Статистика набора 2011, МФТИ, ФУПМ

stats.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:e2bf6f96bacb69efa975d094c1e3816e9739fdf7081fc0005b8c0326f76ff37a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot = (iggplot(data=df) +\n",
      "        gg.aes_string(x='factor(\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435, levels=c(%s))' % ', '.join('\"%s\"' % s for s in data['\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435'][data['\u041f\u043e\u0441\u0442\u0443\u043f\u0438\u043b\u0438'].argsort()][::-1]),\n",
      "                      y='\u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e', \n",
      "                      fill='factor(\u0422\u0438\u043f, level=c(\"\u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b\", \"\u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c\"))') +\n",
      "        gg.scale_x_discrete('\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435') +\n",
      "        gg.scale_fill_discrete('\u0422\u0438\u043f') +\n",
      "        gg.geom_bar(stat='identity', color='black', size=0.5, alpha=0.8))\n",
      "plot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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zM3gTj8cT3OpwUbW1tbqu+/1+M8KHStM0KaVNwgQCAZ/Ppz6S1nK73R6PxyZD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\nKQsqezuzsUYAAAAASUVORK5CYII=\n",
       "prompt_number": 7,
       "text": [
        "<GGPlot - Python:0x7f7cc217cfc8 / R:0x2baf078>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from rpy2.robjects import pandas2ri\n",
      "pandas2ri.activate()\n",
      "import rpy2.robjects as robj\n",
      "from rpy2.robjects.lib import ggplot2 as gg\n",
      "from rpy2.ipython.ggplot import ggplot as iggplot\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/dist-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
        "  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.DataFrame([('\u0411\u044e\u0434\u0436\u0435\u0442', 106, 23), ('\u041f\u043b\u0430\u0442\u043d\u043e\u0435', 10, 5), ('\u0426\u0435\u043b\u0435\u0432\u043e\u0435', 5, 1), ('\u041a\u0438\u0435\u0432\u0441\u043a\u043e\u0435', 7, 0), ('\u041c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u044b\u0435', 4, 1)], columns=['\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435', '\u041f\u043e\u0441\u0442\u0443\u043f\u0438\u043b\u0438', '\u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['\u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c'] = data['\u041f\u043e\u0441\u0442\u0443\u043f\u0438\u043b\u0438'] - data['\u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435</th>\n",
        "      <th>\u041f\u043e\u0441\u0442\u0443\u043f\u0438\u043b\u0438</th>\n",
        "      <th>\u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b</th>\n",
        "      <th>\u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>        \u0411\u044e\u0434\u0436\u0435\u0442</td>\n",
        "      <td> 106</td>\n",
        "      <td> 23</td>\n",
        "      <td> 83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>       \u041f\u043b\u0430\u0442\u043d\u043e\u0435</td>\n",
        "      <td>  10</td>\n",
        "      <td>  5</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>       \u0426\u0435\u043b\u0435\u0432\u043e\u0435</td>\n",
        "      <td>   5</td>\n",
        "      <td>  1</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>      \u041a\u0438\u0435\u0432\u0441\u043a\u043e\u0435</td>\n",
        "      <td>   7</td>\n",
        "      <td>  0</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> \u041c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u044b\u0435</td>\n",
        "      <td>   4</td>\n",
        "      <td>  1</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "       \u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435  \u041f\u043e\u0441\u0442\u0443\u043f\u0438\u043b\u0438  \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b  \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c\n",
        "0         \u0411\u044e\u0434\u0436\u0435\u0442        106         23        83\n",
        "1        \u041f\u043b\u0430\u0442\u043d\u043e\u0435         10          5         5\n",
        "2        \u0426\u0435\u043b\u0435\u0432\u043e\u0435          5          1         4\n",
        "3       \u041a\u0438\u0435\u0432\u0441\u043a\u043e\u0435          7          0         7\n",
        "4  \u041c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u044b\u0435          4          1         3"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = data[['\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435', '\u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c']]\n",
      "df.columns = ['\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435', '\u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e']\n",
      "df['\u0422\u0438\u043f'] = '\u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c'\n",
      "\n",
      "df_ = data[['\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435', '\u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b']]\n",
      "df_.columns = ['\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435', '\u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e']\n",
      "df_['\u0422\u0438\u043f'] = '\u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b'\n",
      "\n",
      "df = pd.concat((df, df_))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>\u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435</th>\n",
        "      <th>\u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e</th>\n",
        "      <th>\u0422\u0438\u043f</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>        \u0411\u044e\u0434\u0436\u0435\u0442</td>\n",
        "      <td> 83</td>\n",
        "      <td>  \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>       \u041f\u043b\u0430\u0442\u043d\u043e\u0435</td>\n",
        "      <td>  5</td>\n",
        "      <td>  \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>       \u0426\u0435\u043b\u0435\u0432\u043e\u0435</td>\n",
        "      <td>  4</td>\n",
        "      <td>  \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>      \u041a\u0438\u0435\u0432\u0441\u043a\u043e\u0435</td>\n",
        "      <td>  7</td>\n",
        "      <td>  \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> \u041c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u044b\u0435</td>\n",
        "      <td>  3</td>\n",
        "      <td>  \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>        \u0411\u044e\u0434\u0436\u0435\u0442</td>\n",
        "      <td> 23</td>\n",
        "      <td> \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>       \u041f\u043b\u0430\u0442\u043d\u043e\u0435</td>\n",
        "      <td>  5</td>\n",
        "      <td> \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>       \u0426\u0435\u043b\u0435\u0432\u043e\u0435</td>\n",
        "      <td>  1</td>\n",
        "      <td> \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>      \u041a\u0438\u0435\u0432\u0441\u043a\u043e\u0435</td>\n",
        "      <td>  0</td>\n",
        "      <td> \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> \u041c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u044b\u0435</td>\n",
        "      <td>  1</td>\n",
        "      <td> \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "       \u041e\u0442\u0434\u0435\u043b\u0435\u043d\u0438\u0435  \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e        \u0422\u0438\u043f\n",
        "0         \u0411\u044e\u0434\u0436\u0435\u0442          83   \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c\n",
        "1        \u041f\u043b\u0430\u0442\u043d\u043e\u0435           5   \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c\n",
        "2        \u0426\u0435\u043b\u0435\u0432\u043e\u0435           4   \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c\n",
        "3       \u041a\u0438\u0435\u0432\u0441\u043a\u043e\u0435           7   \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c\n",
        "4  \u041c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u044b\u0435           3   \u041e\u0441\u0442\u0430\u043b\u0438\u0441\u044c\n",
        "0         \u0411\u044e\u0434\u0436\u0435\u0442          23  \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b\n",
        "1        \u041f\u043b\u0430\u0442\u043d\u043e\u0435           5  \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b\n",
        "2        \u0426\u0435\u043b\u0435\u0432\u043e\u0435           1  \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b\n",
        "3       \u041a\u0438\u0435\u0432\u0441\u043a\u043e\u0435           0  \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b\n",
        "4  \u041c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u044b\u0435           1  \u041e\u0442\u0447\u0438\u0441\u043b\u0435\u043d\u044b"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}