{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Программирование и анализ данных\n",
    "## Визуализация данных с `seaborn`\n",
    "\n",
    "*Алла Тамбовцева, НИУ ВШЭ*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этой лекции мы будем строить графики для визуализации распределения данных с помощью библиотеки `seaborn`. Эту библиотеку можно считать некоторой «надстройкой» над библиотекой `matplotlib`, которая предлагает более удобные функции для построения графиков разных видов и более симпатичный дизайн для графиков. Для начала установим её. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь импортируем эту библиотеку с сокращённым названием `sns`, а заодно импортируем модуль `pyplot` из `matplotlib`, который нам тоже понадобится для настройки графиков. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь сделаем так, чтобы все графики далее строились с использованием настроек по умолчанию из `seaborn`, а не из `matplotlib` (более красиво, так как автоматически добавляется светло-серый фон графика, сетка и прочее)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Загрузим данные из файла `Chile.csv`, сохраним их в датафрейм и удалим строки с пропущенными значениями (многие функции для графики не работают в случае, если в визуализируемых данных остались `NaN`, при желании пропуски можно не удалять, а заполнить с помощью метода `.fillna()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>income</th>\n",
       "      <th>statusquo</th>\n",
       "      <th>vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>175000</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>P</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>1.00820</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>175000</td>\n",
       "      <td>M</td>\n",
       "      <td>29.0</td>\n",
       "      <td>PS</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>-1.29617</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>N</td>\n",
       "      <td>175000</td>\n",
       "      <td>F</td>\n",
       "      <td>38.0</td>\n",
       "      <td>P</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>1.23072</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>175000</td>\n",
       "      <td>F</td>\n",
       "      <td>49.0</td>\n",
       "      <td>P</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>-1.03163</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>175000</td>\n",
       "      <td>F</td>\n",
       "      <td>23.0</td>\n",
       "      <td>S</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>-1.10496</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 region  population sex   age education   income  statusquo vote\n",
       "0           1      N      175000   M  65.0         P  35000.0    1.00820    Y\n",
       "1           2      N      175000   M  29.0        PS   7500.0   -1.29617    N\n",
       "2           3      N      175000   F  38.0         P  15000.0    1.23072    Y\n",
       "3           4      N      175000   F  49.0         P  35000.0   -1.03163    N\n",
       "4           5      N      175000   F  23.0         S  35000.0   -1.10496    N"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "ch = pd.read_csv(\"Chile.csv\")\n",
    "ch.dropna(inplace = True)\n",
    "ch.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Показатели:**\n",
    "\n",
    "* `region`: регион, где живет респондент (`C`, Central; `M`, Metropolitan Santiago area; `N`, North; `S`, South; `SA`, City of Santiago);\n",
    "* `sex`: пол респондента;\n",
    "* `population`: численность населения в регионе респондента;\n",
    "* `age`: возраст респондента;\n",
    "* `education`: уровень образования респондента (`P`, Primary; `PS`, Post-secondary; `S`, Secondary);\n",
    "* `income`: доход респондента;\n",
    "* `statusquo`: индекс отношения к статус-кво (чем больше значение, тем больше человек склонен одобрять сохранение статус-кво – нахождение Пиночета у власти);\n",
    "* `vote`: как респондент собирается голосовать (`A`, will abstain; `N`, will vote no – against Pinochet; `U`, undecided; `Y`, will vote yes – for Pinochet)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Гистограмма и график плотности распределения\n",
    "\n",
    "Построим простенькую гистограмму для столбца `age`. В `seaborn` нет отдельной функции для гистограммы, но есть функция `distplot()`, которая по умолчанию строит гистограмму и накладывает на неё сглаженный график плотности распределения (отсюда и название, *distplot* – от *distribution*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e64b650>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ch[\"age\"]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте теперь уберём сглаженный график плотности и изменим цвет гистограммы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1e553350>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ch[\"age\"], kde = False, \n",
    "             color = \"limegreen\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аргумент `kde` отвечает за наличие или отсутствие сглаженного графика плотности, и название его неслучайно. Аббревиатура `kde` происходит от *kernel density estimation* (ядерная оценка плотности), почитать можно [здесь](https://ru.wikipedia.org/wiki/%D0%AF%D0%B4%D0%B5%D1%80%D0%BD%D0%B0%D1%8F_%D0%BE%D1%86%D0%B5%D0%BD%D0%BA%D0%B0_%D0%BF%D0%BB%D0%BE%D1%82%D0%BD%D0%BE%D1%81%D1%82%D0%B8)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь добавим аргумент `bins` для настройки числа столбцов и особый аргумент `hist_kws` с перечнем аргументов для построения гистограммы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f587290>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# alpha – прозрачность (1 - нет прозрачности)\n",
    "# edgecolor – цвет границ столбцов\n",
    "\n",
    "sns.distplot(ch[\"age\"], \n",
    "             kde = False, \n",
    "             color = \"limegreen\", \n",
    "             bins = 10, \n",
    "             hist_kws = {'alpha' : 0.75, \n",
    "                        'edgecolor' : 'black'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аргументы для настройки гистограммы указываются особым образом, в виде словаря. На первый взгляд это кажется необычным, но если вспомнить про `kwargs` (*keywords arguments*) в функциях, всё встанет на свои места. Здесь внутри `hist_kws` (как раз *histogram keywords*) можно указывать сколько угодно аргументов для настройки вида гистограммы, но при этом нужно обязательно фиксировать их названия. В итоге получаем словарь с ключами в виде названий аргументов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если бы мы хотели сохранить наложенный график плотности на рисунке, можно было бы и его вид настроить аналогичным образом:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f415b10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ch[\"age\"], \n",
    "             color = \"limegreen\", \n",
    "             bins = 10, \n",
    "             hist_kws = {'alpha' : 0.75, \n",
    "                        'edgecolor' : 'black'},\n",
    "             kde_kws = {'color' : 'red'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь добавим заголовок графика и подписи к осям. Сделать это можно с помощью методов из модуля `pyplot`, который мы сократили до `plt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of age')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ch[\"age\"], \n",
    "             color = \"limegreen\", \n",
    "             bins = 10, \n",
    "             hist_kws = {'alpha' : 0.75, \n",
    "                        'edgecolor' : 'black'},\n",
    "             kde_kws = {'color' : 'red'})\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"Distribution of age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавить горизонтальную линию, которая отчерчивает определённую частоту можно с помощью того же модуля `plt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1a2090e050>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ch[\"age\"], \n",
    "             color = \"limegreen\", \n",
    "             kde = False,\n",
    "             bins = 10, \n",
    "             hist_kws = {'alpha' : 0.75, \n",
    "                        'edgecolor' : 'black'})\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"Distribution of age\")\n",
    "\n",
    "# метод hlines\n",
    "plt.hlines(y = 250, xmin = 10, xmax = 80, \n",
    "          color = \"red\", linestyles = \"dashed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В методе `.hlines()` необходимо указать не только необходимое значение по вертикальной оси (`y`), но и установить рамки по горизонтальной оси (`xmin` и `xmax`), даже если прямая должна проходить через весь график. Согласно документации, стилей для линии всего 4: `'solid'` (непрерывная линия), `'dashed'` (пунктирная линия), `'dashdot'` (точка-тире), `'dotted'` (линия из точек).\n",
    "\n",
    "Аналогичным образом можно добавить вертикальную линию через `.vlines()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1a20677650>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ch[\"age\"], \n",
    "             color = \"limegreen\", \n",
    "             kde = False,\n",
    "             bins = 10, \n",
    "             hist_kws = {'alpha' : 0.75, \n",
    "                        'edgecolor' : 'black'})\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"Distribution of age\")\n",
    "\n",
    "# метод vlines\n",
    "# вертикальная линия отмечает среднее значение по столбцу\n",
    "plt.vlines(x = ch[\"age\"].mean(), ymin = 0, ymax = 500, \n",
    "          color = \"red\", linestyles = \"dotted\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если нам нужен только сглаженный график плотности, можно воспользоваться функцией `kdeplot()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age of responsents')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(ch[\"age\"], shade = True)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"Age of responsents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В данном примере мы добавили опцию `shade=True`, чтобы площадь под графиком была закрашена. Сглаженные графики плотности иногда полезны, но стоит помнить, что из-за сглаживания они получаются менее точными, чем гистограммы, поэтому их не очень хорошо полезно использовать, когда данных мало или когда нам важны точные частоты или «пики» распределения.\n",
    "\n",
    "Иногда на такие графики добавляют так называемые *rugs* (нет точного термина на русском, можно рассматривать как засечки), которые будут показывать, сколько наблюдений на том или ином участке графика. Но такая опция есть только у `distplot()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age of responsents')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# «выключаем» гистограмму\n",
    "# «включаем» rugs\n",
    "\n",
    "sns.distplot(ch[\"age\"], \n",
    "             hist = False, \n",
    "             rug = True)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"Age of responsents\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь попробуем построить графики по группам. Посмотрим на распределение возраста респондентов, сгруппированных по региону и по полу. Для начала создадим шаблон для графика — график с множеством ячеек-фасеток, где каждая ячейка-фасетка соответствует одной группе. По строкам у нас будут идти значения пола (их два), по столбцам – значения региона (их пять):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2084ca90>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# функция FacetGrid создает сетку\n",
    "# margin_titles – наличие подписей по строкам и столбцам\n",
    "\n",
    "g = sns.FacetGrid(ch, row=\"sex\", \n",
    "                  col=\"region\", \n",
    "                  margin_titles=True)\n",
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Осталось только сообщить, что за графики будут в каждой ячейке. Для этого применим метод `.map()` и укажем, что мы строим гистограмму (`hist`) для столбца `age`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a24980210>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(ch, row=\"sex\", \n",
    "                  col=\"region\", \n",
    "                  margin_titles=True)\n",
    "g.map(plt.hist, \"age\", color=\"steelblue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Важно:** строки с `FacetGrid()` и `.map()` должны быть в одной ячейке, они связаны. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Столбиковая диаграмма\n",
    "\n",
    "Построим столбиковую диаграмму (*bar plot* или *bar chart*), чтобы показать, сколько респондентов из разных регионов участвовало в опросе. Для этого нам понадобится табличка с частотами для каждого названия региона. Создадим её с помощью уже знакомого метода `.value_counts()` и сохраним в переменную `tab`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SA    848\n",
       "S     655\n",
       "C     548\n",
       "N     305\n",
       "M      75\n",
       "Name: region, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab = ch[\"region\"].value_counts()\n",
    "tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция `barplot()`, которую мы будем использовать для построения столбиковой диаграммы, принимает на вход два набора значений: список или массив значений по оси `x` (подписи к столбикам) и список или массив значений по оси `y` (частоты). Извлечём их из `tab`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tab.index\n",
    "y = tab.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['SA', 'S', 'C', 'N', 'M'], dtype='object'),\n",
       " array([848, 655, 548, 305,  75]))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь поменяем фон у графика на белый и построим диаграмму:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24fa5610>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.barplot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь попробуем построить похожий график, только с группировкой по полу. Другими словами, мы хотим получить график, где для каждого региона строится столбик отдельно для мужчин и отдельно для женщин. Опять же, сначала сгруппируем данные:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>sex</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>population</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>income</th>\n",
       "      <th>statusquo</th>\n",
       "      <th>vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>F</td>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>M</td>\n",
       "      <td>279</td>\n",
       "      <td>279</td>\n",
       "      <td>279</td>\n",
       "      <td>279</td>\n",
       "      <td>279</td>\n",
       "      <td>279</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>F</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>M</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>S</td>\n",
       "      <td>F</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>S</td>\n",
       "      <td>M</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>SA</td>\n",
       "      <td>F</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>SA</td>\n",
       "      <td>M</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region sex  Unnamed: 0  population  age  education  income  statusquo  vote\n",
       "0      C   F         269         269  269        269     269        269   269\n",
       "1      C   M         279         279  279        279     279        279   279\n",
       "2      M   F          43          43   43         43      43         43    43\n",
       "3      M   M          32          32   32         32      32         32    32\n",
       "4      N   F         159         159  159        159     159        159   159\n",
       "5      N   M         146         146  146        146     146        146   146\n",
       "6      S   F         330         330  330        330     330        330   330\n",
       "7      S   M         325         325  325        325     325        325   325\n",
       "8     SA   F         449         449  449        449     449        449   449\n",
       "9     SA   M         399         399  399        399     399        399   399"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = ch.groupby(['region', 'sex'], as_index=False).count()\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Опция `as_index = False` нужна для того, чтобы `region` и `sex` не записывались как уровни (отдельный атрибут), а чтобы для каждой пары *регион-пол* создавалась отдельная строка. Сравните результат выше с результатом, полученным без `as_index=False`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Unnamed: 0</th>\n",
       "      <th>population</th>\n",
       "      <th>age</th>\n",
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       "      <th>vote</th>\n",
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       "      <th>region</th>\n",
       "      <th>sex</th>\n",
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       "      <td>32</td>\n",
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       "      <td rowspan=\"2\" valign=\"top\">N</td>\n",
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       "      <td>330</td>\n",
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       "      <td>M</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">SA</td>\n",
       "      <td>F</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
       "      <td>449</td>\n",
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       "    <tr>\n",
       "      <td>M</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0  population  age  education  income  statusquo  vote\n",
       "region sex                                                                 \n",
       "C      F           269         269  269        269     269        269   269\n",
       "       M           279         279  279        279     279        279   279\n",
       "M      F            43          43   43         43      43         43    43\n",
       "       M            32          32   32         32      32         32    32\n",
       "N      F           159         159  159        159     159        159   159\n",
       "       M           146         146  146        146     146        146   146\n",
       "S      F           330         330  330        330     330        330   330\n",
       "       M           325         325  325        325     325        325   325\n",
       "SA     F           449         449  449        449     449        449   449\n",
       "       M           399         399  399        399     399        399   399"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch.groupby(['region', 'sex']).count() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вся таблица `res` нам не нужна, поскольку в ней много одинаковых числовых столбцов. Выберем столбцы `region` и `sex` (нужны для группировки) и столбец `vote` (один из числовых столбцов, могли взять другой):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>sex</th>\n",
       "      <th>vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>32</td>\n",
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       "      <td>4</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>S</td>\n",
       "      <td>F</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>S</td>\n",
       "      <td>M</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>SA</td>\n",
       "      <td>F</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>SA</td>\n",
       "      <td>M</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region sex  vote\n",
       "0      C   F   269\n",
       "1      C   M   279\n",
       "2      M   F    43\n",
       "3      M   M    32\n",
       "4      N   F   159\n",
       "5      N   M   146\n",
       "6      S   F   330\n",
       "7      S   M   325\n",
       "8     SA   F   449\n",
       "9     SA   M   399"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = res[['region', 'sex', 'vote']]\n",
    "freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Осталось, используя табличку `freq` выше, построить график! Применим функцию `catplot()`, от *categorical plot*, график для категориальных (нечисловых) данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(x = \"region\", \n",
    "            y = \"vote\", \n",
    "            hue = \"sex\", \n",
    "            data=freq,\n",
    "            kind=\"bar\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.xlabel(\"Region\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По оси `x` указаны значения из `region`, по оси `y` – частоты из `vote`, внутри каждого региона добавляем группировку по значениям `sex` и делаем столбцы разного цвета (`hue`), сообщаем, что все эти столбцы берутся из таблицы `freq`. \n",
    "\n",
    "Если мы хотим поменять цвета у столбцов, можем указать другую цветовую палитру (нашли её в [документации](https://matplotlib.org/3.1.3/tutorials/colors/colormaps.html) `matplotlib`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 22.960000000000015, 'Region')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 408.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# аргумент palette\n",
    "\n",
    "sns.catplot(x = \"region\", \n",
    "            y = \"vote\", \n",
    "            hue = \"sex\", \n",
    "            data=freq,\n",
    "            kind=\"bar\",\n",
    "            palette = \"Pastel1\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.xlabel(\"Region\")"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}