{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Основы программирования в Python\n",
    "\n",
    "*Алла Тамбовцева*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Основы работы с библиотекой `matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С библиотекой `matplotlib` мы уже сталкивались, когда строили статистические графики на основе данных из датафреймов `pandas`. Но с помощью `matplotlib` можно строить не только гистограммы, графики плотности и диаграммы рассеяния, но и вообще любые графики. \n",
    "\n",
    "Для начала построим простенький график для визуализации данных в двух списках. Импортируем модуль pyplot из библиотеки и добавим питоновскую \"магическую\" строчку для того, чтобы графики отображались прямо в ipynb-файле."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим два небольших списка."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [-2, -0.5, 0, 2, 5, 8, 9, 10]\n",
    "Y = [4, 0.25, 0, 4, 25, 64, 81, 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим график."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca728b2a20>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca74ba1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как можно заметить, в списке `Y` сохранены элементы списка `X`, возведенные в квадрат. Однако наш график не похож на ветвь параболы, он какой-то угловатый. Это нормально, потому что в списках у нас всего по 8 элементов, то есть, всего 8 точек на графике соединяются линиями. Если бы точек было больше, график был бы более гладким. Воспользуемся функцией `linspace` из библиотеки `numpy` (вот она нам и пригодилась!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.        , -1.87878788, -1.75757576, -1.63636364, -1.51515152,\n",
       "       -1.39393939, -1.27272727, -1.15151515, -1.03030303, -0.90909091,\n",
       "       -0.78787879, -0.66666667, -0.54545455, -0.42424242, -0.3030303 ,\n",
       "       -0.18181818, -0.06060606,  0.06060606,  0.18181818,  0.3030303 ,\n",
       "        0.42424242,  0.54545455,  0.66666667,  0.78787879,  0.90909091,\n",
       "        1.03030303,  1.15151515,  1.27272727,  1.39393939,  1.51515152,\n",
       "        1.63636364,  1.75757576,  1.87878788,  2.        ,  2.12121212,\n",
       "        2.24242424,  2.36363636,  2.48484848,  2.60606061,  2.72727273,\n",
       "        2.84848485,  2.96969697,  3.09090909,  3.21212121,  3.33333333,\n",
       "        3.45454545,  3.57575758,  3.6969697 ,  3.81818182,  3.93939394,\n",
       "        4.06060606,  4.18181818,  4.3030303 ,  4.42424242,  4.54545455,\n",
       "        4.66666667,  4.78787879,  4.90909091,  5.03030303,  5.15151515,\n",
       "        5.27272727,  5.39393939,  5.51515152,  5.63636364,  5.75757576,\n",
       "        5.87878788,  6.        ,  6.12121212,  6.24242424,  6.36363636,\n",
       "        6.48484848,  6.60606061,  6.72727273,  6.84848485,  6.96969697,\n",
       "        7.09090909,  7.21212121,  7.33333333,  7.45454545,  7.57575758,\n",
       "        7.6969697 ,  7.81818182,  7.93939394,  8.06060606,  8.18181818,\n",
       "        8.3030303 ,  8.42424242,  8.54545455,  8.66666667,  8.78787879,\n",
       "        8.90909091,  9.03030303,  9.15151515,  9.27272727,  9.39393939,\n",
       "        9.51515152,  9.63636364,  9.75757576,  9.87878788, 10.        ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(-2, 10, 100) # 100 точек\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca728561d0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca7285c048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так график больше похож на параболу. Могли бы изобразить ее полностью, определенную на участке от -10 до 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca727c6780>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca727e1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 200)\n",
    "plt.plot(x, x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поменяем цвет линии по умолчанию на какой-нибудь другой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca72735978>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca7275c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'lightblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Или так:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca7268bac8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca726b03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список цветов в Python см. [здесь](https://matplotlib.org/users/colors.html). \n",
    "\n",
    "Теперь изменим тип линии. По умолчанию используется сплошная линия, но ее можно заменить на пунктирную или что-то подобное:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca725fb940>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca7261b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linestyle = '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca72564e10>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca7260fc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linestyle = '-.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список всех типов линий см. [здесь](https://matplotlib.org/gallery/lines_bars_and_markers/line_styles_reference.html). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кроме того, можно изменить толщину линии, добавив аргумент `linewidth`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca721eaa58>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca72226400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linewidth = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь построим график, состоящий только из точек (можно считать диаграммой рассеяния)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fca721d69e8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca721ff358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь будем менять цвет точек и тип точек (тип маркера) одновременно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fca7213fa20>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca7217b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fca720a5a20>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca721fae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = '*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fca7208b9b0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca720c91d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='green', marker = '2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список маркеров смотри [здесь](https://matplotlib.org/api/markers_api.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если бы все строки кода со `scatter()` были в одной ячейке, то графики бы просто накладывались друг на друга."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fca71f63d30>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca720089e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o')\n",
    "plt.scatter(X, Y, color ='blue', marker = '*')\n",
    "plt.scatter(X, Y, color ='green', marker = '2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если присмотреться, то на красных точках можно увидеть синие звездочки и зеленые треугольники. Чтобы такого не происходило (например, если вы создаете и сохраняете графики в цикле в пределах одной ячейки), нужно добавить строку с функцией `clf()`, которая очищает координатную плоскость для следующего графика (*clf* ‒ от *clear figure*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fca7236b470>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca71f73588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o')\n",
    "plt.clf()\n",
    "plt.scatter(X, Y, color ='blue', marker = '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На плоскости представлен только последний график с синими звездочками, красные точки от первого графика был стерты с помощью `clf()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При построении графиков стоит иметь в виду, что функция `plot()` всегда соединяет точки, причем последовательно, в том порядке, в котором они следуют в списках или массивах. Из-за этой особенности, допустив ошибку, связанную с заданием неверной области определения функции, можно получить некорректные графики. Построим для примера гиперболу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca71f32898>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca71f94978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-100, 100, 100)\n",
    "y = 1/x\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Полученный график не совсем похож на гиперболу! Как известно, в точке $x=0$ график уходит на бесконечность, линия при $x=0$ отсутствует. А здесь она есть! Избавимся от нее, построив график \"по кусочкам\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oem/.local/lib/python3.5/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \n",
      "/home/oem/.local/lib/python3.5/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca71ecdcf8>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca71ecdbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.linspace(-100, 0, 100) # x < 0\n",
    "y1 = 1/x1\n",
    "\n",
    "x2 = np.linspace(0, 100, 100) # x > 0\n",
    "y2 = 1/x2\n",
    "\n",
    "plt.plot(x1, y1, 'blue')\n",
    "plt.plot(x2, y2, 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В завершение попробуем построить целый рисунок (*figure*), состоящий сразу из нескольких графиков (подграфиков). Построим разные типы функций: $y=x^2$, $y=x^3$, $y=e^x$ и $y=|x|$. Сначала создадим соответствующие массивы значений:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-100, 100, 100)\n",
    "y = x ** 2\n",
    "z = x ** 3\n",
    "r = np.exp(x)\n",
    "m = abs(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим рисунок (*figure*):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca71ed5240>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca71ed5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А теперь будем добавлять в него графики, указывая их расположение. В функции `subplot()` указывается число. Первые две цифры ‒ это число графиков в строке и столбце (здесь 2 на 2, поэтому `22`). Последняя цифра ‒ это положение графика: левый верхний угол (`1`), правый верхний угол (`2`), левый нижний угол (`3`), правый нижний угол (`4`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca71ca26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.plot(x, y) # x^2\n",
    "plt.title('parabola')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(x, z) # x^3\n",
    "plt.title('hyperbola')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.plot(x, r) # e^x\n",
    "plt.title('exponent')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot(x, m) # |x|\n",
    "plt.title('abs')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Строка `plt.grid(True)` нужна для того, чтобы на графиках были добавлены линии разметки, привычные нам \"клеточки\", которые позволяют удобным образом определять координаты точек на графике."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И напоследок: как сохранить график в файл. Очень просто. Например, так."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca71d2d048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.scatter(X, Y, color ='red', marker = '*')\n",
    "plt.savefig('MyScatter.png') # ищем файл в рабочей папке (рядом с текущим ipynb-файлом)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы достаточно кратко обсудили возможности библиотеки `matplotlib`, но на этом ее возможности не заканчиваются. Кому интересно, стоит посмотреть документацию по `matplotlib`, а также заглянуть в галерею с примерами графиков, которые можно адаптировать под свои задачи и данные.\n",
    "\n",
    "Если кто-то привык работать в R и полюбил вид графиков `ggplot2`, можно установить [библиотеку](http://ggplot.yhathq.com/install.html) `ggplot` для Python. Кроме того, можно поработать с библиотекой [seaborn](https://seaborn.pydata.org/), она предоставляет много возможностей для построения статистических графиков, и выглядят ти графики тоже очень симпатично.\n",
    "\n",
    "Если хочется более продвинутой интерактивной графики, связанной со статистическими моделями, стоит обратить внимание на библиотеку GraphLab. Она интересна не только графикой, но и другими вещами, но есть один минус: библиотека платная. Однако есть возможность получить доступ по учебной лицензии и даже продлевать ее, особенно, если учесть, что GraphLab используется в некоторых курсах на Coursera, что тоже облегачает получение доступа ([пример](https://www.coursera.org/learn/ml-foundations/home/welcome) такого курса)."
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}