groupby/2.use-groupby-multiple-columns-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to Use GroupBy with Multiple Columns in Pandas\n",
    "\n",
    "https://datascientyst.com/use-groupby-multiple-columns-pandas/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('../data/medium_data.csv.zip')\n",
    "\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df['date_m'] = df['date'].dt.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>title</th>\n",
       "      <th>date</th>\n",
       "      <th>publication</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3974</th>\n",
       "      <td>How to Start a Motorcycle Ride sharing Company...</td>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>Data Driven Investor</td>\n",
       "      <td>https://medium.com/datadriveninvestor/how-to-s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4318</th>\n",
       "      <td>How To Build a Pitch Deck</td>\n",
       "      <td>2019-10-23</td>\n",
       "      <td>Data Driven Investor</td>\n",
       "      <td>https://medium.com/datadriveninvestor/how-to-b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>What is Marketing Segmentation, and why you ne...</td>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>The Startup</td>\n",
       "      <td>https://medium.com/swlh/what-is-marketing-segm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4203</th>\n",
       "      <td>How to create a meaningful relationship</td>\n",
       "      <td>2019-10-23</td>\n",
       "      <td>UX Collective</td>\n",
       "      <td>https://uxdesign.cc/how-to-create-a-meaningful...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>&lt;strong class=\"markup--strong markup--h3-stron...</td>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>The Startup</td>\n",
       "      <td>https://medium.com/swlh/how-to-navigate-the-st...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title       date  \\\n",
       "3974  How to Start a Motorcycle Ride sharing Company... 2019-05-24   \n",
       "4318                          How To Build a Pitch Deck 2019-10-23   \n",
       "1679  What is Marketing Segmentation, and why you ne... 2019-10-28   \n",
       "4203            How to create a meaningful relationship 2019-10-23   \n",
       "3926  <strong class=\"markup--strong markup--h3-stron... 2019-05-24   \n",
       "\n",
       "               publication                                                url  \n",
       "3974  Data Driven Investor  https://medium.com/datadriveninvestor/how-to-s...  \n",
       "4318  Data Driven Investor  https://medium.com/datadriveninvestor/how-to-b...  \n",
       "1679           The Startup  https://medium.com/swlh/what-is-marketing-segm...  \n",
       "4203         UX Collective  https://uxdesign.cc/how-to-create-a-meaningful...  \n",
       "3926           The Startup  https://medium.com/swlh/how-to-navigate-the-st...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['title', 'date', 'publication', 'url']\n",
    "df[cols].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Group by multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7ff1d16349a0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('publication')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: GroupBy SeriesGroupBy vs DataFrameGroupBy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7ff1d1634d00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['publication'])['url']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7ff2243ef2b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['publication'])[['url', 'date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7ff1d15cf700>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['publication', 'date_m'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "publication    date_m \n",
       "Better Humans  2019-03     5\n",
       "               2019-04     4\n",
       "               2019-05     4\n",
       "               2019-06     1\n",
       "               2019-07     3\n",
       "                          ..\n",
       "UX Collective  2019-08    24\n",
       "               2019-09    33\n",
       "               2019-10    86\n",
       "               2019-11    28\n",
       "               2019-12    46\n",
       "Name: url, Length: 79, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['publication', 'date_m'])['url'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publication</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Better Humans</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Better Marketing</th>\n",
       "      <td>242</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Driven Investor</th>\n",
       "      <td>778</td>\n",
       "      <td>778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Startup</th>\n",
       "      <td>3041</td>\n",
       "      <td>3041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Writing Cooperative</th>\n",
       "      <td>403</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Towards Data Science</th>\n",
       "      <td>1461</td>\n",
       "      <td>1461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UX Collective</th>\n",
       "      <td>555</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          url  date\n",
       "publication                        \n",
       "Better Humans              28    28\n",
       "Better Marketing          242   242\n",
       "Data Driven Investor      778   778\n",
       "The Startup              3041  3041\n",
       "The Writing Cooperative   403   403\n",
       "Towards Data Science     1461  1461\n",
       "UX Collective             555   555"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['publication'])[['url', 'date']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Apply multiple agg functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">claps</th>\n",
       "      <th colspan=\"3\" halign=\"left\">reading_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publication</th>\n",
       "      <th>date_m</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Better Humans</th>\n",
       "      <th>2019-03</th>\n",
       "      <td>1710.800000</td>\n",
       "      <td>5</td>\n",
       "      <td>8554</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>3942.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>15769</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05</th>\n",
       "      <td>1187.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4748</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06</th>\n",
       "      <td>9700.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>9700</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07</th>\n",
       "      <td>1183.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>3550</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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 rowspan=\"5\" valign=\"top\">UX Collective</th>\n",
       "      <th>2019-08</th>\n",
       "      <td>960.750000</td>\n",
       "      <td>24</td>\n",
       "      <td>23058</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>24</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09</th>\n",
       "      <td>144.606061</td>\n",
       "      <td>33</td>\n",
       "      <td>4772</td>\n",
       "      <td>5.757576</td>\n",
       "      <td>33</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10</th>\n",
       "      <td>417.383721</td>\n",
       "      <td>86</td>\n",
       "      <td>35895</td>\n",
       "      <td>6.104651</td>\n",
       "      <td>86</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11</th>\n",
       "      <td>345.642857</td>\n",
       "      <td>28</td>\n",
       "      <td>9678</td>\n",
       "      <td>5.535714</td>\n",
       "      <td>28</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12</th>\n",
       "      <td>338.847826</td>\n",
       "      <td>46</td>\n",
       "      <td>15587</td>\n",
       "      <td>6.326087</td>\n",
       "      <td>46</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             claps              reading_time           \n",
       "                              mean count    sum         mean count  sum\n",
       "publication   date_m                                                   \n",
       "Better Humans 2019-03  1710.800000     5   8554    12.000000     5   60\n",
       "              2019-04  3942.250000     4  15769     7.500000     4   30\n",
       "              2019-05  1187.000000     4   4748    25.000000     4  100\n",
       "              2019-06  9700.000000     1   9700     5.000000     1    5\n",
       "              2019-07  1183.333333     3   3550    22.333333     3   67\n",
       "...                            ...   ...    ...          ...   ...  ...\n",
       "UX Collective 2019-08   960.750000    24  23058     6.333333    24  152\n",
       "              2019-09   144.606061    33   4772     5.757576    33  190\n",
       "              2019-10   417.383721    86  35895     6.104651    86  525\n",
       "              2019-11   345.642857    28   9678     5.535714    28  155\n",
       "              2019-12   338.847826    46  15587     6.326087    46  291\n",
       "\n",
       "[79 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['publication', 'date_m'])[['claps', 'reading_time']].agg(['mean', 'count', 'sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>claps</th>\n",
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       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publication</th>\n",
       "      <th>date_m</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Better Humans</th>\n",
       "      <th>2019-03</th>\n",
       "      <td>5138.200000</td>\n",
       "      <td>1710.800000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2019-03-17 04:48:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>2308.250000</td>\n",
       "      <td>3942.250000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>2019-04-24 18:00:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05</th>\n",
       "      <td>3382.000000</td>\n",
       "      <td>1187.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2019-05-14 06:00:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06</th>\n",
       "      <td>6326.000000</td>\n",
       "      <td>9700.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2019-06-27 00:00:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07</th>\n",
       "      <td>1189.333333</td>\n",
       "      <td>1183.333333</td>\n",
       "      <td>22.333333</td>\n",
       "      <td>2019-07-24 00:00:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">UX Collective</th>\n",
       "      <th>2019-08</th>\n",
       "      <td>2702.583333</td>\n",
       "      <td>960.750000</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>2019-08-09 06:00:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09</th>\n",
       "      <td>2142.909091</td>\n",
       "      <td>144.606061</td>\n",
       "      <td>5.757576</td>\n",
       "      <td>2019-09-15 13:05:27.272727296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10</th>\n",
       "      <td>2927.406977</td>\n",
       "      <td>417.383721</td>\n",
       "      <td>6.104651</td>\n",
       "      <td>2019-10-20 05:18:08.372092928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11</th>\n",
       "      <td>2297.750000</td>\n",
       "      <td>345.642857</td>\n",
       "      <td>5.535714</td>\n",
       "      <td>2019-11-06 04:17:08.571428608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12</th>\n",
       "      <td>3570.260870</td>\n",
       "      <td>338.847826</td>\n",
       "      <td>6.326087</td>\n",
       "      <td>2019-12-19 22:26:05.217391360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                id        claps reading_time  \\\n",
       "                              mean         mean         mean   \n",
       "publication   date_m                                           \n",
       "Better Humans 2019-03  5138.200000  1710.800000    12.000000   \n",
       "              2019-04  2308.250000  3942.250000     7.500000   \n",
       "              2019-05  3382.000000  1187.000000    25.000000   \n",
       "              2019-06  6326.000000  9700.000000     5.000000   \n",
       "              2019-07  1189.333333  1183.333333    22.333333   \n",
       "...                            ...          ...          ...   \n",
       "UX Collective 2019-08  2702.583333   960.750000     6.333333   \n",
       "              2019-09  2142.909091   144.606061     5.757576   \n",
       "              2019-10  2927.406977   417.383721     6.104651   \n",
       "              2019-11  2297.750000   345.642857     5.535714   \n",
       "              2019-12  3570.260870   338.847826     6.326087   \n",
       "\n",
       "                                               date  \n",
       "                                               mean  \n",
       "publication   date_m                                 \n",
       "Better Humans 2019-03 2019-03-17 04:48:00.000000000  \n",
       "              2019-04 2019-04-24 18:00:00.000000000  \n",
       "              2019-05 2019-05-14 06:00:00.000000000  \n",
       "              2019-06 2019-06-27 00:00:00.000000000  \n",
       "              2019-07 2019-07-24 00:00:00.000000000  \n",
       "...                                             ...  \n",
       "UX Collective 2019-08 2019-08-09 06:00:00.000000000  \n",
       "              2019-09 2019-09-15 13:05:27.272727296  \n",
       "              2019-10 2019-10-20 05:18:08.372092928  \n",
       "              2019-11 2019-11-06 04:17:08.571428608  \n",
       "              2019-12 2019-12-19 22:26:05.217391360  \n",
       "\n",
       "[79 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['publication', 'date_m']).agg(['mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Pandas groupby and named aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_mean</th>\n",
       "      <th>claps_mean</th>\n",
       "      <th>claps_count</th>\n",
       "      <th>claps_range</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publication</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Better Humans</th>\n",
       "      <td>3004.285714</td>\n",
       "      <td>1827.785714</td>\n",
       "      <td>28</td>\n",
       "      <td>9660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Better Marketing</th>\n",
       "      <td>2954.578512</td>\n",
       "      <td>829.347107</td>\n",
       "      <td>242</td>\n",
       "      <td>22971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Driven Investor</th>\n",
       "      <td>3491.115681</td>\n",
       "      <td>95.034704</td>\n",
       "      <td>778</td>\n",
       "      <td>4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Startup</th>\n",
       "      <td>3238.427162</td>\n",
       "      <td>303.403815</td>\n",
       "      <td>3041</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Writing Cooperative</th>\n",
       "      <td>3539.476427</td>\n",
       "      <td>372.744417</td>\n",
       "      <td>403</td>\n",
       "      <td>12600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Towards Data Science</th>\n",
       "      <td>3181.948665</td>\n",
       "      <td>283.631759</td>\n",
       "      <td>1461</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UX Collective</th>\n",
       "      <td>3138.336937</td>\n",
       "      <td>380.924324</td>\n",
       "      <td>555</td>\n",
       "      <td>10496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id_mean   claps_mean  claps_count  claps_range\n",
       "publication                                                                \n",
       "Better Humans            3004.285714  1827.785714           28         9660\n",
       "Better Marketing         2954.578512   829.347107          242        22971\n",
       "Data Driven Investor     3491.115681    95.034704          778         4100\n",
       "The Startup              3238.427162   303.403815         3041        38000\n",
       "The Writing Cooperative  3539.476427   372.744417          403        12600\n",
       "Towards Data Science     3181.948665   283.631759         1461         8000\n",
       "UX Collective            3138.336937   380.924324          555        10496"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('publication').agg(\n",
    "             id_mean = ('id', 'mean'),\n",
    "             claps_mean = ('claps', 'mean'),\n",
    "             claps_count = ('claps', 'count'),\n",
    "             claps_range = ('claps', lambda x: x.max() - x.min()))"
   ]
  }
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