vander_plus_3_Pandas_05.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "510086eb",
   "metadata": {},
   "source": [
    "## Иерархическая индексация (heirarchical indexing)\n",
    "    Многомерные данные могут храниться путем использования объектов Panel - 3D, и Panel4D - для 4D или\n",
    "    Иерархической индексации (или мультииндекс multi-indexing) для N-мерных структур."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aba7a333",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16a469b",
   "metadata": {},
   "source": [
    "### Мультииндексированный объект Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ca88247",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [\n",
    "    ('c', 2000), ('c', 2010),\n",
    "    ('n', 2000), ('n', 2010),\n",
    "    ('t', 2000), ('t', 2010)\n",
    "]\n",
    "populations = [3387, 3725,\n",
    "              1897, 1937,\n",
    "              2085, 2514]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7676d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = pd.Series(populations, index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a57c919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(c, 2000)    3387\n",
       "(c, 2010)    3725\n",
       "(n, 2000)    1897\n",
       "(n, 2010)    1937\n",
       "(t, 2000)    2085\n",
       "(t, 2010)    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop\n",
    "# это еще не мультииндекс, это картежи-ключи"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8acde486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('c', 2000),\n",
       "            ('c', 2010),\n",
       "            ('n', 2000),\n",
       "            ('n', 2010),\n",
       "            ('t', 2000),\n",
       "            ('t', 2010)],\n",
       "           )"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_tuples(index)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "afa263ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = pop.reindex(index)\n",
    "# вот это мультииндекс"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b5c78312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c  2000    3387\n",
       "   2010    3725\n",
       "n  2000    1897\n",
       "   2010    1937\n",
       "t  2000    2085\n",
       "   2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "046d0776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3725\n",
       "n    1937\n",
       "t    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# мультииндекс любит срезы:\n",
    "pop[:, 2010]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d8bc97",
   "metadata": {},
   "source": [
    "#### Мультииндекс как доп. измерение:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4fd2a98f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# мультииндексный Series м.б. преобразован в обычный DataFrame\n",
    "pop_df = pop.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92d73ea3",
   "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>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3387</td>\n",
       "      <td>3725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>1897</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>2085</td>\n",
       "      <td>2514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2000  2010\n",
       "c  3387  3725\n",
       "n  1897  1937\n",
       "t  2085  2514"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6e049caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c  2000    3387\n",
       "   2010    3725\n",
       "n  2000    1897\n",
       "   2010    1937\n",
       "t  2000    2085\n",
       "   2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# и наоборот, DataFrame м.б. превращен в мультииндексный Series\n",
    "pop_df.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f83910c2",
   "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></th>\n",
       "      <th>total</th>\n",
       "      <th>under18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">c</th>\n",
       "      <th>2000</th>\n",
       "      <td>3387</td>\n",
       "      <td>926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>3725</td>\n",
       "      <td>928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">n</th>\n",
       "      <th>2000</th>\n",
       "      <td>1897</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1937</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">t</th>\n",
       "      <th>2000</th>\n",
       "      <td>2085</td>\n",
       "      <td>590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2514</td>\n",
       "      <td>687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total  under18\n",
       "c 2000   3387      926\n",
       "  2010   3725      928\n",
       "n 2000   1897      468\n",
       "  2010   1937      431\n",
       "t 2000   2085      590\n",
       "  2010   2514      687"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# можно добавать доп столбец\n",
    "pop_df = pd.DataFrame({'total': pop,\n",
    "                      'under18': [926, 928,\n",
    "                                 468, 431,\n",
    "                                 590, 687]})\n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "296a9b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c  2000    0.273398\n",
       "   2010    0.249128\n",
       "n  2000    0.246705\n",
       "   2010    0.222509\n",
       "t  2000    0.282974\n",
       "   2010    0.273270\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_u18 = pop_df['under18'] / pop_df['total']\n",
    "f_u18\n",
    "# сакой способ позволяет легко манипулировать многомерными данными"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1643ed54",
   "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>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.273398</td>\n",
       "      <td>0.249128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.246705</td>\n",
       "      <td>0.222509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.282974</td>\n",
       "      <td>0.273270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       2000      2010\n",
       "c  0.273398  0.249128\n",
       "n  0.246705  0.222509\n",
       "t  0.282974  0.273270"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_u18.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "769f3145",
   "metadata": {},
   "source": [
    "### Методы создания мультииндексов:\n",
    "    Наиболее простой метод создания мультииндекса - передать в конструктор список из двух и более индексных массивов (стр. 168)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82ff733c",
   "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></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0.272002</td>\n",
       "      <td>0.711487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.879524</td>\n",
       "      <td>0.594409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>0.216776</td>\n",
       "      <td>0.009556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.562513</td>\n",
       "      <td>0.208733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        data1     data2\n",
       "a 1  0.272002  0.711487\n",
       "  2  0.879524  0.594409\n",
       "b 1  0.216776  0.009556\n",
       "  2  0.562513  0.208733"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(4,2),\n",
    "                 index=[['a', 'a', 'b', 'b'],\n",
    "                         [1, 2, 1, 2]],\n",
    "                 columns=['data1', 'data2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa556d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Если передавать словарь, с кортежами в качестве ключей Pandas автоматически распознает мультииндекс:\n",
    "data = {\n",
    "    ('c', 2000): 3387,\n",
    "    ('c', 2010): 3725,\n",
    "    ('n', 2000): 2085,\n",
    "    ('n', 2010): 2514,\n",
    "    ('t', 2000): 1897,\n",
    "    ('t', 2010): 1937,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e50af65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c  2000    3387\n",
       "   2010    3725\n",
       "n  2000    2085\n",
       "   2010    2514\n",
       "t  2000    1897\n",
       "   2010    1937\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a83933",
   "metadata": {},
   "source": [
    "### Явные конструкторы для MultiIndex\n",
    "    pd.MultiIndex.методы-конструкторы класса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c5b0f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# из списка массивов\n",
    "pd.MultiIndex.from_arrays([\n",
    "    ['a', 'a', 'b', 'b'],\n",
    "    [1, 2, 1, 2]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3b87a382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# из списка кортежей\n",
    "pd.MultiIndex.from_tuples([\n",
    "    ('a', 1),\n",
    "    ('a', 2),\n",
    "    ('b', 1),\n",
    "    ('b', 2)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a489dccd",
   "metadata": {},
   "source": [
    "##### из декартова произведения обычных индексов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e6eb9013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_product([\n",
    "    ['a', 'b'],\n",
    "    [1, 2]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "99da8c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# с помощью внутреннего представления MultyIndex, передав:\n",
    "# Levels - список списков значений для каждого уровня и\n",
    "# codes - список списков меток\n",
    "pd.MultiIndex(\n",
    "    levels=[['a', 'b'], [1, 2]],\n",
    "    codes=[[0, 0, 1, 1], [0, 1, 0, 1]]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2def357b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.MultiIndex?\n",
    "# pd.MultiIndex(\n",
    "#     levels=None,\n",
    "#     codes=None,\n",
    "#     sortorder=None,\n",
    "#     names=None,\n",
    "#     dtype=None,\n",
    "#     copy=False,\n",
    "#     name=None,\n",
    "#     verify_integrity: 'bool' = True,\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5342caff",
   "metadata": {},
   "source": [
    "#### Названия уровней мультииндексов\n",
    "    Иногда бывает удобно давать названия уровням мультииндексов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e01629dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c  2000    3387\n",
       "   2010    3725\n",
       "n  2000    1897\n",
       "   2010    1937\n",
       "t  2000    2085\n",
       "   2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a573753f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop.index.names = ['state', 'year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "886da530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  year\n",
       "c      2000    3387\n",
       "       2010    3725\n",
       "n      2000    1897\n",
       "       2010    1937\n",
       "t      2000    2085\n",
       "       2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed80c08f",
   "metadata": {},
   "source": [
    "#### Мультииндекс для столбцов:\n",
    "    В объектах DataFrame строки и столбцы полностью симметричны, и у строк и у столбцов может быть несколько уровней индексов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7cdf578f",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_product(\n",
    "    [[2013, 2014], [1, 2]],\n",
    "    names=['year', 'visit']\n",
    ")\n",
    "columns = pd.MultiIndex.from_product(\n",
    "    [['Bob', 'Guido', 'Sue'], ['HR', 'Temp']],\n",
    "    names=['subject', 'type']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c201afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# создаем имитационные данные\n",
    "data = np.round(np.random.randn(4, 6), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "65276cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.4,  0.6, -0.4,  1.5, -1. ,  0.1],\n",
       "       [-0.1, -0.7,  1.6, -1.3, -1. , -1. ],\n",
       "       [-2.5,  0.8, -1.7,  0. ,  0.7, -0.5],\n",
       "       [-1.9,  0.8,  0.2, -1.5, -0.8, -1.1]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4277c092",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[:, ::2] *= 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eb9b13ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "data += 37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d07ae770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33. , 37.6, 33. , 38.5, 27. , 37.1],\n",
       "       [36. , 36.3, 53. , 35.7, 27. , 36. ],\n",
       "       [12. , 37.8, 20. , 37. , 44. , 36.5],\n",
       "       [18. , 37.8, 39. , 35.5, 29. , 35.9]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f0320a80",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>39.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob       Guido         Sue      \n",
       "type          HR  Temp    HR  Temp    HR  Temp\n",
       "year visit                                    \n",
       "2013 1      33.0  37.6  33.0  38.5  27.0  37.1\n",
       "     2      36.0  36.3  53.0  35.7  27.0  36.0\n",
       "2014 1      12.0  37.8  20.0  37.0  44.0  36.5\n",
       "     2      18.0  37.8  39.0  35.5  29.0  35.9"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helth_data = pd.DataFrame(data, index=index, columns=columns)\n",
    "helth_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49909763",
   "metadata": {},
   "source": [
    "###### по сути это четырехмерные данные с измерениями:\n",
    "- субъект\n",
    "- измеряемый параметр type\n",
    "- год\n",
    "- номер посещения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b1664e32",
   "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>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.0</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.0</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type          HR  Temp\n",
       "year visit            \n",
       "2013 1      33.0  38.5\n",
       "     2      53.0  35.7\n",
       "2014 1      20.0  37.0\n",
       "     2      39.0  35.5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helth_data['Guido']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "377efb03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# это удобно для сложных записей, содержащих несколько маркированных неоднократно измеряемых параметров для многих субъектов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34bd2ef5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "24102126",
   "metadata": {},
   "source": [
    "### Индексация и срезы по мультииндексу\n",
    "    Объект MultiIndex сконструирован так, чтобы индексация и срезы по мультииндексу были интуитивно понятны, особенно если думать об индексации как о дополнительных измерениях."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3dc50e5",
   "metadata": {},
   "source": [
    "#### Мультииндексация объектов Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c9edb8ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  year\n",
       "c      2000    3387\n",
       "       2010    3725\n",
       "n      2000    1897\n",
       "       2010    1937\n",
       "t      2000    2085\n",
       "       2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "15d7c753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3387"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop['c', 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d904ee21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2000    3387\n",
       "2010    3725\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# частичная индексация (partial indexing):\n",
    "pop['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4da23fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  year\n",
       "c      2000    3387\n",
       "       2010    3725\n",
       "n      2000    1897\n",
       "       2010    1937\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# частичные срезы:\n",
    "pop['c':'n']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "df20a494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  year\n",
       "c      2000    3387\n",
       "       2010    3725\n",
       "n      2000    1897\n",
       "       2010    1937\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.loc['c':'n']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "801a2679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "c    3387\n",
       "n    1897\n",
       "t    2085\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# частичная индексация по нижним уровням:\n",
    "pop[:, 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5e8192ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  year\n",
       "c      2000    3387\n",
       "       2010    3725\n",
       "t      2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выборка данных на основе булевой маски:\n",
    "pop[pop > 2500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2a769136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  year\n",
       "c      2000    3387\n",
       "       2010    3725\n",
       "t      2000    2085\n",
       "       2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# выборка на основе прихотливой индексации:\n",
    "pop[['c', 't']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a10c18c5",
   "metadata": {},
   "source": [
    "#### Мультииндексация объектов DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fcdfb359",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>39.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob       Guido         Sue      \n",
       "type          HR  Temp    HR  Temp    HR  Temp\n",
       "year visit                                    \n",
       "2013 1      33.0  37.6  33.0  38.5  27.0  37.1\n",
       "     2      36.0  36.3  53.0  35.7  27.0  36.0\n",
       "2014 1      12.0  37.8  20.0  37.0  44.0  36.5\n",
       "     2      18.0  37.8  39.0  35.5  29.0  35.9"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helth_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c21f5aa",
   "metadata": {},
   "source": [
    "###### В объектах DataFrame основными являются столбцы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5667acbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  visit\n",
       "2013  1        33.0\n",
       "      2        53.0\n",
       "2014  1        20.0\n",
       "      2        39.0\n",
       "Name: (Guido, HR), dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helth_data['Guido', 'HR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "820031c3",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>37.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob      \n",
       "type          HR  Temp\n",
       "year visit            \n",
       "2013 1      33.0  37.6\n",
       "     2      36.0  36.3"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helth_data.iloc[:2, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a8b165d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  visit\n",
       "2013  1        33.0\n",
       "      2        36.0\n",
       "2014  1        12.0\n",
       "      2        18.0\n",
       "Name: (Bob, HR), dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helth_data.loc[:, ('Bob', 'HR')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "90dfcf7f",
   "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>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>37.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type          HR  Temp\n",
       "year visit            \n",
       "2013 1      33.0  37.6\n",
       "     2      36.0  36.3\n",
       "2014 1      12.0  37.8\n",
       "     2      18.0  37.8"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# доступ как в матрице\n",
    "helth_data.loc[:, 'Bob']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "202a1c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# helth_data['Guido', 2013] - приведет к ошибке\n",
    "# helth_data['Guido'][2013] - приведет к ошибке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "162f8158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helth_data.loc[(2013, 2), ('Bob', 'HR')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "399764dd",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (Temp/ipykernel_9536/3397362960.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\Admin\\AppData\\Local\\Temp/ipykernel_9536/3397362960.py\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    helth_data.loc[(:, 2), ('Bob', 'HR')]\u001b[0m\n\u001b[1;37m                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# НО! срезы в виде кортежей могут привести к ошибке\n",
    "helth_data.loc[(:, 2), ('Bob', 'HR')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8ad59218",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>Bob</th>\n",
       "      <th>Guido</th>\n",
       "      <th>Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>HR</th>\n",
       "      <th>HR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <th>1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob Guido   Sue\n",
       "type          HR    HR    HR\n",
       "year visit                  \n",
       "2013 1      33.0  33.0  27.0\n",
       "2014 1      12.0  20.0  44.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# для работы со срезами в виде кортежей необходимо сформировать срез явным образом \n",
    "idx = pd.IndexSlice\n",
    "helth_data.loc[idx[:, 1], idx[:, 'HR']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71bf80af",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bbbb97c4",
   "metadata": {},
   "source": [
    "### Перегруппировка мультииндексов\n",
    "    Один из ключей к эффективной работе с данными умение эффективно их преобразовывать ради удобства вычислений.\n",
    "    stack() unstack() и др."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729559c1",
   "metadata": {},
   "source": [
    "#### Отсортированные и неотсортированные индексы\n",
    "##### Большинство операций срезов с мультииндексами завершиться ошибкой, если индекс не отсортирован!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "84da25fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('c', 1),\n",
       "            ('c', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_product(\n",
    "    [['a', 'c', 'b'], [1, 2]]\n",
    ")\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a8ac58ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# index можно передавать как позиционный параметр\n",
    "data = pd.Series(np.random.rand(6), index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b653fb9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1    0.080243\n",
       "   2    0.144768\n",
       "c  1    0.607155\n",
       "   2    0.371881\n",
       "b  1    0.900655\n",
       "   2    0.828185\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "411b1bcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.errors.UnsortedIndexError'>\n",
      "'Key length (1) was greater than MultiIndex lexsort depth (0)'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    data['a':'b']\n",
    "except KeyError as e:\n",
    "    print(type(e))\n",
    "    print(e)\n",
    "# возникла ошибка, по причине того, что индекс не отсортирован"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f26b1247",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "23ae3746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1    0.080243\n",
       "   2    0.144768\n",
       "b  1    0.900655\n",
       "   2    0.828185\n",
       "c  1    0.607155\n",
       "   2    0.371881\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e26116be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1    0.080243\n",
       "   2    0.144768\n",
       "b  1    0.900655\n",
       "   2    0.828185\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['a':'b']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f5a9e1",
   "metadata": {},
   "source": [
    "#### Выполнение операции stack() unstack() над индексами\n",
    "    Существует возможность преобразовать набор данных из вертикального мультииндекса в простое двумерное представление, при необходимости указывая требуемый уровнь."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "321a925d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  year\n",
       "c      2000    3387\n",
       "       2010    3725\n",
       "n      2000    1897\n",
       "       2010    1937\n",
       "t      2000    2085\n",
       "       2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "77b0c779",
   "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>year</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3387</td>\n",
       "      <td>3725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>1897</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>2085</td>\n",
       "      <td>2514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year   2000  2010\n",
       "state            \n",
       "c      3387  3725\n",
       "n      1897  1937\n",
       "t      2085  2514"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popa = pop.unstack()\n",
    "popa\n",
    "# level=1 преобразует 1 уровень индекса в имена столбцов, по умолчанию level=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5477839b",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>c</th>\n",
       "      <th>n</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>3387</td>\n",
       "      <td>1897</td>\n",
       "      <td>2085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>3725</td>\n",
       "      <td>1937</td>\n",
       "      <td>2514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state     c     n     t\n",
       "year                   \n",
       "2000   3387  1897  2085\n",
       "2010   3725  1937  2514"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.unstack(level=0)\n",
    "# level=0 преобразует 0 уровень индекса в имена столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6c68972d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3387</td>\n",
       "      <td>3725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>1897</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>2085</td>\n",
       "      <td>2514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year   2000  2010\n",
       "state            \n",
       "c      3387  3725\n",
       "n      1897  1937\n",
       "t      2085  2514"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.unstack(level=1)\n",
    "# level=1 преобразует 1 уровень индекса в имена столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f14ffad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state  year\n",
       "c      2000    3387\n",
       "       2010    3725\n",
       "n      2000    1897\n",
       "       2010    1937\n",
       "t      2000    2085\n",
       "       2010    2514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popa.stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c31d99d",
   "metadata": {},
   "source": [
    "#### Создание и перестройка индексов\n",
    "    Преобразование метки индекса в столбцы с помощью метода reset_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "40104614",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_flat = pop.reset_index(name='population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ea91136e",
   "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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>2000</td>\n",
       "      <td>3387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c</td>\n",
       "      <td>2010</td>\n",
       "      <td>3725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>n</td>\n",
       "      <td>2000</td>\n",
       "      <td>1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>n</td>\n",
       "      <td>2010</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>t</td>\n",
       "      <td>2000</td>\n",
       "      <td>2085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>t</td>\n",
       "      <td>2010</td>\n",
       "      <td>2514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  year  population\n",
       "0     c  2000        3387\n",
       "1     c  2010        3725\n",
       "2     n  2000        1897\n",
       "3     n  2010        1937\n",
       "4     t  2000        2085\n",
       "5     t  2010        2514"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_flat\n",
    "# часто реальные данные имеют подобный вид:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90f98ed",
   "metadata": {},
   "source": [
    "# САМЫЙ УДОБНЫЙ ПАТТЕРН ДЛЯ РАБОТЫ С РЕАЛЬНЫМИ ДАННЫМИ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9082c182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">c</th>\n",
       "      <th>2000</th>\n",
       "      <td>3387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>3725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">n</th>\n",
       "      <th>2000</th>\n",
       "      <td>1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">t</th>\n",
       "      <th>2000</th>\n",
       "      <td>2085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population\n",
       "state year            \n",
       "c     2000        3387\n",
       "      2010        3725\n",
       "n     2000        1897\n",
       "      2010        1937\n",
       "t     2000        2085\n",
       "      2010        2514"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# поэтому удобно создать MultiIndex из значений столбцов\n",
    "pop_flat.set_index(['state', 'year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6408c5d",
   "metadata": {},
   "source": [
    "### Агрегирование по мультииндексам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "796ee7c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>33.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>39.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob       Guido         Sue      \n",
       "type          HR  Temp    HR  Temp    HR  Temp\n",
       "year visit                                    \n",
       "2013 1      33.0  37.6  33.0  38.5  27.0  37.1\n",
       "     2      36.0  36.3  53.0  35.7  27.0  36.0\n",
       "2014 1      12.0  37.8  20.0  37.0  44.0  36.5\n",
       "     2      18.0  37.8  39.0  35.5  29.0  35.9"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helth_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "20ca9885",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp/ipykernel_9536/3790674258.py:1: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "  data_mean = helth_data.mean(level='year')\n"
     ]
    }
   ],
   "source": [
    "# вычисление среднего значения\n",
    "data_mean = helth_data.mean(level='year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "87c3fa21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
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       "    <tr>\n",
       "      <th>type</th>\n",
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       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>34.5</td>\n",
       "      <td>36.95</td>\n",
       "      <td>43.0</td>\n",
       "      <td>37.10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>15.0</td>\n",
       "      <td>37.80</td>\n",
       "      <td>29.5</td>\n",
       "      <td>36.25</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject   Bob        Guido          Sue       \n",
       "type       HR   Temp    HR   Temp    HR   Temp\n",
       "year                                          \n",
       "2013     34.5  36.95  43.0  37.10  27.0  36.55\n",
       "2014     15.0  37.80  29.5  36.25  36.5  36.20"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0b24b8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
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       "    <tr>\n",
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       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>34.5</td>\n",
       "      <td>36.95</td>\n",
       "      <td>43.0</td>\n",
       "      <td>37.10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>15.0</td>\n",
       "      <td>37.80</td>\n",
       "      <td>29.5</td>\n",
       "      <td>36.25</td>\n",
       "      <td>36.5</td>\n",
       "      <td>36.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject   Bob        Guido          Sue       \n",
       "type       HR   Temp    HR   Temp    HR   Temp\n",
       "year                                          \n",
       "2013     34.5  36.95  43.0  37.10  27.0  36.55\n",
       "2014     15.0  37.80  29.5  36.25  36.5  36.20"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# новый синтаксис для агрегаций\n",
    "data_mean_f = helth_data.groupby('year').mean()\n",
    "data_mean_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b78e42af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>34.833333</td>\n",
       "      <td>36.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>36.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type         HR       Temp\n",
       "year                      \n",
       "2013  34.833333  36.866667\n",
       "2014  27.000000  36.750000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# далее, воспользовавшись axis можно получить среднее значение по уровням по столбцам\n",
    "data_mean_f.groupby(axis=1, level='type').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b3dd549e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp/ipykernel_9536/3676335015.py:1: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.median(level=1) should use df.groupby(level=1).median().\n",
      "  data_mean_f.mean(axis=1, level='type')\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>34.833333</td>\n",
       "      <td>36.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>36.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type         HR       Temp\n",
       "year                      \n",
       "2013  34.833333  36.866667\n",
       "2014  27.000000  36.750000"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mean_f.mean(axis=1, level='type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77a5f7b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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