research/Wk2 Plotly Intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOTLY INTRO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "below is following a tutorial (https://www.kaggle.com/kanncaa1/plotly-tutorial-for-beginners/notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['educational_attainment_supplementary_data.csv', 'education_expenditure_supplementary_data.csv', 'school_and_country_table.csv', 'cwurData.csv', 'timesData.csv', 'shanghaiData.csv']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# plotly\n",
    "import plotly.plotly as py\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# word cloud library\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"./Tutorial Data\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "timesData = pd.read_csv(\"./Tutorial Data/timesData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2603 entries, 0 to 2602\n",
      "Data columns (total 14 columns):\n",
      "world_rank                2603 non-null object\n",
      "university_name           2603 non-null object\n",
      "country                   2603 non-null object\n",
      "teaching                  2603 non-null float64\n",
      "international             2603 non-null object\n",
      "research                  2603 non-null float64\n",
      "citations                 2603 non-null float64\n",
      "income                    2603 non-null object\n",
      "total_score               2603 non-null object\n",
      "num_students              2544 non-null object\n",
      "student_staff_ratio       2544 non-null float64\n",
      "international_students    2536 non-null object\n",
      "female_male_ratio         2370 non-null object\n",
      "year                      2603 non-null int64\n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 284.8+ KB\n"
     ]
    }
   ],
   "source": [
    "timesData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>world_rank</th>\n",
       "      <th>university_name</th>\n",
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       "      <th>international_students</th>\n",
       "      <th>female_male_ratio</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Harvard University</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>99.7</td>\n",
       "      <td>72.4</td>\n",
       "      <td>98.7</td>\n",
       "      <td>98.8</td>\n",
       "      <td>34.5</td>\n",
       "      <td>96.1</td>\n",
       "      <td>20,152</td>\n",
       "      <td>8.9</td>\n",
       "      <td>25%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>97.7</td>\n",
       "      <td>54.6</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.9</td>\n",
       "      <td>83.7</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2,243</td>\n",
       "      <td>6.9</td>\n",
       "      <td>27%</td>\n",
       "      <td>33 : 67</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Massachusetts Institute of Technology</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>97.8</td>\n",
       "      <td>82.3</td>\n",
       "      <td>91.4</td>\n",
       "      <td>99.9</td>\n",
       "      <td>87.5</td>\n",
       "      <td>95.6</td>\n",
       "      <td>11,074</td>\n",
       "      <td>9.0</td>\n",
       "      <td>33%</td>\n",
       "      <td>37 : 63</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Stanford University</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>98.3</td>\n",
       "      <td>29.5</td>\n",
       "      <td>98.1</td>\n",
       "      <td>99.2</td>\n",
       "      <td>64.3</td>\n",
       "      <td>94.3</td>\n",
       "      <td>15,596</td>\n",
       "      <td>7.8</td>\n",
       "      <td>22%</td>\n",
       "      <td>42 : 58</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Princeton University</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>90.9</td>\n",
       "      <td>70.3</td>\n",
       "      <td>95.4</td>\n",
       "      <td>99.9</td>\n",
       "      <td>-</td>\n",
       "      <td>94.2</td>\n",
       "      <td>7,929</td>\n",
       "      <td>8.4</td>\n",
       "      <td>27%</td>\n",
       "      <td>45 : 55</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>University of Cambridge</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>90.5</td>\n",
       "      <td>77.7</td>\n",
       "      <td>94.1</td>\n",
       "      <td>94.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>91.2</td>\n",
       "      <td>18,812</td>\n",
       "      <td>11.8</td>\n",
       "      <td>34%</td>\n",
       "      <td>46 : 54</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>University of Oxford</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>88.2</td>\n",
       "      <td>77.2</td>\n",
       "      <td>93.9</td>\n",
       "      <td>95.1</td>\n",
       "      <td>73.5</td>\n",
       "      <td>91.2</td>\n",
       "      <td>19,919</td>\n",
       "      <td>11.6</td>\n",
       "      <td>34%</td>\n",
       "      <td>46 : 54</td>\n",
       "      <td>2011</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>University of California, Berkeley</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>84.2</td>\n",
       "      <td>39.6</td>\n",
       "      <td>99.3</td>\n",
       "      <td>97.8</td>\n",
       "      <td>-</td>\n",
       "      <td>91.1</td>\n",
       "      <td>36,186</td>\n",
       "      <td>16.4</td>\n",
       "      <td>15%</td>\n",
       "      <td>50 : 50</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Imperial College London</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>89.2</td>\n",
       "      <td>90.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>88.3</td>\n",
       "      <td>92.9</td>\n",
       "      <td>90.6</td>\n",
       "      <td>15,060</td>\n",
       "      <td>11.7</td>\n",
       "      <td>51%</td>\n",
       "      <td>37 : 63</td>\n",
       "      <td>2011</td>\n",
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       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Yale University</td>\n",
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       "      <td>92.1</td>\n",
       "      <td>59.2</td>\n",
       "      <td>89.7</td>\n",
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       "      <td>2011</td>\n",
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       "  world_rank                        university_name                   country  \\\n",
       "0          1                     Harvard University  United States of America   \n",
       "1          2     California Institute of Technology  United States of America   \n",
       "2          3  Massachusetts Institute of Technology  United States of America   \n",
       "3          4                    Stanford University  United States of America   \n",
       "4          5                   Princeton University  United States of America   \n",
       "5          6                University of Cambridge            United Kingdom   \n",
       "6          6                   University of Oxford            United Kingdom   \n",
       "7          8     University of California, Berkeley  United States of America   \n",
       "8          9                Imperial College London            United Kingdom   \n",
       "9         10                        Yale University  United States of America   \n",
       "\n",
       "   teaching international  research  citations income total_score  \\\n",
       "0      99.7          72.4      98.7       98.8   34.5        96.1   \n",
       "1      97.7          54.6      98.0       99.9   83.7        96.0   \n",
       "2      97.8          82.3      91.4       99.9   87.5        95.6   \n",
       "3      98.3          29.5      98.1       99.2   64.3        94.3   \n",
       "4      90.9          70.3      95.4       99.9      -        94.2   \n",
       "5      90.5          77.7      94.1       94.0   57.0        91.2   \n",
       "6      88.2          77.2      93.9       95.1   73.5        91.2   \n",
       "7      84.2          39.6      99.3       97.8      -        91.1   \n",
       "8      89.2          90.0      94.5       88.3   92.9        90.6   \n",
       "9      92.1          59.2      89.7       91.5      -        89.5   \n",
       "\n",
       "  num_students  student_staff_ratio international_students female_male_ratio  \\\n",
       "0       20,152                  8.9                    25%               NaN   \n",
       "1        2,243                  6.9                    27%           33 : 67   \n",
       "2       11,074                  9.0                    33%           37 : 63   \n",
       "3       15,596                  7.8                    22%           42 : 58   \n",
       "4        7,929                  8.4                    27%           45 : 55   \n",
       "5       18,812                 11.8                    34%           46 : 54   \n",
       "6       19,919                 11.6                    34%           46 : 54   \n",
       "7       36,186                 16.4                    15%           50 : 50   \n",
       "8       15,060                 11.7                    51%           37 : 63   \n",
       "9       11,751                  4.4                    20%           50 : 50   \n",
       "\n",
       "   year  \n",
       "0  2011  \n",
       "1  2011  \n",
       "2  2011  \n",
       "3  2011  \n",
       "4  2011  \n",
       "5  2011  \n",
       "6  2011  \n",
       "7  2011  \n",
       "8  2011  \n",
       "9  2011  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesData.head(10)"
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  },
  {
   "cell_type": "code",
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\"db80d051-933c-4170-beb2-d9b5077477c3\"}], {\"title\": {\"text\": \"Citation and Teaching vs World Rank of Top 100 Universities\"}, \"xaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"World Rank\"}, \"zeroline\": false}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"778f35c3-b8da-408a-8da2-cb66ddfb3996\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare data frame\n",
    "df = timesData.iloc[:100,:]\n",
    "\n",
    "# import graph objects as \"go\"\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Creating trace1\n",
    "trace1 = go.Scatter(\n",
    "                    x = df.world_rank,\n",
    "                    y = df.citations,\n",
    "                    mode = \"lines\",\n",
    "                    name = \"citations\",\n",
    "                    marker = dict(color = 'rgba(16, 112, 2, 0.8)'),\n",
    "                    text= df.university_name)\n",
    "# Creating trace2\n",
    "trace2 = go.Scatter(\n",
    "                    x = df.world_rank,\n",
    "                    y = df.teaching,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"teaching\",\n",
    "                    marker = dict(color = 'rgba(80, 26, 80, 0.8)'),\n",
    "                    text= df.university_name)\n",
    "data = [trace1, trace2]\n",
    "layout = dict(title = 'Citation and Teaching vs World Rank of Top 100 Universities',\n",
    "              xaxis= dict(title= 'World Rank',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
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          "Heidelberg University",
          "McGill University",
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          "University of California, Santa Barbara",
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          "University of Tokyo",
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          "Vanderbilt University",
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          "Maastricht University",
          "Emory University",
          "Lund University",
          "Ohio State University",
          "University of Exeter",
          "University of Bonn",
          "Georgetown University",
          "McMaster University",
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          "Michigan State University"
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          "=39",
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          "=90",
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          "=94",
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          "=99",
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University\", \"Monash University\", \"Middle East Technical University\", \"University of Arizona\", \"University of Notre Dame\", \"Tufts University\", \"University of California, Irvine\", \"Ghent University\", \"University of Massachusetts\", \"University of Pittsburgh\", \"Emory University\", \"McMaster University\", \"University of Glasgow\", \"Vanderbilt University\", \"University of Colorado Boulder\", \"Stockholm University\", \"Technical University of Munich\", \"Uppsala University\"], \"x\": [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"9\", \"11\", \"12\", \"13\", \"14\", \"15\", \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"29\", \"29\", \"32\", \"33\", \"34\", \"34\", \"36\", \"37\", \"38\", \"39\", \"40\", \"41\", \"42\", \"43\", \"44\", \"45\", \"46\", \"46\", \"48\", \"49\", \"50\", \"51\", \"52\", \"52\", \"54\", \"55\", \"55\", \"57\", \"58\", \"59\", \"60\", \"61\", \"61\", 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{\"marker\": {\"color\": \"rgba(0, 255, 200, 0.8)\"}, \"mode\": \"markers\", \"name\": \"2016\", \"text\": [\"California Institute of Technology\", \"University of Oxford\", \"Stanford University\", \"University of Cambridge\", \"Massachusetts Institute of Technology\", \"Harvard University\", \"Princeton University\", \"Imperial College London\", \"ETH Zurich \\u2013 Swiss Federal Institute of Technology Zurich\", \"University of Chicago\", \"Johns Hopkins University\", \"Yale University\", \"University of California, Berkeley\", \"University College London\", \"Columbia University\", \"University of California, Los Angeles\", \"University of Pennsylvania\", \"Cornell University\", \"University of Toronto\", \"Duke University\", \"University of Michigan\", \"Carnegie Mellon University\", \"London School of Economics and Political Science\", \"University of Edinburgh\", \"Northwestern University\", \"National University of Singapore\", \"King\\u2019s College London\", \"Karolinska Institute\", \"LMU Munich\", \"New York University\", \"\\u00c9cole Polytechnique F\\u00e9d\\u00e9rale de Lausanne\", \"University of Washington\", \"University of Melbourne\", \"University of British Columbia\", \"KU Leuven\", \"University of Illinois at Urbana-Champaign\", \"Heidelberg University\", \"McGill University\", \"University of California, San Diego\", \"University of California, Santa Barbara\", \"Georgia Institute of Technology\", \"Peking University\", \"University of Tokyo\", \"University of California, Davis\", \"University of Hong Kong\", \"University of Texas at Austin\", \"Tsinghua University\", \"Wageningen University and Research Center\", \"Humboldt University of Berlin\", \"University of Wisconsin-Madison\", \"Brown University\", \"Australian National University\", \"Technical University of Munich\", \"\\u00c9cole Normale Sup\\u00e9rieure\", \"Nanyang Technological University\", \"University of Manchester\", \"University of Sydney\", \"University of Amsterdam\", \"Hong Kong University of Science and Technology\", \"The University of Queensland\", \"Washington University in St Louis\", \"Utrecht University\", \"University of North Carolina at Chapel Hill\", \"Boston University\", \"Delft University of Technology\", \"University of Minnesota\", \"Leiden University\", \"University of Southern California\", \"University of Bristol\", \"Durham University\", \"Erasmus University Rotterdam\", \"Free University of Berlin\", \"Monash University\", \"University of Groningen\", \"Pennsylvania State University\", \"University of Glasgow\", \"University of Helsinki\", \"University of T\\u00fcbingen\", \"University of Pittsburgh\", \"University of Warwick\", \"Uppsala University\", \"University of Copenhagen\", \"University of New South Wales\", \"University of Freiburg\", \"Seoul National University\", \"University of St Andrews\", \"Vanderbilt University\", \"Kyoto University\", \"Maastricht University\", \"Emory University\", \"Lund University\", \"Ohio State University\", \"University of Exeter\", \"University of Bonn\", \"Georgetown University\", \"McMaster University\", \"University of Sheffield\", \"Queen Mary University of London\", \"University of G\\u00f6ttingen\", \"Michigan State University\"], \"x\": [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\", \"24\", \"25\", \"26\", \"27\", \"28\", \"29\", \"30\", \"31\", \"32\", \"33\", \"34\", \"35\", \"36\", \"37\", \"38\", \"=39\", \"=39\", \"41\", \"42\", \"43\", \"=44\", \"=44\", \"46\", \"=47\", \"=47\", \"49\", \"50\", \"51\", \"52\", \"53\", \"54\", \"55\", \"=56\", \"=56\", \"58\", \"59\", \"=60\", \"=60\", \"62\", \"63\", \"64\", \"=65\", \"=65\", \"67\", \"68\", \"69\", \"70\", \"71\", \"72\", \"73\", \"74\", \"75\", \"=76\", \"=76\", \"78\", \"79\", \"80\", \"81\", \"=82\", \"=82\", \"84\", \"85\", \"86\", \"87\", \"=88\", \"=88\", \"=90\", \"=90\", \"=90\", \"93\", \"=94\", \"=94\", \"=94\", \"97\", \"98\", \"=99\", \"=99\"], \"y\": [99.8, 98.8, 99.9, 97.0, 99.7, 99.8, 99.3, 96.7, 91.1, 99.2, 98.2, 97.2, 99.7, 94.2, 98.1, 98.5, 98.6, 97.2, 90.9, 99.0, 94.4, 99.1, 94.3, 96.3, 96.5, 79.4, 93.8, 92.2, 85.7, 95.3, 94.6, 98.6, 86.6, 91.5, 87.3, 86.8, 88.2, 78.9, 98.7, 99.2, 86.0, 69.1, 60.9, 84.3, 70.1, 92.2, 58.8, 87.8, 73.6, 86.6, 96.4, 72.3, 80.1, 87.1, 85.6, 77.3, 77.5, 87.4, 82.6, 82.4, 99.2, 88.1, 95.9, 97.7, 61.5, 88.0, 85.2, 93.0, 91.2, 87.7, 95.6, 60.2, 73.3, 84.0, 80.5, 92.3, 82.1, 79.3, 92.3, 78.2, 80.9, 86.2, 82.1, 80.5, 50.0, 78.8, 95.7, 46.6, 82.7, 96.7, 81.3, 83.2, 94.3, 85.3, 82.5, 84.9, 80.1, 93.3, 72.7, 76.8], \"type\": \"scatter\", \"uid\": \"aaff5fbf-21ef-4e64-ba3a-8adde17b4627\"}], {\"title\": {\"text\": \"Citation vs world rank of top 100 universities with 2014, 2015 and 2016 years\"}, \"xaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"World Rank\"}, \"zeroline\": false}, \"yaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"Citation\"}, \"zeroline\": false}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"ebab399b-34a5-4f71-acae-21c430739a83\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare data frames\n",
    "df2014 = timesData[timesData.year == 2014].iloc[:100,:]\n",
    "df2015 = timesData[timesData.year == 2015].iloc[:100,:]\n",
    "df2016 = timesData[timesData.year == 2016].iloc[:100,:]\n",
    "# import graph objects as \"go\"\n",
    "import plotly.graph_objs as go\n",
    "# creating trace1\n",
    "trace1 =go.Scatter(\n",
    "                    x = df2014.world_rank,\n",
    "                    y = df2014.citations,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"2014\",\n",
    "                    marker = dict(color = 'rgba(255, 128, 255, 0.8)'),\n",
    "                    text= df2014.university_name)\n",
    "# creating trace2\n",
    "trace2 =go.Scatter(\n",
    "                    x = df2015.world_rank,\n",
    "                    y = df2015.citations,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"2015\",\n",
    "                    marker = dict(color = 'rgba(255, 128, 2, 0.8)'),\n",
    "                    text= df2015.university_name)\n",
    "# creating trace3\n",
    "trace3 =go.Scatter(\n",
    "                    x = df2016.world_rank,\n",
    "                    y = df2016.citations,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"2016\",\n",
    "                    marker = dict(color = 'rgba(0, 255, 200, 0.8)'),\n",
    "                    text= df2016.university_name)\n",
    "data = [trace1, trace2, trace3]\n",
    "layout = dict(title = 'Citation vs world rank of top 100 universities with 2014, 2015 and 2016 years',\n",
    "              xaxis= dict(title= 'World Rank',ticklen= 5,zeroline= False),\n",
    "              yaxis= dict(title= 'Citation',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>world_rank</th>\n",
       "      <th>university_name</th>\n",
       "      <th>country</th>\n",
       "      <th>teaching</th>\n",
       "      <th>international</th>\n",
       "      <th>research</th>\n",
       "      <th>citations</th>\n",
       "      <th>income</th>\n",
       "      <th>total_score</th>\n",
       "      <th>num_students</th>\n",
       "      <th>student_staff_ratio</th>\n",
       "      <th>international_students</th>\n",
       "      <th>female_male_ratio</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>1</td>\n",
       "      <td>California Institute of Technology</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>94.4</td>\n",
       "      <td>65.8</td>\n",
       "      <td>98.2</td>\n",
       "      <td>99.8</td>\n",
       "      <td>91.2</td>\n",
       "      <td>94.9</td>\n",
       "      <td>2,243</td>\n",
       "      <td>6.9</td>\n",
       "      <td>27%</td>\n",
       "      <td>33 : 67</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>2</td>\n",
       "      <td>Harvard University</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>95.3</td>\n",
       "      <td>66.2</td>\n",
       "      <td>98.5</td>\n",
       "      <td>99.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>93.9</td>\n",
       "      <td>20,152</td>\n",
       "      <td>8.9</td>\n",
       "      <td>25%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>2</td>\n",
       "      <td>University of Oxford</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>89.0</td>\n",
       "      <td>90.2</td>\n",
       "      <td>98.5</td>\n",
       "      <td>95.4</td>\n",
       "      <td>90.3</td>\n",
       "      <td>93.9</td>\n",
       "      <td>19,919</td>\n",
       "      <td>11.6</td>\n",
       "      <td>34%</td>\n",
       "      <td>46 : 54</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     world_rank                     university_name                   country  \\\n",
       "1002          1  California Institute of Technology  United States of America   \n",
       "1003          2                  Harvard University  United States of America   \n",
       "1004          2                University of Oxford            United Kingdom   \n",
       "\n",
       "      teaching international  research  citations income total_score  \\\n",
       "1002      94.4          65.8      98.2       99.8   91.2        94.9   \n",
       "1003      95.3          66.2      98.5       99.1   40.6        93.9   \n",
       "1004      89.0          90.2      98.5       95.4   90.3        93.9   \n",
       "\n",
       "     num_students  student_staff_ratio international_students  \\\n",
       "1002        2,243                  6.9                    27%   \n",
       "1003       20,152                  8.9                    25%   \n",
       "1004       19,919                 11.6                    34%   \n",
       "\n",
       "     female_male_ratio  year  \n",
       "1002           33 : 67  2014  \n",
       "1003               NaN  2014  \n",
       "1004           46 : 54  2014  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare data frames\n",
    "df2014 = timesData[timesData.year == 2014].iloc[:3,:]\n",
    "df2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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         },
         "name": "citations",
         "text": [
          "United States of America",
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         ],
         "type": "bar",
         "uid": "c6cb0a82-1391-4e38-a38f-b5d95ec91cad",
         "x": [
          "California Institute of Technology",
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          99.8,
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          "color": "rgba(10, 255, 128, 0.5)",
          "line": {
           "color": "rgb(0,0,0)",
           "width": 1.5
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         "x": [
          "California Institute of Technology",
          "Harvard University",
          "University of Oxford"
         ],
         "y": [
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       "layout": {
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      "text/html": [
       "<div id=\"5073bf27-a828-43f3-b912-9860bfee5e7f\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"5073bf27-a828-43f3-b912-9860bfee5e7f\", [{\"marker\": {\"color\": \"rgba(255, 174, 255, 0.5)\", \"line\": {\"color\": \"rgb(0,0,0)\", \"width\": 1.5}}, \"name\": \"citations\", \"text\": [\"United States of America\", \"United States of America\", \"United Kingdom\"], \"x\": [\"California Institute of Technology\", \"Harvard University\", \"University of Oxford\"], \"y\": [99.8, 99.1, 95.4], \"type\": \"bar\", \"uid\": \"c6cb0a82-1391-4e38-a38f-b5d95ec91cad\"}, {\"marker\": {\"color\": \"rgba(10, 255, 128, 0.5)\", \"line\": {\"color\": \"rgb(0,0,0)\", \"width\": 1.5}}, \"name\": \"teaching\", \"text\": [\"United States of America\", \"United States of America\", \"United Kingdom\"], \"x\": [\"California Institute of Technology\", \"Harvard University\", \"University of Oxford\"], \"y\": [94.4, 95.3, 89.0], \"type\": \"bar\", \"uid\": \"bdd6961e-9ba6-4230-b791-ac975eea958a\"}], {\"barmode\": \"group\"}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"5073bf27-a828-43f3-b912-9860bfee5e7f\"));});</script>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare data frames\n",
    "df2014 = timesData[timesData.year == 2014].iloc[:3,:]\n",
    "# import graph objects as \"go\"\n",
    "import plotly.graph_objs as go\n",
    "# create trace1 \n",
    "trace1 = go.Bar(\n",
    "                x = df2014.university_name,\n",
    "                y = df2014.citations,\n",
    "                name = \"citations\",\n",
    "                marker = dict(color = 'rgba(255, 174, 255, 0.5)',\n",
    "                             line=dict(color='rgb(0,0,0)',width=1.5)),\n",
    "                text = df2014.country)\n",
    "# create trace2 \n",
    "trace2 = go.Bar(\n",
    "                x = df2014.university_name,\n",
    "                y = df2014.teaching,\n",
    "                name = \"teaching\",\n",
    "                marker = dict(color = 'rgba(10, 255, 128, 0.5)',\n",
    "                              line=dict(color='rgb(0,0,0)',width=1.5)),\n",
    "                text = df2014.country)\n",
    "data = [trace1, trace2]\n",
    "layout = go.Layout(barmode = \"group\")\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
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         "y": [
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       ],
       "layout": {
        "barmode": "relative",
        "title": {
         "text": "citations and teaching of top 3 universities in 2014"
        },
        "xaxis": {
         "title": {
          "text": "Top 3 universities"
         }
        }
       }
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     "metadata": {},
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    }
   ],
   "source": [
    "# prepare data frames\n",
    "df2014 = timesData[timesData.year == 2014].iloc[:3,:]\n",
    "# import graph objects as \"go\"\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "x = df2014.university_name\n",
    "\n",
    "trace1 = {\n",
    "  'x': x,\n",
    "  'y': df2014.citations,\n",
    "  'name': 'citation',\n",
    "  'type': 'bar'\n",
    "};\n",
    "trace2 = {\n",
    "  'x': x,\n",
    "  'y': df2014.teaching,\n",
    "  'name': 'teaching',\n",
    "  'type': 'bar'\n",
    "};\n",
    "data = [trace1, trace2];\n",
    "layout = {\n",
    "  'xaxis': {'title': 'Top 3 universities'},\n",
    "  'barmode': 'relative',\n",
    "  'title': 'citations and teaching of top 3 universities in 2014'\n",
    "};\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import graph objects as \"go\" and import tools\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import matplotlib.pyplot as plt\n",
    "# prepare data frames\n",
    "df2016 = timesData[timesData.year == 2016].iloc[:7,:]\n",
    "\n",
    "y_saving = [each for each in df2016.research]\n",
    "y_net_worth  = [float(each) for each in df2016.income]\n",
    "x_saving = [each for each in df2016.university_name]\n",
    "x_net_worth  = [each for each in df2016.university_name]\n",
    "trace0 = go.Bar(\n",
    "                x=y_saving,\n",
    "                y=x_saving,\n",
    "                marker=dict(color='rgba(171, 50, 96, 0.6)',line=dict(color='rgba(171, 50, 96, 1.0)',width=1)),\n",
    "                name='research',\n",
    "                orientation='h',\n",
    ")\n",
    "trace1 = go.Scatter(\n",
    "                x=y_net_worth,\n",
    "                y=x_net_worth,\n",
    "                mode='lines+markers',\n",
    "                line=dict(color='rgb(63, 72, 204)'),\n",
    "                name='income',\n",
    ")\n",
    "layout = dict(\n",
    "                title='Citations and income',\n",
    "                yaxis=dict(showticklabels=True,domain=[0, 0.85]),\n",
    "                yaxis2=dict(showline=True,showticklabels=False,linecolor='rgba(102, 102, 102, 0.8)',linewidth=2,domain=[0, 0.85]),\n",
    "                xaxis=dict(zeroline=False,showline=False,showticklabels=True,showgrid=True,domain=[0, 0.42]),\n",
    "                xaxis2=dict(zeroline=False,showline=False,showticklabels=True,showgrid=True,domain=[0.47, 1],side='top',dtick=25),\n",
    "                legend=dict(x=0.029,y=1.038,font=dict(size=10) ),\n",
    "                margin=dict(l=200, r=20,t=70,b=70),\n",
    "                paper_bgcolor='rgb(248, 248, 255)',\n",
    "                plot_bgcolor='rgb(248, 248, 255)',\n",
    ")\n",
    "annotations = []\n",
    "y_s = np.round(y_saving, decimals=2)\n",
    "y_nw = np.rint(y_net_worth)\n",
    "# Adding labels\n",
    "for ydn, yd, xd in zip(y_nw, y_s, x_saving):\n",
    "    # labeling the scatter savings\n",
    "    annotations.append(dict(xref='x2', yref='y2', y=xd, x=ydn - 4,text='{:,}'.format(ydn),font=dict(family='Arial', size=12,color='rgb(63, 72, 204)'),showarrow=False))\n",
    "    # labeling the bar net worth\n",
    "    annotations.append(dict(xref='x1', yref='y1', y=xd, x=yd + 3,text=str(yd),font=dict(family='Arial', size=12,color='rgb(171, 50, 96)'),showarrow=False))\n",
    "\n",
    "layout['annotations'] = annotations\n",
    "\n",
    "# Creating two subplots\n",
    "fig = tools.make_subplots(rows=1, cols=2, specs=[[{}, {}]], shared_xaxes=True,\n",
    "                          shared_yaxes=False, vertical_spacing=0.001)\n",
    "\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 2)\n",
    "\n",
    "fig['layout'].update(layout)\n",
    "iplot(fig)"
   ]
  },
  {
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   "execution_count": 12,
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         "name": "Number Of Students Rates",
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   ],
   "source": [
    "# data preparation\n",
    "df2016 = timesData[timesData.year == 2016].iloc[:7,:]\n",
    "pie1 = df2016.num_students\n",
    "pie1_list = [float(each.replace(',', '.')) for each in df2016.num_students]  # str(2,4) => str(2.4) = > float(2.4) = 2.4\n",
    "labels = df2016.university_name\n",
    "# figure\n",
    "fig = {\n",
    "  \"data\": [\n",
    "    {\n",
    "      \"values\": pie1_list,\n",
    "      \"labels\": labels,\n",
    "      \"domain\": {\"x\": [0, .5]},\n",
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    "      \"hole\": .3,\n",
    "      \"type\": \"pie\"\n",
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    "        \"title\":\"Universities Number of Students rates\",\n",
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    "              \"showarrow\": False,\n",
    "              \"text\": \"Number of Students\",\n",
    "                \"x\": 0.20,\n",
    "                \"y\": 1\n",
    "            },\n",
    "        ]\n",
    "    }\n",
    "}\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7 entries, 1803 to 1809\n",
      "Data columns (total 14 columns):\n",
      "world_rank                7 non-null object\n",
      "university_name           7 non-null object\n",
      "country                   7 non-null object\n",
      "teaching                  7 non-null float64\n",
      "international             7 non-null object\n",
      "research                  7 non-null float64\n",
      "citations                 7 non-null float64\n",
      "income                    7 non-null object\n",
      "total_score               7 non-null object\n",
      "num_students              7 non-null object\n",
      "student_staff_ratio       7 non-null float64\n",
      "international_students    7 non-null object\n",
      "female_male_ratio         6 non-null object\n",
      "year                      7 non-null int64\n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 840.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df2016.info()"
   ]
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   "source": [
    "# data preparation\n",
    "df2016 = timesData[timesData.year == 2016].iloc[:20,:]\n",
    "num_students_size  = [float(each.replace(',', '.')) for each in df2016.num_students]\n",
    "international_color = [float(each) for each in df2016.international]\n",
    "data = [\n",
    "    {\n",
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    "        'x': df2016.world_rank,\n",
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    "        'marker': {\n",
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    "iplot(data)"
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          13.8,
          10.8,
          13.7,
          17.4,
          5.7,
          18.7,
          11.5,
          6.5,
          17.6,
          27.3,
          14.1,
          23.7,
          19.2,
          13.9,
          15.5,
          5.7,
          18,
          7.3,
          6.8,
          15.5,
          10.5,
          null,
          15.3,
          10.1,
          8.3,
          12.4,
          5.6,
          10.1,
          8.6,
          12.9,
          11.4,
          13.4,
          20.2,
          5.8,
          7.9,
          6.5,
          23.8,
          4.8,
          13,
          19.3,
          14,
          41.9,
          15.4,
          25.6,
          3.6,
          13.7,
          9,
          24.5,
          32.7,
          4.5,
          17,
          10,
          16.1,
          17.1,
          11.5,
          17.3,
          4.5,
          15.7,
          8.7,
          12.7,
          16.1,
          18.2,
          31.5,
          13.8,
          6.6,
          15.1,
          14.4,
          6.4,
          10,
          8.4,
          15.7,
          12.7,
          18.1,
          17.4,
          21.6,
          15.5,
          16,
          12.1,
          19.4,
          20.3,
          34.1,
          15.9,
          7.3,
          52.5,
          22.7,
          17.3,
          22.5,
          16.9,
          10.9,
          16,
          13.1,
          40.5,
          18,
          7.8,
          5,
          10.7,
          null,
          13.6,
          13.9,
          13.6,
          18.7,
          29.9,
          14,
          13.5,
          15,
          14.9,
          23.7,
          14.7,
          25.6,
          12,
          4.1,
          10.2,
          8.5,
          20.5,
          15,
          11.6,
          8,
          22,
          15,
          14.7,
          15.2,
          11.8,
          16.4,
          17.2,
          12.6,
          10.2,
          39.3,
          15.8,
          11.9,
          5.5,
          18.8,
          18.9,
          16.6,
          13,
          24.5,
          16.3,
          null,
          4.6,
          21.4,
          14.8,
          24.4,
          9.2,
          27.6,
          16.2,
          14.8,
          42,
          5.7,
          22.1,
          18.9,
          25.9,
          15.3,
          21.5,
          5,
          14.6,
          15.9,
          9.3,
          13.6,
          18.8,
          17.4,
          26.3,
          18.3,
          14.6,
          38.9,
          31.2,
          20.7,
          11.1,
          7.9,
          7.1,
          59.1,
          17.4,
          24.6,
          null,
          18.9,
          13.4,
          12,
          12.3,
          null,
          13.1,
          18.2,
          7.9,
          13.5,
          15.1,
          19.4,
          8.4,
          11.2,
          11.9,
          19,
          30.1,
          12.2,
          15.7,
          19.8,
          19.9,
          16.4,
          16.9,
          null,
          20.3,
          16.3,
          17.8,
          24.2,
          20.7,
          38.4,
          26.1,
          29.8,
          11.6,
          19,
          8.3,
          14.4,
          10.8,
          12.2,
          15.8,
          20,
          16.6,
          28.3,
          18.5,
          4.6,
          9.9,
          22.9,
          16.5,
          null,
          24.3,
          72.5,
          14.1,
          18.3,
          25.8,
          11.6,
          19.5,
          67.8,
          17.6,
          25.9,
          29.1,
          6.7,
          13.3,
          19.6,
          13.9,
          17.9,
          46.3,
          23,
          22.4,
          37.4,
          18.8,
          26.8,
          null,
          23.8,
          12.7,
          22.8,
          18.6,
          12.6,
          25.4,
          21.1,
          18.6,
          10.7,
          19.2,
          13,
          8.2,
          11,
          16,
          7.7,
          22.2,
          18.4,
          35.8,
          15.3,
          16.6,
          53.6,
          3.3,
          12.2,
          30.3,
          26.9,
          28.3,
          25.9,
          null,
          null,
          46.1,
          21.9,
          26.9,
          43.2,
          39.4,
          15.9,
          24.2,
          23.3,
          16,
          23.3,
          25,
          25.4,
          13,
          17,
          14.9,
          11.5,
          15.7,
          11.9,
          19,
          11.3,
          7.7,
          17.4,
          25.9,
          22.2,
          32.3,
          12.8,
          18.7,
          20.3,
          22.3,
          17.8,
          16.5,
          17.3,
          30.2,
          19.4,
          43.7,
          18.7,
          25.9,
          27.8,
          18.1,
          25.3,
          null,
          18,
          18.8,
          21.5,
          15.9,
          16.6,
          9.5,
          23.8,
          15.4,
          17,
          14.1,
          52,
          17.1,
          18.3,
          15.9,
          null,
          17.6,
          17.1,
          17.1,
          30.4,
          22.6,
          20.4,
          22,
          10.8,
          17.8,
          8.2,
          15.1,
          8.4,
          17.4,
          29,
          62.7,
          19.8,
          15.9,
          7.5,
          16.8,
          null,
          9.8,
          20.9,
          26.8,
          28,
          26.2,
          17.9,
          null,
          11.7,
          19.6,
          13.6,
          15.3,
          5.2,
          17.1,
          36.5,
          14.4,
          65.1,
          19,
          21.5,
          14.4,
          23.4,
          58.4,
          26.2,
          20.2,
          13,
          14.6,
          null,
          11.8,
          16.9,
          14.4,
          20.6
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare data\n",
    "x2011 = timesData.student_staff_ratio[timesData.year == 2011]\n",
    "x2012 = timesData.student_staff_ratio[timesData.year == 2012]\n",
    "\n",
    "trace1 = go.Histogram(\n",
    "    x=x2011,\n",
    "    opacity=0.75,\n",
    "    name = \"2011\",\n",
    "    marker=dict(color='rgba(171, 50, 96, 0.6)'))\n",
    "trace2 = go.Histogram(\n",
    "    x=x2012,\n",
    "    opacity=0.75,\n",
    "    name = \"2012\",\n",
    "    marker=dict(color='rgba(12, 50, 196, 0.6)'))\n",
    "\n",
    "data = [trace1, trace2]\n",
    "layout = go.Layout(barmode='overlay',\n",
    "                   title=' students-staff ratio in 2011 and 2012',\n",
    "                   xaxis=dict(title='students-staff ratio'),\n",
    "                   yaxis=dict( title='Count'),\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data prepararion\n",
    "x2011 = timesData.country[timesData.year == 2011]\n",
    "plt.subplots(figsize=(8,8))\n",
    "wordcloud = WordCloud(\n",
    "                          background_color='white',\n",
    "                          width=512,\n",
    "                          height=384\n",
    "                         ).generate(\" \".join(x2011))\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.savefig('graph.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "marker": {
          "color": "rgb(12, 12, 140)"
         },
         "name": "total score of universities in 2015",
         "type": "box",
         "uid": "12d49a72-8ce3-4965-ad81-cd83c1ea5dad",
         "y": [
          "94.3",
          "93.3",
          "93.2",
          "92.9",
          "92.0",
          "91.9",
          "90.9",
          "89.5",
          "87.5",
          "87.5",
          "87.1",
          "85.5",
          "84.6",
          "84.4",
          "83.0",
          "81.0",
          "80.9",
          "79.9",
          "79.4",
          "79.3",
          "79.2",
          "78.7",
          "76.1",
          "74.3",
          "73.3",
          "73.2",
          "72.8",
          "72.3",
          "71.9",
          "71.9",
          "71.9",
          "71.8",
          "71.2",
          "70.9",
          "70.9",
          "70.4",
          "70.0",
          "69.9",
          "69.6",
          "69.4",
          "68.6",
          "67.8",
          "67.5",
          "66.8",
          "66.5",
          "65.9",
          "65.9",
          "65.2",
          "65.1",
          "64.8",
          "64.7",
          "64.5",
          "64.5",
          "64.1",
          "63.7",
          "63.7",
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    "# data preparation\n",
    "x2015 = timesData[timesData.year == 2015]\n",
    "\n",
    "trace0 = go.Box(\n",
    "    y=x2015.total_score,\n",
    "    name = 'total score of universities in 2015',\n",
    "    marker = dict(\n",
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    "    )\n",
    ")\n",
    "trace1 = go.Box(\n",
    "    y=x2015.research,\n",
    "    name = 'research of universities in 2015',\n",
    "    marker = dict(\n",
    "        color = 'rgb(12, 128, 128)',\n",
    "    )\n",
    ")\n",
    "data = [trace0, trace1]\n",
    "iplot(data)"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import figure factory\n",
    "import plotly.figure_factory as ff\n",
    "# prepare data\n",
    "dataframe = timesData[timesData.year == 2015]\n",
    "data2015 = dataframe.loc[:,[\"research\",\"international\", \"total_score\"]]\n",
    "data2015[\"index\"] = np.arange(1,len(data2015)+1)\n",
    "# scatter matrix\n",
    "fig = ff.create_scatterplotmatrix(data2015, diag='box', index='index',colormap='Portland',\n",
    "                                  colormap_type='cat',\n",
    "                                  height=700, width=700)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first line plot\n",
    "trace1 = go.Scatter(\n",
    "    x=dataframe.world_rank,\n",
    "    y=dataframe.teaching,\n",
    "    name = \"teaching\",\n",
    "    marker = dict(color = 'rgba(16, 112, 2, 0.8)'),\n",
    ")\n",
    "# second line plot\n",
    "trace2 = go.Scatter(\n",
    "    x=dataframe.world_rank,\n",
    "    y=dataframe.income,\n",
    "    xaxis='x2',\n",
    "    yaxis='y2',\n",
    "    name = \"income\",\n",
    "    marker = dict(color = 'rgba(160, 112, 20, 0.8)'),\n",
    ")\n",
    "data = [trace1, trace2]\n",
    "layout = go.Layout(\n",
    "    xaxis2=dict(\n",
    "        domain=[0.6, 0.95],\n",
    "        anchor='y2',        \n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        domain=[0.6, 0.95],\n",
    "        anchor='x2',\n",
    "    ),\n",
    "    title = 'Income and Teaching vs World Rank of Universities'\n",
    "\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create trace 1 that is 3d scatter\n",
    "trace1 = go.Scatter3d(\n",
    "    x=dataframe.world_rank,\n",
    "    y=dataframe.research,\n",
    "    z=dataframe.citations,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size=10,\n",
    "        color='rgb(255,0,0)',                # set color to an array/list of desired values      \n",
    "    )\n",
    ")\n",
    "\n",
    "data = [trace1]\n",
    "layout = go.Layout(\n",
    "    margin=dict(\n",
    "        l=0,\n",
    "        r=0,\n",
    "        b=0,\n",
    "        t=0  \n",
    "    )\n",
    "    \n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now I'm going to use the CSV from our warm-up activity that used the spotify API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['song_list.csv']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# plotly\n",
    "import plotly.plotly as py\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# word cloud library\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"./Spotify Data\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data that we will use.\n",
    "songlist = pd.read_csv(\"./Spotify Data/song_list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3988 entries, 0 to 3987\n",
      "Data columns (total 21 columns):\n",
      "artists             3983 non-null object\n",
      "songs               3988 non-null object\n",
      "danceability        3988 non-null float64\n",
      "energy              3988 non-null float64\n",
      "key                 3988 non-null int64\n",
      "loudness            3988 non-null float64\n",
      "mode                3988 non-null int64\n",
      "speechiness         3988 non-null float64\n",
      "acousticness        3988 non-null float64\n",
      "instrumentalness    3988 non-null float64\n",
      "liveness            3988 non-null float64\n",
      "valence             3988 non-null float64\n",
      "tempo               3988 non-null float64\n",
      "type                3988 non-null object\n",
      "id                  3988 non-null object\n",
      "uri                 3988 non-null object\n",
      "track_href          3988 non-null object\n",
      "analysis_url        3988 non-null object\n",
      "duration_ms         3988 non-null int64\n",
      "time_signature      3988 non-null int64\n",
      "happy/sad           3988 non-null int64\n",
      "dtypes: float64(9), int64(5), object(7)\n",
      "memory usage: 654.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# information about timesData\n",
    "songlist.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>artists</th>\n",
       "      <th>songs</th>\n",
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       "      <th>...</th>\n",
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       "      <th>duration_ms</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>happy/sad</th>\n",
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       "  </thead>\n",
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       "      <td>Merry Christmas (I Don't Wanna Fight Tonight) ...</td>\n",
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       "      <td>7</td>\n",
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       "      <td>0.7590</td>\n",
       "      <td>0</td>\n",
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       "      <td>37wcYprGuVZE4TFMRugoKh</td>\n",
       "      <td>spotify:track:37wcYprGuVZE4TFMRugoKh</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/37wcYprGuVZE...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/37wc...</td>\n",
       "      <td>240185</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toby Keith</td>\n",
       "      <td>Red Solo Cup</td>\n",
       "      <td>0.5490</td>\n",
       "      <td>0.5630</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.655</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0669</td>\n",
       "      <td>0.27900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>163.991</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>47nm8czanMUzIqHsnr5x61</td>\n",
       "      <td>spotify:track:47nm8czanMUzIqHsnr5x61</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/47nm8czanMUz...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/47nm...</td>\n",
       "      <td>223387</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Matthew And The Atlas</td>\n",
       "      <td>I Will Remain</td>\n",
       "      <td>0.7440</td>\n",
       "      <td>0.4320</td>\n",
       "      <td>8</td>\n",
       "      <td>-11.173</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0383</td>\n",
       "      <td>0.79000</td>\n",
       "      <td>0.013500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6820</td>\n",
       "      <td>123.933</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>1pRb7DlOyLPGcBJUJoAKOo</td>\n",
       "      <td>spotify:track:1pRb7DlOyLPGcBJUJoAKOo</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/1pRb7DlOyLPG...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/1pRb...</td>\n",
       "      <td>206547</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Benjamin Godard</td>\n",
       "      <td>Symphonie gothique, Op. 23: V. Allegro non troppo</td>\n",
       "      <td>0.3820</td>\n",
       "      <td>0.0965</td>\n",
       "      <td>9</td>\n",
       "      <td>-22.290</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0343</td>\n",
       "      <td>0.97000</td>\n",
       "      <td>0.879000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3120</td>\n",
       "      <td>104.629</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>0tqy02sjAeGfXlWQyBnUrD</td>\n",
       "      <td>spotify:track:0tqy02sjAeGfXlWQyBnUrD</td>\n",
       "      <td>https://api.spotify.com/v1/tracks/0tqy02sjAeGf...</td>\n",
       "      <td>https://api.spotify.com/v1/audio-analysis/0tqy...</td>\n",
       "      <td>236747</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>John Morgan</td>\n",
       "      <td>The Uninvited (reconstructed by J. Morgan): Gr...</td>\n",
       "      <td>0.0645</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>0</td>\n",
       "      <td>-20.949</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0405</td>\n",
       "      <td>0.94500</td>\n",
       "      <td>0.859000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0309</td>\n",
       "      <td>70.799</td>\n",
       "      <td>audio_features</td>\n",
       "      <td>7h50wXOMKvnPhNtrhpIgKy</td>\n",
       "      <td>spotify:track:7h50wXOMKvnPhNtrhpIgKy</td>\n",
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       "      <td>https://api.spotify.com/v1/audio-analysis/7h50...</td>\n",
       "      <td>282667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Righteous Brothers</td>\n",
       "      <td>Unchained Melody</td>\n",
       "      <td>0.3670</td>\n",
       "      <td>0.2150</td>\n",
       "      <td>0</td>\n",
       "      <td>-16.652</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0288</td>\n",
       "      <td>0.51700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2660</td>\n",
       "      <td>98.742</td>\n",
       "      <td>audio_features</td>\n",
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       "      <td>https://api.spotify.com/v1/audio-analysis/1jFh...</td>\n",
       "      <td>215840</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 artists                                              songs  \\\n",
       "0                Ramones  Merry Christmas (I Don't Wanna Fight Tonight) ...   \n",
       "1           David Guetta                      Would I Lie To You - Extended   \n",
       "2       Hailee Steinfeld                                        Love Myself   \n",
       "3                Tove Lo                                          Influence   \n",
       "4             Triple One                                              Tarlo   \n",
       "5             Toby Keith                                       Red Solo Cup   \n",
       "6  Matthew And The Atlas                                      I Will Remain   \n",
       "7        Benjamin Godard  Symphonie gothique, Op. 23: V. Allegro non troppo   \n",
       "8            John Morgan  The Uninvited (reconstructed by J. Morgan): Gr...   \n",
       "9     Righteous Brothers                                   Unchained Melody   \n",
       "\n",
       "   danceability  energy  key  loudness  mode  speechiness  acousticness  \\\n",
       "0        0.4750  0.9350    0    -6.454     1       0.0488       0.03600   \n",
       "1        0.7400  0.7670    7    -6.264     0       0.2260       0.00334   \n",
       "2        0.6170  0.7590    0    -6.785     0       0.0677       0.00260   \n",
       "3        0.7680  0.5930    5    -9.643     0       0.0463       0.09930   \n",
       "4        0.6250  0.5890    2    -8.192     1       0.0666       0.15500   \n",
       "5        0.5490  0.5630    4    -6.655     1       0.0669       0.27900   \n",
       "6        0.7440  0.4320    8   -11.173     1       0.0383       0.79000   \n",
       "7        0.3820  0.0965    9   -22.290     1       0.0343       0.97000   \n",
       "8        0.0645  0.1130    0   -20.949     1       0.0405       0.94500   \n",
       "9        0.3670  0.2150    0   -16.652     1       0.0288       0.51700   \n",
       "\n",
       "   instrumentalness    ...      valence    tempo            type  \\\n",
       "0          0.000003    ...       0.8180  151.902  audio_features   \n",
       "1          0.000007    ...       0.7090  124.023  audio_features   \n",
       "2          0.000000    ...       0.3240  122.925  audio_features   \n",
       "3          0.000184    ...       0.3290  104.954  audio_features   \n",
       "4          0.000009    ...       0.6410   81.961  audio_features   \n",
       "5          0.000000    ...       0.8100  163.991  audio_features   \n",
       "6          0.013500    ...       0.6820  123.933  audio_features   \n",
       "7          0.879000    ...       0.3120  104.629  audio_features   \n",
       "8          0.859000    ...       0.0309   70.799  audio_features   \n",
       "9          0.000000    ...       0.2660   98.742  audio_features   \n",
       "\n",
       "                       id                                   uri  \\\n",
       "0  7EGQaR9y8XyKeDW7jiDndI  spotify:track:7EGQaR9y8XyKeDW7jiDndI   \n",
       "1  6rdMvRjvA5518Q5rmqZLH0  spotify:track:6rdMvRjvA5518Q5rmqZLH0   \n",
       "2  36phXHOKx8rO7CMp2vNX4x  spotify:track:36phXHOKx8rO7CMp2vNX4x   \n",
       "3  6IFwG4pnVPcpwX4kXFUbsV  spotify:track:6IFwG4pnVPcpwX4kXFUbsV   \n",
       "4  37wcYprGuVZE4TFMRugoKh  spotify:track:37wcYprGuVZE4TFMRugoKh   \n",
       "5  47nm8czanMUzIqHsnr5x61  spotify:track:47nm8czanMUzIqHsnr5x61   \n",
       "6  1pRb7DlOyLPGcBJUJoAKOo  spotify:track:1pRb7DlOyLPGcBJUJoAKOo   \n",
       "7  0tqy02sjAeGfXlWQyBnUrD  spotify:track:0tqy02sjAeGfXlWQyBnUrD   \n",
       "8  7h50wXOMKvnPhNtrhpIgKy  spotify:track:7h50wXOMKvnPhNtrhpIgKy   \n",
       "9  1jFhnVoJkcB4lf9tT0rSZS  spotify:track:1jFhnVoJkcB4lf9tT0rSZS   \n",
       "\n",
       "                                          track_href  \\\n",
       "0  https://api.spotify.com/v1/tracks/7EGQaR9y8XyK...   \n",
       "1  https://api.spotify.com/v1/tracks/6rdMvRjvA551...   \n",
       "2  https://api.spotify.com/v1/tracks/36phXHOKx8rO...   \n",
       "3  https://api.spotify.com/v1/tracks/6IFwG4pnVPcp...   \n",
       "4  https://api.spotify.com/v1/tracks/37wcYprGuVZE...   \n",
       "5  https://api.spotify.com/v1/tracks/47nm8czanMUz...   \n",
       "6  https://api.spotify.com/v1/tracks/1pRb7DlOyLPG...   \n",
       "7  https://api.spotify.com/v1/tracks/0tqy02sjAeGf...   \n",
       "8  https://api.spotify.com/v1/tracks/7h50wXOMKvnP...   \n",
       "9  https://api.spotify.com/v1/tracks/1jFhnVoJkcB4...   \n",
       "\n",
       "                                        analysis_url duration_ms  \\\n",
       "0  https://api.spotify.com/v1/audio-analysis/7EGQ...      123600   \n",
       "1  https://api.spotify.com/v1/audio-analysis/6rdM...      288070   \n",
       "2  https://api.spotify.com/v1/audio-analysis/36ph...      218773   \n",
       "3  https://api.spotify.com/v1/audio-analysis/6IFw...      224507   \n",
       "4  https://api.spotify.com/v1/audio-analysis/37wc...      240185   \n",
       "5  https://api.spotify.com/v1/audio-analysis/47nm...      223387   \n",
       "6  https://api.spotify.com/v1/audio-analysis/1pRb...      206547   \n",
       "7  https://api.spotify.com/v1/audio-analysis/0tqy...      236747   \n",
       "8  https://api.spotify.com/v1/audio-analysis/7h50...      282667   \n",
       "9  https://api.spotify.com/v1/audio-analysis/1jFh...      215840   \n",
       "\n",
       "   time_signature  happy/sad  \n",
       "0               4          1  \n",
       "1               4          1  \n",
       "2               4          1  \n",
       "3               4          1  \n",
       "4               4          0  \n",
       "5               4          1  \n",
       "6               4          1  \n",
       "7               4          1  \n",
       "8               3          1  \n",
       "9               3          0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songlist.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
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       "<div id=\"923cad1c-dcd1-4af2-b164-1f426d8527c2\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"923cad1c-dcd1-4af2-b164-1f426d8527c2\", [{\"marker\": {\"color\": \"rgba(16, 112, 2, 0.8)\"}, \"mode\": \"lines\", \"name\": \"energy\", \"text\": [\"Merry Christmas (I Don't Wanna Fight Tonight) - Single Version\", \"Would I Lie To You - Extended\", \"Love Myself\", \"Influence\", \"Tarlo\", \"Red Solo Cup\", \"I Will Remain\", \"Symphonie gothique, Op. 23: V. Allegro non troppo\", \"The Uninvited (reconstructed by J. Morgan): Grandfather and the Cliff\", \"Unchained Melody\", \"Astounded\", \"Outta Control - Remix\", \"Happy Birthday (MORRIS)\", \"Whiskey in My Water\", \"I Need You (feat. Olaf Blackwood)\", \"You Were Meant For Me\", \"For A Friend\", \"Dancing in the Moonlight\", \"These Are the Days of Our Lives\", \"Because the Night - Nightcore Edit\", \"As Tears Go By\", \"Felt Good On My Lips\", \"Let It Go\", \"Happy\", \"Beautiful Life\"], \"x\": [0.475, 0.74, 0.617, 0.768, 0.625, 0.5489999999999999, 0.7440000000000001, 0.382, 0.0645, 0.36700000000000005, 0.662, 0.7609999999999999, 0.5579999999999999, 0.526, 0.6, 0.5820000000000001, 0.659, 0.639, 0.677, 0.54, 0.34600000000000003, 0.56, 0.38799999999999996, 0.555, 0.732], \"y\": [0.935, 0.767, 0.759, 0.593, 0.589, 0.563, 0.43200000000000005, 0.0965, 0.113, 0.215, 0.802, 0.537, 0.904, 0.872, 0.863, 0.33, 0.654, 0.845, 0.40299999999999997, 0.9540000000000001, 0.305, 0.8909999999999999, 0.9129999999999999, 0.897, 0.52], \"type\": \"scatter\", \"uid\": \"a39b039b-ae45-4de0-b21f-b41219e7fa04\"}, {\"marker\": {\"color\": \"rgba(40, 26, 80, 0.8)\"}, \"mode\": \"lines+markers\", \"name\": \"valence\", \"text\": [\"Merry Christmas (I Don't Wanna Fight Tonight) - Single Version\", \"Would I Lie To You - Extended\", \"Love Myself\", \"Influence\", \"Tarlo\", \"Red Solo Cup\", \"I Will Remain\", \"Symphonie gothique, Op. 23: V. Allegro non troppo\", \"The Uninvited (reconstructed by J. Morgan): Grandfather and the Cliff\", \"Unchained Melody\", \"Astounded\", \"Outta Control - Remix\", \"Happy Birthday (MORRIS)\", \"Whiskey in My Water\", \"I Need You (feat. Olaf Blackwood)\", \"You Were Meant For Me\", \"For A Friend\", \"Dancing in the Moonlight\", \"These Are the Days of Our Lives\", \"Because the Night - Nightcore Edit\", \"As Tears Go By\", \"Felt Good On My Lips\", \"Let It Go\", \"Happy\", \"Beautiful Life\"], \"x\": [0.475, 0.74, 0.617, 0.768, 0.625, 0.5489999999999999, 0.7440000000000001, 0.382, 0.0645, 0.36700000000000005, 0.662, 0.7609999999999999, 0.5579999999999999, 0.526, 0.6, 0.5820000000000001, 0.659, 0.639, 0.677, 0.54, 0.34600000000000003, 0.56, 0.38799999999999996, 0.555, 0.732], \"y\": [0.818, 0.7090000000000001, 0.324, 0.32899999999999996, 0.6409999999999999, 0.81, 0.682, 0.312, 0.0309, 0.266, 0.655, 0.555, 0.843, 0.583, 0.223, 0.40700000000000003, 0.6629999999999999, 0.871, 0.568, 0.358, 0.35200000000000004, 0.603, 0.565, 0.965, 0.33899999999999997], \"type\": \"scatter\", \"uid\": \"368fa41b-1ca0-41fc-bae2-ce53f109ea5c\"}], {\"title\": {\"text\": \"Energy and Valence vs Danceability of First 25 Songs\"}, \"xaxis\": {\"ticklen\": 10, \"title\": {\"text\": \"Danceability\"}, \"zeroline\": false}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"923cad1c-dcd1-4af2-b164-1f426d8527c2\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare data frame\n",
    "df = songlist.iloc[:25,:]\n",
    "\n",
    "# import graph objects as \"go\"\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Creating trace1\n",
    "trace1 = go.Scatter(\n",
    "                    x = df.danceability,\n",
    "                    y = df.energy,\n",
    "                    mode = \"lines\",\n",
    "                    name = \"energy\",\n",
    "                    marker = dict(color = 'rgba(16, 112, 2, 0.8)'),\n",
    "                    text= df.songs)\n",
    "# Creating trace2\n",
    "trace2 = go.Scatter(\n",
    "                    x = df.danceability,\n",
    "                    y = df.valence,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"valence\",\n",
    "                    marker = dict(color = 'rgba(40, 26, 80, 0.8)'),\n",
    "                    text= df.songs)\n",
    "data = [trace1, trace2]\n",
    "layout = dict(title = 'Energy and Valence vs Danceability of First 25 Songs',\n",
    "              xaxis= dict(title= 'Danceability',ticklen= 10,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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          "color": "rgba(255, 128, 255, 0.8)"
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         "mode": "markers",
         "name": "Madonna",
         "text": [
          "Like A Virgin",
          "Love Don't Live Here Anymore",
          "Vogue",
          "B-Day Song",
          "Give Me All Your Luvin'",
          "Holiday",
          "Celebration",
          "Material Girl"
         ],
         "type": "scatter",
         "uid": "d5f313ec-351f-4811-ac7d-2c9431b20075",
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        {
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          "color": "rgba(255, 128, 2, 0.8)"
         },
         "mode": "markers",
         "name": "Cascada",
         "text": [
          "Because the Night - Nightcore Edit",
          "Everytime We Touch",
          "The World Is in My Hands - Ryan Thistlebeck vs. Manila Nightcore Edit",
          "Evacuate the Dancefloor - Nightcore Edit",
          "Jingle Bell Rock",
          "Summer of Love - Ryan T. & Rick M. Nightcore Edit",
          "Everytime We Touch - Nightcore Edit",
          "Everytime We Touch - Fallen Superhero Remix",
          "The Rhythm of the Night - Ryan T. & Rick M. Nightcore Edit",
          "Santa Clause Is Coming to Town",
          "Miracle - Nightcore Edit",
          "Let It Snow",
          "Last Christmas"
         ],
         "type": "scatter",
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        {
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          "color": "rgba(0, 255, 200, 0.8)"
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         "mode": "markers",
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        "title": {
         "text": "Loudness vs Key of songs by Madonna, Cascada and Queen"
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        "xaxis": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare data frames\n",
    "dfMadonna = songlist[songlist.artists == 'Madonna'].iloc[:200,:]\n",
    "dfCascada = songlist[songlist.artists == 'Cascada'].iloc[:200,:]\n",
    "dfQueen = songlist[songlist.artists == 'Queen'].iloc[:200,:]\n",
    "# import graph objects as \"go\"\n",
    "import plotly.graph_objs as go\n",
    "# creating trace1\n",
    "trace1 =go.Scatter(\n",
    "                    x = dfMadonna.key,\n",
    "                    y = dfMadonna.loudness,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"Madonna\",\n",
    "                    marker = dict(color = 'rgba(255, 128, 255, 0.8)'),\n",
    "                    text= dfRollingStones.songs)\n",
    "# creating trace2\n",
    "trace2 =go.Scatter(\n",
    "                    x = dfCascada.key,\n",
    "                    y = dfCascada.loudness,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"Cascada\",\n",
    "                    marker = dict(color = 'rgba(255, 128, 2, 0.8)'),\n",
    "                    text= dfCascada.songs)\n",
    "# creating trace3\n",
    "trace3 =go.Scatter(\n",
    "                    x = dfQueen.key,\n",
    "                    y = dfQueen.loudness,\n",
    "                    mode = \"markers\",\n",
    "                    name = \"Queen\",\n",
    "                    marker = dict(color = 'rgba(0, 255, 200, 0.8)'),\n",
    "                    text= dfQueen.songs)\n",
    "data = [trace1, trace2, trace3]\n",
    "layout = dict(title = 'Loudness vs Key of songs by Madonna, Cascada and Queen',\n",
    "              xaxis= dict(title= 'Key',ticklen= 5,zeroline= False),\n",
    "              yaxis= dict(title= 'Loudness',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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