{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Введение в анализ данных\n",
    "\n",
    "## Семинар 3. Продолжение знакомства с библиотеками для анализа данных\n",
    "\n",
    "### Полезная информация\n",
    " - [Страница курса на вики](http://wiki.cs.hse.ru/Майнор_Интеллектуальный_анализ_данных/Введение_в_анализ_данных)\n",
    " - [Страница семинаров на вики](http://wiki.cs.hse.ru/Майнор_Интеллектуальный_анализ_данных/Введение_в_анализ_данных/ИАД-11,12)\n",
    " - [Таблица с оценками](https://docs.google.com/spreadsheets/d/1jZL_-ELf0Ogj2XHa6VVbkg8vrInycv2-Z9UR5keLDfM/edit?usp=sharing)\n",
    " - Почта курса *hse.minor.dm@gmail.com* (Формат темы: \"[ИАД-NN] - Вопрос - Фамилия Имя Отчество\")\n",
    " - Виртуальная машина для майнора (подробности см. на странице семинара)\n",
    " - **Подписаться на рассылку**: написать пустое письмо на hse-minor-datamining-2+subscribe@googlegroups.com\n",
    " - Первое ДЗ!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Что было в прошлый раз\n",
    "\n",
    "**Типы ответов**\n",
    " - вещественные ответы (регрессия)\n",
    " - конечное число ответов (классификация: бинарная/многоклассовая/пересекающиеся классы)\n",
    " - временной ряд\n",
    " - ранжирование (?)\n",
    " - отсутствие ответа (кластеризация)\n",
    " \n",
    "**Типы признаков**\n",
    " - бинарный ({0, 1})\n",
    " - вещественный\n",
    " - категориальный (неупорядоченное конечное множество)\n",
    " - порядковый (упорядоченное конечное множество)\n",
    " - множественные признаки\n",
    " \n",
    "**Обобщающая способность**\n",
    " - недообучение\n",
    " - переобучение\n",
    " \n",
    "**Задачи анализа данных**\n",
    " - медицинская диагностика: объект — пациент, ответ — диагноз, классификация с пересекающимися классами; признаки: бинарные (пол), порядковые (тяжесть состояния), вещественные (вес)\n",
    " - кредитный скоринг\n",
    " - предсказание оттока клиентов\n",
    " - стоимость недвижимости\n",
    " - прогнозированние продаж (временные ряды)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(15).reshape(3, 5)\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Транспонирование матрицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  5, 10],\n",
       "       [ 1,  6, 11],\n",
       "       [ 2,  7, 12],\n",
       "       [ 3,  8, 13],\n",
       "       [ 4,  9, 14]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = np.arange(6).reshape(2, 3)\n",
    "B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Конкатенация:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  5, 10],\n",
       "       [ 1,  6, 11],\n",
       "       [ 2,  7, 12],\n",
       "       [ 3,  8, 13],\n",
       "       [ 4,  9, 14],\n",
       "       [ 0,  1,  2],\n",
       "       [ 3,  4,  5]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((A.T, B), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  0,  3],\n",
       "       [ 5,  6,  7,  8,  9,  1,  4],\n",
       "       [10, 11, 12, 13, 14,  2,  5]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((A, B.T), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В чем разница между *concatenate* и *vstack*/*hstack*? [Stackoverflow!](http://stackoverflow.com/questions/33356442/when-should-i-use-hstack-vstack-vs-append-vs-concatenate-vs-column-stack)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Изменение размера матрицы (количество элементов должно оставаться тем же)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(6).reshape(2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если задать один один из параметров равным -1, то он будет вычислен автоматически"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(6).reshape(2, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вытягивание в вектор"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ravel(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10,  1,  6, 11,  2,  7, 12,  3,  8, 13,  4,  9, 14])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ravel(A, order='F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?np.ravel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[flatten vs ravel](http://stackoverflow.com/questions/28930465/what-is-the-difference-between-flatten-and-ravel-functions-in-numpy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Логические операции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(10).reshape(2, 5)\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применить одно условие достаточно просто"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[A > 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если нужно сделать несколько условий?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-2852d2c8b339>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mA\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m4\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mA\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "A[A > 4 and A < 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[np.logical_and(A > 4, A < 8)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы нейти все ненулевые элементы матрицы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 0, 1, 1, 1, 1, 1]), array([1, 2, 3, 4, 0, 1, 2, 3, 4]))"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.nonzero()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[A.nonzero()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?np.nonzero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matrix\n",
    "Ещё один тип данных в NumPy — matrix. Является производным классом от ndarray, в связи с чем можно использовать все методы и функции, применимые к array. Однако:\n",
    " - matrix — строго 2мерные;\n",
    " - матричное умножение осуществляется через * (в отличие от dot для array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = np.arange(0, 4).reshape(2, 2)\n",
    "B = np.arange(3, 7).reshape(2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4],\n",
       "       [10, 18]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.matrix(A)\n",
    "b = np.matrix(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 5,  6],\n",
       "        [21, 26]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas\n",
    " \n",
    " ![](http://i.imgur.com/PADRo1K.png)\n",
    " \n",
    " \n",
    " - [Working with missing data](http://pandas.pydata.org/pandas-docs/stable/missing_data.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male   22      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('titanic.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n",
       "       u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Survived'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pclass'].unique()"
   ]
  },
  {
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass\n",
       "0         0       3\n",
       "1         1       1\n",
       "2         1       3\n",
       "3         1       1\n",
       "4         0       3\n",
       "5         0       3\n",
       "6         0       1\n",
       "7         0       3\n",
       "8         1       3\n",
       "9         1       2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Survived', 'Pclass']][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если хочется делать неожиданные срезы по столбцам и строкам одновременно, можно воспользоваться методом iloc: он работает с целочисленной индексацией"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass\n",
       "1         1       1\n",
       "2         1       3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1:3, 1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Группировка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Группировка позволяет объединять данные и считать по ним общую статистику.\n",
    "Для группировки данных есть метод groupby, который возвращает специальный объект. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10bf46510>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       \n",
       "1       count    216.000000\n",
       "        mean      84.154687\n",
       "        std       78.380373\n",
       "        min        0.000000\n",
       "        25%       30.923950\n",
       "        50%       60.287500\n",
       "        75%       93.500000\n",
       "        max      512.329200\n",
       "2       count    184.000000\n",
       "        mean      20.662183\n",
       "        std       13.417399\n",
       "        min        0.000000\n",
       "        25%       13.000000\n",
       "        50%       14.250000\n",
       "        75%       26.000000\n",
       "        max       73.500000\n",
       "3       count    491.000000\n",
       "        mean      13.675550\n",
       "        std       11.778142\n",
       "        min        0.000000\n",
       "        25%        7.750000\n",
       "        50%        8.050000\n",
       "        75%       15.500000\n",
       "        max       69.550000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Pclass')['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  1           233\n",
       "        0            81\n",
       "male    0           468\n",
       "        1           109\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Sex')['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Pclass  Survived\n",
       "female  1       1            91\n",
       "                0             3\n",
       "        2       1            70\n",
       "                0             6\n",
       "        3       0            72\n",
       "                1            72\n",
       "male    1       0            77\n",
       "                1            45\n",
       "        2       0            91\n",
       "                1            17\n",
       "        3       0           300\n",
       "                1            47\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['Sex', 'Pclass'])['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Sex     Survived\n",
       "1       female  1            91\n",
       "                0             3\n",
       "        male    0            77\n",
       "                1            45\n",
       "2       female  1            70\n",
       "                0             6\n",
       "        male    0            91\n",
       "                1            17\n",
       "3       female  0            72\n",
       "                1            72\n",
       "        male    0           300\n",
       "                1            47\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['Pclass', 'Sex'])['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Объединение датафреймов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "male = data[data['Sex'] == 'male']\n",
    "female = data[data['Sex'] == 'female']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
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       "      <td>54</td>\n",
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       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
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       "      <td>S</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass                            Name   Sex  Age  \\\n",
       "0            1         0       3         Braund, Mr. Owen Harris  male   22   \n",
       "4            5         0       3        Allen, Mr. William Henry  male   35   \n",
       "5            6         0       3                Moran, Mr. James  male  NaN   \n",
       "6            7         0       1         McCarthy, Mr. Timothy J  male   54   \n",
       "7            8         0       3  Palsson, Master. Gosta Leonard  male    2   \n",
       "\n",
       "   SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "0      1      0  A/5 21171   7.2500   NaN        S  \n",
       "4      0      0     373450   8.0500   NaN        S  \n",
       "5      0      0     330877   8.4583   NaN        Q  \n",
       "6      0      0      17463  51.8625   E46        S  \n",
       "7      3      1     349909  21.0750   NaN        S  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([male, female]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>female</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "\n",
       "                                                Name     Sex  Age  SibSp  \\\n",
       "4                           Allen, Mr. William Henry    male   35      0   \n",
       "5                                   Moran, Mr. James    male  NaN      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([male.iloc[1:3], female]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Пропуски в данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     22\n",
       "1     38\n",
       "2     26\n",
       "3     35\n",
       "4     35\n",
       "5    NaN\n",
       "6     54\n",
       "7      2\n",
       "8     27\n",
       "9     14\n",
       "10     4\n",
       "11    58\n",
       "12    20\n",
       "13    39\n",
       "14    14\n",
       "15    55\n",
       "16     2\n",
       "17   NaN\n",
       "18    31\n",
       "19   NaN\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "count у метода describe вернет количество объектов, имеющих значение данного признака\n",
    "\n",
    "чтобы посмотреть какие объекты имеют пропуски можно воспользоваться методом isnull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5      True\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17     True\n",
       "18    False\n",
       "19     True\n",
       "Name: Age, dtype: bool"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].isnull()[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Glynn, Miss. Mary Agatha</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kraeff, Mr. Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349253</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "5             6         0       3   \n",
       "17           18         1       2   \n",
       "19           20         1       3   \n",
       "26           27         0       3   \n",
       "28           29         1       3   \n",
       "29           30         0       3   \n",
       "31           32         1       1   \n",
       "32           33         1       3   \n",
       "36           37         1       3   \n",
       "42           43         0       3   \n",
       "\n",
       "                                              Name     Sex  Age  SibSp  Parch  \\\n",
       "5                                 Moran, Mr. James    male  NaN      0      0   \n",
       "17                    Williams, Mr. Charles Eugene    male  NaN      0      0   \n",
       "19                         Masselmani, Mrs. Fatima  female  NaN      0      0   \n",
       "26                         Emir, Mr. Farred Chehab    male  NaN      0      0   \n",
       "28                   O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0      0   \n",
       "29                             Todoroff, Mr. Lalio    male  NaN      0      0   \n",
       "31  Spencer, Mrs. William Augustus (Marie Eugenie)  female  NaN      1      0   \n",
       "32                        Glynn, Miss. Mary Agatha  female  NaN      0      0   \n",
       "36                                Mamee, Mr. Hanna    male  NaN      0      0   \n",
       "42                             Kraeff, Mr. Theodor    male  NaN      0      0   \n",
       "\n",
       "      Ticket      Fare Cabin Embarked  \n",
       "5     330877    8.4583   NaN        Q  \n",
       "17    244373   13.0000   NaN        S  \n",
       "19      2649    7.2250   NaN        C  \n",
       "26      2631    7.2250   NaN        C  \n",
       "28    330959    7.8792   NaN        Q  \n",
       "29    349216    7.8958   NaN        S  \n",
       "31  PC 17569  146.5208   B78        C  \n",
       "32    335677    7.7500   NaN        Q  \n",
       "36      2677    7.2292   NaN        C  \n",
       "42    349253    7.8958   NaN        C  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Age'].isnull()][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для удаления объектов с пропусками можно воспользоваться методом dropna. В отличии от None, nan нельзя сравнивать сам с собой. Сравните:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "None == None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183, 12)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = data.dropna()\n",
    "new_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если нужно удалить только объекты, имеющие пропуски в определенных колонкач, можно в параметре subset указать колонки, на которые обращать внимание:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(714, 12)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = data.dropna(subset=['Age'])\n",
    "new_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы сохранить данные можно воспользоваться методом to_csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_data.to_csv(\"titanic2.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "\n",
    " - [matplotlib](http://matplotlib.org)\n",
    " - [matplotlib - 2D and 3D plotting in Python](http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb)\n",
    " - [visualization in pandas](http://pandas.pydata.org/pandas-docs/stable/visualization.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = linspace(1, 10, 20)\n",
    "y = x ** 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib позволяет строить как непрерывные линии:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d1c6350>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vMN3dvwvMAPoBmNlRwEVAG+Bs4B4z26PxrCKSXo89Bn36wNSp8L3vxY5GqqpaScDMWgA/\nAh6ssPs8YESyPQI4P9nuDox2963uvhxYBnSozueLSDo88gj8/vdhVbC2bWNHI3uiui2Bu4A/ABUf\n723q7qUA7r4GOCjZ3xxYWeG4Vck+Eclhw4dD377w/PNwzDGxo5E9lXESMLNzgFJ3nwd8XVlH8z+I\n5KkHH4T/+z+YMQPatIkdjWSiOtNGdAa6m9mPgG8A+5rZI8AaM2vq7qVm1gz4KDl+FXBIhfe3SPZV\nqri4+MvtoqIiioqKqhGqiNS0+++H224LCaB169jRFKaSkhJKSkqqdY4amUDOzE4Drnf37mY2CFjr\n7gPN7Eagibv3TTqGRwInEcpA04DWlc0UpwnkRNJt6FAYNCgkAM0FlB5pmUBuADDGzC4HVhBGBOHu\ni8xsDGEk0RbgGl3pRXLP4MHw17+G9QBatYodjVSXppIWkSr7y19CK2DGDGjZMnY0srO0tAREJA8N\nGgTDhoUWwCGH7PZwyRFKAiKyW7ffHoaCzpoFzTWwO68oCYjILrmHNYHHjAkJ4OCDY0ckNU1JQEQq\ntXlzmAhu4UKYOROaNYsdkdQGJQER+Yp16+DCC6FRo9ACaNQodkRSWzSVtIjsYPly6NQpTAExdqwS\nQL5TEhCRL82eHRLAr34VngfYa6/YEUltUzlIRIBw13/VVfD3v0O3brGjkWxREhApcO5w113hQbDn\nntNiMIVGSUCkgG3dGhaCeeEFePllOPTQ2BFJtikJiBSoTz6BSy4JieDFF2G//WJHJDGoY1ikAK1a\nBaeeGp7+nThRCaCQKQmIFJj58+H734dLLw1rAtSrFzsiiUnlIJECMnkyXHZZmAn0pz+NHY2kgVoC\nIgXivvvg8sth3DglANlOLQGRPFdWBjfcEGr/L76olcBkR0oCInls0yb4+c9h7dowBPSAA2JHJGmj\ncpBInlqzBrp0CXP/TJmiBCCVUxIQyUMzZoQnf889F0aMgL33jh2RpJXKQSJ5ZNs2uOWWsAzkI49A\n166xI5K0UxIQyRNr1kCPHmAGb7yhRWCkalQOEskD06fD8cfDaafB1KlKAFJ1agmI5LBt2+Dmm+GB\nB1T+kcwoCYjkqA8/DOWfOnVU/pHMqRwkkoOmTQujf7p0UflHqkctAZEcsnUr3HRTWP1r5MiQBESq\nQ0lAJEesXh3KP/XqhfJP06axI5J8oHKQSA6YOjWUf7p2DUtAKgFITVFLQCTFtm6F4mIYPhxGjYKi\nosgBSd5REhBJqVWrwsIvDRrAnDm6+5faoXKQSApNmQLt28OZZ6r8I7VLLQGRFFm3Dv7wh3DhHz06\nPAEsUpvUEhBJibFj4eijoW5dePNNJQDJDrUERCL78EO49tpw4R81Ck49NXZEUkjUEhCJxB0efBDa\ntYM2bWD+fCUAyT61BEQieOcduOoq+OSTMAVEu3axI5JClXFLwMxamNkMM3vLzBaaWe9kfxMzm2pm\nS81sipk1rvCefma2zMwWm9kZNfEHEMklW7fCoEHQsWNY9euVV5QAJC5z98zeaNYMaObu88xsH2AO\ncB7wS2Ctuw8ysxuBJu7e18yOAkYCJwItgOlAa68kADOrbLdITps7F3r1ggMPhPvvh8MOix2R5Bsz\nw91tT96TcUvA3de4+7xkeyOwmHBxPw8YkRw2Ajg/2e4OjHb3re6+HFgGdMj080VyxWefwY03hjH/\nvXuHKSCUACQtaqRj2My+DXwP+CfQ1N1LISQK4KDksObAygpvW5XsE8lbM2dC27awYgUsXAg9e4bl\nH0XSotodw0kp6Emgj7tvNLOd6zgZ1XWKi4u/3C4qKqJIk6ZIDqn40NfQodC9e+yIJB+VlJRQUlJS\nrXNk3CcAYGZ1gYnAZHcfnOxbDBS5e2nSbzDT3duYWV/A3X1gctxzQH93f7WS86pPQHLW00/Db38L\n550Hf/4zNG68+/eI1ISs9gkk/g4sKk8AifFAz2T7MmBchf2XmFl9M2sFHA7Mrubni6TG4sXhjv+P\nfwxTPtxzjxKApF91hoh2Bn4GnG5mc83sDTM7CxgI/NDMlgJdgQEA7r4IGAMsAiYB1+h2X/LB6tVh\nzP+pp4apHubNg1NOiR2VSNVUqxxUW1QOklywYQPccUe44+/VC/r1gyZNYkclhSxGOUik4GzeDEOG\nwBFHwPvvh/H/gwYpAUhu0rQRIlXkDk8+Ge74Dz88zPmvp30l1ykJiFTBrFlwww2wZQvcdx/84Aex\nIxKpGUoCIl/jrbegb98wzfNtt8Ell0AdFVElj+ifs0glVq+GK6+ELl3g9NNhyRLo0UMJQPKP/kmL\nVLB+PfzpT3DssWGit7ffhuuug733jh2ZSO1QEhABPv8c/va3MOJn9eow1n/AANh//9iRidQu9QlI\nQfvvf+Hee8OQzxNOCAu8tG0bOyqR7FFLQArSe++FaZ0PPzys8jVtGkycqAQghUdJQArK66+HET4n\nnggNG4ZRPw8/DMccEzsykThUDpK8V1YGkyeHKR7eew/+93/hgQdg331jRyYSn5KA5K0vvoCRI+HO\nO6F+/TC//09/CvXqxY5MJD2UBCTvfPxxeKr3b38L0zrcfXcY668VvUS+Sn0CkjeWLw+lnu98B5Yu\nDXP7TJ4MXbsqAYjsipKA5DR3eO01uPRSaN8+PNS1cCEMHx4e+BKRr6dykOSkNWtCvX/ECNi4Ea69\nFu6/H/bbL3ZkIrlFi8pIzvjiC5gwIdzlv/QSXHAB9OwJJ5+sOX1EILNFZdQSkFQrL/eMGAGPPx46\nenv2DNuNGsWOTiT3KQlIKq1eDY8+Gu76N28OF/45c6Bly9iRieQXJQFJjc8+g3Hjwl3/P/8JP/lJ\neKirUyeN7hGpLUoCEpV7uOAPHx6WbjzhhHDX/9RTYVoHEaldSgKSdWVlobQzcSKMHh329ewZpm8+\n5JCooYkUHI0Okqz49FN4/vkwumfiRGjcGLp1gx//GDp2VLlHpCZkMjpISUBqzQcfhAv+hAnwwgvh\nYa5u3eDcc6F169jRieQfJQGJqrzMM2FCeL3/Ppx9drjon3WWVukSqW1KApJ1n34K06eHi/6zz4YL\n/bnnhjv+Tp2grnqdRLJGSUBqnTssWQIlJaHUU7HM061bWKlLROJQEpAat3lzKPG8+GJ4vfRSWIzl\nlFPgnHPgzDNV5hFJCyUBqbZ16+CVV7Zf9OfMgSOOgM6dwxw9nTtDixaxoxSRyigJyB774INQ0im/\n6L/7blh/9+STw6tjR83MKZIrlATka33xBSxeHJ7QLb/ob9q0/YJ/8slw3HFaflEkVykJCBA6b1es\ngAULwgIr5a933w2rbnXosP2i37q1HtQSyRdKAgXo44+3X+TLL/pvvhlKOMceC23bhq/HHgtHHhlW\n3hKR/KQkkMc2bYJly3a8s1+wADZsgGOO2X6hb9s2fH/AAbEjFpFsUxLIYZs3hyds33svvJYv33F7\n3To47LCv3t23bKlVtUQkyIkkYGZnAX8lLHL/kLsPrOSYvEsC27aFkTiVXeDfew8++giaN4dWreDb\n3w5fK243a6aLvYh8vdQvL2lmdYAhQFdgNfCamY1z9yXZjCMTJSUlFBUV7bBv61ZYuzZcwL/utWZN\nWCnroIN2vMB36bJ9u3nzPZ9iobKYYlNMVZfGuBRT1aQ1pkxke2aXDsAyd18BYGajgfOAqElg2zb4\n5JNQXy//Wr5dfpGfMKGEVq2Kdri4r1sHTZqEi/vOrw4ddvy+RYua75RN6z9ExVQ1aYxLMVVNWmPK\nRLaTQHNgZYXvPyAkhkqVlYWx7bt6ff75rn/22WdfvbDv6kL/+eewzz5hRM2++27/uu++cOCB4SJ+\nwAFw/vlhu2nT8PXAA2GvvWr9dyYiUmtSO8djvXqh3FK/PjRoEO6id/Wq7OcNGoSLeZMmcOihX73A\nV/zasOHu6+3FxXDppVn5o4uIZE1WO4bNrCNQ7O5nJd/3BXznzmEzy69eYRGRLEn16CAz2wtYSugY\n/hCYDVzq7ouzFoSIiHwpq+Ugd99mZtcCU9k+RFQJQEQkklQ+LCYiItmRqsePzOwhMys1swWxYwEw\nsxZmNsPM3jKzhWbWO3ZMAGa2t5m9amZzk9hujx1TOTOrY2ZvmNn42LEAmNlyM5uf/K5mx44HwMwa\nm9kTZrY4+fs7KQUxHZH8jt5Ivq5Pw793M+uX/I4WmNlIM6ufgpj6JNeDaNeEyq6VZtbEzKaa2VIz\nm2JmjatyrlQlAeBh4MzYQVSwFfidux8NfB/4jZkdGTkm3P0LoIu7Hwe0BU43s86RwyrXB1gUO4gK\nyoAidz/O3Xc5HDnLBgOT3L0N0A6IXhJ197eT39HxwAnAp8DYmDGZWUvgSuA4d29LKF9fEjmmo4Fe\nQHvge8C5ZnZYhFAqu1b2Baa7+3eBGUC/qpwoVUnA3V8EPo4dRzl3X+Pu85LtjYT/rM3jRhW4+6Zk\nc2/C32P035uZtQB+BDwYO5YKjBT9Ozez/YBT3P1hAHff6u4bIoe1sx8A/3L3lbs9snZtADYDjcys\nLtCQMNNATG2AV939C3ffBvw/4MfZDmIX18rzgBHJ9gjg/KqcKzX/OdLOzL5NyPyvxo0kSMouc4E1\nQIm7p+Hu+y7gD0CaOpocmGZmr5nZlbGDAVoB/zGzh5PSyzAz+0bsoHZyMTAqdhDu/jFwJ/A+sApY\n5+7T40bFm8ApSemlIeGm55DIMZU7yN1LIdzAAgdV5U1KAlVgZvsATwJ9khZBdO5elpSDWgCnmtlp\nMeMxs3OA0qTlZMkrDTonJY4fEcp5J0eOpy5wPDA0iWsToRmfCmZWD+gOPJGCWA4DrgNaAt8C9jGz\nHjFjSuY5GwhMAyYBc4FtMWP6GlW6GVMS2I2kGfok8Ii7j4sdz86SUsKzhBplTJ2B7mb2LuEusouZ\n/SNyTLj7h8nXfxNq3LH7BT4AVrr768n3TxKSQlqcDcxJfl+xtQdecvf/JqWXp4FOkWPC3R929/bu\nXgSsA96OHFK5UjNrCmBmzYCPqvKmNCaBNN1FAvwdWOTug2MHUs7Mvlne85+UEn4IzIsZk7v/0d0P\ndffDCJ13M9z9FzFjMrOGSSsOM2sEnEFozkeTNNdXmtkRya6upKsj/VJSUApKLAU6mlkDMzPC7yp6\nJ7qZ/U/y9VDgAuCxWKGw47VyPNAz2b4MqNJNa6rmDjKzx4Ai4EAzex/oX96BFimezsDPgIVJ/d2B\nP7r7c7FiShwMjEj+Y9QhtFKejxxTGjUFxibTkNQFRrr71MgxAfQGRiall3eBX0aOBwhJk9ApfFXs\nWADcfX7SmpxDKLnMBYbFjQqAp8zsAGALcE2Mjv3KrpXAAOAJM7scWAFcVKVz6WExEZHClcZykIiI\nZImSgIhIAVMSEBEpYEoCIiIFTElARKSAKQmIiBQwJQERkQKmJCAiUsD+P5mmFThVFwffAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0e1690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так и отдельно точки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10d007d10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce543d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И все вместе!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1100c1e90>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w85/DmDFRR5MZdLOYiKSFjRvhjDOgQwcoLYUc1SSaRCpJQB+9iLQod7j8ctiwAe67Twkg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1100209d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y, 'bo-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Но вернемся к реальным данным. Иногда хочется посмотреть на данные, \"не отходя\" от pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e431310>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VZlbRsU4z+11g0d13c+ynHIZfn8CrvDdq+B16I7tX07Hrk949zxcBf163/gu9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\nFnoRkQmnhV5EZMJpoRcRmXD/H2wOWRSNtNcCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d9beb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "?pd.DataFrame.hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e4a5dd0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e687d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Группировки также работают (параметр by):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x10f868b10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x10fa00050>], dtype=object)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pk35EjETEA+n588CjwBzgVGBF+tgK4LR2VdLMzIrR1Ji+pHnAkcA9wFBE1CD7wwDMLrpy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\n38ysRJz0zcxKxEnfzKxEnPTNzErk/wG44fgk6fSbsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f885690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age'].hist(by=data['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Такие гистограммы не удобно сравнивать, хотя бы потому что они имеют разный масштаб по оси y. Испраим это:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1113f0190>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x111578990>], dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V9y9Wxpe//BV27fqnwJtHqPlr/NVfvbdrs6U1Z8+e+9sFZtJ4xhkhPAq8s6puTHI4cF2S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111404ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age'].hist(by=data['Sex'], sharey=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аналогично можно задать одинаковый масштаб по оси x:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x10d9ffdd0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x10e0cef10>], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M9vXCF57ITTedDRw/QI172bjxNO6//wdd2sgsX5jmhWzDmd0L04b5/f0lyVFUPTBC7bN3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\nDARJUmMgSJIAA0GS1BgIkiQA/j8YQbjzXHCFmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e05a950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age'].hist(by=data['Sex'], sharey=True, sharex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Но все же строить более сложные графики лучше с помощью matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qcDIAoKFhfZcHfluwYFIPR2Nm+4pS3DMI4JeSnpL0+XTbqIhoBIiINcDIEsRhZmbtKMWV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\nZjgZmJkZTgZmZgb8fz7ZYDf8kzWqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d2050d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 20\n",
    "index = np.arange(bins)\n",
    "male = plt.hist(data[data['Sex'] == 'male']['Age'].dropna(), bins=bins, alpha=0.6, label='male')\n",
    "female = plt.hist(data[data['Sex'] == 'female']['Age'].dropna(), bins=bins, alpha=0.6, label='female')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Age by gender')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если нужно сравнить данные по процентому соотношению, можно задать параметр normed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GDOGqq67igQce2FVvwIABXHPNNfTv35/zzz+ft956i6985SsMHjyY448/nuOPP55nn30W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\nP9+Rys25JFD4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d893690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 20\n",
    "index = np.arange(bins)\n",
    "male = plt.hist(data[data['Sex'] == 'male']['Age'].dropna(), bins=bins, alpha=0.6, normed=True, label='male')\n",
    "male = plt.hist(data[data['Sex'] == 'female']['Age'].dropna(), bins=bins, alpha=0.6, normed=True, label='female')\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Age by gender')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Не забывайте, что порой играет роль как мы смотрим на данные."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://pbs.twimg.com/media/Bpx6cTlCMAE38VL.jpg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}