2016/pr201_bp_03_03_describe_random.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ура! Мы смогли запустить Jupyter! \n",
    "Подключаем нужные модули :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # всякие математические функции\n",
    "import pandas as pd # работа с табличками данных\n",
    "import scipy.stats as stats # работа со случайными величинами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns # для графиков (на вышкинских не сработает)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/boris/Documents/pr201/2016/red_rose_python'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы попросить python смотреть в другую папку:\n",
    "\n",
    "cd полный путь к нужной папке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/boris/Downloads\n"
     ]
    }
   ],
   "source": [
    "cd /Users/boris/Downloads/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/boris/Downloads/pokemon-sun-and-moon-gen-7-stats-1\n"
     ]
    }
   ],
   "source": [
    "cd /Users/boris/Downloads/pokemon-sun-and-moon-gen-7-stats-1/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pok = pd.read_csv(\"pokemon.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>id</th>\n",
       "      <th>ndex</th>\n",
       "      <th>species</th>\n",
       "      <th>forme</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>ability1</th>\n",
       "      <th>ability2</th>\n",
       "      <th>abilityH</th>\n",
       "      <th>hp</th>\n",
       "      <th>...</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>dex1</th>\n",
       "      <th>dex2</th>\n",
       "      <th>class</th>\n",
       "      <th>percent-male</th>\n",
       "      <th>percent-female</th>\n",
       "      <th>pre-evolution</th>\n",
       "      <th>egg-group1</th>\n",
       "      <th>egg-group2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>Overgrow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chlorophyll</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>15.2 lbs.</td>\n",
       "      <td>2'04\"</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Seed Pokémon</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monster</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  ndex    species      forme  type1   type2  ability1 ability2  \\\n",
       "0   1     1  Bulbasaur  Bulbasaur  Grass  Poison  Overgrow      NaN   \n",
       "\n",
       "      abilityH  hp    ...         weight  height  dex1  dex2         class  \\\n",
       "0  Chlorophyll  45    ...      15.2 lbs.   2'04\"   NaN   NaN  Seed Pokémon   \n",
       "\n",
       "   percent-male percent-female pre-evolution egg-group1 egg-group2  \n",
       "0         0.875          0.125           NaN    Monster      Grass  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pok.head(1) # начало таблички"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>id</th>\n",
       "      <th>ndex</th>\n",
       "      <th>species</th>\n",
       "      <th>forme</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>ability1</th>\n",
       "      <th>ability2</th>\n",
       "      <th>abilityH</th>\n",
       "      <th>hp</th>\n",
       "      <th>...</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>dex1</th>\n",
       "      <th>dex2</th>\n",
       "      <th>class</th>\n",
       "      <th>percent-male</th>\n",
       "      <th>percent-female</th>\n",
       "      <th>pre-evolution</th>\n",
       "      <th>egg-group1</th>\n",
       "      <th>egg-group2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>1062</td>\n",
       "      <td>801</td>\n",
       "      <td>Magearna</td>\n",
       "      <td>Magearna (Original Color)</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Fairy</td>\n",
       "      <td>Soul-Heart</td>\n",
       "      <td>Soul-Heart</td>\n",
       "      <td>Soul-Heart</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>177.5 lbs.</td>\n",
       "      <td>3'03\"</td>\n",
       "      <td>This beautiful form reflects Magearna’s appear...</td>\n",
       "      <td>This gorgeous Magearna looks as it did at the ...</td>\n",
       "      <td>Artificial Pokémon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Undiscovered</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  ndex   species                      forme  type1  type2  \\\n",
       "1060  1062   801  Magearna  Magearna (Original Color)  Steel  Fairy   \n",
       "\n",
       "        ability1    ability2    abilityH  hp    ...          weight  height  \\\n",
       "1060  Soul-Heart  Soul-Heart  Soul-Heart  80    ...      177.5 lbs.   3'03\"   \n",
       "\n",
       "                                                   dex1  \\\n",
       "1060  This beautiful form reflects Magearna’s appear...   \n",
       "\n",
       "                                                   dex2               class  \\\n",
       "1060  This gorgeous Magearna looks as it did at the ...  Artificial Pokémon   \n",
       "\n",
       "      percent-male percent-female pre-evolution    egg-group1 egg-group2  \n",
       "1060           NaN            NaN           NaN  Undiscovered        NaN  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pok.tail(1) # хвостик таблички"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/boris/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>ndex</th>\n",
       "      <th>hp</th>\n",
       "      <th>attack</th>\n",
       "      <th>defense</th>\n",
       "      <th>spattack</th>\n",
       "      <th>spdefense</th>\n",
       "      <th>speed</th>\n",
       "      <th>total</th>\n",
       "      <th>percent-male</th>\n",
       "      <th>percent-female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1061.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>1061.000000</td>\n",
       "      <td>859.000000</td>\n",
       "      <td>859.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>531.244109</td>\n",
       "      <td>417.471254</td>\n",
       "      <td>70.041470</td>\n",
       "      <td>79.602262</td>\n",
       "      <td>73.730443</td>\n",
       "      <td>74.550424</td>\n",
       "      <td>72.911404</td>\n",
       "      <td>70.321395</td>\n",
       "      <td>441.157399</td>\n",
       "      <td>0.544529</td>\n",
       "      <td>0.455471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>306.748505</td>\n",
       "      <td>239.020743</td>\n",
       "      <td>25.893508</td>\n",
       "      <td>31.378369</td>\n",
       "      <td>30.394899</td>\n",
       "      <td>31.975146</td>\n",
       "      <td>27.995681</td>\n",
       "      <td>29.328288</td>\n",
       "      <td>121.289505</td>\n",
       "      <td>0.207969</td>\n",
       "      <td>0.207969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>266.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>531.000000</td>\n",
       "      <td>423.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>796.000000</td>\n",
       "      <td>646.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1062.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id         ndex           hp       attack      defense  \\\n",
       "count  1061.000000  1061.000000  1061.000000  1061.000000  1061.000000   \n",
       "mean    531.244109   417.471254    70.041470    79.602262    73.730443   \n",
       "std     306.748505   239.020743    25.893508    31.378369    30.394899   \n",
       "min       1.000000     1.000000     1.000000     5.000000     5.000000   \n",
       "25%     266.000000   201.000000    50.000000    55.000000    50.000000   \n",
       "50%     531.000000   423.000000    68.000000    75.000000    70.000000   \n",
       "75%     796.000000   646.000000    80.000000   100.000000    91.000000   \n",
       "max    1062.000000   802.000000   255.000000   190.000000   230.000000   \n",
       "\n",
       "          spattack    spdefense        speed        total  percent-male  \\\n",
       "count  1061.000000  1061.000000  1061.000000  1061.000000    859.000000   \n",
       "mean     74.550424    72.911404    70.321395   441.157399      0.544529   \n",
       "std      31.975146    27.995681    29.328288   121.289505      0.207969   \n",
       "min      10.000000    20.000000     5.000000   175.000000      0.000000   \n",
       "25%      50.000000    50.000000    48.000000   336.000000           NaN   \n",
       "50%      70.000000    70.000000    68.000000   455.000000           NaN   \n",
       "75%      95.000000    90.000000    93.000000   520.000000           NaN   \n",
       "max     194.000000   230.000000   180.000000   780.000000      1.000000   \n",
       "\n",
       "       percent-female  \n",
       "count      859.000000  \n",
       "mean         0.455471  \n",
       "std          0.207969  \n",
       "min          0.000000  \n",
       "25%               NaN  \n",
       "50%               NaN  \n",
       "75%               NaN  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pok.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        49\n",
       "1        62\n",
       "2        82\n",
       "3        52\n",
       "4        64\n",
       "5        84\n",
       "6        48\n",
       "7        63\n",
       "8        83\n",
       "9        30\n",
       "10       20\n",
       "11       45\n",
       "12       35\n",
       "13       25\n",
       "14       90\n",
       "15       45\n",
       "16       60\n",
       "17       80\n",
       "18       56\n",
       "19       81\n",
       "20       60\n",
       "21       90\n",
       "22       60\n",
       "23       95\n",
       "24       55\n",
       "25       90\n",
       "26       75\n",
       "27      100\n",
       "28       47\n",
       "29       62\n",
       "       ... \n",
       "1031     95\n",
       "1032     95\n",
       "1033     95\n",
       "1034     95\n",
       "1035     95\n",
       "1036     95\n",
       "1037     95\n",
       "1038     95\n",
       "1039     95\n",
       "1040     95\n",
       "1041     95\n",
       "1042     95\n",
       "1043     60\n",
       "1044     60\n",
       "1045     60\n",
       "1046     60\n",
       "1047     60\n",
       "1048     60\n",
       "1049    100\n",
       "1050    100\n",
       "1051    100\n",
       "1052    100\n",
       "1053    100\n",
       "1054    100\n",
       "1055    100\n",
       "1056    100\n",
       "1057    100\n",
       "1058    100\n",
       "1059    110\n",
       "1060     95\n",
       "Name: attack, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pok['attack']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы поставить модуль seaborn под windows:\n",
    "1. Жмакаем кнопку Пуск\n",
    "2. Вводим в строке снизу \"cmd\" и жмакаем enter\n",
    "3. Набираем \"conda install seaborn\" и enter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/boris/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c665048>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q1RufClnnk7uCfHDlhiVYDQsHm/uIRGOzFxBiniQoiAt09cWDQm21BIV84C6z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\nxKT/Hxjv7WxaAIJ2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c56dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.distplot(pok['attack'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x11c70f0b8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yLEehirRWSjWNYnx2KP90TRwWBOFOAOcC+BziQe4SQRDuKmTDiDLJ59pEWvty\nKuYy5VI9YuWK+Why2TSPpxxSkB5rHbuUazLl43qUg2J8dij/9PawVgA4DsAWURSHBEE4C8D7AL5V\nsJYRZZDPQXitfeVj+Q2XvRa3fW2Z5jjRdV86Bvc8JR/DSj52YDgCR136ZTmKNY6Sr+VISq0Ynx3K\nP13LiwiC8C6AEwC8K4ricYIgOAG8JYpiKWftcXmRKsPz1qZMsli2YGpBbgkWM8Ggit9v3et7PLqx\nLZbNGNYpx0w3xBjWRJcX+R8ATwOYJAjCtQC+CuCJPLWNKK8yrS+VzZdtvtaeyuXY2ZSkUo6bbN/d\nl7ZiRa7UEgxWnj0/r0Es21JcQH6yOan86QpYoij+RBCEFQD2AjgMwE2iKL5U0JYR5UjP+lJ6s7ny\ntfZULsfOpiSVWommdBUrcqWWYJDvLDmt804X9CslezFbeudh1dXZEQyW/zyrTPQmXdQC+EQUxTUA\n3gXwKUEQphe0ZUQ5ymdpn3ytPZXaA4qXTVq/sR2+YFjXa9K1d+WK+WMTk/Vtnyu1BIN8Z8lp7S9d\nxftKyV7MljQPK93/BfxDWNzqxFnL55b9PKtM9N4SfAzADkEQ6gDcAmADgEcBnF2ohhHlSnNCckwx\nwVL5WIVy7SkpK07reb1t0lM2KZtSRC57LY5unaRrsvBEqCUYbNjUIWtno6s2UVU+l1uEWuedLjBm\n+35UCj3zsCph/pVEb8BqFUXx3wVBWAvgobFbhG2FbBhRrrSytvZ0BWTbKR+r0cqKyzZb7vxTWxMV\nIkZHoxhNynXKV0mqYmSrqc3vUh53ZDQ6oVuEWuedLoBXSvYipac3YFkEQZgC4DwAnxMEYRoAR+Ga\nRUZRjmsU5XM9LJfNirkzGxLPu+qs8AXCeOaVjxAKj8JiNuHwFlfiea1r8cJfdqeseyXR6pGolSjy\nBcL45Yvt+HCvB1EADY5a3HDRUrhs1rTvQyHep+R9NrlsaHTVJqrGJ8v2FqFWaaZ0AXlak7Mqxqyq\nnd6AtQ7AWwBeFEWxXRCEDgA3Fa5ZZBSVskZRNokaAGS1Abfu6kuMp2hdC+WXtsNmwdQme9Y9kg2b\nO9C+Z7xarccfxrontmLuzIa0+yjE+yTbJ7TTz/N1a7JQ1TvIOPRmCT4BeRr7QlEU9VVcpIpmpLI0\nTU4zBvyjsscSvYkanX1+9A2l9pTUzjv5OeXtrKNbJyW+fG9/pE3zdb5AGA//pg0HurxobrSjsy81\n08sfjGRkMEYUAAAgAElEQVR8HwrxPqXbh8NmTqx8zIm0lC+6AtZYSvsPAUzCWOUYQRAgiuIRBWwb\nGYCR1iiaO3uyLClh7uzJiX/rXYPJNzwiWyRR0uiqhdVi1rwW6W5npbuGyp5Rk1te4gmIJxhkeh8K\n8T6p1UiUHN06mb0hyju9twR/BuB6AO1Iv6acKkEQlgO4UxTF0wVBOBLAIwCiANpFUbxqbJuvA7gc\nQATAHZznZQxGKkuTrq1af1M+3zXgVx2LMplMafef7nZWutcpezFuhwUzJ9sTY1huuwWzmp3oGvCj\nyWWDy27BtMnOtGtl5et9St5nk9uGWCwGjy9c9p8DMi69AatXFMXf53IAQRDWAFgJQFoE6G4A3xNF\n8TVBENYLgvBZAG8CWI14vUIHgNcFQdgsimJ1TKYwsGzHFSZSbUJaFypTFYNNb+3G03/enXj85TNb\ncdYJrel/amn9TfH8ZLcd+7pSb8u1f9SLG3/xJmKxGKzWGkRjUWzY1KEvuUHj2K++tz+lB9PS5JRV\nlhj0h/HBx/2Jv8+d1aD6fmi9T1prcb29/RP84ncfJrZbdf5CLBOmo7PPj7VPbMFgIIIamLDw8EZc\n/8XFJU+0yfZzVejtc31NtvRMHB4OBuD1Frfqh9tdD5NJd4Up3fQGrNcEQbgbwB8BDEtPiqL4Fx2v\n3QXgfMTnbgHA8aIovjb275cRn8sVBfC6KIojAIYEQdgJ4FjEJylTBZlotYnkdaG0vhCSgxUAPPm/\nu3HWCa1pj6036WLJ3MlYtmBqSiWGSBSIjN0qDIZHMeSPJAJbNhUtko/96KadKdtK856Ux5dkOzal\ntRZXcrACgPUvfIhlN07Huqe2wuOP/44cRQztewYKUlEjW9l+rgq9fa6vyZaeBRxrbTZs3e2DyVSc\nShfBgB9nLZ9bkHlfegPWiWP/uzTpuRiAMzK9UBTFFwRBODzpqeSw6wVQD8ANYDDpeR+AjGfb3Kyv\n6GMlMuq5e/zhlMeZziUwHEl53NzsxsO/aZN9IdhsFnznq8tU99Hc7E57bK2/KZ/3h0Zx96pT8K/f\n+m3aNqsdI902el/TetjklO2TzWpxZ/XZUFuLS+v1zc3ulPcC0HeOudK732w/V4XePtfXZGvW7Nlw\nl9mEYL/PhilT3GhoyP9nQm+W4Ol5PGY06d9uAB4AQ4gHLuXzaVVjJWfA2FWsG521KY8znYvDZkUo\nMj5u5KizoqfHiwNd8tcd6PJq7qunx5v22Fp/03reajYhMpp5OFfP+WVzTdTa1OS2ocFZi+ZGO/79\nU0dM+LOR7tjK9yJTeycim895tp+rQm+f62uA7H6MBoJhoGY484ZFFPCH0NvrRTisq/KfKq1roDdL\n8HAAvwIwB8A/IZ7ifqkointyaMsWQRBOHbudeA6AVwC0AbhjrGahHcACxBM8qMLkMvivtS6UVubb\nl89sxZP/Kx/DynRsvUkX0uMbLlqKHz26RdZDMZtMqLXWwGqtQaOrNj7epOP8tI5xybnz8Os/jN8W\nvOTceZrb5zo20lxvQ09Smn5zfTwLcdX5C7H+BfkYFhB/L9Y+ljSGNaexLBIssv1cFXr7XF9D6em9\nJfjfiE8e/gmALgBPAvgNgFNzOOa3ATwoCIIVwIcAnhVFMSYIwn0AXkf8luH3RFHUvu9BhpXT5M/k\nPnlShDh72Sxs3dmDyGgMVrMJZy+fBQA46aiZ2HXAn/iiOOnomQAAXyCSKI806AvDNxwBYpCNg6Uk\nECg6Ub5gBBs2deBAtzfldprZbMLcmQ3Y2+1FZ18QXv8IfMORjMHk1gdfR/9Ylag9nV7sPtCNtd88\nA0vntqB9wWCiRNHSeS0AtK9hLoP8TrsZPUPyxwCwTJiOZTem1rd22ayYNskOjz+CUcQg7vNgT+cg\nXtvWVdQqGyntyvKaZPs5zOVzy4nO+ac3YE0RRXGzIAg/EUUxhnjAuUrvQURR3AvgH8f+vRPAp1S2\neQjAQ3r3SdVDK+nigY3bE7flIqMxPPD8dtx11cmag91qS1BkWyFi18FBzRJL4ZEoPtg9nrGnd5mL\nfkVJw15f6rEl+U4MyLa+4obNHdixfzzCRUZjuOfp8cSNYlXZ0KtSKrFQnN6AFRQEYRbGfm8KgnAK\nAPX/aonyLNslPrSqOqhtn21FiGyXrZjIMhfZVqcoRtURtX0qe5rbd/fh9kfaEj2aUlZDMVIlFspM\nb8C6DsDvARwpCMJWxCtefKFgrSJKku0SH1pjW2rbZ1shQrmPjG2fwDIX2VanKEbVEbXqFibIg1Yg\nNCpbOqWU1VCMVIklF3oXcMxWXZ1dns+dhWCgcOnzaQPW2C3A7wCYAmAZgPkAzAB2cIyJikUr6UJr\nSQmtwW617V118YDS1e+HNziCzj4/1m9sT4x1KPd1/mmteOHV3TjQEx+nigEwm4D5hzUiGBqF22HF\n3i4vhkOjupe5mOIavw0oPU4+j0IuL6KVXJHuGF7/cOK2oNVswuovLMJrW7sSldqTS1f1eIK4/ouL\ns25XvlR64oOeeVjZGg76sXzhlAkt9liohSJNsZh2aq4gCHsAXAbgAQBfgyLm6pw4XCgxo6Z2T5SR\n09pzIQ2cJ39x53vQfv3Gdtl40bIFU9OOdRRzWRUjvd/ZXsd0jHTe+dTc7Nbdt3l0Y1ss0wKO2Qr4\nvTjlmOklXfBR6xpkuiV4B4DvApgO4HbF33RNHCZKlu6LXutvv355h2w5j/bdfVjUOhnnn9qKF/6y\nO2PQkPbb1e+HNzAiq7cnbb9fMaerbUc3dt3/Br54xhF46KUdiUzEGy5aiiOnN+L+599Hx4F4L2NP\npxeD/mHceOEJmuctlZfyByNw1snLS2mdt1ZJKq1ySh8d8GDtk++ltDXdMbReo9z+/FNb8cyfP8KO\nvQMIhuO/6BudVtxw0XGyMlnZ9Gi03pezl83CAxu36y7FRdUjbQ9LIgjCTaIo/mcR2pMN9rAMKN0v\ncK2/ffOev6hWSG9y22QZe1q/5pX7Vdv+a3e+oquqs9Vswn+vOR2X3vlKyt8evlH799u3fv6GrK1N\nblsie1DrvK/92WsY8o8nbdQ7rbh39T+ltNUE4KEbz8A31v1ZNpnZbAKOE6bGaw76whjwpV4r5Wuk\n81O2SXmt1c4jW1rvi3JS9kSOYUTsYeXew5KsFQThewAExIvUXot49XWOY1FW0mVtaf9NPZQoM/C0\nMsD0PK93CQI91S2A1B6NT9FWjy+UyKTrGpAPUkvt8gbkr5Eeq5VTUmvbaAwZaw4qXyM91psdOZEs\nyK5+9cF5ZZsmcgyqLHprZ9wPwAXgeAAjAOaCc6YoB2pZeJn+Nn92o+q+lBl4Whlgep63mvX9qNW7\nnTT/Z0+nNx40FHcyYjEk/pbci0puV42i2rX0WNkC6bHetiUfQ/ka6bHymjnr1LMdJ5IF6Q2k9prV\n2jSRY1Bl0dvDOl4UxeMEQThHFMWAIAj/AeCDQjaMykO+kwtyKY906acXYsOmDvQODcPjDcHtsKCl\nyZnI2FNunzL+clq8NFPXwNhYSV3qmlE3XLQUax97L2MP6sgZbviCYVxx3kL8YqN2dp2yh9LcUIdg\nJAp/MILwSFT2t+HQCJYtmJpyHkfNaZItHXLUnCYA8TGre56Sj2Epz8FqNmHurAZ8uHe8JGdyzcHk\nElPJr7nhoqWq78X5p7XimVdSx7D0ZEFqcdktstuUlhpg6fypOHv5LDzw/PaUrFAivQErNlbnT/qv\neQpyWMiRjCdTpYDs1xVKLY8kba9VykZ6Xjl25wuk3pH2BcL4wUNvJXotezq9iIyM4tJzF+LhP3yI\nXo8HofAoGt02PPzSh+gbHIZveARuhwVL5jVnXL5jx/4hbNjUgZFRedB5s70nsV7Uuqe2wqOYqxUI\njyIcicJiNmFkFIgm/dcTjcbwzo5uxADs7fTin5a0YNGcZpx5/Ey0f9yfCExnnRgvMTXF7UCj25ZI\n4JjS4AAAHDm9Ef+9ZrxOtS8YxoZN8ffGbbdib7cXh3r9suve0uDAknnNifevpdGh+V6svuBY1Wsi\nXfdsf9hMm+zE/p7x24JL54+PKd511cmGHqulwtAbsO4F8L8ApguCcC/i61vdVrBWUdnIVCkg29I3\nauWRch1QVzs2gJRbbB37PdiwuQNbd/UlnkvuuQDAgDeUWL8quXehtgR814AfvR55QBL3eVLODwDM\nNSbUO2s1yzkBQDipV5e8JtX9z7fL1qr62TPt+O81p+u+hslB51s/fyNxXZJfk6/SRbnsp9LnSBVD\nISYOT2TBx0It3CjJNHH4q0kPn0R8zMsM4C7Ex7KowmWqFJBt6Rutckq50H9sk+6SPD2eYMZ7B/Gx\nF/lGofAobn+kLaVnZa4xoUElYDlsFkxtsqO50Z7Sm9NKopAe53INsy1jla1c9sPisBNXiInDuS74\nWMiFGyWZeljS/YUjx/7vDwBGAfwLgO2IV2ynCpbpV3C2pW+0yinlQuvYyl6RcFgjLOYa1d6S2j7V\nis4mc9VZcHiLS9ZjG43FVPevVv4JAI5unZT4sm5TSZEHUtO7pWSEXK5htmWsslXpJZDK1aQpLch3\nWns5SxuwRFG8BAAEQfgzgMWiKPaOPW4CsLHwzaNSy/QrONvbOlrllHKhdezIyCg69nsAmCAc1ohL\nzl0gez4UjmJUZf6hw2bGyhXzcffT29IeV0rYkMaHugcCCITGf+WaTYDZXCMr/zQyGh27bRjD/Nny\nNaTmzarHzgNDssfAeELEyGgMlqSEiFyuYbZlrLLF23tUDHrHsGYASL7p70e8+gVVuazXFbJZMXdm\nQ+KLzZWULp1txQdlAkfXQACb3z6AvsFh2GotcDssQAz45Yvb8fEhL6LRUdTVWtDkrkNgeATBUATD\nkfHkichIFN/++V+hnEyfXNx1wex6rFwxH3//uE+zF1bvGs/Gc9VZ4bLX4tzlh6H94z5ERmPYvrsf\nXZ5AIikhMCy/pZN4rLjTExyOYP3GdvR4gpg7syElsSFdNQ1pTbHY2HX++XPvY8YUN84/tTWl/b5A\nGL98sR0f7vUgCqDBUYsbLlqatspGqVOwilkqi0pHb8B6CcCfBEF4HvFxrC8AeLpgraKKlW5wXs86\nVsnrYSmTD9Y+Lk9LT06kkAxHIvCMJR8snTcFFnMNejxBHOzxjb02/nqr2YSZzS7s7ZQv1PjRIS9c\n9lr84ncfQmnONDcG/WEMeEMY8IZk5yCVPwLiY1FrH3svkdHXPSBfg0p6rHyNct2p7bv7cHTr5MSX\nc7pkDGUyyMHeIA72BrHzgAceXzixz5HRKCzmGrTvGU+H9/jDWPv4e7j7m6ckghUgTxAp9bpTpT4+\nFYeuicOiKF6PeAHcBQDmAfipKIo3FbJhVJlyqXShdz0svVUoJAPeEFadtwg3X7wsJbMpMhpDc6M9\npeOQ7hiRyEhKckWmihIAUnp00mPla5RHDoRG0bajGxs2dQBIn4yhlZgx6JdPDRD3edDZlzrYLm2n\nVWWj1OtOlfr4VBx6e1gQRfE5AM8VsC1UBZSD890DwcRyHloD93W1Ztlk2zpbfBl3ZSKBcl0mPW2R\nqK1z1bajW3Wf6ze2q+7vYF/ql2Rzox2+QDhlP8nVHNwOm2wCrdtpS2yjJwhLX87pkjG01vGqgfLO\nYwy+4dQEYOmXrdr1uP2RNgz65IGv2EkXTPqoDroDFlE+SIPx23f3IRAaRSA0kriVkzxw77JbsHP/\nAK746f8hquiBHN4Sz4pac+ES3PKrtxNf6jHEqzkEh+VjU/UOS3yy7tgYVr2rFi1N8cQJaezDUVcD\nXzA1QLQ02dA3FJY937ajG0fMcOHjQz6kYzIhkZyhLFYrJVBI56GWEKG3+ob05Xzl545O3Ba1mk24\n8nNHpxzDF4wAsRimNtVhxhQ3gsNh2e2/+bMb0e8dTukpLjw8Xh4rucqGRAoUapU0iqVakz4KtYBj\nLnKdv5XN3C0GLCoqKUnj9kfaZL+I40Eq/jdfIIxv/fwNzS/qRFHYqLxiBAA0OGtx8b/Mx73PjCcG\nXPZvC7FoTrNsYD4yMopf/2EHxH0eWSV4ZVXyoUAYIyrtGBmJJcopKSuhJ8QA33Ak5fbUjMl2PPD8\n9kRyxJWfO1o1EUWqXPHjx7dg535P6v7HeHzDuP2RNvQPDcvGvF56Yy+u/nw80CiTXRLLixz0QNw/\nHuTOOH4Gfv1Sh2z/JgCfPe0IAMCcqQ04Yey8lYs1NjhrcfPFyzTbWUhayT+VnoxRiHlYucpl/la2\nc7cYsKgk0t3C2bC5I22vQtp23VNbMaqIWM2Nds0KEZnmVwGIZxYCiaAVCGkEzeAI9imW33DYanCw\nV14Bft0TWzF3ZoP8NqhnPLAok0XUEgZaJjnSBqzklPhkHUmv0UpKeGDjdlmQ+9mz7apjZw88vz2l\nMoZSOd6Gq/RkDM7DIiqCdLdw1AbMrWYT5sxokC0Vr0wkkG7BKb9QtZbMUNPS5ESNKZi2lJLDZoar\nziLbRupdXPHT/5ONt/mDkZRz3bqrF8kjQcoAoWznqgsWIxQawbZdvSmFc9Mbv82iN6FF64eCVmUM\nh82MqU2Osr0Nx2SMysKARSWRbv6WsvdlNZtw22Un4pj502TFUJWJBI0uG1z2Ws0KEWoVJyR2mxl1\ntRZ09vnhC6avOmartaBvSB7QpN6FWuKD8ly/df8bsluIyvYqeyr1zvjrtRY81CIcNr4si1aPVtle\nrUQPrcoYR7dOLuseC5MxKgsDFpUdtd6X2riDnmQFrSUzGl21GI3GsOvgIMLhKIZDowiGRhO9JnON\nCdFYTLmMFQDIelYOmzkxFypdm5LHUmZOcSAWiyIQGoXTHh/D2vzWAc1lUjz+MBqdtYllUg70eNHZ\nF0QMgMVswryZ9QiGo2h01cJkMmHAG9K9dIuyvVJbDvX60T0QQCwWg9tpy3tljGIxWnspPZNyDoiB\nxKp16YFqXXYh3+edbY9FjfKWmFpg9QXCuOXXbbJAJy1Rn87dT22RZfAtmtOIy/9tUUUnESSr4s+5\n7nLnj25sixl5DCvg9+KUY6anJF1oXQP2sKggMpVZUisfpPW35NJMNqsZh09zwxuIwFVnwYEeX7yn\nkrT929s/kVWiWHX+QiwTpifatPvgAHq9+qrE15gAmICoxtBRIDSKPZ3eRJWIgaEA9nSNV65Ydf5C\nvPNhX8qY2AEdX8TJwUp6rEwi2LqzB7dddqLsOqW7tlJae3NDHWZOdePsZbPwwMbtKa/JZ3Zd9mum\nyXuWlRyUKTvsYRmQEX55KnsvUo/iWz9/Q/bl3eS2JcoHaf1N+bwWaftLVaqfP3zjGXnpUaXjsFlk\nad6SOdPcKWNnJgAP3XgGAO0vdLXzUNuX1nVKd20lyjEr6TVa718ust1XPo9tRNn0sP7r4T/G6uyO\nQjYnrbo6e3JuT9a00trZw6KiyrbMUrq/6V0zK9N2u/b3pf27XssWTFUNfGrBClBP9kj+mZhN6rXa\nvrSuk57STMoEi3yvk5XLvpjZp18p52ENB/1YvnAK3O76Ce0nm9czYFFB6M1KS1c+SPqbVlkhpXTr\nQt3+SBsG/BP/D7vJbcOq8xZh67o/665dGBlJPa7ZhETl9QNd8gC075NBzX2tXDEfW3f2yI6tdZ30\nlGZS9rDqbGas39ieUpB3Itl1Wp8FrZ4lM/v0K+U8rIDfC7e7vqALNiqZb7311qIdLM9uDQTCmbeq\nQE6nDeV+7gsPb0Tv4DBqLTWYNyu+/lOt1YzF8yZjS0cvotEY6p218fWixsYnkv/mtFtw2FQX/tS2\nH82NdRgOj6TMQap3WmGvNSMag2xfM5rr8M6OXtm2Hl/212vGJDsOb3EhPBJNae9RRzThzfZORGPx\nL/2aGlNK1Q1JZ38QCw9vxKAvlNh+3uxGbPuoDx5fGMrhMX9oFId6/Zg30469SeNhZ53QguOF6Vh2\n1FTVa6jn2o5GYzCbgGlNdiw4fBK+eq6A9o/7E685bKoL7+3sTQQxh82CJfOmJN6/XGh9Fh566UO0\n7eiGxxfGoV4/egeHsWzB1MT2TrsVR86on9CxjcjptN2md9ttOw7daq21FbI5miKRMA5rccNmq8v7\nvrWuAXtYlJDPgXateVbTmpyJcZUUSd/cweERfPDx+BJsVrMJVnONLGi56ywIhKMARmX32BbOnowl\ncyejY78HwfCoamp6sniVCst4GrfDJl9LSo2i02SvNcObZv7WkD8Cl6MW/mAEteYYPtyrXbkCiNcr\nbHLLv4g8vrETiQKj0Sgio1EM+cN48k878fV/PSrtWmN+fwS+QDiR6n/Jvy7EkdMb4QuEZa851Cvv\n7YUj6eek6aJx/bVu/UmfHSOM1VJxMWBRQqnL2CjXbEqWvFaVRFniSFr/acPmDtny9ek0uW1w1VnQ\n2R9I7GtAsZaUWiBXrlUVyTDZuHtgfP9hnTFAOe4kfaGve2orhsbW9BqNxfDBx/2JJUa03j+t9biU\n73lyFXkAGImO7zPXz4LW54q3/ihbDFi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c70f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=pok, x='attack', y='defense')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?sns.distplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/boris/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d255a90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cztl9Dz55w0r+5dXzHG3p5e5b1bximE6mv5/plM91m49kksIh4A7gaaXUDuBk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\n/f/ShDoC0r9UmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d2a4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pok['attack'], bins=5, rug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$X \\sim Bin(n = 50, p = 1/3)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = stats.binom(n=50, p=1.0/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.666666666666575"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.expect() # ожидание, E(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.111111111111111"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.var() # дисперсия, Var(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0012238402794417023"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.pmf(7) # probability mass function, P(X=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001744022609223021"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.cdf(7) # cumulative distribution function, P(X<=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98729240821878073"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.cdf(40) - X.cdf(9) # P(X \\in [10; 40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = X.rvs(10000) # генерируем X_1, X_2, ..., X_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 11, 21, 20, 14, 19, 17, 10, 11, 11])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/boris/anaconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11edc8390>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TrX5S426TzqQ4fCpOeZmfzbctoCpYRqLn8jYjbWv4dq72OsZ6/pHabVzZwI/3tvDtnxzl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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11edcd940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = 1.0 / (x ** 2 + 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0039991746771476221"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23, 17, 23, 19, 18, 14, 23,  9, 14, 16])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.rvs(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}