vander_plus_3_Pandas_08.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "202805d2",
   "metadata": {},
   "source": [
    "## Агрегирование и группировка"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5c8ec1",
   "metadata": {},
   "source": [
    "### Данные о планетах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53eda460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2207df8d220>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 804.125x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df = sns.load_dataset(\"penguins\")\n",
    "sns.pairplot(df, hue=\"species\")\n",
    "# прикольно! матрица из всех возможных графиков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96a10d64",
   "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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>222.0</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>212.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>213.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0    Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1    Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2    Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3    Adelie  Torgersen             NaN            NaN                NaN   \n",
       "4    Adelie  Torgersen            36.7           19.3              193.0   \n",
       "..      ...        ...             ...            ...                ...   \n",
       "339  Gentoo     Biscoe             NaN            NaN                NaN   \n",
       "340  Gentoo     Biscoe            46.8           14.3              215.0   \n",
       "341  Gentoo     Biscoe            50.4           15.7              222.0   \n",
       "342  Gentoo     Biscoe            45.2           14.8              212.0   \n",
       "343  Gentoo     Biscoe            49.9           16.1              213.0   \n",
       "\n",
       "     body_mass_g     sex  \n",
       "0         3750.0    Male  \n",
       "1         3800.0  Female  \n",
       "2         3250.0  Female  \n",
       "3            NaN     NaN  \n",
       "4         3450.0  Female  \n",
       "..           ...     ...  \n",
       "339          NaN     NaN  \n",
       "340       4850.0  Female  \n",
       "341       5750.0    Male  \n",
       "342       5200.0  Female  \n",
       "343       5400.0    Male  \n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89a3de99",
   "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>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300000</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774000</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030000</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220000</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.941507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.00</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>2.615864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.191524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.125083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.187757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1035 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               method  number  orbital_period   mass  distance  year\n",
       "0     Radial Velocity       1      269.300000   7.10     77.40  2006\n",
       "1     Radial Velocity       1      874.774000   2.21     56.95  2008\n",
       "2     Radial Velocity       1      763.000000   2.60     19.84  2011\n",
       "3     Radial Velocity       1      326.030000  19.40    110.62  2007\n",
       "4     Radial Velocity       1      516.220000  10.50    119.47  2009\n",
       "...               ...     ...             ...    ...       ...   ...\n",
       "1030          Transit       1        3.941507    NaN    172.00  2006\n",
       "1031          Transit       1        2.615864    NaN    148.00  2007\n",
       "1032          Transit       1        3.191524    NaN    174.00  2007\n",
       "1033          Transit       1        4.125083    NaN    293.00  2008\n",
       "1034          Transit       1        4.187757    NaN    260.00  2008\n",
       "\n",
       "[1035 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets = sns.load_dataset('planets')\n",
    "planets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86acbd4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1035, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c25497",
   "metadata": {},
   "source": [
    "### Простое агрегирование в библиотеке Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd4d41f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8327268",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "ser = pd.Series(rng.rand(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86bd5a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.374540\n",
       "1    0.950714\n",
       "2    0.731994\n",
       "3    0.598658\n",
       "4    0.156019\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be43d711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.811925491708157"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3fde5f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5623850983416314"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b93d05e3",
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.155995</td>\n",
       "      <td>0.020584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.058084</td>\n",
       "      <td>0.969910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.866176</td>\n",
       "      <td>0.832443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.601115</td>\n",
       "      <td>0.212339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.708073</td>\n",
       "      <td>0.181825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B\n",
       "0  0.155995  0.020584\n",
       "1  0.058084  0.969910\n",
       "2  0.866176  0.832443\n",
       "3  0.601115  0.212339\n",
       "4  0.708073  0.181825"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': rng.rand(5),\n",
    "                   'B': rng.rand(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d2d520c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.477888\n",
       "B    0.443420\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()\n",
    "# в случае DataFrame агрегирующие ф-ии возвращают сводные показатели по каджому столбцу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f9d53c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.088290\n",
       "1    0.513997\n",
       "2    0.849309\n",
       "3    0.406727\n",
       "4    0.444949\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# можно агрегировать и по строкам, задав ось (направление)\n",
    "df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0770ca9e",
   "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>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>498.00000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "      <td>498.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.73494</td>\n",
       "      <td>835.778671</td>\n",
       "      <td>2.509320</td>\n",
       "      <td>52.068213</td>\n",
       "      <td>2007.377510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.17572</td>\n",
       "      <td>1469.128259</td>\n",
       "      <td>3.636274</td>\n",
       "      <td>46.596041</td>\n",
       "      <td>4.167284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.328300</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>38.272250</td>\n",
       "      <td>0.212500</td>\n",
       "      <td>24.497500</td>\n",
       "      <td>2005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>357.000000</td>\n",
       "      <td>1.245000</td>\n",
       "      <td>39.940000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>999.600000</td>\n",
       "      <td>2.867500</td>\n",
       "      <td>59.332500</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.00000</td>\n",
       "      <td>17337.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          number  orbital_period        mass    distance         year\n",
       "count  498.00000      498.000000  498.000000  498.000000   498.000000\n",
       "mean     1.73494      835.778671    2.509320   52.068213  2007.377510\n",
       "std      1.17572     1469.128259    3.636274   46.596041     4.167284\n",
       "min      1.00000        1.328300    0.003600    1.350000  1989.000000\n",
       "25%      1.00000       38.272250    0.212500   24.497500  2005.000000\n",
       "50%      1.00000      357.000000    1.245000   39.940000  2009.000000\n",
       "75%      2.00000      999.600000    2.867500   59.332500  2011.000000\n",
       "max      6.00000    17337.500000   25.000000  354.000000  2014.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.dropna().describe()\n",
    "#  метод .describe() вычисляет сразу несколько распространенных сводных показателей по каждому столбцу"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fdafbad",
   "metadata": {},
   "source": [
    "##### Агрегирующая ф-ия объектов Pandas                                       Описание\n",
    "    count()                                           Общее кол-во эл-тов\n",
    "    first(), last()                                   Первый и последний эл-ты\n",
    "    mean(), median()                                  Среднее значение и медиана\n",
    "    min(), max()                                      Минимум и максимум\n",
    "    std(), var()                                      Стандартное отклонение и дисперсия\n",
    "    mad()                                             Среднее абсолютное отклонение\n",
    "    prod()                                            Произведение всех эл-тов\n",
    "    sum()                                             сумма всех элементов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588d3d68",
   "metadata": {},
   "source": [
    "### GroupBy: разбиение, применение, объединение\n",
    "    операция groupby - позволяет быстро вычислять сводные показатели по подмножествам данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ddd11fa",
   "metadata": {},
   "source": [
    "#### Разбиение, применение и объединение\n",
    "- разбиение включает разделение на части и группировку объекта DataFrame на основе значений заданного ключа.\n",
    "- применение включает вычисление какой-либо ф-ии, обычно агрегирующей, преобразование или фильтрацию в пределах отдельных групп\n",
    "- объединение - выполняется слияние результатов этих операций в выходной массив."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1be06546",
   "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>key</th>\n",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data\n",
       "0   A     0\n",
       "1   B     1\n",
       "2   C     2\n",
       "3   A     3\n",
       "4   B     4\n",
       "5   C     5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "     'data': range(6)},\n",
    "    columns=['key', 'data']\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c195993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000022004616A90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key')\n",
    "# возвращается спец объект DataFrameGroupBy - это спец представление объекта DF готовое к руппировке, но не выполняющее пока\n",
    "# никаких вычислений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "49d45459",
   "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>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data\n",
       "key      \n",
       "A       3\n",
       "B       5\n",
       "C       7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  для получения результата необходимо вызвать один из агрегирующих методов\n",
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2de67bf",
   "metadata": {},
   "source": [
    "#### Объект GroupBy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6773ca9d",
   "metadata": {},
   "source": [
    "###### Индексация по столбцам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32d80a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000022004625640>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "578a3650",
   "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>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300000</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774000</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030000</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220000</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.941507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.00</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>2.615864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>3.191524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.00</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.125083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>Transit</td>\n",
       "      <td>1</td>\n",
       "      <td>4.187757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260.00</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1035 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               method  number  orbital_period   mass  distance  year\n",
       "0     Radial Velocity       1      269.300000   7.10     77.40  2006\n",
       "1     Radial Velocity       1      874.774000   2.21     56.95  2008\n",
       "2     Radial Velocity       1      763.000000   2.60     19.84  2011\n",
       "3     Radial Velocity       1      326.030000  19.40    110.62  2007\n",
       "4     Radial Velocity       1      516.220000  10.50    119.47  2009\n",
       "...               ...     ...             ...    ...       ...   ...\n",
       "1030          Transit       1        3.941507    NaN    172.00  2006\n",
       "1031          Transit       1        2.615864    NaN    148.00  2007\n",
       "1032          Transit       1        3.191524    NaN    174.00  2007\n",
       "1033          Transit       1        4.125083    NaN    293.00  2008\n",
       "1034          Transit       1        4.187757    NaN    260.00  2008\n",
       "\n",
       "[1035 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "036b4931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x0000022004625040>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['orbital_period']\n",
    "# здесь мы выбрали конкретную группу Series сославшись на имя столбца"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86e3c3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method\n",
       "Astrometry                         631.180000\n",
       "Eclipse Timing Variations         4343.500000\n",
       "Imaging                          27500.000000\n",
       "Microlensing                      3300.000000\n",
       "Orbital Brightness Modulation        0.342887\n",
       "Pulsar Timing                       66.541900\n",
       "Pulsation Timing Variations       1170.000000\n",
       "Radial Velocity                    360.200000\n",
       "Transit                              5.714932\n",
       "Transit Timing Variations           57.011000\n",
       "Name: orbital_period, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['orbital_period'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea6a38e7",
   "metadata": {},
   "source": [
    "###### Цикл по группам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a241d50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry                     shape=(2, 6)\n",
      "Eclipse Timing Variations      shape=(9, 6)\n",
      "Imaging                        shape=(38, 6)\n",
      "Microlensing                   shape=(23, 6)\n",
      "Orbital Brightness Modulation  shape=(3, 6)\n",
      "Pulsar Timing                  shape=(5, 6)\n",
      "Pulsation Timing Variations    shape=(1, 6)\n",
      "Radial Velocity                shape=(553, 6)\n",
      "Transit                        shape=(397, 6)\n",
      "Transit Timing Variations      shape=(4, 6)\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print('{0:30s} shape={1}'.format(method, group.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1111fc8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry                     shape=         method  number  orbital_period  mass  distance  year\n",
      "113  Astrometry       1          246.36   NaN     20.77  2013\n",
      "537  Astrometry       1         1016.00   NaN     14.98  2010\n",
      "Eclipse Timing Variations      shape=                       method  number  orbital_period  mass  distance  year\n",
      "32  Eclipse Timing Variations       1        10220.00  6.05       NaN  2009\n",
      "37  Eclipse Timing Variations       2         5767.00   NaN    130.72  2008\n",
      "38  Eclipse Timing Variations       2         3321.00   NaN    130.72  2008\n",
      "39  Eclipse Timing Variations       2         5573.55   NaN    500.00  2010\n",
      "40  Eclipse Timing Variations       2         2883.50   NaN    500.00  2010\n",
      "41  Eclipse Timing Variations       1         2900.00   NaN       NaN  2011\n",
      "42  Eclipse Timing Variations       1         4343.50  4.20       NaN  2012\n",
      "43  Eclipse Timing Variations       2         5840.00   NaN       NaN  2011\n",
      "44  Eclipse Timing Variations       2         1916.25   NaN       NaN  2011\n",
      "Imaging                        shape=       method  number  orbital_period  mass  distance  year\n",
      "29    Imaging       1             NaN   NaN     45.52  2005\n",
      "30    Imaging       1             NaN   NaN    165.00  2007\n",
      "31    Imaging       1             NaN   NaN    140.00  2004\n",
      "33    Imaging       1             NaN   NaN       NaN  2008\n",
      "34    Imaging       1             NaN   NaN    145.00  2013\n",
      "35    Imaging       1             NaN   NaN    139.00  2004\n",
      "36    Imaging       1             NaN   NaN     18.39  2006\n",
      "47    Imaging       1         6000.00   NaN     19.28  2008\n",
      "54    Imaging       1             NaN   NaN     52.03  2012\n",
      "68    Imaging       1       318280.00   NaN      7.69  2008\n",
      "69    Imaging       1             NaN   NaN    145.00  2008\n",
      "70    Imaging       1             NaN   NaN     36.00  2013\n",
      "71    Imaging       1             NaN   NaN    140.00  2010\n",
      "72    Imaging       1         4639.15   NaN     12.21  2009\n",
      "73    Imaging       1             NaN   NaN     52.40  2004\n",
      "74    Imaging       1         7336.50   NaN     25.00  2009\n",
      "75    Imaging       1         8679.70   NaN     26.67  2009\n",
      "89    Imaging       1        10037.50   NaN     23.10  2011\n",
      "90    Imaging       1             NaN   NaN       NaN  2006\n",
      "124   Imaging       1             NaN   NaN     17.95  2013\n",
      "150   Imaging       1             NaN   NaN    145.00  2010\n",
      "392   Imaging       1             NaN   NaN     91.57  2013\n",
      "423   Imaging       1             NaN   NaN     91.83  2013\n",
      "577   Imaging       1             NaN   NaN     40.85  2006\n",
      "635   Imaging       1             NaN   NaN     11.43  2010\n",
      "639   Imaging       1             NaN   NaN    131.93  2010\n",
      "643   Imaging       4       170000.00   NaN     39.94  2008\n",
      "644   Imaging       4        69000.00   NaN     39.94  2008\n",
      "645   Imaging       4        37000.00   NaN     39.94  2008\n",
      "646   Imaging       4        18000.00   NaN     39.94  2010\n",
      "900   Imaging       1        40000.00   NaN       NaN  2011\n",
      "937   Imaging       1       730000.00   NaN       NaN  2006\n",
      "948   Imaging       1             NaN   NaN    135.00  2013\n",
      "949   Imaging       1             NaN   NaN    120.00  2013\n",
      "950   Imaging       1             NaN   NaN       NaN  2010\n",
      "957   Imaging       1             NaN   NaN       NaN  2008\n",
      "1027  Imaging       1             NaN   NaN     19.20  2011\n",
      "1029  Imaging       1             NaN   NaN     10.10  2012\n",
      "Microlensing                   shape=           method  number  orbital_period  mass  distance  year\n",
      "902  Microlensing       1             NaN   NaN       NaN  2008\n",
      "903  Microlensing       1             NaN   NaN       NaN  2008\n",
      "904  Microlensing       1             NaN   NaN       NaN  2009\n",
      "905  Microlensing       1             NaN   NaN    3600.0  2013\n",
      "906  Microlensing       1          2780.0   NaN       NaN  2011\n",
      "907  Microlensing       1             NaN   NaN       NaN  2010\n",
      "908  Microlensing       1          1970.0   NaN       NaN  2010\n",
      "909  Microlensing       1             NaN   NaN    2300.0  2012\n",
      "910  Microlensing       1             NaN   NaN    2800.0  2012\n",
      "911  Microlensing       1             NaN   NaN    7720.0  2012\n",
      "912  Microlensing       1             NaN   NaN    7560.0  2013\n",
      "917  Microlensing       1             NaN   NaN       NaN  2004\n",
      "918  Microlensing       1          3600.0   NaN       NaN  2005\n",
      "919  Microlensing       1          3300.0   NaN       NaN  2006\n",
      "920  Microlensing       1          3500.0   NaN       NaN  2005\n",
      "921  Microlensing       2          1825.0   NaN       NaN  2008\n",
      "922  Microlensing       2          5100.0   NaN       NaN  2008\n",
      "923  Microlensing       1             NaN   NaN       NaN  2009\n",
      "924  Microlensing       1             NaN   NaN    2570.0  2012\n",
      "925  Microlensing       2             NaN   NaN    4080.0  2012\n",
      "926  Microlensing       2             NaN   NaN    4080.0  2012\n",
      "927  Microlensing       1             NaN   NaN    1760.0  2013\n",
      "928  Microlensing       1             NaN   NaN    4970.0  2013\n",
      "Orbital Brightness Modulation  shape=                            method  number  orbital_period  mass  distance  \\\n",
      "787  Orbital Brightness Modulation       2        0.240104   NaN    1180.0   \n",
      "788  Orbital Brightness Modulation       2        0.342887   NaN    1180.0   \n",
      "792  Orbital Brightness Modulation       1        1.544929   NaN       NaN   \n",
      "\n",
      "     year  \n",
      "787  2011  \n",
      "788  2011  \n",
      "792  2013  \n",
      "Pulsar Timing                  shape=            method  number  orbital_period  mass  distance  year\n",
      "941  Pulsar Timing       3       25.262000   NaN       NaN  1992\n",
      "942  Pulsar Timing       3       66.541900   NaN       NaN  1992\n",
      "943  Pulsar Timing       3       98.211400   NaN       NaN  1994\n",
      "944  Pulsar Timing       1    36525.000000   NaN       NaN  2003\n",
      "945  Pulsar Timing       1        0.090706   NaN    1200.0  2011\n",
      "Pulsation Timing Variations    shape=                          method  number  orbital_period  mass  distance  year\n",
      "958  Pulsation Timing Variations       1          1170.0   NaN       NaN  2007\n",
      "Radial Velocity                shape=              method  number  orbital_period   mass  distance  year\n",
      "0    Radial Velocity       1        269.3000   7.10     77.40  2006\n",
      "1    Radial Velocity       1        874.7740   2.21     56.95  2008\n",
      "2    Radial Velocity       1        763.0000   2.60     19.84  2011\n",
      "3    Radial Velocity       1        326.0300  19.40    110.62  2007\n",
      "4    Radial Velocity       1        516.2200  10.50    119.47  2009\n",
      "..               ...     ...             ...    ...       ...   ...\n",
      "914  Radial Velocity       1          6.9580   0.34       NaN  2014\n",
      "915  Radial Velocity       1          5.1180   0.40       NaN  2014\n",
      "916  Radial Velocity       1        121.7100   1.54       NaN  2014\n",
      "939  Radial Velocity       1          4.4264    NaN       NaN  2012\n",
      "940  Radial Velocity       1          2.1451    NaN       NaN  2012\n",
      "\n",
      "[553 rows x 6 columns]\n",
      "Transit                        shape=       method  number  orbital_period  mass  distance  year\n",
      "91    Transit       1        1.508956   NaN       NaN  2008\n",
      "92    Transit       1        1.742994   NaN     200.0  2008\n",
      "93    Transit       1        4.256800   NaN     680.0  2008\n",
      "94    Transit       1        9.202050   NaN       NaN  2008\n",
      "95    Transit       1        4.037896   NaN       NaN  2009\n",
      "...       ...     ...             ...   ...       ...   ...\n",
      "1030  Transit       1        3.941507   NaN     172.0  2006\n",
      "1031  Transit       1        2.615864   NaN     148.0  2007\n",
      "1032  Transit       1        3.191524   NaN     174.0  2007\n",
      "1033  Transit       1        4.125083   NaN     293.0  2008\n",
      "1034  Transit       1        4.187757   NaN     260.0  2008\n",
      "\n",
      "[397 rows x 6 columns]\n",
      "Transit Timing Variations      shape=                        method  number  orbital_period  mass  distance  year\n",
      "680  Transit Timing Variations       2        160.0000   NaN    2119.0  2011\n",
      "736  Transit Timing Variations       2         57.0110   NaN     855.0  2012\n",
      "749  Transit Timing Variations       3             NaN   NaN       NaN  2014\n",
      "813  Transit Timing Variations       2         22.3395   NaN     339.0  2013\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print('{0:30s} shape={1}'.format(method, group))\n",
    "# shape=(9, 6) - означает, что Eclipse Timing Variations имеет 9 записей по 6 столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70e2e979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry shape=((2, 6))\n",
      "Eclipse Timing Variations shape=((9, 6))\n",
      "Imaging shape=((38, 6))\n",
      "Microlensing shape=((23, 6))\n",
      "Orbital Brightness Modulation shape=((3, 6))\n",
      "Pulsar Timing shape=((5, 6))\n",
      "Pulsation Timing Variations shape=((1, 6))\n",
      "Radial Velocity shape=((553, 6))\n",
      "Transit shape=((397, 6))\n",
      "Transit Timing Variations shape=((4, 6))\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print(f'{method} shape=({group.shape})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "49e39dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry                                                                                           shape=(2, 6)\n",
      "Eclipse Timing Variations                                                                            shape=(9, 6)\n",
      "Imaging                                                                                              shape=(38, 6)\n",
      "Microlensing                                                                                         shape=(23, 6)\n",
      "Orbital Brightness Modulation                                                                        shape=(3, 6)\n",
      "Pulsar Timing                                                                                        shape=(5, 6)\n",
      "Pulsation Timing Variations                                                                          shape=(1, 6)\n",
      "Radial Velocity                                                                                      shape=(553, 6)\n",
      "Transit                                                                                              shape=(397, 6)\n",
      "Transit Timing Variations                                                                            shape=(4, 6)\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print('{0:100s} shape={1}'.format(method, group.shape))\n",
    "# {0:100s} - означает размер столбца в символах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "147ff1d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry           shape=(2, 6)\n",
      "Eclipse Timing Variations shape=(9, 6)\n",
      "Imaging              shape=(38, 6)\n",
      "Microlensing         shape=(23, 6)\n",
      "Orbital Brightness Modulation shape=(3, 6)\n",
      "Pulsar Timing        shape=(5, 6)\n",
      "Pulsation Timing Variations shape=(1, 6)\n",
      "Radial Velocity      shape=(553, 6)\n",
      "Transit              shape=(397, 6)\n",
      "Transit Timing Variations shape=(4, 6)\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print('{0:20s} shape={1}'.format(method, group.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "59c7e29c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry                    0 shape=((2, 6))\n",
      "Eclipse Timing Variations                    0 shape=((9, 6))\n",
      "Imaging                    0 shape=((38, 6))\n",
      "Microlensing                    0 shape=((23, 6))\n",
      "Orbital Brightness Modulation                    0 shape=((3, 6))\n",
      "Pulsar Timing                    0 shape=((5, 6))\n",
      "Pulsation Timing Variations                    0 shape=((1, 6))\n",
      "Radial Velocity                    0 shape=((553, 6))\n",
      "Transit                    0 shape=((397, 6))\n",
      "Transit Timing Variations                    0 shape=((4, 6))\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print(f'{method} {0:20} shape=({group.shape})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e7f0207c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Astrometry                     shape=(2, 6)\n",
      "Eclipse Timing Variations      shape=(9, 6)\n",
      "Imaging                        shape=(38, 6)\n",
      "Microlensing                   shape=(23, 6)\n",
      "Orbital Brightness Modulation  shape=(3, 6)\n",
      "Pulsar Timing                  shape=(5, 6)\n",
      "Pulsation Timing Variations    shape=(1, 6)\n",
      "Radial Velocity                shape=(553, 6)\n",
      "Transit                        shape=(397, 6)\n",
      "Transit Timing Variations      shape=(4, 6)\n"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method'):\n",
    "    print(f'{method:30} shape={group.shape}')\n",
    "# САМЫЙ ЧЁТКИЙ СПОСОБ!!!\n",
    "# f' строка была послана БОГОМ!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea664e81",
   "metadata": {},
   "source": [
    "###### Методы диспетчеризации.\n",
    "    Все методы, не реализованные явным образом объектом GroupBy, будут передаваться далее и выполняться для групп, вне зависимости от того, являются они объектами Series или DF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "193b211b",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></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>Astrometry</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2011.500000</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010.75</td>\n",
       "      <td>2011.5</td>\n",
       "      <td>2012.25</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>38.0</td>\n",
       "      <td>2009.131579</td>\n",
       "      <td>2.781901</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2008.00</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>23.0</td>\n",
       "      <td>2009.782609</td>\n",
       "      <td>2.859697</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2008.00</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2012.00</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2011.666667</td>\n",
       "      <td>1.154701</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2012.00</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1998.400000</td>\n",
       "      <td>8.384510</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1992.00</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>2003.00</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>2007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>553.0</td>\n",
       "      <td>2007.518987</td>\n",
       "      <td>4.249052</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>2005.00</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>397.0</td>\n",
       "      <td>2011.236776</td>\n",
       "      <td>2.077867</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2010.00</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2012.500000</td>\n",
       "      <td>1.290994</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2011.75</td>\n",
       "      <td>2012.5</td>\n",
       "      <td>2013.25</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               count         mean       std     min      25%  \\\n",
       "method                                                                         \n",
       "Astrometry                       2.0  2011.500000  2.121320  2010.0  2010.75   \n",
       "Eclipse Timing Variations        9.0  2010.000000  1.414214  2008.0  2009.00   \n",
       "Imaging                         38.0  2009.131579  2.781901  2004.0  2008.00   \n",
       "Microlensing                    23.0  2009.782609  2.859697  2004.0  2008.00   \n",
       "Orbital Brightness Modulation    3.0  2011.666667  1.154701  2011.0  2011.00   \n",
       "Pulsar Timing                    5.0  1998.400000  8.384510  1992.0  1992.00   \n",
       "Pulsation Timing Variations      1.0  2007.000000       NaN  2007.0  2007.00   \n",
       "Radial Velocity                553.0  2007.518987  4.249052  1989.0  2005.00   \n",
       "Transit                        397.0  2011.236776  2.077867  2002.0  2010.00   \n",
       "Transit Timing Variations        4.0  2012.500000  1.290994  2011.0  2011.75   \n",
       "\n",
       "                                  50%      75%     max  \n",
       "method                                                  \n",
       "Astrometry                     2011.5  2012.25  2013.0  \n",
       "Eclipse Timing Variations      2010.0  2011.00  2012.0  \n",
       "Imaging                        2009.0  2011.00  2013.0  \n",
       "Microlensing                   2010.0  2012.00  2013.0  \n",
       "Orbital Brightness Modulation  2011.0  2012.00  2013.0  \n",
       "Pulsar Timing                  1994.0  2003.00  2011.0  \n",
       "Pulsation Timing Variations    2007.0  2007.00  2007.0  \n",
       "Radial Velocity                2009.0  2011.00  2014.0  \n",
       "Transit                        2012.0  2013.00  2014.0  \n",
       "Transit Timing Variations      2012.5  2013.25  2014.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['year'].describe()\n",
    "# КОРОЧЕ ТЕПЕРЬ ТАК ПРАВИЛЬНО!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7eb817ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       method                       \n",
       "count  Astrometry                          2.0\n",
       "       Eclipse Timing Variations           9.0\n",
       "       Imaging                            38.0\n",
       "       Microlensing                       23.0\n",
       "       Orbital Brightness Modulation       3.0\n",
       "                                         ...  \n",
       "max    Pulsar Timing                    2011.0\n",
       "       Pulsation Timing Variations      2007.0\n",
       "       Radial Velocity                  2014.0\n",
       "       Transit                          2014.0\n",
       "       Transit Timing Variations        2014.0\n",
       "Length: 80, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['year'].describe().unstack()\n",
    "# поведение изменилось (не как в книжке) - так неправильно!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca33d8ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       method                       \n",
      "count  Astrometry                          2.0\n",
      "       Eclipse Timing Variations           9.0\n",
      "       Imaging                            38.0\n",
      "       Microlensing                       23.0\n",
      "       Orbital Brightness Modulation       3.0\n",
      "                                         ...  \n",
      "max    Pulsar Timing                    2011.0\n",
      "       Pulsation Timing Variations      2007.0\n",
      "       Radial Velocity                  2014.0\n",
      "       Transit                          2014.0\n",
      "       Transit Timing Variations        2014.0\n",
      "Length: 80, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(planets.groupby('method')['year'].describe().unstack())\n",
    "# не хочет печатать всё строки (как в книжке)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c6ef4505",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot unpack non-iterable float object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_14764/3631352662.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplanets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'method'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'year'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{method} {group}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot unpack non-iterable float object"
     ]
    }
   ],
   "source": [
    "for (method, group) in planets.groupby('method')['year'].describe().unstack():\n",
    "    print(f'{method} {group}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "928fffbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       method                       \n",
       "count  Astrometry                          2.000000\n",
       "       Eclipse Timing Variations           9.000000\n",
       "       Imaging                            38.000000\n",
       "       Microlensing                       23.000000\n",
       "       Orbital Brightness Modulation       3.000000\n",
       "       Pulsar Timing                       5.000000\n",
       "       Pulsation Timing Variations         1.000000\n",
       "       Radial Velocity                   553.000000\n",
       "       Transit                           397.000000\n",
       "       Transit Timing Variations           4.000000\n",
       "mean   Astrometry                       2011.500000\n",
       "       Eclipse Timing Variations        2010.000000\n",
       "       Imaging                          2009.131579\n",
       "       Microlensing                     2009.782609\n",
       "       Orbital Brightness Modulation    2011.666667\n",
       "       Pulsar Timing                    1998.400000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2007.518987\n",
       "       Transit                          2011.236776\n",
       "       Transit Timing Variations        2012.500000\n",
       "std    Astrometry                          2.121320\n",
       "       Eclipse Timing Variations           1.414214\n",
       "       Imaging                             2.781901\n",
       "       Microlensing                        2.859697\n",
       "       Orbital Brightness Modulation       1.154701\n",
       "       Pulsar Timing                       8.384510\n",
       "       Pulsation Timing Variations              NaN\n",
       "       Radial Velocity                     4.249052\n",
       "       Transit                             2.077867\n",
       "       Transit Timing Variations           1.290994\n",
       "min    Astrometry                       2010.000000\n",
       "       Eclipse Timing Variations        2008.000000\n",
       "       Imaging                          2004.000000\n",
       "       Microlensing                     2004.000000\n",
       "       Orbital Brightness Modulation    2011.000000\n",
       "       Pulsar Timing                    1992.000000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  1989.000000\n",
       "       Transit                          2002.000000\n",
       "       Transit Timing Variations        2011.000000\n",
       "25%    Astrometry                       2010.750000\n",
       "       Eclipse Timing Variations        2009.000000\n",
       "       Imaging                          2008.000000\n",
       "       Microlensing                     2008.000000\n",
       "       Orbital Brightness Modulation    2011.000000\n",
       "       Pulsar Timing                    1992.000000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2005.000000\n",
       "       Transit                          2010.000000\n",
       "       Transit Timing Variations        2011.750000\n",
       "50%    Astrometry                       2011.500000\n",
       "       Eclipse Timing Variations        2010.000000\n",
       "       Imaging                          2009.000000\n",
       "       Microlensing                     2010.000000\n",
       "       Orbital Brightness Modulation    2011.000000\n",
       "       Pulsar Timing                    1994.000000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2009.000000\n",
       "       Transit                          2012.000000\n",
       "       Transit Timing Variations        2012.500000\n",
       "75%    Astrometry                       2012.250000\n",
       "       Eclipse Timing Variations        2011.000000\n",
       "       Imaging                          2011.000000\n",
       "       Microlensing                     2012.000000\n",
       "       Orbital Brightness Modulation    2012.000000\n",
       "       Pulsar Timing                    2003.000000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2011.000000\n",
       "       Transit                          2013.000000\n",
       "       Transit Timing Variations        2013.250000\n",
       "max    Astrometry                       2013.000000\n",
       "       Eclipse Timing Variations        2012.000000\n",
       "       Imaging                          2013.000000\n",
       "       Microlensing                     2013.000000\n",
       "       Orbital Brightness Modulation    2013.000000\n",
       "       Pulsar Timing                    2011.000000\n",
       "       Pulsation Timing Variations      2007.000000\n",
       "       Radial Velocity                  2014.000000\n",
       "       Transit                          2014.000000\n",
       "       Transit Timing Variations        2014.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 80\n",
    "planets.groupby('method')['year'].describe().unstack()\n",
    "# но всё равно такой себе способ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "096e556c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ae91ff3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       method                       \n",
       "count  Astrometry                          2.0\n",
       "       Eclipse Timing Variations           9.0\n",
       "       Imaging                            38.0\n",
       "       Microlensing                       23.0\n",
       "       Orbital Brightness Modulation       3.0\n",
       "                                         ...  \n",
       "max    Pulsar Timing                    2011.0\n",
       "       Pulsation Timing Variations      2007.0\n",
       "       Radial Velocity                  2014.0\n",
       "       Transit                          2014.0\n",
       "       Transit Timing Variations        2014.0\n",
       "Length: 80, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby('method')['year'].describe().unstack()\n",
    "# ааа, он обрезает массив как только тот превышает допустимый лимит строк"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54dad85f",
   "metadata": {},
   "source": [
    "#### Агрегирование, фильтрация, преобразование, применение\n",
    "    методы эффективно выполняющие операции до объединения сгруппированых строк"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a0f9db22",
   "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>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "df = pd.DataFrame(\n",
    "    {'key': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "     'data1': range(6),\n",
    "     'data2': rng.randint(0, 10, 6)\n",
    "    },\n",
    "    columns = ['key', 'data1', 'data2']\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889e2944",
   "metadata": {},
   "source": [
    "###### Агрегирование.\n",
    "    метод .aggregate() может вычислять все сводные показатели сразу! [в отличии от mean(), sum() и т.д.] \n",
    "    Принимает на вход строку, ф-ию, список."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ad66ec27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\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 colspan=\"3\" halign=\"left\">data1</th>\n",
       "      <th colspan=\"3\" halign=\"left\">data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</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>A</th>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data1            data2           \n",
       "      min median max   min median max\n",
       "key                                  \n",
       "A       0    1.5   3     3    4.0   5\n",
       "B       1    2.5   4     0    3.5   7\n",
       "C       2    3.5   5     3    6.0   9"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').aggregate(['min', np.median, max])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "25f2072c",
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        0      5\n",
       "B        1      7\n",
       "C        2      9"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# можно передать в метод стоварь, связывающий имена столбцов с операциями, которые д.б к ним применены\n",
    "df.groupby('key').aggregate({'data1': 'min',\n",
    "                             'data2': max})\n",
    "# ЧЁТКО!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "415db709",
   "metadata": {},
   "source": [
    "###### Фильтрация.\n",
    "    позволяет опускать данные, в зависимости от свойств группы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d0b81317",
   "metadata": {},
   "outputs": [],
   "source": [
    "# фильтр по стандартному отклонению\n",
    "def filter_func(x):\n",
    "    return x['data2'].std() > 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "709f9932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
       "      <td>A</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "75d69007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>4.949747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>4.242641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       data1     data2\n",
       "key                   \n",
       "A    2.12132  1.414214\n",
       "B    2.12132  4.949747\n",
       "C    2.12132  4.242641"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "076461c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\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>key</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').filter(filter_func)\n",
    "# фильтр по стандартному отклонению > 4\n",
    "# ф-ия filter_func возвращает булево значение"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56dd78ce",
   "metadata": {},
   "source": [
    "###### Преобразование.\n",
    "    Может вернуть полный набор преобразованных данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e5bf3552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  data2\n",
       "0   -1.5    1.0\n",
       "1   -1.5   -3.5\n",
       "2   -1.5   -3.0\n",
       "3    1.5   -1.0\n",
       "4    1.5    3.5\n",
       "5    1.5    3.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').transform(lambda x: x - x.mean())\n",
    "# центрирование путем вычитания среднего по группам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "83730ecc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1  data2\n",
       "0    1.5    4.0\n",
       "1    2.5    3.5\n",
       "2    3.5    6.0\n",
       "3    1.5    4.0\n",
       "4    2.5    3.5\n",
       "5    3.5    6.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').transform(lambda x: x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4e2992cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
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       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8513c62b",
   "metadata": {},
   "source": [
    "###### Метод apply().\n",
    "    Позволяет применять произвольную ф-ию к результатам группировки [типа map()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4f3de5b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_by_data2(x):\n",
    "    \"\"\" x - объект DF сгруппированых значений\"\"\"\n",
    "    x['data1'] /= x['data2'].sum()\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "044d951a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>data2</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key     data1  data2\n",
       "0   A  0.000000      5\n",
       "1   B  0.142857      0\n",
       "2   C  0.166667      3\n",
       "3   A  0.375000      3\n",
       "4   B  0.571429      7\n",
       "5   C  0.416667      9"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').apply(norm_by_data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010944bd",
   "metadata": {},
   "source": [
    "#### Задание ключа разбиения"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c329a0f",
   "metadata": {},
   "source": [
    "###### Список массивов, объект Series и индекс как ключи группировки.\n",
    "    Ключ может быть любым рядом или списком такой же длины, как у объекта DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "efa698a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>17</td>\n",
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       "      <th>1</th>\n",
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      ],
      "text/plain": [
       "   data1  data2\n",
       "0      7     17\n",
       "1      4      3\n",
       "2      4      7"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = [0, 1, 0, 1, 2, 0]\n",
    "df.groupby(L).sum()\n",
    "# 0: 0+2+5=7   1: 1+3=4  2: 4\n",
    "# 0: 5+3+9=17  1: 0+3=3  2: 7\n",
    "# т.е. он произвел группировку согласно новым предложенным ключам из L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9dc7d01c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>3</th>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
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       "      <th>5</th>\n",
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       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key  data1  data2\n",
       "0   A      0      5\n",
       "1   B      1      0\n",
       "2   C      2      3\n",
       "3   A      3      3\n",
       "4   B      4      7\n",
       "5   C      5      9"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5d90418d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>8</td>\n",
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       "      <th>C</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        3      8\n",
       "B        5      7\n",
       "C        7     12"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "14c33c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        3      8\n",
       "B        5      7\n",
       "C        7     12"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.groupby(df['key']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "16efbeac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    A\n",
       "1    B\n",
       "2    C\n",
       "3    A\n",
       "4    B\n",
       "5    C\n",
       "Name: key, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['key']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b758efca",
   "metadata": {},
   "source": [
    "###### Словарь или объект Series, связывающий индекс и группу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "253b75bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "A        0      5\n",
       "B        1      0\n",
       "C        2      3\n",
       "A        3      3\n",
       "B        4      7\n",
       "C        5      9"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.set_index('key')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d85cbcb3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>consonant</th>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vowel</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           data1  data2\n",
       "key                    \n",
       "consonant     12     19\n",
       "vowel          3      8"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = {'A': 'vowel', 'B': 'consonant', 'C': 'consonant'}\n",
    "df2.groupby(mapping).sum()\n",
    "# задали новое соответствие для ключей"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d7f588",
   "metadata": {},
   "source": [
    "###### Любая ф-ия языка Python\n",
    "    можно передать любую ф-ию, принимающую на входе значение индекса и возвращающую группу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "07a42722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data1  data2\n",
       "key              \n",
       "a      1.5    4.0\n",
       "b      2.5    3.5\n",
       "c      3.5    6.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(str.lower).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd1b1217",
   "metadata": {},
   "source": [
    "###### Список допустимых ключей\n",
    "    Комбинация для группировки по мультииндексу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8e86319d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th>vowel</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>consonant</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>consonant</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               data1  data2\n",
       "key key                    \n",
       "a   vowel        1.5    4.0\n",
       "b   consonant    2.5    3.5\n",
       "c   consonant    3.5    6.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby([str.lower, mapping]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09425a61",
   "metadata": {},
   "source": [
    "#### Пример группировки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "34110bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2000\n",
       "1       2000\n",
       "2       2010\n",
       "3       2000\n",
       "4       2000\n",
       "        ... \n",
       "1030    2000\n",
       "1031    2000\n",
       "1032    2000\n",
       "1033    2000\n",
       "1034    2000\n",
       "Name: year, Length: 1035, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade = 10 * (planets['year'] // 10)\n",
    "decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "21f97c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2000s\n",
       "1       2000s\n",
       "2       2010s\n",
       "3       2000s\n",
       "4       2000s\n",
       "        ...  \n",
       "1030    2000s\n",
       "1031    2000s\n",
       "1032    2000s\n",
       "1033    2000s\n",
       "1034    2000s\n",
       "Name: year, Length: 1035, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade = decade.astype(str) + 's'\n",
    "decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "24e184cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2000s\n",
       "1       2000s\n",
       "2       2010s\n",
       "3       2000s\n",
       "4       2000s\n",
       "        ...  \n",
       "1030    2000s\n",
       "1031    2000s\n",
       "1032    2000s\n",
       "1033    2000s\n",
       "1034    2000s\n",
       "Name: decade, Length: 1035, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decade.name = 'decade'\n",
    "decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a351e33d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "method                         decade\n",
       "Astrometry                     2010s       2\n",
       "Eclipse Timing Variations      2000s       5\n",
       "                               2010s      10\n",
       "Imaging                        2000s      29\n",
       "                               2010s      21\n",
       "Microlensing                   2000s      12\n",
       "                               2010s      15\n",
       "Orbital Brightness Modulation  2010s       5\n",
       "Pulsar Timing                  1990s       9\n",
       "                               2000s       1\n",
       "                               2010s       1\n",
       "Pulsation Timing Variations    2000s       1\n",
       "Radial Velocity                1980s       1\n",
       "                               1990s      52\n",
       "                               2000s     475\n",
       "                               2010s     424\n",
       "Transit                        2000s      64\n",
       "                               2010s     712\n",
       "Transit Timing Variations      2010s       9\n",
       "Name: number, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby(['method', decade])['number'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a3c389da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>decade</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "      <th>2010s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astrometry</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "decade                         1980s  1990s  2000s  2010s\n",
       "method                                                   \n",
       "Astrometry                       NaN    NaN    NaN    2.0\n",
       "Eclipse Timing Variations        NaN    NaN    5.0   10.0\n",
       "Imaging                          NaN    NaN   29.0   21.0\n",
       "Microlensing                     NaN    NaN   12.0   15.0\n",
       "Orbital Brightness Modulation    NaN    NaN    NaN    5.0\n",
       "Pulsar Timing                    NaN    9.0    1.0    1.0\n",
       "Pulsation Timing Variations      NaN    NaN    1.0    NaN\n",
       "Radial Velocity                  1.0   52.0  475.0  424.0\n",
       "Transit                          NaN    NaN   64.0  712.0\n",
       "Transit Timing Variations        NaN    NaN    NaN    9.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby(['method', decade])['number'].sum().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1d49b1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>decade</th>\n",
       "      <th>1980s</th>\n",
       "      <th>1990s</th>\n",
       "      <th>2000s</th>\n",
       "      <th>2010s</th>\n",
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       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Astrometry</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Imaging</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Microlensing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Velocity</th>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transit Timing Variations</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "decade                         1980s  1990s  2000s  2010s\n",
       "method                                                   \n",
       "Astrometry                       0.0    0.0    0.0    2.0\n",
       "Eclipse Timing Variations        0.0    0.0    5.0   10.0\n",
       "Imaging                          0.0    0.0   29.0   21.0\n",
       "Microlensing                     0.0    0.0   12.0   15.0\n",
       "Orbital Brightness Modulation    0.0    0.0    0.0    5.0\n",
       "Pulsar Timing                    0.0    9.0    1.0    1.0\n",
       "Pulsation Timing Variations      0.0    0.0    1.0    0.0\n",
       "Radial Velocity                  1.0   52.0  475.0  424.0\n",
       "Transit                          0.0    0.0   64.0  712.0\n",
       "Transit Timing Variations        0.0    0.0    0.0    9.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby(['method', decade])['number'].sum().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "78d0263a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "decade  method                       \n",
       "1980s   Astrometry                         NaN\n",
       "        Eclipse Timing Variations          NaN\n",
       "        Imaging                            NaN\n",
       "        Microlensing                       NaN\n",
       "        Orbital Brightness Modulation      NaN\n",
       "        Pulsar Timing                      NaN\n",
       "        Pulsation Timing Variations        NaN\n",
       "        Radial Velocity                    1.0\n",
       "        Transit                            NaN\n",
       "        Transit Timing Variations          NaN\n",
       "1990s   Astrometry                         NaN\n",
       "        Eclipse Timing Variations          NaN\n",
       "        Imaging                            NaN\n",
       "        Microlensing                       NaN\n",
       "        Orbital Brightness Modulation      NaN\n",
       "        Pulsar Timing                      9.0\n",
       "        Pulsation Timing Variations        NaN\n",
       "        Radial Velocity                   52.0\n",
       "        Transit                            NaN\n",
       "        Transit Timing Variations          NaN\n",
       "2000s   Astrometry                         NaN\n",
       "        Eclipse Timing Variations          5.0\n",
       "        Imaging                           29.0\n",
       "        Microlensing                      12.0\n",
       "        Orbital Brightness Modulation      NaN\n",
       "        Pulsar Timing                      1.0\n",
       "        Pulsation Timing Variations        1.0\n",
       "        Radial Velocity                  475.0\n",
       "        Transit                           64.0\n",
       "        Transit Timing Variations          NaN\n",
       "2010s   Astrometry                         2.0\n",
       "        Eclipse Timing Variations         10.0\n",
       "        Imaging                           21.0\n",
       "        Microlensing                      15.0\n",
       "        Orbital Brightness Modulation      5.0\n",
       "        Pulsar Timing                      1.0\n",
       "        Pulsation Timing Variations        NaN\n",
       "        Radial Velocity                  424.0\n",
       "        Transit                          712.0\n",
       "        Transit Timing Variations          9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets.groupby(['method', decade])['number'].sum().unstack().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbc8670c",
   "metadata": {},
   "outputs": [],
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