notebooks/3.2 Skills, Wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2 Skills/Wrangling\n",
    "\n",
    "In this notebook, we will focus on two essential skills in data analysis:\n",
    "\n",
    "1. The ability to add select, aggregate and transform data in a dataframe (**part 1**)\n",
    "2. The ability to get insights about a dataset by means of plotting and summary statistics (**part 2**)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read in a CSV file containing an export of [Elon Musk's tweets](https://twitter.com/elonmusk), exported from Twitter's API. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = '../data/musk_tweets/elonmusk_tweets.csv'\n",
    "df = pd.read_csv(dataset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2819 entries, 0 to 2818\n",
      "Data columns (total 3 columns):\n",
      "id            2819 non-null int64\n",
      "created_at    2819 non-null object\n",
      "text          2819 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 66.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('id', drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's give this dataset a bit more structure:\n",
    "- the `id` column can be transformed into the dataframe's index, thus enabling us e.g. to select a tweet by id;\n",
    "- `created_at` contains a timestamp, thus it can easily be converted into a `datetime` value\n",
    "- but what's going on with the text column ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.created_at = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2819 entries, 849636868052275200 to 15434727182\n",
      "Data columns (total 2 columns):\n",
      "created_at    2819 non-null datetime64[ns]\n",
      "text          2819 non-null object\n",
      "dtypes: datetime64[ns](1), object(1)\n",
      "memory usage: 66.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Renaming columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An operation on dataframes that you'll find yourself doing very often is to rename the columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first way of renaming columns is by manipulating directly the dataframe's index via the `columns` property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['created_at', 'text'], dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the column names by assigning to `columns` a list having as values the new column names.\n",
    "\n",
    "**NB**: the size of the list and new number of colums must match!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we renamed the column `text` => `tweet`\n",
    "df.columns = ['created_at', 'tweet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
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       "      <th>848943072423497728</th>\n",
       "      <td>2017-04-03 16:59:35</td>\n",
       "      <td>b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848935705057280001</th>\n",
       "      <td>2017-04-03 16:30:19</td>\n",
       "      <td>b'Stormy weather in Shortville ...'</td>\n",
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       "      <th>848416049573658624</th>\n",
       "      <td>2017-04-02 06:05:23</td>\n",
       "      <td>b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"</td>\n",
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      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "849636868052275200 2017-04-05 14:56:29   \n",
       "848988730585096192 2017-04-03 20:01:01   \n",
       "848943072423497728 2017-04-03 16:59:35   \n",
       "848935705057280001 2017-04-03 16:30:19   \n",
       "848416049573658624 2017-04-02 06:05:23   \n",
       "\n",
       "                                                                                                                                                                         tweet  \n",
       "id                                                                                                                                                                              \n",
       "849636868052275200                                                                                            b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'  \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"  \n",
       "848943072423497728                                                                                                               b'@waltmossberg @mims @defcon_5 Et tu, Walt?'  \n",
       "848935705057280001                                                                                                                         b'Stormy weather in Shortville ...'  \n",
       "848416049573658624                                                                           b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check that the change did take place\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second way of renaming colums is to use the method `rename()` of a dataframe. The `columns` parameter takes a dictionary of mappings between old and new column names.\n",
    "\n",
    "```python\n",
    "mapping_dict = {\n",
    "    \"old_column_name\": \"new_column_name\"\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's change column `tweet` => `text`\n",
    "df = df.rename(columns={\"tweet\": \"text\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>b'Stormy weather in Shortville ...'</td>\n",
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       "    <tr>\n",
       "      <th>848416049573658624</th>\n",
       "      <td>2017-04-02 06:05:23</td>\n",
       "      <td>b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"</td>\n",
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      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "849636868052275200 2017-04-05 14:56:29   \n",
       "848988730585096192 2017-04-03 20:01:01   \n",
       "848943072423497728 2017-04-03 16:59:35   \n",
       "848935705057280001 2017-04-03 16:30:19   \n",
       "848416049573658624 2017-04-02 06:05:23   \n",
       "\n",
       "                                                                                                                                                                          text  \n",
       "id                                                                                                                                                                              \n",
       "849636868052275200                                                                                            b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'  \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"  \n",
       "848943072423497728                                                                                                               b'@waltmossberg @mims @defcon_5 Et tu, Walt?'  \n",
       "848935705057280001                                                                                                                         b'Stormy weather in Shortville ...'  \n",
       "848416049573658624                                                                           b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question**: in which cases is it more convenient to use the second method over the first?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "849636868052275200   2017-04-05 14:56:29\n",
       "848988730585096192   2017-04-03 20:01:01\n",
       "848943072423497728   2017-04-03 16:59:35\n",
       "848935705057280001   2017-04-03 16:30:19\n",
       "848416049573658624   2017-04-02 06:05:23\n",
       "Name: created_at, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this selects one single column and returns as a Series\n",
    "df[\"created_at\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"created_at\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                            created_at\n",
       "id                                    \n",
       "849636868052275200 2017-04-05 14:56:29\n",
       "848988730585096192 2017-04-03 20:01:01\n",
       "848943072423497728 2017-04-03 16:59:35\n",
       "848935705057280001 2017-04-03 16:30:19\n",
       "848416049573658624 2017-04-02 06:05:23"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# whereas this syntax selects one single column\n",
    "# but returns a Dataframe\n",
    "df[[\"created_at\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[[\"created_at\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Selecting rows\n",
    "\n",
    "Filtering rows in `pandas` is done by means of `[ ]`, which can contain the row number as well as a condition for the selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
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       "      <th>849636868052275200</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'</td>\n",
       "      <td>https://twitter.com/i/web/status/849636868052275200</td>\n",
       "      <td>2</td>\n",
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       "      <td>Wednesday</td>\n",
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       "      <th>848988730585096192</th>\n",
       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
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       "      <td>0</td>\n",
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       "      <td>[@ForIn2020, @waltmossberg, @mims, @defcon_5]</td>\n",
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       "                            created_at  \\\n",
       "id                                       \n",
       "849636868052275200 2017-04-05 14:56:29   \n",
       "848988730585096192 2017-04-03 20:01:01   \n",
       "\n",
       "                                                                                                                                                                          text  \\\n",
       "id                                                                                                                                                                               \n",
       "849636868052275200                                                                                            b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'   \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"   \n",
       "\n",
       "                                                             tweet_link  \\\n",
       "id                                                                        \n",
       "849636868052275200  https://twitter.com/i/web/status/849636868052275200   \n",
       "848988730585096192  https://twitter.com/i/web/status/848988730585096192   \n",
       "\n",
       "                    week_day  day_hour  \\\n",
       "id                                       \n",
       "849636868052275200         2        14   \n",
       "848988730585096192         0        20   \n",
       "\n",
       "                                                   tweet_mentions  n_mentions  \\\n",
       "id                                                                              \n",
       "849636868052275200                                             []           0   \n",
       "848988730585096192  [@ForIn2020, @waltmossberg, @mims, @defcon_5]           4   \n",
       "\n",
       "                    year week_day_name  \n",
       "id                                      \n",
       "849636868052275200  2017     Wednesday  \n",
       "848988730585096192  2017        Monday  "
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1674, 9)"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# equivalent of `df.query('n_mentions > 0')`\n",
    "\n",
    "df[df.n_mentions > 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1145, 9)"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.n_mentions <= 0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426, 9)"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.week_day_name == 'Saturday'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(848, 9)"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.week_day_name.str.startswith('S')].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Multiple conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 9)"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AND condition with `&`\n",
    "\n",
    "df[\n",
    "    (df.week_day_name == 'Saturday') & (df.n_mentions == 0)\n",
    "].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 9)"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# equivalent expression with `query()`\n",
    "\n",
    "df.query(\"week_day_name == 'Saturday' and n_mentions == 0\").shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1384, 9)"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OR condition with `|`\n",
    "\n",
    "df[\n",
    "    (df.week_day_name == 'Saturday') | (df.n_mentions == 0)\n",
    "].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation\n",
    "\n",
    "\n",
    "The two main functions used to manipulate and transform values in a dataframe are:\n",
    "- `map()`\n",
    "- `apply()`\n",
    "\n",
    "In this section we'll be using both to enrich our datasets with useful information (useful for exploration, for later visualizations, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add link to original tweet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `map()` method can be called on a column, as well as on the dataframe's index.\n",
    "\n",
    "When passed as a parameter to `map`, the functional programming-stlye function `lambda` can be used to transform any value from that column into another one.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tweet_link'] = df.index.map(lambda x: f'https://twitter.com/i/web/status/{x}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015d\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >created_at</th>        <th class=\"col_heading level0 col1\" >text</th>        <th class=\"col_heading level0 col2\" >tweet_link</th>        <th class=\"col_heading level0 col3\" >week_day</th>        <th class=\"col_heading level0 col4\" >day_hour</th>    </tr>    <tr>        <th class=\"index_name level0\" >id</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015dlevel0_row0\" class=\"row_heading level0 row0\" >849636868052275200</th>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow0_col0\" class=\"data row0 col0\" >2017-04-05 14:56:29</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow0_col1\" class=\"data row0 col1\" >b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow0_col2\" class=\"data row0 col2\" ><a target=\"_blank\" href=\"https://twitter.com/i/web/status/849636868052275200\">https://twitter.com/i/web/status/849636868052275200</a></td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow0_col3\" class=\"data row0 col3\" >Wednesday</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow0_col4\" class=\"data row0 col4\" >14</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015dlevel0_row1\" class=\"row_heading level0 row1\" >848988730585096192</th>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow1_col0\" class=\"data row1 col0\" >2017-04-03 20:01:01</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow1_col1\" class=\"data row1 col1\" >b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow1_col2\" class=\"data row1 col2\" ><a target=\"_blank\" href=\"https://twitter.com/i/web/status/848988730585096192\">https://twitter.com/i/web/status/848988730585096192</a></td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow1_col3\" class=\"data row1 col3\" >Monday</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow1_col4\" class=\"data row1 col4\" >20</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015dlevel0_row2\" class=\"row_heading level0 row2\" >848943072423497728</th>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow2_col0\" class=\"data row2 col0\" >2017-04-03 16:59:35</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow2_col1\" class=\"data row2 col1\" >b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow2_col2\" class=\"data row2 col2\" ><a target=\"_blank\" href=\"https://twitter.com/i/web/status/848943072423497728\">https://twitter.com/i/web/status/848943072423497728</a></td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow2_col3\" class=\"data row2 col3\" >Monday</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow2_col4\" class=\"data row2 col4\" >16</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015dlevel0_row3\" class=\"row_heading level0 row3\" >848935705057280001</th>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow3_col0\" class=\"data row3 col0\" >2017-04-03 16:30:19</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow3_col1\" class=\"data row3 col1\" >b'Stormy weather in Shortville ...'</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow3_col2\" class=\"data row3 col2\" ><a target=\"_blank\" href=\"https://twitter.com/i/web/status/848935705057280001\">https://twitter.com/i/web/status/848935705057280001</a></td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow3_col3\" class=\"data row3 col3\" >Monday</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow3_col4\" class=\"data row3 col4\" >16</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015dlevel0_row4\" class=\"row_heading level0 row4\" >848416049573658624</th>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow4_col0\" class=\"data row4 col0\" >2017-04-02 06:05:23</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow4_col1\" class=\"data row4 col1\" >b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow4_col2\" class=\"data row4 col2\" ><a target=\"_blank\" href=\"https://twitter.com/i/web/status/848416049573658624\">https://twitter.com/i/web/status/848416049573658624</a></td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow4_col3\" class=\"data row4 col3\" >Sunday</td>\n",
       "                        <td id=\"T_e7016102_abcc_11e9_bbfe_f45c89be015drow4_col4\" class=\"data row4 col4\" >6</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11727e160>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_colwidth\", 10000)\n",
    "\n",
    "def make_clickable(val):\n",
    "    # target _blank to open new window\n",
    "    return '<a target=\"_blank\" href=\"{}\">{}</a>'.format(val, val)\n",
    "\n",
    "df.head().style.format({'tweet_link': make_clickable})\n",
    "\n",
    "# to apply the style to the entire dataframe just remove\n",
    "# `.head` from the line above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add colums with mentions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def find_mentions(tweet_text):\n",
    "    handle_regexp = r'@[a-zA-Z0-9_]{1,15}'\n",
    "    return re.findall(handle_regexp, tweet_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tweet_mentions'] = df.text.apply(find_mentions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['n_mentions'] = df.tweet_mentions.apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>849636868052275200</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'</td>\n",
       "      <td>https://twitter.com/i/web/status/849636868052275200</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>14</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848988730585096192</th>\n",
       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
       "      <td>https://twitter.com/i/web/status/848988730585096192</td>\n",
       "      <td>Monday</td>\n",
       "      <td>20</td>\n",
       "      <td>[@ForIn2020, @waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848943072423497728</th>\n",
       "      <td>2017-04-03 16:59:35</td>\n",
       "      <td>b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "      <td>https://twitter.com/i/web/status/848943072423497728</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "      <td>[@waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848935705057280001</th>\n",
       "      <td>2017-04-03 16:30:19</td>\n",
       "      <td>b'Stormy weather in Shortville ...'</td>\n",
       "      <td>https://twitter.com/i/web/status/848935705057280001</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848416049573658624</th>\n",
       "      <td>2017-04-02 06:05:23</td>\n",
       "      <td>b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"</td>\n",
       "      <td>https://twitter.com/i/web/status/848416049573658624</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>6</td>\n",
       "      <td>[@DaveLeeBBC, @verge]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "849636868052275200 2017-04-05 14:56:29   \n",
       "848988730585096192 2017-04-03 20:01:01   \n",
       "848943072423497728 2017-04-03 16:59:35   \n",
       "848935705057280001 2017-04-03 16:30:19   \n",
       "848416049573658624 2017-04-02 06:05:23   \n",
       "\n",
       "                                                                                                                                                                          text  \\\n",
       "id                                                                                                                                                                               \n",
       "849636868052275200                                                                                            b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'   \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"   \n",
       "848943072423497728                                                                                                               b'@waltmossberg @mims @defcon_5 Et tu, Walt?'   \n",
       "848935705057280001                                                                                                                         b'Stormy weather in Shortville ...'   \n",
       "848416049573658624                                                                           b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"   \n",
       "\n",
       "                                                             tweet_link  \\\n",
       "id                                                                        \n",
       "849636868052275200  https://twitter.com/i/web/status/849636868052275200   \n",
       "848988730585096192  https://twitter.com/i/web/status/848988730585096192   \n",
       "848943072423497728  https://twitter.com/i/web/status/848943072423497728   \n",
       "848935705057280001  https://twitter.com/i/web/status/848935705057280001   \n",
       "848416049573658624  https://twitter.com/i/web/status/848416049573658624   \n",
       "\n",
       "                     week_day  day_hour  \\\n",
       "id                                        \n",
       "849636868052275200  Wednesday        14   \n",
       "848988730585096192     Monday        20   \n",
       "848943072423497728     Monday        16   \n",
       "848935705057280001     Monday        16   \n",
       "848416049573658624     Sunday         6   \n",
       "\n",
       "                                                   tweet_mentions  n_mentions  \n",
       "id                                                                             \n",
       "849636868052275200                                             []           0  \n",
       "848988730585096192  [@ForIn2020, @waltmossberg, @mims, @defcon_5]           4  \n",
       "848943072423497728              [@waltmossberg, @mims, @defcon_5]           3  \n",
       "848935705057280001                                             []           0  \n",
       "848416049573658624                          [@DaveLeeBBC, @verge]           2  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add column with week day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "849636868052275200    Wednesday\n",
       "848988730585096192       Monday\n",
       "848943072423497728       Monday\n",
       "848935705057280001       Monday\n",
       "848416049573658624       Sunday\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.created_at.dt.weekday_name.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"week_day_name\"] = df.created_at.dt.weekday_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"week_day\"] = df.created_at.dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
       "      <th>year</th>\n",
       "      <th>week_day_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>849636868052275200</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'</td>\n",
       "      <td>https://twitter.com/i/web/status/849636868052275200</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848988730585096192</th>\n",
       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
       "      <td>https://twitter.com/i/web/status/848988730585096192</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>[@ForIn2020, @waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848943072423497728</th>\n",
       "      <td>2017-04-03 16:59:35</td>\n",
       "      <td>b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "      <td>https://twitter.com/i/web/status/848943072423497728</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>[@waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "849636868052275200 2017-04-05 14:56:29   \n",
       "848988730585096192 2017-04-03 20:01:01   \n",
       "848943072423497728 2017-04-03 16:59:35   \n",
       "\n",
       "                                                                                                                                                                          text  \\\n",
       "id                                                                                                                                                                               \n",
       "849636868052275200                                                                                            b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'   \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"   \n",
       "848943072423497728                                                                                                               b'@waltmossberg @mims @defcon_5 Et tu, Walt?'   \n",
       "\n",
       "                                                             tweet_link  \\\n",
       "id                                                                        \n",
       "849636868052275200  https://twitter.com/i/web/status/849636868052275200   \n",
       "848988730585096192  https://twitter.com/i/web/status/848988730585096192   \n",
       "848943072423497728  https://twitter.com/i/web/status/848943072423497728   \n",
       "\n",
       "                    week_day  day_hour  \\\n",
       "id                                       \n",
       "849636868052275200         2        14   \n",
       "848988730585096192         0        20   \n",
       "848943072423497728         0        16   \n",
       "\n",
       "                                                   tweet_mentions  n_mentions  \\\n",
       "id                                                                              \n",
       "849636868052275200                                             []           0   \n",
       "848988730585096192  [@ForIn2020, @waltmossberg, @mims, @defcon_5]           4   \n",
       "848943072423497728              [@waltmossberg, @mims, @defcon_5]           3   \n",
       "\n",
       "                    year week_day_name  \n",
       "id                                      \n",
       "849636868052275200  2017     Wednesday  \n",
       "848988730585096192  2017        Monday  \n",
       "848943072423497728  2017        Monday  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add column with day hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.created_at.dt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "849636868052275200    14\n",
       "848988730585096192    20\n",
       "848943072423497728    16\n",
       "848935705057280001    16\n",
       "848416049573658624     6\n",
       "Name: created_at, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.created_at.dt.hour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"day_hour\"] = df.created_at.dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>849636868052275200</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848988730585096192</th>\n",
       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>Monday</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848943072423497728</th>\n",
       "      <td>2017-04-03 16:59:35</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848935705057280001</th>\n",
       "      <td>2017-04-03 16:30:19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            created_at   week_day  day_hour\n",
       "id                                                         \n",
       "849636868052275200 2017-04-05 14:56:29  Wednesday        14\n",
       "848988730585096192 2017-04-03 20:01:01     Monday        20\n",
       "848943072423497728 2017-04-03 16:59:35     Monday        16\n",
       "848935705057280001 2017-04-03 16:30:19     Monday        16"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display_cols = ['created_at', 'week_day', 'day_hour']\n",
    "df[display_cols].head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregation\n",
    "\n",
    "(Figure taken from W. Mckinney's *Python for Data Analysis* 2013, p. 252)\n",
    "\n",
    "<img src='figures/groupby-mechanics.png' width='600px'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "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>n_mentions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>2277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     n_mentions\n",
       "min           0\n",
       "max           6\n",
       "sum        2277"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg({'n_mentions': ['min', 'max', 'sum']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "grp_by_day = df.groupby('week_day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object returned by `gropuby` is a `DataFrameGroupBy` **not** a normal `DataFrame`.\n",
    "\n",
    "However, some methods of the latter work also on the former, e.g. `head` and `tail`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>849636868052275200</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'</td>\n",
       "      <td>https://twitter.com/i/web/status/849636868052275200</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>14</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848988730585096192</th>\n",
       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
       "      <td>https://twitter.com/i/web/status/848988730585096192</td>\n",
       "      <td>Monday</td>\n",
       "      <td>20</td>\n",
       "      <td>[@ForIn2020, @waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848416049573658624</th>\n",
       "      <td>2017-04-02 06:05:23</td>\n",
       "      <td>b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"</td>\n",
       "      <td>https://twitter.com/i/web/status/848416049573658624</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>6</td>\n",
       "      <td>[@DaveLeeBBC, @verge]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848244577521647616</th>\n",
       "      <td>2017-04-01 18:44:01</td>\n",
       "      <td>b\"Why did we waste so much time developing silly rockets? Damn you, aliens! So obtuse! You have all this crazy tech, but can't speak English!?\"</td>\n",
       "      <td>https://twitter.com/i/web/status/848244577521647616</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>18</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847958571895619584</th>\n",
       "      <td>2017-03-31 23:47:32</td>\n",
       "      <td>b'@BadAstronomer We can def bring it back like Dragon. Just a question of how much weight we need to add.'</td>\n",
       "      <td>https://twitter.com/i/web/status/847958571895619584</td>\n",
       "      <td>Friday</td>\n",
       "      <td>23</td>\n",
       "      <td>[@BadAstronomer]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847594208219336705</th>\n",
       "      <td>2017-03-30 23:39:41</td>\n",
       "      <td>b'Incredibly proud of the SpaceX team for achieving this milestone in space! Next goal is reflight within 24 hours.'</td>\n",
       "      <td>https://twitter.com/i/web/status/847594208219336705</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>23</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846772378067648513</th>\n",
       "      <td>2017-03-28 17:14:01</td>\n",
       "      <td>b'@danahull Very few. We have yet to do a China (or Europe) launch of Model 3.'</td>\n",
       "      <td>https://twitter.com/i/web/status/846772378067648513</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>17</td>\n",
       "      <td>[@danahull]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "849636868052275200 2017-04-05 14:56:29   \n",
       "848988730585096192 2017-04-03 20:01:01   \n",
       "848416049573658624 2017-04-02 06:05:23   \n",
       "848244577521647616 2017-04-01 18:44:01   \n",
       "847958571895619584 2017-03-31 23:47:32   \n",
       "847594208219336705 2017-03-30 23:39:41   \n",
       "846772378067648513 2017-03-28 17:14:01   \n",
       "\n",
       "                                                                                                                                                                          text  \\\n",
       "id                                                                                                                                                                               \n",
       "849636868052275200                                                                                            b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'   \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"   \n",
       "848416049573658624                                                                           b\"@DaveLeeBBC @verge Coal is dying due to nat gas fracking. It's basically dead.\"   \n",
       "848244577521647616             b\"Why did we waste so much time developing silly rockets? Damn you, aliens! So obtuse! You have all this crazy tech, but can't speak English!?\"   \n",
       "847958571895619584                                                  b'@BadAstronomer We can def bring it back like Dragon. Just a question of how much weight we need to add.'   \n",
       "847594208219336705                                        b'Incredibly proud of the SpaceX team for achieving this milestone in space! Next goal is reflight within 24 hours.'   \n",
       "846772378067648513                                                                             b'@danahull Very few. We have yet to do a China (or Europe) launch of Model 3.'   \n",
       "\n",
       "                                                             tweet_link  \\\n",
       "id                                                                        \n",
       "849636868052275200  https://twitter.com/i/web/status/849636868052275200   \n",
       "848988730585096192  https://twitter.com/i/web/status/848988730585096192   \n",
       "848416049573658624  https://twitter.com/i/web/status/848416049573658624   \n",
       "848244577521647616  https://twitter.com/i/web/status/848244577521647616   \n",
       "847958571895619584  https://twitter.com/i/web/status/847958571895619584   \n",
       "847594208219336705  https://twitter.com/i/web/status/847594208219336705   \n",
       "846772378067648513  https://twitter.com/i/web/status/846772378067648513   \n",
       "\n",
       "                     week_day  day_hour  \\\n",
       "id                                        \n",
       "849636868052275200  Wednesday        14   \n",
       "848988730585096192     Monday        20   \n",
       "848416049573658624     Sunday         6   \n",
       "848244577521647616   Saturday        18   \n",
       "847958571895619584     Friday        23   \n",
       "847594208219336705   Thursday        23   \n",
       "846772378067648513    Tuesday        17   \n",
       "\n",
       "                                                   tweet_mentions  n_mentions  \n",
       "id                                                                             \n",
       "849636868052275200                                             []           0  \n",
       "848988730585096192  [@ForIn2020, @waltmossberg, @mims, @defcon_5]           4  \n",
       "848416049573658624                          [@DaveLeeBBC, @verge]           2  \n",
       "848244577521647616                                             []           0  \n",
       "847958571895619584                               [@BadAstronomer]           1  \n",
       "847594208219336705                                             []           0  \n",
       "846772378067648513                                    [@danahull]           1  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the head of a DataFrameGroupBy consists of the first\n",
    "# n records for each group (see `help(grp_by_day.head)`)\n",
    "\n",
    "grp_by_day.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`agg` is used to pass an aggregation function to be applied to each group resulting from `groupby`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_day</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>Friday</th>\n",
       "      <td>530</td>\n",
       "      <td>530</td>\n",
       "      <td>530</td>\n",
       "      <td>530</td>\n",
       "      <td>530</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>426</td>\n",
       "      <td>426</td>\n",
       "      <td>426</td>\n",
       "      <td>426</td>\n",
       "      <td>426</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>385</td>\n",
       "      <td>385</td>\n",
       "      <td>385</td>\n",
       "      <td>385</td>\n",
       "      <td>385</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>380</td>\n",
       "      <td>380</td>\n",
       "      <td>380</td>\n",
       "      <td>380</td>\n",
       "      <td>380</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           created_at  text  tweet_link  day_hour  tweet_mentions  n_mentions\n",
       "week_day                                                                     \n",
       "Friday            530   530         530       530             530         530\n",
       "Monday            315   315         315       315             315         315\n",
       "Saturday          426   426         426       426             426         426\n",
       "Sunday            422   422         422       422             422         422\n",
       "Thursday          361   361         361       361             361         361\n",
       "Tuesday           385   385         385       385             385         385\n",
       "Wednesday         380   380         380       380             380         380"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we are interested in how many tweets\n",
    "# there are for each group, so we pass `len()`\n",
    "\n",
    "grp_by_day.agg(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>tweet_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_count\n",
       "week_day              \n",
       "Friday             530\n",
       "Monday             315\n",
       "Saturday           426\n",
       "Sunday             422\n",
       "Thursday           361\n",
       "Tuesday            385\n",
       "Wednesday          380"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# however, we are not interested in having the count for all\n",
    "# columns. rather we want to create a new dataframe with renamed\n",
    "# column names\n",
    "\n",
    "grp_by_day.agg({'text': len}).rename({'text': 'tweet_count'}, axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### By label (column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously we've added a column indicating on which day of the week a given tweet appeared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "grpby_result_as_series = df.groupby('day_hour')['text'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_hour\n",
       "0    144\n",
       "1    183\n",
       "2    119\n",
       "3    104\n",
       "4     98\n",
       "Name: text, dtype: int64"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpby_result_as_series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "grpby_result_as_df = df.groupby('day_hour')[['text']]\\\n",
    "    .count()\\\n",
    "    .rename({'text': 'count'}, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>183</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count\n",
       "day_hour       \n",
       "0           144\n",
       "1           183\n",
       "2           119\n",
       "3           104\n",
       "4            98"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpby_result_as_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### By series or dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 <class 'pandas.core.frame.DataFrame'>\n",
      "2 <class 'pandas.core.frame.DataFrame'>\n",
      "3 <class 'pandas.core.frame.DataFrame'>\n",
      "4 <class 'pandas.core.frame.DataFrame'>\n",
      "5 <class 'pandas.core.frame.DataFrame'>\n",
      "6 <class 'pandas.core.frame.DataFrame'>\n",
      "7 <class 'pandas.core.frame.DataFrame'>\n",
      "8 <class 'pandas.core.frame.DataFrame'>\n",
      "9 <class 'pandas.core.frame.DataFrame'>\n",
      "10 <class 'pandas.core.frame.DataFrame'>\n",
      "11 <class 'pandas.core.frame.DataFrame'>\n",
      "12 <class 'pandas.core.frame.DataFrame'>\n",
      "13 <class 'pandas.core.frame.DataFrame'>\n",
      "14 <class 'pandas.core.frame.DataFrame'>\n",
      "15 <class 'pandas.core.frame.DataFrame'>\n",
      "16 <class 'pandas.core.frame.DataFrame'>\n",
      "17 <class 'pandas.core.frame.DataFrame'>\n",
      "18 <class 'pandas.core.frame.DataFrame'>\n",
      "19 <class 'pandas.core.frame.DataFrame'>\n",
      "20 <class 'pandas.core.frame.DataFrame'>\n",
      "21 <class 'pandas.core.frame.DataFrame'>\n",
      "22 <class 'pandas.core.frame.DataFrame'>\n",
      "23 <class 'pandas.core.frame.DataFrame'>\n",
      "24 <class 'pandas.core.frame.DataFrame'>\n",
      "25 <class 'pandas.core.frame.DataFrame'>\n",
      "26 <class 'pandas.core.frame.DataFrame'>\n",
      "27 <class 'pandas.core.frame.DataFrame'>\n",
      "28 <class 'pandas.core.frame.DataFrame'>\n",
      "29 <class 'pandas.core.frame.DataFrame'>\n",
      "30 <class 'pandas.core.frame.DataFrame'>\n",
      "31 <class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "for group, rows in df.groupby(df.created_at.dt.day):\n",
    "    print(group, type(rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\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>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97</td>\n",
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       "      <th>5</th>\n",
       "      <td>117</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "            text\n",
       "created_at      \n",
       "1            107\n",
       "2            107\n",
       "3            114\n",
       "4             97\n",
       "5            117"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we pass the groups as a series\n",
    "df.groupby(df.created_at.dt.day).agg({'text':len}).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
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      "text/plain": [
       "            text\n",
       "created_at      \n",
       "1            107\n",
       "2            107\n",
       "3            114\n",
       "4             97\n",
       "5            117"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# here we pass the groups as a series\n",
    "df.groupby(df.created_at.dt.day)[['text']].count().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>98</td>\n",
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      "text/plain": [
       "            text\n",
       "created_at      \n",
       "0            144\n",
       "1            183\n",
       "2            119\n",
       "3            104\n",
       "4             98"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.created_at.dt.hour)[['text']].count().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### By multiple labels (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we group based on the values of two columns\n",
    "# instead of one\n",
    "\n",
    "x = df.groupby(['week_day', 'day_hour'])[['text']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Friday</th>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   text\n",
       "week_day day_hour      \n",
       "Friday   0           28\n",
       "         1           29\n",
       "         2           20\n",
       "         3           20\n",
       "         4           15"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregation methods\n",
    "\n",
    "**Summary**:\n",
    "\n",
    "- `count`: Number of non-NA values\n",
    "- `sum`: Sum of non-NA values\n",
    "- `mean`: Mean of non-NA values\n",
    "- `median`: Arithmetic median of non-NA values\n",
    "- `std`, `var`: standard deviation and variance\n",
    "- `min`, `max`: Minimum and maximum of non-NA values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They can be used on a single series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.n_mentions.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the entire dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "week_day         3.196169\n",
       "day_hour        12.782547\n",
       "n_mentions       0.807733\n",
       "year          2014.777226\n",
       "dtype: float64"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or also as aggregation functions within a groupby:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">n_mentions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_day</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>0</th>\n",
       "      <td>315</td>\n",
       "      <td>0.812698</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.964248</td>\n",
       "      <td>0.929775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>385</td>\n",
       "      <td>0.729870</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.874861</td>\n",
       "      <td>0.765381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>380</td>\n",
       "      <td>0.786842</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.768328</td>\n",
       "      <td>0.590328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>361</td>\n",
       "      <td>0.781163</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.832853</td>\n",
       "      <td>0.693644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>530</td>\n",
       "      <td>0.879245</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.901869</td>\n",
       "      <td>0.813368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>426</td>\n",
       "      <td>0.727700</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.800607</td>\n",
       "      <td>0.640972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>422</td>\n",
       "      <td>0.907583</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.851707</td>\n",
       "      <td>0.725406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         n_mentions                                      \n",
       "              count      mean min max       std       var\n",
       "week_day                                                 \n",
       "0               315  0.812698   0   5  0.964248  0.929775\n",
       "1               385  0.729870   0   6  0.874861  0.765381\n",
       "2               380  0.786842   0   4  0.768328  0.590328\n",
       "3               361  0.781163   0   4  0.832853  0.693644\n",
       "4               530  0.879245   0   5  0.901869  0.813368\n",
       "5               426  0.727700   0   5  0.800607  0.640972\n",
       "6               422  0.907583   0   6  0.851707  0.725406"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('week_day').agg(\n",
    "    {\n",
    "        # each key in this dict specifies\n",
    "        # a given column\n",
    "        'n_mentions':[\n",
    "            # the list contains aggregation functions\n",
    "            # to be applied to this column\n",
    "            'count',\n",
    "            'mean',\n",
    "            'min',\n",
    "            'max',\n",
    "            'std',\n",
    "            'var'\n",
    "        ]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sorting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To sort the values of  a dataframe we use its `sort_values` method:\n",
    "- `by`: specifies the name of the column to be used for sorting\n",
    "- `ascending` (default = `True`): specifies whether the sorting should be *ascending* (A-Z, 0-9) or `descending` (Z-A, 9-0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>15434727182</th>\n",
       "      <td>2010-06-04 18:31:57</td>\n",
       "      <td>b'Please ignore prior tweets, as that was someone pretending to be me :)  This is actually me.'</td>\n",
       "      <td>https://twitter.com/i/web/status/15434727182</td>\n",
       "      <td>Friday</td>\n",
       "      <td>18</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142179928203460608</th>\n",
       "      <td>2011-12-01 09:55:11</td>\n",
       "      <td>b\"Went to Iceland on Sat to ride bumper cars on ice!  No, not the country, Vlad's rink in Van Nuys. Awesome family fun :) http://t.co/rBQXJ9IT\"</td>\n",
       "      <td>https://twitter.com/i/web/status/142179928203460608</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>9</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142188458125963264</th>\n",
       "      <td>2011-12-01 10:29:04</td>\n",
       "      <td>b'I made the volume on the Model S http://t.co/wMCnT53M go to 11.  Now I just need to work in a miniature Stonehenge...'</td>\n",
       "      <td>https://twitter.com/i/web/status/142188458125963264</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>10</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142880871391838208</th>\n",
       "      <td>2011-12-03 08:20:28</td>\n",
       "      <td>b'Great Voltaire quote, arguably better than Twain. Hearing news of his own death, Voltaire replied the reports were true, only premature.'</td>\n",
       "      <td>https://twitter.com/i/web/status/142880871391838208</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>8</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142881284019060736</th>\n",
       "      <td>2011-12-03 08:22:07</td>\n",
       "      <td>b'That was a total non sequitur btw'</td>\n",
       "      <td>https://twitter.com/i/web/status/142881284019060736</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>8</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "15434727182        2010-06-04 18:31:57   \n",
       "142179928203460608 2011-12-01 09:55:11   \n",
       "142188458125963264 2011-12-01 10:29:04   \n",
       "142880871391838208 2011-12-03 08:20:28   \n",
       "142881284019060736 2011-12-03 08:22:07   \n",
       "\n",
       "                                                                                                                                                               text  \\\n",
       "id                                                                                                                                                                    \n",
       "15434727182                                                         b'Please ignore prior tweets, as that was someone pretending to be me :)  This is actually me.'   \n",
       "142179928203460608  b\"Went to Iceland on Sat to ride bumper cars on ice!  No, not the country, Vlad's rink in Van Nuys. Awesome family fun :) http://t.co/rBQXJ9IT\"   \n",
       "142188458125963264                         b'I made the volume on the Model S http://t.co/wMCnT53M go to 11.  Now I just need to work in a miniature Stonehenge...'   \n",
       "142880871391838208      b'Great Voltaire quote, arguably better than Twain. Hearing news of his own death, Voltaire replied the reports were true, only premature.'   \n",
       "142881284019060736                                                                                                             b'That was a total non sequitur btw'   \n",
       "\n",
       "                                                             tweet_link  \\\n",
       "id                                                                        \n",
       "15434727182                https://twitter.com/i/web/status/15434727182   \n",
       "142179928203460608  https://twitter.com/i/web/status/142179928203460608   \n",
       "142188458125963264  https://twitter.com/i/web/status/142188458125963264   \n",
       "142880871391838208  https://twitter.com/i/web/status/142880871391838208   \n",
       "142881284019060736  https://twitter.com/i/web/status/142881284019060736   \n",
       "\n",
       "                    week_day  day_hour tweet_mentions  n_mentions  \n",
       "id                                                                 \n",
       "15434727182           Friday        18             []           0  \n",
       "142179928203460608  Thursday         9             []           0  \n",
       "142188458125963264  Thursday        10             []           0  \n",
       "142880871391838208  Saturday         8             []           0  \n",
       "142881284019060736  Saturday         8             []           0  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='created_at', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>752721031735812096</th>\n",
       "      <td>2016-07-12 04:27:33</td>\n",
       "      <td>b\"@kumailn @RealDaveBarton @JuddApatow @SiliconHBO @FredericLambert @MikeJudge can't wait to see the costume\"</td>\n",
       "      <td>https://twitter.com/i/web/status/752721031735812096</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "      <td>[@kumailn, @RealDaveBarton, @JuddApatow, @SiliconHBO, @FredericLambert, @MikeJudge]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303244501957365760</th>\n",
       "      <td>2013-02-17 20:48:17</td>\n",
       "      <td>b'RT @LawrenceChanin: @TeslaRoadTrip @avantgame @TEDchris @elonmusk @nytimes   \\nThe driving public is the winner thanks to the efforts of  ...'</td>\n",
       "      <td>https://twitter.com/i/web/status/303244501957365760</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>20</td>\n",
       "      <td>[@LawrenceChanin, @TeslaRoadTrip, @avantgame, @TEDchris, @elonmusk, @nytimes]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845289977051148289</th>\n",
       "      <td>2017-03-24 15:03:29</td>\n",
       "      <td>b'@faultywarrior @matt_trulli @FredericLambert @TimShelton @JimPengelly Let me just go fetch my magic wand ...'</td>\n",
       "      <td>https://twitter.com/i/web/status/845289977051148289</td>\n",
       "      <td>Friday</td>\n",
       "      <td>15</td>\n",
       "      <td>[@faultywarrior, @matt_trulli, @FredericLambert, @TimShelton, @JimPengelly]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672792504895434753</th>\n",
       "      <td>2015-12-04 15:00:07</td>\n",
       "      <td>b\"RT @WSJLife: Supermodel @KarlieKloss blasts off to the future at @elonmusk's @spacex HQ https://t.co/PfE2bWcQwM @wsjmag https://t.co/zc7QM9\\xe2\\x80\\xa6\"</td>\n",
       "      <td>https://twitter.com/i/web/status/672792504895434753</td>\n",
       "      <td>Friday</td>\n",
       "      <td>15</td>\n",
       "      <td>[@WSJLife, @KarlieKloss, @elonmusk, @spacex, @wsjmag]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191005784862236672</th>\n",
       "      <td>2012-04-14 03:31:42</td>\n",
       "      <td>b'RT @SethGreen: My love @ClareGrant &amp; @elonmusk partied with the space cheese on our tour of @SpaceX &amp; @TeslaMotors ...which was AWESO\\xe2\\x80\\xa6 h ...'</td>\n",
       "      <td>https://twitter.com/i/web/status/191005784862236672</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3</td>\n",
       "      <td>[@SethGreen, @ClareGrant, @elonmusk, @SpaceX, @TeslaMotors]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "752721031735812096 2016-07-12 04:27:33   \n",
       "303244501957365760 2013-02-17 20:48:17   \n",
       "845289977051148289 2017-03-24 15:03:29   \n",
       "672792504895434753 2015-12-04 15:00:07   \n",
       "191005784862236672 2012-04-14 03:31:42   \n",
       "\n",
       "                                                                                                                                                                          text  \\\n",
       "id                                                                                                                                                                               \n",
       "752721031735812096                                               b\"@kumailn @RealDaveBarton @JuddApatow @SiliconHBO @FredericLambert @MikeJudge can't wait to see the costume\"   \n",
       "303244501957365760            b'RT @LawrenceChanin: @TeslaRoadTrip @avantgame @TEDchris @elonmusk @nytimes   \\nThe driving public is the winner thanks to the efforts of  ...'   \n",
       "845289977051148289                                             b'@faultywarrior @matt_trulli @FredericLambert @TimShelton @JimPengelly Let me just go fetch my magic wand ...'   \n",
       "672792504895434753  b\"RT @WSJLife: Supermodel @KarlieKloss blasts off to the future at @elonmusk's @spacex HQ https://t.co/PfE2bWcQwM @wsjmag https://t.co/zc7QM9\\xe2\\x80\\xa6\"   \n",
       "191005784862236672  b'RT @SethGreen: My love @ClareGrant & @elonmusk partied with the space cheese on our tour of @SpaceX & @TeslaMotors ...which was AWESO\\xe2\\x80\\xa6 h ...'   \n",
       "\n",
       "                                                             tweet_link  \\\n",
       "id                                                                        \n",
       "752721031735812096  https://twitter.com/i/web/status/752721031735812096   \n",
       "303244501957365760  https://twitter.com/i/web/status/303244501957365760   \n",
       "845289977051148289  https://twitter.com/i/web/status/845289977051148289   \n",
       "672792504895434753  https://twitter.com/i/web/status/672792504895434753   \n",
       "191005784862236672  https://twitter.com/i/web/status/191005784862236672   \n",
       "\n",
       "                    week_day  day_hour  \\\n",
       "id                                       \n",
       "752721031735812096   Tuesday         4   \n",
       "303244501957365760    Sunday        20   \n",
       "845289977051148289    Friday        15   \n",
       "672792504895434753    Friday        15   \n",
       "191005784862236672  Saturday         3   \n",
       "\n",
       "                                                                                         tweet_mentions  \\\n",
       "id                                                                                                        \n",
       "752721031735812096  [@kumailn, @RealDaveBarton, @JuddApatow, @SiliconHBO, @FredericLambert, @MikeJudge]   \n",
       "303244501957365760        [@LawrenceChanin, @TeslaRoadTrip, @avantgame, @TEDchris, @elonmusk, @nytimes]   \n",
       "845289977051148289          [@faultywarrior, @matt_trulli, @FredericLambert, @TimShelton, @JimPengelly]   \n",
       "672792504895434753                                [@WSJLife, @KarlieKloss, @elonmusk, @spacex, @wsjmag]   \n",
       "191005784862236672                          [@SethGreen, @ClareGrant, @elonmusk, @SpaceX, @TeslaMotors]   \n",
       "\n",
       "                    n_mentions  \n",
       "id                              \n",
       "752721031735812096           6  \n",
       "303244501957365760           6  \n",
       "845289977051148289           5  \n",
       "672792504895434753           5  \n",
       "191005784862236672           5  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='n_mentions', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save\n",
    "\n",
    "Before continuing with the plotting, let's save our enhanced dataframe, so that we can come back to it without having to redo the same manipulations on it.\n",
    "\n",
    "`pandas` provides a number of handy functions to export dataframes in a variety of formats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use `to_pickle` to serialize the dataframe into a binary format, by using behind the scenes Python's `pickle` library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle(\"./musk_tweets_enhanced.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--   1 matteo  staff   602215 Jul 21 17:42 musk_tweets_enhanced.pkl\r\n"
     ]
    }
   ],
   "source": [
    "ls -al | grep pkl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `describe()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        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>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>n_mentions</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2819.000000</td>\n",
       "      <td>2819.000000</td>\n",
       "      <td>2819.000000</td>\n",
       "      <td>2819.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.196169</td>\n",
       "      <td>12.782547</td>\n",
       "      <td>0.807733</td>\n",
       "      <td>2014.777226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.946637</td>\n",
       "      <td>7.611198</td>\n",
       "      <td>0.859091</td>\n",
       "      <td>1.687017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          week_day     day_hour   n_mentions         year\n",
       "count  2819.000000  2819.000000  2819.000000  2819.000000\n",
       "mean      3.196169    12.782547     0.807733  2014.777226\n",
       "std       1.946637     7.611198     0.859091     1.687017\n",
       "min       0.000000     0.000000     0.000000  2010.000000\n",
       "25%       2.000000     5.000000     0.000000  2013.000000\n",
       "50%       3.000000    15.000000     1.000000  2015.000000\n",
       "75%       5.000000    19.000000     1.000000  2016.000000\n",
       "max       6.000000    23.000000     6.000000  2017.000000"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the default behavior is to include only\n",
    "# column with numerical values\n",
    "\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in this case fails as pandas does not \n",
    "# now how to handle a column with values of type list (fair enough)\n",
    "\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>last</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.196169</td>\n",
       "      <td>12.782547</td>\n",
       "      <td>0.807733</td>\n",
       "      <td>2014.777226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.946637</td>\n",
       "      <td>7.611198</td>\n",
       "      <td>0.859091</td>\n",
       "      <td>1.687017</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>2010.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>2013.000000</td>\n",
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       "    <tr>\n",
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       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at     week_day     day_hour   n_mentions  \\\n",
       "count                  2819  2819.000000  2819.000000  2819.000000   \n",
       "unique                 2819          NaN          NaN          NaN   \n",
       "top     2013-02-21 06:48:55          NaN          NaN          NaN   \n",
       "freq                      1          NaN          NaN          NaN   \n",
       "first   2010-06-04 18:31:57          NaN          NaN          NaN   \n",
       "last    2017-04-05 14:56:29          NaN          NaN          NaN   \n",
       "mean                    NaN     3.196169    12.782547     0.807733   \n",
       "std                     NaN     1.946637     7.611198     0.859091   \n",
       "min                     NaN     0.000000     0.000000     0.000000   \n",
       "25%                     NaN     2.000000     5.000000     0.000000   \n",
       "50%                     NaN     3.000000    15.000000     1.000000   \n",
       "75%                     NaN     5.000000    19.000000     1.000000   \n",
       "max                     NaN     6.000000    23.000000     6.000000   \n",
       "\n",
       "               year  \n",
       "count   2819.000000  \n",
       "unique          NaN  \n",
       "top             NaN  \n",
       "freq            NaN  \n",
       "first           NaN  \n",
       "last            NaN  \n",
       "mean    2014.777226  \n",
       "std        1.687017  \n",
       "min     2010.000000  \n",
       "25%     2013.000000  \n",
       "50%     2015.000000  \n",
       "75%     2016.000000  \n",
       "max     2017.000000  "
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# that's a workaround to include all other columns\n",
    "\n",
    "df.describe(exclude=[list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                    2819\n",
       "unique                   2819\n",
       "top       2013-02-21 06:48:55\n",
       "freq                        1\n",
       "first     2010-06-04 18:31:57\n",
       "last      2017-04-05 14:56:29\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.created_at.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['week_day_name'] = df['week_day_name'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>0.807733</td>\n",
       "      <td>2014.777226</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
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       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>6.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at     week_day     day_hour   n_mentions  \\\n",
       "count                  2819  2819.000000  2819.000000  2819.000000   \n",
       "unique                 2819          NaN          NaN          NaN   \n",
       "top     2013-02-21 06:48:55          NaN          NaN          NaN   \n",
       "freq                      1          NaN          NaN          NaN   \n",
       "first   2010-06-04 18:31:57          NaN          NaN          NaN   \n",
       "last    2017-04-05 14:56:29          NaN          NaN          NaN   \n",
       "mean                    NaN     3.196169    12.782547     0.807733   \n",
       "std                     NaN     1.946637     7.611198     0.859091   \n",
       "min                     NaN     0.000000     0.000000     0.000000   \n",
       "25%                     NaN     2.000000     5.000000     0.000000   \n",
       "50%                     NaN     3.000000    15.000000     1.000000   \n",
       "75%                     NaN     5.000000    19.000000     1.000000   \n",
       "max                     NaN     6.000000    23.000000     6.000000   \n",
       "\n",
       "               year week_day_name  \n",
       "count   2819.000000          2819  \n",
       "unique          NaN             7  \n",
       "top             NaN        Friday  \n",
       "freq            NaN           530  \n",
       "first           NaN           NaN  \n",
       "last            NaN           NaN  \n",
       "mean    2014.777226           NaN  \n",
       "std        1.687017           NaN  \n",
       "min     2010.000000           NaN  \n",
       "25%     2013.000000           NaN  \n",
       "50%     2015.000000           NaN  \n",
       "75%     2016.000000           NaN  \n",
       "max     2017.000000           NaN  "
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(exclude=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms\n",
    "\n",
    "They are useful to see the distribution of a certain variable in your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_mentions</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            text\n",
       "n_mentions      \n",
       "0           1145\n",
       "1           1231\n",
       "2            329\n",
       "3             78\n",
       "4             28\n",
       "5              6\n",
       "6              2"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['n_mentions'])[['text']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df.n_mentions, bins='auto', rwidth=1.0)\n",
    "plt.title('Distribution of the number of mentions per tweet')\n",
    "plt.ylabel(\"Tweets\")\n",
    "plt.xlabel(\"Mentions (per tweet)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df.day_hour, bins='auto', rwidth=0.6)\n",
    "plt.title('Distribution of the number of mentions per tweet')\n",
    "plt.ylabel(\"Tweets\")\n",
    "plt.xlabel(\"Hour of the day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2017 = df[df.created_at.dt.year == 2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df_2017.day_hour, bins='auto', rwidth=0.6)\n",
    "plt.title('Year 2017')\n",
    "plt.ylabel(\"Tweets\")\n",
    "plt.xlabel(\"Hour of the day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we have used directly `matplotlib` to generate our plots.\n",
    "\n",
    "`pandas`'s dataframes provide some methods that directly call `matplotlib`'s API behind the scenes:\n",
    "- `hist()` for histograms\n",
    "- `boxplot()` for boxplots\n",
    "- `plot()` for other types of plots (specified with e.g. `any='scatter'`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By passing the `by` parameter to e.g. `hist()` it is possible to produce one histogram plot of a given variable for each value in another column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how we can plot the number of mentions by year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = df.created_at.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = df.hist(column='day_hour', by='year', figsize=(10,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter plots\n",
    "\n",
    "They are useful to plot the relation between two variables in your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>849636868052275200</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'</td>\n",
       "      <td>https://twitter.com/i/web/status/849636868052275200</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>14</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848988730585096192</th>\n",
       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
       "      <td>https://twitter.com/i/web/status/848988730585096192</td>\n",
       "      <td>Monday</td>\n",
       "      <td>20</td>\n",
       "      <td>[@ForIn2020, @waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848943072423497728</th>\n",
       "      <td>2017-04-03 16:59:35</td>\n",
       "      <td>b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "      <td>https://twitter.com/i/web/status/848943072423497728</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "      <td>[@waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "849636868052275200 2017-04-05 14:56:29   \n",
       "848988730585096192 2017-04-03 20:01:01   \n",
       "848943072423497728 2017-04-03 16:59:35   \n",
       "\n",
       "                                                                                                                                                                          text  \\\n",
       "id                                                                                                                                                                               \n",
       "849636868052275200                                                                                            b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'   \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"   \n",
       "848943072423497728                                                                                                               b'@waltmossberg @mims @defcon_5 Et tu, Walt?'   \n",
       "\n",
       "                                                             tweet_link  \\\n",
       "id                                                                        \n",
       "849636868052275200  https://twitter.com/i/web/status/849636868052275200   \n",
       "848988730585096192  https://twitter.com/i/web/status/848988730585096192   \n",
       "848943072423497728  https://twitter.com/i/web/status/848943072423497728   \n",
       "\n",
       "                     week_day  day_hour  \\\n",
       "id                                        \n",
       "849636868052275200  Wednesday        14   \n",
       "848988730585096192     Monday        20   \n",
       "848943072423497728     Monday        16   \n",
       "\n",
       "                                                   tweet_mentions  n_mentions  \n",
       "id                                                                             \n",
       "849636868052275200                                             []           0  \n",
       "848988730585096192  [@ForIn2020, @waltmossberg, @mims, @defcon_5]           4  \n",
       "848943072423497728              [@waltmossberg, @mims, @defcon_5]           3  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# specify the type of plot and the two\n",
    "# variables to be plotted against one another\n",
    "plt.scatter(df.n_mentions, df.day_hour)\n",
    "\n",
    "# give a title to the plot\n",
    "plt.title('Number of mentions vs hour of the day')\n",
    "\n",
    "# give a label to the axes\n",
    "plt.ylabel(\"Day hour\")\n",
    "plt.xlabel(\"Number of mentions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bar charts\n",
    "\n",
    "They are useful to plot categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_by_weekday = df.groupby(df.created_at.dt.weekday)[['text']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_days = [\n",
    "    \"Mon\",\n",
    "    \"Tue\",\n",
    "    \"Wed\",\n",
    "    \"Thur\",\n",
    "    \"Fri\",\n",
    "    \"Sat\",\n",
    "    \"Sun\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# specify the type of plot and the labels\n",
    "# for the y axis (the bars)\n",
    "plt.bar(\n",
    "    tweets_by_weekday.index,\n",
    "    tweets_by_weekday.text,\n",
    "    tick_label=week_days,\n",
    "    width=0.5\n",
    ")\n",
    "\n",
    "# give a title to the plot\n",
    "plt.title('Elon Musk\\'s week on Twitter')\n",
    "\n",
    "# give a label to the axes\n",
    "plt.ylabel(\"Number of tweets\")\n",
    "plt.xlabel(\"Week day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Box plots\n",
    "\n",
    "![box plot explained](./figures/eda-boxplot.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "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>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            text\n",
       "created_at      \n",
       "0            315\n",
       "1            385\n",
       "2            380\n",
       "3            361\n",
       "4            530\n",
       "5            426\n",
       "6            422"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_by_weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "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>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>402.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>67.551744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>315.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>370.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>385.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>424.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>530.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             text\n",
       "count    7.000000\n",
       "mean   402.714286\n",
       "std     67.551744\n",
       "min    315.000000\n",
       "25%    370.500000\n",
       "50%    385.000000\n",
       "75%    424.000000\n",
       "max    530.000000"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_by_weekday.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12971a9b0>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets_by_weekday.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>849636868052275200</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'</td>\n",
       "      <td>https://twitter.com/i/web/status/849636868052275200</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848988730585096192</th>\n",
       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"</td>\n",
       "      <td>https://twitter.com/i/web/status/848988730585096192</td>\n",
       "      <td>[@ForIn2020, @waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848943072423497728</th>\n",
       "      <td>2017-04-03 16:59:35</td>\n",
       "      <td>b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "      <td>https://twitter.com/i/web/status/848943072423497728</td>\n",
       "      <td>[@waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            created_at  \\\n",
       "id                                       \n",
       "849636868052275200 2017-04-05 14:56:29   \n",
       "848988730585096192 2017-04-03 20:01:01   \n",
       "848943072423497728 2017-04-03 16:59:35   \n",
       "\n",
       "                                                                                                                                                                          text  \\\n",
       "id                                                                                                                                                                               \n",
       "849636868052275200                                                                                            b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'   \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exactly. Tesla is absurdly overvalued if based on the past, but that's irr\\xe2\\x80\\xa6 https://t.co/qQcTqkzgMl\"   \n",
       "848943072423497728                                                                                                               b'@waltmossberg @mims @defcon_5 Et tu, Walt?'   \n",
       "\n",
       "                                                             tweet_link  \\\n",
       "id                                                                        \n",
       "849636868052275200  https://twitter.com/i/web/status/849636868052275200   \n",
       "848988730585096192  https://twitter.com/i/web/status/848988730585096192   \n",
       "848943072423497728  https://twitter.com/i/web/status/848943072423497728   \n",
       "\n",
       "                                                   tweet_mentions  n_mentions  \\\n",
       "id                                                                              \n",
       "849636868052275200                                             []           0   \n",
       "848988730585096192  [@ForIn2020, @waltmossberg, @mims, @defcon_5]           4   \n",
       "848943072423497728              [@waltmossberg, @mims, @defcon_5]           3   \n",
       "\n",
       "                    week_day  day_hour  \n",
       "id                                      \n",
       "849636868052275200         2        14  \n",
       "848988730585096192         0        20  \n",
       "848943072423497728         0        16  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "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>day_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2819.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.782547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.611198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day_hour\n",
       "count  2819.000000\n",
       "mean     12.782547\n",
       "std       7.611198\n",
       "min       0.000000\n",
       "25%       5.000000\n",
       "50%      15.000000\n",
       "75%      19.000000\n",
       "max      23.000000"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['day_hour']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_hour    5.0\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['day_hour']].quantile(.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.boxplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "df[['day_hour', 'week_day_name']].boxplot(\n",
    "    by='week_day_name',\n",
    "    grid=False,\n",
    "    figsize=(8,6),\n",
    "    fontsize=10\n",
    ")\n",
    "\n",
    "# give a title to the plot\n",
    "plt.title('')\n",
    "\n",
    "# give a label to the axes\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "df[['day_hour', 'week_day']].boxplot(\n",
    "    by='week_day',\n",
    "    grid=True, # just to show the difference with/without\n",
    "    figsize=(8,6),\n",
    "    fontsize=10\n",
    ")\n",
    "\n",
    "# give a title to the plot\n",
    "plt.title('')\n",
    "\n",
    "# give a label to the axes\n",
    "plt.xlabel(\"Day of the week\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
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