13 Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Intro\n",
    "\n",
    "Matplotlib - модуль для создания графиков. Описание с [официального сайта](https://matplotlib.org/index.html):\n",
    "\n",
    ">Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.\n",
    "\n",
    "## Установка\n",
    "\n",
    "Матплотлиб нужно установить командой `pip install matplotlib`. \n",
    "\n",
    "Чтобы проверить, установился ли модуль, нужно написать `import matplotlib` в консоли питона."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Простые графики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нарисуем какой-нибудь простой график. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5rmb9Pn9lAL2Z2slheugN17/fOhw1h47huuc/w+8WfoWaQ/ZsuM4rKUPPjPYY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\nP26SXwBOAeqqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24a976e99b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X и Y - координаты точек\n",
    "X = [1,   1.5, 2,   3, 5, 6]\n",
    "Y = [1.8, 1,   3.6, 2, 1, 3]\n",
    "\n",
    "plt.plot(X, Y) # рисуем график - последовательно соединяем точки с координатами из X и Y\n",
    "plt.show()     # показываем, что получилось"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можем просто нарисовать точки - **scatterplot**. Кроме того, добавим нашему графику заголовок и подписи осей:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c546a70b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y)  # рисуем точки\n",
    "plt.title('Очень простой график')  # добавляем заголовок\n",
    "plt.ylabel('ось Y')  # подписи к осям\n",
    "plt.xlabel('ось X')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С помощью `xlim` и `ylim` можно указать масштаб графика по оси X и Y:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD8CAYAAABuHP8oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAJAUAQeApP4P6ml0UoHUgcoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c54708c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y)\n",
    "plt.xlim(0,10)\n",
    "plt.ylim(0,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Когда мы рисуем точки, кроме координат в `plt.scatter()` можно передать параметры:\n",
    "\n",
    "* `s`: размер точки, по умолчанию 20;\n",
    "* `c`: цвет, по умолчанию 'b' - голубой;\n",
    "* `marker`: символ точки, по умолчанию 'o' - круглая точка.\n",
    "\n",
    "\n",
    "Список цветов можно посмотреть [тут](https://matplotlib.org/examples/color/named_colors.html), а маркеров - [тут](https://matplotlib.org/examples/lines_bars_and_markers/marker_reference.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c546bc7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, s=60, c='red', marker='^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В качестве размера и цвета можно задавать массивы с соответствующими значениями:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bkmxNck+Su5OcP+pMw5bkuUl+muSOzjl/ctSZlkOSY5L8PMkNo86yHJL8MsldSW5PMjXq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x257732e9780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = [60, 70, 80, 90, 100, 130]\n",
    "colors = ['red', 'green', 'blue', 'black', 'yellow', 'brown']\n",
    "plt.scatter(X, Y, s=size, c=colors, marker='^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Естественно, любой график можно сохранить в виде картинки на ваш компьютер в одном из доступных форматов: emf, eps, pdf, png, ps, raw, rgba, svg, svgz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.savefig('plot_with_colors.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ggplot\n",
    "\n",
    "Можно не задавать каждый параметр графика вручную, а использовать готовые стилевые шаблоны для графиков. Одним из популярных шаблонов является `ggplot`. \n",
    "\n",
    "Возьмем два набора точек - с координатами (X, Y) и (X2, Y2):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1.5, 2, 3, 5, 6] [1.8, 1, 3.6, 2, 1, 3] [2, 3.0, 4, 6, 10, 12] [2.7, 1.5, 5.4, 3.0, 1.5, 4.5]\n"
     ]
    }
   ],
   "source": [
    "X2 = [i*2 for i in X]\n",
    "Y2 = [i*1.5 for i in Y]\n",
    "print(X, Y, X2, Y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dw731FUOTwiT45o8SvLAtH1lUMdRq1Fod0i6qMCjSF8E+7jlgMir5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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24a977d6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import style  # добавляем стили\n",
    "style.use('ggplot')  # выбираем стиль ggplot\n",
    "\n",
    "plt.plot(X,Y)\n",
    "plt.plot(X2, Y2)\n",
    "\n",
    "plt.title('График с ggplot')\n",
    "plt.ylabel('значения Y')\n",
    "plt.xlabel('значения X')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В `plot` так же как в `scatter` выше можно указывать разные параметры. Например, мы можем поменять цвет линии, ее ширину.  \n",
    "\n",
    "Кроме того мы можем добавить в график легенду. Для этого каждой линии нужно приписать `label`, а затем нарисовать легенду с помощью `legend()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l2LN1Y03x4vEwleXsf8jPR5FWW8UZFIpjwp7oHSyVGhYz2psqI/9ly5ahbdu2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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c547f20b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, Y,'g',label='зеленая линия', linewidth=5)\n",
    "plt.plot(X2, Y2,'c',label='голубая линия',linewidth=3)\n",
    "\n",
    "plt.title('График с ggplot')\n",
    "plt.ylabel('значения Y')\n",
    "plt.xlabel('значения X')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.grid(True,color='orchid')  # а еще можно менять вид сетки\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Соберем новый набор данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = [1, 2, 3, 4, 5]\n",
    "Y = [11, 12, 13, 14, 15]\n",
    "\n",
    "X2 = [6, 7, 8, 9, 10]\n",
    "Y2 = [15, 14, 13, 12, 11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы можем нарисовать __столбчатую диаграмму__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kpMbHx51eKeYGBwf17bffqra2VsnJyTp79qw6OjpUVFTk9GoxUVRUpDfeeEMNDQ2z97W0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\nHQAM8R+UiKnpaw+hOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c54877518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(X, Y)\n",
    "plt.bar(X2, Y2, color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Пример\n",
    "\n",
    "Есть вот такой [файл](https://github.com/elmiram/2017learnpython/blob/master/data/Genproc.csv) с твитами генпрокуратуры. Попробуем создать графики на этом датасете. \n",
    "\n",
    "### Сколько твитов было в каждый месяц в 2013 году?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'01': 460,\n",
       " '02': 503,\n",
       " '03': 377,\n",
       " '04': 435,\n",
       " '05': 337,\n",
       " '06': 347,\n",
       " '07': 438,\n",
       " '08': 207}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "with open('data/genproc.csv', 'r', encoding='utf-8') as f:\n",
    "    lines = f.readlines()\n",
    "    \n",
    "dates = Counter([line.split()[1][5:7] for line in lines if '2013-' in line])\n",
    "dates = dict(dates)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0devWyFMBAKKGJ2MBwHAUPQAYjqIHAMNR9ABgOIoeAAxH0QOA4Sh6ADAcRQ8AhqPoAcBw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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c549b0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days_labels = sorted(dates.keys())\n",
    "days = [i for i in range(len(days_labels))]\n",
    "\n",
    "twit_nums = [dates[key] for key in days_labels]\n",
    "\n",
    "plt.bar(days, twit_nums)\n",
    "plt.xticks(days, days_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сколько Твитов было в каждый час в аккаунте Генпрокуратуры и Сколково?\n",
    "\n",
    "[Твиты сколково](https://github.com/elmiram/2017learnpython/blob/master/data/skolkovo_ru.csv)\n",
    "\n",
    "[Твиты прокуратуры](https://github.com/elmiram/2017learnpython/blob/master/data/Genproc.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23']\n"
     ]
    }
   ],
   "source": [
    "hours = ['{:02}'.format(i) for i in range(24)]\n",
    "print(hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 1, 1, 62, 255, 534, 430, 372, 396, 420, 448, 276, 28, 3, 1, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "hours_genproc = Counter([line.split()[2][:2] for line in lines])\n",
    "hours_genproc = [hours_genproc[hour] for hour in hours]\n",
    "print(hours_genproc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 12, 0, 0, 0, 0, 0, 14, 13, 129, 158, 165, 319, 277, 207, 234, 293, 243, 211, 188, 66, 24, 10, 10]\n"
     ]
    }
   ],
   "source": [
    "with open('data/skolkovo_ru.csv', 'r', encoding='utf-8') as f2:\n",
    "    lines2 = f2.readlines()\n",
    "    \n",
    "hours_skolkovo = Counter([line.split()[2][:2] for line in lines2])\n",
    "hours_skolkovo = [hours_skolkovo[hour] for hour in hours]\n",
    "print(hours_skolkovo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VFsVyOycnJ6vKWlOuvpVxZF01/Uw5mTkA3N/w/nLvaav4uim7MfnBySw/vJzx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c5461ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(hours)), hours_genproc, 'g', label='генпрокуратура')\n",
    "plt.plot(range(len(hours)), hours_skolkovo, 'c', label='сколково')\n",
    "\n",
    "plt.title('Твиты по часам')\n",
    "plt.ylabel('Количество твитов')\n",
    "plt.xlabel('Время')\n",
    "plt.xticks(range(len(hours)), hours)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Найти самые частые существительные в первых 100 твитах Сколково (не ретвитах)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analyzing...\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "from pymystem3 import Mystem  # импортируем майстем\n",
    "m = Mystem()\n",
    "\n",
    "with open('data/skolkovo_ru.csv', 'r', encoding='utf-8') as f2:\n",
    "    lines2 = f2.readlines()\n",
    "    \n",
    "tweets_skolkovo = [line.split(' <sk_ru> ')[1] for line in lines2 if '<sk_ru> RT' not in line]\n",
    "print('analyzing...')\n",
    "analyzed = m.analyze(' '.join(tweets_skolkovo[:100]))\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['виктор', 'вексельберг', 'представитель', 'процесс', 'потенциал', 'россия', 'горизонт', 'планирование', 'компонент', 'космос']\n"
     ]
    }
   ],
   "source": [
    "nouns = [i['analysis'][0]['lex'] for i in analyzed if i['text'].strip() and 'analysis' in i and i['analysis'] and i['analysis'][0]['gr'].split('=')[0].split(',')[0] == 'S']\n",
    "print(nouns[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "nouns = Counter(nouns).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CCy9IlrNhwwYkJiaif//+OHjwIKytreHn54eRI0dKlgkAOTk5cHR0LPdceno63N3dJc1V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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24a9781eac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(nouns)), [i[1] for i in nouns])\n",
    "plt.xticks(range(len(nouns)), [i[0] for i in nouns], rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задания\n",
    "\n",
    "1) Составьте список 10 аккаунтов, которые чаще всего ретвитил аккаунт Сколково. Ретвиты начинаются с RT, аккаунт автора указывается сразу после RT, например: `RT @sk_ru_live`. Наприсуйте столбчатую диаграмму, показывающую количество ретвитов из каждого найденного аккаунта.\n",
    "\n",
    "2) Составьте график длины твитов аккаунта Сколково (ретвиты не считать). На то же изображение добавьте такой же график, построенный по аккаунту генпрокуратуры."
   ]
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}