2016-fall/seminars/sem01-tools.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Машинное обучение, ФКН ВШЭ\n",
    "\n",
    "# Семинар 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Знакомство с jupyter\n",
    "\n",
    "Документ jupyter notebook имеет расширение .ipynb, состоит из множества ячеек. В ячейках можно писать программный код, делать размеченные и неразмеченные заметки. Этим функциям соответствует три типа ячеек:\n",
    "    \n",
    "    1. code\n",
    "    2. markdown\n",
    "    3. raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для работы с содержимым ячейки используется *режим редактирования* (*Edit mode*, включается нажатием клавиши **Enter** после выбора ячейки), а для навигации между ячейками искользуется *командный режим* (*Command mode*, включается нажатием клавиши **Esc**). \n",
    "\n",
    "Тип ячейки можно задать в командном режиме либо с помощью горячих клавиш (**y** to code, **m** to markdown, **r** to edit raw text), либо в меню *Cell -> Cell type*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ячейка с кодом\n",
    "a = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ячейка с размеченным текстом"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Ячейка с неразмеченным текстом"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "После заполнения ячейки нужно нажать *Shift + Enter*, эта команда обработает содержимое ячейки: проинтерпретирует код или сверстает размеченный текст.\n",
    "\n",
    "Ячейки с программным кодом разделяют область видимости:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Пара слов о вёрстке \n",
    "[Здесь](https://athena.brynmawr.edu/jupyter/hub/dblank/public/Jupyter%20Notebook%20Users%20Manual.ipynb) находится <s>не</s>большая заметка о используемом языке разметки Markdown. Он позволяет:\n",
    "\n",
    "0. Составлять упорядоченные списки\n",
    "1. #Делать \n",
    "##заголовки \n",
    "###разного уровня\n",
    "3. Выделять *текст* <s>при</s> **необходимости**\n",
    "4. Добавлять [ссылки](http://imgs.xkcd.com/comics/the_universal_label.png)\n",
    "\n",
    "\n",
    "* Составлять неупорядоченные списки\n",
    "\n",
    "Делать вставки с помощью LaTex:\n",
    "    \n",
    "$\n",
    "\\left\\{\n",
    "\\begin{array}{ll}\n",
    "x = 16 \\sin^3 (t) \\\\ \n",
    "y = 13 \\cos (t) - 5 \\cos (2t) - 2 \\cos (3t) - \\cos (4t) \\\\\n",
    "t \\in [0, 2 \\pi]\n",
    "\\end{array}\n",
    "\\right.$\n",
    "\n",
    "Можно также вставлять изображения:\n",
    "<img src = \"http://cs625727.vk.me/v625727306/3e600/rEqFs3UK3-s.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy\n",
    "\n",
    "**NumPy** — библиотека языка Python, позволяющая [удобно] работать с многомерными массивами и матрицами, содержащая математические функции. Кроме того, NumPy позволяет векторизовать многие вычисления, имеющие место в машинном обучении. \n",
    "\n",
    " - [numpy](http://www.numpy.org)\n",
    " - [numpy tutorial](http://cs231n.github.io/python-numpy-tutorial/)\n",
    " - [100 numpy exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Основным типом данных NumPy является многомерный массив элементов одного типа — [numpy.ndarray](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.array.html). Каждый подобный массив имеет несколько *измерений* или *осей* — в частности, вектор (в классическом понимании) является одномерным массивом и имеет 1 ось, матрица является двумерным массивом и имеет 2 оси и т.д."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec = np.array([1, 2, 3])\n",
    "vec.ndim # количество осей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "mat.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы узнать длину массива по каждой из осей, можно воспользоваться атрибутом shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы узнать тип элементов и их размер в байтах:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'int64'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.dtype.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.itemsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Создание массивов\n",
    "\n",
    "* Передать итерируемый объект в качестве параметра функции array (можно также явно указать тип элементов):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([1, 2, 3])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([1, 2, 3], dtype = float)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = np.array([(1, 2, 3), (4, 5, 6)])\n",
    "B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Создание массивов специального вида при помощи функций zeros, ones, empty, identity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.72723371e-077,   2.68679363e+154,   2.22586790e-314,\n",
       "          2.22594686e-314,   2.22594008e-314],\n",
       "       [  2.22598140e-314,   2.22236417e-314,   0.00000000e+000,\n",
       "          2.22024022e-314,   6.95337803e-309]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((2, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обратите внимание, что содержимое массива, созданного при помощи функции empty, **не инициализируется**, то есть в качестве значений он **может содержать \"мусор\"**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Создание последовательностей при помощи функций arange (в качестве парметров принимает левую и правую границы последовательности и **шаг**) и linspace (принимает левую и правую границы и **количество элементов**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  5,  8, 11, 14, 17])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(2, 20, 3) # аналогично стандартной функции range python, правая граница не включается"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.5,  3.4,  4.3,  5.2,  6.1,  7. ,  7.9])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(2.5, 8.7, 0.9) # но может работать и с вещественными числами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.        ,   3.23076923,   4.46153846,   5.69230769,\n",
       "         6.92307692,   8.15384615,   9.38461538,  10.61538462,\n",
       "        11.84615385,  13.07692308,  14.30769231,  15.53846154,\n",
       "        16.76923077,  18.        ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(2, 18, 14) # правая граница включается (по умолчанию)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Для изменения размеров существующего массива можно воспользоваться функцией reshape (при этом количество элементов должно оставаться неизменным):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(9).reshape(3, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вместо значения длины массива по одному из измерений можно указать -1 — в этом случае значение будет рассчитано автоматически:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(8).reshape(2, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Транспонирование существующего массива:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = np.arange(6).reshape(2, -1)\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 3],\n",
       "       [1, 4],\n",
       "       [2, 5]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Объединение существующих массивов по заданной оси:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  0,  1,  4],\n",
       "       [ 3,  4,  5,  9, 16, 25]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(6).reshape(2, -1)\n",
    "np.hstack((A, A**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 0,  1,  4],\n",
       "       [ 9, 16, 25]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((A, A**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  0,  1,  4],\n",
       "       [ 3,  4,  5,  9, 16, 25]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((A, A**2), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Повторение существующего массива"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 0, 1, 2],\n",
       "       [0, 1, 2, 0, 1, 2]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(3)\n",
    "np.tile(a, (2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [0, 1, 2],\n",
       "       [0, 1, 2],\n",
       "       [0, 1, 2]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(a, (4, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Базовые операции\n",
    "\n",
    "* Базовые арифметические операции над массивами выполняются поэлементно:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A = np.arange(9).reshape(3, 3)\n",
    "B = np.arange(1, 10).reshape(3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n",
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "print A\n",
    "print B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  3,  5],\n",
       "       [ 7,  9, 11],\n",
       "       [13, 15, 17]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.5       ,  0.66666667],\n",
       "       [ 0.75      ,  0.8       ,  0.83333333],\n",
       "       [ 0.85714286,  0.875     ,  0.88888889]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * 1.0 / B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  6],\n",
       "       [ 9, 12, 15],\n",
       "       [18, 21, 24]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 * A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  4],\n",
       "       [ 9, 16, 25],\n",
       "       [36, 49, 64]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отдельно обратим внимание на то, что умножение массивов также является **поэлементным**, а не матричным:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  6],\n",
       "       [12, 20, 30],\n",
       "       [42, 56, 72]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для выполнения матричного умножения необходимо использовать функцию dot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 18,  21,  24],\n",
       "       [ 54,  66,  78],\n",
       "       [ 90, 111, 132]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поскольку операции выполняются поэлементно, операнды бинарных операций должны иметь одинаковый размер. Тем не менее, операция может быть корректно выполнена, если размеры операндов таковы, что они могут быть расширены до одинаковых размеров. Данная возможность называется [broadcasting](http://www.scipy-lectures.org/intro/numpy/operations.html#broadcasting):\n",
    "<img src = \"http://www.scipy-lectures.org/_images/numpy_broadcasting.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [10, 11, 12],\n",
       "       [20, 21, 22],\n",
       "       [30, 31, 32]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(np.arange(0, 40, 10), (3, 1)).T + np.array([0, 1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Универсальные функции (sin, cos, exp и т.д.) также применяются поэлементно:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00000000e+00,   2.71828183e+00,   7.38905610e+00],\n",
       "       [  2.00855369e+01,   5.45981500e+01,   1.48413159e+02],\n",
       "       [  4.03428793e+02,   1.09663316e+03,   2.98095799e+03]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Некоторые операции над массивами (например, вычисления минимума, максимума, суммы элементов) выполняются над всеми элементами вне зависимости от формы массива, однако при указании оси выполняются вдоль нее (например, для нахождения максимума каждой строки или каждого столбца):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7, 8])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.max(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 12, 21])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.sum(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Индексация\n",
    "\n",
    "Для доступа к элементам может использоваться [много различных способов](http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html), рассмотрим основные.\n",
    "\n",
    "* Для индексации могут использоваться конкретные значения индексов и срезы (slice), как и в стандартных типах Python. Для многомерных массивов индексы для различных осей разделяются запятой. Если для многомерного массива указаны индексы не для всех измерений, недостающие заполняются полным срезом (:)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 7])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3:8:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "       [27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
       "       [36, 37, 38, 39, 40, 41, 42, 43, 44],\n",
       "       [45, 46, 47, 48, 49, 50, 51, 52, 53],\n",
       "       [54, 55, 56, 57, 58, 59, 60, 61, 62],\n",
       "       [63, 64, 65, 66, 67, 68, 69, 70, 71],\n",
       "       [72, 73, 74, 75, 76, 77, 78, 79, 80]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(81).reshape(9, -1)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "       [27, 28, 29, 30, 31, 32, 33, 34, 35]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3],\n",
       "       [11, 12],\n",
       "       [20, 21],\n",
       "       [29, 30],\n",
       "       [38, 39],\n",
       "       [47, 48],\n",
       "       [56, 57],\n",
       "       [65, 66],\n",
       "       [74, 75]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[:, 2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 21],\n",
       "       [29, 30]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[2:4, 2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([72, 73, 74, 75, 76, 77, 78, 79, 80])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Также может использоваться индексация при помощи списков индексов (по каждой из осей):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "       [27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
       "       [36, 37, 38, 39, 40, 41, 42, 43, 44],\n",
       "       [45, 46, 47, 48, 49, 50, 51, 52, 53],\n",
       "       [54, 55, 56, 57, 58, 59, 60, 61, 62],\n",
       "       [63, 64, 65, 66, 67, 68, 69, 70, 71],\n",
       "       [72, 73, 74, 75, 76, 77, 78, 79, 80]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(81).reshape(9, -1)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18, 37, 48])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[[2, 4, 5], [0, 1, 3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Может также применяться логическая индексация (при помощи логических массивов):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(11)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  4,  5,  6,  7,  9, 10])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[A % 5 != 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  4,  5,  6,  9, 10])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[np.logical_and(A != 7, A % 5 != 3)] # также можно использовать логические операции"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Примеры"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11],\n",
       "       [ 12,  13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23],\n",
       "       [ 24,  25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35],\n",
       "       [ 36,  37,  38,  39,  40,  41,  42,  43,  44,  45,  46,  47],\n",
       "       [ 48,  49,  50,  51,  52,  53,  54,  55,  56,  57,  58,  59],\n",
       "       [ 60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71],\n",
       "       [ 72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83],\n",
       "       [ 84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95],\n",
       "       [ 96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107],\n",
       "       [108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(120).reshape(10, -1)\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Выбрать все четные строки матрицы A.\n",
    "2. Составить одномерный массив из всех не делящихся на 3 элементов нечетных столбцов А.\n",
    "3. Посчитать сумму диагональных элементов A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Зачем?\n",
    "\n",
    "Зачем необходимо использовать NumPy, если существуют стандартные списки/кортежи и циклы?\n",
    "\n",
    "Причина заключается в скорости работы. Попробуем посчитать сумму поэлементых произведений 2 больших векторов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "A_quick_arr = np.random.normal(size = (1000000,))\n",
    "B_quick_arr = np.random.normal(size = (1000000,))\n",
    "\n",
    "A_slow_list, B_slow_list = list(A_quick_arr), list(B_quick_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.310231\n"
     ]
    }
   ],
   "source": [
    "start = time.clock()\n",
    "ans = 0\n",
    "for i in xrange(len(A_slow_list)):\n",
    "    ans += A_slow_list[i] * B_slow_list[i]\n",
    "print(time.clock() - start) # время выполнения в секундах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.322319\n"
     ]
    }
   ],
   "source": [
    "start = time.clock()\n",
    "ans = sum([A_slow_list[i] * B_slow_list[i] for i in range(1000000)])\n",
    "print(time.clock() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.005845\n"
     ]
    }
   ],
   "source": [
    "start = time.clock()\n",
    "ans = np.sum(A_quick_arr * B_quick_arr)\n",
    "print(time.clock() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.003035\n"
     ]
    }
   ],
   "source": [
    "start = time.clock()\n",
    "ans = A_quick_arr.dot(B_quick_arr)\n",
    "print(time.clock() - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "\n",
    "<img src = \"http://bconnelly.net/assets/img/pandas1.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pandas** (Python Data Analysis Library) — библиотека языка Python для [удобных] обработки и анализа данных.\n",
    "\n",
    "\n",
    "Рассмотрим данную библиотеку на примере [данных](https://www.kaggle.com/c/titanic/data) [соревнования](https://www.kaggle.com/c/titanic) о предсказании судьбы пассажиров лайнера \"Титаник\". Имеется некоторая информация о пассажирах, по которой необходимо предсказать выживаемость каждого из них.\n",
    "* Какого типа данная задача?\n",
    "* Что является объектами?\n",
    "* Что является ответами?\n",
    "* Какие могут быть признаки? Какие у них типы?\n",
    "\n",
    "Загрузка данных в **pandas** происходит в специальный объект типа **DataFrame**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Anderson, Mr. Harry</td>\n",
       "      <td>male</td>\n",
       "      <td>48.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19952</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>E12</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
       "      <td>female</td>\n",
       "      <td>63.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>D7</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hudson, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast, NI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
       "      <td>female</td>\n",
       "      <td>53.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11769</td>\n",
       "      <td>51.4792</td>\n",
       "      <td>C101</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bayside, Queens, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Artagaveytia, Mr. Ramon</td>\n",
       "      <td>male</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17609</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Montevideo, Uruguay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Astor, Col. John Jacob</td>\n",
       "      <td>male</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17757</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>C62 C64</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124.0</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Astor, Mrs. John Jacob (Madeleine Talmadge Force)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17757</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>C62 C64</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Aubart, Mme. Leontine Pauline</td>\n",
       "      <td>female</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17477</td>\n",
       "      <td>69.3000</td>\n",
       "      <td>B35</td>\n",
       "      <td>C</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paris, France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barber, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19877</td>\n",
       "      <td>78.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
       "      <td>male</td>\n",
       "      <td>80.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27042</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>A23</td>\n",
       "      <td>S</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hessle, Yorks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Baumann, Mr. John D</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17318</td>\n",
       "      <td>25.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Baxter, Mr. Quigg Edmond</td>\n",
       "      <td>male</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Baxter, Mrs. James (Helene DeLaudeniere Chaput)</td>\n",
       "      <td>female</td>\n",
       "      <td>50.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bazzani, Miss. Albina</td>\n",
       "      <td>female</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11813</td>\n",
       "      <td>76.2917</td>\n",
       "      <td>D15</td>\n",
       "      <td>C</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Beattie, Mr. Thomson</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13050</td>\n",
       "      <td>75.2417</td>\n",
       "      <td>C6</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Winnipeg, MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mr. Richard Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>37.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bidois, Miss. Rosalie</td>\n",
       "      <td>female</td>\n",
       "      <td>42.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17757</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bird, Miss. Ellen</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17483</td>\n",
       "      <td>221.7792</td>\n",
       "      <td>C97</td>\n",
       "      <td>S</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Birnbaum, Mr. Jakob</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13905</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.0</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bishop, Mr. Dickinson H</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11967</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>B49</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dowagiac, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bishop, Mrs. Dickinson H (Helen Walton)</td>\n",
       "      <td>female</td>\n",
       "      <td>19.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11967</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>B49</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dowagiac, MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bissette, Miss. Amelia</td>\n",
       "      <td>female</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17760</td>\n",
       "      <td>135.6333</td>\n",
       "      <td>C99</td>\n",
       "      <td>S</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bjornstrom-Steffansson, Mr. Mauritz Hakan</td>\n",
       "      <td>male</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110564</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stockholm, Sweden / Washington, DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Vovk, Mr. Janko</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349252</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Waelens, Mr. Achille</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345767</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Antwerp, Belgium / Stanton, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Warren, Mr. Charles William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 49867</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Webber, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 3101316</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Wenzel, Mr. Linhart</td>\n",
       "      <td>male</td>\n",
       "      <td>32.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345775</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Whabee, Mrs. George Joseph (Shawneene Abi-Saab)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2688</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Widegren, Mr. Carl/Charles Peter</td>\n",
       "      <td>male</td>\n",
       "      <td>51.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347064</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Wiklund, Mr. Jakob Alfred</td>\n",
       "      <td>male</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3101267</td>\n",
       "      <td>6.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Wiklund, Mr. Karl Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3101266</td>\n",
       "      <td>6.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Willer, Mr. Aaron (\"Abi Weller\")</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3410</td>\n",
       "      <td>8.7125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Willey, Mr. Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.O./P.P. 751</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 2466</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Williams, Mr. Leslie</td>\n",
       "      <td>male</td>\n",
       "      <td>28.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54636</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Windelov, Mr. Einar</td>\n",
       "      <td>male</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 3101317</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Wiseman, Mr. Phillippe</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4. 34244</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Wittevrongel, Mr. Camille</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345771</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Yasbeck, Mr. Antoni</td>\n",
       "      <td>male</td>\n",
       "      <td>27.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2659</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n",
       "      <td>female</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2659</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Youseff, Mr. Gerious</td>\n",
       "      <td>male</td>\n",
       "      <td>45.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2628</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Yousif, Mr. Wazli</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2647</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Yousseff, Mr. Gerious</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2627</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zabour, Miss. Hileni</td>\n",
       "      <td>female</td>\n",
       "      <td>14.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zakarian, Mr. Mapriededer</td>\n",
       "      <td>male</td>\n",
       "      <td>26.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2656</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>304.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>male</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2670</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Zimmerman, Mr. Leo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315082</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived                                               name  \\\n",
       "0          1         1                      Allen, Miss. Elisabeth Walton   \n",
       "1          1         1                     Allison, Master. Hudson Trevor   \n",
       "2          1         0                       Allison, Miss. Helen Loraine   \n",
       "3          1         0               Allison, Mr. Hudson Joshua Creighton   \n",
       "4          1         0    Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
       "5          1         1                                Anderson, Mr. Harry   \n",
       "6          1         1                  Andrews, Miss. Kornelia Theodosia   \n",
       "7          1         0                             Andrews, Mr. Thomas Jr   \n",
       "8          1         1      Appleton, Mrs. Edward Dale (Charlotte Lamson)   \n",
       "9          1         0                            Artagaveytia, Mr. Ramon   \n",
       "10         1         0                             Astor, Col. John Jacob   \n",
       "11         1         1  Astor, Mrs. John Jacob (Madeleine Talmadge Force)   \n",
       "12         1         1                      Aubart, Mme. Leontine Pauline   \n",
       "13         1         1                       Barber, Miss. Ellen \"Nellie\"   \n",
       "14         1         1               Barkworth, Mr. Algernon Henry Wilson   \n",
       "15         1         0                                Baumann, Mr. John D   \n",
       "16         1         0                           Baxter, Mr. Quigg Edmond   \n",
       "17         1         1    Baxter, Mrs. James (Helene DeLaudeniere Chaput)   \n",
       "18         1         1                              Bazzani, Miss. Albina   \n",
       "19         1         0                               Beattie, Mr. Thomson   \n",
       "20         1         1                      Beckwith, Mr. Richard Leonard   \n",
       "21         1         1   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)   \n",
       "22         1         1                              Behr, Mr. Karl Howell   \n",
       "23         1         1                              Bidois, Miss. Rosalie   \n",
       "24         1         1                                  Bird, Miss. Ellen   \n",
       "25         1         0                                Birnbaum, Mr. Jakob   \n",
       "26         1         1                            Bishop, Mr. Dickinson H   \n",
       "27         1         1            Bishop, Mrs. Dickinson H (Helen Walton)   \n",
       "28         1         1                             Bissette, Miss. Amelia   \n",
       "29         1         1          Bjornstrom-Steffansson, Mr. Mauritz Hakan   \n",
       "...      ...       ...                                                ...   \n",
       "1279       3         0               Vestrom, Miss. Hulda Amanda Adolfina   \n",
       "1280       3         0                                    Vovk, Mr. Janko   \n",
       "1281       3         0                               Waelens, Mr. Achille   \n",
       "1282       3         0                                Ware, Mr. Frederick   \n",
       "1283       3         0                        Warren, Mr. Charles William   \n",
       "1284       3         0                                  Webber, Mr. James   \n",
       "1285       3         0                                Wenzel, Mr. Linhart   \n",
       "1286       3         1    Whabee, Mrs. George Joseph (Shawneene Abi-Saab)   \n",
       "1287       3         0                   Widegren, Mr. Carl/Charles Peter   \n",
       "1288       3         0                          Wiklund, Mr. Jakob Alfred   \n",
       "1289       3         0                            Wiklund, Mr. Karl Johan   \n",
       "1290       3         1                   Wilkes, Mrs. James (Ellen Needs)   \n",
       "1291       3         0                   Willer, Mr. Aaron (\"Abi Weller\")   \n",
       "1292       3         0                                 Willey, Mr. Edward   \n",
       "1293       3         0                  Williams, Mr. Howard Hugh \"Harry\"   \n",
       "1294       3         0                               Williams, Mr. Leslie   \n",
       "1295       3         0                                Windelov, Mr. Einar   \n",
       "1296       3         0                                   Wirz, Mr. Albert   \n",
       "1297       3         0                             Wiseman, Mr. Phillippe   \n",
       "1298       3         0                          Wittevrongel, Mr. Camille   \n",
       "1299       3         0                                Yasbeck, Mr. Antoni   \n",
       "1300       3         1            Yasbeck, Mrs. Antoni (Selini Alexander)   \n",
       "1301       3         0                               Youseff, Mr. Gerious   \n",
       "1302       3         0                                  Yousif, Mr. Wazli   \n",
       "1303       3         0                              Yousseff, Mr. Gerious   \n",
       "1304       3         0                               Zabour, Miss. Hileni   \n",
       "1305       3         0                              Zabour, Miss. Thamine   \n",
       "1306       3         0                          Zakarian, Mr. Mapriededer   \n",
       "1307       3         0                                Zakarian, Mr. Ortin   \n",
       "1308       3         0                                 Zimmerman, Mr. Leo   \n",
       "\n",
       "         sex    age  sibsp  parch            ticket      fare    cabin  \\\n",
       "0     female  29.00      0      0             24160  211.3375       B5   \n",
       "1       male   0.92      1      2            113781  151.5500  C22 C26   \n",
       "2     female   2.00      1      2            113781  151.5500  C22 C26   \n",
       "3       male  30.00      1      2            113781  151.5500  C22 C26   \n",
       "4     female  25.00      1      2            113781  151.5500  C22 C26   \n",
       "5       male  48.00      0      0             19952   26.5500      E12   \n",
       "6     female  63.00      1      0             13502   77.9583       D7   \n",
       "7       male  39.00      0      0            112050    0.0000      A36   \n",
       "8     female  53.00      2      0             11769   51.4792     C101   \n",
       "9       male  71.00      0      0          PC 17609   49.5042      NaN   \n",
       "10      male  47.00      1      0          PC 17757  227.5250  C62 C64   \n",
       "11    female  18.00      1      0          PC 17757  227.5250  C62 C64   \n",
       "12    female  24.00      0      0          PC 17477   69.3000      B35   \n",
       "13    female  26.00      0      0             19877   78.8500      NaN   \n",
       "14      male  80.00      0      0             27042   30.0000      A23   \n",
       "15      male    NaN      0      0          PC 17318   25.9250      NaN   \n",
       "16      male  24.00      0      1          PC 17558  247.5208  B58 B60   \n",
       "17    female  50.00      0      1          PC 17558  247.5208  B58 B60   \n",
       "18    female  32.00      0      0             11813   76.2917      D15   \n",
       "19      male  36.00      0      0             13050   75.2417       C6   \n",
       "20      male  37.00      1      1             11751   52.5542      D35   \n",
       "21    female  47.00      1      1             11751   52.5542      D35   \n",
       "22      male  26.00      0      0            111369   30.0000     C148   \n",
       "23    female  42.00      0      0          PC 17757  227.5250      NaN   \n",
       "24    female  29.00      0      0          PC 17483  221.7792      C97   \n",
       "25      male  25.00      0      0             13905   26.0000      NaN   \n",
       "26      male  25.00      1      0             11967   91.0792      B49   \n",
       "27    female  19.00      1      0             11967   91.0792      B49   \n",
       "28    female  35.00      0      0          PC 17760  135.6333      C99   \n",
       "29      male  28.00      0      0            110564   26.5500      C52   \n",
       "...      ...    ...    ...    ...               ...       ...      ...   \n",
       "1279  female  14.00      0      0            350406    7.8542      NaN   \n",
       "1280    male  22.00      0      0            349252    7.8958      NaN   \n",
       "1281    male  22.00      0      0            345767    9.0000      NaN   \n",
       "1282    male    NaN      0      0            359309    8.0500      NaN   \n",
       "1283    male    NaN      0      0        C.A. 49867    7.5500      NaN   \n",
       "1284    male    NaN      0      0  SOTON/OQ 3101316    8.0500      NaN   \n",
       "1285    male  32.50      0      0            345775    9.5000      NaN   \n",
       "1286  female  38.00      0      0              2688    7.2292      NaN   \n",
       "1287    male  51.00      0      0            347064    7.7500      NaN   \n",
       "1288    male  18.00      1      0           3101267    6.4958      NaN   \n",
       "1289    male  21.00      1      0           3101266    6.4958      NaN   \n",
       "1290  female  47.00      1      0            363272    7.0000      NaN   \n",
       "1291    male    NaN      0      0              3410    8.7125      NaN   \n",
       "1292    male    NaN      0      0     S.O./P.P. 751    7.5500      NaN   \n",
       "1293    male    NaN      0      0          A/5 2466    8.0500      NaN   \n",
       "1294    male  28.50      0      0             54636   16.1000      NaN   \n",
       "1295    male  21.00      0      0  SOTON/OQ 3101317    7.2500      NaN   \n",
       "1296    male  27.00      0      0            315154    8.6625      NaN   \n",
       "1297    male    NaN      0      0        A/4. 34244    7.2500      NaN   \n",
       "1298    male  36.00      0      0            345771    9.5000      NaN   \n",
       "1299    male  27.00      1      0              2659   14.4542      NaN   \n",
       "1300  female  15.00      1      0              2659   14.4542      NaN   \n",
       "1301    male  45.50      0      0              2628    7.2250      NaN   \n",
       "1302    male    NaN      0      0              2647    7.2250      NaN   \n",
       "1303    male    NaN      0      0              2627   14.4583      NaN   \n",
       "1304  female  14.50      1      0              2665   14.4542      NaN   \n",
       "1305  female    NaN      1      0              2665   14.4542      NaN   \n",
       "1306    male  26.50      0      0              2656    7.2250      NaN   \n",
       "1307    male  27.00      0      0              2670    7.2250      NaN   \n",
       "1308    male  29.00      0      0            315082    7.8750      NaN   \n",
       "\n",
       "     embarked boat   body                           home.dest  \n",
       "0           S    2    NaN                        St Louis, MO  \n",
       "1           S   11    NaN     Montreal, PQ / Chesterville, ON  \n",
       "2           S  NaN    NaN     Montreal, PQ / Chesterville, ON  \n",
       "3           S  NaN  135.0     Montreal, PQ / Chesterville, ON  \n",
       "4           S  NaN    NaN     Montreal, PQ / Chesterville, ON  \n",
       "5           S    3    NaN                        New York, NY  \n",
       "6           S   10    NaN                          Hudson, NY  \n",
       "7           S  NaN    NaN                         Belfast, NI  \n",
       "8           S    D    NaN                 Bayside, Queens, NY  \n",
       "9           C  NaN   22.0                 Montevideo, Uruguay  \n",
       "10          C  NaN  124.0                        New York, NY  \n",
       "11          C    4    NaN                        New York, NY  \n",
       "12          C    9    NaN                       Paris, France  \n",
       "13          S    6    NaN                                 NaN  \n",
       "14          S    B    NaN                       Hessle, Yorks  \n",
       "15          S  NaN    NaN                        New York, NY  \n",
       "16          C  NaN    NaN                        Montreal, PQ  \n",
       "17          C    6    NaN                        Montreal, PQ  \n",
       "18          C    8    NaN                                 NaN  \n",
       "19          C    A    NaN                        Winnipeg, MN  \n",
       "20          S    5    NaN                        New York, NY  \n",
       "21          S    5    NaN                        New York, NY  \n",
       "22          C    5    NaN                        New York, NY  \n",
       "23          C    4    NaN                                 NaN  \n",
       "24          S    8    NaN                                 NaN  \n",
       "25          C  NaN  148.0                   San Francisco, CA  \n",
       "26          C    7    NaN                        Dowagiac, MI  \n",
       "27          C    7    NaN                        Dowagiac, MI  \n",
       "28          S    8    NaN                                 NaN  \n",
       "29          S    D    NaN  Stockholm, Sweden / Washington, DC  \n",
       "...       ...  ...    ...                                 ...  \n",
       "1279        S  NaN    NaN                                 NaN  \n",
       "1280        S  NaN    NaN                                 NaN  \n",
       "1281        S  NaN    NaN      Antwerp, Belgium / Stanton, OH  \n",
       "1282        S  NaN    NaN                                 NaN  \n",
       "1283        S  NaN    NaN                                 NaN  \n",
       "1284        S  NaN    NaN                                 NaN  \n",
       "1285        S  NaN  298.0                                 NaN  \n",
       "1286        C    C    NaN                                 NaN  \n",
       "1287        S  NaN    NaN                                 NaN  \n",
       "1288        S  NaN  314.0                                 NaN  \n",
       "1289        S  NaN    NaN                                 NaN  \n",
       "1290        S  NaN    NaN                                 NaN  \n",
       "1291        S  NaN    NaN                                 NaN  \n",
       "1292        S  NaN    NaN                                 NaN  \n",
       "1293        S  NaN    NaN                                 NaN  \n",
       "1294        S  NaN   14.0                                 NaN  \n",
       "1295        S  NaN    NaN                                 NaN  \n",
       "1296        S  NaN  131.0                                 NaN  \n",
       "1297        S  NaN    NaN                                 NaN  \n",
       "1298        S  NaN    NaN                                 NaN  \n",
       "1299        C    C    NaN                                 NaN  \n",
       "1300        C  NaN    NaN                                 NaN  \n",
       "1301        C  NaN  312.0                                 NaN  \n",
       "1302        C  NaN    NaN                                 NaN  \n",
       "1303        C  NaN    NaN                                 NaN  \n",
       "1304        C  NaN  328.0                                 NaN  \n",
       "1305        C  NaN    NaN                                 NaN  \n",
       "1306        C  NaN  304.0                                 NaN  \n",
       "1307        C  NaN    NaN                                 NaN  \n",
       "1308        S  NaN    NaN                                 NaN  \n",
       "\n",
       "[1309 rows x 14 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data = pd.read_csv('titanic.csv')\n",
    "pass_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно, данные представляют из себя таблицу, где строка — объект, столбец — признак. Для экономии места можно выводить заданное количество первых строк объекта при помощи метода head():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                            name     sex    age  sibsp  \\\n",
       "0       1         1   Allen, Miss. Elisabeth Walton  female  29.00      0   \n",
       "1       1         1  Allison, Master. Hudson Trevor    male   0.92      1   \n",
       "2       1         0    Allison, Miss. Helen Loraine  female   2.00      1   \n",
       "\n",
       "   parch  ticket      fare    cabin embarked boat  body  \\\n",
       "0      0   24160  211.3375       B5        S    2   NaN   \n",
       "1      2  113781  151.5500  C22 C26        S   11   NaN   \n",
       "2      2  113781  151.5500  C22 C26        S  NaN   NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на признаки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'pclass', u'survived', u'name', u'sex', u'age', u'sibsp', u'parch',\n",
       "       u'ticket', u'fare', u'cabin', u'embarked', u'boat', u'body',\n",
       "       u'home.dest'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По данным можно индексироваться при помощи номеров строк/столбцов или названий признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.55</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.55</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.55</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "    age  sibsp  parch  ticket    fare    cabin embarked boat   body  \\\n",
       "2   2.0      1      2  113781  151.55  C22 C26        S  NaN    NaN   \n",
       "3  30.0      1      2  113781  151.55  C22 C26        S  NaN  135.0   \n",
       "4  25.0      1      2  113781  151.55  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived                                             name\n",
       "1         1                   Allison, Master. Hudson Trevor\n",
       "2         0                     Allison, Miss. Helen Loraine\n",
       "3         0             Allison, Mr. Hudson Joshua Creighton\n",
       "4         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.iloc[1:5, 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      Allen, Miss. Elisabeth Walton\n",
       "1                     Allison, Master. Hudson Trevor\n",
       "2                       Allison, Miss. Helen Loraine\n",
       "3               Allison, Mr. Hudson Joshua Creighton\n",
       "4    Allison, Mrs. Hudson J C (Bessie Waldo Daniels)\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data['name'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              name     sex  parch\n",
       "0                    Allen, Miss. Elisabeth Walton  female      0\n",
       "1                   Allison, Master. Hudson Trevor    male      2\n",
       "2                     Allison, Miss. Helen Loraine  female      2\n",
       "3             Allison, Mr. Hudson Joshua Creighton    male      2\n",
       "4  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female      2"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data[['name', 'sex', 'parch']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также действует и логическая индексация, которая позволяет рассматривать определенные группы объектов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
       "      <td>female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>D7</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hudson, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
       "      <td>female</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11769</td>\n",
       "      <td>51.4792</td>\n",
       "      <td>C101</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bayside, Queens, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "6       1         1                Andrews, Miss. Kornelia Theodosia  female   \n",
       "8       1         1    Appleton, Mrs. Edward Dale (Charlotte Lamson)  female   \n",
       "\n",
       "    age  sibsp  parch  ticket      fare    cabin embarked boat  body  \\\n",
       "0  29.0      0      0   24160  211.3375       B5        S    2   NaN   \n",
       "2   2.0      1      2  113781  151.5500  C22 C26        S  NaN   NaN   \n",
       "4  25.0      1      2  113781  151.5500  C22 C26        S  NaN   NaN   \n",
       "6  63.0      1      0   13502   77.9583       D7        S   10   NaN   \n",
       "8  53.0      2      0   11769   51.4792     C101        S    D   NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  \n",
       "6                       Hudson, NY  \n",
       "8              Bayside, Queens, NY  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data[pass_data['sex'] == 'female'].head() # женщины на борту"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Anderson, Mr. Harry</td>\n",
       "      <td>male</td>\n",
       "      <td>48.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19952</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>E12</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
       "      <td>female</td>\n",
       "      <td>63.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>D7</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hudson, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast, NI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                  name     sex    age  \\\n",
       "1       1         1        Allison, Master. Hudson Trevor    male   0.92   \n",
       "3       1         0  Allison, Mr. Hudson Joshua Creighton    male  30.00   \n",
       "5       1         1                   Anderson, Mr. Harry    male  48.00   \n",
       "6       1         1     Andrews, Miss. Kornelia Theodosia  female  63.00   \n",
       "7       1         0                Andrews, Mr. Thomas Jr    male  39.00   \n",
       "\n",
       "   sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "1      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "3      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "5      0      0   19952   26.5500      E12        S    3    NaN   \n",
       "6      1      0   13502   77.9583       D7        S   10    NaN   \n",
       "7      0      0  112050    0.0000      A36        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "5                     New York, NY  \n",
       "6                       Hudson, NY  \n",
       "7                      Belfast, NI  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# женщины старше 60 и мужчины на борту\n",
    "pass_data[(pass_data['sex'] == 'female') & (pass_data['age'] >= 60) | (pass_data['sex'] == 'male')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Пример\n",
    "Посмотрим, сколько на борту было относительно молодых женщин, путешествующих в одиночку. Скорее всего, довольно мало, потому что в такое длительное путешествие молодых девушек одних не отпустили бы опекающие родственники."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 14)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data[(pass_data.sex == 'female') & \n",
    "          (pass_data.age > 18) & \n",
    "          (pass_data.age < 25) & \n",
    "          (pass_data.sibsp == 0) &\n",
    "          (pass_data.parch == 0)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кроме того, для заданного признака можно построить гистограмму:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f7b47d0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11022b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pass_data.age.hist(bins = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Редактирование DataFrame\n",
    "\n",
    "* Переименование признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     Sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "     age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.00      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.92      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.00      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.rename(columns={'sex': 'Sex'}, inplace=True)\n",
    "pass_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Применение преобразования к существующему признаку. Например, выделим фамилию:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Allen\n",
       "1    Allison\n",
       "2    Allison\n",
       "3    Allison\n",
       "4    Allison\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_last_name(name):\n",
    "    return name.split(',')[0].strip()\n",
    "\n",
    "last_names = pass_data['name'].apply(get_last_name)\n",
    "last_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Добавление признака"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "      <th>Last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "      <td>Allen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "      <td>Allison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "      <td>Allison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "      <td>Allison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "      <td>Allison</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     Sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "     age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.00      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.92      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.00      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest Last_name  \n",
       "0                     St Louis, MO     Allen  \n",
       "1  Montreal, PQ / Chesterville, ON   Allison  \n",
       "2  Montreal, PQ / Chesterville, ON   Allison  \n",
       "3  Montreal, PQ / Chesterville, ON   Allison  \n",
       "4  Montreal, PQ / Chesterville, ON   Allison  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data['Last_name'] = last_names\n",
    "pass_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Удаление признака"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>pclass</th>\n",
       "      <th>survived</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",
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       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
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       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
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       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     Sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "     age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.00      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.92      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.00      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.00      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.drop('Last_name', axis=1, inplace=True)\n",
    "pass_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Работа с пропущенными данными\n",
    "\n",
    "Методы isnull() и notnull() позволяют получить бинарный массив, отражающий отсутствие или наличие данных для каждого из объектов соответственно:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "Name: boat, dtype: bool"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data['boat'].isnull().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>Sex</th>\n",
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       "      <td>St Louis, MO</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Allison, Master. Hudson Trevor</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Anderson, Mr. Harry</td>\n",
       "      <td>male</td>\n",
       "      <td>48.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19952</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>E12</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
       "      <td>female</td>\n",
       "      <td>63.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13502</td>\n",
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       "      <td>D7</td>\n",
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       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hudson, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
       "      <td>female</td>\n",
       "      <td>53.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11769</td>\n",
       "      <td>51.4792</td>\n",
       "      <td>C101</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bayside, Queens, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                           name     Sex  \\\n",
       "0       1         1                  Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                 Allison, Master. Hudson Trevor    male   \n",
       "5       1         1                            Anderson, Mr. Harry    male   \n",
       "6       1         1              Andrews, Miss. Kornelia Theodosia  female   \n",
       "8       1         1  Appleton, Mrs. Edward Dale (Charlotte Lamson)  female   \n",
       "\n",
       "     age  sibsp  parch  ticket      fare    cabin embarked boat  body  \\\n",
       "0  29.00      0      0   24160  211.3375       B5        S    2   NaN   \n",
       "1   0.92      1      2  113781  151.5500  C22 C26        S   11   NaN   \n",
       "5  48.00      0      0   19952   26.5500      E12        S    3   NaN   \n",
       "6  63.00      1      0   13502   77.9583       D7        S   10   NaN   \n",
       "8  53.00      2      0   11769   51.4792     C101        S    D   NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "5                     New York, NY  \n",
       "6                       Hudson, NY  \n",
       "8              Bayside, Queens, NY  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data[pass_data['boat'].notnull()].head() # пассажиры с известным номером шлюпки эвакуации"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Сортировка объектов/признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belfast, NI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Chisholm, Mr. Roderick Robert Crispin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Liverpool, England / Belfast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Harrison, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B94</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ismay, Mr. Joseph Bruce</td>\n",
       "      <td>male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B52 B54 B56</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Liverpool</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass  survived                                   name   Sex   age  \\\n",
       "7         1         0                 Andrews, Mr. Thomas Jr  male  39.0   \n",
       "70        1         0  Chisholm, Mr. Roderick Robert Crispin  male   NaN   \n",
       "125       1         0                       Fry, Mr. Richard  male   NaN   \n",
       "150       1         0                  Harrison, Mr. William  male  40.0   \n",
       "170       1         1                Ismay, Mr. Joseph Bruce  male  49.0   \n",
       "\n",
       "     sibsp  parch  ticket  fare        cabin embarked boat   body  \\\n",
       "7        0      0  112050   0.0          A36        S  NaN    NaN   \n",
       "70       0      0  112051   0.0          NaN        S  NaN    NaN   \n",
       "125      0      0  112058   0.0         B102        S  NaN    NaN   \n",
       "150      0      0  112059   0.0          B94        S  NaN  110.0   \n",
       "170      0      0  112058   0.0  B52 B54 B56        S    C    NaN   \n",
       "\n",
       "                        home.dest  \n",
       "7                     Belfast, NI  \n",
       "70   Liverpool, England / Belfast  \n",
       "125                           NaN  \n",
       "150                           NaN  \n",
       "170                     Liverpool  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.sort_values(by=['pclass', 'fare'], ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austria-Hungary / Germantown, Philadelphia, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mrs. James Warburton Martinez (Charlo...</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germantown, Philadelphia, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Alice Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Winnipeg, MB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass  survived                                               name  \\\n",
       "49        1         1                 Cardeza, Mr. Thomas Drake Martinez   \n",
       "50        1         1  Cardeza, Mrs. James Warburton Martinez (Charlo...   \n",
       "183       1         1                             Lesurer, Mr. Gustave J   \n",
       "302       1         1                                   Ward, Miss. Anna   \n",
       "111       1         1                     Fortune, Miss. Alice Elizabeth   \n",
       "\n",
       "        Sex   age  sibsp  parch    ticket      fare        cabin embarked  \\\n",
       "49     male  36.0      0      1  PC 17755  512.3292  B51 B53 B55        C   \n",
       "50   female  58.0      0      1  PC 17755  512.3292  B51 B53 B55        C   \n",
       "183    male  35.0      0      0  PC 17755  512.3292         B101        C   \n",
       "302  female  35.0      0      0  PC 17755  512.3292          NaN        C   \n",
       "111  female  24.0      3      2     19950  263.0000  C23 C25 C27        S   \n",
       "\n",
       "    boat  body                                       home.dest  \n",
       "49     3   NaN  Austria-Hungary / Germantown, Philadelphia, PA  \n",
       "50     3   NaN                    Germantown, Philadelphia, PA  \n",
       "183    3   NaN                                             NaN  \n",
       "302    3   NaN                                             NaN  \n",
       "111   10   NaN                                    Winnipeg, MB  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.sort_values(by=['pclass', 'fare'], ascending=[True, False]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Группировка данных\n",
    "\n",
    "Группировка при помощи метода groupby позволяет объединять данные в группы по одному или нескольким признакам и считать по ним общую статистику."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x1102bf4d0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.groupby('Sex') # разбиение всех объектов на 2 группы по полу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     pclass\n",
       "female  3         216\n",
       "        1         144\n",
       "        2         106\n",
       "male    3         493\n",
       "        1         179\n",
       "        2         171\n",
       "Name: pclass, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.groupby('Sex')['pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/numpy/lib/function_base.py:3823: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pclass       \n",
       "1       count    323.000000\n",
       "        mean      87.508992\n",
       "        std       80.447178\n",
       "        min        0.000000\n",
       "        25%       30.695800\n",
       "        50%       60.000000\n",
       "        75%      107.662500\n",
       "        max      512.329200\n",
       "2       count    277.000000\n",
       "        mean      21.179196\n",
       "        std       13.607122\n",
       "        min        0.000000\n",
       "        25%       13.000000\n",
       "        50%       15.045800\n",
       "        75%       26.000000\n",
       "        max       73.500000\n",
       "3       count    708.000000\n",
       "        mean      13.302889\n",
       "        std       11.494358\n",
       "        min        0.000000\n",
       "        25%             NaN\n",
       "        50%             NaN\n",
       "        75%             NaN\n",
       "        max       69.550000\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.groupby('pclass')['fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    28.687088\n",
       "male      30.585228\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.groupby('Sex')['age'].mean() # средний возраст для пассажиров каждого из полов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как известно, в первую очередь спасали женщин и детей в порядке повышения номера класса, в связи с этим доля выживших женщин, вероятно, будет выше, чем доля выживших мужчин. Проверим:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.727468\n",
       "male      0.190985\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.groupby('Sex')['survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аналогично для пассажиров различных классов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass\n",
       "1    0.619195\n",
       "2    0.429603\n",
       "3    0.255289\n",
       "Name: survived, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.groupby('pclass')['survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Рассмотренные выше статистические данные могут быть рассмотрены и в виде стандартного DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Sex</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.727468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.190985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  survived\n",
       "0  female  0.727468\n",
       "1    male  0.190985"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pass_data.groupby('Sex', as_index=False)['survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Сохранение данных\n",
    "\n",
    "Также данные можно сохранить в файл:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pass_data.to_csv('titanic_2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* [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)\n",
    "\n",
    "**Matplotlib** — библиотека языка Python, используемая для визуализации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(1, 10, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для построения графиков в matplotlib используются фигуры и заданные для них оси, что довольно удобно в случае, когда необходимо строить несколько графиков или их расположение нестандартное."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1109eaa10>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sUN6lSWXJlkBJUquICCKCmppa+vUbRU3Nwo+vSWp9hkBJUotZdemXqqpqKisH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\ngZIkSWXIEChJklSGDIGSJEll6P8DBX1FL7tASu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110248710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "axes.plot(x, x ** 2, 'r')\n",
    "axes.plot(x, x ** 3, 'b*--')\n",
    "\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title')\n",
    "axes.legend([r'$x^2$', 'x^3'], loc = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x112c5c810>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8av/+9zMvuijz+OOzvNCdr+OP74w/+eT8zpvZ+e/4tNP2P++aNYvz3/c113TO\n9573ZP7gB5lXXNF5v3Xrws99++2Zt922/9htt3XGJWmByvwzp1zVd9PEEXEUnRD4K8B3gEng7Zn5\nwAHHZb/9bs/q1TRgD6cXi9/5Hdof/zitK69k+GMfW5xz9mIqt6sTNv53d7Px09ezkhczy3cZ++13\ncsk5Z8+vE9bPq1B7vY+cK1wlacnMZ5q48i7fXL+A1wNf7Hq/iYN0B+njzuCeK6/MuyH3XHnl8j1v\nV7fqxt++PBs0Ot07Gnnjb//7hXWrynPf/aEP5YmNf75f42dV49V594c+tGidsD1DQ51rMjS0sE5Y\nP3fYMnvbZfvWtzKPPrpzHfZd56OP7owvRC+7d5LUp5hHZ7DycDfnguHXgU93vf+3wCcOctwiXNIl\n0qtg1cvAVoafPZANGvtPt9LIPQsJP708d2bvgpWB7eB6FdqcbpWkn2EY7Ncw2KvwswSh6u5zz80T\nefn+3Ttennefe+7Cwk8ZrG4cOiYbHJureFk2ODZvHDpmeQcrA9vPMrRJ0pKZTxjsx9XEu4CXdL1f\nU479jC1btjz7emRkhJHFXtW5WMrVke0LL2Torx9hpnvDYk6lfe4ahj//+bnft9Wr8+5z4om0br6Z\nvae0IKfYd1/fbHyX1s1/u7D7zMr92C45/XR+ddcu2nybFjB80prOfmwLXbHcq9Wi/boKdd8+cvvu\nkfvUp+CCCxZnZbgrXCWpZyYmJpiYmFjYSeaaHqv+Ao4G/hFoAkPAPcCZBzluURL2Utqze3c24oAO\nXhyXe3bvXpbnzczMb30rb4yjskEjV+2bgo6jFqdb1Y+dMDtskqQKMY/OYN89gSQzfwq8F7gD+AZw\nU2beX21Vi2P4hz9kjKdo8HpW8QoavJ4xfsLwD3+4LM8LwC23cEk+w/Rll3DXjs8wfenbuSSfWZzH\nVPXyEVi9eqJCL5/U4GO7JEk90HdbyxypvtxaZutW2LSJ4rLLaP/Wb9G6/nqGr722M76QacBenRd6\nuwWH23tIkjQn89laxjC4nPQq/BiqJEmqBcNgl74Mg5IkSQswnzDYd/cMSpIkafEYBiVJkmrMMChJ\nklRjhkFJkqQaMwxKkiTVmGFQkiSpxgyDkiRJNWYYlCRJqjHDoCRJUo0ZBiVJkmrMMChJklRjhkFJ\nkqQaMwxKkiTVmGFQkiSpxgyDkiRJNWYYlCRJqjHDoCRJUo0ZBiVJkmrMMChJklRjhkFJkqQaMwxK\nkiTVmGEpkmu/AAAGMUlEQVRQkiSpxgyDkiRJNWYYlCRJqjHDoCRJUo0ZBiVJkmrMMChJklRjhkFJ\nkqQaMwxKkiTVmGFQkiSpxgyDkiRJNWYYlCRJqjHDoCRJUo0ZBiVJkmrMMChJklRjhkFJkqQaMwxK\nkiTVmGFQkiSpxgyDkiRJNVZZGIyIj0bE/RFxT0T8eUSs6vre5ojYWX7/vK7x9RExFRHfjIiPVVO5\nJEnS4KiyM3gH8OrMfA2wE9gMEBFnARcDZwJvAbZFRJSf+RNgY2aeAZwREW9a+rJ1JCYmJqouoda8\n/tXy+lfL618tr3//qSwMZuZdmflM+fYrwJry9QXATZn5dGa26QTFsyPiFOCEzJwsj7seuGgpa9aR\n8y+Dann9q+X1r5bXv1pe//6zXO4ZvAz4Qvn6NODhru/tKsdOAx7pGn+kHJMkSdI8rejlySPiTuDk\n7iEggQ9m5l+Wx3wQmM3M8V7WIkmSpJ8VmVndD494F/Bu4I2Z+VQ5tgnIzNxavr8N+DAwDezIzDPL\n8bcD52bmfzjEuav7xSRJkiqSmfH8Rz2np53Bw4mINwMfAP7VviBY+jxwQ0T8MZ1p4FcAd2dmRsST\nEXE2MAm8E/jEoc4/1wshSZJUR5V1BiNiJzAEPF4OfSUzryi/txnYCMwCV2bmHeX4vwD+DDgW+EJm\nXrnUdUuSJA2SSqeJJUmSVK3lspp40UTEmyPigXJj6quqrqdOImJNRHwpIr4REV+PiPdXXVMdRcRR\nEfEPEfH5qmupm4g4MSJuKTfM/0ZEnFN1TXVSPrDgG+XDCW6IiKGqaxpkETEWEbsjYqprbHVE3BER\nD0bE7RFxYpU1DrJDXP9DPtDjcAYqDEbEUcAngTcBrwYuiYi11VZVK08Dv5eZrwb+JfAer38lrgTu\nq7qImvo4nVtYzgR+Abi/4npqIyKadBYkvjYz19G5J/7t1VY18K6l8//bbpuAuzLzVcCXKB8ooZ44\n2PU/6AM9ns9AhUHgbGBnZk5n5ixwE3BhxTXVRmY+mpn3lK9/SOd/hO4FuYQiYg3wVuAzVddSN+W/\nwH85M68FKDfO/0HFZdXJD4C9wPERsQI4DvhOtSUNtsz8G+B7BwxfCFxXvr4OHw7RMwe7/od5oMdh\nDVoYPHDDajemrkhEtIDXAH9XbSW188d0Vul7M/DSeynwWERcW07TfzoiGlUXVReZ+T3gj4CH6Dys\n4PuZeVe1VdXSSZm5GzoNAuCkiuups8uALx7JgYMWBrUMRMQLgFvprAT/YdX11EVEnA/sLruzUX5p\n6awA1gOfysz1wI/pTJlpCUTEy4DfBZrAqcALIuId1VYl/IdpJboe6HHjkRw/aGFwF/CSrvdryjEt\nkXJ65lbgs5n5uarrqZk3ABdExLeBcWA0Iq6vuKY6eQR4ODP/vnx/K51wqKXxOuDLmflEZv4U+Avg\nlyquqY52R8TJABFxCrCn4npqp3ygx1uBI/7H0KCFwUngFRHRLFeRvZ3OJtZaOn8K3JeZH6+6kLrJ\nzKsz8yWZ+TI6/+1/KTPfWXVddVFOjT0cEWeUQ7+CC3mW0oPA6yPi2IgIOtffBTy9d+AsxOeBd5Wv\n/x1gU6C39rv+XQ/0uOCAB3ocVmVPIOmFzPxpRLyXzmqao4CxzPQvgyUSEW8AfhP4ekR8jc70wNWZ\neVu1lUlL5v10nqC0Evg2cGnF9dRGZt5bdsK/CvwU+Brw6WqrGmwRcSMwArwoIh6i8+jYa4BbIuIy\nOo+Rvbi6CgfbIa7/1XQe6HFn599Ezz3Q47DnctNpSZKk+hq0aWJJkiTNgWFQkiSpxgyDkiRJNWYY\nlCRJqjHDoCRJUo0ZBiVJkmrMMChJklRjhkFJkqQaMwxK0iKIiNdFxL0RMRQRx0fE/4uIs6quS5Ke\nj08gkaRFEhH/BWiUXw9n5taKS5Kk52UYlKRFUj6TeBKYAX4p/QtWUh9wmliSFs8/A14AnAAcW3Et\nknRE7AxK0iKJiM8B48BLgVMz830VlyRJz2tF1QVI0iCIiN8C9mbmTRFxFPDliBjJzImKS5Okw7Iz\nKEmSVGPeMyhJklRjhkFJkqQaMwxKkiTVmGFQkiSpxgyDkiRJNWYYlCRJqjHDoCRJUo0ZBiVJkmrs\n/wO95B+M88fuNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a850d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "axes.scatter(x, x ** 2, color='red', marker='*', s=80)\n",
    "axes.scatter(x, x ** 3)\n",
    "\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib позволяет настраивать многие детали создаваемых графиков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x112d50fd0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HaNkyeOQRuPBC6NYt7Wqk5km1JTCE0CGE8EoI4ZHMz31DCE+EEN4KIcwLIfRp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112c3dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "axes.plot(x, x ** 2, 'r^-', label = r'$y = x^2$', markersize=8, markerfacecolor=\"yellow\", \n",
    "          markeredgewidth=1, markeredgecolor=\"green\")\n",
    "axes.plot(x, x ** 3, 'b*--', label = r'$y = x^3$', alpha = 0.5)\n",
    "\n",
    "axes.set_xlabel('x')\n",
    "axes.set_ylabel('y')\n",
    "axes.set_title('title')\n",
    "axes.legend(loc = 0, fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как было упомянуто, matplotlib предоставляет определенную свободу в расположении подграфиков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1131e3750>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sNpuNGjVqcPLkSQYMGODQLVhu227z9qq32XF+B9tf3U7R3EUddt9CuB2bDQYP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\nCooQQgiHkIIihBDCIf4f34fNJF4COrYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d69a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
    "\n",
    "# main figure\n",
    "axes1.plot(x, x ** 2, 'r')\n",
    "axes1.set_xlabel('x')\n",
    "axes1.set_ylabel('y')\n",
    "axes1.set_title('title')\n",
    "\n",
    "# insert\n",
    "axes2.plot(x ** 2, x, 'g')\n",
    "axes2.set_xlabel('y')\n",
    "axes2.set_ylabel('x')\n",
    "axes2.set_title('insert title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Впрочем, можно использовать и одну из классических конфигураций:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sH2P8ZC5/7vGJUiE89RTsvnvqLH/YYbnTqBY8QldSRZkxA1q1gs03/992rQrl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\ncdWpjlwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132635d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize = (16, 5))\n",
    "\n",
    "for pow_num, ax in enumerate(axes):\n",
    "    ax.plot(x, x ** (pow_num + 1), 'r')\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_title(r'$y = x^' + str(pow_num + 1)+ r'$', fontsize = 18)\n",
    "fig.tight_layout() # борьба с пересечением подграфиков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получившийся график можно сохранить в файл:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig(\"pows.png\", dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib также позволяет строить surface plot, используя значения функции в узлах сетки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "alpha = 0.7\n",
    "phi_ext = 2 * np.pi * 0.5\n",
    "\n",
    "def flux_qubit_potential(phi_m, phi_p):\n",
    "    return 2 + alpha - 2 * np.cos(phi_p) * np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)\n",
    "\n",
    "phi_m = np.linspace(0, 2*np.pi, 100)\n",
    "phi_p = np.linspace(0, 2*np.pi, 100)\n",
    "X,Y = np.meshgrid(phi_p, phi_m)\n",
    "Z = flux_qubit_potential(X, Y).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ihxGd/MY32Pfbvw1lhhRZpsmV559nxcaNXOjvJz4yknnN39HBouuuY46q0jk5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110622190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "fig = plt.figure(figsize=(14,6))\n",
    "\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "p = ax.plot_surface(X, Y, Z, rstride=4, cstride=4, linewidth=0, cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кроме того, можно строить более детализированные гистограммы по сравнению с теми, что мы рассмотрели ранее:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1112ba350>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DPKX0ZErp3vz71cAfgJ2BI4Ar892uBN7XnAolSWoNLRnk1SKiHXgzcAcwOaW0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\n5JIklZhBLklSiRnkkiSV2P8Hb7vUPXR4PfYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bd8190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.0, 0.0, 1.0, 1.0])\n",
    "bins = 20\n",
    "index = np.arange(bins)\n",
    "axes.hist(pass_data[pass_data['Sex'] == 'male']['age'].dropna(), bins=bins, alpha=0.6, label='male')\n",
    "axes.hist(pass_data[pass_data['Sex'] == 'female']['age'].dropna(), bins=bins, alpha=0.6, label='female')\n",
    "\n",
    "axes.legend()\n",
    "axes.set_xlabel('Age', fontsize=18)\n",
    "axes.set_ylabel('Count', fontsize=18)\n",
    "axes.set_title('Age by gender', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заметим также, что Matplotlib позволяет использовать многие другие типы визуализации, подробнее про них можно почитать, например, [здесь](http://matplotlib.org/gallery.html) или [здесь](http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb)."
   ]
  }
 ],
 "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
}