise - Street/Sidewalk\n",
+ "111049 Noise - Commercial\n",
+ "111050 Noise\n",
+ "111051 Noise - Commercial\n",
+ "111052 Water System\n",
+ "111053 Derelict Vehicles\n",
+ "111054 Noise - Street/Sidewalk\n",
+ "111055 Noise - Commercial\n",
+ "111056 Street Sign - Missing\n",
+ "111057 Noise\n",
+ "111058 Noise - Commercial\n",
+ "111059 Noise - Street/Sidewalk\n",
+ "111060 Noise\n",
+ "111061 Noise - Commercial\n",
+ "111062 Water System\n",
+ "111063 Water System\n",
+ "111064 Maintenance or Facility\n",
+ "111065 Illegal Parking\n",
+ "111066 Noise - Street/Sidewalk\n",
+ "111067 Noise - Commercial\n",
+ "111068 Blocked Driveway\n",
+ "Name: Complaint Type, Length: 111069, dtype: object"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "complaints['Complaint Type']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To get the first 5 rows of a dataframe, we can use a slice: `df[:5]`.\n",
+ "\n",
+ "This is a great way to get a sense for what kind of information is in the dataframe -- take a minute to look at the contents and get a feel for this dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Unique Key
\n",
+ "
Created Date
\n",
+ "
Closed Date
\n",
+ "
Agency
\n",
+ "
Agency Name
\n",
+ "
Complaint Type
\n",
+ "
Descriptor
\n",
+ "
Location Type
\n",
+ "
Incident Zip
\n",
+ "
Incident Address
\n",
+ "
...
\n",
+ "
Bridge Highway Name
\n",
+ "
Bridge Highway Direction
\n",
+ "
Road Ramp
\n",
+ "
Bridge Highway Segment
\n",
+ "
Garage Lot Name
\n",
+ "
Ferry Direction
\n",
+ "
Ferry Terminal Name
\n",
+ "
Latitude
\n",
+ "
Longitude
\n",
+ "
Location
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
26589651
\n",
+ "
10/31/2013 02:08:41 AM
\n",
+ "
NaN
\n",
+ "
NYPD
\n",
+ "
New York City Police Department
\n",
+ "
Noise - Street/Sidewalk
\n",
+ "
Loud Talking
\n",
+ "
Street/Sidewalk
\n",
+ "
11432
\n",
+ "
90-03 169 STREET
\n",
+ "
...
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
40.708275
\n",
+ "
-73.791604
\n",
+ "
(40.70827532593202, -73.79160395779721)
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
26593698
\n",
+ "
10/31/2013 02:01:04 AM
\n",
+ "
NaN
\n",
+ "
NYPD
\n",
+ "
New York City Police Department
\n",
+ "
Illegal Parking
\n",
+ "
Commercial Overnight Parking
\n",
+ "
Street/Sidewalk
\n",
+ "
11378
\n",
+ "
58 AVENUE
\n",
+ "
...
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
40.721041
\n",
+ "
-73.909453
\n",
+ "
(40.721040535628305, -73.90945306791765)
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
+ "
26594139
\n",
+ "
10/31/2013 02:00:24 AM
\n",
+ "
10/31/2013 02:40:32 AM
\n",
+ "
NYPD
\n",
+ "
New York City Police Department
\n",
+ "
Noise - Commercial
\n",
+ "
Loud Music/Party
\n",
+ "
Club/Bar/Restaurant
\n",
+ "
10032
\n",
+ "
4060 BROADWAY
\n",
+ "
...
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
40.843330
\n",
+ "
-73.939144
\n",
+ "
(40.84332975466513, -73.93914371913482)
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
26595721
\n",
+ "
10/31/2013 01:56:23 AM
\n",
+ "
10/31/2013 02:21:48 AM
\n",
+ "
NYPD
\n",
+ "
New York City Police Department
\n",
+ "
Noise - Vehicle
\n",
+ "
Car/Truck Horn
\n",
+ "
Street/Sidewalk
\n",
+ "
10023
\n",
+ "
WEST 72 STREET
\n",
+ "
...
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
40.778009
\n",
+ "
-73.980213
\n",
+ "
(40.7780087446372, -73.98021349023975)
\n",
+ "
\n",
+ "
\n",
+ "
4
\n",
+ "
26590930
\n",
+ "
10/31/2013 01:53:44 AM
\n",
+ "
NaN
\n",
+ "
DOHMH
\n",
+ "
Department of Health and Mental Hygiene
\n",
+ "
Rodent
\n",
+ "
Condition Attracting Rodents
\n",
+ "
Vacant Lot
\n",
+ "
10027
\n",
+ "
WEST 124 STREET
\n",
+ "
...
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
40.807691
\n",
+ "
-73.947387
\n",
+ "
(40.80769092704951, -73.94738703491433)
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 52 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unique Key Created Date Closed Date Agency \\\n",
+ "0 26589651 10/31/2013 02:08:41 AM NaN NYPD \n",
+ "1 26593698 10/31/2013 02:01:04 AM NaN NYPD \n",
+ "2 26594139 10/31/2013 02:00:24 AM 10/31/2013 02:40:32 AM NYPD \n",
+ "3 26595721 10/31/2013 01:56:23 AM 10/31/2013 02:21:48 AM NYPD \n",
+ "4 26590930 10/31/2013 01:53:44 AM NaN DOHMH \n",
+ "\n",
+ " Agency Name Complaint Type \\\n",
+ "0 New York City Police Department Noise - Street/Sidewalk \n",
+ "1 New York City Police Department Illegal Parking \n",
+ "2 New York City Police Department Noise - Commercial \n",
+ "3 New York City Police Department Noise - Vehicle \n",
+ "4 Department of Health and Mental Hygiene Rodent \n",
+ "\n",
+ " Descriptor Location Type Incident Zip \\\n",
+ "0 Loud Talking Street/Sidewalk 11432 \n",
+ "1 Commercial Overnight Parking Street/Sidewalk 11378 \n",
+ "2 Loud Music/Party Club/Bar/Restaurant 10032 \n",
+ "3 Car/Truck Horn Street/Sidewalk 10023 \n",
+ "4 Condition Attracting Rodents Vacant Lot 10027 \n",
+ "\n",
+ " Incident Address ... \\\n",
+ "0 90-03 169 STREET ... \n",
+ "1 58 AVENUE ... \n",
+ "2 4060 BROADWAY ... \n",
+ "3 WEST 72 STREET ... \n",
+ "4 WEST 124 STREET ... \n",
+ "\n",
+ " Bridge Highway Name Bridge Highway Direction Road Ramp \\\n",
+ "0 NaN NaN NaN \n",
+ "1 NaN NaN NaN \n",
+ "2 NaN NaN NaN \n",
+ "3 NaN NaN NaN \n",
+ "4 NaN NaN NaN \n",
+ "\n",
+ " Bridge Highway Segment Garage Lot Name Ferry Direction Ferry Terminal Name \\\n",
+ "0 NaN NaN NaN NaN \n",
+ "1 NaN NaN NaN NaN \n",
+ "2 NaN NaN NaN NaN \n",
+ "3 NaN NaN NaN NaN \n",
+ "4 NaN NaN NaN NaN \n",
+ "\n",
+ " Latitude Longitude Location \n",
+ "0 40.708275 -73.791604 (40.70827532593202, -73.79160395779721) \n",
+ "1 40.721041 -73.909453 (40.721040535628305, -73.90945306791765) \n",
+ "2 40.843330 -73.939144 (40.84332975466513, -73.93914371913482) \n",
+ "3 40.778009 -73.980213 (40.7780087446372, -73.98021349023975) \n",
+ "4 40.807691 -73.947387 (40.80769092704951, -73.94738703491433) \n",
+ "\n",
+ "[5 rows x 52 columns]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "complaints[:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can combine these to get the first 5 rows of a column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Noise - Street/Sidewalk\n",
+ "1 Illegal Parking\n",
+ "2 Noise - Commercial\n",
+ "3 Noise - Vehicle\n",
+ "4 Rodent\n",
+ "Name: Complaint Type, dtype: object"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "complaints['Complaint Type'][:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "and it doesn't matter which direction we do it in:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Noise - Street/Sidewalk\n",
+ "1 Illegal Parking\n",
+ "2 Noise - Commercial\n",
+ "3 Noise - Vehicle\n",
+ "4 Rodent\n",
+ "Name: Complaint Type, dtype: object"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "complaints[:5]['Complaint Type']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2.3 Selecting multiple columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What if we just want to know the complaint type and the borough, but not the rest of the information? Pandas makes it really easy to select a subset of the columns: just index with list of columns you want."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ ""
+ ],
+ "text/plain": [
+ " Unique Key Created Date Closed Date Agency \\\n",
+ "0 26589651 10/31/2013 02:08:41 AM NaN NYPD \n",
+ "1 26593698 10/31/2013 02:01:04 AM NaN NYPD \n",
+ "2 26594139 10/31/2013 02:00:24 AM 10/31/2013 02:40:32 AM NYPD \n",
+ "3 26595721 10/31/2013 01:56:23 AM 10/31/2013 02:21:48 AM NYPD \n",
+ "4 26590930 10/31/2013 01:53:44 AM NaN DOHMH \n",
+ "\n",
+ " Agency Name Complaint Type \\\n",
+ "0 New York City Police Department Noise - Street/Sidewalk \n",
+ "1 New York City Police Department Illegal Parking \n",
+ "2 New York City Police Department Noise - Commercial \n",
+ "3 New York City Police Department Noise - Vehicle \n",
+ "4 Department of Health and Mental Hygiene Rodent \n",
+ "\n",
+ " Descriptor Location Type Incident Zip \\\n",
+ "0 Loud Talking Street/Sidewalk 11432 \n",
+ "1 Commercial Overnight Parking Street/Sidewalk 11378 \n",
+ "2 Loud Music/Party Club/Bar/Restaurant 10032 \n",
+ "3 Car/Truck Horn Street/Sidewalk 10023 \n",
+ "4 Condition Attracting Rodents Vacant Lot 10027 \n",
+ "\n",
+ " Incident Address ... \\\n",
+ "0 90-03 169 STREET ... \n",
+ "1 58 AVENUE ... \n",
+ "2 4060 BROADWAY ... \n",
+ "3 WEST 72 STREET ... \n",
+ "4 WEST 124 STREET ... \n",
+ "\n",
+ " Bridge Highway Name Bridge Highway Direction Road Ramp \\\n",
+ "0 NaN NaN NaN \n",
+ "1 NaN NaN NaN \n",
+ "2 NaN NaN NaN \n",
+ "3 NaN NaN NaN \n",
+ "4 NaN NaN NaN \n",
+ "\n",
+ " Bridge Highway Segment Garage Lot Name Ferry Direction Ferry Terminal Name \\\n",
+ "0 NaN NaN NaN NaN \n",
+ "1 NaN NaN NaN NaN \n",
+ "2 NaN NaN NaN NaN \n",
+ "3 NaN NaN NaN NaN \n",
+ "4 NaN NaN NaN NaN \n",
+ "\n",
+ " Latitude Longitude Location \n",
+ "0 40.708275 -73.791604 (40.70827532593202, -73.79160395779721) \n",
+ "1 40.721041 -73.909453 (40.721040535628305, -73.90945306791765) \n",
+ "2 40.843330 -73.939144 (40.84332975466513, -73.93914371913482) \n",
+ "3 40.778009 -73.980213 (40.7780087446372, -73.98021349023975) \n",
+ "4 40.807691 -73.947387 (40.80769092704951, -73.94738703491433) \n",
+ "\n",
+ "[5 rows x 52 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "complaints[:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To get the noise complaints, we need to find the rows where the \"Complaint Type\" column is \"Noise - Street/Sidewalk\". I'll show you how to do that, and then explain what's going on."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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Agency Name
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Complaint Type
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+ "
Descriptor
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+ "
Location Type
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+ "
Incident Zip
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+ "
Incident Address
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+ "
...
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+ "
Bridge Highway Name
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Bridge Highway Direction
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Bridge Highway Segment
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Garage Lot Name
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Ferry Terminal Name
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NYPD
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New York City Police Department
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Street/Sidewalk
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New York City Police Department
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...
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NYPD
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New York City Police Department
\n",
+ "
Noise - Street/Sidewalk
\n",
+ "
Loud Talking
\n",
+ "
Street/Sidewalk
\n",
+ "
10312
\n",
+ "
24 PRINCETON LANE
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+ "
...
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NaN
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NaN
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NaN
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NaN
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NaN
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40.553421
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\n",
+ "
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+ " \n",
+ "
\n",
+ "
3 rows × 52 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unique Key Created Date Closed Date Agency \\\n",
+ "0 26589651 10/31/2013 02:08:41 AM NaN NYPD \n",
+ "16 26594086 10/31/2013 12:54:03 AM 10/31/2013 02:16:39 AM NYPD \n",
+ "25 26591573 10/31/2013 12:35:18 AM 10/31/2013 02:41:35 AM NYPD \n",
+ "\n",
+ " Agency Name Complaint Type \\\n",
+ "0 New York City Police Department Noise - Street/Sidewalk \n",
+ "16 New York City Police Department Noise - Street/Sidewalk \n",
+ "25 New York City Police Department Noise - Street/Sidewalk \n",
+ "\n",
+ " Descriptor Location Type Incident Zip Incident Address \\\n",
+ "0 Loud Talking Street/Sidewalk 11432 90-03 169 STREET \n",
+ "16 Loud Music/Party Street/Sidewalk 10310 173 CAMPBELL AVENUE \n",
+ "25 Loud Talking Street/Sidewalk 10312 24 PRINCETON LANE \n",
+ "\n",
+ " ... Bridge Highway Name \\\n",
+ "0 ... NaN \n",
+ "16 ... NaN \n",
+ "25 ... NaN \n",
+ "\n",
+ " Bridge Highway Direction Road Ramp Bridge Highway Segment Garage Lot Name \\\n",
+ "0 NaN NaN NaN NaN \n",
+ "16 NaN NaN NaN NaN \n",
+ "25 NaN NaN NaN NaN \n",
+ "\n",
+ " Ferry Direction Ferry Terminal Name Latitude Longitude \\\n",
+ "0 NaN NaN 40.708275 -73.791604 \n",
+ "16 NaN NaN 40.636182 -74.116150 \n",
+ "25 NaN NaN 40.553421 -74.196743 \n",
+ "\n",
+ " Location \n",
+ "0 (40.70827532593202, -73.79160395779721) \n",
+ "16 (40.63618202176914, -74.1161500428337) \n",
+ "25 (40.55342078716953, -74.19674315017886) \n",
+ "\n",
+ "[3 rows x 52 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "noise_complaints = complaints[complaints['Complaint Type'] == \"Noise - Street/Sidewalk\"]\n",
+ "noise_complaints[:3]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you look at `noise_complaints`, you'll see that this worked, and it only contains complaints with the right complaint type. But how does this work? Let's deconstruct it into two pieces"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 True\n",
+ "1 False\n",
+ "2 False\n",
+ "3 False\n",
+ "4 False\n",
+ "5 False\n",
+ "6 False\n",
+ "7 False\n",
+ "8 False\n",
+ "9 False\n",
+ "10 False\n",
+ "11 False\n",
+ "12 False\n",
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+ "14 False\n",
+ "15 False\n",
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+ "26 False\n",
+ "27 False\n",
+ "28 True\n",
+ "29 False\n",
+ " ... \n",
+ "111039 False\n",
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+ "111042 True\n",
+ "111043 False\n",
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+ "111046 False\n",
+ "111047 False\n",
+ "111048 True\n",
+ "111049 False\n",
+ "111050 False\n",
+ "111051 False\n",
+ "111052 False\n",
+ "111053 False\n",
+ "111054 True\n",
+ "111055 False\n",
+ "111056 False\n",
+ "111057 False\n",
+ "111058 False\n",
+ "111059 True\n",
+ "111060 False\n",
+ "111061 False\n",
+ "111062 False\n",
+ "111063 False\n",
+ "111064 False\n",
+ "111065 False\n",
+ "111066 True\n",
+ "111067 False\n",
+ "111068 False\n",
+ "Name: Complaint Type, Length: 111069, dtype: bool"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "complaints['Complaint Type'] == \"Noise - Street/Sidewalk\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is a big array of `True`s and `False`s, one for each row in our dataframe. When we index our dataframe with this array, we get just the rows where our boolean array evaluated to `True`. It's important to note that for row filtering by a boolean array the length of our dataframe's index must be the same length as the boolean array used for filtering.\n",
+ "\n",
+ "You can also combine more than one condition with the `&` operator like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
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+ "
Incident Address
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...
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+ "
10/31/2013 12:30:36 AM
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NaN
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+ "
New York City Police Department
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Noise - Street/Sidewalk
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Street/Sidewalk
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+ "
...
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
10/31/2013 12:18:54 AM
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NYPD
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+ "
New York City Police Department
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Noise - Street/Sidewalk
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236
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+ "
10/30/2013 10:23:20 PM
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+ "
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+ "
New York City Police Department
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+ "
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+ "
Loud Talking
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Street/Sidewalk
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+ "
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...
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NaN
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NaN
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NaN
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NaN
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
10/30/2013 10:26:28 PM
\n",
+ "
NYPD
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+ "
New York City Police Department
\n",
+ "
Noise - Street/Sidewalk
\n",
+ "
Loud Music/Party
\n",
+ "
Street/Sidewalk
\n",
+ "
11218
\n",
+ "
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\n",
+ "
...
\n",
+ "
NaN
\n",
+ "
NaN
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+ "
NaN
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+ "
NaN
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+ "
NaN
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+ "
NaN
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+ "
NaN
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+ "
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\n",
+ "
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\n",
+ "
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\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 52 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Unique Key Created Date Closed Date Agency \\\n",
+ "31 26595564 10/31/2013 12:30:36 AM NaN NYPD \n",
+ "49 26595553 10/31/2013 12:05:10 AM 10/31/2013 02:43:43 AM NYPD \n",
+ "109 26594653 10/30/2013 11:26:32 PM 10/31/2013 12:18:54 AM NYPD \n",
+ "236 26591992 10/30/2013 10:02:58 PM 10/30/2013 10:23:20 PM NYPD \n",
+ "370 26594167 10/30/2013 08:38:25 PM 10/30/2013 10:26:28 PM NYPD \n",
+ "\n",
+ " Agency Name Complaint Type \\\n",
+ "31 New York City Police Department Noise - Street/Sidewalk \n",
+ "49 New York City Police Department Noise - Street/Sidewalk \n",
+ "109 New York City Police Department Noise - Street/Sidewalk \n",
+ "236 New York City Police Department Noise - Street/Sidewalk \n",
+ "370 New York City Police Department Noise - Street/Sidewalk \n",
+ "\n",
+ " Descriptor Location Type Incident Zip Incident Address \\\n",
+ "31 Loud Music/Party Street/Sidewalk 11236 AVENUE J \n",
+ "49 Loud Talking Street/Sidewalk 11225 25 LEFFERTS AVENUE \n",
+ "109 Loud Music/Party Street/Sidewalk 11222 NaN \n",
+ "236 Loud Talking Street/Sidewalk 11218 DITMAS AVENUE \n",
+ "370 Loud Music/Party Street/Sidewalk 11218 126 BEVERLY ROAD \n",
+ "\n",
+ " ... Bridge Highway Name \\\n",
+ "31 ... NaN \n",
+ "49 ... NaN \n",
+ "109 ... NaN \n",
+ "236 ... NaN \n",
+ "370 ... NaN \n",
+ "\n",
+ " Bridge Highway Direction Road Ramp Bridge Highway Segment Garage Lot Name \\\n",
+ "31 NaN NaN NaN NaN \n",
+ "49 NaN NaN NaN NaN \n",
+ "109 NaN NaN NaN NaN \n",
+ "236 NaN NaN NaN NaN \n",
+ "370 NaN NaN NaN NaN \n",
+ "\n",
+ " Ferry Direction Ferry Terminal Name Latitude Longitude \\\n",
+ "31 NaN NaN 40.634104 -73.911055 \n",
+ "49 NaN NaN 40.661793 -73.959934 \n",
+ "109 NaN NaN 40.724600 -73.954271 \n",
+ "236 NaN NaN 40.636169 -73.972455 \n",
+ "370 NaN NaN 40.642922 -73.978762 \n",
+ "\n",
+ " Location \n",
+ "31 (40.634103775951736, -73.91105541883589) \n",
+ "49 (40.6617931276793, -73.95993363978067) \n",
+ "109 (40.724599563793525, -73.95427134534344) \n",
+ "236 (40.63616876563881, -73.97245504682485) \n",
+ "370 (40.6429222774404, -73.97876175474585) \n",
+ "\n",
+ "[5 rows x 52 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "is_noise = complaints['Complaint Type'] == \"Noise - Street/Sidewalk\"\n",
+ "in_brooklyn = complaints['Borough'] == \"BROOKLYN\"\n",
+ "complaints[is_noise & in_brooklyn][:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or if we just wanted a few columns:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
]
},
"metadata": {},
@@ -1455,12 +84,10 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
- "url_template = \"http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data\""
+ "url_template = \"http://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID=5415&Year={year}&Month={month}&Day=1&timeframe=1&submit=Download+Data\""
]
},
{
@@ -1474,12 +101,21 @@
"cell_type": "code",
"execution_count": 4,
"metadata": {
- "collapsed": false
+ "scrolled": true
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "http://climate.weather.gc.ca/climate_data/bulk_data_e.html?format=csv&stationID=5415&Year=2012&Month=3&Day=1&timeframe=1&submit=Download+Data\n"
+ ]
+ }
+ ],
"source": [
"url = url_template.format(month=3, year=2012)\n",
- "weather_mar2012 = pd.read_csv(url, skiprows=15, index_col='Date/Time', parse_dates=True, encoding='latin1', header=True)"
+ "print(url)\n",
+ "weather_mar2012 = pd.read_csv(url, skiprows=15, index_col='Date/Time', parse_dates=True, encoding='latin1', header=0)"
]
},
{
@@ -1488,20 +124,31 @@
"source": [
"This is super great! We can just use the same `read_csv` function as before, and just give it a URL as a filename. Awesome.\n",
"\n",
- "There are 16 rows of metadata at the top of this CSV, but pandas knows CSVs are weird, so there's a `skiprows` options. We parse the dates again, and set 'Date/Time' to be the index column. Here's the resulting dataframe."
+ "There are 15 rows of metadata at the top of this CSV, but pandas knows CSVs are weird, so there's a `skiprows` options. We parse the dates again, and set 'Date/Time' to be the index column. Here's the resulting dataframe."
]
},
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
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- "
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+ "
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+ "\n",
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Month
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Day
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Temp Flag
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Dew Point Temp (°C)
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Dew Point Temp Flag
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Rel Hum (%)
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+ "
Rel Hum Flag
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...
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Wind Spd Flag
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Visibility (km)
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@@ -1556,723 +203,723 @@
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- "
NaN
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- "
Snow
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-8.0
\n",
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NaN
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+ "
Snow
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"
\n",
"
\n",
"
2012-03-01 17:00:00
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2012
\n",
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3
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1
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17:00
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- "
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2012
\n",
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3
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1
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+ "
17:00
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"
-2.9
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"
NaN
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"
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- "
80
\n",
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80
\n",
+ "
NaN
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"
...
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NaN
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- "
4.0
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NaN
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100.80
\n",
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4.0
\n",
"
NaN
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+ "
100.80
\n",
"
NaN
\n",
"
NaN
\n",
- "
-9
\n",
- "
NaN
\n",
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Snow
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NaN
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+ "
-9.0
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NaN
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+ "
Snow
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"
\n",
"
\n",
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2012-03-01 18:00:00
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2012
\n",
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3
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1
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18:00
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2012
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1
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18:00
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"
-3.0
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80
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80
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4.0
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NaN
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100.87
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4.0
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"
NaN
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100.87
\n",
"
NaN
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"
NaN
\n",
- "
-9
\n",
- "
NaN
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Snow
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NaN
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+ "
-9.0
\n",
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NaN
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+ "
Snow
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"
\n",
"
\n",
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2012-03-01 19:00:00
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2012
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3
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1
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19:00
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2012
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1
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19:00
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"
-3.6
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"
NaN
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81
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81
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...
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3.2
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NaN
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3.2
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NaN
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100.93
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"
NaN
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"
NaN
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"
NaN
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- "
-9
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NaN
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Snow
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-9.0
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NaN
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Snow
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"
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"
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2012-03-01 20:00:00
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2012
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1
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81
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...
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4.8
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100.95
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4.8
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NaN
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100.95
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NaN
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NaN
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+ "
-10.0
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-10
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Snow
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2012-03-01 21:00:00
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2012
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2012
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1
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21:00
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81
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81
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NaN
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...
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6.4
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6.4
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NaN
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100.98
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NaN
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NaN
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NaN
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-10.0
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-10
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NaN
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Snow
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Snow
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2012-03-01 22:00:00
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2012
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2012
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22:00
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-4.3
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82
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...
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2.4
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2.4
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101.00
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NaN
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-11
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"
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2012-03-01 23:00:00
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2012
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2012
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81
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81
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...
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4.8
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4.8
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-11.0
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Snow
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2012-03-02 00:00:00
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83
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83
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...
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3.2
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3.2
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101.04
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"
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-12
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2012-03-02 01:00:00
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2012
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2012-03-02 02:00:00
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\n",
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2012-03-02 03:00:00
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83
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83
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4.8
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4.8
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101.15
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-12
\n",
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\n",
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\n",
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2012-03-02 04:00:00
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...
\n",
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6.4
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-13
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Snow
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\n",
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\n",
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2012-03-02 05:00:00
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81
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81
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...
\n",
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12.9
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101.15
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12.9
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+ "
NaN
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+ "
101.15
\n",
"
NaN
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NaN
\n",
"
NaN
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- "
-12
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- "
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\n",
@@ -2300,1049 +947,1049 @@
"
\n",
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\n",
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2012-03-30 18:00:00
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30
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18:00
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3.9
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2012
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+ "
3
\n",
+ "
30
\n",
+ "
18:00
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+ "
3.9
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"
NaN
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-7.9
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"
NaN
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42
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+ "
42
\n",
+ "
NaN
\n",
"
...
\n",
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24.1
\n",
"
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- "
101.26
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+ "
24.1
\n",
+ "
NaN
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+ "
101.26
\n",
+ "
NaN
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"
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"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mostly Cloudy
\n",
+ "
Mostly Cloudy
\n",
"
\n",
"
\n",
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2012-03-30 19:00:00
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- "
2012
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30
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19:00
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3.1
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2012
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3
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30
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+ "
19:00
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+ "
3.1
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"
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-6.7
\n",
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NaN
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49
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49
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+ "
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...
\n",
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25.0
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101.29
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25.0
\n",
+ "
NaN
\n",
+ "
101.29
\n",
"
NaN
\n",
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\n",
"
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\n",
"
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- "
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- "
Mostly Cloudy
\n",
+ "
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\n",
+ "
Mostly Cloudy
\n",
"
\n",
"
\n",
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2012-03-30 20:00:00
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2012
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30
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20:00
\n",
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3.0
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2012
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3
\n",
+ "
30
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+ "
20:00
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+ "
3.0
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"
NaN
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"
-8.4
\n",
"
NaN
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43
\n",
+ "
43
\n",
+ "
NaN
\n",
"
...
\n",
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\n",
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25.0
\n",
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101.30
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
101.30
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mostly Cloudy
\n",
+ "
NaN
\n",
+ "
Mostly Cloudy
\n",
"
\n",
"
\n",
"
2012-03-30 21:00:00
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2012
\n",
- "
3
\n",
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30
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21:00
\n",
- "
\n",
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1.7
\n",
+ "
2012
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+ "
3
\n",
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30
\n",
+ "
21:00
\n",
+ "
1.7
\n",
"
NaN
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-9.0
\n",
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45
\n",
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45
\n",
+ "
NaN
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...
\n",
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25.0
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NaN
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101.32
\n",
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25.0
\n",
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NaN
\n",
+ "
101.32
\n",
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NaN
\n",
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NaN
\n",
"
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\n",
- "
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\n",
- "
Cloudy
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
Cloudy
\n",
"
\n",
"
\n",
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2012-03-30 22:00:00
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2012
\n",
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3
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30
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22:00
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\n",
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0.4
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2012
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3
\n",
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30
\n",
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22:00
\n",
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0.4
\n",
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-8.1
\n",
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NaN
\n",
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\n",
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53
\n",
+ "
NaN
\n",
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...
\n",
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\n",
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25.0
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101.30
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25.0
\n",
+ "
NaN
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+ "
101.30
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NaN
\n",
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\n",
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- "
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\n",
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Mostly Cloudy
\n",
+ "
NaN
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+ "
Mostly Cloudy
\n",
"
\n",
"
\n",
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2012-03-30 23:00:00
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2012
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3
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30
\n",
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23:00
\n",
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\n",
- "
1.4
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
30
\n",
+ "
23:00
\n",
+ "
1.4
\n",
"
NaN
\n",
"
-7.7
\n",
"
NaN
\n",
- "
51
\n",
+ "
51
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
101.34
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
101.34
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mainly Clear
\n",
+ "
NaN
\n",
+ "
Mainly Clear
\n",
"
\n",
"
\n",
"
2012-03-31 00:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
00:00
\n",
- "
\n",
- "
1.5
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
00:00
\n",
+ "
1.5
\n",
"
NaN
\n",
"
-8.6
\n",
"
NaN
\n",
- "
47
\n",
+ "
47
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
101.33
\n",
+ "
25.0
\n",
"
NaN
\n",
+ "
101.33
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mostly Cloudy
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
Mostly Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 01:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
01:00
\n",
- "
\n",
- "
1.3
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
01:00
\n",
+ "
1.3
\n",
"
NaN
\n",
"
-9.6
\n",
"
NaN
\n",
- "
44
\n",
+ "
44
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
101.31
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
101.31
\n",
+ "
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mostly Cloudy
\n",
+ "
Mostly Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 02:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
02:00
\n",
- "
\n",
- "
1.3
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
02:00
\n",
+ "
1.3
\n",
"
NaN
\n",
"
-9.7
\n",
"
NaN
\n",
- "
44
\n",
+ "
44
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
101.29
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
101.29
\n",
+ "
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Cloudy
\n",
+ "
Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 03:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
03:00
\n",
- "
\n",
- "
0.7
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
03:00
\n",
+ "
0.7
\n",
"
NaN
\n",
"
-8.8
\n",
"
NaN
\n",
- "
49
\n",
+ "
49
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
101.30
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
101.30
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Cloudy
\n",
+ "
NaN
\n",
+ "
Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 04:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
04:00
\n",
- "
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
04:00
\n",
"
-0.9
\n",
"
NaN
\n",
"
-8.5
\n",
"
NaN
\n",
- "
56
\n",
+ "
56
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
101.32
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
101.32
\n",
+ "
NaN
\n",
"
NaN
\n",
"
NaN
\n",
+ "
-5.0
\n",
"
NaN
\n",
- "
-5
\n",
- "
NaN
\n",
- "
Cloudy
\n",
+ "
Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 05:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
05:00
\n",
- "
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
05:00
\n",
"
-0.6
\n",
"
NaN
\n",
"
-9.2
\n",
"
NaN
\n",
- "
52
\n",
+ "
52
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
101.30
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
101.30
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
-5
\n",
- "
NaN
\n",
- "
Cloudy
\n",
+ "
-5.0
\n",
+ "
NaN
\n",
+ "
Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 06:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
06:00
\n",
- "
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
06:00
\n",
"
-0.5
\n",
"
NaN
\n",
"
-9.2
\n",
"
NaN
\n",
- "
52
\n",
+ "
52
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.32
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
101.32
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
-5
\n",
- "
NaN
\n",
- "
Cloudy
\n",
+ "
-5.0
\n",
+ "
NaN
\n",
+ "
Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 07:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
07:00
\n",
- "
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
07:00
\n",
"
-0.3
\n",
"
NaN
\n",
"
-9.2
\n",
"
NaN
\n",
- "
51
\n",
+ "
51
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.32
\n",
+ "
48.3
\n",
"
NaN
\n",
+ "
101.32
\n",
"
NaN
\n",
"
NaN
\n",
- "
-5
\n",
- "
NaN
\n",
- "
Cloudy
\n",
+ "
NaN
\n",
+ "
-5.0
\n",
+ "
NaN
\n",
+ "
Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 08:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
08:00
\n",
- "
\n",
- "
0.7
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
08:00
\n",
+ "
0.7
\n",
"
NaN
\n",
"
-8.5
\n",
"
NaN
\n",
- "
50
\n",
+ "
50
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.33
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
101.33
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Cloudy
\n",
+ "
NaN
\n",
+ "
Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 09:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
09:00
\n",
- "
\n",
- "
1.5
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
09:00
\n",
+ "
1.5
\n",
"
NaN
\n",
"
-7.8
\n",
"
NaN
\n",
- "
50
\n",
+ "
50
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.34
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
101.34
\n",
+ "
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mostly Cloudy
\n",
+ "
Mostly Cloudy
\n",
"
\n",
"
\n",
"
2012-03-31 10:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
10:00
\n",
- "
\n",
- "
2.9
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
10:00
\n",
+ "
2.9
\n",
"
NaN
\n",
"
-8.1
\n",
"
NaN
\n",
- "
44
\n",
+ "
44
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.30
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
101.30
\n",
+ "
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mainly Clear
\n",
+ "
Mainly Clear
\n",
"
\n",
"
\n",
"
2012-03-31 11:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
11:00
\n",
- "
\n",
- "
4.6
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
11:00
\n",
+ "
4.6
\n",
"
NaN
\n",
"
-9.7
\n",
"
NaN
\n",
- "
35
\n",
+ "
35
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.24
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
101.24
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Clear
\n",
+ "
NaN
\n",
+ "
Clear
\n",
"
\n",
"
\n",
"
2012-03-31 12:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
12:00
\n",
- "
\n",
- "
6.4
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
12:00
\n",
+ "
6.4
\n",
"
NaN
\n",
"
-7.1
\n",
"
NaN
\n",
- "
37
\n",
+ "
37
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.16
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
101.16
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Clear
\n",
+ "
NaN
\n",
+ "
Clear
\n",
"
\n",
"
\n",
"
2012-03-31 13:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
13:00
\n",
- "
\n",
- "
6.5
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
13:00
\n",
+ "
6.5
\n",
"
NaN
\n",
"
-9.7
\n",
"
NaN
\n",
- "
30
\n",
+ "
30
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.08
\n",
+ "
48.3
\n",
"
NaN
\n",
+ "
101.08
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Clear
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
Clear
\n",
"
\n",
"
\n",
"
2012-03-31 14:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
14:00
\n",
- "
\n",
- "
7.7
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
14:00
\n",
+ "
7.7
\n",
"
NaN
\n",
"
-8.5
\n",
"
NaN
\n",
- "
31
\n",
+ "
31
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
101.01
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
101.01
\n",
+ "
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mainly Clear
\n",
+ "
Mainly Clear
\n",
"
\n",
"
\n",
"
2012-03-31 15:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
15:00
\n",
- "
\n",
- "
7.7
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
15:00
\n",
+ "
7.7
\n",
"
NaN
\n",
"
-8.6
\n",
"
NaN
\n",
- "
30
\n",
+ "
30
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
100.94
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
100.94
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mainly Clear
\n",
+ "
NaN
\n",
+ "
Mainly Clear
\n",
"
\n",
"
\n",
"
2012-03-31 16:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
16:00
\n",
- "
\n",
- "
8.4
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
16:00
\n",
+ "
8.4
\n",
"
NaN
\n",
"
-7.7
\n",
"
NaN
\n",
- "
31
\n",
+ "
31
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
100.89
\n",
+ "
48.3
\n",
"
NaN
\n",
+ "
100.89
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mainly Clear
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
Mainly Clear
\n",
"
\n",
"
\n",
"
2012-03-31 17:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
17:00
\n",
- "
\n",
- "
7.9
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
17:00
\n",
+ "
7.9
\n",
"
NaN
\n",
"
-8.1
\n",
"
NaN
\n",
- "
31
\n",
+ "
31
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
100.88
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
100.88
\n",
+ "
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mainly Clear
\n",
+ "
Mainly Clear
\n",
"
\n",
"
\n",
"
2012-03-31 18:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
18:00
\n",
- "
\n",
- "
7.0
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
18:00
\n",
+ "
7.0
\n",
"
NaN
\n",
"
-8.2
\n",
"
NaN
\n",
- "
33
\n",
+ "
33
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
48.3
\n",
"
NaN
\n",
- "
100.87
\n",
+ "
48.3
\n",
+ "
NaN
\n",
+ "
100.87
\n",
+ "
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Mainly Clear
\n",
+ "
Mainly Clear
\n",
"
\n",
"
\n",
"
2012-03-31 19:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
19:00
\n",
- "
\n",
- "
5.9
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
19:00
\n",
+ "
5.9
\n",
"
NaN
\n",
"
-8.0
\n",
"
NaN
\n",
- "
36
\n",
+ "
36
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
100.88
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
100.88
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Clear
\n",
+ "
NaN
\n",
+ "
Clear
\n",
"
\n",
"
\n",
"
2012-03-31 20:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
20:00
\n",
- "
\n",
- "
4.4
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
20:00
\n",
+ "
4.4
\n",
"
NaN
\n",
"
-7.2
\n",
"
NaN
\n",
- "
43
\n",
+ "
43
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
100.85
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
100.85
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Clear
\n",
+ "
NaN
\n",
+ "
Clear
\n",
"
\n",
"
\n",
"
2012-03-31 21:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
21:00
\n",
- "
\n",
- "
2.6
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
21:00
\n",
+ "
2.6
\n",
"
NaN
\n",
"
-6.3
\n",
"
NaN
\n",
- "
52
\n",
+ "
52
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
100.86
\n",
+ "
25.0
\n",
"
NaN
\n",
+ "
100.86
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Clear
\n",
+ "
NaN
\n",
+ "
NaN
\n",
+ "
Clear
\n",
"
\n",
"
\n",
"
2012-03-31 22:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
22:00
\n",
- "
\n",
- "
2.7
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
22:00
\n",
+ "
2.7
\n",
"
NaN
\n",
"
-6.7
\n",
"
NaN
\n",
- "
50
\n",
+ "
50
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
100.82
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
100.82
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Clear
\n",
+ "
NaN
\n",
+ "
Clear
\n",
"
\n",
"
\n",
"
2012-03-31 23:00:00
\n",
- "
2012
\n",
- "
3
\n",
- "
31
\n",
- "
23:00
\n",
- "
\n",
- "
1.5
\n",
+ "
2012
\n",
+ "
3
\n",
+ "
31
\n",
+ "
23:00
\n",
+ "
1.5
\n",
"
NaN
\n",
"
-6.9
\n",
"
NaN
\n",
- "
54
\n",
+ "
54
\n",
+ "
NaN
\n",
"
...
\n",
- "
NaN
\n",
- "
25.0
\n",
"
NaN
\n",
- "
100.79
\n",
+ "
25.0
\n",
+ "
NaN
\n",
+ "
100.79
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
"
NaN
\n",
- "
NaN
\n",
- "
Clear
\n",
+ "
NaN
\n",
+ "
Clear
\n",
"
\n",
" \n",
"
\n",
- "
744 rows × 24 columns
\n",
+ "
744 rows × 23 columns
\n",
"
"
],
"text/plain": [
- " Year Month Day Time Data Quality Temp (°C) \\\n",
- "Date/Time \n",
- "2012-03-01 00:00:00 2012 3 1 00:00 -5.5 \n",
- "2012-03-01 01:00:00 2012 3 1 01:00 -5.7 \n",
- "2012-03-01 02:00:00 2012 3 1 02:00 -5.4 \n",
- "2012-03-01 03:00:00 2012 3 1 03:00 -4.7 \n",
- "2012-03-01 04:00:00 2012 3 1 04:00 -5.4 \n",
- "2012-03-01 05:00:00 2012 3 1 05:00 -5.3 \n",
- "2012-03-01 06:00:00 2012 3 1 06:00 -5.2 \n",
- "2012-03-01 07:00:00 2012 3 1 07:00 -4.9 \n",
- "2012-03-01 08:00:00 2012 3 1 08:00 -5.0 \n",
- "2012-03-01 09:00:00 2012 3 1 09:00 -4.9 \n",
- "2012-03-01 10:00:00 2012 3 1 10:00 -4.7 \n",
- "2012-03-01 11:00:00 2012 3 1 11:00 -4.4 \n",
- "2012-03-01 12:00:00 2012 3 1 12:00 -4.3 \n",
- "2012-03-01 13:00:00 2012 3 1 13:00 -4.3 \n",
- "2012-03-01 14:00:00 2012 3 1 14:00 -3.9 \n",
- "2012-03-01 15:00:00 2012 3 1 15:00 -3.3 \n",
- "2012-03-01 16:00:00 2012 3 1 16:00 -2.7 \n",
- "2012-03-01 17:00:00 2012 3 1 17:00 -2.9 \n",
- "2012-03-01 18:00:00 2012 3 1 18:00 -3.0 \n",
- "2012-03-01 19:00:00 2012 3 1 19:00 -3.6 \n",
- "2012-03-01 20:00:00 2012 3 1 20:00 -3.7 \n",
- "2012-03-01 21:00:00 2012 3 1 21:00 -3.9 \n",
- "2012-03-01 22:00:00 2012 3 1 22:00 -4.3 \n",
- "2012-03-01 23:00:00 2012 3 1 23:00 -4.3 \n",
- "2012-03-02 00:00:00 2012 3 2 00:00 -4.8 \n",
- "2012-03-02 01:00:00 2012 3 2 01:00 -5.3 \n",
- "2012-03-02 02:00:00 2012 3 2 02:00 -5.2 \n",
- "2012-03-02 03:00:00 2012 3 2 03:00 -5.5 \n",
- "2012-03-02 04:00:00 2012 3 2 04:00 -5.6 \n",
- "2012-03-02 05:00:00 2012 3 2 05:00 -5.5 \n",
- "... ... ... ... ... ... ... \n",
- "2012-03-30 18:00:00 2012 3 30 18:00 3.9 \n",
- "2012-03-30 19:00:00 2012 3 30 19:00 3.1 \n",
- "2012-03-30 20:00:00 2012 3 30 20:00 3.0 \n",
- "2012-03-30 21:00:00 2012 3 30 21:00 1.7 \n",
- "2012-03-30 22:00:00 2012 3 30 22:00 0.4 \n",
- "2012-03-30 23:00:00 2012 3 30 23:00 1.4 \n",
- "2012-03-31 00:00:00 2012 3 31 00:00 1.5 \n",
- "2012-03-31 01:00:00 2012 3 31 01:00 1.3 \n",
- "2012-03-31 02:00:00 2012 3 31 02:00 1.3 \n",
- "2012-03-31 03:00:00 2012 3 31 03:00 0.7 \n",
- "2012-03-31 04:00:00 2012 3 31 04:00 -0.9 \n",
- "2012-03-31 05:00:00 2012 3 31 05:00 -0.6 \n",
- "2012-03-31 06:00:00 2012 3 31 06:00 -0.5 \n",
- "2012-03-31 07:00:00 2012 3 31 07:00 -0.3 \n",
- "2012-03-31 08:00:00 2012 3 31 08:00 0.7 \n",
- "2012-03-31 09:00:00 2012 3 31 09:00 1.5 \n",
- "2012-03-31 10:00:00 2012 3 31 10:00 2.9 \n",
- "2012-03-31 11:00:00 2012 3 31 11:00 4.6 \n",
- "2012-03-31 12:00:00 2012 3 31 12:00 6.4 \n",
- "2012-03-31 13:00:00 2012 3 31 13:00 6.5 \n",
- "2012-03-31 14:00:00 2012 3 31 14:00 7.7 \n",
- "2012-03-31 15:00:00 2012 3 31 15:00 7.7 \n",
- "2012-03-31 16:00:00 2012 3 31 16:00 8.4 \n",
- "2012-03-31 17:00:00 2012 3 31 17:00 7.9 \n",
- "2012-03-31 18:00:00 2012 3 31 18:00 7.0 \n",
- "2012-03-31 19:00:00 2012 3 31 19:00 5.9 \n",
- "2012-03-31 20:00:00 2012 3 31 20:00 4.4 \n",
- "2012-03-31 21:00:00 2012 3 31 21:00 2.6 \n",
- "2012-03-31 22:00:00 2012 3 31 22:00 2.7 \n",
- "2012-03-31 23:00:00 2012 3 31 23:00 1.5 \n",
+ " Year Month Day Time Temp (°C) Temp Flag \\\n",
+ "Date/Time \n",
+ "2012-03-01 00:00:00 2012 3 1 00:00 -5.5 NaN \n",
+ "2012-03-01 01:00:00 2012 3 1 01:00 -5.7 NaN \n",
+ "2012-03-01 02:00:00 2012 3 1 02:00 -5.4 NaN \n",
+ "2012-03-01 03:00:00 2012 3 1 03:00 -4.7 NaN \n",
+ "2012-03-01 04:00:00 2012 3 1 04:00 -5.4 NaN \n",
+ "2012-03-01 05:00:00 2012 3 1 05:00 -5.3 NaN \n",
+ "2012-03-01 06:00:00 2012 3 1 06:00 -5.2 NaN \n",
+ "2012-03-01 07:00:00 2012 3 1 07:00 -4.9 NaN \n",
+ "2012-03-01 08:00:00 2012 3 1 08:00 -5.0 NaN \n",
+ "2012-03-01 09:00:00 2012 3 1 09:00 -4.9 NaN \n",
+ "2012-03-01 10:00:00 2012 3 1 10:00 -4.7 NaN \n",
+ "2012-03-01 11:00:00 2012 3 1 11:00 -4.4 NaN \n",
+ "2012-03-01 12:00:00 2012 3 1 12:00 -4.3 NaN \n",
+ "2012-03-01 13:00:00 2012 3 1 13:00 -4.3 NaN \n",
+ "2012-03-01 14:00:00 2012 3 1 14:00 -3.9 NaN \n",
+ "2012-03-01 15:00:00 2012 3 1 15:00 -3.3 NaN \n",
+ "2012-03-01 16:00:00 2012 3 1 16:00 -2.7 NaN \n",
+ "2012-03-01 17:00:00 2012 3 1 17:00 -2.9 NaN \n",
+ "2012-03-01 18:00:00 2012 3 1 18:00 -3.0 NaN \n",
+ "2012-03-01 19:00:00 2012 3 1 19:00 -3.6 NaN \n",
+ "2012-03-01 20:00:00 2012 3 1 20:00 -3.7 NaN \n",
+ "2012-03-01 21:00:00 2012 3 1 21:00 -3.9 NaN \n",
+ "2012-03-01 22:00:00 2012 3 1 22:00 -4.3 NaN \n",
+ "2012-03-01 23:00:00 2012 3 1 23:00 -4.3 NaN \n",
+ "2012-03-02 00:00:00 2012 3 2 00:00 -4.8 NaN \n",
+ "2012-03-02 01:00:00 2012 3 2 01:00 -5.3 NaN \n",
+ "2012-03-02 02:00:00 2012 3 2 02:00 -5.2 NaN \n",
+ "2012-03-02 03:00:00 2012 3 2 03:00 -5.5 NaN \n",
+ "2012-03-02 04:00:00 2012 3 2 04:00 -5.6 NaN \n",
+ "2012-03-02 05:00:00 2012 3 2 05:00 -5.5 NaN \n",
+ "... ... ... ... ... ... ... \n",
+ "2012-03-30 18:00:00 2012 3 30 18:00 3.9 NaN \n",
+ "2012-03-30 19:00:00 2012 3 30 19:00 3.1 NaN \n",
+ "2012-03-30 20:00:00 2012 3 30 20:00 3.0 NaN \n",
+ "2012-03-30 21:00:00 2012 3 30 21:00 1.7 NaN \n",
+ "2012-03-30 22:00:00 2012 3 30 22:00 0.4 NaN \n",
+ "2012-03-30 23:00:00 2012 3 30 23:00 1.4 NaN \n",
+ "2012-03-31 00:00:00 2012 3 31 00:00 1.5 NaN \n",
+ "2012-03-31 01:00:00 2012 3 31 01:00 1.3 NaN \n",
+ "2012-03-31 02:00:00 2012 3 31 02:00 1.3 NaN \n",
+ "2012-03-31 03:00:00 2012 3 31 03:00 0.7 NaN \n",
+ "2012-03-31 04:00:00 2012 3 31 04:00 -0.9 NaN \n",
+ "2012-03-31 05:00:00 2012 3 31 05:00 -0.6 NaN \n",
+ "2012-03-31 06:00:00 2012 3 31 06:00 -0.5 NaN \n",
+ "2012-03-31 07:00:00 2012 3 31 07:00 -0.3 NaN \n",
+ "2012-03-31 08:00:00 2012 3 31 08:00 0.7 NaN \n",
+ "2012-03-31 09:00:00 2012 3 31 09:00 1.5 NaN \n",
+ "2012-03-31 10:00:00 2012 3 31 10:00 2.9 NaN \n",
+ "2012-03-31 11:00:00 2012 3 31 11:00 4.6 NaN \n",
+ "2012-03-31 12:00:00 2012 3 31 12:00 6.4 NaN \n",
+ "2012-03-31 13:00:00 2012 3 31 13:00 6.5 NaN \n",
+ "2012-03-31 14:00:00 2012 3 31 14:00 7.7 NaN \n",
+ "2012-03-31 15:00:00 2012 3 31 15:00 7.7 NaN \n",
+ "2012-03-31 16:00:00 2012 3 31 16:00 8.4 NaN \n",
+ "2012-03-31 17:00:00 2012 3 31 17:00 7.9 NaN \n",
+ "2012-03-31 18:00:00 2012 3 31 18:00 7.0 NaN \n",
+ "2012-03-31 19:00:00 2012 3 31 19:00 5.9 NaN \n",
+ "2012-03-31 20:00:00 2012 3 31 20:00 4.4 NaN \n",
+ "2012-03-31 21:00:00 2012 3 31 21:00 2.6 NaN \n",
+ "2012-03-31 22:00:00 2012 3 31 22:00 2.7 NaN \n",
+ "2012-03-31 23:00:00 2012 3 31 23:00 1.5 NaN \n",
"\n",
- " Temp Flag Dew Point Temp (°C) Dew Point Temp Flag \\\n",
- "Date/Time \n",
- "2012-03-01 00:00:00 NaN -9.7 NaN \n",
- "2012-03-01 01:00:00 NaN -8.7 NaN \n",
- "2012-03-01 02:00:00 NaN -8.3 NaN \n",
- "2012-03-01 03:00:00 NaN -7.7 NaN \n",
- "2012-03-01 04:00:00 NaN -7.8 NaN \n",
- "2012-03-01 05:00:00 NaN -7.9 NaN \n",
- "2012-03-01 06:00:00 NaN -7.8 NaN \n",
- "2012-03-01 07:00:00 NaN -7.4 NaN \n",
- "2012-03-01 08:00:00 NaN -7.5 NaN \n",
- "2012-03-01 09:00:00 NaN -7.5 NaN \n",
- "2012-03-01 10:00:00 NaN -7.3 NaN \n",
- "2012-03-01 11:00:00 NaN -6.8 NaN \n",
- "2012-03-01 12:00:00 NaN -6.8 NaN \n",
- "2012-03-01 13:00:00 NaN -6.9 NaN \n",
- "2012-03-01 14:00:00 NaN -6.6 NaN \n",
- "2012-03-01 15:00:00 NaN -6.2 NaN \n",
- "2012-03-01 16:00:00 NaN -5.7 NaN \n",
- "2012-03-01 17:00:00 NaN -5.9 NaN \n",
- "2012-03-01 18:00:00 NaN -6.0 NaN \n",
- "2012-03-01 19:00:00 NaN -6.4 NaN \n",
- "2012-03-01 20:00:00 NaN -6.4 NaN \n",
- "2012-03-01 21:00:00 NaN -6.7 NaN \n",
- "2012-03-01 22:00:00 NaN -6.9 NaN \n",
- "2012-03-01 23:00:00 NaN -7.1 NaN \n",
- "2012-03-02 00:00:00 NaN -7.3 NaN \n",
- "2012-03-02 01:00:00 NaN -7.9 NaN \n",
- "2012-03-02 02:00:00 NaN -7.8 NaN \n",
- "2012-03-02 03:00:00 NaN -7.9 NaN \n",
- "2012-03-02 04:00:00 NaN -8.2 NaN \n",
- "2012-03-02 05:00:00 NaN -8.3 NaN \n",
- "... ... ... ... \n",
- "2012-03-30 18:00:00 NaN -7.9 NaN \n",
- "2012-03-30 19:00:00 NaN -6.7 NaN \n",
- "2012-03-30 20:00:00 NaN -8.4 NaN \n",
- "2012-03-30 21:00:00 NaN -9.0 NaN \n",
- "2012-03-30 22:00:00 NaN -8.1 NaN \n",
- "2012-03-30 23:00:00 NaN -7.7 NaN \n",
- "2012-03-31 00:00:00 NaN -8.6 NaN \n",
- "2012-03-31 01:00:00 NaN -9.6 NaN \n",
- "2012-03-31 02:00:00 NaN -9.7 NaN \n",
- "2012-03-31 03:00:00 NaN -8.8 NaN \n",
- "2012-03-31 04:00:00 NaN -8.5 NaN \n",
- "2012-03-31 05:00:00 NaN -9.2 NaN \n",
- "2012-03-31 06:00:00 NaN -9.2 NaN \n",
- "2012-03-31 07:00:00 NaN -9.2 NaN \n",
- "2012-03-31 08:00:00 NaN -8.5 NaN \n",
- "2012-03-31 09:00:00 NaN -7.8 NaN \n",
- "2012-03-31 10:00:00 NaN -8.1 NaN \n",
- "2012-03-31 11:00:00 NaN -9.7 NaN \n",
- "2012-03-31 12:00:00 NaN -7.1 NaN \n",
- "2012-03-31 13:00:00 NaN -9.7 NaN \n",
- "2012-03-31 14:00:00 NaN -8.5 NaN \n",
- "2012-03-31 15:00:00 NaN -8.6 NaN \n",
- "2012-03-31 16:00:00 NaN -7.7 NaN \n",
- "2012-03-31 17:00:00 NaN -8.1 NaN \n",
- "2012-03-31 18:00:00 NaN -8.2 NaN \n",
- "2012-03-31 19:00:00 NaN -8.0 NaN \n",
- "2012-03-31 20:00:00 NaN -7.2 NaN \n",
- "2012-03-31 21:00:00 NaN -6.3 NaN \n",
- "2012-03-31 22:00:00 NaN -6.7 NaN \n",
- "2012-03-31 23:00:00 NaN -6.9 NaN \n",
+ " Dew Point Temp (°C) Dew Point Temp Flag Rel Hum (%) \\\n",
+ "Date/Time \n",
+ "2012-03-01 00:00:00 -9.7 NaN 72 \n",
+ "2012-03-01 01:00:00 -8.7 NaN 79 \n",
+ "2012-03-01 02:00:00 -8.3 NaN 80 \n",
+ "2012-03-01 03:00:00 -7.7 NaN 79 \n",
+ "2012-03-01 04:00:00 -7.8 NaN 83 \n",
+ "2012-03-01 05:00:00 -7.9 NaN 82 \n",
+ "2012-03-01 06:00:00 -7.8 NaN 82 \n",
+ "2012-03-01 07:00:00 -7.4 NaN 83 \n",
+ "2012-03-01 08:00:00 -7.5 NaN 83 \n",
+ "2012-03-01 09:00:00 -7.5 NaN 82 \n",
+ "2012-03-01 10:00:00 -7.3 NaN 82 \n",
+ "2012-03-01 11:00:00 -6.8 NaN 83 \n",
+ "2012-03-01 12:00:00 -6.8 NaN 83 \n",
+ "2012-03-01 13:00:00 -6.9 NaN 82 \n",
+ "2012-03-01 14:00:00 -6.6 NaN 81 \n",
+ "2012-03-01 15:00:00 -6.2 NaN 80 \n",
+ "2012-03-01 16:00:00 -5.7 NaN 80 \n",
+ "2012-03-01 17:00:00 -5.9 NaN 80 \n",
+ "2012-03-01 18:00:00 -6.0 NaN 80 \n",
+ "2012-03-01 19:00:00 -6.4 NaN 81 \n",
+ "2012-03-01 20:00:00 -6.4 NaN 81 \n",
+ "2012-03-01 21:00:00 -6.7 NaN 81 \n",
+ "2012-03-01 22:00:00 -6.9 NaN 82 \n",
+ "2012-03-01 23:00:00 -7.1 NaN 81 \n",
+ "2012-03-02 00:00:00 -7.3 NaN 83 \n",
+ "2012-03-02 01:00:00 -7.9 NaN 82 \n",
+ "2012-03-02 02:00:00 -7.8 NaN 82 \n",
+ "2012-03-02 03:00:00 -7.9 NaN 83 \n",
+ "2012-03-02 04:00:00 -8.2 NaN 82 \n",
+ "2012-03-02 05:00:00 -8.3 NaN 81 \n",
+ "... ... ... ... \n",
+ "2012-03-30 18:00:00 -7.9 NaN 42 \n",
+ "2012-03-30 19:00:00 -6.7 NaN 49 \n",
+ "2012-03-30 20:00:00 -8.4 NaN 43 \n",
+ "2012-03-30 21:00:00 -9.0 NaN 45 \n",
+ "2012-03-30 22:00:00 -8.1 NaN 53 \n",
+ "2012-03-30 23:00:00 -7.7 NaN 51 \n",
+ "2012-03-31 00:00:00 -8.6 NaN 47 \n",
+ "2012-03-31 01:00:00 -9.6 NaN 44 \n",
+ "2012-03-31 02:00:00 -9.7 NaN 44 \n",
+ "2012-03-31 03:00:00 -8.8 NaN 49 \n",
+ "2012-03-31 04:00:00 -8.5 NaN 56 \n",
+ "2012-03-31 05:00:00 -9.2 NaN 52 \n",
+ "2012-03-31 06:00:00 -9.2 NaN 52 \n",
+ "2012-03-31 07:00:00 -9.2 NaN 51 \n",
+ "2012-03-31 08:00:00 -8.5 NaN 50 \n",
+ "2012-03-31 09:00:00 -7.8 NaN 50 \n",
+ "2012-03-31 10:00:00 -8.1 NaN 44 \n",
+ "2012-03-31 11:00:00 -9.7 NaN 35 \n",
+ "2012-03-31 12:00:00 -7.1 NaN 37 \n",
+ "2012-03-31 13:00:00 -9.7 NaN 30 \n",
+ "2012-03-31 14:00:00 -8.5 NaN 31 \n",
+ "2012-03-31 15:00:00 -8.6 NaN 30 \n",
+ "2012-03-31 16:00:00 -7.7 NaN 31 \n",
+ "2012-03-31 17:00:00 -8.1 NaN 31 \n",
+ "2012-03-31 18:00:00 -8.2 NaN 33 \n",
+ "2012-03-31 19:00:00 -8.0 NaN 36 \n",
+ "2012-03-31 20:00:00 -7.2 NaN 43 \n",
+ "2012-03-31 21:00:00 -6.3 NaN 52 \n",
+ "2012-03-31 22:00:00 -6.7 NaN 50 \n",
+ "2012-03-31 23:00:00 -6.9 NaN 54 \n",
"\n",
- " Rel Hum (%) ... Wind Spd Flag \\\n",
- "Date/Time ... \n",
- "2012-03-01 00:00:00 72 ... NaN \n",
- "2012-03-01 01:00:00 79 ... NaN \n",
- "2012-03-01 02:00:00 80 ... NaN \n",
- "2012-03-01 03:00:00 79 ... NaN \n",
- "2012-03-01 04:00:00 83 ... NaN \n",
- "2012-03-01 05:00:00 82 ... NaN \n",
- "2012-03-01 06:00:00 82 ... NaN \n",
- "2012-03-01 07:00:00 83 ... NaN \n",
- "2012-03-01 08:00:00 83 ... NaN \n",
- "2012-03-01 09:00:00 82 ... NaN \n",
- "2012-03-01 10:00:00 82 ... NaN \n",
- "2012-03-01 11:00:00 83 ... NaN \n",
- "2012-03-01 12:00:00 83 ... NaN \n",
- "2012-03-01 13:00:00 82 ... NaN \n",
- "2012-03-01 14:00:00 81 ... NaN \n",
- "2012-03-01 15:00:00 80 ... NaN \n",
- "2012-03-01 16:00:00 80 ... NaN \n",
- "2012-03-01 17:00:00 80 ... NaN \n",
- "2012-03-01 18:00:00 80 ... NaN \n",
- "2012-03-01 19:00:00 81 ... NaN \n",
- "2012-03-01 20:00:00 81 ... NaN \n",
- "2012-03-01 21:00:00 81 ... NaN \n",
- "2012-03-01 22:00:00 82 ... NaN \n",
- "2012-03-01 23:00:00 81 ... NaN \n",
- "2012-03-02 00:00:00 83 ... NaN \n",
- "2012-03-02 01:00:00 82 ... NaN \n",
- "2012-03-02 02:00:00 82 ... NaN \n",
- "2012-03-02 03:00:00 83 ... NaN \n",
- "2012-03-02 04:00:00 82 ... NaN \n",
- "2012-03-02 05:00:00 81 ... NaN \n",
- "... ... ... ... \n",
- "2012-03-30 18:00:00 42 ... NaN \n",
- "2012-03-30 19:00:00 49 ... NaN \n",
- "2012-03-30 20:00:00 43 ... NaN \n",
- "2012-03-30 21:00:00 45 ... NaN \n",
- "2012-03-30 22:00:00 53 ... NaN \n",
- "2012-03-30 23:00:00 51 ... NaN \n",
- "2012-03-31 00:00:00 47 ... NaN \n",
- "2012-03-31 01:00:00 44 ... NaN \n",
- "2012-03-31 02:00:00 44 ... NaN \n",
- "2012-03-31 03:00:00 49 ... NaN \n",
- "2012-03-31 04:00:00 56 ... NaN \n",
- "2012-03-31 05:00:00 52 ... NaN \n",
- "2012-03-31 06:00:00 52 ... NaN \n",
- "2012-03-31 07:00:00 51 ... NaN \n",
- "2012-03-31 08:00:00 50 ... NaN \n",
- "2012-03-31 09:00:00 50 ... NaN \n",
- "2012-03-31 10:00:00 44 ... NaN \n",
- "2012-03-31 11:00:00 35 ... NaN \n",
- "2012-03-31 12:00:00 37 ... NaN \n",
- "2012-03-31 13:00:00 30 ... NaN \n",
- "2012-03-31 14:00:00 31 ... NaN \n",
- "2012-03-31 15:00:00 30 ... NaN \n",
- "2012-03-31 16:00:00 31 ... NaN \n",
- "2012-03-31 17:00:00 31 ... NaN \n",
- "2012-03-31 18:00:00 33 ... NaN \n",
- "2012-03-31 19:00:00 36 ... NaN \n",
- "2012-03-31 20:00:00 43 ... NaN \n",
- "2012-03-31 21:00:00 52 ... NaN \n",
- "2012-03-31 22:00:00 50 ... NaN \n",
- "2012-03-31 23:00:00 54 ... NaN \n",
+ " Rel Hum Flag ... Wind Spd Flag \\\n",
+ "Date/Time ... \n",
+ "2012-03-01 00:00:00 NaN ... NaN \n",
+ "2012-03-01 01:00:00 NaN ... NaN \n",
+ "2012-03-01 02:00:00 NaN ... NaN \n",
+ "2012-03-01 03:00:00 NaN ... NaN \n",
+ "2012-03-01 04:00:00 NaN ... NaN \n",
+ "2012-03-01 05:00:00 NaN ... NaN \n",
+ "2012-03-01 06:00:00 NaN ... NaN \n",
+ "2012-03-01 07:00:00 NaN ... NaN \n",
+ "2012-03-01 08:00:00 NaN ... NaN \n",
+ "2012-03-01 09:00:00 NaN ... NaN \n",
+ "2012-03-01 10:00:00 NaN ... NaN \n",
+ "2012-03-01 11:00:00 NaN ... NaN \n",
+ "2012-03-01 12:00:00 NaN ... NaN \n",
+ "2012-03-01 13:00:00 NaN ... NaN \n",
+ "2012-03-01 14:00:00 NaN ... NaN \n",
+ "2012-03-01 15:00:00 NaN ... NaN \n",
+ "2012-03-01 16:00:00 NaN ... NaN \n",
+ "2012-03-01 17:00:00 NaN ... NaN \n",
+ "2012-03-01 18:00:00 NaN ... NaN \n",
+ "2012-03-01 19:00:00 NaN ... NaN \n",
+ "2012-03-01 20:00:00 NaN ... NaN \n",
+ "2012-03-01 21:00:00 NaN ... NaN \n",
+ "2012-03-01 22:00:00 NaN ... NaN \n",
+ "2012-03-01 23:00:00 NaN ... NaN \n",
+ "2012-03-02 00:00:00 NaN ... NaN \n",
+ "2012-03-02 01:00:00 NaN ... NaN \n",
+ "2012-03-02 02:00:00 NaN ... NaN \n",
+ "2012-03-02 03:00:00 NaN ... NaN \n",
+ "2012-03-02 04:00:00 NaN ... NaN \n",
+ "2012-03-02 05:00:00 NaN ... NaN \n",
+ "... ... ... ... \n",
+ "2012-03-30 18:00:00 NaN ... NaN \n",
+ "2012-03-30 19:00:00 NaN ... NaN \n",
+ "2012-03-30 20:00:00 NaN ... NaN \n",
+ "2012-03-30 21:00:00 NaN ... NaN \n",
+ "2012-03-30 22:00:00 NaN ... NaN \n",
+ "2012-03-30 23:00:00 NaN ... NaN \n",
+ "2012-03-31 00:00:00 NaN ... NaN \n",
+ "2012-03-31 01:00:00 NaN ... NaN \n",
+ "2012-03-31 02:00:00 NaN ... NaN \n",
+ "2012-03-31 03:00:00 NaN ... NaN \n",
+ "2012-03-31 04:00:00 NaN ... NaN \n",
+ "2012-03-31 05:00:00 NaN ... NaN \n",
+ "2012-03-31 06:00:00 NaN ... NaN \n",
+ "2012-03-31 07:00:00 NaN ... NaN \n",
+ "2012-03-31 08:00:00 NaN ... NaN \n",
+ "2012-03-31 09:00:00 NaN ... NaN \n",
+ "2012-03-31 10:00:00 NaN ... NaN \n",
+ "2012-03-31 11:00:00 NaN ... NaN \n",
+ "2012-03-31 12:00:00 NaN ... NaN \n",
+ "2012-03-31 13:00:00 NaN ... NaN \n",
+ "2012-03-31 14:00:00 NaN ... NaN \n",
+ "2012-03-31 15:00:00 NaN ... NaN \n",
+ "2012-03-31 16:00:00 NaN ... NaN \n",
+ "2012-03-31 17:00:00 NaN ... NaN \n",
+ "2012-03-31 18:00:00 NaN ... NaN \n",
+ "2012-03-31 19:00:00 NaN ... NaN \n",
+ "2012-03-31 20:00:00 NaN ... NaN \n",
+ "2012-03-31 21:00:00 NaN ... NaN \n",
+ "2012-03-31 22:00:00 NaN ... NaN \n",
+ "2012-03-31 23:00:00 NaN ... NaN \n",
"\n",
- " Visibility (km) Visibility Flag Stn Press (kPa) \\\n",
- "Date/Time \n",
- "2012-03-01 00:00:00 4.0 NaN 100.97 \n",
- "2012-03-01 01:00:00 2.4 NaN 100.87 \n",
- "2012-03-01 02:00:00 4.8 NaN 100.80 \n",
- "2012-03-01 03:00:00 4.0 NaN 100.69 \n",
- "2012-03-01 04:00:00 1.6 NaN 100.62 \n",
- "2012-03-01 05:00:00 2.4 NaN 100.58 \n",
- "2012-03-01 06:00:00 4.0 NaN 100.57 \n",
- "2012-03-01 07:00:00 1.6 NaN 100.59 \n",
- "2012-03-01 08:00:00 1.2 NaN 100.59 \n",
- "2012-03-01 09:00:00 1.6 NaN 100.60 \n",
- "2012-03-01 10:00:00 1.2 NaN 100.62 \n",
- "2012-03-01 11:00:00 1.0 NaN 100.66 \n",
- "2012-03-01 12:00:00 1.2 NaN 100.66 \n",
- "2012-03-01 13:00:00 1.2 NaN 100.65 \n",
- "2012-03-01 14:00:00 1.2 NaN 100.67 \n",
- "2012-03-01 15:00:00 1.6 NaN 100.71 \n",
- "2012-03-01 16:00:00 2.4 NaN 100.74 \n",
- "2012-03-01 17:00:00 4.0 NaN 100.80 \n",
- "2012-03-01 18:00:00 4.0 NaN 100.87 \n",
- "2012-03-01 19:00:00 3.2 NaN 100.93 \n",
- "2012-03-01 20:00:00 4.8 NaN 100.95 \n",
- "2012-03-01 21:00:00 6.4 NaN 100.98 \n",
- "2012-03-01 22:00:00 2.4 NaN 101.00 \n",
- "2012-03-01 23:00:00 4.8 NaN 101.04 \n",
- "2012-03-02 00:00:00 3.2 NaN 101.04 \n",
- "2012-03-02 01:00:00 4.8 NaN 101.09 \n",
- "2012-03-02 02:00:00 6.4 NaN 101.11 \n",
- "2012-03-02 03:00:00 4.8 NaN 101.15 \n",
- "2012-03-02 04:00:00 6.4 NaN 101.15 \n",
- "2012-03-02 05:00:00 12.9 NaN 101.15 \n",
- "... ... ... ... \n",
- "2012-03-30 18:00:00 24.1 NaN 101.26 \n",
- "2012-03-30 19:00:00 25.0 NaN 101.29 \n",
- "2012-03-30 20:00:00 25.0 NaN 101.30 \n",
- "2012-03-30 21:00:00 25.0 NaN 101.32 \n",
- "2012-03-30 22:00:00 25.0 NaN 101.30 \n",
- "2012-03-30 23:00:00 25.0 NaN 101.34 \n",
- "2012-03-31 00:00:00 25.0 NaN 101.33 \n",
- "2012-03-31 01:00:00 25.0 NaN 101.31 \n",
- "2012-03-31 02:00:00 25.0 NaN 101.29 \n",
- "2012-03-31 03:00:00 25.0 NaN 101.30 \n",
- "2012-03-31 04:00:00 25.0 NaN 101.32 \n",
- "2012-03-31 05:00:00 25.0 NaN 101.30 \n",
- "2012-03-31 06:00:00 48.3 NaN 101.32 \n",
- "2012-03-31 07:00:00 48.3 NaN 101.32 \n",
- "2012-03-31 08:00:00 48.3 NaN 101.33 \n",
- "2012-03-31 09:00:00 48.3 NaN 101.34 \n",
- "2012-03-31 10:00:00 48.3 NaN 101.30 \n",
- "2012-03-31 11:00:00 48.3 NaN 101.24 \n",
- "2012-03-31 12:00:00 48.3 NaN 101.16 \n",
- "2012-03-31 13:00:00 48.3 NaN 101.08 \n",
- "2012-03-31 14:00:00 48.3 NaN 101.01 \n",
- "2012-03-31 15:00:00 48.3 NaN 100.94 \n",
- "2012-03-31 16:00:00 48.3 NaN 100.89 \n",
- "2012-03-31 17:00:00 48.3 NaN 100.88 \n",
- "2012-03-31 18:00:00 48.3 NaN 100.87 \n",
- "2012-03-31 19:00:00 25.0 NaN 100.88 \n",
- "2012-03-31 20:00:00 25.0 NaN 100.85 \n",
- "2012-03-31 21:00:00 25.0 NaN 100.86 \n",
- "2012-03-31 22:00:00 25.0 NaN 100.82 \n",
- "2012-03-31 23:00:00 25.0 NaN 100.79 \n",
+ " Visibility (km) Visibility Flag Stn Press (kPa) \\\n",
+ "Date/Time \n",
+ "2012-03-01 00:00:00 4.0 NaN 100.97 \n",
+ "2012-03-01 01:00:00 2.4 NaN 100.87 \n",
+ "2012-03-01 02:00:00 4.8 NaN 100.80 \n",
+ "2012-03-01 03:00:00 4.0 NaN 100.69 \n",
+ "2012-03-01 04:00:00 1.6 NaN 100.62 \n",
+ "2012-03-01 05:00:00 2.4 NaN 100.58 \n",
+ "2012-03-01 06:00:00 4.0 NaN 100.57 \n",
+ "2012-03-01 07:00:00 1.6 NaN 100.59 \n",
+ "2012-03-01 08:00:00 1.2 NaN 100.59 \n",
+ "2012-03-01 09:00:00 1.6 NaN 100.60 \n",
+ "2012-03-01 10:00:00 1.2 NaN 100.62 \n",
+ "2012-03-01 11:00:00 1.0 NaN 100.66 \n",
+ "2012-03-01 12:00:00 1.2 NaN 100.66 \n",
+ "2012-03-01 13:00:00 1.2 NaN 100.65 \n",
+ "2012-03-01 14:00:00 1.2 NaN 100.67 \n",
+ "2012-03-01 15:00:00 1.6 NaN 100.71 \n",
+ "2012-03-01 16:00:00 2.4 NaN 100.74 \n",
+ "2012-03-01 17:00:00 4.0 NaN 100.80 \n",
+ "2012-03-01 18:00:00 4.0 NaN 100.87 \n",
+ "2012-03-01 19:00:00 3.2 NaN 100.93 \n",
+ "2012-03-01 20:00:00 4.8 NaN 100.95 \n",
+ "2012-03-01 21:00:00 6.4 NaN 100.98 \n",
+ "2012-03-01 22:00:00 2.4 NaN 101.00 \n",
+ "2012-03-01 23:00:00 4.8 NaN 101.04 \n",
+ "2012-03-02 00:00:00 3.2 NaN 101.04 \n",
+ "2012-03-02 01:00:00 4.8 NaN 101.09 \n",
+ "2012-03-02 02:00:00 6.4 NaN 101.11 \n",
+ "2012-03-02 03:00:00 4.8 NaN 101.15 \n",
+ "2012-03-02 04:00:00 6.4 NaN 101.15 \n",
+ "2012-03-02 05:00:00 12.9 NaN 101.15 \n",
+ "... ... ... ... \n",
+ "2012-03-30 18:00:00 24.1 NaN 101.26 \n",
+ "2012-03-30 19:00:00 25.0 NaN 101.29 \n",
+ "2012-03-30 20:00:00 25.0 NaN 101.30 \n",
+ "2012-03-30 21:00:00 25.0 NaN 101.32 \n",
+ "2012-03-30 22:00:00 25.0 NaN 101.30 \n",
+ "2012-03-30 23:00:00 25.0 NaN 101.34 \n",
+ "2012-03-31 00:00:00 25.0 NaN 101.33 \n",
+ "2012-03-31 01:00:00 25.0 NaN 101.31 \n",
+ "2012-03-31 02:00:00 25.0 NaN 101.29 \n",
+ "2012-03-31 03:00:00 25.0 NaN 101.30 \n",
+ "2012-03-31 04:00:00 25.0 NaN 101.32 \n",
+ "2012-03-31 05:00:00 25.0 NaN 101.30 \n",
+ "2012-03-31 06:00:00 48.3 NaN 101.32 \n",
+ "2012-03-31 07:00:00 48.3 NaN 101.32 \n",
+ "2012-03-31 08:00:00 48.3 NaN 101.33 \n",
+ "2012-03-31 09:00:00 48.3 NaN 101.34 \n",
+ "2012-03-31 10:00:00 48.3 NaN 101.30 \n",
+ "2012-03-31 11:00:00 48.3 NaN 101.24 \n",
+ "2012-03-31 12:00:00 48.3 NaN 101.16 \n",
+ "2012-03-31 13:00:00 48.3 NaN 101.08 \n",
+ "2012-03-31 14:00:00 48.3 NaN 101.01 \n",
+ "2012-03-31 15:00:00 48.3 NaN 100.94 \n",
+ "2012-03-31 16:00:00 48.3 NaN 100.89 \n",
+ "2012-03-31 17:00:00 48.3 NaN 100.88 \n",
+ "2012-03-31 18:00:00 48.3 NaN 100.87 \n",
+ "2012-03-31 19:00:00 25.0 NaN 100.88 \n",
+ "2012-03-31 20:00:00 25.0 NaN 100.85 \n",
+ "2012-03-31 21:00:00 25.0 NaN 100.86 \n",
+ "2012-03-31 22:00:00 25.0 NaN 100.82 \n",
+ "2012-03-31 23:00:00 25.0 NaN 100.79 \n",
"\n",
- " Stn Press Flag Hmdx Hmdx Flag Wind Chill \\\n",
- "Date/Time \n",
- "2012-03-01 00:00:00 NaN NaN NaN -13 \n",
- "2012-03-01 01:00:00 NaN NaN NaN -13 \n",
- "2012-03-01 02:00:00 NaN NaN NaN -13 \n",
- "2012-03-01 03:00:00 NaN NaN NaN -12 \n",
- "2012-03-01 04:00:00 NaN NaN NaN -14 \n",
- "2012-03-01 05:00:00 NaN NaN NaN -14 \n",
- "2012-03-01 06:00:00 NaN NaN NaN -14 \n",
- "2012-03-01 07:00:00 NaN NaN NaN -13 \n",
- "2012-03-01 08:00:00 NaN NaN NaN -13 \n",
- "2012-03-01 09:00:00 NaN NaN NaN -13 \n",
- "2012-03-01 10:00:00 NaN NaN NaN -13 \n",
- "2012-03-01 11:00:00 NaN NaN NaN -12 \n",
- "2012-03-01 12:00:00 NaN NaN NaN -12 \n",
- "2012-03-01 13:00:00 NaN NaN NaN -12 \n",
- "2012-03-01 14:00:00 NaN NaN NaN -11 \n",
- "2012-03-01 15:00:00 NaN NaN NaN -10 \n",
- "2012-03-01 16:00:00 NaN NaN NaN -8 \n",
- "2012-03-01 17:00:00 NaN NaN NaN -9 \n",
- "2012-03-01 18:00:00 NaN NaN NaN -9 \n",
- "2012-03-01 19:00:00 NaN NaN NaN -9 \n",
- "2012-03-01 20:00:00 NaN NaN NaN -10 \n",
- "2012-03-01 21:00:00 NaN NaN NaN -10 \n",
- "2012-03-01 22:00:00 NaN NaN NaN -11 \n",
- "2012-03-01 23:00:00 NaN NaN NaN -11 \n",
- "2012-03-02 00:00:00 NaN NaN NaN -12 \n",
- "2012-03-02 01:00:00 NaN NaN NaN -12 \n",
- "2012-03-02 02:00:00 NaN NaN NaN -12 \n",
- "2012-03-02 03:00:00 NaN NaN NaN -12 \n",
- "2012-03-02 04:00:00 NaN NaN NaN -13 \n",
- "2012-03-02 05:00:00 NaN NaN NaN -12 \n",
- "... ... ... ... ... \n",
- "2012-03-30 18:00:00 NaN NaN NaN NaN \n",
- "2012-03-30 19:00:00 NaN NaN NaN NaN \n",
- "2012-03-30 20:00:00 NaN NaN NaN NaN \n",
- "2012-03-30 21:00:00 NaN NaN NaN NaN \n",
- "2012-03-30 22:00:00 NaN NaN NaN NaN \n",
- "2012-03-30 23:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 00:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 01:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 02:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 03:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 04:00:00 NaN NaN NaN -5 \n",
- "2012-03-31 05:00:00 NaN NaN NaN -5 \n",
- "2012-03-31 06:00:00 NaN NaN NaN -5 \n",
- "2012-03-31 07:00:00 NaN NaN NaN -5 \n",
- "2012-03-31 08:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 09:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 10:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 11:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 12:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 13:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 14:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 15:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 16:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 17:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 18:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 19:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 20:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 21:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 22:00:00 NaN NaN NaN NaN \n",
- "2012-03-31 23:00:00 NaN NaN NaN NaN \n",
+ " Stn Press Flag Hmdx Hmdx Flag Wind Chill \\\n",
+ "Date/Time \n",
+ "2012-03-01 00:00:00 NaN NaN NaN -13.0 \n",
+ "2012-03-01 01:00:00 NaN NaN NaN -13.0 \n",
+ "2012-03-01 02:00:00 NaN NaN NaN -13.0 \n",
+ "2012-03-01 03:00:00 NaN NaN NaN -12.0 \n",
+ "2012-03-01 04:00:00 NaN NaN NaN -14.0 \n",
+ "2012-03-01 05:00:00 NaN NaN NaN -14.0 \n",
+ "2012-03-01 06:00:00 NaN NaN NaN -14.0 \n",
+ "2012-03-01 07:00:00 NaN NaN NaN -13.0 \n",
+ "2012-03-01 08:00:00 NaN NaN NaN -13.0 \n",
+ "2012-03-01 09:00:00 NaN NaN NaN -13.0 \n",
+ "2012-03-01 10:00:00 NaN NaN NaN -13.0 \n",
+ "2012-03-01 11:00:00 NaN NaN NaN -12.0 \n",
+ "2012-03-01 12:00:00 NaN NaN NaN -12.0 \n",
+ "2012-03-01 13:00:00 NaN NaN NaN -12.0 \n",
+ "2012-03-01 14:00:00 NaN NaN NaN -11.0 \n",
+ "2012-03-01 15:00:00 NaN NaN NaN -10.0 \n",
+ "2012-03-01 16:00:00 NaN NaN NaN -8.0 \n",
+ "2012-03-01 17:00:00 NaN NaN NaN -9.0 \n",
+ "2012-03-01 18:00:00 NaN NaN NaN -9.0 \n",
+ "2012-03-01 19:00:00 NaN NaN NaN -9.0 \n",
+ "2012-03-01 20:00:00 NaN NaN NaN -10.0 \n",
+ "2012-03-01 21:00:00 NaN NaN NaN -10.0 \n",
+ "2012-03-01 22:00:00 NaN NaN NaN -11.0 \n",
+ "2012-03-01 23:00:00 NaN NaN NaN -11.0 \n",
+ "2012-03-02 00:00:00 NaN NaN NaN -12.0 \n",
+ "2012-03-02 01:00:00 NaN NaN NaN -12.0 \n",
+ "2012-03-02 02:00:00 NaN NaN NaN -12.0 \n",
+ "2012-03-02 03:00:00 NaN NaN NaN -12.0 \n",
+ "2012-03-02 04:00:00 NaN NaN NaN -13.0 \n",
+ "2012-03-02 05:00:00 NaN NaN NaN -12.0 \n",
+ "... ... ... ... ... \n",
+ "2012-03-30 18:00:00 NaN NaN NaN NaN \n",
+ "2012-03-30 19:00:00 NaN NaN NaN NaN \n",
+ "2012-03-30 20:00:00 NaN NaN NaN NaN \n",
+ "2012-03-30 21:00:00 NaN NaN NaN NaN \n",
+ "2012-03-30 22:00:00 NaN NaN NaN NaN \n",
+ "2012-03-30 23:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 00:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 01:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 02:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 03:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 04:00:00 NaN NaN NaN -5.0 \n",
+ "2012-03-31 05:00:00 NaN NaN NaN -5.0 \n",
+ "2012-03-31 06:00:00 NaN NaN NaN -5.0 \n",
+ "2012-03-31 07:00:00 NaN NaN NaN -5.0 \n",
+ "2012-03-31 08:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 09:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 10:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 11:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 12:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 13:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 14:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 15:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 16:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 17:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 18:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 19:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 20:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 21:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 22:00:00 NaN NaN NaN NaN \n",
+ "2012-03-31 23:00:00 NaN NaN NaN NaN \n",
"\n",
" Wind Chill Flag Weather \n",
"Date/Time \n",
@@ -3408,7 +2055,7 @@
"2012-03-31 22:00:00 NaN Clear \n",
"2012-03-31 23:00:00 NaN Clear \n",
"\n",
- "[744 rows x 24 columns]"
+ "[744 rows x 23 columns]"
]
},
"execution_count": 5,
@@ -3430,14 +2077,12 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 6,
@@ -3446,652 +2091,9 @@
},
{
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\n",
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+ "
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- "29136 26550551 10/24/2013 06:16:34 PM NaN DCA Department of Consumer Affairs Consumer Complaint False Advertising NaN 77092-2016 2700 EAST SELTICE WAY EAST SELTICE WAY NaN NaN NaN NaN NaN HOUSTON NaN NaN Assigned 11/13/2013 11:15:20 AM 10/29/2013 11:16:16 AM 0 Unspecified Unspecified NaN NaN Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified N NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
- "30939 26548831 10/24/2013 09:35:10 AM NaN DCA Department of Consumer Affairs Consumer Complaint Harassment NaN 55164-0737 P.O. BOX 64437 64437 NaN NaN NaN NaN NaN ST. PAUL NaN NaN Assigned 11/13/2013 02:30:21 PM 10/29/2013 02:31:06 PM 0 Unspecified Unspecified NaN NaN Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified N NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
- "70539 26488417 10/15/2013 03:40:33 PM NaN TLC Taxi and Limousine Commission Taxi Complaint Driver Complaint Street 11549-3650 365 HOFSTRA UNIVERSITY HOFSTRA UNIVERSITY NaN NaN NaN NaN NaN HEMSTEAD NaN NaN Assigned 11/30/2013 01:20:33 PM 10/16/2013 01:21:39 PM 0 Unspecified Unspecified NaN NaN Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified N NaN NaN NaN La Guardia Airport NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
- "85821 26468296 10/10/2013 12:36:43 PM 10/26/2013 01:07:07 AM DCA Department of Consumer Affairs Consumer Complaint Debt Not Owed NaN 29616-0759 PO BOX 25759 BOX 25759 NaN NaN NaN NaN NaN GREENVILLE NaN NaN Closed 10/26/2013 09:20:28 AM 10/26/2013 01:07:07 AM 0 Unspecified Unspecified NaN NaN Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified N NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
- "89304 26461137 10/09/2013 05:23:46 PM 10/25/2013 01:06:41 AM DCA Department of Consumer Affairs Consumer Complaint Harassment NaN 35209-3114 600 BEACON PKWY BEACON PKWY NaN NaN NaN NaN NaN BIRMINGHAM NaN NaN Closed 10/25/2013 02:43:42 PM 10/25/2013 01:06:41 AM 0 Unspecified Unspecified NaN NaN Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified N NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN"
+ " Unique Key Created Date Closed Date Agency \\\n",
+ "29136 26550551 10/24/2013 06:16:34 PM NaN DCA \n",
+ "30939 26548831 10/24/2013 09:35:10 AM NaN DCA \n",
+ "70539 26488417 10/15/2013 03:40:33 PM NaN TLC \n",
+ "85821 26468296 10/10/2013 12:36:43 PM 10/26/2013 01:07:07 AM DCA \n",
+ "89304 26461137 10/09/2013 05:23:46 PM 10/25/2013 01:06:41 AM DCA \n",
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@@ -654,10 +508,8 @@
},
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- "execution_count": 44,
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@@ -666,7 +518,7 @@
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@@ -685,10 +537,8 @@
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"source": [
"requests['Incident Zip'] = requests['Incident Zip'].str.slice(0, 5)"
@@ -710,15 +560,26 @@
},
{
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- "execution_count": 46,
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- " Unique Key Created Date Closed Date Agency Agency Name Complaint Type Descriptor Location Type Incident Zip Incident Address Street Name Cross Street 1 Cross Street 2 Intersection Street 1 Intersection Street 2 Address Type City Landmark Facility Type Status Due Date Resolution Action Updated Date Community Board Borough X Coordinate (State Plane) Y Coordinate (State Plane) Park Facility Name Park Borough School Name School Number School Region School Code School Phone Number School Address School City School State School Zip School Not Found School or Citywide Complaint Vehicle Type Taxi Company Borough Taxi Pick Up Location Bridge Highway Name Bridge Highway Direction Road Ramp Bridge Highway Segment Garage Lot Name Ferry Direction Ferry Terminal Name Latitude Longitude Location\n",
- "42600 26529313 10/22/2013 02:51:06 PM NaN TLC Taxi and Limousine Commission Taxi Complaint Driver Complaint NaN 00000 EWR EWR EWR NaN NaN NaN NaN NaN NEWARK NaN NaN Assigned 12/07/2013 09:53:51 AM 10/23/2013 09:54:43 AM 0 Unspecified Unspecified NaN NaN Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified Unspecified N NaN NaN NaN Other NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN\n",
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},
- "execution_count": 46,
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}
@@ -916,10 +707,8 @@
},
{
"cell_type": "code",
- "execution_count": 47,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 11,
+ "metadata": {},
"outputs": [],
"source": [
"zero_zips = requests['Incident Zip'] == '00000'\n",
@@ -935,15 +724,13 @@
},
{
"cell_type": "code",
- "execution_count": 48,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 12,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([nan, '00083', '02061', '06901', '07020', '07087', '07093', '07109',\n",
+ "array(['00083', '02061', '06901', '07020', '07087', '07093', '07109',\n",
" '07114', '07201', '07208', '07306', '07604', '08807', '10000',\n",
" '10001', '10002', '10003', '10004', '10005', '10006', '10007',\n",
" '10009', '10010', '10011', '10012', '10013', '10014', '10016',\n",
@@ -977,17 +764,17 @@
" '11590', '11691', '11692', '11693', '11694', '11697', '11716',\n",
" '11722', '11735', '11747', '11776', '11788', '11797', '13221',\n",
" '14225', '19711', '23502', '23541', '29616', '35209', '41042',\n",
- " '55164', '61702', '70711', '77056', '77092', '90010', '92123'], dtype=object)"
+ " '55164', '61702', '70711', '77056', '77092', '90010', '92123', nan],\n",
+ " dtype=object)"
]
},
- "execution_count": 48,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "unique_zips = requests['Incident Zip'].unique()\n",
- "unique_zips.sort()\n",
+ "unique_zips = requests['Incident Zip'].sort_values().unique()\n",
"unique_zips"
]
},
@@ -1002,10 +789,8 @@
},
{
"cell_type": "code",
- "execution_count": 49,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 13,
+ "metadata": {},
"outputs": [],
"source": [
"zips = requests['Incident Zip']\n",
@@ -1017,10 +802,8 @@
},
{
"cell_type": "code",
- "execution_count": 50,
- "metadata": {
- "collapsed": false
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+ "execution_count": 14,
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@@ -1041,7 +824,7 @@
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@@ -1052,15 +835,26 @@
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@@ -1170,13 +964,13 @@
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},
- "execution_count": 51,
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],
"source": [
- "requests[is_far][['Incident Zip', 'Descriptor', 'City']].sort('Incident Zip')"
+ "requests[is_far][['Incident Zip', 'Descriptor', 'City']].sort_values('Incident Zip')"
]
},
{
@@ -1188,49 +982,77 @@
},
{
"cell_type": "code",
- "execution_count": 52,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 16,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "BROOKLYN 31662\n",
- "NEW YORK 22664\n",
- "BRONX 18438\n",
- "STATEN ISLAND 4766\n",
- "JAMAICA 2246\n",
- "FLUSHING 1803\n",
- "ASTORIA 1568\n",
- "RIDGEWOOD 1073\n",
- "CORONA 707\n",
- "OZONE PARK 693\n",
- "LONG ISLAND CITY 678\n",
- "FAR ROCKAWAY 652\n",
- "ELMHURST 647\n",
- "WOODSIDE 609\n",
- "EAST ELMHURST 562\n",
- "...\n",
- "MELVILLE 1\n",
- "PORT JEFFERSON STATION 1\n",
- "NORWELL 1\n",
- "EAST ROCKAWAY 1\n",
- "BIRMINGHAM 1\n",
- "ROSLYN 1\n",
- "LOS ANGELES 1\n",
- "MINEOLA 1\n",
- "JERSEY CITY 1\n",
- "ST. PAUL 1\n",
- "CLIFTON 1\n",
- "COL.ANVURES 1\n",
- "EDGEWATER 1\n",
- "ROSELYN 1\n",
- "CENTRAL ISLIP 1\n",
- "Length: 100, dtype: int64"
+ "BROOKLYN 31662\n",
+ "NEW YORK 22664\n",
+ "BRONX 18438\n",
+ "STATEN ISLAND 4766\n",
+ "JAMAICA 2246\n",
+ "FLUSHING 1803\n",
+ "ASTORIA 1568\n",
+ "RIDGEWOOD 1073\n",
+ "CORONA 707\n",
+ "OZONE PARK 693\n",
+ "LONG ISLAND CITY 678\n",
+ "FAR ROCKAWAY 652\n",
+ "ELMHURST 647\n",
+ "WOODSIDE 609\n",
+ "EAST ELMHURST 562\n",
+ "QUEENS VILLAGE 549\n",
+ "JACKSON HEIGHTS 541\n",
+ "FOREST HILLS 541\n",
+ "SOUTH RICHMOND HILL 521\n",
+ "MASPETH 473\n",
+ "WOODHAVEN 464\n",
+ "FRESH MEADOWS 435\n",
+ "SPRINGFIELD GARDENS 434\n",
+ "BAYSIDE 411\n",
+ "SOUTH OZONE PARK 410\n",
+ "RICHMOND HILL 404\n",
+ "REGO PARK 402\n",
+ "MIDDLE VILLAGE 396\n",
+ "SAINT ALBANS 387\n",
+ "WHITESTONE 348\n",
+ " ... \n",
+ "LYNBROOK 1\n",
+ "ROSELYN 1\n",
+ "JERSEY CITY 1\n",
+ "FREEPORT 1\n",
+ "BRIDGE WATER 1\n",
+ "FARMINGDALE 1\n",
+ "CLIFTON 1\n",
+ "NEWARK AIRPORT 1\n",
+ "MELVILLE 1\n",
+ "NEW YORK CITY 1\n",
+ "WOODBURY 1\n",
+ "NANUET 1\n",
+ "EAST ROCKAWAY 1\n",
+ "CHEEKTOWAGA 1\n",
+ "WEST NEW YORK 1\n",
+ "BOHIEMA 1\n",
+ "EDGEWATER 1\n",
+ "BIRMINGHAM 1\n",
+ "VALLEY STREAM 1\n",
+ "ST. PAUL 1\n",
+ "ROSLYN 1\n",
+ "ELIZABETH 1\n",
+ "UNION CITY 1\n",
+ "RYEBROOK 1\n",
+ "CENTRAL ISLIP 1\n",
+ "LAWRENCE 1\n",
+ "NEW YOR 1\n",
+ "SYRACUSE 1\n",
+ "STAMFORD 1\n",
+ "BELLEVILLE 1\n",
+ "Name: City, Length: 100, dtype: int64"
]
},
- "execution_count": 52,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -1262,10 +1084,8 @@
},
{
"cell_type": "code",
- "execution_count": 53,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 17,
+ "metadata": {},
"outputs": [],
"source": [
"na_values = ['NO CLUE', 'N/A', '0']\n",
@@ -1276,10 +1096,8 @@
},
{
"cell_type": "code",
- "execution_count": 54,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 18,
+ "metadata": {},
"outputs": [],
"source": [
"def fix_zip_codes(zips):\n",
@@ -1295,10 +1113,8 @@
},
{
"cell_type": "code",
- "execution_count": 55,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 19,
+ "metadata": {},
"outputs": [],
"source": [
"requests['Incident Zip'] = fix_zip_codes(requests['Incident Zip'])"
@@ -1306,10 +1122,8 @@
},
{
"cell_type": "code",
- "execution_count": 56,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 20,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1348,10 +1162,11 @@
" '11722', '11549', '10162', '23502', '11518', '07020', '08807',\n",
" '11577', '07114', '11003', '07201', '61702', '10103', '29616',\n",
" '35209', '11520', '11735', '10129', '11005', '41042', '11590',\n",
- " '06901', '07208', '11530', '13221', '10954', '11111', '10107'], dtype=object)"
+ " '06901', '07208', '11530', '13221', '10954', '11111', '10107'],\n",
+ " dtype=object)"
]
},
- "execution_count": 56,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -1410,23 +1225,23 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 2",
+ "display_name": "Python 3",
"language": "python",
- "name": "python2"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.6"
+ "pygments_lexer": "ipython3",
+ "version": "3.7.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/cookbook/Chapter 8 - How to deal with timestamps.ipynb b/cookbook/Chapter 8 - How to deal with timestamps.ipynb
index 60c23b11c..cecf62e4d 100644
--- a/cookbook/Chapter 8 - How to deal with timestamps.ipynb
+++ b/cookbook/Chapter 8 - How to deal with timestamps.ipynb
@@ -1 +1,596 @@
-{"nbformat": 4, "metadata": {"orig_nbformat": 3}, "cells": [{"cell_type": "code", "execution_count": 1, "outputs": [], "source": "import pandas as pd", "metadata": {"collapsed": false, "trusted": false}}, {"source": "# 8.1 Parsing Unix timestamps", "cell_type": "markdown", "metadata": {}}, {"source": "It's not obvious how to deal with Unix timestamps in pandas -- it took me quite a while to figure this out. The file we're using here is a popularity-contest file I found on my system at `/var/log/popularity-contest`.\n\nHere's an [explanation of how this file works](http://popcon.ubuntu.com/README).\n\nI'm going to hope that nothing in it is sensitive :)", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 2, "outputs": [], "source": "# Read it, and remove the last row\npopcon = pd.read_csv('../data/popularity-contest', sep=' ', )[:-1]\npopcon.columns = ['atime', 'ctime', 'package-name', 'mru-program', 'tag']", "metadata": {"collapsed": false, "trusted": false}}, {"source": "The colums are the access time, created time, package name, recently used program, and a tag", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 3, "outputs": [{"execution_count": 3, "output_type": "execute_result", "data": {"text/plain": " atime ctime package-name mru-program tag\n0 1387295797 1367633260 perl-base /usr/bin/perl NaN\n1 1387295796 1354370480 login /bin/su NaN\n2 1387295743 1354341275 libtalloc2 /usr/lib/x86_64-linux-gnu/libtalloc.so.2.0.7 NaN\n3 1387295743 1387224204 libwbclient0 /usr/lib/x86_64-linux-gnu/libwbclient.so.0 \n4 1387295742 1354341253 libselinux1 /lib/x86_64-linux-gnu/libselinux.so.1 NaN", "text/html": "
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\n \n
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\n
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mru-program
\n
tag
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\n
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\n
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\n
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\n
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1387295742
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libselinux1
\n
/lib/x86_64-linux-gnu/libselinux.so.1
\n
NaN
\n
\n \n
\n
"}, "metadata": {}}], "source": "popcon[:5]", "metadata": {"collapsed": false, "trusted": false}}, {"source": "The magical part about parsing timestamps in pandas is that numpy datetimes are already stored as Unix timestamps. So all we need to do is tell pandas that these integers are actually datetimes -- it doesn't need to do any conversion at all.\n\nWe need to convert these to ints to start:", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 4, "outputs": [], "source": "popcon['atime'] = popcon['atime'].astype(int)\npopcon['ctime'] = popcon['ctime'].astype(int)", "metadata": {"collapsed": false, "trusted": false}}, {"source": "Every numpy array and pandas series has a dtype -- this is usually `int64`, `float64`, or `object`. Some of the time types available are `datetime64[s]`, `datetime64[ms]`, and `datetime64[us]`. There are also `timedelta` types, similarly.\n\nWe can use the `pd.to_datetime` function to convert our integer timestamps into datetimes. This is a constant-time operation -- we're not actually changing any of the data, just how pandas thinks about it.", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 5, "outputs": [], "source": "popcon['atime'] = pd.to_datetime(popcon['atime'], unit='s')\npopcon['ctime'] = pd.to_datetime(popcon['ctime'], unit='s')", "metadata": {"collapsed": false, "trusted": false}}, {"source": "If we look at the dtype now, it's `\n4 2013-12-17 15:55:42 2012-12-01 05:54:13 libselinux1 /lib/x86_64-linux-gnu/libselinux.so.1 NaN", "text/html": "
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mru-program
\n
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\n
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\n
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\n
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\n
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"}, "metadata": {}}], "source": "popcon[:5]", "metadata": {"collapsed": false, "trusted": false}}, {"source": "Now suppose we want to look at all packages that aren't libraries.\n\nFirst, I want to get rid of everything with timestamp 0. Notice how we can just use a string in this comparison, even though it's actually a timestamp on the inside? That is because pandas is amazing.", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 8, "outputs": [], "source": "popcon = popcon[popcon['atime'] > '1970-01-01']", "metadata": {"collapsed": false, "trusted": false}}, {"source": "Now we can use pandas' magical string abilities to just look at rows where the package name doesn't contain 'lib'.", "cell_type": "markdown", "metadata": {}}, {"cell_type": "code", "execution_count": 9, "outputs": [], "source": "nonlibraries = popcon[~popcon['package-name'].str.contains('lib')]", "metadata": {"collapsed": false, "trusted": false}}, {"cell_type": "code", "execution_count": 10, "outputs": [{"execution_count": 10, "output_type": "execute_result", "data": {"text/plain": " atime ctime package-name \\\n57 2013-12-17 04:55:39 2013-12-17 04:55:42 ddd \n450 2013-12-16 20:03:20 2013-12-16 20:05:13 nodejs \n454 2013-12-16 20:03:20 2013-12-16 20:05:04 switchboard-plug-keyboard \n445 2013-12-16 20:03:20 2013-12-16 20:05:04 thunderbird-locale-en \n396 2013-12-16 20:08:27 2013-12-16 20:05:03 software-center \n449 2013-12-16 20:03:20 2013-12-16 20:05:00 samba-common-bin \n397 2013-12-16 20:08:25 2013-12-16 20:04:59 postgresql-client-9.1 \n398 2013-12-16 20:08:23 2013-12-16 20:04:58 postgresql-9.1 \n452 2013-12-16 20:03:20 2013-12-16 20:04:55 php5-dev \n440 2013-12-16 20:03:20 2013-12-16 20:04:54 php-pear \n\n mru-program tag \n57 /usr/bin/ddd \n450 /usr/bin/npm \n454 /usr/lib/plugs/pantheon/keyboard/options.txt \n445 /usr/lib/thunderbird-addons/extensions/langpac... \n396 /usr/sbin/update-software-center \n449 /usr/bin/net.samba3 \n397 /usr/lib/postgresql/9.1/bin/psql \n398 /usr/lib/postgresql/9.1/bin/postmaster \n452 /usr/include/php5/main/snprintf.h \n440 /usr/share/php/XML/Util.php ", "text/html": "
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\n
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\n
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"}, "metadata": {}}], "source": "nonlibraries.sort('ctime', ascending=False)[:10]", "metadata": {"collapsed": false, "trusted": false}}, {"source": "Okay, cool, it says that I I installed ddd recently. And postgresql! I remember installing those things. Neat.", "cell_type": "markdown", "metadata": {}}, {"source": "The whole message here is that if you have a timestamp in seconds or milliseconds or nanoseconds, then you can just \"cast\" it to a `'datetime64[the-right-thing]'` and pandas/numpy will take care of the rest.", "cell_type": "markdown", "metadata": {}}, {"source": "\n",
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+ "
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+ "text/plain": [
+ " atime ctime package-name \\\n",
+ "0 1387295797 1367633260 perl-base \n",
+ "1 1387295796 1354370480 login \n",
+ "2 1387295743 1354341275 libtalloc2 \n",
+ "3 1387295743 1387224204 libwbclient0 \n",
+ "4 1387295742 1354341253 libselinux1 \n",
+ "\n",
+ " mru-program tag \n",
+ "0 /usr/bin/perl NaN \n",
+ "1 /bin/su NaN \n",
+ "2 /usr/lib/x86_64-linux-gnu/libtalloc.so.2.0.7 NaN \n",
+ "3 /usr/lib/x86_64-linux-gnu/libwbclient.so.0 \n",
+ "4 /lib/x86_64-linux-gnu/libselinux.so.1 NaN "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "popcon[:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The magical part about parsing timestamps in pandas is that numpy datetimes are already stored as Unix timestamps. So all we need to do is tell pandas that these integers are actually datetimes -- it doesn't need to do any conversion at all.\n",
+ "\n",
+ "We need to convert these to ints to start:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "popcon['atime'] = popcon['atime'].astype(int)\n",
+ "popcon['ctime'] = popcon['ctime'].astype(int)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Every numpy array and pandas series has a dtype -- this is usually `int64`, `float64`, or `object`. Some of the time types available are `datetime64[s]`, `datetime64[ms]`, and `datetime64[us]`. There are also `timedelta` types, similarly.\n",
+ "\n",
+ "We can use the `pd.to_datetime` function to convert our integer timestamps into datetimes. This is a constant-time operation -- we're not actually changing any of the data, just how pandas thinks about it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "popcon['atime'] = pd.to_datetime(popcon['atime'], unit='s')\n",
+ "popcon['ctime'] = pd.to_datetime(popcon['ctime'], unit='s')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we look at the dtype now, it's `\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
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+ "
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\n",
+ "
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+ " \n",
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+ "
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+ "
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+ "
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+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
2013-12-17 15:56:36
\n",
+ "
2012-12-01 14:01:20
\n",
+ "
login
\n",
+ "
/bin/su
\n",
+ "
NaN
\n",
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\n",
+ "
\n",
+ "
2
\n",
+ "
2013-12-17 15:55:43
\n",
+ "
2012-12-01 05:54:35
\n",
+ "
libtalloc2
\n",
+ "
/usr/lib/x86_64-linux-gnu/libtalloc.so.2.0.7
\n",
+ "
NaN
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
2013-12-17 15:55:43
\n",
+ "
2013-12-16 20:03:24
\n",
+ "
libwbclient0
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+ "
/usr/lib/x86_64-linux-gnu/libwbclient.so.0
\n",
+ "
<RECENT-CTIME>
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+ "
\n",
+ "
\n",
+ "
4
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+ "
2013-12-17 15:55:42
\n",
+ "
2012-12-01 05:54:13
\n",
+ "
libselinux1
\n",
+ "
/lib/x86_64-linux-gnu/libselinux.so.1
\n",
+ "
NaN
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " atime ctime package-name \\\n",
+ "0 2013-12-17 15:56:37 2013-05-04 02:07:40 perl-base \n",
+ "1 2013-12-17 15:56:36 2012-12-01 14:01:20 login \n",
+ "2 2013-12-17 15:55:43 2012-12-01 05:54:35 libtalloc2 \n",
+ "3 2013-12-17 15:55:43 2013-12-16 20:03:24 libwbclient0 \n",
+ "4 2013-12-17 15:55:42 2012-12-01 05:54:13 libselinux1 \n",
+ "\n",
+ " mru-program tag \n",
+ "0 /usr/bin/perl NaN \n",
+ "1 /bin/su NaN \n",
+ "2 /usr/lib/x86_64-linux-gnu/libtalloc.so.2.0.7 NaN \n",
+ "3 /usr/lib/x86_64-linux-gnu/libwbclient.so.0 \n",
+ "4 /lib/x86_64-linux-gnu/libselinux.so.1 NaN "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "popcon[:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now suppose we want to look at all packages that aren't libraries.\n",
+ "\n",
+ "First, I want to get rid of everything with timestamp 0. Notice how we can just use a string in this comparison, even though it's actually a timestamp on the inside? That is because pandas is amazing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "popcon = popcon[popcon['atime'] > '1970-01-01']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can use pandas' magical string abilities to just look at rows where the package name doesn't contain 'lib'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nonlibraries = popcon[~popcon['package-name'].str.contains('lib')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
atime
\n",
+ "
ctime
\n",
+ "
package-name
\n",
+ "
mru-program
\n",
+ "
tag
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
57
\n",
+ "
2013-12-17 04:55:39
\n",
+ "
2013-12-17 04:55:42
\n",
+ "
ddd
\n",
+ "
/usr/bin/ddd
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
450
\n",
+ "
2013-12-16 20:03:20
\n",
+ "
2013-12-16 20:05:13
\n",
+ "
nodejs
\n",
+ "
/usr/bin/npm
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
454
\n",
+ "
2013-12-16 20:03:20
\n",
+ "
2013-12-16 20:05:04
\n",
+ "
switchboard-plug-keyboard
\n",
+ "
/usr/lib/plugs/pantheon/keyboard/options.txt
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
445
\n",
+ "
2013-12-16 20:03:20
\n",
+ "
2013-12-16 20:05:04
\n",
+ "
thunderbird-locale-en
\n",
+ "
/usr/lib/thunderbird-addons/extensions/langpac...
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
396
\n",
+ "
2013-12-16 20:08:27
\n",
+ "
2013-12-16 20:05:03
\n",
+ "
software-center
\n",
+ "
/usr/sbin/update-software-center
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
449
\n",
+ "
2013-12-16 20:03:20
\n",
+ "
2013-12-16 20:05:00
\n",
+ "
samba-common-bin
\n",
+ "
/usr/bin/net.samba3
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
397
\n",
+ "
2013-12-16 20:08:25
\n",
+ "
2013-12-16 20:04:59
\n",
+ "
postgresql-client-9.1
\n",
+ "
/usr/lib/postgresql/9.1/bin/psql
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
398
\n",
+ "
2013-12-16 20:08:23
\n",
+ "
2013-12-16 20:04:58
\n",
+ "
postgresql-9.1
\n",
+ "
/usr/lib/postgresql/9.1/bin/postmaster
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
452
\n",
+ "
2013-12-16 20:03:20
\n",
+ "
2013-12-16 20:04:55
\n",
+ "
php5-dev
\n",
+ "
/usr/include/php5/main/snprintf.h
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ "
\n",
+ "
440
\n",
+ "
2013-12-16 20:03:20
\n",
+ "
2013-12-16 20:04:54
\n",
+ "
php-pear
\n",
+ "
/usr/share/php/XML/Util.php
\n",
+ "
<RECENT-CTIME>
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " atime ctime package-name \\\n",
+ "57 2013-12-17 04:55:39 2013-12-17 04:55:42 ddd \n",
+ "450 2013-12-16 20:03:20 2013-12-16 20:05:13 nodejs \n",
+ "454 2013-12-16 20:03:20 2013-12-16 20:05:04 switchboard-plug-keyboard \n",
+ "445 2013-12-16 20:03:20 2013-12-16 20:05:04 thunderbird-locale-en \n",
+ "396 2013-12-16 20:08:27 2013-12-16 20:05:03 software-center \n",
+ "449 2013-12-16 20:03:20 2013-12-16 20:05:00 samba-common-bin \n",
+ "397 2013-12-16 20:08:25 2013-12-16 20:04:59 postgresql-client-9.1 \n",
+ "398 2013-12-16 20:08:23 2013-12-16 20:04:58 postgresql-9.1 \n",
+ "452 2013-12-16 20:03:20 2013-12-16 20:04:55 php5-dev \n",
+ "440 2013-12-16 20:03:20 2013-12-16 20:04:54 php-pear \n",
+ "\n",
+ " mru-program tag \n",
+ "57 /usr/bin/ddd \n",
+ "450 /usr/bin/npm \n",
+ "454 /usr/lib/plugs/pantheon/keyboard/options.txt \n",
+ "445 /usr/lib/thunderbird-addons/extensions/langpac... \n",
+ "396 /usr/sbin/update-software-center \n",
+ "449 /usr/bin/net.samba3 \n",
+ "397 /usr/lib/postgresql/9.1/bin/psql \n",
+ "398 /usr/lib/postgresql/9.1/bin/postmaster \n",
+ "452 /usr/include/php5/main/snprintf.h \n",
+ "440 /usr/share/php/XML/Util.php "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nonlibraries.sort_values('ctime', ascending=False)[:10]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Okay, cool, it says that I I installed ddd recently. And postgresql! I remember installing those things. Neat."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The whole message here is that if you have a timestamp in seconds or milliseconds or nanoseconds, then you can just \"cast\" it to a `'datetime64[the-right-thing]'` and pandas/numpy will take care of the rest."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
"
![](\"files/images/tab-once.png\")
\n![](\"files/images/tab-twice.png\")
\n![](\"files/images/tab-4-times.png\")
\n![](\"files/images/function-completion.png\")
\n