old.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ranking analysis\n",
    "\n",
    "Recently my alma-mater, EPFL, has been ranked as the best new university. Following that article I decided to anaylise university rankings and see how EPFL evolved in the ranking and what are the main criteria that influenced it's rise. Also I will analyse which criteria they can improve and which ranking they could expect in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arnaudbenard/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timesData = pd.read_csv('./timesData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "    </tr>\n",
       "    <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  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",
       "\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",
       "\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",
       "\n",
       "   year  \n",
       "0  2011  \n",
       "1  2011  \n",
       "2  2011  \n",
       "3  2011  \n",
       "4  2011  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timesData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "first200timesData = timesData[:200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arnaudbenard/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "/Users/arnaudbenard/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "world_rank                  int64\n",
       "university_name            object\n",
       "country                    object\n",
       "teaching                  float64\n",
       "international              object\n",
       "research                  float64\n",
       "citations                 float64\n",
       "income                     object\n",
       "total_score               float64\n",
       "num_students               object\n",
       "student_staff_ratio       float64\n",
       "international_students     object\n",
       "female_male_ratio          object\n",
       "year                        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first200timesData['world_rank'] = first200timesData['world_rank'].astype(int)\n",
    "first200timesData['total_score'] = first200timesData['total_score'].astype(float)\n",
    "\n",
    "first200timesData.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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76twzW9vVM0Ekj8JWjlkiHuNMMzl3nHGhp2/Ix4pEyo/CVsbFJWfOGLX1ee9AysdqRMqP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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d00ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot(x='total_score',y='world_rank', data=first200timesData, order=3)\n",
    "g = g.set(ylim=(200, 1), xlim=(100, 0))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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