Lecture 11.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Программирование на языке Python для сбора и анализа данных\n",
    "\n",
    "*Текст лекции: Щуров И.В., НИУ ВШЭ*\n",
    "\n",
    "Данный notebook является конспектом лекции по курсу «Программирование на языке Python для сбора и анализа данных» (НИУ ВШЭ, 2015-16). Он распространяется на условиях лицензии [Creative Commons Attribution-Share Alike 4.0](http://creativecommons.org/licenses/by-sa/4.0/). При использовании обязательно упоминание автора курса и аффилиации. При наличии технической возможности необходимо также указать активную гиперссылку на [страницу курса](http://math-info.hse.ru/s15/m). Фрагменты кода, включенные в этот notebook, публикуются как [общественное достояние](http://creativecommons.org/publicdomain/zero/1.0/).\n",
    "\n",
    "Другие материалы курса, включая конспекты и видеозаписи лекций, а также наборы задач, можно найти на [странице курса](http://math-info.hse.ru/s15/m)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Лекция №11: Библиотеки numpy и matplotlib\n",
    "### Библиотека numpy: эффективные массивы\n",
    "Писать программы на Python легко и приятно. Гораздо легче и приятнее, чем на низкоуровневых языках программирования, таких как C или C++. Но, увы, чудес не бывает: за простоту написания кода мы платим скоростью его исполнения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2, 1.2]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = [1.2] * 10000\n",
    "numbers[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы создали список из 10000 чисел. Для простоты они все одинаковые, но Python об этом не знает. Как быстро мы возведём каждое из них в квадрат?\n",
    "\n",
    "Для проверки, с какой скоростью выполняется некоторый фрагмент кода, полезно использовать магическое слово `%%timeit`. Оно говорит, что ячейку нужно выполнить несколько раз и засечь, сколько времени на это ушло."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.39 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "\n",
    "squares = [x**2 for x in numbers]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Больше миллисекунды на один проход! (Кстати, `x*x` будет в два раза быстрее — попробуйте!) Не очень-то быстро, на самом деле. Для «тяжелой» математики, часто возникающей при обработке больших массивов данных, хочется использовать все возможности компьютера.\n",
    "\n",
    "Но не надо отчаиваться: для быстрой работы с числами есть специальные библиотеки, и главная из них — `numpy`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Главный объект, с которым мы будем работать — это `np.array` (на самом деле он называется `np.ndarray`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np_numbers = np.array(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.2,  1.2,  1.2, ...,  1.2,  1.2,  1.2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`np.array` — это специальный тип данных, похожий на список, но содержащий данные только одного типа (в данном случае — только вещественные числа). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_numbers[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np_numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " С математической точки зрения, `np.array` — это что-то, похожее на вектор. Но практически все операции выполняются поэлементно. Например, возведение в квадрат каждого элемента можно реализовать как `np_numbers**2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.44,  1.44,  1.44, ...,  1.44,  1.44,  1.44])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_squares = np_numbers**2\n",
    "np_squares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, как быстро работает эта операция:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 17.01 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "100000 loops, best of 3: 5.04 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "\n",
    "np_squares = np_numbers**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь 5 микросекунд, в 200 раз быстрее! Правда, нас предупреждают, что это может быть последствия кеширования — но в любом случае, работа с массивами чисел с помощью `numpy` происходит гораздо быстрее, чем с помощью обычных списков и циклов.\n",
    "\n",
    "Давайте посмотрим на `np.array` более подробно."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Массивы похожи на списки…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import array\n",
    "# чтобы не писать каждый раз np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "q = array([4, 5, 8, 9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Я дальше буду называть `np.array` массивами (в отличие от списком, которые мы так в Python не называем). Итак, можно обращаться к элементам массива по индексам, как и к спискам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И менять их тоже можно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  5,  8,  9])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q[0] = 12\n",
    "q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно итерировать элементы списка, хотя этого следует по возможности избегать — массивы numpy нужны как раз для того, чтобы не использовать циклы для выполнения массовых операций. Ниже будет понятно, как это можно делать."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n",
      "8\n",
      "14\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for x in q:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно делать срезы (но с ними тоже есть хитрости, об этом ниже)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 14])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### …но не всегда похожи!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте заведём ещё один массив."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w = array([2, 3, 6, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Все арифметические операции над массивами выполняются поэлементно. Поэтому `+` означает сложение, а не конкатенацию. В отличие от обычных списков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 11, 20, 19])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q + w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если вы хотели сделать конкатенацию, то нужно использовать не оператор сложения, а специальную функцию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14,  8, 14,  9,  2,  3,  6, 10])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate( [q, w] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аналогично сложению работают и другие операции. Например, умножение:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28, 24, 84, 90])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q * w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если у массивов будет разная длина, то ничего не получится:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,) (3,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-191-a14671572ef0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mq\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,) (3,) "
     ]
    }
   ],
   "source": [
    "q = np.array([14, 8, 14, 9])\n",
    "w = np.array([1, 3, 4])\n",
    "q + w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно применять различные математические операции к массивам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = array([1,2,3,4,5])\n",
    "y = array([4, 5, 6, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   2.71828183,    7.3890561 ,   20.08553692,   54.59815003,\n",
       "        148.4131591 ])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заметим, что мы должны были использовать функцию `exp` из `numpy`, а не из обычного `math`. Если бы мы взяли эту функцию из `math`, ничего бы не сработало."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "only length-1 arrays can be converted to Python scalars",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-195-655644d1b4c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: only length-1 arrays can be converted to Python scalars"
     ]
    }
   ],
   "source": [
    "math.sqrt(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вообще в `numpy` много математических функций. Вот, например, квадратный корень:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.41421356,  1.73205081,  2.        ,  2.23606798])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Типы элементов в массивах"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вообще, в массивах могут храниться не только числа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mixed_array = np.array([1, 2, 3, \"Hello\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Однако, все элементы, лежащие в одном массиве, должны быть одного типа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3', 'Hello'], \n",
       "      dtype='<U21')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь видно, что числа `1`, `2`, `3` превратились в строчки `'1'`, `'2'`, `'3'`. Параметр `dtype` содержит информацию о типе объектов, хранящихся в массиве. `<U21` означает юникодную строку длиной максимум 21 байт. При попытке записать более длинную строку она будет обрезана."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, World, This is'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed_array[0] = 'Hello, World, This is a Test'\n",
    "mixed_array[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вообще `numpy` при создании массива старается не терять информацию и выбирает самый «вместительный» тип."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  5.])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array([1,2,3, 5.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Все числа превратились в вещественные, поскольку в исходном списке, из которого делается массив, есть вещественное число."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Коварные срезы\n",
    "\n",
    "Давайте посмотрим внимательно на срезы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = array([1.1, 2.2, 3.3, 4.4, 5.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = x[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.2,  3.3])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пока всё идёт как обычно: мы создали срез, начинающийся с элемента с индексом 1 (то есть второй элемент, нумерация с нуля) и заканчивающийся элементом с индексом 3 (последний элемент всегда не включается).\n",
    "\n",
    "Теперь попробуем изменить значение элемента *в срезе*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s[0] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 100. ,    3.3])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как вы думаете, что произойдёт с исходным массивом `x`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Он тоже изменился! Раньше мы видели подобную штуку в ситуациях, когда один список имел несколько имён (то есть несколько переменных на него ссылались), но создание среза раньше приводило к копированию информации. Оказывается, в numpy создание среза ничего не копирует: срез — это не новый массив, содержащий те же элементы, что старый, а так называемый *view* (вид), то есть своего рода интерфейс к старому массиву. Грубо говоря, наш срез `s` просто помнит, что «его» элемент с индексом 0 — это на самом деле элемент с индексом 1 от исходного массива `x`, а его элемент с индексом 1 — это на самом деле элемент с индексом 2 от исходного массива, а других элементов у него нет. Можно думать про срез как про такие специальные очки, через которые мы смотрим на исходный массив.\n",
    "\n",
    "Преимущество и этого подхода два: во-первых, непосвященные сломают голову, пытаясь понять, что тут происходит, а во-вторых если у нас есть огромный массив данных, то нам не придётся тратить ресурсы на то, чтобы его скопировать, если нам нужно сделать срез. Недостатки тоже есть, но они являются продолжением первого из преимуществ.\n",
    "\n",
    "Если вам всё-таки нужно сделать копию массива, нужно использовать метод `copy()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = x.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y[0] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  12. ,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Продвинутая индексация"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Помимо коварных срезов есть ещё некоторые возможности создания новых массивов из старых. Например, можно выбрать из массива элементы с нужными номерами вот так:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = x[ [1, 3, 4] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 100. ,    4.4,    5.5])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно даже использовать одинаковые номера."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = x[ [1, 1, 1] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 100.,  100.,  100.])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кстати, создаваемый таким образом объект уже является честной копией исходного, а не каким-нибудь коварным видом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y[0] = 123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 123.,  100.,  100.])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Есть ещё один хитрый способ выбора элементов из массива. Допустим, мы хотим выбрать только те элементы, которые обладают каким-то свойством — скажем, меньше 50. Можно было бы использовать цикл с условием или аналогичный ему list comprehension, но в `numpy` используют другой синтаксис."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = x[ x < 50 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.1,  3.3,  4.4,  5.5])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как он работает? Очень просто. (Ну ок, не очень.) Для начала, что такое `x < 50`? Это результат применения операции «сравнение с 50» к каждому элементу массива. То есть это новый массив."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x < 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если в каком-то месте стоит `True`, то это означает, что на соответствующем месте в `x` стоит элемент, который удовлетворяет условию, а если `False`, то не удовлетворяет.\n",
    "\n",
    "Теперь можно попробовать подставить массив из `True` и `False` в качестве индекса в `x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    5.5])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[ array([True, True, False, False, True]) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Эта штука выбирает ровно те элементы, на чьих местах стоит `True` — то есть ровно те, для которых выполнялось условие. То, что нам нужно!\n",
    "\n",
    "А вот так можно проверить несколько условий одновременно:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.3,  4.4,  5.5])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[ (x < 50) & (x > 2) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Скобочки очень важны, иначе ничего не заработает. Операция `&` соответствует логическому И и опять же выполняется поэлементно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x < 50) & (x > 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для логического ИЛИ мы бы исползовали `|`, а для отрицания `~`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x < 50) | (x > 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~ (x < 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Результатом такого выбора снова является *вид*, и это очень удобно, потому что позволяет заменять одни элементы на другие в зависимости от условий и таким образом избавляться от операторов `if`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.1,  100. ,    3.3,    4.4,    5.5])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x[ x>50 ] = 0\n",
    "# заменить все элементы, большие 50, на 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.1,  0. ,  3.3,  4.4,  5.5])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кстати, чтобы узнать, правда ли, что два массива равны (в том числе, что состоят из одних и тех же элементов, находящихся в одном и том же порядке), теперь нельзя использовать `==` — ведь это тоже поэлементная операция!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3]) == np.array([1, 2, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы понять, правда ли, что массивы равны, можно использовать такой синтаксис:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.array([1, 2, 3]) == np.array([1,2,3])).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь мы сначала сравниваем массивы поэлементно, а потом применяем к результату метод `all()`, возвращающий истину только если все элементы являются истиными. Этот подход часто используется, хотя имеет свои подводные камни (см. [на stackoverflow](http://stackoverflow.com/questions/10580676/comparing-two-numpy-arrays-for-equality-element-wise)). Есть и специализированная функция для проверки на равенство:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal(np.array([1, 2, 3]), np.array([1, 2, 3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Построение графиков в matplotlib\n",
    "В Python существует много способов строить графики. Мы сейчас рассмотрим самый простой из них, а позже поговорим про более сложные. Для этого нам потребуется библиотека `matplotlib`, а точнее её часть под названием `pyplot`. Стандартный способ её импорта выглядит вот так:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы графики рисовались прямо в ноутбуке, нужно дать вот такую магическую команду:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Простейшее рисование — это функция `plot`, она принимает на вход список $x$-координат, список $y$-координат и рисует соответствующую картинку либо в виде ломаной:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10ff2ef60>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F0iRYfpkyyyuSJsnyyxQ89BBce20vK//hDy2vSKoeM/UB9pZXNm2C+XnLK5KmY+rll4hY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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fee97f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "либо в виде отдельный точек:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1112d2550>]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1111d4860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Либо ещё кучей способов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111394470>]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vcBBwKPDlzDxr1nGmXwpsagpuvhk+9zm48UY4+eRptm1bw09+8sfpl2XL1rB163rGxky/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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112f5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], '-o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, как `numpy` работает в связке с `matplotlib.pyplot`. Вообще это всё очень похое на *MATLAB*, и если вы знаете *MATLAB*, то для вас многое здесь будет знакомо — и наоборот, после `numpy` и `matplotlib.pyplot` будете чувствовать себя как дома в *MATLAB*.\n",
    "\n",
    "> Chewie, we're home. [Здесь должна была быть картинка из «Звёздных войн», но я не могу её включить по соображениям авторских прав.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-5, 5, 200)\n",
    "# это массив из 200 элементов, состоящий из равномерно разбросанных чисел от -5 до 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.        , -4.94974874, -4.89949749, -4.84924623, -4.79899497,\n",
       "       -4.74874372, -4.69849246, -4.64824121, -4.59798995, -4.54773869])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вот так можно нарисовать параболу:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1115d62b0>]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SpTBggF+c1LgxTJjge74/+8z3exfivHchfe9yIe7PL11V2WqnIbBjV+8qfGEX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\nCFMRFxGJMBVxEZEIUxEXEYmw/weSGV9p1kuYiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111540550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Действительно, `x**2` — это массив, элементами которого являются квадраты чисел, лежащих в `x`. Значит, построив график, состоящий из точек, $x$-координаты которых записаны в `x`, а $y$-координаты с `x**2`, мы построим график функции $y=x^2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А вот, например, синусоида:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111758710>]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HTgJPPgnt28Mxx4ROIknx/e/DH/8IW7eGThKGCr7k7PLLfbHdsCFsjjvuUOte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\nRDJCBV9EJCP+Fwl+1H5x90rFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11166f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А вот что-то посложнее:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11180f7b8>]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JG5FO3g4doFhbZhaHL1m0BVTwWxYbkY4rh5+niAbUV/AB4AMfAF55BfjZz9L/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\naoIKvqIoSk1QwVcURakJKviKoig14f8Dll7LimMomQgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111768b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вот так можно построить несколько графиков и сделать подписи."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x111b369b0>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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INhu8+67ZB2DLlvvvB5yWEetHcDLyJCG9Q1w6AdhLkoAFqlY121W2bQsnTphPLLlzWx2V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\n1RJQsnjxmJMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111816898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1,1,201)\n",
    "plt.plot(x,x**2, label = '$y = x^2$')\n",
    "plt.plot(x,x**3, label = '$y = x^3$')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Знак `$` в `label` используется для того, чтобы записывать формулы — это делается в [LaTeX](https://en.wikibooks.org/wiki/LaTeX/Mathematics)-нотации и долларами там обозначается начало и конец формулы. (Кстати, в IPython Notebook в ячейках типа `Markdown` тоже можно записывать формулы в LaTeX-нотации.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Конечно, мы могли бы получить `x` и `y` не в результате вычисления значений какой-то функции, а откуда-то извне. Возьмём для примера случайные числа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.random.random(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1118ee668>]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H8PMnhGrEfg0T5DN/nwxtRJ56ksigijqqOs2eQJsfO2xJ9UYLnRbyJow6YymO1YLB1NTB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\n/x+PCv3uCogSnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1116bbb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.random.random(100)\n",
    "plt.plot(x,y, 'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Есть и специализированная функция для создания *scatter plot*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1119a8048>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qqqI+JHz+KrMIV3lVv+3PsoNelkVPs7OzXHPNZ9m9+3mWLXsr119/TSV/50ajQaPRSGZh\nUbPH/ASsAGba3q+jo3IYeKo1/Qz4JfA8cH6P5aWWLbOS56vQYa7aVMGaf3m4Cs9qP5mZmfHR0WMc\nFu//vqOjb8jdMRYCIQeQAw4DngCWAaPANuDkPvOXujgoDwdlP8McsEoCxRD6oiOrfb65P67QPtlF\nnCQQuzjI3V81syuBzTT7Hdzu7jvM7IpWYOs7/yXuOvOsTK0yqlTBWmShi740NlSxJVIn4O4zwDs7\nPvuXHvP+eRLrlGiGObHr4JZBZZGI6vVJ7r//Il5++er9n42OfpJ6/SuprrfsNHZQwjorU8fGpnJX\nmarmfFJUqhjuLs7YQUoCKdBJVqpOx0C2lAQKRgdIGNru2Ujrbli/X29xkkDs1kFJTxS8ddBC8t56\nqKy03bOTRqsy/X79UYDOYtJSptZDRaLtXmz6/dKjJCAiiUq6afHs7Cw/+tF24PxkApSDRb2FSGtC\nxUESUb9OU6G2e+iOXKEk+fjJ5u9WP6insI6bgxGyx3DSU9mTgHt1TwxpGuQkn/V2D5nwy7KPHVy/\nMOOwwhctOqHQ3ykNSgIFVJaDNC/yOMRFqJg6k8/IyLG+fPlZhdzP8vi75lGcJKDHSwagxx9Kmg6u\nRF3La699ga1bX01sP8vysZV6JGQGomaPtCYqcCegq5vk5bGuJVRM3fYvWFnYppq6a14YaiIqVZfH\ncY5CxdQcY+dSXn55/pOrgVeAR2MvO0RTzdAD5JWdkkAAGp0zHXk8WYSIaWJiglNOOZGtW2+l+VC/\nrwLPMzJSp17/WqaxSP6pTiCA+SvE8fFNjI9vyt0Ac1J8ixcvAT4O3AU0963TTvv92PtZnDL6LOsS\nZHAaO0ikhNIczTbKGD5FGF23yDSAnIgcIk8Drp177oVs2XI+83UJ0LwT3rz5rmAxlUmcJKA6AZGS\nymMdieSP6gQSpDJPke7U3j+/VByUEJV5ivSXp+KpslGdQA6ozFNEQomTBFQcJCJSYUoCCVGZp0i6\nVOeWDhUHJUhlniLpUJ1bf8HrBMxsFXATzTuL2939ho6/XwJMtd7+EvgLd+86kEmRk4CIpEN1bv0F\nrRMwsxHgFpp9008BLjazkzpmewp4v7ufBnwOuC3uekXyREUVUlRJdBY7E9jl7rsBzGwjsBrYOT+D\nuz/YNv+DwNIE1iuSC51FFXNza1VUkTANupieJJLAUuDptvfP0EwMvXwUuCeB9YrkQojhlasmj0OF\nl0Wmw0aY2TnA5cDZ/eabnp7e/7pWq1Gr1VKNS0TyT8NgHNBoNGg0GoksK3bFsJmtAKbdfVXr/Tqa\nT7nprBw+lea4tqvc/ck+y1PFcIbUoik+tVyR0IK2DjKzw4DHgA8C/wX8ELjY3Xe0zXM8cC9waUf9\nQLflKQlkJMrJS0mju/ntsmfPi8ArLF68pNLbR/tJtuIkgaSeC7yKZiLYBaxrfXYFMNl6fRvwIvBj\nYCvwwz7LGvLpmhLVsM86zuNzfEPo9cxbbZ8mbYfsEeMZw8EfLH9IQEoCqek8eQ2bBIadv4z6neC0\nfZpCb4cqPpg+ThLQ8wQqolszxk9/+irm5qbU7G4IagmUb2quG0HU7JHWhO4EIlno6qfX1dkwV026\nze9/lavt0xRyO4S+CwkFFQdV2yAHXVIHRxVvtdsttK2rvn3mhdoOSgJKApU0yI4/7NWZTma9VWnb\nFO27VvVuTEmg4ga9+hn0gK7qgSQHK+p+ULTElYQ4SUBDSZdA0p2VNGKjgPaDIonTT0Ctg0pA46qI\nSFS6E5BDaBgEAe0HRRL8oTJJUhLIB3X7Dysv2z8vcUh/SgIiJaIrcBmWkoBIiahCVoYV9PGSIiJS\nXEoCIjlTr08yNjYFbAA2tMZ0mgwdVi7p2c7xqThIJIdUIbsw1Z0coDoBEakc1Z0coDoBEdlPRSQy\nDPUYFimRKo2nX69PMje3Vs/DiEl3AhnTVVr55Ok3PfihN81kMF+3UDbzw6WMj29ifHxTaZNd2nQn\nkKEqXaVVRdl+0zxUSA8Tw8TERGG3dW5EHX40rYkSDyVd1QdelFneftM4wz/nYejoPMRQROgZwyIC\n8UaUzcPzk/MQQ9UoCWRIFVnlk8ffVEUkMpSotxBpTZS4OMi9mk89Kruy/KZ5KIrJQwxFROgni5nZ\nKuAmmq2Nbnf3G7rMczNwHvBr4DJ339ZjWZ5ETCIyvKJVDEtT0B7DZjYCPA58EHgOeBi4yN13ts1z\nHnClu/+Rmb0P+KK7r+ixPCUBEZEhhO4xfCawy913u/s+YCOwumOe1cCXAdz9IeBoM1uSwLpFRCSG\nJJLAUuDptvfPtD7rN8+zXeYREZGM5bJ10PT09P7XtVqNWq0WLBYRkbxpNBo0Go1ElpVEncAKYNrd\nV7Xer6NZU31D2zy3Ave5+zda73cCK939hS7LU52AiMgQQtcJPAy83cyWmdkocBGwqWOeTcBHYH/S\neKlbAhARkWzFLg5y91fN7EpgMweaiO4wsyuaf/b17v5dM/uQmT1Bs4no5XHXKyIi8emhMiIiBRe6\nOEhERApKSUBEpMKUBEREKkxJQESkwpQEREQqTElARKTClARERCpMSUBEpMKUBEREKkxJQESkwpQE\nREQqTElARKTClARERCpMSUBEpMKUBEREKkxJQESkwpQEREQqTElARKTClARERCpMSUBEpMKUBERE\nKkxJQESkwmIlATM71sw2m9ljZjZrZkd3meetZvZ9M/upmT1qZn8dZ50iIpKcuHcC64Dvufs7ge8D\n13SZ5xXgb939FOAPgL8ys5NirjeXGo1G6BBiUfxhKf6wih5/VHGTwGpgQ+v1BuDDnTO4+/Puvq31\n+lfADmBpzPXmUtF3IsUfluIPq+jxRxU3CbzR3V+A5skeeGO/mc3st4HTgYdirldERBJw+EIzmNkW\nYEn7R4ADf99ldu+znCOBbwKfaN0RiIhIYObe87y98D+b7QBq7v6Cmb0JuM/dT+4y3+HAd4B73P2L\nCywzekAiIhXl7hbl/xa8E1jAJuAy4AZgLfCtHvN9CfjPhRIARP8iIiIyvLh3AouAfwPeBuwG/sTd\nXzKzNwO3ufsfm9lZwA+AR2kWFznwd+4+Ezt6ERGJJVYSEBGRYgvaY7ionc3MbJWZ7TSzx81sqsc8\nN5vZLjPbZmanZx1jPwvFb2aXmNn21jRnZu8KEWcvg2z/1nzvNbN9ZrYmy/gWMuD+UzOzrWb2EzO7\nL+sYexlg3znOzO5p7fePmtllAcLsycxuN7MXzOyRPvPk+djtG3+kY9fdg0006xI+1Xo9BXy+yzxv\nAk5vvT4SeAw4KWDMI8ATwDLgCGBbZzzAecC/t16/D3gw5HaOEP8K4OjW61VFi79tvntpNkhYEzru\nIbf/0cBPgaWt94tDxz1E7NcC18/HDbwIHB469rb4zqbZTP2RHn/P7bE7YPxDH7uhxw4qYmezM4Fd\n7r7b3fcBG2l+j3argS8DuPtDwNFmtoR8WDB+d3/Q3f+39fZB8tW5b5DtD3AVzSbJ/51lcAMYJP5L\ngLvc/VkAd9+TcYy9DBL788BRrddHAS+6+ysZxtiXu88Bv+gzS56P3QXjj3Lshk4CRexsthR4uu39\nMxy6oTvnebbLPKEMEn+7jwL3pBrRcBaM38zeAnzY3f+ZZr+WPBlk+58ILDKz+8zsYTO7NLPo+hsk\n9tuAU8zsOWA78ImMYktKno/dYQ107MZtIrogdTYrLjM7B7ic5i1okdxEs3hxXt4SwUIOB84APgC8\nHnjAzB5w9yfChjWQa4Dt7n6OmZ0AbDGzU3XMZmuYYzf1JODu473+1qrgWOIHOpt1vXVvdTb7JvAV\nd+/VFyErzwLHt71/a+uzznnetsA8oQwSP2Z2KrAeWOXu/W6fszZI/O8BNpqZ0SyXPs/M9rn7poxi\n7GeQ+J8B9rj7b4DfmNkPgNNolseHNEjsZwHXAbj7k2b2M+Ak4D8yiTC+PB+7Axn22A1dHDTf2QwS\n6myWgYeBt5vZMjMbBS6i+T3abQI+AmBmK4CX5ou9cmDB+M3seOAu4FJ3fzJAjP0sGL+7/25r+h2a\nFw9/mZMEAIPtP98Czjazw8zsdTQrKHdkHGc3g8S+A/hDgFZZ+onAU5lGuTCj991hno/deT3jj3Ts\nBq7pXgR8j2aLn83AMa3P3wx8p/X6LOBVmi0RtgI/ppnhQsa9qhXzLmBd67MrgMm2eW6heeW2HTgj\nZLzDxk+zXPfF1rbeCvwwdMzDbv+2eb9EjloHDbH/XE2zhdAjwFWhYx5i31kMfLu13z8CXBw65o74\nvw48B/wf8HOaRSZFOnb7xh/l2FVnMRGRCgtdHCQiIgEpCYiIVJiSgIhIhSkJiIhUmJKAiEiFKQmI\niFSYkoCISIUpCYiIVNj/A8CsRKSu03zuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118c1940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ещё можно построить [гистограмму](https://ru.wikipedia.org/wiki/%D0%93%D0%B8%D1%81%D1%82%D0%BE%D0%B3%D1%80%D0%B0%D0%BC%D0%BC%D0%B0#.D0.92_.D1.81.D1.82.D0.B0.D1.82.D0.B8.D1.81.D1.82.D0.B8.D0.BA.D0.B5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 10.,   9.,   8.,   8.,   7.,  11.,   9.,  12.,  12.,  14.]),\n",
       " array([ 0.00350726,  0.10110942,  0.19871157,  0.29631372,  0.39391588,\n",
       "         0.49151803,  0.58912018,  0.68672234,  0.78432449,  0.88192664,\n",
       "         0.9795288 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yy2eAn05yVpKTgIuB9W/MG4FL4dGrUfdV1WpP4y+STeciydOADwGvq6qvDZBxHjadh6ra\nMd6eztq6+hsaLHSY7P3xYeBFSbYmOYW1fxRr8bqPSebibuClAOP143OAr8815XyFjX9Lnbo3ezlT\nrw0uREryW2vfrqur6iNJXpHkq8AB1pYdmjPJXAB/BJwG/PX4LPVQVT1vuNT9m3Ae/s9L5h5yTiZ8\nf3w5yUeBvcAjwNVVddeAsU+ICX8u3glcm2QPa2X3B1X1P8OlPnGSXA+sAE9Och9rn/w5iQ696cVH\nktQQ/zs7SWqIpS5JDbHUJakhlrokNcRSl6SGWOqS1BBLXZIaYqlLUkP+F6GMfF3Kb4QmAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111768da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно строить трёхмерные картинки, но тут уже нужна магия и я не буду вдаваться в детали."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b6d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X21kUtlSmz+eLfY/12VVjo8GRLlRGWN1+MHPmGDWsli1b1sgNvbNfopNsnc0s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5zb+uabbygsLOTGG29k/vz5bL/99nTr1o3u3bvTuXNnHMeJ6wRLxqdVJRLnYG3M39bF2JRC2aa2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\nKTx/xb8BrANQSv0CtPQknisEwP8BOIV0I+dBi6QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b6d668>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = np.mgrid[-1:1:0.01, -1:1:0.01]\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_surface(x,y,x**2+y**2)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наконец, можно строить интерактивные картинки!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed\n",
    "import ipywidgets as widgets\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_pic(a, b):\n",
    "    x = np.linspace(-3,3,200)\n",
    "    plt.plot(x, np.sin(x*a+b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_pic>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lItQxx8C99/qPDRtg8mT/IvD88zB/vm/HHHwwVKjg/yxTxi91sHWrv0lq+XJf\n9I880vfiTz8dbrkFTjih4BcKEZF0Zs65kj+J2UTgDufcJ3l8rz7Q3TnXJPdxZ8A553rl81wlDyQi\nkmGcc4U2peM53s3vZDOBY8ysKrAKuAK4Mr8niSW0iIgUXUmnZTY3sxVAfWCsmf039+t/NrOxAM65\nnUAnYDwwH3jFObewZLFFRKSo4tLSERGR1JdySyuY2QNm9qmZzTWz98zssNCZ4snMHjWzhbm/3ygz\n2z90pniK9Wa8KEnlGwfjwcyGmNl3ZvZZ6CzxZmaHmdkHZjbfzD43s5tDZ4onMytrZtPNbE7u79iz\nwONTbYRvZvs65zbmfn4TUMs51y5wrLgxs8bAB865HDN7BH8B+97QueLFzI4DcoBBwJ15XciPktwb\nB5cAjYBv8dekrnDOLQoaLI7M7ExgIzDUOXdS6DzxZGaVgcrOublmti8wG2iWZn9/+zjnNpnZXsBH\n+Ak0H+V1bMqN8HcV+1zlge9DZUkE59x7zrmc3IfTgLR6BxPjzXhRUuQbB6PGOTcF+Cl0jkRwzq12\nzs3N/XwjsBB/b1DacM7tuj21LL6m5/t3mXIFH8DM/p+ZLQdaAw8HjpNIbYH/hg4hBcrrxsG0KhiZ\nwsyqAbWB6WGTxJeZlTKzOcBqINs5tyC/Y4PchlTYzVzOua5A19x+aV+gTYCYxRbLzWpm1gXY7px7\nOUDEEglxM55ISeS2c14DbtmjixB5uR2DOrnXA8ebWUPnXJ7rmwUp+M65c2M89GVgXCKzJEJhv5+Z\ntQbOB85JSqA4K8LfXzpYCey+Vf1huV+TiDCz0vhi/6Jz7s3QeRLFObfezN4GTiWfBS1TrqVjZsfs\n9rA5MDdUlkQwsybAXcDFzrmtofMkWDr08X+9cdDMyuBvHBwTOFMiGOnx95WXZ4EFzrknQweJNzM7\nxMwOyP28HHAuBdTMVJyl8xpQHdgJfAXc4JxbEzZV/JjZUqAM8EPul6Y55zoGjBRXZtYc6A8cAqwD\n5jrnmoZNVTK5L9JP4gdIQ5xzjwSOFFdm9jKQBRwMfAd0c849FzRUnJjZGcBk/FLuLvfjPufcO0GD\nxYmZnQi8gH+xLoV/F9M73+NTreCLiEhipFxLR0REEkMFX0QkQ6jgi4hkCBV8EZEMoYIvIpIhVPBF\nRDKECr6ISIZQwRcRyRD/H907M/N2Qx4+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ddbfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_pic, a=[0, 3, 0.1], b=[0, 3, 0.1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция `interact` создаёт несколько бегунков и позволяет с их помощью задавать параметры у функции (в данном случае `plot_pic`), которая строит график.\n",
    "\n",
    "> Эта картинка не будет интерактивной при просмотре IPython Notebook, но если вы скачаете его и запустите у себя на компьютере, то там будет."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ещё `interact` можно вызывать так:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lItQxx8C99/qPDRtg8mT/IvD88zB/vm/HHHwwVKjg/yxTxi91sHWrv0lq+XJf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\nRDKECr6ISIZQwRcRyRD/H907M/N2Qx4+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11214ebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(a=[0, 3, 0.1], b=[0, 3, 0.1])\n",
    "def plot_pic(a, b):\n",
    "    x = np.linspace(-3,3,200)\n",
    "    plt.plot(x, np.sin(x*a+b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На сегодня всё!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}