notebooks/4_DataWrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data wrangling\n",
    "\n",
    "In this notebook, we will - **learn** how to **wrangle data**: transforming data for our needs.\n",
    "\n",
    "We consider a dataset of tweets from Elon Musk, SpaceX and Tesla founder, and ask the following questions:\n",
    "* When is Elon most actively tweeting?\n",
    "\n",
    "While this question is a bit trivial, it will allow us to learn how to wrangle data."
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = 'data/elonmusk_tweets.csv'\n",
    "df = pd.read_csv(dataset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   id          2819 non-null   int64 \n",
      " 1   created_at  2819 non-null   object\n",
      " 2   text        2819 non-null   object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 66.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.created_at = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   created_at  2819 non-null   datetime64[ns]\n",
      " 1   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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['created_at', 'text'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we renamed the column `text` => `tweet`\n",
    "df.columns = ['created_at', 'tweet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
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       "      <td>2017-04-03 16:30:19</td>\n",
       "      <td>b'Stormy weather in Shortville ...'</td>\n",
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       "      <td>b\"@DaveLeeBBC @verge Coal is dying due to nat ...</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:...  \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exa...  \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 ...  "
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's change column `tweet` => `text`\n",
    "df = df.rename(columns={\"tweet\": \"text\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>b'Stormy weather in Shortville ...'</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:...  \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exa...  \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 ...  "
      ]
     },
     "execution_count": 11,
     "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": 12,
   "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": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"created_at\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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"
      ]
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     "execution_count": 14,
     "metadata": {},
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   "source": [
    "# whereas this syntax selects one single column\n",
    "# but returns a Dataframe\n",
    "df[[\"created_at\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "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": 16,
   "metadata": {},
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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:...  \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exa...  "
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[0:2]"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tweet_link'] = df.index.map(lambda x: f'https://twitter.com/i/web/status/{x}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "</style><table id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57\" ><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>    </tr>    <tr>        <th class=\"index_name level0\" >id</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57level0_row0\" class=\"row_heading level0 row0\" >849636868052275200</th>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row0_col0\" class=\"data row0 col0\" >2017-04-05 14:56:29</td>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row0_col1\" class=\"data row0 col1\" >b'And so the robots spared humanity ... https://t.co/v7JUJQWfCv'</td>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row0_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",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57level0_row1\" class=\"row_heading level0 row1\" >848988730585096192</th>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row1_col0\" class=\"data row1 col0\" >2017-04-03 20:01:01</td>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row1_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_75dbceb2_a41f_11ea_8372_8c8590554d57row1_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",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57level0_row2\" class=\"row_heading level0 row2\" >848943072423497728</th>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row2_col0\" class=\"data row2 col0\" >2017-04-03 16:59:35</td>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row2_col1\" class=\"data row2 col1\" >b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row2_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",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57level0_row3\" class=\"row_heading level0 row3\" >848935705057280001</th>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row3_col0\" class=\"data row3 col0\" >2017-04-03 16:30:19</td>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row3_col1\" class=\"data row3 col1\" >b'Stormy weather in Shortville ...'</td>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row3_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",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57level0_row4\" class=\"row_heading level0 row4\" >848416049573658624</th>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row4_col0\" class=\"data row4 col0\" >2017-04-02 06:05:23</td>\n",
       "                        <td id=\"T_75dbceb2_a41f_11ea_8372_8c8590554d57row4_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_75dbceb2_a41f_11ea_8372_8c8590554d57row4_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",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x114441198>"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tweet_mentions'] = df.text.apply(find_mentions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['n_mentions'] = df.tweet_mentions.apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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",
       "    </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",
       "    </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",
       "    </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>[]</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>[@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",
       "                                                   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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add column with week day and hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def day_of_week(t):\n",
    "    if t==0:\n",
    "        return \"Monday\"\n",
    "    elif t==1:\n",
    "        return \"Tuesday\"\n",
    "    elif t==2:\n",
    "        return \"Wednesday\"\n",
    "    elif t==3:\n",
    "        return \"Thursday\"\n",
    "    elif t==4:\n",
    "        return \"Friday\"\n",
    "    elif t==5:\n",
    "        return \"Saturday\"\n",
    "    return \"Sunday\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"week_day\"] = df.created_at.dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"week_day_name\"] = df[\"week_day\"].apply(day_of_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>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",
       "    </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>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>[@ForIn2020, @waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>4</td>\n",
       "      <td>0</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>[@waltmossberg, @mims, @defcon_5]</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "                                                   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 week_day_name  \n",
       "id                                          \n",
       "849636868052275200         2     Wednesday  \n",
       "848988730585096192         0        Monday  \n",
       "848943072423497728         0        Monday  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add column with day hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.created_at.dt?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.created_at.dt.hour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"day_hour\"] = df.created_at.dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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>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>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848935705057280001</th>\n",
       "      <td>2017-04-03 16:30:19</td>\n",
       "      <td>0</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         2        14\n",
       "848988730585096192 2017-04-03 20:01:01         0        20\n",
       "848943072423497728 2017-04-03 16:59:35         0        16\n",
       "848935705057280001 2017-04-03 16:30:19         0        16"
      ]
     },
     "execution_count": 30,
     "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": [
    "##### Multiple conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 8)"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 8)"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1384, 8)"
      ]
     },
     "execution_count": 33,
     "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": [
    "### Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg({'n_mentions': ['min', 'max', 'sum']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "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>week_day_name</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",
       "      <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>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>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>Monday</td>\n",
       "      <td>20</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>[@DaveLeeBBC, @verge]</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>6</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>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>18</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>[@BadAstronomer]</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>23</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>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>23</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>[@danahull]</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>17</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",
       "                                                   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   \n",
       "\n",
       "                    week_day week_day_name  day_hour  \n",
       "id                                                    \n",
       "849636868052275200         2     Wednesday        14  \n",
       "848988730585096192         0        Monday        20  \n",
       "848416049573658624         6        Sunday         6  \n",
       "848244577521647616         5      Saturday        18  \n",
       "847958571895619584         4        Friday        23  \n",
       "847594208219336705         3      Thursday        23  \n",
       "846772378067648513         1       Tuesday        17  "
      ]
     },
     "execution_count": 37,
     "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": 38,
   "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_name</th>\n",
       "      <th>day_hour</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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",
       "      <td>422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          created_at  text  tweet_link  tweet_mentions  n_mentions  \\\n",
       "week_day                                                             \n",
       "0                315   315         315             315         315   \n",
       "1                385   385         385             385         385   \n",
       "2                380   380         380             380         380   \n",
       "3                361   361         361             361         361   \n",
       "4                530   530         530             530         530   \n",
       "5                426   426         426             426         426   \n",
       "6                422   422         422             422         422   \n",
       "\n",
       "          week_day_name  day_hour  \n",
       "week_day                           \n",
       "0                   315       315  \n",
       "1                   385       385  \n",
       "2                   380       380  \n",
       "3                   361       361  \n",
       "4                   530       530  \n",
       "5                   426       426  \n",
       "6                   422       422  "
      ]
     },
     "execution_count": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>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": [
       "          tweet_count\n",
       "week_day             \n",
       "0                 315\n",
       "1                 385\n",
       "2                 380\n",
       "3                 361\n",
       "4                 530\n",
       "5                 426\n",
       "6                 422"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "grpby_result_as_series = df.groupby('day_hour')['text'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpby_result_as_series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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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       "      <th>1</th>\n",
       "      <td>183</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>119</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
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       "      <th>4</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "          count\n",
       "day_hour       \n",
       "0           144\n",
       "1           183\n",
       "2           119\n",
       "3           104\n",
       "4            98"
      ]
     },
     "execution_count": 43,
     "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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 45,
     "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "      <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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<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>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            text\n",
       "created_at      \n",
       "0            144\n",
       "1            183\n",
       "2            119\n",
       "3            104\n",
       "4             98"
      ]
     },
     "execution_count": 47,
     "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": 48,
   "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </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\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   text\n",
       "week_day day_hour      \n",
       "0        0           11\n",
       "         1           23\n",
       "         2           17\n",
       "         3            7\n",
       "         4           12"
      ]
     },
     "execution_count": 49,
     "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.n_mentions.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the entire dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n_mentions     0.807733\n",
       "week_day       3.196169\n",
       "day_hour      12.782547\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "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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       "    .dataframe thead tr:last-of-type th {\n",
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       "</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": 52,
     "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>week_day_name</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",
       "      <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>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>18</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>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>9</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>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>10</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>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>8</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>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>8</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",
       "                   tweet_mentions  n_mentions  week_day week_day_name  \\\n",
       "id                                                                      \n",
       "15434727182                    []           0         4        Friday   \n",
       "142179928203460608             []           0         3      Thursday   \n",
       "142188458125963264             []           0         3      Thursday   \n",
       "142880871391838208             []           0         5      Saturday   \n",
       "142881284019060736             []           0         5      Saturday   \n",
       "\n",
       "                    day_hour  \n",
       "id                            \n",
       "15434727182               18  \n",
       "142179928203460608         9  \n",
       "142188458125963264        10  \n",
       "142880871391838208         8  \n",
       "142881284019060736         8  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='created_at', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>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>week_day_name</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",
       "      <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>[@kumailn, @RealDaveBarton, @JuddApatow, @SiliconHBO, @FredericLambert, @MikeJudge]</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</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>[@LawrenceChanin, @TeslaRoadTrip, @avantgame, @TEDchris, @elonmusk, @nytimes]</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>20</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>[@faultywarrior, @matt_trulli, @FredericLambert, @TimShelton, @JimPengelly]</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>15</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>[@WSJLife, @KarlieKloss, @elonmusk, @spacex, @wsjmag]</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>15</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>[@SethGreen, @ClareGrant, @elonmusk, @SpaceX, @TeslaMotors]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>3</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",
       "                                                                                         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  week_day week_day_name  day_hour  \n",
       "id                                                                \n",
       "752721031735812096           6         1       Tuesday         4  \n",
       "303244501957365760           6         6        Sunday        20  \n",
       "845289977051148289           5         4        Friday        15  \n",
       "672792504895434753           5         4        Friday        15  \n",
       "191005784862236672           5         5      Saturday         3  "
      ]
     },
     "execution_count": 54,
     "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": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_pickle(\"stuff/musk_tweets_enhanced.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `describe()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_mentions</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
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       "  </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.807733</td>\n",
       "      <td>3.196169</td>\n",
       "      <td>12.782547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.859091</td>\n",
       "      <td>1.946637</td>\n",
       "      <td>7.611198</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        n_mentions     week_day     day_hour\n",
       "count  2819.000000  2819.000000  2819.000000\n",
       "mean      0.807733     3.196169    12.782547\n",
       "std       0.859091     1.946637     7.611198\n",
       "min       0.000000     0.000000     0.000000\n",
       "25%       0.000000     2.000000     5.000000\n",
       "50%       1.000000     3.000000    15.000000\n",
       "75%       1.000000     5.000000    19.000000\n",
       "max       6.000000     6.000000    23.000000"
      ]
     },
     "execution_count": 56,
     "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_mentions</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\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>0.807733</td>\n",
       "      <td>3.196169</td>\n",
       "      <td>12.782547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.859091</td>\n",
       "      <td>1.946637</td>\n",
       "      <td>7.611198</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        n_mentions     week_day     day_hour\n",
       "count  2819.000000  2819.000000  2819.000000\n",
       "mean      0.807733     3.196169    12.782547\n",
       "std       0.859091     1.946637     7.611198\n",
       "min       0.000000     0.000000     0.000000\n",
       "25%       0.000000     2.000000     5.000000\n",
       "50%       1.000000     3.000000    15.000000\n",
       "75%       1.000000     5.000000    19.000000\n",
       "max       6.000000     6.000000    23.000000"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>n_mentions</th>\n",
       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2819</td>\n",
       "      <td>2819.000000</td>\n",
       "      <td>2819.000000</td>\n",
       "      <td>2819.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2013-02-21 06:48:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>2010-06-04 18:31:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.807733</td>\n",
       "      <td>3.196169</td>\n",
       "      <td>12.782547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.859091</td>\n",
       "      <td>1.946637</td>\n",
       "      <td>7.611198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at   n_mentions     week_day     day_hour\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     0.807733     3.196169    12.782547\n",
       "std                     NaN     0.859091     1.946637     7.611198\n",
       "min                     NaN     0.000000     0.000000     0.000000\n",
       "25%                     NaN     0.000000     2.000000     5.000000\n",
       "50%                     NaN     1.000000     3.000000    15.000000\n",
       "75%                     NaN     1.000000     5.000000    19.000000\n",
       "max                     NaN     6.000000     6.000000    23.000000"
      ]
     },
     "execution_count": 58,
     "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": 59,
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.created_at.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['week_day_name'] = df['week_day_name'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th>created_at</th>\n",
       "      <th>n_mentions</th>\n",
       "      <th>week_day</th>\n",
       "      <th>week_day_name</th>\n",
       "      <th>day_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2819</td>\n",
       "      <td>2819.000000</td>\n",
       "      <td>2819.000000</td>\n",
       "      <td>2819</td>\n",
       "      <td>2819.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2819</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2013-02-21 06:48:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>2010-06-04 18:31:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.807733</td>\n",
       "      <td>3.196169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.782547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.859091</td>\n",
       "      <td>1.946637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.611198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at   n_mentions     week_day week_day_name  \\\n",
       "count                  2819  2819.000000  2819.000000          2819   \n",
       "unique                 2819          NaN          NaN             7   \n",
       "top     2013-02-21 06:48:55          NaN          NaN        Friday   \n",
       "freq                      1          NaN          NaN           530   \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     0.807733     3.196169           NaN   \n",
       "std                     NaN     0.859091     1.946637           NaN   \n",
       "min                     NaN     0.000000     0.000000           NaN   \n",
       "25%                     NaN     0.000000     2.000000           NaN   \n",
       "50%                     NaN     1.000000     3.000000           NaN   \n",
       "75%                     NaN     1.000000     5.000000           NaN   \n",
       "max                     NaN     6.000000     6.000000           NaN   \n",
       "\n",
       "           day_hour  \n",
       "count   2819.000000  \n",
       "unique          NaN  \n",
       "top             NaN  \n",
       "freq            NaN  \n",
       "first           NaN  \n",
       "last            NaN  \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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(exclude=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 63,
   "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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       "    }\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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['n_mentions'])[['text']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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": 65,
   "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": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2017 = df[df.created_at.dt.year == 2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = df.created_at.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": [
    "#### Bar charts\n",
    "\n",
    "They are useful to plot categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_by_weekday = df.groupby(df.created_at.dt.weekday)[['text']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 74,
   "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](images/eda-boxplot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers, missing values\n",
    "\n",
    "An *outlier* is an observation far from the center of mass of the distribution. It might be an error or a genuine observation: this distinction requires domain knowledge. Outliers infuence the outcomes of several statistics and machine learning methods: it is important to decide how to deal with them.\n",
    "\n",
    "A *missing value* is an observation without a value. There can be many reasons for a missing value: the value might not exist (hence its absence is informative and it should be left empty) or might not be known (hence the value is existing but missing in the dataset and it should be marked as NA).\n",
    "\n",
    "*One way to think about the difference is with this Zen-like koan: An explicit missing value is the presence of an absence; an implicit missing value is the absence of a presence.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_by_weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_by_weekday.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116a0a550>"
      ]
     },
     "execution_count": 77,
     "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": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>week_day_name</th>\n",
       "      <th>day_hour</th>\n",
       "      <th>year</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>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</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>Monday</td>\n",
       "      <td>20</td>\n",
       "      <td>2017</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>Monday</td>\n",
       "      <td>16</td>\n",
       "      <td>2017</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 week_day_name  day_hour  year  \n",
       "id                                                          \n",
       "849636868052275200         2     Wednesday        14  2017  \n",
       "848988730585096192         0        Monday        20  2017  \n",
       "848943072423497728         0        Monday        16  2017  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['day_hour']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_hour    5.0\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['day_hour']].quantile(.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.boxplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 84,
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1.\n",
    "\n",
    "* Create a function that calculates the frequency of hashtags in tweets.\n",
    "* Test it on toy examples, to make sure it works.\n",
    "* Applly it to Elon Musk's tweets.\n",
    "* List the top 10 hashtags in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
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    "### Exercise 2.\n",
    "\n",
    "Read the file `data/adams-hhgttg.txt` and:\n",
    "\n",
    "- Count the number of occurrences per distinct word in the text.\n",
    "\n",
    "- Create a data frame with two columns: word and counts.\n",
    "\n",
    "- Plot the histogram of the word frequencies and think about what is happening."
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    "---"
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