notebooks/3.1 Exploratory data analysis basics.ipynb

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   "attachments": {},
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   "source": [
    "# 3.1 Basic of exploratory data analysis with 🐼\n",
    "\n",
    "In this notebook, we will focus on another essential skill in data analysis, namely the ability to get insights about a dataset by means of plotting and summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's resume things from where we've left them. We load back the dataframe that we had picled at the end of the previous notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"./musk_tweets_enhanced.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2819 entries, 849636868052275200 to 15434727182\n",
      "Data columns (total 8 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",
      " 2   tweet_link      2819 non-null   object        \n",
      " 3   tweet_mentions  2819 non-null   object        \n",
      " 4   n_mentions      2819 non-null   int64         \n",
      " 5   week_day_name   2819 non-null   object        \n",
      " 6   week_day        2819 non-null   int64         \n",
      " 7   day_hour        2819 non-null   int64         \n",
      "dtypes: datetime64[ns](1), int64(3), object(4)\n",
      "memory usage: 198.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>text</th>\n",
       "      <th>tweet_link</th>\n",
       "      <th>tweet_mentions</th>\n",
       "      <th>n_mentions</th>\n",
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       "      <th>week_day</th>\n",
       "      <th>day_hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
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       "  <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:...</td>\n",
       "      <td>https://twitter.com/i/web/status/8496368680522...</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848988730585096192</th>\n",
       "      <td>2017-04-03 20:01:01</td>\n",
       "      <td>b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exa...</td>\n",
       "      <td>https://twitter.com/i/web/status/8489887305850...</td>\n",
       "      <td>@ForIn2020, @waltmossberg, @mims, @defcon_5</td>\n",
       "      <td>4</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848943072423497728</th>\n",
       "      <td>2017-04-03 16:59:35</td>\n",
       "      <td>b'@waltmossberg @mims @defcon_5 Et tu, Walt?'</td>\n",
       "      <td>https://twitter.com/i/web/status/8489430724234...</td>\n",
       "      <td>@waltmossberg, @mims, @defcon_5</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</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>b'Stormy weather in Shortville ...'</td>\n",
       "      <td>https://twitter.com/i/web/status/8489357050572...</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>848416049573658624</th>\n",
       "      <td>2017-04-02 06:05:23</td>\n",
       "      <td>b\"@DaveLeeBBC @verge Coal is dying due to nat ...</td>\n",
       "      <td>https://twitter.com/i/web/status/8484160495736...</td>\n",
       "      <td>@DaveLeeBBC, @verge</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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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 ...   \n",
       "\n",
       "                                                           tweet_link  \\\n",
       "id                                                                      \n",
       "849636868052275200  https://twitter.com/i/web/status/8496368680522...   \n",
       "848988730585096192  https://twitter.com/i/web/status/8489887305850...   \n",
       "848943072423497728  https://twitter.com/i/web/status/8489430724234...   \n",
       "848935705057280001  https://twitter.com/i/web/status/8489357050572...   \n",
       "848416049573658624  https://twitter.com/i/web/status/8484160495736...   \n",
       "\n",
       "                                                 tweet_mentions  n_mentions  \\\n",
       "id                                                                            \n",
       "849636868052275200                                                        1   \n",
       "848988730585096192  @ForIn2020, @waltmossberg, @mims, @defcon_5           4   \n",
       "848943072423497728              @waltmossberg, @mims, @defcon_5           3   \n",
       "848935705057280001                                                        1   \n",
       "848416049573658624                          @DaveLeeBBC, @verge           2   \n",
       "\n",
       "                   week_day_name  week_day  day_hour  \n",
       "id                                                    \n",
       "849636868052275200     Wednesday         2        14  \n",
       "848988730585096192        Monday         0        20  \n",
       "848943072423497728        Monday         0        16  \n",
       "848935705057280001        Monday         0        16  \n",
       "848416049573658624        Sunday         6         6  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `describe()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>mean</th>\n",
       "      <td>2015-03-24 20:24:24.578928896</td>\n",
       "      <td>1.213906</td>\n",
       "      <td>3.196169</td>\n",
       "      <td>12.782547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010-06-04 18:31:57</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013-06-28 18:27:19</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015-10-21 23:14:33</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016-08-30 04:08:30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.568269</td>\n",
       "      <td>1.946637</td>\n",
       "      <td>7.611198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                          created_at   n_mentions     week_day     day_hour\n",
       "count                           2819  2819.000000  2819.000000  2819.000000\n",
       "mean   2015-03-24 20:24:24.578928896     1.213906     3.196169    12.782547\n",
       "min              2010-06-04 18:31:57     1.000000     0.000000     0.000000\n",
       "25%              2013-06-28 18:27:19     1.000000     2.000000     5.000000\n",
       "50%              2015-10-21 23:14:33     1.000000     3.000000    15.000000\n",
       "75%              2016-08-30 04:08:30     1.000000     5.000000    19.000000\n",
       "max              2017-04-05 14:56:29     6.000000     6.000000    23.000000\n",
       "std                              NaN     0.568269     1.946637     7.611198"
      ]
     },
     "execution_count": 4,
     "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": 8,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>b'And so the robots spared humanity ... https:...</td>\n",
       "      <td>https://twitter.com/i/web/status/8496368680522...</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2015-03-24 20:24:24.578928896</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.213906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.196169</td>\n",
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       "      <th>min</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>25%</th>\n",
       "      <td>2013-06-28 18:27:19</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>2015-10-21 23:14:33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016-08-30 04:08:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2017-04-05 14:56:29</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "                           created_at  \\\n",
       "count                            2819   \n",
       "unique                            NaN   \n",
       "top                               NaN   \n",
       "freq                              NaN   \n",
       "mean    2015-03-24 20:24:24.578928896   \n",
       "min               2010-06-04 18:31:57   \n",
       "25%               2013-06-28 18:27:19   \n",
       "50%               2015-10-21 23:14:33   \n",
       "75%               2016-08-30 04:08:30   \n",
       "max               2017-04-05 14:56:29   \n",
       "std                               NaN   \n",
       "\n",
       "                                                     text  \\\n",
       "count                                                2819   \n",
       "unique                                               2819   \n",
       "top     b'And so the robots spared humanity ... https:...   \n",
       "freq                                                    1   \n",
       "mean                                                  NaN   \n",
       "min                                                   NaN   \n",
       "25%                                                   NaN   \n",
       "50%                                                   NaN   \n",
       "75%                                                   NaN   \n",
       "max                                                   NaN   \n",
       "std                                                   NaN   \n",
       "\n",
       "                                               tweet_link tweet_mentions  \\\n",
       "count                                                2819           2819   \n",
       "unique                                               2819           1181   \n",
       "top     https://twitter.com/i/web/status/8496368680522...                  \n",
       "freq                                                    1           1145   \n",
       "mean                                                  NaN            NaN   \n",
       "min                                                   NaN            NaN   \n",
       "25%                                                   NaN            NaN   \n",
       "50%                                                   NaN            NaN   \n",
       "75%                                                   NaN            NaN   \n",
       "max                                                   NaN            NaN   \n",
       "std                                                   NaN            NaN   \n",
       "\n",
       "         n_mentions week_day_name     week_day     day_hour  \n",
       "count   2819.000000          2819  2819.000000  2819.000000  \n",
       "unique          NaN             7          NaN          NaN  \n",
       "top             NaN        Friday          NaN          NaN  \n",
       "freq            NaN           530          NaN          NaN  \n",
       "mean       1.213906           NaN     3.196169    12.782547  \n",
       "min        1.000000           NaN     0.000000     0.000000  \n",
       "25%        1.000000           NaN     2.000000     5.000000  \n",
       "50%        1.000000           NaN     3.000000    15.000000  \n",
       "75%        1.000000           NaN     5.000000    19.000000  \n",
       "max        6.000000           NaN     6.000000    23.000000  \n",
       "std        0.568269           NaN     1.946637     7.611198  "
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                             2819\n",
       "mean     2015-03-24 20:24:24.578928896\n",
       "min                2010-06-04 18:31:57\n",
       "25%                2013-06-28 18:27:19\n",
       "50%                2015-10-21 23:14:33\n",
       "75%                2016-08-30 04:08:30\n",
       "max                2017-04-05 14:56:29\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.created_at.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['week_day_name'] = df['week_day_name'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>n_mentions</th>\n",
       "      <th>week_day_name</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</td>\n",
       "      <td>2819.000000</td>\n",
       "      <td>2819.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015-03-24 20:24:24.578928896</td>\n",
       "      <td>1.213906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.196169</td>\n",
       "      <td>12.782547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010-06-04 18:31:57</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013-06-28 18:27:19</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015-10-21 23:14:33</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016-08-30 04:08:30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017-04-05 14:56:29</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.568269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.946637</td>\n",
       "      <td>7.611198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           created_at   n_mentions week_day_name     week_day  \\\n",
       "count                            2819  2819.000000          2819  2819.000000   \n",
       "unique                            NaN          NaN             7          NaN   \n",
       "top                               NaN          NaN        Friday          NaN   \n",
       "freq                              NaN          NaN           530          NaN   \n",
       "mean    2015-03-24 20:24:24.578928896     1.213906           NaN     3.196169   \n",
       "min               2010-06-04 18:31:57     1.000000           NaN     0.000000   \n",
       "25%               2013-06-28 18:27:19     1.000000           NaN     2.000000   \n",
       "50%               2015-10-21 23:14:33     1.000000           NaN     3.000000   \n",
       "75%               2016-08-30 04:08:30     1.000000           NaN     5.000000   \n",
       "max               2017-04-05 14:56:29     6.000000           NaN     6.000000   \n",
       "std                               NaN     0.568269           NaN     1.946637   \n",
       "\n",
       "           day_hour  \n",
       "count   2819.000000  \n",
       "unique          NaN  \n",
       "top             NaN  \n",
       "freq            NaN  \n",
       "mean      12.782547  \n",
       "min        0.000000  \n",
       "25%        5.000000  \n",
       "50%       15.000000  \n",
       "75%       19.000000  \n",
       "max       23.000000  \n",
       "std        7.611198  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(exclude=['object'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_mentions</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2376</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",
       "1           2376\n",
       "2            329\n",
       "3             78\n",
       "4             28\n",
       "5              6\n",
       "6              2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['n_mentions'])[['text']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n_mentions\n",
       "1    2376\n",
       "2     329\n",
       "3      78\n",
       "4      28\n",
       "5       6\n",
       "6       2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['n_mentions'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u87t376733ntPgwYN0ujRo9WyZUt16dJFXbt2VbFiefu7Trly5fI1GMYdd9zhNO1wOFSlSpVc71cqKMeOHVNYWJjTL+XSn5cjZs2/WsWKFbMto2TJkjne53Hteu64445s+y+39RSUa+stWbKkJOn333+Xv7+/Dh48KMuysu3/LHkdUORG67kZ1y4zICBAXl5e9iVXV7efPXs22/tvdExl/YLer1+/XGtISkqyt0X68/6avDh27Jgdgq529eddu3btPC3rWjntF0mqUKFCju1Zx2bW9rZo0SLH5V77OXl5eWW7hDUvx3pujh07pmLFiqlKlSpO7aGhoQoMDLypn7VJkyapc+fOqlq1qmrXrq22bduqT58+172U8WrXHiO+vr4qW7ZstmMkr/vM3d1d5cuXz9O6c+Pm5qbo6Ght3rxZ0p9hq2nTpmrSpIkyMjK0fft2lSlTRufOnXMKWwcPHtSePXtyvew4MTHR7peUlJTr/wFZ/QBkR9gCcEO//PKLkpKSsv3CczVvb29t2rRJ69ev14oVK7Ry5Up98sknatGihVatWiU3N7cbric/91nlVW6DB2RkZOSppoKQ23qs69y/40o3qjczM9O+yT6nvr6+vgWynut9dvlZZkHu/6yzVi+//LLuuuuuHPtcu/0mjuv8ym0f5OWzlv68Byk0NDRbv2tHDjT1M5XXBx3n5bOOiYnR4cOHtWzZMq1atUrvvfeeZsyYoTlz5jidGb1Z+d1nnp6eef6D1PU0adJEL774oi5evKjNmzdr7NixCgwMVO3atbV582b73qmrw1ZmZqZat26tf/3rXzkus2rVqna/kJAQLViwIMd+uYU1AIQtAHkwf/58SVJsbOx1+xUrVkwtW7ZUy5Yt9dprr2nKlCkaO3as1q9fr1atWuX5F6a8uvYyIMuydOjQIae/UJcsWTLHZ9IcO3ZMlSpVsqfzU1t4eLjWrFmjCxcuOJ3d+umnn+z5BSE8PFx79uxRZmam0y9jf3U9f/VzqFy5sizLUmRkpP3LmAlZZ4eu/fxMndGTbnxMZV0O6u/vr1atWhXousPDw3N8nltBH1f5kbW9ISEhBba9+f1Zy8zM1MGDB+0zfNKfg4mcP3/+pvdJqVKlNGDAAA0YMEApKSmKiYnRhAkT8hS2Dh48qPvuu8+eTklJ0alTp9S+fXtJZvZZluvtu6ZNmyo9PV0LFy7Ur7/+aoeqmJgYO2xVrVrVacCKypUrKyUl5YZ1Vq5cWWvWrFHjxo1v+MeDgv6eB4o67tkCcF3r1q3T5MmTFRkZmeuQxJJyHC466y//WUM0Zz07pqAeyPnhhx863Ue2ePFinTp1Su3atbPbKleurO3btys9Pd1uW758ebbhvPNTW/v27ZWRkaE333zTqX3GjBlyOBxO6/8r2rdvr4SEBH3yySd225UrVzRr1iz5+vqqWbNmN7VcHx+fv/QZdOnSRW5ubpo4cWK2s0OWZeV4ed7N8Pf3V+nSpe37RrK8/fbbBbL8nNzomIqKilLlypX1yiuvKCUlJdv7z5w5c9Prbt++vXbs2KFt27bZbampqZo7d64iIiJc8hy12NhY+fv7a8qUKbp8+XK2+TezvVmj7eX1Z02S06h4kvTaa69Jkjp06JDv9V97fPr6+qpKlSrZhpLPzdy5c532xezZs3XlyhX7GDGxz7Jc73uqUaNGKl68uKZNm6ZSpUrZoy82bdpU27dv18aNG53Oakl/jgi6bds2ffXVV9mWd/78eV25csXul5GRocmTJ2frd+XKFad6/ur3C3Cr4cwWANuXX36pn376SVeuXNHp06e1bt06rV69WuHh4fr888/l5eWV63snTZqkTZs2qUOHDgoPD1diYqLefvttlS9f3r7hv3LlygoMDNScOXPk5+cnHx8fNWrUKM/3tFyrVKlSatKkiQYMGKDTp09r5syZqlKlitPw9IMGDdLixYvVtm1bdevWTYcPH9Z//vOfbMOd56e2jh076r777tPYsWN19OhR1a1bV6tWrdKyZcs0bNiwbMu+WYMHD9Y777yj/v37a/fu3YqIiNDixYu1ZcsWzZw5M9s9Y3kVFRWlNWvW6LXXXlNYWJgiIyNzvFcoN5UrV9YLL7ygMWPG6OjRo3rggQfk5+enI0eOaMmSJRo8eLBGjRp1U7Vda9CgQXrppZc0aNAgNWjQQJs2bdLPP/9cIMvOyY2OqWLFium9995Tu3btVKtWLQ0YMEDlypXTr7/+qvXr18vf31//93//d1PrHj16tBYuXKh27drpqaeeUqlSpTRv3jwdOXJE//3vfwvkUrP88vf31+zZs9WnTx/Vr19fPXr0UHBwsI4fP64VK1aocePG2f7ocCPe3t6qWbOmPvnkE1WtWlWlSpVS7dq1c7wfrW7duurXr5/mzp2r8+fPq1mzZtqxY4fmzZunBx54wOkMU17VrFlTzZs3V1RUlEqVKqVdu3Zp8eLFGjp0aJ7en56erpYtW6pbt246cOCA3n77bTVp0sQeXt3EPssSFRUlSRo7dqx69Oih4sWLq2PHjvLx8VGJEiUUFRWl7du328/Ykv48s5WamqrU1NRsYeuZZ57R559/rvvvv98eIj81NVV79+7V4sWLdfToUZUuXVrNmjXT448/rqlTpyo+Pl5t2rRR8eLFdfDgQS1atEivv/66unbtatc4e/ZsvfDCC6pSpYpCQkJyvX8NuC24YAREAIVM1pDGWS8PDw8rNDTUat26tfX66687DYWd5dohmteuXWt17tzZCgsLszw8PKywsDCrZ8+e2YYUXrZsmVWzZk3L3d3daVjvZs2aWbVq1cqxvtyGfl+4cKE1ZswYKyQkxPL29rY6dOhgHTt2LNv7X331VatcuXKWp6en1bhxY2vXrl3Zlnm92q4d+t2y/hzeefjw4VZYWJhVvHhx64477rBefvllp+HALSv3IbpzG5L+WqdPn7YGDBhglS5d2vLw8LDuvPPOHIdCz8/Q7z/99JMVExNjeXt7W5LsOrI+02uH7886Pq4dbvq///2v1aRJE8vHx8fy8fGxqlevbg0ZMsQ6cODAddefn/WkpaVZAwcOtAICAiw/Pz+rW7duVmJiYq5Dv1+7zH79+lk+Pj7Zarj2eMvvMfXdd99ZXbp0sYKCgixPT08rPDzc6tatm7V27dob1nQ9hw8ftrp27WoFBgZaXl5eVsOGDa3ly5dn65fbcXWtrKHfX375Zaf2rO1dtGiRU3tuj4FYv369FRsbawUEBFheXl5W5cqVrf79+1u7du2y++S2r6/9rrAsy9q6dasVFRVleXh4OH2WOfW9fPmyNXHiRCsyMtIqXry4VaFCBWvMmDH28PRZcvsZuPZn/YUXXrAaNmxoBQYGWt7e3lb16tWtF1980Wk495xk7ZuNGzdagwcPtkqWLGn5+vpavXr1ss6ePZut/1/ZZ9czefJkq1y5claxYsWy/bw888wzliRr2rRpTu+pUqWKJck6fPhwtuVduHDBGjNmjFWlShXLw8PDKl26tHXvvfdar7zySrZ9MnfuXCsqKsry9va2/Pz8rDvvvNP617/+ZZ08edLuk5CQYHXo0MHy8/OzJDEMPG57DssqpHdoAwAAFBJZD/TduXOnGjRo4OpyABQR3LMFAAAAAAYQtgAAAADAAMIWAAAAABjAPVsAAAAAYABntgAAAADAAMIWAAAAABjAQ43zIDMzUydPnpSfn5/9kEAAAAAAtx/LsnThwgWFhYXd8IHzhK08OHnypCpUqODqMgAAAAAUEidOnFD58uWv24ewlQd+fn6S/tyh/v7+Lq4GAAAAgKskJyerQoUKdka4HsJWHmRdOujv70/YAgAAAJCn24sYIAMAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwwN3VBeDmRIxe4eoSCqWjL3VwdQkAAACAJM5sAQAAAIARhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMcGnYmjp1qu6++275+fkpJCREDzzwgA4cOODU5+LFixoyZIiCgoLk6+urhx56SKdPn3bqc/z4cXXo0EElSpRQSEiInnnmGV25csWpz4YNG1S/fn15enqqSpUqiouLM715AAAAAG5jLg1bGzdu1JAhQ7R9+3atXr1aly9fVps2bZSammr3GT58uP7v//5PixYt0saNG3Xy5El16dLFnp+RkaEOHTooPT1dW7du1bx58xQXF6dx48bZfY4cOaIOHTrovvvuU3x8vIYNG6ZBgwbpq6+++lu3FwAAAMDtw2FZluXqIrKcOXNGISEh2rhxo2JiYpSUlKTg4GB99NFH6tq1qyTpp59+Uo0aNbRt2zbdc889+vLLL3X//ffr5MmTKlOmjCRpzpw5evbZZ3XmzBl5eHjo2Wef1YoVK/TDDz/Y6+rRo4fOnz+vlStX3rCu5ORkBQQEKCkpSf7+/mY2Pp8iRq9wdQmF0tGXOri6BAAAANzC8pMNCtU9W0lJSZKkUqVKSZJ2796ty5cvq1WrVnaf6tWrq2LFitq2bZskadu2bbrzzjvtoCVJsbGxSk5O1r59++w+Vy8jq0/WMq516dIlJScnO70AAAAAID8KTdjKzMzUsGHD1LhxY9WuXVuSlJCQIA8PDwUGBjr1LVOmjBISEuw+VwetrPlZ867XJzk5WX/88Ue2WqZOnaqAgAD7VaFChQLZRgAAAAC3j0ITtoYMGaIffvhBH3/8satL0ZgxY5SUlGS/Tpw44eqSAAAAABQx7q4uQJKGDh2q5cuXa9OmTSpfvrzdHhoaqvT0dJ0/f97p7Nbp06cVGhpq99mxY4fT8rJGK7y6z7UjGJ4+fVr+/v7y9vbOVo+np6c8PT0LZNsAAAAA3J5cembLsiwNHTpUS5Ys0bp16xQZGek0PyoqSsWLF9fatWvttgMHDuj48eOKjo6WJEVHR2vv3r1KTEy0+6xevVr+/v6qWbOm3efqZWT1yVoGAAAAABQ0l57ZGjJkiD766CMtW7ZMfn5+9j1WAQEB8vb2VkBAgAYOHKgRI0aoVKlS8vf315NPPqno6Gjdc889kqQ2bdqoZs2a6tOnj6ZPn66EhAQ9//zzGjJkiH126h//+IfefPNN/etf/9Kjjz6qdevW6dNPP9WKFYzoBwAAAMAMl57Zmj17tpKSktS8eXOVLVvWfn3yySd2nxkzZuj+++/XQw89pJiYGIWGhuqzzz6z57u5uWn58uVyc3NTdHS0evfurb59+2rSpEl2n8jISK1YsUKrV69W3bp19eqrr+q9995TbGzs37q9AAAAAG4fheo5W4UVz9kqOnjOFgAAAEwqss/ZAgAAAIBbBWELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMcGnY2rRpkzp27KiwsDA5HA4tXbrUaX7//v3lcDicXm3btnXqc+7cOfXq1Uv+/v4KDAzUwIEDlZKS4tRnz549atq0qby8vFShQgVNnz7d9KYBAAAAuM25NGylpqaqbt26euutt3Lt07ZtW506dcp+LVy40Gl+r169tG/fPq1evVrLly/Xpk2bNHjwYHt+cnKy2rRpo/DwcO3evVsvv/yyJkyYoLlz5xrbLgAAAABwd+XK27Vrp3bt2l23j6enp0JDQ3Oc9+OPP2rlypXauXOnGjRoIEmaNWuW2rdvr1deeUVhYWFasGCB0tPT9f7778vDw0O1atVSfHy8XnvtNadQBgAAAAAFqdDfs7VhwwaFhISoWrVqeuKJJ3T27Fl73rZt2xQYGGgHLUlq1aqVihUrpm+++cbuExMTIw8PD7tPbGysDhw4oN9//z3HdV66dEnJyclOLwAAAADIj0Idttq2basPP/xQa9eu1bRp07Rx40a1a9dOGRkZkqSEhASFhIQ4vcfd3V2lSpVSQkKC3adMmTJOfbKms/pca+rUqQoICLBfFSpUKOhNAwAAAHCLc+llhDfSo0cP+9933nmn6tSpo8qVK2vDhg1q2bKlsfWOGTNGI0aMsKeTk5MJXAAAAADypVCf2bpWpUqVVLp0aR06dEiSFBoaqsTERKc+V65c0blz5+z7vEJDQ3X69GmnPlnTud0L5unpKX9/f6cXAAAAAORHkQpbv/zyi86ePauyZctKkqKjo3X+/Hnt3r3b7rNu3TplZmaqUaNGdp9Nmzbp8uXLdp/Vq1erWrVqKlmy5N+7AQAAAABuGy4NWykpKYqPj1d8fLwk6ciRI4qPj9fx48eVkpKiZ555Rtu3b9fRo0e1du1ade7cWVWqVFFsbKwkqUaNGmrbtq0ee+wx7dixQ1u2bNHQoUPVo0cPhYWFSZIeeeQReXh4aODAgdq3b58++eQTvf76606XCQIAAABAQXNp2Nq1a5fq1aunevXqSZJGjBihevXqady4cXJzc9OePXvUqVMnVa1aVQMHDlRUVJQ2b94sT09PexkLFixQ9erV1bJlS7Vv315NmjRxeoZWQECAVq1apSNHjigqKkojR47UuHHjGPYdAAAAgFEOy7IsVxdR2CUnJysgIEBJSUmF5v6tiNErXF1CoXT0pQ6uLgEAAAC3sPxkgyJ1zxYAAAAAFBWELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADAg32HrxIkT+uWXX+zpHTt2aNiwYZo7d26BFgYAAAAARVm+w9Yjjzyi9evXS5ISEhLUunVr7dixQ2PHjtWkSZMKvEAAAAAAKIryHbZ++OEHNWzYUJL06aefqnbt2tq6dasWLFiguLi4gq4PAAAAAIqkfIety5cvy9PTU5K0Zs0aderUSZJUvXp1nTp1qmCrAwAAAIAiKt9hq1atWpozZ442b96s1atXq23btpKkkydPKigoqMALBAAAAICiKN9ha9q0aXrnnXfUvHlz9ezZU3Xr1pUkff755/blhQAAAABwu3PP7xuaN2+u3377TcnJySpZsqTdPnjwYPn4+BRocQAAAABQVOX7zFaLFi104cIFp6AlSaVKlVL37t0LrDAAAAAAKMryHbY2bNig9PT0bO0XL17U5s2bC6QoAAAAACjq8nwZ4Z49e+x/79+/XwkJCfZ0RkaGVq5cqXLlyhVsdQAAAABQROU5bN11111yOBxyOBxq0aJFtvne3t6aNWtWgRYHAAAAAEVVnsPWkSNHZFmWKlWqpB07dig4ONie5+HhoZCQELm5uRkpEgAAAACKmjyHrfDwcElSZmamsWIAAAAA4FaR7wEyJGn+/Plq3LixwsLCdOzYMUnSjBkztGzZsgItDgAAAACKqnyHrdmzZ2vEiBFq3769zp8/r4yMDElSyZIlNXPmzIKuDwAAAACKpHyHrVmzZundd9/V2LFjne7RatCggfbu3VugxQEAAABAUZXvsHXkyBHVq1cvW7unp6dSU1MLpCgAAAAAKOryHbYiIyMVHx+frX3lypWqUaNGQdQEAAAAAEVenkcjzDJixAgNGTJEFy9elGVZ2rFjhxYuXKipU6fqvffeM1EjAAAAABQ5+Q5bgwYNkre3t55//nmlpaXpkUceUVhYmF5//XX16NHDRI0AAAAAUOTkO2xJUq9evdSrVy+lpaUpJSVFISEhBV0XAAAAABRpN/WcrStXrmjNmjWaP3++vL29JUknT55USkpKgRYHAAAAAEVVvs9sHTt2TG3bttXx48d16dIltW7dWn5+fpo2bZouXbqkOXPmmKgTAAAAAIqUfJ/Zevrpp9WgQQP9/vvv9lktSXrwwQe1du3aAi0OAAAAAIqqfJ/Z2rx5s7Zu3SoPDw+n9oiICP36668FVhgAAAAAFGX5PrOVmZmpjIyMbO2//PKL/Pz8CqQoAAAAACjq8h222rRpo5kzZ9rTDodDKSkpGj9+vNq3b1+QtQEAAABAkZXvywhfffVVxcbGqmbNmrp48aIeeeQRHTx4UKVLl9bChQtN1AgAAAAARU6+w1b58uX1/fff6+OPP9aePXuUkpKigQMHqlevXk4DZgAAAADA7eymHmrs7u6u3r17F3QtAAAAAHDLyHfYqlixopo3b65mzZrpvvvuU6VKlUzUBQAAAABFWr4HyJgyZYq8vLw0bdo0ValSRRUqVFDv3r317rvv6uDBgyZqBAAAAIAiJ99ntnr37m1fQnjq1Clt3LhRy5cv1z//+c9ch4UHAAAAgNvNTd2zlZaWpq+//lobNmzQ+vXr9d1336l27dpq3rx5AZcHAAAAAEVTvsPWvffeq++++041atRQ8+bNNXr0aMXExKhkyZIm6gMAAACAIinf92z99NNP8vHxUfXq1VW9enXVqFGDoAUAAAAA18h32Dp79qzWrVune+65R1999ZUaN26scuXK6ZFHHtG7775rokYAAAAAKHIclmVZN/tmy7K0e/duvfnmm1qwYMEtO0BGcnKyAgIClJSUJH9/f1eXI0mKGL3C1SUUSkdf6uDqEgAAAHALy082yPOZrUmTJiktLU3ffvutXnvtNXXq1ElBQUGKjo7Wnj179OSTT+qzzz77y8UDAAAAwK0gz2e23NzcdOrUKYWFhalevXpq1qyZmjVrppiYGAUEBJiu06U4s1V0cGYLAAAAJuUnG+R5NMKsTHbu3LlCEzgAAAAAoLDK1wAZDoeDoAUAAAAAeZCv52xVrVpVDofjun3OnTv3lwoCAAAAgFtBvsLWxIkTb/n7swAAAACgIOQrbPXo0UMhISGmagEAAACAW0ae79m60eWDAAAAAID/J89h6y88+xgAAAAAbjt5vowwMzPTZB0AAAAAcEvJ19DvAAAAAIC8IWwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABggEvD1qZNm9SxY0eFhYXJ4XBo6dKlTvMty9K4ceNUtmxZeXt7q1WrVjp48KBTn3PnzqlXr17y9/dXYGCgBg4cqJSUFKc+e/bsUdOmTeXl5aUKFSpo+vTppjcNAAAAwG3OpWErNTVVdevW1VtvvZXj/OnTp+uNN97QnDlz9M0338jHx0exsbG6ePGi3adXr17at2+fVq9ereXLl2vTpk0aPHiwPT85OVlt2rRReHi4du/erZdfflkTJkzQ3LlzjW8fAAAAgNuXw7Isy9VFSJLD4dCSJUv0wAMPSPrzrFZYWJhGjhypUaNGSZKSkpJUpkwZxcXFqUePHvrxxx9Vs2ZN7dy5Uw0aNJAkrVy5Uu3bt9cvv/yisLAwzZ49W2PHjlVCQoI8PDwkSaNHj9bSpUv1008/5am25ORkBQQEKCkpSf7+/gW/8TchYvQKV5dQKB19qYOrSwAAAMAtLD/ZoNDes3XkyBElJCSoVatWdltAQIAaNWqkbdu2SZK2bdumwMBAO2hJUqtWrVSsWDF98803dp+YmBg7aElSbGysDhw4oN9//z3HdV+6dEnJyclOLwAAAADIj0IbthISEiRJZcqUcWovU6aMPS8hIUEhISFO893d3VWqVCmnPjkt4+p1XGvq1KkKCAiwXxUqVPjrGwQAAADgtlJow5YrjRkzRklJSfbrxIkTri4JAAAAQBFTaMNWaGioJOn06dNO7adPn7bnhYaGKjEx0Wn+lStXdO7cOac+OS3j6nVcy9PTU/7+/k4vAAAAAMiPQhu2IiMjFRoaqrVr19ptycnJ+uabbxQdHS1Jio6O1vnz57V79267z7p165SZmalGjRrZfTZt2qTLly/bfVavXq1q1aqpZMmSf9PWAAAAALjduDRspaSkKD4+XvHx8ZL+HBQjPj5ex48fl8Ph0LBhw/TCCy/o888/1969e9W3b1+FhYXZIxbWqFFDbdu21WOPPaYdO3Zoy5YtGjp0qHr06KGwsDBJ0iOPPCIPDw8NHDhQ+/bt0yeffKLXX39dI0aMcNFWAwAAALgduLty5bt27dJ9991nT2cFoH79+ikuLk7/+te/lJqaqsGDB+v8+fNq0qSJVq5cKS8vL/s9CxYs0NChQ9WyZUsVK1ZMDz30kN544w17fkBAgFatWqUhQ4YoKipKpUuX1rhx45yexQUAAAAABa3QPGerMOM5W0UHz9kCAACASbfEc7YAAAAAoCgjbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMKNRha8KECXI4HE6v6tWr2/MvXryoIUOGKCgoSL6+vnrooYd0+vRpp2UcP35cHTp0UIkSJRQSEqJnnnlGV65c+bs3BQAAAMBtxt3VBdxIrVq1tGbNGnva3f3/lTx8+HCtWLFCixYtUkBAgIYOHaouXbpoy5YtkqSMjAx16NBBoaGh2rp1q06dOqW+ffuqePHimjJlyt++LQAAAABuH4U+bLm7uys0NDRbe1JSkv6//+//00cffaQWLVpIkj744APVqFFD27dv1z333KNVq1Zp//79WrNmjcqUKaO77rpLkydP1rPPPqsJEybIw8Pj794cAAAAALeJQn0ZoSQdPHhQYWFhqlSpknr16qXjx49Lknbv3q3Lly+rVatWdt/q1aurYsWK2rZtmyRp27ZtuvPOO1WmTBm7T2xsrJKTk7Vv375c13np0iUlJyc7vQAAAAAgPwp12GrUqJHi4uK0cuVKzZ49W0eOHFHTpk114cIFJSQkyMPDQ4GBgU7vKVOmjBISEiRJCQkJTkEra37WvNxMnTpVAQEB9qtChQoFu2EAAAAAbnmF+jLCdu3a2f+uU6eOGjVqpPDwcH366afy9vY2tt4xY8ZoxIgR9nRycjKBCwAAAEC+FOozW9cKDAxU1apVdejQIYWGhio9PV3nz5936nP69Gn7Hq/Q0NBsoxNmTed0H1gWT09P+fv7O70AAAAAID+KVNhKSUnR4cOHVbZsWUVFRal48eJau3atPf/AgQM6fvy4oqOjJUnR0dHau3evEhMT7T6rV6+Wv7+/atas+bfXDwAAAOD2UagvIxw1apQ6duyo8PBwnTx5UuPHj5ebm5t69uypgIAADRw4UCNGjFCpUqXk7++vJ598UtHR0brnnnskSW3atFHNmjXVp08fTZ8+XQkJCXr++ec1ZMgQeXp6unjrAAAAANzKCnXY+uWXX9SzZ0+dPXtWwcHBatKkibZv367g4GBJ0owZM1SsWDE99NBDunTpkmJjY/X222/b73dzc9Py5cv1xBNPKDo6Wj4+PurXr58mTZrkqk0CAAAAcJtwWJZlubqIwi45OVkBAQFKSkoqNPdvRYxe4eoSCqWjL3VwdQkAAAC4heUnGxSpe7YAAAAAoKggbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAxwd3UBAP4eEaNXuLqEQunoSx1cXQIAALhFcWYLAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAAAAAYABhCwAAAAAMIGwBAAAAgAGELQAAAAAwgLAFAAAAAAYQtgAAAADAAMIWAAAAABhA2AIAAAAAAwhbAAAAAGAAYQsAAAAADCBsAQAAAIABhC0AAAAAMICwBQAAAAAGELYAAAAAwAB3VxcAACh8IkavcHUJhdbRlzq4ugQAQBHBmS0AAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADDA3dUFAACAoi9i9ApXl1BoHX2pg6tLAOAit9WZrbfeeksRERHy8vJSo0aNtGPHDleXBAAAAOAWdduErU8++UQjRozQ+PHj9e2336pu3bqKjY1VYmKiq0sDAAAAcAu6bcLWa6+9pscee0wDBgxQzZo1NWfOHJUoUULvv/++q0sDAAAAcAu6Le7ZSk9P1+7duzVmzBi7rVixYmrVqpW2bduWrf+lS5d06dIlezopKUmSlJycbL7YPMq8lObqEgqlwvQZFTYcMznjmMkZx0vuOGZyxjGTO46ZnNUe/5WrS0AR88PEWFeXIOn//UxblnXDvrdF2Prtt9+UkZGhMmXKOLWXKVNGP/30U7b+U6dO1cSJE7O1V6hQwViNKBgBM11dAYoajhnkF8cM8otjBigYhe1n6cKFCwoICLhun9sibOXXmDFjNGLECHs6MzNT586dU1BQkBwOhwsr+1NycrIqVKigEydOyN/f39XloAjgmEF+cLwgvzhmkF8cM8ivwnTMWJalCxcuKCws7IZ9b4uwVbp0abm5uen06dNO7adPn1ZoaGi2/p6envL09HRqCwwMNFniTfH393f5wYaihWMG+cHxgvzimEF+ccwgvwrLMXOjM1pZbosBMjw8PBQVFaW1a9fabZmZmVq7dq2io6NdWBkAAACAW9VtcWZLkkaMGKF+/fqpQYMGatiwoWbOnKnU1FQNGDDA1aUBAAAAuAXdNmGre/fuOnPmjMaNG6eEhATdddddWrlyZbZBM4oCT09PjR8/PtuljkBuOGaQHxwvyC+OGeQXxwzyq6geMw4rL2MWAgAAAADy5ba4ZwsAAAAA/m6ELQAAAAAwgLAFAAAAAAYQtgAAAADAAMJWEbJp0yZ17NhRYWFhcjgcWrp0qatLQiE2depU3X333fLz81NISIgeeOABHThwwNVloRCbPXu26tSpYz8wMjo6Wl9++aWry0IR8tJLL8nhcGjYsGGuLgWF1IQJE+RwOJxe1atXd3VZKOR+/fVX9e7dW0FBQfL29tadd96pXbt2ubqsPCFsFSGpqamqW7eu3nrrLVeXgiJg48aNGjJkiLZv367Vq1fr8uXLatOmjVJTU11dGgqp8uXL66WXXtLu3bu1a9cutWjRQp07d9a+fftcXRqKgJ07d+qdd95RnTp1XF0KCrlatWrp1KlT9uvrr792dUkoxH7//Xc1btxYxYsX15dffqn9+/fr1VdfVcmSJV1dWp7cNs/ZuhW0a9dO7dq1c3UZKCJWrlzpNB0XF6eQkBDt3r1bMTExLqoKhVnHjh2dpl988UXNnj1b27dvV61atVxUFYqClJQU9erVS++++65eeOEFV5eDQs7d3V2hoaGuLgNFxLRp01ShQgV98MEHdltkZKQLK8ofzmwBt4mkpCRJUqlSpVxcCYqCjIwMffzxx0pNTVV0dLSry0EhN2TIEHXo0EGtWrVydSkoAg4ePKiwsDBVqlRJvXr10vHjx11dEgqxzz//XA0aNNDDDz+skJAQ1atXT++++66ry8ozzmwBt4HMzEwNGzZMjRs3Vu3atV1dDgqxvXv3Kjo6WhcvXpSvr6+WLFmimjVrurosFGIff/yxvv32W+3cudPVpaAIaNSokeLi4lStWjWdOnVKEydOVNOmTfXDDz/Iz8/P1eWhEPrf//6n2bNna8SIEXruuee0c+dOPfXUU/Lw8FC/fv1cXd4NEbaA28CQIUP0ww8/cF08bqhatWqKj49XUlKSFi9erH79+mnjxo0ELuToxIkTevrpp7V69Wp5eXm5uhwUAVffDlGnTh01atRI4eHh+vTTTzVw4EAXVobCKjMzUw0aNNCUKVMkSfXq1dMPP/ygOXPmFImwxWWEwC1u6NChWr58udavX6/y5cu7uhwUch4eHqpSpYqioqI0depU1a1bV6+//rqry0IhtXv3biUmJqp+/fpyd3eXu7u7Nm7cqDfeeEPu7u7KyMhwdYko5AIDA1W1alUdOnTI1aWgkCpbtmy2P/jVqFGjyFx+ypkt4BZlWZaefPJJLVmyRBs2bChSN5Oi8MjMzNSlS5dcXQYKqZYtW2rv3r1ObQMGDFD16tX17LPPys3NzUWVoahISUnR4cOH1adPH1eXgkKqcePG2R5d8/PPPys8PNxFFeUPYasISUlJcfrLz5EjRxQfH69SpUqpYsWKLqwMhdGQIUP00UcfadmyZfLz81NCQoIkKSAgQN7e3i6uDoXRmDFj1K5dO1WsWFEXLlzQRx99pA0bNuirr75ydWkopPz8/LLdB+rj46OgoCDuD0WORo0apY4dOyo8PFwnT57U+PHj5ebmpp49e7q6NBRSw4cP17333qspU6aoW7du2rFjh+bOnau5c+e6urQ8IWwVIbt27dJ9991nT48YMUKS1K9fP8XFxbmoKhRWs2fPliQ1b97cqf2DDz5Q//79//6CUOglJiaqb9++OnXqlAICAlSnTh199dVXat26tatLA3CL+OWXX9SzZ0+dPXtWwcHBatKkibZv367g4GBXl4ZC6u6779aSJUs0ZswYTZo0SZGRkZo5c6Z69erl6tLyxGFZluXqIgAAAADgVsMAGQAAAABgAGELAAAAAAwgbAEAAACAAYQtAAAAADCAsAUAAAAABhC2AAAAAMAAwhYAAAAAGEDYAgAAAAADCFsAgELL4XBo6dKlri5Dffr00ZQpU1xdRqGTnp6uiIgI7dq1y9WlAEChRNgCADjp37+/HA6H/vGPf2SbN2TIEDkcDvXv379A1zlhwgTddddd2dpPnTqldu3aFei68uv777/XF198oaeeesol64+Li1NgYKBL1n21nD4jDw8PjRo1Ss8++6xrigKAQo6wBQDIpkKFCvr444/1xx9/2G0XL17URx99pIoVK/5tdYSGhsrT0/NvW19OZs2apYcffli+vr5G15Oenm50+ab06tVLX3/9tfbt2+fqUgCg0CFsAQCyqV+/vipUqKDPPvvMbvvss89UsWJF1atXz6lvZmampk6dqsjISHl7e6tu3bpavHixPX/Dhg1yOBxau3atGjRooBIlSujee+/VgQMHJP155mbixIn6/vvv5XA45HA4FBcXJyn7ZYR79+5VixYt5O3traCgIA0ePFgpKSn2/P79++uBBx7QK6+8orJlyyooKEhDhgzR5cuX7T5vv/227rjjDnl5ealMmTLq2rVrrvshIyNDixcvVseOHZ3aIyIiNHnyZPXs2VM+Pj4qV66c3nrrLac+58+f16BBgxQcHCx/f3+1aNFC33//vT0/60zRe++9p8jISHl5eWVb/4YNGzRgwAAlJSXZ+2bChAl68803Vbt2bbvf0qVL5XA4NGfOHLutVatWev755+3pZcuWqX79+vLy8lKlSpU0ceJEXblyJU/1Xu8zKlmypBo3bqyPP/441/0IALcrwhYAIEePPvqoPvjgA3v6/fff14ABA7L1mzp1qj788EPNmTNH+/bt0/Dhw9W7d29t3LjRqd/YsWP16quvateuXXJ3d9ejjz4qSerevbtGjhypWrVq6dSpUzp16pS6d++ebT2pqamKjY1VyZIltXPnTi1atEhr1qzR0KFDnfqtX79ehw8f1vr16zVv3jzFxcXZwWDXrl166qmnNGnSJB04cEArV65UTExMrvtgz549SkpKUoMGDbLNe/nll1W3bl199913Gj16tJ5++mmtXr3anv/www8rMTFRX375pXbv3q369eurZcuWOnfunN3n0KFD+u9//6vPPvtM8fHx2dZx7733aubMmfL397f3zahRo9SsWTPt379fZ86ckSRt3LhRpUuX1oYNGyRJly9f1rZt29S8eXNJ0ubNm9W3b189/fTT2r9/v9555x3FxcXpxRdfzFO9N/qMGjZsqM2bN+e6HwHgtmUBAHCVfv36WZ07d7YSExMtT09P6+jRo9bRo0ctLy8v68yZM1bnzp2tfv36WZZlWRcvXrRKlChhbd261WkZAwcOtHr27GlZlmWtX7/ekmStWbPGnr9ixQpLkvXHH39YlmVZ48ePt+rWrZutFknWkiVLLMuyrLlz51olS5a0UlJSnJZTrFgxKyEhwa49PDzcunLlit3n4Ycftrp3725ZlmX997//tfz9/a3k5OQ87YslS5ZYbm5uVmZmplN7eHi41bZtW6e27t27W+3atbMsy7I2b95s+fv7WxcvXnTqU7lyZeudd96xt7l48eJWYmLidWv44IMPrICAAKe2zMxMKygoyFq0aJFlWZZ11113WVOnTrVCQ0Mty7Ksr7/+2ipevLiVmppqWZZltWzZ0poyZYrTMubPn2+VLVs2X/Xm9BlZlmW9/vrrVkRExHW3AwBuR+4uznoAgEIqODhYHTp0UFxcnCzLUocOHVS6dGmnPocOHVJaWppat27t1J6enp7tcsM6derY/y5btqwkKTExMc/3gP3444+qW7eufHx87LbGjRsrMzNTBw4cUJkyZSRJtWrVkpubm9O69u7dK0lq3bq1wsPDValSJbVt21Zt27bVgw8+qBIlSuS4zj/++EOenp5yOBzZ5kVHR2ebnjlzpqQ/B9VISUlRUFBQtuUdPnzYng4PD1dwcHCetv9qDodDMTEx2rBhg1q1aqX9+/frn//8p6ZPn66ffvpJGzdu1N13321v1/fff68tW7Y4ncnKyMjQxYsXlZaWlud6c+Pt7a20tLR8bwcA3OoIWwCAXD366KP2ZXrX3pMkyb5fasWKFSpXrpzTvGsHtihevLj976zwkpmZWaD1XruerHVlrcfPz0/ffvutNmzYoFWrVmncuHGaMGGCdu7cmeOIf6VLl1ZaWprS09Pl4eGR5xpSUlJUtmxZ+7K+q129nquDY341b95cc+fO1ebNm1WvXj35+/vbAWzjxo1q1qyZUz0TJ05Uly5dsi3Hy8srz/Xm5ty5czcVGgHgVkfYAgDkqm3btkpPT5fD4VBsbGy2+TVr1pSnp6eOHz/u9Mt9fnl4eCgjI+O6fWrUqKG4uDilpqbaIWXLli0qVqyYqlWrlud1ubu7q1WrVmrVqpXGjx+vwMBArVu3LscgkjXU+f79+7MNe759+/Zs0zVq1JD05wAjCQkJcnd3V0RERJ5ry0lu+6ZZs2YaNmyYFi1aZN+b1bx5c61Zs0ZbtmzRyJEj7b7169fXgQMHVKVKlRzXkZd6r/cZ/fDDD9nOZAIAGCADAHAdbm5u+vHHH7V//36nS/Oy+Pn5adSoURo+fLjmzZunw4cP69tvv9WsWbM0b968PK8nIiJCR44cUXx8vH777TddunQpW59evXrJy8tL/fr10w8//KD169frySefVJ8+fexLCG9k+fLleuONNxQfH69jx47pww8/VGZmZq5hLTg4WPXr19fXX3+dbd6WLVs0ffp0/fzzz3rrrbe0aNEiPf3005L+HAkwOjpaDzzwgFatWqWjR49q69atGjt2bL4fABwREaGUlBStXbtWv/32m325Xp06dVSyZEl99NFHTmFr6dKlunTpkho3bmwvY9y4cfrwww81ceJE7du3Tz/++KM+/vhje7TCvNR7vc9o8+bNatOmTb62CwBuB4QtAMB1+fv7y9/fP9f5kydP1r///W9NnTpVNWrUUNu2bbVixQpFRkbmeR0PPfSQ2rZtq/vuu0/BwcFauHBhtj4lSpTQV199pXPnzunuu+9W165d1bJlS7355pt5Xk9gYKA+++wztWjRQjVq1NCcOXO0cOFC1apVK9f3DBo0SAsWLMjWPnLkSO3atUv16tXTCy+8oNdee80+++dwOPTFF18oJiZGAwYMUNWqVdWjRw8dO3Ysz8Ewy7333qt//OMf6t69u4KDgzV9+nR7HU2bNpXD4VCTJk0k/RnA/P391aBBA6dLFGNjY7V8+XKtWrVKd999t+655x7NmDFD4eHhea43t89o27ZtSkpKuu4Q+gBwu3JYlmW5uggAAAqrP/74Q9WqVdMnn3xiD4oRERGhYcOGadiwYa4trhDo3r276tatq+eee87VpQBAocOZLQAArsPb21sffvihfvvtN1eXUuikp6frzjvv1PDhw11dCgAUSgyQAQDADWTdEwVnHh4e9n1fAIDsuIwQAAAAAAzgMkIAAAAAMICwBQAAAAAGELYAAAAAwADCFgAAAAAYQNgCAAAAAAMIWwAAAABgAGELAAAAAAwgbAEAAACAAf8/vr9iXHI0xQUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df['n_mentions'], 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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df.day_hour, 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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2017 = df[df['created_at'].dt.year == 2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df_2017['day_hour'], rwidth=0.6)\n",
    "plt.title('Year 2017')\n",
    "plt.ylabel(\"Tweets\")\n",
    "plt.xlabel(\"Hour of the day\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "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'`)"
   ]
  },
  {
   "attachments": {},
   "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."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how we can plot the number of mentions by year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = df['created_at'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JCTp+/Hip+69cuVKTJk3StGnTtHv3bi1ZskSvv/66/va3v1U2FAAAAABwq0oXWHPmzNGoUaM0YsQItWrVSosWLVKdOnX0yiuvlLr/559/rh49euiPf/yjmjdvrr59+2rYsGGXHfUCAAAAgOquUgXWuXPnlJ2drfj4+P816OOj+Ph4ZWVllXpM9+7dlZ2d7SioDhw4oI8++ki33XZbme9TVFQku93utAAAAABAdeNbmYN/+uknnT9/XqGhoU7rQ0ND9d1335V6zB//+Ef99NNP6tmzpwzD0H//+1/95S9/ueQlgmlpaZo+fXplQgUAAAAAl6vyWQTXr1+vp59+Wi+++KJ27typd955R2vWrNGTTz5Z5jGpqanKz893LDk5OVUYMQAAAACUT6VGsBo1aqRatWopNzfXaX1ubq7CwsJKPWbKlCm677779Kc//UmS1LZtWxUUFGj06NH6+9//Lh+fi2s+q9Uqq9VamVABAAAAwOUqNYLl5+enTp06KTMz07GupKREmZmZiouLK/WYM2fOXFRE1apVS5JkGEZlwgEAAAAAt6rUCJYkpaSkaPjw4ercubO6du2qefPmqaCgQCNGjJAk3X///brqqquUlpYmSRowYIDmzJmj2NhYdevWTfv379eUKVM0YMAAR6EFAAAAAJ6o0gXWkCFDdOLECU2dOlU2m00dOnRQRkaGY+KLI0eOOI1YTZ48WRaLRZMnT9bRo0fVuHFjDRgwQE899VRlQwGAcjl69Kgef/xxffzxxzpz5oyuvfZaLV26VJ07d3Z3aAAAwMNVusCSpDFjxmjMmDGlblu/fr3zG/r6atq0aZo2bZoZbw0AFfLLL7+oR48euvnmm/Xxxx+rcePG2rdvnxo0aODu0AAAgBcwpcACAE8xe/ZsRUZGaunSpY510dHRbowIAAB4EwosADXK+++/r4SEBN1zzz3asGGDrrrqKv31r3/VqFGjyjymqKhIRUVFjtc87BwAgPJrPmmNqe0dmtXf1PbMRoEFoEY5cOCAFi5cqJSUFP3tb3/T9u3b9fDDD8vPz0/Dhw8v9Rgedl4xZv+PtLpzRX+r+48HAEDZqvxBwwDgTiUlJerYsaOefvppxcbGavTo0Ro1apQWLVpU5jE87BwAAJQXBRaAGiU8PFytWrVyWteyZUsdOXKkzGOsVqsCAwOdFgAAgNJQYAGoUXr06KE9e/Y4rdu7d6+aNWvmpogAAIA3ocACUKNMmDBBW7Zs0dNPP639+/dr5cqVevnll5WcnOzu0AAAgBdgkgsANUqXLl307rvvKjU1VTNmzFB0dLTmzZunpKQkd4cGAADKobrPSkiBBaDGuf3223X77be7OwwAAOCFuEQQAAAAAExCgQUAAAAAJqHAAgAAAACTcA8WAAAA4KGq+4QPNREjWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAADgArNmzZLFYtH48eMd6woLC5WcnKyGDRuqXr16SkxMVG5urvuCBGA6CiwAAACTbd++XS+99JLatWvntH7ChAn64IMP9Oabb2rDhg06duyY7rrrLjdFCcAVKLAAAABMdPr0aSUlJWnx4sVq0KCBY31+fr6WLFmiOXPmqHfv3urUqZOWLl2qzz//XFu2bHFjxADMRIEFAABgouTkZPXv31/x8fFO67Ozs1VcXOy0PiYmRlFRUcrKyiqzvaKiItntdqcFQPXFg4YBAABMsmrVKu3cuVPbt2+/aJvNZpOfn5+Cg4Od1oeGhspms5XZZlpamqZPn252qABchBEsAAAAE+Tk5GjcuHFasWKF/P39TWs3NTVV+fn5jiUnJ8e0tgGYjwILAADABNnZ2Tp+/Lg6duwoX19f+fr6asOGDZo/f758fX0VGhqqc+fOKS8vz+m43NxchYWFldmu1WpVYGCg0wKg+uISQQAAABP06dNHu3btclo3YsQIxcTE6PHHH1dkZKRq166tzMxMJSYmSpL27NmjI0eOKC4uzh0hA3ABU0aw0tPT1bx5c/n7+6tbt27atm3bJffPy8tTcnKywsPDZbVadd111+mjjz4yIxQAAAC3qF+/vtq0aeO01K1bVw0bNlSbNm0UFBSkkSNHKiUlRZ999pmys7M1YsQIxcXF6YYbbnB3+ABMUukRrNdff10pKSlatGiRunXrpnnz5ikhIUF79uxRkyZNLtr/3LlzuuWWW9SkSRO99dZbuuqqq3T48OGLbvgEAADwNnPnzpWPj48SExNVVFSkhIQEvfjii+4OC4CJKl1gzZkzR6NGjdKIESMkSYsWLdKaNWv0yiuvaNKkSRft/8orr+jkyZP6/PPPVbt2bUlS8+bNKxsGAABAtbN+/Xqn1/7+/kpPT1d6erp7AgIuo/mkNe4OweNV6hLBc+fOKTs72+l5Dj4+PoqPjy/zeQ7vv/++4uLilJycrNDQULVp00ZPP/20zp8/X+b78PwHAAAAAJ6gUgXWTz/9pPPnzys0NNRp/aWe53DgwAG99dZbOn/+vD766CNNmTJFzz33nGbOnFnm+6SlpSkoKMixREZGViZsAAAAAHCJKp+mvaSkRE2aNNHLL7+sTp06aciQIfr73/+uRYsWlXkMz38AAAAA4AkqdQ9Wo0aNVKtWLeXm5jqtv9TzHMLDw1W7dm3VqlXLsa5ly5ay2Ww6d+6c/Pz8LjrGarXKarVWJlQAAAAAcLlKjWD5+fmpU6dOyszMdKwrKSlRZmZmmc9z6NGjh/bv36+SkhLHur179yo8PLzU4goAAAAAPEWlLxFMSUnR4sWLtXz5cu3evVsPPfSQCgoKHLMK3n///UpNTXXs/9BDD+nkyZMaN26c9u7dqzVr1ujpp59WcnJyZUMBAAAAALeq9DTtQ4YM0YkTJzR16lTZbDZ16NBBGRkZjokvjhw5Ih+f/9VxkZGRWrt2rSZMmKB27drpqquu0rhx4/T4449XNhQAAAAAcKtKF1iSNGbMGI0ZM6bUbb9//oMkxcXFacuWLWa8NQAAAABUG1U+iyAAAAAAeCsKLAAAAAAwCQUWAAAAAJiEAgsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASSiwANRos2bNksVi0fjx490dCgAA8AIUWABqrO3bt+ull15Su3bt3B0KAADwEhRYAGqk06dPKykpSYsXL1aDBg3cHQ4AAPASFFgAaqTk5GT1799f8fHxl923qKhIdrvdaQEAACiNr7sDAICqtmrVKu3cuVPbt28v1/5paWmaPn26i6Mqn+aT1pja3qFZ/U1tzxOY/RkCAPBbjGABqFFycnI0btw4rVixQv7+/uU6JjU1Vfn5+Y4lJyfHxVECAABPxQgWgBolOztbx48fV8eOHR3rzp8/r40bN2rBggUqKipSrVq1nI6xWq2yWq1VHSoAAPBAFFgAapQ+ffpo165dTutGjBihmJgYPf744xcVVwAAABVBgQWgRqlfv77atGnjtK5u3bpq2LDhResBAAAqinuwAAAAAMAkjGABqPHWr1/v7hAAAICXYAQLAAAAAExCgQUAAAAAJqHAAgAAAACTUGABAAAAgEkosAAAAADAJBRYAAAAAGASCiwAAAAAMIkpBVZ6erqaN28uf39/devWTdu2bSvXcatWrZLFYtGgQYPMCAMAAAAA3KrSBdbrr7+ulJQUTZs2TTt37lT79u2VkJCg48ePX/K4Q4cO6dFHH9WNN95Y2RAAAAAAoFqodIE1Z84cjRo1SiNGjFCrVq20aNEi1alTR6+88kqZx5w/f15JSUmaPn26rr766sqGAAAAAADVgm9lDj537pyys7OVmprqWOfj46P4+HhlZWWVedyMGTPUpEkTjRw5Uv/+978v+z5FRUUqKipyvLbb7ZUJGwAAAF6q+aQ1prZ3aFZ/U9uD96vUCNZPP/2k8+fPKzQ01Gl9aGiobDZbqcds2rRJS5Ys0eLFi8v9PmlpaQoKCnIskZGRlQkbAAAAAFyiSmcRPHXqlO677z4tXrxYjRo1Kvdxqampys/Pdyw5OTkujBIAAAAArkylLhFs1KiRatWqpdzcXKf1ubm5CgsLu2j/77//XocOHdKAAQMc60pKSn4NxNdXe/bs0TXXXHPRcVarVVartTKhAgAAoJK4/A64vEqNYPn5+alTp07KzMx0rCspKVFmZqbi4uIu2j8mJka7du3Sl19+6VjuuOMO3Xzzzfryyy+59A8AAACAR6vUCJYkpaSkaPjw4ercubO6du2qefPmqaCgQCNGjJAk3X///brqqquUlpYmf39/tWnTxun44OBgSbpoPQAAAAB4mkoXWEOGDNGJEyc0depU2Ww2dejQQRkZGY6JL44cOSIfnyq91QsAAAAA3KLSBZYkjRkzRmPGjCl12/r16y957LJly8wIAQAAAADcjqElAAAAADCJKSNYAAAAQEWZPSshUB0wggUAAAAAJqHAAgAAMElaWpq6dOmi+vXrq0mTJho0aJD27NnjtE9hYaGSk5PVsGFD1atXT4mJiRc9UxSA56LAAgAAMMmGDRuUnJysLVu2aN26dSouLlbfvn1VUFDg2GfChAn64IMP9Oabb2rDhg06duyY7rrrLjdGDcBM3IMFAABgkoyMDKfXy5YtU5MmTZSdna0//OEPys/P15IlS7Ry5Ur17t1bkrR06VK1bNlSW7Zs0Q033OCOsAGYiBEsAAAAF8nPz5ckhYSESJKys7NVXFys+Ph4xz4xMTGKiopSVlZWqW0UFRXJbrc7LQCqLwosAAAAFygpKdH48ePVo0cPtWnTRpJks9nk5+en4OBgp31DQ0Nls9lKbSctLU1BQUGOJTIy0tWhA6gECiwAAAAXSE5O1jfffKNVq1ZVqp3U1FTl5+c7lpycHJMiBOAK3IMFAABgsjFjxujDDz/Uxo0b1bRpU8f6sLAwnTt3Tnl5eU6jWLm5uQoLCyu1LavVKqvV6uqQAZiEESwAAACTGIahMWPG6N1339Wnn36q6Ohop+2dOnVS7dq1lZmZ6Vi3Z88eHTlyRHFxcVUdLgAXYAQLAADAJMnJyVq5cqXee+891a9f33FfVVBQkAICAhQUFKSRI0cqJSVFISEhCgwM1NixYxUXF8cMgoCXoMACAAAwycKFCyVJvXr1clq/dOlSPfDAA5KkuXPnysfHR4mJiSoqKlJCQoJefPHFKo4UgKtQYAEAAJjEMIzL7uPv76/09HSlp6dXQUQAqhr3YAEAAACASSiwAAAAAMAkFFgAapy0tDR16dJF9evXV5MmTTRo0CDt2bPH3WEBAAAvQIEFoMbZsGGDkpOTtWXLFq1bt07FxcXq27evCgoK3B0aAADwcExyAaDGycjIcHq9bNkyNWnSRNnZ2frDH/7gpqgAAIA3oMACUOPl5+dLkkJCQkrdXlRUpKKiIsdru91eJXEBAADPwyWCAGq0kpISjR8/Xj169FCbNm1K3SctLU1BQUGOJTIysoqjBAAAnoICC0CNlpycrG+++UarVq0qc5/U1FTl5+c7lpycnCqMEAAAeBIuEQRQY40ZM0YffvihNm7cqKZNm5a5n9VqldVqrcLIAACAp6LAAlDjGIahsWPH6t1339X69esVHR3t7pAAAICXoMACUOMkJydr5cqVeu+991S/fn3ZbDZJUlBQkAICAtwcHQAA8GSm3IOVnp6u5s2by9/fX926ddO2bdvK3Hfx4sW68cYb1aBBAzVo0EDx8fGX3B8AzLZw4ULl5+erV69eCg8Pdyyvv/66u0MDAAAertIF1uuvv66UlBRNmzZNO3fuVPv27ZWQkKDjx4+Xuv/69es1bNgwffbZZ8rKylJkZKT69u2ro0ePVjYUACgXwzBKXR544AF3hwYAADxcpQusOXPmaNSoURoxYoRatWqlRYsWqU6dOnrllVdK3X/FihX661//qg4dOigmJkb//Oc/VVJSoszMzDLfo6ioSHa73WkBAAAAgOqmUgXWuXPnlJ2drfj4+P816OOj+Ph4ZWVllauNM2fOqLi4uMwHfEo8gwYAAACAZ6hUgfXTTz/p/PnzCg0NdVofGhrquGn8ch5//HFFREQ4FWm/xzNoAAAAAHgCt84iOGvWLK1atUrr16+Xv79/mfvxDBoAAAAAnqBSBVajRo1Uq1Yt5ebmOq3Pzc1VWFjYJY999tlnNWvWLH3yySdq165dZcIAAAAAgGqhUpcI+vn5qVOnTk4TVFyYsCIuLq7M45555hk9+eSTysjIUOfOnSsTAgAAAABUG5W+RDAlJUXDhw9X586d1bVrV82bN08FBQUaMWKEJOn+++/XVVddpbS0NEnS7NmzNXXqVK1cuVLNmzd33KtVr1491atXr7LhAAAAAIDbVLrAGjJkiE6cOKGpU6fKZrOpQ4cOysjIcEx8ceTIEfn4/G+gbOHChTp37pzuvvtup3amTZumJ554orLhAAAAAIDbmDLJxZgxYzRmzJhSt61fv97p9aFDh8x4SwAAAACodir9oGEAAAAAwK8osAAAAADAJBRYAAAAAGASCiwAAAAAMAkFFgAAAACYhAILAAAAAExiyjTtAACg+mo+aY27QwCAGoMRLAAAAAAwCQUWAAAAAJiEAgsAAAAATEKBBQAAAAAmYZILAAAAoAxmTxJzaFZ/U9tD9cMIFgAAAACYhAILAAAAAExCgQUAAAAAJqHAAgAAAACTUGABAAAAgEmYRRAAAACoImbPSojqhxEsAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJTCmw0tPT1bx5c/n7+6tbt27atm3bJfd/8803FRMTI39/f7Vt21YfffSRGWEAQLlVNG8BgJnIQYD3qnSB9frrryslJUXTpk3Tzp071b59eyUkJOj48eOl7v/5559r2LBhGjlypL744gsNGjRIgwYN0jfffFPZUACgXCqatwDATOQgwLtVusCaM2eORo0apREjRqhVq1ZatGiR6tSpo1deeaXU/Z9//nndeuuteuyxx9SyZUs9+eST6tixoxYsWFDZUACgXCqatwDATOQgwLv5Vubgc+fOKTs7W6mpqY51Pj4+io+PV1ZWVqnHZGVlKSUlxWldQkKCVq9eXeb7FBUVqaioyPE6Pz9fkmS32ysRPdylpOiMqe3x76BqXPicDcNwcySVcyV5qzI5yOx/72Zzxd9Pde+zJzD7e6nu30l5+ksO8s4cBFQHZuegShVYP/30k86fP6/Q0FCn9aGhofruu+9KPcZms5W6v81mK/N90tLSNH369IvWR0ZGXkHU8DZB89wdQc1y6tQpBQUFuTuMK3YlecubcxB/P9VTTfteKtJfctD/eEMOAqoDs3NQpQqsqpKamuo06lVSUqKTJ0+qYcOGslgsZR5nt9sVGRmpnJwcBQYGVkWoLuMtffGWfkje05fy9sMwDJ06dUoRERFVGF31UFoOOnz4sDp06OBx37+n/rsl7qpVHeMmB9Xs30HVEZ+va1W3z7ciOahSBVajRo1Uq1Yt5ebmOq3Pzc1VWFhYqceEhYVVaH9JslqtslqtTuuCg4PLHWdgYGC1+GLM4C198ZZ+SN7Tl/L0w5PPGl9wJXmrtBzk4/PrLaye+v0Td9UibnOQg/6npv4Oqo74fF2rOn2+5c1BlZrkws/PT506dVJmZqZjXUlJiTIzMxUXF1fqMXFxcU77S9K6devK3B8AzHQleQsAzEIOArxfpS8RTElJ0fDhw9W5c2d17dpV8+bNU0FBgUaMGCFJuv/++3XVVVcpLS1NkjRu3DjddNNNeu6559S/f3+tWrVKO3bs0Msvv1zZUACgXC6XtwDAlchBgHerdIE1ZMgQnThxQlOnTpXNZlOHDh2UkZHhuHnzyJEjjktpJKl79+5auXKlJk+erL/97W9q0aKFVq9erTZt2lQ2lItYrVZNmzbtomF1T+QtffGWfkje0xdv6UdFXC5vlYenfm7EXbWIG6UxIweVB9+ja/H5upYnf74Ww9PnOwUAAACAaqLSDxoGAAAAAPyKAgsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASSo9TTsA1AQ//fSTXnnlFWVlZclms0mSwsLC1L17dz3wwANq3LixmyMEUNORp4DqgWnaAeAytm/froSEBNWpU0fx8fGOZ9Xk5uYqMzNTZ86c0dq1a9W5c2c3R3ppBQUFeuONN7R//36Fh4dr2LBhatiwobvDQjWxbdu2i36Yx8XFqWvXrm6ODOXhLXkKNZu35CGvLLC85cv5rYMHDzp+FLniocyudO7cOa1evbrUM2oDBw6Un5+fmyOsOMMwtH79esd3kpCQoNq1a7s7rHLzxu/ElW644Qa1b99eixYtksVicdpmGIb+8pe/6Ouvv1ZWVpabIixdq1attGnTJoWEhCgnJ0d/+MMf9Msvv+i6667T999/L19fX23ZskXR0dHuDvUi3vBv1FPyxPHjx5WYmKjNmzcrKirK6Yf5kSNH1KNHD7399ttq0qSJmyPFpXhqnvIk3pCXqiuvy0OGF8nNzTV69uxpWCwWo1mzZkbXrl2Nrl27Gs2aNTMsFovRs2dPIzc3191hXtZDDz1knDp1yjAMwzhz5oyRmJho+Pj4GBaLxfDx8TFuvvlmx/bqbt++fcbVV19t+Pv7GzfddJMxePBgY/DgwcZNN91k+Pv7G9dee62xb98+d4d5Wf369TPy8vIMwzCMn3/+2ejWrZthsViMxo0bGz4+PkZMTIxx/PhxN0dZPt7ynVQlf39/Y/fu3WVu3717t+Hv71+FEZWPxWJx5LykpCSje/fujn/Hp06dMuLj441hw4a5M8RSeeq/UU/NE4mJiUZcXJzx3XffXbTtu+++M7p3727cfffdbogMFeGpecpTeGpe8hTeloe8qsDyli/Hx8fH8aMoNTXVaNq0qfHpp58aBQUFxqZNm4xrrrnGmDRpkpujLJ/4+Hhj4MCBRn5+/kXb8vPzjYEDBxp9+/Z1Q2QV89sfqg899JDRqlUr48CBA4ZhGEZOTo7RqVMn4y9/+Ys7Qyw3b/lOqlLz5s2N5cuXl7l9+fLlRrNmzaouoHL67b/bq6++2vh//+//OW3fvHmzERkZ6Y7QLslT/416ap6oV6+esXPnzjK379ixw6hXr14VRoQr4al5ylN4al7yFN6Wh7yqwPKWL+e3/5Nu06aNsXLlSqft7733nnHddde5I7QKCwgIMHbt2lXm9q+//toICAiowoiuzG+/k+uvv9547733nLZ/8sknRnR0tDtCqzBv+U6q0oIFCwyr1Wo8/PDDxnvvvWds2bLF2LJli/Hee+8ZDz/8sBEQEGCkp6e7O8yLWCwWx4hJRETERd/7oUOHquUZbU/9N+qpeaJhw4bG+vXry9z+2WefGQ0bNqzCiHAlPDVPeQpPzUuewtvykFfNImi1WmW328vcfurUKVmt1iqM6MpduH7aZrOpXbt2Ttvat2+vnJwcd4RVYcHBwTp06FCZ940dOnRIwcHBVRvUFbrwnfzyyy+65pprnLZde+21OnbsmDvCqjBv+k6qSnJysho1aqS5c+fqxRdf1Pnz5yVJtWrVUqdOnbRs2TINHjzYzVGWrk+fPvL19ZXdbteePXucvvfDhw9Xy0kuPPnfqCfmiSFDhmj48OGaO3eu+vTpo8DAQEmS3W5XZmamUlJSNGzYMDdHicvx5DzlCTw5L3kCb8tDXlVgedOXM2XKFNWpU0c+Pj46duyYWrdu7dj2888/q27dum6Mrvz+9Kc/6f7779eUKVPUp0+fi2Y1mjlzpsaOHevmKMvngQcekNVqVXFxsQ4ePOj0ndhsNo9JrN70nVSlIUOGaMiQISouLtZPP/0kSWrUqFG1nLTggmnTpjm9rlevntPrDz74QDfeeGNVhlQunvxv1BPzxJw5c1RSUqKhQ4fqv//9r+NG/XPnzsnX11cjR47Us88+6+YoUR6emKc8hSfnJU/gbXnIq2YRLCoq0vjx4/XKK6+U+eXMnTu32o9i9erVy2kGoKSkJP3pT39yvJ45c6Y++eQTrV+/3g3RVdzs2bP1/PPPy2azOfplGIbCwsI0fvx4TZw40c0RXt6IESOcXvfr18/pTODEiRP19ddfKyMjo6pDuyLe8J3Au3niv1FPzxN2u13Z2dlOs6N16tTJcbISqOk8MS95Gm/JQ15VYF3gLV9OWQ4cOCA/Pz81bdrU3aFUyIEDB5Sbmyvp1++kOk4NfaUKCgpUq1Yt+fv7uzuUCjl48KDT34k3fSfwDt70b9RT8wQAZ96Ul+AaXllgAQC8V05OjqZNm6ZXXnnF3aFUSHWO++zZs8rOzlZISIhatWrltK2wsFBvvPGG7r//fjdFB1R/1fnv21N4Ux7y6gKroKBAb7zxhuMhj8OGDauWN3T/3tixYzV48OBqeW/Elfj222+1YMGCUh/+PGbMmIv+iKqrH3/8UZmZmQoJCVF8fLzTAwULCgr03HPPaerUqW6M8MocO3ZML730kuPv5E9/+pNiYmLcHRZQpq+++kodO3Z03MTvKapr3Hv37lXfvn115MgRWSwW9ezZU6+99poiIiIk/XqPSURERLWLG6hOquvft6fwtjzkVQVWq1attGnTJoWEhCgnJ0c33nij8vLydN111+n777+Xr6+vtmzZUu2Hcn18fGSxWHTNNddo5MiRGj58uMLCwtwd1hX5+OOPNWjQIHXs2FEJCQlON4WuW7dO2dnZeu+995SQkODmSC9t+/bt6tu3r0pKSlRcXKyrrrpKq1evdtzA7kl/+HXq1NHhw4fVuHFjffvtt+revbsaN26s2NhY7dq1S0eOHFFWVtZFs1cCVeX999+/5PYDBw7okUceqXZ/b54a95133qni4mItW7ZMeXl5Gj9+vL799lutX79eUVFRHpXfAFfx1L9vT+FtecirCiwfHx/ZbDY1adJE9957rw4ePKiPPvpIQUFBOn36tO688041btxYK1eudHeol+Tj46N169bpgw8+0IoVK5Sfn69+/fpp1KhRuu222+Tj4+PuEMutffv2GjhwoGbMmFHq9ieeeELvvPOOvv766yqOrGJuueUWRUZG6p///KcKCgr0+OOP64033tC6desUGxvrUX/4v/07GTRokEpKSvTOO+/I19dXJSUlSkpK0unTp/XBBx+4O1TUUBdOMl3qf08Wi6Xa/b15atyhoaH65JNP1LZtW0m/3rT/17/+VR999JE+++wz1a1b12PyG+Aqnvr37Sm8LQ95zi/1CsrKytITTzyhoKAgSb9OTzx9+nRt2rTJzZGVT9u2bTVv3jwdO3ZM//d//6eioiINGjRIkZGR+vvf/679+/e7O8Ry2bt3r5KSksrcPmzYMO3bt68KI7oy2dnZmjRpknx8fFS/fn29+OKLevTRR9WnTx9t377d3eFdsZ07d+qxxx6Tr++vT2zw8fHRxIkTlZ2d7ebIUJOFh4frnXfeUUlJSanLzp073R1iqTw17rNnzzpygPTrj8SFCxdqwIABuummm7R37143RgdUD5769+0pvC0PeV2BdWHazMLCQoWHhzttu+qqq3TixAl3hHXFateurcGDBysjI0MHDhzQqFGjtGLFCl1//fXuDq1cmjdvrjVr1pS5fc2aNWrWrFkVRnTlCgsLnV5PmjRJf/vb39S3b199/vnnboqq4iwWi+PvxMfHx3ES4oLg4GD98ssv7ggNkCR16tTpkkX+5c4iu4unxh0TE6MdO3ZctH7BggUaOHCg7rjjDjdEBVQvnvr37Sm8LQ951YOGJalPnz7y9fWV3W7Xnj17nJ64ffjwYY+Y5KIsUVFReuKJJzRt2jR98skn7g6nXGbMmKE//vGPWr9+veLj4y96MF9GRka1v2RTktq0aaPPP//8ovuSHn30UZWUlHjMA6ylX4fdr7vuOlksFp0+fVpff/21U7/279/vsff8wTs89thjKigoKHP7tddeq88++6wKIyofT437zjvv1Guvvab77rvvom0LFixQSUmJFi1a5IbIgOrDU/++PYW35SGvugdr+vTpTq9vuOEGp8kTHnvsMf3www967bXXqjq0ComOjtaOHTs8uhj8rc8//1zz588vdRbBcePGKS4uzs0RXt4///lPbdiwQf/6179K3T579mwtWrRIBw8erOLIKm758uVOr6+//nrdcMMNjtdPPvmkfvnlF82ZM6eqQwMAAPB4XlVgAQAAAIA7ed09WAAAAADgLjWqwHrxxRfLnC7ck3hLPyTpb3/7mx588EF3h1Fp3tIPybv6AgAAUNVqVIH19ttva9myZe4Oo9K8pR+SdPToUR06dMjdYVSat/RD8q6+AAAAVDXuwQIAAAAAk9SoESwAAAAAcCWvKrCee+45HT582N1hVJq39OOCDz/8UFOnTtXmzZslSZ9++qluu+023XrrrXr55ZfdHF35eUs/JO/qCwAAQHXiVQXWY489pmuuuUa33HKLXn/9dZ07d87dIV0Rb+mHJL300ku688479dFHH+m2227T//3f/2nQoEG66qqr1Lx5c40fP17PP/+8u8O8LG/ph+RdfQEAAKh2DC9isViMpUuXGgMHDjRq165tNGzY0Bg3bpyxa9cud4dWId7SD8MwjFatWhkvv/yyYRiG8emnnxr+/v5Genq6Y/vSpUuNli1buiu8cvOWfhiGd/UFAACguvGqSS58fHxks9nUpEkTHT9+XMuWLdPSpUu1d+9ederUSaNGjdLQoUNVv359d4d6Sd7SD0mqU6eOvvvuO0VFRUmS/Pz8tHPnTrVp00aSdOjQIbVu3VoFBQXuDPOyvKUfknf1BQAAoLrxqksEf6tJkyaaOHGidu/erfXr16tVq1aaMGGCwsPD3R1ahXh6Pxo2bOi4n+zYsWP673//qyNHjji2Hz58WCEhIe4Kr9y8pR+Sd/UFAACguvF1dwBmslgspa6/8cYbdeONN2r+/Pl6/fXXqziqivOWfkjSwIEDNXLkSA0fPlzvv/++7r//fj3yyCPy8fGRxWLRY489pr59+7o7zMvyln5I3tUXAACA6sZrLxH0ZN7SD0kqKCjQhAkTlJWVpe7du+uFF17Q/Pnz9fe//13FxcW66aab9Prrr1f7vnpLPyTv6gsAAEB141UFFjxHYWGhiouLPeI+skvxln5I3tUXAAAAd/Hae7BKk5OTowcffNDdYVSaN/TD399f9evX9/i+eEs/JO/qCwAAgLvUqBGsr776Sh07dtT58+fdHUqleEs/JO/pi7f0Q/KuvgAAAFQ1r5rk4v3337/k9gMHDlRRJJXjLf2QvKcv3tIPybv6AgAAUN141QjWhVnQLtUli8VS7c/Me0s/JO/pi7f0Q/KuvgAAAFQ3XnUPVnh4uN555x2VlJSUuuzcudPdIZaLt/RD8p6+eEs/JO/qCwAAQHXjVQVWp06dlJ2dXeb2y521ry68pR+S9/TFW/oheVdfAAAAqhuvugfrscceU0FBQZnbr732Wn322WdVGNGV8ZZ+SN7TF2/ph+RdfQEAAKhuvOoeLAAAAABwJ6+6RBAAAAAA3IkCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLBwWdu3b9eYMWPUunVr1a1bV1FRURo8eLD27t170b67d+/Wrbfeqnr16ikkJET33XefTpw4cdF+Tz31lO644w6FhobKYrHoiSeeKPW93333XSUkJCgiIkJWq1VNmzbV3XffrW+++cbsbgKoptyZg37vlltukcVi0ZgxYyrbLQAewp056IknnpDFYrlo8ff3N7ubMJGvuwNA9Td79mxt3rxZ99xzj9q1ayebzaYFCxaoY8eO2rJli9q0aSNJ+uGHH/SHP/xBQUFBevrpp3X69Gk9++yz2rVrl7Zt2yY/Pz9Hm5MnT1ZYWJhiY2O1du3aMt97165datCggcaNG6dGjRrJZrPplVdeUdeuXZWVlaX27du7vP8A3MudOei33nnnHWVlZbmkjwCqr+qQgxYuXKh69eo5XteqVcv8jsI8BnAZmzdvNoqKipzW7d2717BarUZSUpJj3UMPPWQEBAQYhw8fdqxbt26dIcl46aWXnI4/ePCgYRiGceLECUOSMW3atHLHY7PZDF9fX+PPf/5zxTsDwONUhxx09uxZo3nz5saMGTMMSUZycnLlOgXAY7gzB02bNs2QZJw4ccKczqBKcIkgLqt79+5OZ10kqUWLFmrdurV2797tWPf222/r9ttvV1RUlGNdfHy8rrvuOr3xxhtOxzdv3vyK42nSpInq1KmjvLy8K24DgOeoDjnomWeeUUlJiR599NGKdwCAR6sOOcgwDNntdhmGUfEOoMpRYOGKGIah3NxcNWrUSJJ09OhRHT9+XJ07d75o365du+qLL76o1Pvl5eXpxIkT2rVrl/70pz/JbrerT58+lWoTgOeqyhx05MgRzZo1S7Nnz1ZAQMAVtwPAe1T176Crr75aQUFBql+/vu69917l5uZWqj24Fvdg4YqsWLFCR48e1YwZMyRJP/74oyQpPDz8on3Dw8N18uRJFRUVyWq1XtH73XDDDdqzZ48kqV69epo8ebJGjhx5hdED8HRVmYMeeeQRxcbGaujQoZULGoDXqKoc1KBBA40ZM0ZxcXGyWq3697//rfT0dG3btk07duxQYGBg5TsD01FgocK+++47JScnKy4uTsOHD5cknT17VpJKTRwXZro5e/bsFRdYS5culd1u14EDB7R06VKdPXtW58+fl48Pg7BATVOVOeizzz7T22+/ra1bt1YyagDeoipz0Lhx45xeJyYmqmvXrkpKStKLL76oSZMmXUkX4GIUWKgQm82m/v37KygoSG+99ZZjFpsLl80UFRVddExhYaHTPlciLi7O8d9Dhw5Vy5YtJUnPPvvsFbcJwPNUZQ7673//q4cfflj33XefunTpUsnIAXgDd/0O+q0//vGPeuSRR/TJJ59QYFVTnP5HueXn56tfv37Ky8tTRkaGIiIiHNsuDIlfGCL/rR9//FEhISFXPHr1ew0aNFDv3r21YsUKU9oD4BmqOge9+uqr2rNnj/785z/r0KFDjkWSTp06pUOHDunMmTNX3iEAHqW6/A6SpMjISJ08edK09mAuCiyUS2FhoQYMGKC9e/fqww8/VKtWrZy2X3XVVWrcuLF27Nhx0bHbtm1Thw4dTI3n7Nmzys/PN7VNANWXO3LQkSNHVFxcrB49eig6OtqxSL8WX9HR0fp//+//XVF/AHiW6vQ7yDAMHTp0SI0bNzatTZiLSwRxWefPn9eQIUOUlZWl9957z+lyvd9KTEzU8uXLlZOTo8jISElSZmam9u7dqwkTJlzRex8/flxNmjRxWnfo0CFlZmaWOlMPAO/jrhw0dOjQUn8U3Xnnnbrttts0atQodevWrcLtAvAs7vwddOLEiYsKqYULF+rEiRO69dZbr6hNuJ7FYEJ9XMb48eP1/PPPa8CAARo8ePBF2++9915JUk5OjmJjYxUcHKxx48bp9OnT+sc//qGmTZtq+/btTkPj//rXv3T48GGdOXNGaWlpuvnmm9W7d29J0n333admzZpJkkJDQ9WnTx916NBBDRo00L59+7RkyRKdOXNGmZmZ6t69exV8AgDcyZ05qDQWi0XJyclasGCByT0FUB25MwfVqVNHQ4YMUdu2beXv769NmzZp1apVat++vTZv3qw6depUwSeACnPfM47hKW666SZDUpnLb33zzTdG3759jTp16hjBwcFGUlKSYbPZKtTmZ5995thv2rRpRufOnY0GDRoYvr6+RkREhDF06FDj66+/dnW3AVQT7sxBpZFkJCcnm9lFANWYO3PQn/70J6NVq1ZG/fr1jdq1axvXXnut8fjjjxt2u93V3UYlMIIFAAAAACZhkgsAAAAAMAkFFgAAAACYhAILAAAAAExCgQUAAAAAJqHAAgAAAACTUGABAAAAgEl8K3rAxo0b9Y9//EPZ2dn68ccf9e6772rQoEGO7YZhaNq0aVq8eLHy8vLUo0cPLVy4UC1atHDsc/LkSY0dO1YffPCBfHx8lJiYqOeff1716tUrVwwlJSU6duyY6tevL4vFUtEuALgChmHo1KlTioiIkI9PzT43Qw4Cqh456H/IQUDVq0gOqnCBVVBQoPbt2+vBBx/UXXfdddH2Z555RvPnz9fy5csVHR2tKVOmKCEhQd9++638/f0lSUlJSfrxxx+1bt06FRcXa8SIERo9erRWrlxZrhiOHTumyMjIioYOwAQ5OTlq2rSpu8NwK3IQ4D7kIHIQ4E7lyUGVetCwxWJxGsEyDEMRERF65JFH9Oijj0qS8vPzFRoaqmXLlmno0KHavXu3WrVqpe3bt6tz586SpIyMDN1222364YcfFBERcdn3zc/PV3BwsHJychQYGHil4QOoALvdrsjISOXl5SkoKMjd4bgVOQioeuSg/yEHAVWvIjmowiNYl3Lw4EHZbDbFx8c71gUFBalbt27KysrS0KFDlZWVpeDgYEdxJUnx8fHy8fHR1q1bdeedd17UblFRkYqKihyvT506JUkKDAwksQBVjMtR/vcZkIOAqkcOIgcB7lSeHGTqRcw2m02SFBoa6rQ+NDTUsc1ms6lJkyZO2319fRUSEuLY5/fS0tIUFBTkWBgWBwAAVW3jxo0aMGCAIiIiZLFYtHr1aqfthmFo6tSpCg8PV0BAgOLj47Vv3z6nfU6ePKmkpCQFBgYqODhYI0eO1OnTp6uwFwBczSPuEk1NTVV+fr5jycnJcXdIAACghrlwH3p6enqp2y/ch75o0SJt3bpVdevWVUJCggoLCx37JCUl6T//+Y/WrVunDz/8UBs3btTo0aOrqgsAqoCplwiGhYVJknJzcxUeHu5Yn5ubqw4dOjj2OX78uNNx//3vf3Xy5EnH8b9ntVpltVrNDBUAAKBC+vXrp379+pW6zTAMzZs3T5MnT9bAgQMlSa+++qpCQ0O1evVqx33oGRkZTvehv/DCC7rtttv07LPPlus+dADVn6kjWNHR0QoLC1NmZqZjnd1u19atWxUXFydJiouLU15enrKzsx37fPrppyopKVG3bt3MDAcAAKBKXO4+dEmXvQ+9LEVFRbLb7U4LgOqrwiNYp0+f1v79+x2vDx48qC+//FIhISGKiorS+PHjNXPmTLVo0cIxTXtERIRjpsGWLVvq1ltv1ahRo7Ro0SIVFxdrzJgxGjp0KGduAACAR3LVfejSr/eiT58+3eSIAbhKhUewduzYodjYWMXGxkqSUlJSFBsbq6lTp0qSJk6cqLFjx2r06NHq0qWLTp8+rYyMDMczsCRpxYoViomJUZ8+fXTbbbepZ8+eevnll03qEgAAgPfgXnTAs1R4BKtXr1661KOzLBaLZsyYoRkzZpS5T0hISLkfKlxZzSetMbW9Q7P6m9oeAMCzmf3/GYn/13giV92HLnEvOvB71f33vUfMIggAAFCdcR86gAtMnUUQAADAW3EfOoDyYAQLgFd54oknZLFYnJaYmBjH9sLCQiUnJ6thw4aqV6+eEhMTlZub68aIAXgK7kMHUB6MYAHwOq1bt9Ynn3zieO3r+79UN2HCBK1Zs0ZvvvmmgoKCNGbMGN11113avHmzO0IF4EE87T50AO5BgQXA6/j6+pZ6w3h+fr6WLFmilStXqnfv3pKkpUuXqmXLltqyZYtuuOGGqg4VAAB4GS4RBOB19u3bp4iICF199dVKSkrSkSNHJEnZ2dkqLi52ehBoTEyMoqKiHA8CLQ0P+QQAAOVFgQXAq3Tr1k3Lli1TRkaGFi5cqIMHD+rGG2/UqVOnZLPZ5Ofnp+DgYKdjfvsg0NKkpaUpKCjIsURGRrq4FwAAwFNxiSAAr9KvXz/Hf7dr107dunVTs2bN9MYbbyggIOCK2kxNTVVKSorjtd1up8gCAAClYgQLgFcLDg7Wddddp/379yssLEznzp1TXl6e0z65ubmXfchnYGCg0wIAAFAaCiwAXu306dP6/vvvFR4erk6dOql27dpODwLds2ePjhw54ngQKAAAQGVwiSAAr/Loo49qwIABatasmY4dO6Zp06apVq1aGjZsmIKCgjRy5EilpKQoJCREgYGBGjt2rOLi4phBEAAAmIICC4BX+eGHHzRs2DD9/PPPaty4sXr27KktW7aocePGkqS5c+fKx8dHiYmJKioqUkJCgl588UU3Rw0AALwFBRYAr7Jq1apLbvf391d6errS09OrKCIAAFCTcA8WAAAAAJiEAgsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASSiwAAAAAMAkFFgAAAAAYBIKLAAAAAAwCQ8ahsdrPmmN6W0emtXf9DYBAADg/RjBAgAAAACTUGABAAAAgEkosAAAAADAJBRYAAAAAGAS0wus8+fPa8qUKYqOjlZAQICuueYaPfnkkzIMw7GPYRiaOnWqwsPDFRAQoPj4eO3bt8/sUAAAAACgSpleYM2ePVsLFy7UggULtHv3bs2ePVvPPPOMXnjhBcc+zzzzjObPn69FixZp69atqlu3rhISElRYWGh2OAAAAABQZUyfpv3zzz/XwIED1b//r9NcN2/eXK+99pq2bdsm6dfRq3nz5mny5MkaOHCgJOnVV19VaGioVq9eraFDh5odEgAAAABUCdNHsLp3767MzEzt3btXkvTVV19p06ZN6tevnyTp4MGDstlsio+PdxwTFBSkbt26KSsrq9Q2i4qKZLfbnRYAAIDqhlslAJg+gjVp0iTZ7XbFxMSoVq1aOn/+vJ566iklJSVJkmw2myQpNDTU6bjQ0FDHtt9LS0vT9OnTzQ4VAFzO7Adh8xBsoHq7cKvE8uXL1bp1a+3YsUMjRoxQUFCQHn74YUn/u1Vi+fLlio6O1pQpU5SQkKBvv/1W/v7+bu4BgMoyfQTrjTfe0IoVK7Ry5Urt3LlTy5cv17PPPqvly5dfcZupqanKz893LDk5OSZGDAAAYI7f3irRvHlz3X333erbt2+Zt0q0a9dOr776qo4dO6bVq1e7N3gApjC9wHrsscc0adIkDR06VG3bttV9992nCRMmKC0tTZIUFhYmScrNzXU6Ljc317Ht96xWqwIDA50WAACA6oZbJQCYXmCdOXNGPj7OzdaqVUslJSWSpOjoaIWFhSkzM9Ox3W63a+vWrYqLizM7HAAAgCpz4SRzTEyMateurdjYWI0fP77St0oEBQU5lsjISNd2AkClmH4P1oABA/TUU08pKipKrVu31hdffKE5c+bowQcflCRZLBaNHz9eM2fOVIsWLRzXHkdERGjQoEFmhwMAAFBlfnurROvWrfXll19q/PjxioiI0PDhw6+ozdTUVKWkpDhe2+12iiygGjO9wHrhhRc0ZcoU/fWvf9Xx48cVERGhP//5z5o6dapjn4kTJ6qgoECjR49WXl6eevbsqYyMDG7sBAAAHu23t0pIUtu2bXX48GGlpaVp+PDhTrdKhIeHO47Lzc1Vhw4dSm3TarXKarW6PHYA5jC9wKpfv77mzZunefPmlbmPxWLRjBkzNGPGDLPfHqgRmJmufGbNmqXU1FSNGzfOkZMKCwv1yCOPaNWqVSoqKlJCQoJefPHFiy7XAYArUZFbJS4UVBdulXjooYeqOlwALmB6gQUA1cH27dv10ksvqV27dk7rJ0yYoDVr1ujNN99UUFCQxowZo7vuukubN292U6SA5+EkT9m4VQIABRYAr3P69GklJSVp8eLFmjlzpmN9fn6+lixZopUrV6p3796SpKVLl6ply5basmWLbrjhBneFDMBLcKsEANNnEQQAd0tOTlb//v2dpkGWpOzsbBUXFzutj4mJUVRUVJnTI0tMkQyg/C7cKnH48GGdPXtW33//vWbOnCk/Pz/HPhdulbDZbCosLNQnn3yi6667zo1RAzATI1gAvMqqVau0c+dObd++/aJtNptNfn5+Cg4Odlp/qemRpV+nSJ4+fbrZoQIAAC/ECBYAr5GTk6Nx48ZpxYoVpl5qk5qaqvz8fMeSk5NjWtsAAMC7UGAB8BrZ2dk6fvy4OnbsKF9fX/n6+mrDhg2aP3++fH19FRoaqnPnzikvL8/puNzcXMfUyaWxWq0KDAx0WgAAAErDJYIAvEafPn20a9cup3UjRoxQTEyMHn/8cUVGRqp27drKzMxUYmKiJGnPnj06cuSI4uLi3BEyAADwMhRYALxG/fr11aZNG6d1devWVcOGDR3rR44cqZSUFIWEhCgwMFBjx45VXFwcMwgCAABTUGABqFHmzp0rHx8fJSYmOj1oGAAAwAwUWAC82vr1651e+/v7Kz09Xenp6e4JCAAAeDUmuQAAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMwnOwgCrQfNIad4cAAACAKkCBBQAA4KXMPsF3aFZ/U9sDvBGXCAIAAACASSiwAAAAAMAkFFgAAAAAYBIKLAAAAAAwCQUWAAAAAJiEAgsAAAAATEKBBQAAAAAmcUmBdfToUd17771q2LChAgIC1LZtW+3YscOx3TAMTZ06VeHh4QoICFB8fLz27dvnilAAAAAAoMqYXmD98ssv6tGjh2rXrq2PP/5Y3377rZ577jk1aNDAsc8zzzyj+fPna9GiRdq6davq1q2rhIQEFRYWmh0OAAAAAFQZ0wus2bNnKzIyUkuXLlXXrl0VHR2tvn376pprrpH06+jVvHnzNHnyZA0cOFDt2rXTq6++qmPHjmn16tVmhwMAAFCluJIHqNlML7Def/99de7cWffcc4+aNGmi2NhYLV682LH94MGDstlsio+Pd6wLCgpSt27dlJWVVWqbRUVFstvtTgsAAEB1w5U8AEwvsA4cOKCFCxeqRYsWWrt2rR566CE9/PDDWr58uSTJZrNJkkJDQ52OCw0NdWz7vbS0NAUFBTmWyMhIs8MGAACoNK7kAWB6gVVSUqKOHTvq6aefVmxsrEaPHq1Ro0Zp0aJFV9xmamqq8vPzHUtOTo6JEQMAAJiDK3kAmF5ghYeHq1WrVk7rWrZsqSNHjkiSwsLCJEm5ublO++Tm5jq2/Z7ValVgYKDTAgAAUN1wJQ8A0wusHj16aM+ePU7r9u7dq2bNmkmSoqOjFRYWpszMTMd2u92urVu3Ki4uzuxwAAAAqgxX8gAwvcCaMGGCtmzZoqefflr79+/XypUr9fLLLys5OVmSZLFYNH78eM2cOVPvv/++du3apfvvv18REREaNGiQ2eEAqGEWLlyodu3aOUa74+Li9PHHHzu2FxYWKjk5WQ0bNlS9evWUmJh40Yg6AFwpruQBYHqB1aVLF7377rt67bXX1KZNGz355JOaN2+ekpKSHPtMnDhRY8eO1ejRo9WlSxedPn1aGRkZ8vf3NzscADVM06ZNNWvWLGVnZ2vHjh3q3bu3Bg4cqP/85z+Sfj0J9MEHH+jNN9/Uhg0bdOzYMd11111ujhqAt+BKHgC+rmj09ttv1+23317mdovFohkzZmjGjBmueHsANdiAAQOcXj/11FNauHChtmzZoqZNm2rJkiVauXKlevfuLUlaunSpWrZsqS1btuiGG24otc2ioiIVFRU5XnODOYCyTJgwQd27d9fTTz+twYMHa9u2bXr55Zf18ssvS3K+kqdFixaKjo7WlClTuJIH8CKmj2ABQHVx/vx5rVq1SgUFBYqLi1N2draKi4udZu+KiYlRVFRUmbN3SdxgDqD8uJIHgEtGsADAnXbt2qW4uDgVFhaqXr16evfdd9WqVSt9+eWX8vPzU3BwsNP+l5q9S/r1BvOUlBTHa7vdTpEFoExcyQPUbBRYALzO9ddfry+//FL5+fl66623NHz4cG3YsOGK27NarbJarSZGCAAAvBUFFgCv4+fnp2uvvVaS1KlTJ23fvl3PP/+8hgwZonPnzikvL89pFOtSs3cBAABUBPdgAfB6JSUlKioqUqdOnVS7dm2n2bv27NmjI0eOMHsXAAAwBSNYALxKamqq+vXrp6ioKJ06dUorV67U+vXrtXbtWgUFBWnkyJFKSUlRSEiIAgMDNXbsWMXFxZU5gyAAAEBFUGAB8CrHjx/X/fffrx9//FFBQUFq166d1q5dq1tuuUWSNHfuXPn4+CgxMVFFRUVKSEjQiy++6OaoAQC4Ms0nrTG1vUOz+pvaXk1EgQXAqyxZsuSS2/39/ZWenq709PQqiggAANQk3IMFAAAAACahwAIAAAAAk3CJIFAKs69nBgAAqKlq2u8qRrAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEl93BwAAAFyr+aQ17g4BAGoMRrAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmMTlBdasWbNksVg0fvx4x7rCwkIlJyerYcOGqlevnhITE5Wbm+vqUAAAAADApVxaYG3fvl0vvfSS2rVr57R+woQJ+uCDD/Tmm29qw4YNOnbsmO666y5XhgIAAFClOMkM1EwuK7BOnz6tpKQkLV68WA0aNHCsz8/P15IlSzRnzhz17t1bnTp10tKlS/X5559ry5YtrgoHAACgynCSGai5XPYcrOTkZPXv31/x8fGaOXOmY312draKi4sVHx/vWBcTE6OoqChlZWXphhtuuKitoqIiFRUVOV7b7XZXhQ0AAFApvz3J/NvfQBdOMq9cuVK9e/eWJC1dulQtW7bUli1bSv0NhIoz+7lvh2b1N7U9eD+XjGCtWrVKO3fuVFpa2kXbbDab/Pz8FBwc7LQ+NDRUNput1PbS0tIUFBTkWCIjI10RNgAAQKX99iTzb13uJHNZioqKZLfbnRYA1ZfpI1g5OTkaN26c1q1bJ39/f1PaTE1NVUpKiuO13W6nyAJQqrS0NL3zzjv67rvvFBAQoO7du2v27Nm6/vrrHfsUFhbqkUce0apVq1RUVKSEhAS9+OKLCg0NdWPkwP+YfQYeVefCSebt27dftO1KTjJLv+a16dOnmx0qUCryT+WZPoKVnZ2t48ePq2PHjvL19ZWvr682bNig+fPny9fXV6GhoTp37pzy8vKcjsvNzVVYWFipbVqtVgUGBjotAFCaDRs2KDk5WVu2bNG6detUXFysvn37qqCgwLEP90AAcIULJ5lXrFhh2klm6dcTzfn5+Y4lJyfHtLYBmM/0Eaw+ffpo165dTutGjBihmJgYPf7444qMjFTt2rWVmZmpxMRESdKePXt05MgRxcXFmR1OjcS1x6jJMjIynF4vW7ZMTZo0UXZ2tv7whz9wDwQAl/ntSeYLzp8/r40bN2rBggVau3at4yTzb0exLnWSWfr1RLPVanVl6OXGbwzg8kwvsOrXr682bdo4ratbt64aNmzoWD9y5EilpKQoJCREgYGBGjt2rOLi4vhhA8B0+fn5kqSQkBBJTLQDwHU4yQxAcuEsgpcyd+5c+fj4KDEx0en+BwAwU0lJicaPH68ePXo4TvBc6UQ73P8A4HI4yQxAqqICa/369U6v/f39lZ6ervT09Kp4ewA1VHJysr755htt2rSpUu0w0Q4As3CSGfB+bhnBAgBXGzNmjD788ENt3LhRTZs2dawPCwur8D0Q1en+BwCehZPMQM1DgQXAqxiGobFjx+rdd9/V+vXrFR0d7bS9U6dO3APxG66Yjpeb1gEANRkFFgCvkpycrJUrV+q9995T/fr1HfdVBQUFKSAgQEFBQdwDAQAAXIYCC4BXWbhwoSSpV69eTuuXLl2qBx54QBL3QAAAANehwALgVQzDuOw+3AMBAABcxcfdAQAAAACAt2AEq4K4IRwAAABAWRjBAgAAAACTUGABAAAAgEkosAAAAADAJBRYAAAAAGASJrkAAJjK7MmAmAgIAOBJGMECAAAAAJNQYAEAAACASSiwAAAAAMAkFFgAAAAAYBIKLAAAAAAwCQUWAAAAAJiEAgsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASSiwAAAAAMAkvu4OAAAAADVT80lr3B0CYDpGsAAAAADAJIxgAYAH4WwvAADVm+kFVlpamt555x199913CggIUPfu3TV79mxdf/31jn0KCwv1yCOPaNWqVSoqKlJCQoJefPFFhYaGmh2OR+AHEwAAAOAdTL9EcMOGDUpOTtaWLVu0bt06FRcXq2/fviooKHDsM2HCBH3wwQd68803tWHDBh07dkx33XWX2aEAAABUqbS0NHXp0kX169dXkyZNNGjQIO3Zs8dpn8LCQiUnJ6thw4aqV6+eEhMTlZub66aIAZjN9AIrIyNDDzzwgFq3bq327dtr2bJlOnLkiLKzsyVJ+fn5WrJkiebMmaPevXurU6dOWrp0qT7//HNt2bLF7HAA1DAbN27UgAEDFBERIYvFotWrVzttNwxDU6dOVXh4uAICAhQfH699+/a5J1gAXocTzQBcPslFfn6+JCkkJESSlJ2dreLiYsXHxzv2iYmJUVRUlLKyskpto6ioSHa73WkBgNIUFBSoffv2Sk9PL3X7M888o/nz52vRokXaunWr6tatq4SEBBUWFlZxpAC8kStONPM7CPAsLi2wSkpKNH78ePXo0UNt2rSRJNlsNvn5+Sk4ONhp39DQUNlstlLbSUtLU1BQkGOJjIx0ZdgAPFi/fv00c+ZM3XnnnRdtMwxD8+bN0+TJkzVw4EC1a9dOr776qo4dO3bRSBcAmMGME838DgI8i0sLrOTkZH3zzTdatWpVpdpJTU1Vfn6+Y8nJyTEpQgA1ycGDB2Wz2Zx+2AQFBalbt25l/rCROHsM4MqYdaKZ30GAZ3HZNO1jxozRhx9+qI0bN6pp06aO9WFhYTp37pzy8vKckktubq7CwsJKbctqtcpqtboqVAA1xIUfL7+fsfRSP2ykX88eT58+3aWxAfA+F040b9q0qVLt8DsI8Cymj2AZhqExY8bo3Xff1aeffqro6Gin7Z06dVLt2rWVmZnpWLdnzx4dOXJEcXFxZocDAJXG2WMAFXXhRPNnn31W5onm37rUiWYAnsX0Eazk5GStXLlS7733nurXr+84KxwUFKSAgAAFBQVp5MiRSklJUUhIiAIDAzV27FjFxcXphhtuMDscAHC48OMlNzdX4eHhjvW5ubnq0KFDmcdx9hhAeRmGobFjx+rdd9/V+vXrL3miOTExURInmgFvY3qBtXDhQklSr169nNYvXbpUDzzwgCRp7ty58vHxUWJiotODhgHAlaKjoxUWFqbMzExHQWW327V161Y99NBD7g0OgFfgRDMA0wsswzAuu4+/v7/S09PLnEYZAK7U6dOntX//fsfrgwcP6ssvv1RISIiioqI0fvx4zZw5Uy1atFB0dLSmTJmiiIgIDRo0yH1BA/AanGgG4LJJLgDAHXbs2KGbb77Z8TolJUWSNHz4cC1btkwTJ05UQUGBRo8erby8PPXs2VMZGRny9/d3V8gAvAgnmgFQYAHwKr169brkDxyLxaIZM2ZoxowZVRgVAACoKVz6HCwAAAAAqEkosAAAAADAJBRYAAAAAGASCiwAAAAAMAkFFgAAAACYhAILAAAAAExCgQUAAAAAJqHAAgAAAACTUGABAAAAgEl83R0AAAAAUF01n7TG1PYOzepvanuofhjBAgAAAACTMIIFAAAAVBGzR8RQ/TCCBQAAAAAmocACAAAAAJNQYAEAAACASbgHC1WOa48BAADgrRjBAgAAAACTMIIFAKjWeAYNAMCTMIIFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADCJ26ZpT09P1z/+8Q/ZbDa1b99eL7zwgrp27equcADUMOSgmouHnaM6IAcB3sstI1ivv/66UlJSNG3aNO3cuVPt27dXQkKCjh8/7o5wANQw5CAA7kQOArybW0aw5syZo1GjRmnEiBGSpEWLFmnNmjV65ZVXNGnSpIv2LyoqUlFRkeN1fn6+JMlut1/2vUqKzpgUdc0VNeFNd4cAFyvP39KFfQzDcHU4LkcOAqoXchA5CHAn03OQUcWKioqMWrVqGe+++67T+vvvv9+44447Sj1m2rRphiQWFpZqsOTk5FRBpnAdchALi2cv5CAWFhZ3LuXJQVU+gvXTTz/p/PnzCg0NdVofGhqq7777rtRjUlNTlZKS4nhdUlKikydPqmHDhrJYLGW+l91uV2RkpHJychQYGGhOB+DA5+ta1e3zNQxDp06dUkREhLtDqRRykPfg83Wt6vb5koPIQdUNn69rVbfPtyI5yG2TXFSE1WqV1Wp1WhccHFzu4wMDA6vFF+Ot+Hxdqzp9vkFBQe4OwS3IQdUbn69rVafPlxz0P+Sg6oPP17Wq0+db3hxU5ZNcNGrUSLVq1VJubq7T+tzcXIWFhVV1OABqGHIQAHciBwHer8oLLD8/P3Xq1EmZmZmOdSUlJcrMzFRcXFxVhwOghiEHAXAnchDg/dxyiWBKSoqGDx+uzp07q2vXrpo3b54KCgocs+mYxWq1atq0aRcNq8McfL6uxefrOuQg78Dn61p8vq5DDvIOfL6u5cmfr8Uw3DPf6YIFCxwP2OvQoYPmz5+vbt26uSMUADUQOQiAO5GDAO/ltgILAAAAALxNld+DBQAAAADeigILAAAAAExCgQUAAAAAJqHAAgAAAACTUGABAAAAgEnc8hwsV/npp5/0yiuvKCsrSzabTZIUFham7t2764EHHlDjxo3dHCFwadu2bbvo329cXJy6du3q5shQHuQgeDpykGcjB8HTeUsO8ppp2rdv366EhATVqVNH8fHxCg0NlSTl5uYqMzNTZ86c0dq1a9W5c2c3R+rZzp07p9WrV5eavAcOHCg/Pz83R+iZjh8/rsTERG3evFlRUVFO/36PHDmiHj166O2331aTJk3cHCnKQg5yPfKP65CDPB85yPXIQa7jbTnIawqsG264Qe3bt9eiRYtksVicthmGob/85S/6+uuvlZWV5aYIPd/+/fuVkJCgY8eOqVu3bk7/+Ldu3aqmTZvq448/1rXXXuvmSD3P3XffrWPHjmnp0qW6/vrrnbbt2bNHDz74oCIiIvTmm2+6KUJcDjnItcg/rkUO8nzkINciB7mWt+UgrymwAgIC9MUXXygmJqbU7d99951iY2N19uzZKo7Me9xyyy2qW7euXn31VQUGBjpts9vtuv/++3X27FmtXbvWTRF6rvr162vjxo2KjY0tdXt2drZ69eqlU6dOVXFkKC9ykGuRf1yLHOT5yEGuRQ5yLW/LQV5zD1ZYWJi2bdtWZmLZtm2b42wDrszmzZu1bdu2ixKLJAUGBurJJ59Ut27d3BCZ57NarbLb7WVuP3XqlKxWaxVGhIoiB7kW+ce1yEGejxzkWuQg1/K2HOQ1Bdajjz6q0aNHKzs7W3369Lno2uPFixfr2WefdXOUni04OFiHDh1SmzZtSt1+6NAhBQcHV21QXmLIkCEaPny45s6dqz59+jgSuN1uV2ZmplJSUjRs2DA3R4lLIQe5FvnHtchBno8c5FrkINfyuhxkeJFVq1YZ3bp1M3x9fQ2LxWJYLBbD19fX6Natm/H666+7OzyPN2XKFKNBgwbGnDlzjK+++sqw2WyGzWYzvvrqK2POnDlGSEiIMW3aNHeH6ZEKCwuNv/zlL4afn5/h4+Nj+Pv7G/7+/oaPj4/h5+dnPPTQQ0ZhYaG7w8RlkINch/zjWuQg70AOch1ykGt5Ww7ymnuwfqu4uFg//fSTJKlRo0aqXbu2myPyHrNnz9bzzz8vm83muInWMAyFhYVp/Pjxmjhxopsj9Gx2u13Z2dlOsxN16tSp1EsSUH2Rg1yD/ON65CDvQA5yDXKQ63lLDvLKAguud/DgQad//NHR0W6OCEBNQf4B4E7kIFyOj7sDgGeKjo5WXFyc4uLiHIklJydHDz74oJsj81xnz57Vpk2b9O233160rbCwUK+++qobogKqH/KPa5CDgPIhB7mGN+UgRrBgmq+++kodO3bU+fPn3R2Kx9m7d6/69u2rI0eOyGKxqGfPnnrttdcUEREh6deblCMiIvhsgTKQfyqHHARUDjmocrwtB3nNLIJwvffff/+S2w8cOFBFkXifxx9/XG3atNGOHTuUl5en8ePHq2fPnlq/fr2ioqLcHR7gduQf1yIHAZdGDnItb8tBjGCh3Hx8fGSxWHSpfzIWi8Vjzi5UJ6Ghofrkk0/Utm1bSb/eNPvXv/5VH330kT777DPVrVvXo87cAGYj/7gWOQi4NHKQa3lbDuIeLJRbeHi43nnnHZWUlJS67Ny5090heqyzZ8/K1/d/A8oWi0ULFy7UgAEDdNNNN2nv3r1ujA5wP/KPa5GDgEsjB7mWt+UgCiyUW6dOnZSdnV3m9sud2UHZYmJitGPHjovWL1iwQAMHDtQdd9zhhqiA6oP841rkIODSyEGu5W05iAIL5fbYY4+pe/fuZW6/9tpr9dlnn1VhRN7jzjvv1GuvvVbqtgULFmjYsGEkbtRo5B/XIgcBl0YOci1vy0HcgwUAAAAAJmEECwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLBwWdu3b9eYMWPUunVr1a1bV1FRURo8eLD27t170b67d+/Wrbfeqnr16ikkJET33XefTpw4cdF+Tz31lO644w6FhobKYrHoiSeeuGQMr7/+uuLi4lS3bl0FBwere/fu+vTTT83qIoBqzJ05qHnz5rJYLKUuLVq0MLurAAAv4OvuAFD9zZ49W5s3b9Y999yjdu3ayWazacGCBerYsaO2bNmiNm3aSJJ++OEH/eEPf1BQUJCefvppnT59Ws8++6x27dqlbdu2yc/Pz9Hm5MmTFRYWptjYWK1du/aS7//EE09oxowZuvvuu/XAAw+ouLhY33zzjY4ePerSfgOoHtyZg+bNm6fTp087rTt8+LAmT56svn37uqbDAADPZgCXsXnzZqOoqMhp3d69ew2r1WokJSU51j300ENGQECAcfjwYce6devWGZKMl156yen4gwcPGoZhGCdOnDAkGdOmTSv1vbOysgyLxWLMmTPHnM4A8DjuzEGlefLJJw1JxubNmyveGQCA1+MSQVxW9+7dnc78SlKLFi3UunVr7d6927Hu7bff1u23366oqCjHuvj4eF133XV64403nI5v3rx5ud573rx5CgsL07hx42QYxkVnkgF4P3fmoNKsXLlS0dHR6t69+xW3AQDwXhRYuCKGYSg3N1eNGjWSJB09elTHjx9X586dL9q3a9eu+uKLL67ofTIzM9WlSxfNnz9fjRs3Vv369RUeHq4FCxZUKn4Anq2qctDvffHFF9q9e7f++Mc/mtIeAMD7UGDhiqxYsUJHjx7VkCFDJEk//vijJCk8PPyifcPDw3Xy5EkVFRVV6D1++eUX/fTTT9q8ebOmTJmiSZMm6fXXX1eHDh00duxYvfTSS5XvCACPVBU5qKz3laSkpKRKtwUA8E4UWKiw7777TsnJyYqLi9Pw4cMlSWfPnpUkWa3Wi/b39/d32qe8LlwO+PPPP+uf//ynHn30UQ0ePFhr1qxRq1atNHPmzMp0A4CHqqoc9HslJSVatWqVYmNj1bJly0q1BQDwXhRYqBCbzab+/fsrKChIb731lmrVqiVJCggIkKRSzxAXFhY67VNeF/avXbu27r77bsd6Hx8fDRkyRD/88IOOHDlyRf0A4JmqMgf93oYNG3T06FFGrwAAl0SBhXLLz89Xv379lJeXp4yMDEVERDi2Xbgs58JlOr/1448/KiQkpNQzy5cSEhIif39/NWzY0PEj6oImTZpI+vUyQgA1Q1XnoN9bsWKFfHx8NGzYsEq1AwDwbhRYKJfCwkINGDBAe/fu1YcffqhWrVo5bb/qqqvUuHFj7dix46Jjt23bpg4dOlT4PX18fNShQwedOHFC586dc9p27NgxSVLjxo0r3C4Az+OOHPRbRUVFevvtt9WrVy+nwg4AgN+jwMJlnT9/XkOGDFFWVpbefPNNxcXFlbpfYmKiPvzwQ+Xk5DjWZWZmau/evbrnnnuu6L2HDBmi8+fPa/ny5Y51hYWFWrFihVq1asUPHaAGcGcOuuCjjz5SXl4elwcCAC7LYhiG4e4gUL2NHz9ezz//vAYMGKDBgwdftP3ee++VJOXk5Cg2NlbBwcEaN26cTp8+rX/84x9q2rSptm/f7nR5zr/+9S8dPnxYZ86cUVpamm6++Wb17t1bknTfffepWbNmkn69Kb1Lly7au3evxo0bp6ioKP3rX//Szp079cEHH6hfv35V8AkAcCd35qAL7r77bn344YfKzc1VUFCQC3sLAPB0FFi4rF69emnDhg1lbv/tP6H//Oc/SklJ0aZNm+Tn56f+/fvrueeeU2hoaLnb/Oyzz9SrVy/H6+PHj2vixIn64IMPVFBQoA4dOmj69OlKSEioXMcAeAR35yC73a7Q0FDddtttevvttyvXGQCA16PAAgAAAACTcA8WAAAAAJiEAgsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASXzdHcCVKCkp0bFjx1S/fn1ZLBZ3hwPUCIZh6NSpU4qIiJCPT80+N0MOAqoeOQiAp/DIAuvYsWOKjIx0dxhAjZSTk6OmTZu6Owy3IgcB7kMOAlDdeWSBVb9+fUm/JtnAwEA3RwPUDHa7XZGRkY6/v5qMHARUPXIQAE/hkQXWhUtyAgMD+XEDVDEuiSMHAe5EDgJQ3XERMwAAAACYhAILAAAAAExCgQUAAAAAJqHAAgAAAACTUGABAAAAgEkosAAAAADAJB45TTtQ0zWftMbU9g7N6m9qe0BNYvbfo8TfJAB4MkawAAAAAMAkFFgAAAAAYBIKLAAAAAAwCQUWAAAAAJiEAgsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASSiwAAAAAMAkFFgAAAAAYBJfdwcAVFbzSWtMb/PQrP6mtwkAAADvxwgWAAAAAJiEAgsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASSiwAAAAAMAkFFgAvMr58+c1ZcoURUdHKyAgQNdcc42efPJJGYbh2McwDE2dOlXh4eEKCAhQfHy89u3b58aoAQCAt6DAAuBVZs+erYULF2rBggXavXu3Zs+erWeeeUYvvPCCY59nnnlG8+fP16JFi7R161bVrVtXCQkJKiwsdGPkAADAG/CgYQBe5fPPP9fAgQPVv/+vD4tu3ry5XnvtNW3btk3Sr6NX8+bN0+TJkzVw4EBJ0quvvqrQ0FCtXr1aQ4cOdVvswAVmP0Cdh6cDQNVhBAuAV+nevbsyMzO1d+9eSdJXX32lTZs2qV+/fpKkgwcPymazKT4+3nFMUFCQunXrpqysrFLbLCoqkt1ud1oAAABKwwgWAK8yadIk2e12xcTEqFatWjp//ryeeuopJSUlSZJsNpskKTQ01Om40NBQx7bfS0tL0/Tp010bOAAA8AqMYAHwKm+88YZWrFihlStXaufOnVq+fLmeffZZLV++/IrbTE1NVX5+vmPJyckxMWIAAOBNGMEC4FUee+wxTZo0yXEvVdu2bXX48GGlpaVp+PDhCgsLkyTl5uYqPDzccVxubq46dOhQaptWq1VWq9XlsQMAAM9X4RGsjRs3asCAAYqIiJDFYtHq1audtj/wwAOyWCxOy6233uq0z8mTJ5WUlKTAwEAFBwdr5MiROn36dKU6AgCSdObMGfn4OKe2WrVqqaSkRJIUHR2tsLAwZWZmOrbb7XZt3bpVcXFxVRorAADwPhUewSooKFD79u314IMP6q677ip1n1tvvVVLly51vP79md+kpCT9+OOPWrdunYqLizVixAiNHj1aK1eurGg4AOBkwIABeuqppxQVFaXWrVvriy++0Jw5c/Tggw9KkiwWi8aPH6+ZM2eqRYsWio6O1pQpUxQREaFBgwa5N3gAAODxKlxg9evXzzEbV1msVqvjMpzf2717tzIyMrR9+3Z17txZkvTCCy/otttu07PPPquIiIiKhgQADi+88IKmTJmiv/71rzp+/LgiIiL05z//WVOnTnXsM3HiRBUUFGj06NHKy8tTz549lZGRIX9/fzdGDgAAvIFLJrlYv369mjRpouuvv14PPfSQfv75Z8e2rKwsBQcHO4orSYqPj5ePj4+2bt1aantMkQygvOrXr6958+bp8OHDOnv2rL7//nvNnDlTfn5+jn0sFotmzJghm82mwsJCffLJJ7ruuuvcGDUAAPAWphdYt956q1599VVlZmZq9uzZ2rBhg/r166fz589L+nWK5CZNmjgd4+vrq5CQkEtOkRwUFORYIiMjzQ4bAAAAACrN9FkEL8zcJf06e1e7du10zTXXaP369erTp88VtZmamqqUlBTHa7vdTpEFAAAAoNpx+XOwrr76ajVq1Ej79++XJIWFhen48eNO+/z3v//VyZMny7xvy2q1KjAw0GkBAAAAgOrG5QXWDz/8oJ9//tnxvJm4uDjl5eUpOzvbsc+nn36qkpISdevWzdXhAAAAAIDLVPgSwdOnTztGoyTp4MGD+vLLLxUSEqKQkBBNnz5diYmJCgsL0/fff6+JEyfq2muvVUJCgiSpZcuWuvXWWzVq1CgtWrRIxcXFGjNmjIYOHcoMggAAAAA8WoVHsHbs2KHY2FjFxsZKklJSUhQbG6upU6eqVq1a+vrrr3XHHXfouuuu08iRI9WpUyf9+9//dnoW1ooVKxQTE6M+ffrotttuU8+ePfXyyy+b1ysAAAAAcIMKj2D16tVLhmGUuX3t2rWXbSMkJISHCgMAAADwOi6/BwsAAAAAagoKLAAAAAAwCQUWAAAAAJiEAgsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASSiwAAAAAMAkFFgAAAAAYBIKLAAAAAAwCQUWAAAAAJjE190BuFrzSWtMbe/QrP6mtgcAAADAezCCBQAAAAAmocACAAAAAJNQYAEAAACASSiwAAAAAMAkFFgAAAAAYBIKLAAAAAAwCQUWAAAAAJiEAgsAAAAATEKBBQAAAAAmocAC4HWOHj2qe++9Vw0bNlRAQIDatm2rHTt2OLYbhqGpU6cqPDxcAQEBio+P1759+9wYMQAA8BYUWAC8yi+//KIePXqodu3a+vjjj/Xtt9/queeeU4MGDRz7PPPMM5o/f74WLVqkrVu3qm7dukpISFBhYaEbIwcAAN7A190BANVR80lrTG3v0Kz+praHss2ePVuRkZFaunSpY110dLTjvw3D0Lx58zR58mQNHDhQkvTqq68qNDRUq1ev1tChQ6s8ZgAA4D0YwQLgVd5//3117txZ99xzj5o0aaLY2FgtXrzYsf3gwYOy2WyKj493rAsKClK3bt2UlZVVaptFRUWy2+1OCwAAQGkosAB4lQMHDmjhwoVq0aKF1q5dq4ceekgPP/ywli9fLkmy2WySpNDQUKfjQkNDHdt+Ly0tTUFBQY4lMjLStZ0AAAAeiwILgFcpKSlRx44d9fTTTys2NlajR4/WqFGjtGjRoituMzU1Vfn5+Y4lJyfHxIgBAIA3ocAC4FXCw8PVqlUrp3UtW7bUkSNHJElhYWGSpNzcXKd9cnNzHdt+z2q1KjAw0GkBAAAoDQUWAK/So0cP7dmzx2nd3r171axZM0m/TngRFhamzMxMx3a73a6tW7cqLi6uSmMFAADeh1kEAXiVCRMmqHv37nr66ac1ePBgbdu2TS+//LJefvllSZLFYtH48eM1c+ZMtWjRQtHR0ZoyZYoiIiI0aNAg9wYPAAA8HgUWUAXMnvYdZevSpYveffddpaamasaMGYqOjta8efOUlJTk2GfixIkqKCjQ6NGjlZeXp549eyojI0P+/v5ujBwAAHgDCiwAXuf222/X7bffXuZ2i8WiGTNmaMaMGVUYFQAAqAm4BwsAAAAATEKBBQAAAAAmocACAAAAAJNQYAEAAACASSiwAAAAAMAkFFgAAAAAYBIKLAAAAAAwSYULrI0bN2rAgAGKiIiQxWLR6tWrnbYbhqGpU6cqPDxcAQEBio+P1759+5z2OXnypJKSkhQYGKjg4GCNHDlSp0+frlRHAAAAAMDdKlxgFRQUqH379kpPTy91+zPPPKP58+dr0aJF2rp1q+rWrauEhAQVFhY69klKStJ//vMfrVu3Th9++KE2btyo0aNHX3kvAAAAAKAa8K3oAf369VO/fv1K3WYYhubNm6fJkydr4MCBkqRXX31VoaGhWr16tYYOHardu3crIyND27dvV+fOnSVJL7zwgm677TY9++yzioiIqER3AACAqzWftMbU9g7N6m9qewDgTqbeg3Xw4EHZbDbFx8c71gUFBalbt27KysqSJGVlZSk4ONhRXElSfHy8fHx8tHXr1lLbLSoqkt1ud1oAAAAAoLoxtcCy2WySpNDQUKf1oaGhjm02m01NmjRx2u7r66uQkBDHPr+XlpamoKAgxxIZGWlm2AAAAABgCo+YRTA1NVX5+fmOJScnx90hAQAAAMBFTC2wwsLCJEm5ublO63Nzcx3bwsLCdPz4caft//3vf3Xy5EnHPr9ntVoVGBjotAAAAABAdWNqgRUdHa2wsDBlZmY61tntdm3dulVxcXGSpLi4OOXl5Sk7O9uxz6effqqSkhJ169bNzHAAAAAAoEpVeBbB06dPa//+/Y7XBw8e1JdffqmQkBBFRUVp/Pjxmjlzplq0aKHo6GhNmTJFERERGjRokCSpZcuWuvXWWzVq1CgtWrRIxcXFGjNmjIYOHcoMggAAAAA8WoULrB07dujmm292vE5JSZEkDR8+XMuWLdPEiRNVUFCg0aNHKy8vTz179lRGRob8/f0dx6xYsUJjxoxRnz595OPjo8TERM2fP9+E7gAAAACA+1S4wOrVq5cMwyhzu8Vi0YwZMzRjxowy9wkJCdHKlSsr+tYAAAAAUK15xCyCAAAAAOAJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCS+7g4A5ms+aY2p7R2a1d/U9oCqMmvWLKWmpmrcuHGaN2+eJKmwsFCPPPKIVq1apaKiIiUkJOjFF19UaGioe4MFAABegREsAF5p+/bteumll9SuXTun9RMmTNAHH3ygN998Uxs2bNCxY8d01113uSlKAADgbRjBAuB1Tp8+raSkJC1evFgzZ850rM/Pz9eSJUu0cuVK9e7dW5K0dOlStWzZUlu2bNENN9xgeiyMKAMAULMwggXA6yQnJ6t///6Kj493Wp+dna3i4mKn9TExMYqKilJWVlaZ7RUVFclutzstAAAApWEEC4BXWbVqlXbu3Knt27dftM1ms8nPz0/BwcFO60NDQ2Wz2cpsMy0tTdOnTzc7VAAA4IUYwQLgNXJycjRu3DitWLFC/v7+prWbmpqq/Px8x5KTk2Na2wAAwLtQYAHwGtnZ2Tp+/Lg6duwoX19f+fr6asOGDZo/f758fX0VGhqqc+fOKS8vz+m43NxchYWFldmu1WpVYGCg0wIAAFAaLhEE4DX69OmjXbt2Oa0bMWKEYmJi9PjjjysyMlK1a9dWZmamEhMTJUl79uzRkSNHFBcX546QAQCAl6HAAuA16tevrzZt2jitq1u3rho2bOhYP3LkSKWkpCgkJESBgYEaO3as4uLiXDKDIAAAqHkosADUKHPnzpWPj48SExOdHjQMAABgBgosAF5t/fr1Tq/9/f2Vnp6u9PR09wQEAAC8GpNcAAAAAIBJKLAAAAAAwCQUWAAAAABgEgosAAAAADAJBRYAAAAAmIQCCwAAAABMQoEFAAAAACahwAIAAAAAk1BgAQAAAIBJKLAAAAAAwCQUWAAAAABgEl93BwCp+aQ17g4BAAAAgAkYwQIAAAAAk1BgAQAAAIBJKLAAAAAAwCTcg1VB3C8FwJ3MzkGHZvU3tT0AAGo6RrAAAAAAwCQUWAAAAABgEtMLrCeeeEIWi8VpiYmJcWwvLCxUcnKyGjZsqHr16ikxMVG5ublmhwEAAAAAVc4lI1itW7fWjz/+6Fg2bdrk2DZhwgR98MEHevPNN7VhwwYdO3ZMd911lyvCAAAAAIAq5ZJJLnx9fRUWFnbR+vz8fC1ZskQrV65U7969JUlLly5Vy5YttWXLFt1www2uCAcAAAAAqoRLCqx9+/YpIiJC/v7+iouLU1pamqKiopSdna3i4mLFx8c79o2JiVFUVJSysrLKLLCKiopUVFTkeG23210RNgAAXokZcAGg6ph+iWC3bt20bNkyZWRkaOHChTp48KBuvPFGnTp1SjabTX5+fgoODnY6JjQ0VDabrcw209LSFBQU5FgiIyPNDhsAAAAAKs30Eax+/fo5/rtdu3bq1q2bmjVrpjfeeEMBAQFX1GZqaqpSUlIcr+12O0UWAAAAgGrH5dO0BwcH67rrrtP+/fsVFhamc+fOKS8vz2mf3NzcUu/ZusBqtSowMNBpAQAAAIDqxuUF1unTp/X9998rPDxcnTp1Uu3atZWZmenYvmfPHh05ckRxcXGuDgUAAAAAXMr0SwQfffRRDRgwQM2aNdOxY8c0bdo01apVS8OGDVNQUJBGjhyplJQUhYSEKDAwUGPHjlVcXBwzCAIAAADweKYXWD/88IOGDRumn3/+WY0bN1bPnj21ZcsWNW7cWJI0d+5c+fj4KDExUUVFRUpISNCLL75odhgAAAAAUOVML7BWrVp1ye3+/v5KT09Xenq62W8NAKgGzJ4S/NCs/qa2BwCAK7n8HiwAqEppaWnq0qWL6tevryZNmmjQoEHas2eP0z6FhYVKTk5Ww4YNVa9ePSUmJio3N9dNEQMAAG9CgQXAq2zYsEHJycnasmWL1q1bp+LiYvXt21cFBQWOfSZMmKAPPvhAb775pjZs2KBjx47prrvucmPUAADAW5h+iSAAuFNGRobT62XLlqlJkybKzs7WH/7wB+Xn52vJkiVauXKlevfuLUlaunSpWrZsqS1btpQ64U5RUZGKioocr+12u2s7AQAAPBYjWAC8Wn5+viQpJCREkpSdna3i4mLFx8c79omJiVFUVJSysv5/e3cX2kS+h3H8SdXGF0yKdm0sKhYERSq+RKxRQUHZXhxcWSpWb3xX1FooYVe3Fyqi4BHE6mJVEFS8UJZlXxRXt7ChIkoxWkURUREtLUiqRTRlra2anAs5OSc01a7+J9NMvh/oRWeS9MegT+bJvKQh5Wvs2bNHXq838cMXnQMAgJ5QsAA4ViwWU1VVlWbPnq3i4mJJUiQSUW5urvLy8pIeW1BQoEgkkvJ1qqur9erVq8RPS0uL1aMDAIAMxSmCAByroqJCd+/e1ZUrV77oddxut9xut6GpAACAk3EEC4Ajbd68WefPn1d9fb1GjRqVWO7z+dTV1aWXL18mPb61tVU+ny/NUwIAAKfhCBYAR4nH46qsrNRvv/2mS5cuqaioKGm93+/XgAEDFAqFVFZWJkl68OCBmpubFQgE7BjZVqa/swoAgGxHwQLgKBUVFTp9+rTOnj2roUOHJq6r8nq9GjRokLxer9asWaNgMKhhw4bJ4/GosrJSgUAg5R0EAQAA/gkKFgBHOXLkiCRp3rx5SctPnDihlStXSpJqamqUk5OjsrIydXZ2qrS0VIcPH07zpAAAwIkoWAAcJR6Pf/IxAwcOVG1trWpra9MwEQAAyCbc5AIAAAAADKFgAQAAAIAhFCwAAAAAMISCBQAAAACGULAAAAAAwBAKFgAAAAAYQsECAAAAAEMoWAAAAABgCF80DADo08b+8IfR12v697+Mvh4AAP+PI1gAAAAAYAgFCwAAAAAMoWABAAAAgCEULAAAAAAwhIIFAAAAAIZQsAAAAADAEAoWAAAAABhCwQIAAAAAQyhYAAAAAGAIBQsAAAAADOlv9wDIPmN/+MPuEQAAAABLcAQLAAAAAAyhYAEAAACAIZwiCADIKpymDACwEgULn8TOCAAAANA7nCIIAAAAAIZQsAAAAADAEAoWAAAAABhCwQIAAAAAQ2wrWLW1tRo7dqwGDhyokpIShcNhu0YBkIXIIAAAYAVbCtZPP/2kYDCoHTt26ObNm5o8ebJKS0v17NkzO8YBkGXIIAAAYBVbCtb+/fu1bt06rVq1ShMnTtTRo0c1ePBgHT9+3I5xAGQZMggAAFgl7d+D1dXVpcbGRlVXVyeW5eTkaMGCBWpoaEj5nM7OTnV2diZ+f/XqlSQpGo1+8u/FOl9/4cSA8/Xm/9J/HxOPx60ex1JkEND3ZFMGAXC+tBestrY2vX//XgUFBUnLCwoKdP/+/ZTP2bNnj3bu3Nlt+ejRoy2ZEcg23gO9f2x7e7u8Xq9ls1iNDAL6nmzKIADOl/aC9Tmqq6sVDAYTv8diMb148ULDhw+Xy+Xq8XnRaFSjR49WS0uLPB5POkbNKmxfa/W17RuPx9Xe3q7CwkK7R0k7MqhvYvtaq69t32zOIACZJe0FKz8/X/369VNra2vS8tbWVvl8vpTPcbvdcrvdScvy8vJ6/Tc9Hk+feHNwKravtfrS9nXCp8ZkkPOwfa3Vl7avEzIIgPOl/SYXubm58vv9CoVCiWWxWEyhUEiBQCDd4wDIMmQQAACwki2nCAaDQa1YsULTp0/XjBkzdODAAf39999atWqVHeMAyDJkEAAAsIotBau8vFzPnz/X9u3bFYlENGXKFP3555/dLjr/Um63Wzt27Oh2ag/MYPtai+1rHTLIGdi+1mL7AsDnccW53ykAAAAAGGHLFw0DAAAAgBNRsAAAAADAEAoWAAAAABhCwQIAAAAAQyhYAAAAAGCILbdpt0pbW5uOHz+uhoYGRSIRSZLP59OsWbO0cuVKffXVVzZPCMDJyCAAAOCY27Rfv35dpaWlGjx4sBYsWJD4PpvW1laFQiG9fv1adXV1mj59us2TAj0Lh8Pdds4DgYBmzJhh82T4FDIITkAGAcCXc0zBmjlzpiZPnqyjR4/K5XIlrYvH49qwYYPu3LmjhoYGmyZ0hq6uLv3+++8pP6FftGiRcnNzbZ4wMz179kxlZWW6evWqxowZk7Rz3tzcrNmzZ+uXX37RiBEjbJ4UPSGDrEf+WIcMAgBzHFOwBg0apFu3bmnChAkp19+/f19Tp05VR0dHmidzjkePHqm0tFRPnz5VSUlJ0hvwtWvXNGrUKF28eFHjxo2zedLMs3jxYj19+lQnTpzQ+PHjk9Y9ePBAq1evVmFhoX7++WebJsSnkEHWIn+sRQYBgDmOuQbL5/MpHA73uHMTDocTb8j4PBs3btSkSZN069YteTyepHXRaFTLly9XRUWF6urqbJowc9XV1eny5cvddmwkafz48frxxx81b9689A+GXiODrEX+WIsMAgBzHFOwvvvuO61fv16NjY2aP39+t+sfjh07pn379tk8ZWa7evWqwuFwt50bSfJ4PNq1a5dKSkpsmCzzud1uRaPRHte3t7fL7XancSL8U2SQtcgfa5FBAGCOYwpWRUWF8vPzVVNTo8OHD+v9+/eSpH79+snv9+vkyZNasmSJzVNmtry8PDU1Nam4uDjl+qamJuXl5aV3KIcoLy/XihUrVFNTo/nz5yd2IqPRqEKhkILBoJYtW2bzlPgYMsha5I+1yCAAMMcxBUv68AZRXl6ut2/fqq2tTZKUn5+vAQMG2DyZM6xdu1bLly/Xtm3bUn5Cv3v3blVWVto8ZWbav3+/YrGYli5dqnfv3iUu1u/q6lL//v21Zs0ajn5kADLIOuSPtcggADDHMTe5QHrs3btXBw8eVCQSSdwpLR6Py+fzqaqqSlu2bLF5wswWjUbV2NiYdIc0v9+f8rQoINuQP9YjgwDgy1Gw8FmePHmS9AZcVFRk80QAsgX5AwDoy3LsHgCZqaioSIFAQIFAILFz09LSotWrV9s8Webq6OjQlStXdO/evW7r3rx5o1OnTtkwFdD3kD/WIIMAwAyOYMGY27dva9q0aYmL+9F7Dx8+1Ndff63m5ma5XC7NmTNHZ86cUWFhoaQP15kUFhaybYEekD9fhgwCAHMcdZMLWOvcuXMfXf/48eM0TeI8W7duVXFxsW7cuKGXL1+qqqpKc+bM0aVLlzRmzBi7xwNsR/5YiwwCAHM4goVey8nJkcvl0sf+ybhcLj7h/AwFBQX666+/NGnSJEkfLtzftGmTLly4oPr6eg0ZMoRPj5HVyB9rkUEAYA7XYKHXRo4cqV9//VWxWCzlz82bN+0eMWN1dHSof///HVB2uVw6cuSIFi5cqLlz5+rhw4c2TgfYj/yxFhkEAOZQsNBrfr9fjY2NPa7/1KfL6NmECRN048aNbssPHTqkRYsW6ZtvvrFhKqDvIH+sRQYBgDkULPTa999/r1mzZvW4fty4caqvr0/jRM7x7bff6syZMynXHTp0SMuWLWPnEVmN/LEWGQQA5nANFgAAAAAYwhEsAAAAADCEggUAAAAAhlCwAAAAAMAQChYAAAAAGELBAgAAAABDKFgAAAAAYAgFCwAAAAAM+Q+FLI+aEAADGAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = df.hist(column='day_hour', by='year', figsize=(10,10))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter plots\n",
    "\n",
    "They are useful to plot the relation between two variables in your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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>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>week_day</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:...</td>\n",
       "      <td>https://twitter.com/i/web/status/8496368680522...</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2</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 Exa...</td>\n",
       "      <td>https://twitter.com/i/web/status/8489887305850...</td>\n",
       "      <td>@ForIn2020, @waltmossberg, @mims, @defcon_5</td>\n",
       "      <td>4</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</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/8489430724234...</td>\n",
       "      <td>@waltmossberg, @mims, @defcon_5</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>0</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:...   \n",
       "848988730585096192  b\"@ForIn2020 @waltmossberg @mims @defcon_5 Exa...   \n",
       "848943072423497728      b'@waltmossberg @mims @defcon_5 Et tu, Walt?'   \n",
       "\n",
       "                                                           tweet_link  \\\n",
       "id                                                                      \n",
       "849636868052275200  https://twitter.com/i/web/status/8496368680522...   \n",
       "848988730585096192  https://twitter.com/i/web/status/8489887305850...   \n",
       "848943072423497728  https://twitter.com/i/web/status/8489430724234...   \n",
       "\n",
       "                                                 tweet_mentions  n_mentions  \\\n",
       "id                                                                            \n",
       "849636868052275200                                                        1   \n",
       "848988730585096192  @ForIn2020, @waltmossberg, @mims, @defcon_5           4   \n",
       "848943072423497728              @waltmossberg, @mims, @defcon_5           3   \n",
       "\n",
       "                   week_day_name  week_day  day_hour  year  \n",
       "id                                                          \n",
       "849636868052275200     Wednesday         2        14  2017  \n",
       "848988730585096192        Monday         0        20  2017  \n",
       "848943072423497728        Monday         0        16  2017  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# specify the type of plot and the two\n",
    "# variables to be plotted against one another\n",
    "plt.scatter(df['n_mentions'], df['day_hour'])\n",
    "\n",
    "# give a title to the plot\n",
    "plt.title('Number of mentions vs hour of the day')\n",
    "\n",
    "# give a label to the axes\n",
    "plt.ylabel(\"Day hour\")\n",
    "plt.xlabel(\"Number of mentions\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bar charts\n",
    "\n",
    "They are useful to plot categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.bar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_by_weekday = df.groupby(df['created_at'].dt.weekday)[['text']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_by_weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Box plots\n",
    "\n",
    "![box plot explained](./figures/eda-boxplot.png)"
   ]
  },
  {
   "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",
       "    }\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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_by_weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_by_weekday.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets_by_weekday.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['day_hour']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_hour    5.0\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['day_hour']].quantile(.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.boxplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "384px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}