HW2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acbfaa3d-c19a-4a4c-b05c-4d6e6aba2104",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rcParams['figure.dpi'] = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35569ac9-fdcb-4a71-9541-0ac1949241fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_names = ['./DataSets/instagram_posts.csv', './DataSets/instagram_profiles.csv', './DataSets/instagram_locations.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fa00364-a384-4162-9823-ece725963021",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill all the missing values \n",
    "def clean_df(df, col):\n",
    "    df.dropna(subset = col, inplace = True)\n",
    "    df.fillna('', inplace = True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a016180-4d1d-419a-b2d1-1553078b38d4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# RQ 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3b2e05-a832-49ca-8862-fe2a616bcfec",
   "metadata": {},
   "source": [
    "After collecting information, the Data Scientists have to know what dataset they are dealing with, so let's start with an Exploratory Data Analysis (EDA). What can you say about our datasets? Please summarise its main characteristics with visual and tabular methods."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "911d0259-5e00-4e3d-a4c6-a2dba7135fa7",
   "metadata": {},
   "source": [
    "###  instagram_profiles.csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e1f30b08-bb8c-475b-bdc8-624e5e634c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0d7c996-5fe5-4c04-a71b-50d41ce6ae65",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('instagram_profiles.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d0e1958-aced-40a7-b74f-ed5a84df1bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>sid</th>\n",
       "      <th>profile_id</th>\n",
       "      <th>profile_name</th>\n",
       "      <th>firstname_lastname</th>\n",
       "      <th>description</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>n_posts</th>\n",
       "      <th>url</th>\n",
       "      <th>cts</th>\n",
       "      <th>is_business_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4184446</td>\n",
       "      <td>4.721050e+09</td>\n",
       "      <td>jphillip033</td>\n",
       "      <td>John Pierce</td>\n",
       "      <td>\"Document Everything Always\"</td>\n",
       "      <td>250.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-01 14:38:55.394 -0400</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4184457</td>\n",
       "      <td>5.905830e+08</td>\n",
       "      <td>mama_haas</td>\n",
       "      <td>Deanna</td>\n",
       "      <td>Trying to enjoy the simple things in life. Kni...</td>\n",
       "      <td>534.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>www.etsy.com/shop/MamaHaas69</td>\n",
       "      <td>2019-08-01 14:39:36.526 -0400</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4184460</td>\n",
       "      <td>1.074147e+09</td>\n",
       "      <td>yellowlightbreen</td>\n",
       "      <td>Eliza Gray</td>\n",
       "      <td>Maine is for Lovers</td>\n",
       "      <td>469.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>elizajanegray.com</td>\n",
       "      <td>2019-08-01 14:39:54.407 -0400</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4184461</td>\n",
       "      <td>1.472039e+09</td>\n",
       "      <td>tec1025</td>\n",
       "      <td>Thomas Clark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-01 14:40:06.472 -0400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4184462</td>\n",
       "      <td>3.531421e+09</td>\n",
       "      <td>luckyfluff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-01 14:40:07.806 -0400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid    profile_id      profile_name firstname_lastname  \\\n",
       "0  4184446  4.721050e+09       jphillip033        John Pierce   \n",
       "1  4184457  5.905830e+08         mama_haas             Deanna   \n",
       "2  4184460  1.074147e+09  yellowlightbreen         Eliza Gray   \n",
       "3  4184461  1.472039e+09           tec1025       Thomas Clark   \n",
       "4  4184462  3.531421e+09        luckyfluff                NaN   \n",
       "\n",
       "                                         description  following  followers  \\\n",
       "0                       \"Document Everything Always\"      250.0      146.0   \n",
       "1  Trying to enjoy the simple things in life. Kni...      534.0     1145.0   \n",
       "2                                Maine is for Lovers      469.0      324.0   \n",
       "3                                                NaN        NaN        NaN   \n",
       "4                                                NaN        NaN        NaN   \n",
       "\n",
       "   n_posts                           url                            cts  \\\n",
       "0    170.0                           NaN  2019-08-01 14:38:55.394 -0400   \n",
       "1   2878.0  www.etsy.com/shop/MamaHaas69  2019-08-01 14:39:36.526 -0400   \n",
       "2    431.0             elizajanegray.com  2019-08-01 14:39:54.407 -0400   \n",
       "3      NaN                           NaN  2019-08-01 14:40:06.472 -0400   \n",
       "4      NaN                           NaN  2019-08-01 14:40:07.806 -0400   \n",
       "\n",
       "  is_business_account  \n",
       "0               False  \n",
       "1               False  \n",
       "2               False  \n",
       "3                 NaN  \n",
       "4                 NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c3dc80-0477-477d-a66c-8aa75d28d0cc",
   "metadata": {},
   "source": [
    "We can see the different columns and get an idea of the structure of this dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "920c62c6-d776-47e3-bb43-69c81aea576f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4509586, 11)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a514f571-91c5-4a8f-bf65-45267b5aae6f",
   "metadata": {},
   "source": [
    "This dataframe has 4509586 rows and 11 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2b56c13d-9731-4686-824d-454e41c929c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49605446"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc68478-5993-4722-92f4-a9b3acd978ff",
   "metadata": {},
   "source": [
    "This dataframe contains 49605446 elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2fd4a4d-1486-4093-9b31-a58896e1aa03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['sid', 'profile_id', 'profile_name', 'firstname_lastname',\n",
      "       'description', 'following', 'followers', 'n_posts', 'url', 'cts',\n",
      "       'is_business_account'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acdae648-badd-4f1e-94a2-98a4563b5ad5",
   "metadata": {},
   "source": [
    "These are the titles of the columns in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88ac07b9-119f-4b71-bf2f-2322eb5f1c28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4509586 entries, 0 to 4509585\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Dtype  \n",
      "---  ------               -----  \n",
      " 0   sid                  int64  \n",
      " 1   profile_id           float64\n",
      " 2   profile_name         object \n",
      " 3   firstname_lastname   object \n",
      " 4   description          object \n",
      " 5   following            float64\n",
      " 6   followers            float64\n",
      " 7   n_posts              float64\n",
      " 8   url                  object \n",
      " 9   cts                  object \n",
      " 10  is_business_account  object \n",
      "dtypes: float64(4), int64(1), object(6)\n",
      "memory usage: 378.5+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2812c09f-88a1-4651-b14d-730e1c23fb17",
   "metadata": {},
   "source": [
    "Here we have a summary of the data types contained in the dataframe. We see that the majority of columns contain object data types ('profile_name', 'firstname_lastname', 'description', 'url', 'cts', 'is_business_account'), other contain float64 data types('profile_id', 'following', 'followers', 'n_posts'). Only one column ('sid') containts int64 data type. \n",
    "For the columns that contain float64 and int64 data types we can use the describe method which computes the number of non-missing values ('count'), the mean, standard deviation, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c5ab62e-7aa9-4072-a816-d8422a35712f",
   "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>sid</th>\n",
       "      <th>profile_id</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>n_posts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.509586e+06</td>\n",
       "      <td>4.477139e+06</td>\n",
       "      <td>3.452771e+06</td>\n",
       "      <td>3.452771e+06</td>\n",
       "      <td>3.452771e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.255992e+06</td>\n",
       "      <td>3.083302e+09</td>\n",
       "      <td>1.405641e+03</td>\n",
       "      <td>4.739899e+03</td>\n",
       "      <td>4.095414e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.301807e+06</td>\n",
       "      <td>4.266575e+10</td>\n",
       "      <td>8.113473e+03</td>\n",
       "      <td>1.939087e+05</td>\n",
       "      <td>1.041940e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.186000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.128596e+06</td>\n",
       "      <td>2.735177e+08</td>\n",
       "      <td>3.560000e+02</td>\n",
       "      <td>1.590000e+02</td>\n",
       "      <td>2.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.255992e+06</td>\n",
       "      <td>1.649248e+09</td>\n",
       "      <td>7.540000e+02</td>\n",
       "      <td>4.060000e+02</td>\n",
       "      <td>1.200000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.383389e+06</td>\n",
       "      <td>5.514691e+09</td>\n",
       "      <td>1.573000e+03</td>\n",
       "      <td>9.740000e+02</td>\n",
       "      <td>4.200000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.510785e+06</td>\n",
       "      <td>9.001013e+13</td>\n",
       "      <td>4.191080e+06</td>\n",
       "      <td>2.854576e+08</td>\n",
       "      <td>1.837300e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sid    profile_id     following     followers       n_posts\n",
       "count  4.509586e+06  4.477139e+06  3.452771e+06  3.452771e+06  3.452771e+06\n",
       "mean   2.255992e+06  3.083302e+09  1.405641e+03  4.739899e+03  4.095414e+02\n",
       "std    1.301807e+06  4.266575e+10  8.113473e+03  1.939087e+05  1.041940e+03\n",
       "min    1.186000e+03  4.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00\n",
       "25%    1.128596e+06  2.735177e+08  3.560000e+02  1.590000e+02  2.200000e+01\n",
       "50%    2.255992e+06  1.649248e+09  7.540000e+02  4.060000e+02  1.200000e+02\n",
       "75%    3.383389e+06  5.514691e+09  1.573000e+03  9.740000e+02  4.200000e+02\n",
       "max    4.510785e+06  9.001013e+13  4.191080e+06  2.854576e+08  1.837300e+05"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ec0987-8075-4618-a3c6-a0e147ca2ace",
   "metadata": {},
   "source": [
    "The first row of the above table gives us the number of non-missing values in the corresponding columns. The other rows of the above table are only relevant, in this case, for the 'following', 'followers' and 'n_posts' columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f4e02f9-fca3-4377-b1fc-c0599c398cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid                          0\n",
       "profile_id               32447\n",
       "profile_name                 0\n",
       "firstname_lastname      288465\n",
       "description            2055996\n",
       "following              1056815\n",
       "followers              1056815\n",
       "n_posts                1056815\n",
       "url                    3639312\n",
       "cts                     438488\n",
       "is_business_account    1064263\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace34042-af3f-4a36-ac98-2735a6e284a3",
   "metadata": {},
   "source": [
    "The above table shows the number of missing values in the dataframe, for each column. The column with more missing values is the 'url' column with more than 3.6 million missing values, followed by the 'description' column with more than 2 million missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "55ba3e9a-72a8-496a-91e5-b41eb5b835ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10689416\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c39d6df-e710-43eb-b48a-661d590c9894",
   "metadata": {},
   "source": [
    "The number above represents the total number of missing values in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f3e1550-791a-41d0-9941-44d37951de42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.215\n"
     ]
    }
   ],
   "source": [
    "print(round(df.isnull().sum().sum()/df.size , 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6da85e5-094d-46af-ad91-dabc7043de54",
   "metadata": {},
   "source": [
    "Finally the above number represents the ratio of missing values with respect to the total number of values in the dataframe. In this case we have around 22% of missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda65826-efab-449c-afc6-785b688dc70d",
   "metadata": {},
   "source": [
    "###  instagram_locations.csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "606d9831-5cc1-4757-be72-1db36d181e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('instagram_locations.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "79097915-1360-4126-8593-0d9837239fb7",
   "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>sid</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>street</th>\n",
       "      <th>zip</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>cd</th>\n",
       "      <th>phone</th>\n",
       "      <th>aj_exact_city_match</th>\n",
       "      <th>...</th>\n",
       "      <th>dir_city_name</th>\n",
       "      <th>dir_city_slug</th>\n",
       "      <th>dir_country_id</th>\n",
       "      <th>dir_country_name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>primary_alias_on_fb</th>\n",
       "      <th>slug</th>\n",
       "      <th>website</th>\n",
       "      <th>cts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>719981</td>\n",
       "      <td>110296492939207</td>\n",
       "      <td>Playa de Daimuz - Valencia, España</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.139475</td>\n",
       "      <td>38.974391</td>\n",
       "      <td>daimuzplaya</td>\n",
       "      <td>playa-de-daimuz-valencia-espana</td>\n",
       "      <td>https://es.wikipedia.org/wiki/Daimuz</td>\n",
       "      <td>2019-05-29 01:21:29.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>719983</td>\n",
       "      <td>274391278</td>\n",
       "      <td>Nová Vieska</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nová Vieska</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>Kis-Újfalu</td>\n",
       "      <td>kis-ujfalu</td>\n",
       "      <td>SK</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>18.466667</td>\n",
       "      <td>47.866667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nova-vieska</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-29 01:21:38.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>719985</td>\n",
       "      <td>148885595789195</td>\n",
       "      <td>Everest Today</td>\n",
       "      <td>Himalayas</td>\n",
       "      <td>+977</td>\n",
       "      <td>Kathmandu, Nepal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>Pasupati</td>\n",
       "      <td>pasupati</td>\n",
       "      <td>NP</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>85.330150</td>\n",
       "      <td>27.701960</td>\n",
       "      <td>EverestToday</td>\n",
       "      <td>everest-today</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-29 01:21:46.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>719987</td>\n",
       "      <td>263258277</td>\n",
       "      <td>BULAC - Bibliothèque universitaire des langues...</td>\n",
       "      <td>65, rue des Grands-Moulins</td>\n",
       "      <td>75013</td>\n",
       "      <td>Paris, France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FR</td>\n",
       "      <td>01 81 69 18 00</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>13ème Arrondissement Paris</td>\n",
       "      <td>13eme-arrondissement-paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>2.375995</td>\n",
       "      <td>48.827240</td>\n",
       "      <td>BULAC.Paris</td>\n",
       "      <td>bulac-bibliotheque-universitaire-des-langues-e...</td>\n",
       "      <td>www.bulac.fr</td>\n",
       "      <td>2019-05-29 01:21:54.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>326443</td>\n",
       "      <td>406147529857708</td>\n",
       "      <td>ABC Cable Networks Group</td>\n",
       "      <td>3800 W Alameda Ave</td>\n",
       "      <td>91505</td>\n",
       "      <td>Burbank, California</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>(818) 569-7500</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-118.341864</td>\n",
       "      <td>34.153265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abc-cable-networks-group</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-04-02 15:22:55.703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sid               id                                               name  \\\n",
       "0  719981  110296492939207                 Playa de Daimuz - Valencia, España   \n",
       "1  719983        274391278                                        Nová Vieska   \n",
       "2  719985  148885595789195                                      Everest Today   \n",
       "3  719987        263258277  BULAC - Bibliothèque universitaire des langues...   \n",
       "4  326443  406147529857708                           ABC Cable Networks Group   \n",
       "\n",
       "                       street    zip                 city region   cd  \\\n",
       "0                         NaN    NaN                  NaN    NaN  NaN   \n",
       "1                         NaN    NaN          Nová Vieska    NaN   SK   \n",
       "2                   Himalayas   +977     Kathmandu, Nepal    NaN   NP   \n",
       "3  65, rue des Grands-Moulins  75013        Paris, France    NaN   FR   \n",
       "4          3800 W Alameda Ave  91505  Burbank, California    NaN   US   \n",
       "\n",
       "            phone aj_exact_city_match  ...               dir_city_name  \\\n",
       "0             NaN               False  ...                         NaN   \n",
       "1             NaN                True  ...                  Kis-Újfalu   \n",
       "2             NaN               False  ...                    Pasupati   \n",
       "3  01 81 69 18 00               False  ...  13ème Arrondissement Paris   \n",
       "4  (818) 569-7500               False  ...                         NaN   \n",
       "\n",
       "                dir_city_slug dir_country_id dir_country_name         lat  \\\n",
       "0                         NaN            NaN              NaN   -0.139475   \n",
       "1                  kis-ujfalu             SK         Slovakia   18.466667   \n",
       "2                    pasupati             NP            Nepal   85.330150   \n",
       "3  13eme-arrondissement-paris             FR           France    2.375995   \n",
       "4                         NaN            NaN              NaN -118.341864   \n",
       "\n",
       "         lng primary_alias_on_fb  \\\n",
       "0  38.974391         daimuzplaya   \n",
       "1  47.866667                 NaN   \n",
       "2  27.701960        EverestToday   \n",
       "3  48.827240         BULAC.Paris   \n",
       "4  34.153265                 NaN   \n",
       "\n",
       "                                                slug  \\\n",
       "0                    playa-de-daimuz-valencia-espana   \n",
       "1                                        nova-vieska   \n",
       "2                                      everest-today   \n",
       "3  bulac-bibliotheque-universitaire-des-langues-e...   \n",
       "4                           abc-cable-networks-group   \n",
       "\n",
       "                                website                      cts  \n",
       "0  https://es.wikipedia.org/wiki/Daimuz  2019-05-29 01:21:29.987  \n",
       "1                                   NaN  2019-05-29 01:21:38.037  \n",
       "2                                   NaN  2019-05-29 01:21:46.295  \n",
       "3                          www.bulac.fr  2019-05-29 01:21:54.355  \n",
       "4                                   NaN  2019-04-02 15:22:55.703  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad72cad2-494e-4c4f-994d-34153da5ecfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1022658, 23)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7945de-cd54-47b2-9cb7-879c0091cb45",
   "metadata": {},
   "source": [
    "This dataframe has 1022658 rows and 23 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4776c236-5fce-4341-afa5-ac3dc93c96c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23521134\n"
     ]
    }
   ],
   "source": [
    "print(df.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "719fc4d3-929b-4cb5-bd7d-b8f997db17f4",
   "metadata": {},
   "source": [
    "This dataframe contains 23521134 elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2c4d7113-559d-4e14-b31e-8e257a3cc586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['sid', 'id', 'name', 'street', 'zip', 'city', 'region', 'cd', 'phone',\n",
      "       'aj_exact_city_match', 'aj_exact_country_match', 'blurb', 'dir_city_id',\n",
      "       'dir_city_name', 'dir_city_slug', 'dir_country_id', 'dir_country_name',\n",
      "       'lat', 'lng', 'primary_alias_on_fb', 'slug', 'website', 'cts'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a4e7244-88b6-49ae-a865-5aba91dc404d",
   "metadata": {},
   "source": [
    "These are the names of the columns in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5dc103a0-1c45-4d9f-983c-b3788e1270c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1022658 entries, 0 to 1022657\n",
      "Data columns (total 23 columns):\n",
      " #   Column                  Non-Null Count    Dtype  \n",
      "---  ------                  --------------    -----  \n",
      " 0   sid                     1022658 non-null  int64  \n",
      " 1   id                      1022658 non-null  int64  \n",
      " 2   name                    1022658 non-null  object \n",
      " 3   street                  715704 non-null   object \n",
      " 4   zip                     715579 non-null   object \n",
      " 5   city                    937166 non-null   object \n",
      " 6   region                  1760 non-null     object \n",
      " 7   cd                      939010 non-null   object \n",
      " 8   phone                   601688 non-null   object \n",
      " 9   aj_exact_city_match     1000510 non-null  object \n",
      " 10  aj_exact_country_match  1000510 non-null  object \n",
      " 11  blurb                   406705 non-null   object \n",
      " 12  dir_city_id             495698 non-null   object \n",
      " 13  dir_city_name           495698 non-null   object \n",
      " 14  dir_city_slug           495221 non-null   object \n",
      " 15  dir_country_id          495628 non-null   object \n",
      " 16  dir_country_name        495698 non-null   object \n",
      " 17  lat                     1016495 non-null  float64\n",
      " 18  lng                     1016495 non-null  float64\n",
      " 19  primary_alias_on_fb     425531 non-null   object \n",
      " 20  slug                    941668 non-null   object \n",
      " 21  website                 623262 non-null   object \n",
      " 22  cts                     1022658 non-null  object \n",
      "dtypes: float64(2), int64(2), object(19)\n",
      "memory usage: 179.5+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410d3625-d2bd-45ce-9e89-8f47b2b60bbe",
   "metadata": {},
   "source": [
    "The above table summarizes the datatype contained in each column of the dataframe and the number or non-missing values in aech column. The column with the lowest number of Non-Null Count is the 'region' column, with only 1760 elements. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "266e6ff0-df2e-4fae-891b-049b318e0462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sid                             0\n",
      "id                              0\n",
      "name                            0\n",
      "street                     306954\n",
      "zip                        307079\n",
      "city                        85492\n",
      "region                    1020898\n",
      "cd                          83648\n",
      "phone                      420970\n",
      "aj_exact_city_match         22148\n",
      "aj_exact_country_match      22148\n",
      "blurb                      615953\n",
      "dir_city_id                526960\n",
      "dir_city_name              526960\n",
      "dir_city_slug              527437\n",
      "dir_country_id             527030\n",
      "dir_country_name           526960\n",
      "lat                          6163\n",
      "lng                          6163\n",
      "primary_alias_on_fb        597127\n",
      "slug                        80990\n",
      "website                    399396\n",
      "cts                             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99cd2742-c1b1-464f-bdeb-133c519ae407",
   "metadata": {},
   "source": [
    "The above table shows the number of missing values in the dataframe. As seen before, the 'region' column is the one containing the least amount of non-missing data. The 'sid', 'id', 'name' and 'cts' columns, on the other hand, contain zero missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "095ef11b-bea3-40cd-a53c-dd9123547cf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6610476\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a9ce35-5745-473c-8ec3-0a557692f3ec",
   "metadata": {},
   "source": [
    "This is the total number of missing values in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "84665c06-061c-4eca-8363-5b2848b2b6ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.281\n"
     ]
    }
   ],
   "source": [
    "print(round(df.isnull().sum().sum()/df.size , 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d4cda0-df9b-4506-b65b-86a22cb8fdd8",
   "metadata": {},
   "source": [
    "And finally this is the ratio of missing values over the total number of elements of the dataframe. For the 'instagram_locations.csv' dataframe we have a percentage of missing values of aroung 28%, higher than the one of 'instagram_profiles.csv'. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4da219-4151-4550-86ed-b732da14bf9f",
   "metadata": {},
   "source": [
    "###  'instagram_posts.csv' file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffcaa4e7-70e0-4f1b-8ff6-d541443e84d0",
   "metadata": {},
   "source": [
    "Since the dataframe is very large, we can get a first idea of what it contains by loading the first 500000 rows, for example, and printing the head of the dataframe as well as the column names:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f588831a-38bd-4f4c-9d17-6c52ebe0013f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>sid_profile</th>\n",
       "      <th>post_id</th>\n",
       "      <th>profile_id</th>\n",
       "      <th>location_id</th>\n",
       "      <th>cts</th>\n",
       "      <th>post_type</th>\n",
       "      <th>description</th>\n",
       "      <th>numbr_likes</th>\n",
       "      <th>number_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28370919</td>\n",
       "      <td>3496776</td>\n",
       "      <td>BXdjjUlgcgq</td>\n",
       "      <td>2237947779</td>\n",
       "      <td>1.022366e+15</td>\n",
       "      <td>2017-08-06 20:06:57.000</td>\n",
       "      <td>2</td>\n",
       "      <td>Wreckloose! Deevalley bike park laps on the @i...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28370932</td>\n",
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       "      <td>BVg0pbolYBC</td>\n",
       "      <td>5579335020</td>\n",
       "      <td>4.574268e+14</td>\n",
       "      <td>2017-06-19 09:31:16.000</td>\n",
       "      <td>1</td>\n",
       "      <td>🙌🏼 believe in ya dreams 🙌🏼 just like I believe...</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28370933</td>\n",
       "      <td>-1</td>\n",
       "      <td>BRgkjcXFp3Q</td>\n",
       "      <td>313429634</td>\n",
       "      <td>4.574268e+14</td>\n",
       "      <td>2017-03-11 20:05:03.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#meraviglia #incensi #the #candele #profumo #a...</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28370934</td>\n",
       "      <td>-1</td>\n",
       "      <td>BKTKeNhjEA7</td>\n",
       "      <td>1837592700</td>\n",
       "      <td>4.574268e+14</td>\n",
       "      <td>2016-09-13 16:27:16.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#teatime #scorpion #friends #love #mountains #...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28370935</td>\n",
       "      <td>-1</td>\n",
       "      <td>8-NQrvoYLX</td>\n",
       "      <td>1131527143</td>\n",
       "      <td>4.574268e+14</td>\n",
       "      <td>2015-10-18 10:19:27.000</td>\n",
       "      <td>1</td>\n",
       "      <td>thE sky gavE mE a #constEllation</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sid  sid_profile      post_id  profile_id   location_id  \\\n",
       "0  28370919      3496776  BXdjjUlgcgq  2237947779  1.022366e+15   \n",
       "1  28370932           -1  BVg0pbolYBC  5579335020  4.574268e+14   \n",
       "2  28370933           -1  BRgkjcXFp3Q   313429634  4.574268e+14   \n",
       "3  28370934           -1  BKTKeNhjEA7  1837592700  4.574268e+14   \n",
       "4  28370935           -1   8-NQrvoYLX  1131527143  4.574268e+14   \n",
       "\n",
       "                       cts  post_type  \\\n",
       "0  2017-08-06 20:06:57.000          2   \n",
       "1  2017-06-19 09:31:16.000          1   \n",
       "2  2017-03-11 20:05:03.000          1   \n",
       "3  2016-09-13 16:27:16.000          1   \n",
       "4  2015-10-18 10:19:27.000          1   \n",
       "\n",
       "                                         description  numbr_likes  \\\n",
       "0  Wreckloose! Deevalley bike park laps on the @i...           80   \n",
       "1  🙌🏼 believe in ya dreams 🙌🏼 just like I believe...           25   \n",
       "2  #meraviglia #incensi #the #candele #profumo #a...            9   \n",
       "3  #teatime #scorpion #friends #love #mountains #...            4   \n",
       "4                   thE sky gavE mE a #constEllation            8   \n",
       "\n",
       "   number_comments  \n",
       "0                0  \n",
       "1                1  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('instagram_posts.csv', sep = '\\t', nrows = 500000)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1471ebba-e4e2-4721-9f03-c1799d4cf5fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['sid', 'sid_profile', 'post_id', 'profile_id', 'location_id', 'cts',\n",
      "       'post_type', 'description', 'numbr_likes', 'number_comments'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "13d70c69-178a-4649-92c4-b3c8fb9224b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b459afae-7583-4a05-b06e-046f70eff0f3",
   "metadata": {},
   "source": [
    "This dataframe has 10 columns, and we are now considering the first 500000 rows. \n",
    "If we work with chunks we can compute the total number of elements in the dataframe, as well as the ratio of null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c35f50f-83ee-478c-85b3-8c4c6216c9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of values is:  427101970 \n",
      "The number of null values is:  19554876 \n",
      " The ratio of null values over total number of values is : 0.046\n"
     ]
    }
   ],
   "source": [
    "null_values = 0\n",
    "tot_values = 0\n",
    "for chunk in pd.read_csv('instagram_posts.csv', sep='\\t', chunksize = 10 ** 6):\n",
    "    null_values += chunk.isnull().sum().sum()\n",
    "    tot_values += chunk.size\n",
    "    \n",
    "print(\"The total number of values is: \", tot_values, '\\nThe number of null values is: ', null_values, \n",
    "     '\\n The ratio of null values over total number of values is :', round(null_values/tot_values, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb269d2-b6a9-45b6-906d-3aa739dd2f8b",
   "metadata": {},
   "source": [
    "The ratio of null values in this dataset is the lowest of the three datasets, being only 4.6%. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f76e381-effa-4739-bcdc-71c94387ad77",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# RQ2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8dbb946-f4a9-495b-9297-82144030207c",
   "metadata": {},
   "source": [
    "### Plot the number of posts for each profile in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8ccc1cc6-a974-4e42-9911-e479f3cf2255",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('instagram_profiles.csv', sep='\\t', usecols = ['profile_id', 'profile_name', 'n_posts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a83800fe-58ac-45f2-a503-5c2c2d4def70",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile_id</th>\n",
       "      <th>profile_name</th>\n",
       "      <th>n_posts</th>\n",
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       "      <td>1.996648e+08</td>\n",
       "      <td>jasem33312</td>\n",
       "      <td>183730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571453</th>\n",
       "      <td>8.563367e+09</td>\n",
       "      <td>koenigpic</td>\n",
       "      <td>142428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4338921</th>\n",
       "      <td>1.273455e+09</td>\n",
       "      <td>rudyanto62</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>9.813508e+06</td>\n",
       "      <td>hometown_ni</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1.607309e+09</td>\n",
       "      <td>dwfitfiber_thailand</td>\n",
       "      <td>117262.0</td>\n",
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       "    <tr>\n",
       "      <th>2207897</th>\n",
       "      <td>1.087614e+09</td>\n",
       "      <td>waxking911</td>\n",
       "      <td>117099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4313908</th>\n",
       "      <td>1.603890e+09</td>\n",
       "      <td>gamt_official</td>\n",
       "      <td>113780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4219476</th>\n",
       "      <td>2.181730e+08</td>\n",
       "      <td>collarosedw_official</td>\n",
       "      <td>113182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4236057</th>\n",
       "      <td>2.855497e+08</td>\n",
       "      <td>gamt_authentic</td>\n",
       "      <td>112777.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1631526</th>\n",
       "      <td>8.312603e+09</td>\n",
       "      <td>ivankkkka.trmp</td>\n",
       "      <td>112723.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           profile_id          profile_name   n_posts\n",
       "3990313  1.996648e+08            jasem33312  183730.0\n",
       "2571453  8.563367e+09             koenigpic  142428.0\n",
       "4338921  1.273455e+09            rudyanto62  126340.0\n",
       "3942129  9.813508e+06           hometown_ni  125220.0\n",
       "4219642  1.607309e+09   dwfitfiber_thailand  117262.0\n",
       "2207897  1.087614e+09            waxking911  117099.0\n",
       "4313908  1.603890e+09         gamt_official  113780.0\n",
       "4219476  2.181730e+08  collarosedw_official  113182.0\n",
       "4236057  2.855497e+08        gamt_authentic  112777.0\n",
       "1631526  8.312603e+09        ivankkkka.trmp  112723.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('n_posts', ascending = False).head(10) #sort by number of posts in descending order and show the head \n",
    "                                                      #of the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2038ca-0444-439d-a75a-33650f6ac5e7",
   "metadata": {},
   "source": [
    "### What posts have the highest number of likes? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64e04fb3-4e62-42db-8c84-87a7cf19015a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('instagram_posts.csv', sep='\\t', usecols = ['profile_id', 'post_id', 'numbr_likes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09e7d86b-69c9-4776-b364-8adc477bc26b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>30850564</th>\n",
       "      <td>Bw7vqLrFkHi</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              post_id    profile_id  numbr_likes\n",
       "19624085  Bt3yaXmAM0d  4.906778e+07    8822952.0\n",
       "19623988  BuwffB7g8cK  4.906778e+07    5447066.0\n",
       "19624082  Buc1gyZAaMQ  4.906778e+07    5116398.0\n",
       "19623986  BvcukE8AMuG  4.906778e+07    4271466.0\n",
       "39701024  BqSLFVBFLfq  4.275539e+08    4264745.0\n",
       "6293907   BwTSXBKg7gN  1.518284e+09    3586546.0\n",
       "19624086  Btw8pFXAvQm  4.906778e+07    3558599.0\n",
       "19802016  BvC3n6_gqin  2.153681e+09    3533326.0\n",
       "30850564  Bw7vqLrFkHi  2.031501e+07    3340297.0\n",
       "39812576  Bw2gTH1AiDA  3.530696e+07    3311246.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('numbr_likes', ascending = False).head(10)  #sort by number of likes and show the head of the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1072d7a1-d20e-4057-aa6e-e15d64519577",
   "metadata": {},
   "source": [
    "### What posts have the most and the least number of comments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d6c00a4-2a9c-4b4d-a123-0665ada5935e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>31689573</th>\n",
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       "    <tr>\n",
       "      <th>14257307</th>\n",
       "      <td>Bzqb95QI_1E</td>\n",
       "      <td>1.959418e+08</td>\n",
       "      <td>166198.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              post_id    profile_id  number_comments\n",
       "41873060  Bx2sPAjHviO  1.635670e+09         488953.0\n",
       "12164781  BuGjhWAhAW5  4.282994e+08         313997.0\n",
       "33261751  BJ-rkpTgn1N  5.533645e+07         257488.0\n",
       "11149127  BMtisIxjhtT  5.697152e+06         214365.0\n",
       "9383817   BzOOWj3DfZK  1.062916e+09         189888.0\n",
       "5427268   BoQoM-JFkPA  1.440949e+08         180635.0\n",
       "25257169  Bwarws1lCok  1.666815e+07         173988.0\n",
       "6220011   Bt4qlVDAZMH  1.797807e+08         170836.0\n",
       "31689573  Bs9NhQkAcJA  6.760538e+06         166919.0\n",
       "14257307  Bzqb95QI_1E  1.959418e+08         166198.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('instagram_posts.csv', sep='\\t', usecols = ['profile_id', 'post_id', 'number_comments'])\n",
    "\n",
    "df.sort_values('number_comments', ascending = False).head(10)      #sort by number of comments in descending order and show the\n",
    "                                                                   #head of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f26b972c-e847-4e60-ad2a-3a0cbba1a33c",
   "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>post_id</th>\n",
       "      <th>profile_id</th>\n",
       "      <th>number_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13653251</th>\n",
       "      <td>BweQx5xh6R_</td>\n",
       "      <td>6.735518e+09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32092480</th>\n",
       "      <td>BsESs02BToJ</td>\n",
       "      <td>1.413730e+09</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <th>32092484</th>\n",
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       "    <tr>\n",
       "      <th>13653242</th>\n",
       "      <td>Bxo-h05pvYy</td>\n",
       "      <td>1.541710e+07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13653241</th>\n",
       "      <td>ByUB81Spb_0</td>\n",
       "      <td>1.541710e+07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13653240</th>\n",
       "      <td>ByWM8i3pH-9</td>\n",
       "      <td>1.541710e+07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32092488</th>\n",
       "      <td>BxCNk_NnNbC</td>\n",
       "      <td>5.908247e+08</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13653236</th>\n",
       "      <td>Bv_Lkonl1dN</td>\n",
       "      <td>1.109772e+10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BXdjjUlgcgq</td>\n",
       "      <td>2.237948e+09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              post_id    profile_id  number_comments\n",
       "13653251  BweQx5xh6R_  6.735518e+09              0.0\n",
       "32092480  BsESs02BToJ  1.413730e+09              0.0\n",
       "13653249  ByU2tWQBQzu  6.735518e+09              0.0\n",
       "32092484  Bu53yP2HtO9  3.035704e+09              0.0\n",
       "13653242  Bxo-h05pvYy  1.541710e+07              0.0\n",
       "13653241  ByUB81Spb_0  1.541710e+07              0.0\n",
       "13653240  ByWM8i3pH-9  1.541710e+07              0.0\n",
       "32092488  BxCNk_NnNbC  5.908247e+08              0.0\n",
       "13653236  Bv_Lkonl1dN  1.109772e+10              0.0\n",
       "0         BXdjjUlgcgq  2.237948e+09              0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna().sort_values('number_comments', ascending = False).tail(10)   #and then show the tail, dropping the NaN values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b41f23-6c8c-46c0-844e-7db952e0ba2d",
   "metadata": {},
   "source": [
    "### How many posts include tagged locations, and how many do not? Show it using an appropriate chart and comment your results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d764b043-6440-458a-aa6b-4a35e1dd2605",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('instagram_posts.csv', sep='\\t', usecols = ['profile_id', 'post_id', 'location_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "edfcecd4-7f6a-4975-aecb-268e41066b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29737425"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location_id'].count()        #NUMBER OF NOT NAN IN 'LOCATION_ID' COLUMN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a49face-6480-4813-afa6-132a628ae1a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12972772"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location_id'].isna().sum()   #NUMBER OF NAN IN 'LOCATION_ID' COLUMN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de17763c-7614-45be-a0c1-8c022ec3c591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0K9n7fo3AoBFdnre1lp8Dg0bTtHwuw84oJbZtDbEdG3p5y1QrVxeVVgxxuwjlPRp4qrVTM8buPyaQN8y1u5f7cwYRyBtKrHY9AOE1S0gbOo5E0y4AjLGoW/AYudNO6vK8re2a/0/yj/o2WHEwlv2k+DDxSO9tlGorF/iF20Uo79HLEhQAweKS4cAfB5/44+MDOYPHullLdMtqal+6DZOIEygYwZCTf0LT8tdo+KDCrnXyERR8bTYiQryhltqXbmP4N2/ocF5/pn0j7tCnC4lu/YyCo84HYOfc+2n+7APSCosYduq17mysd4WAiTVlsza7XYjyDg08BUCwuOS7mUUHnZ03/dSvNp2U6h131JTNusLtIpR36CFNRbC4ZDRwVPY+M/UeZqov/aCotGJc55MplRwaeArgtMzx04b5g/mj3C5EeUo6cKXbRSjv0MDzuGBxyVjg0ODkw6a4XYvypO8WlVZku12E8gYNPHV82pBx2f7cYZPcLkR5UgEw2+0ilDdo4HmYc93dUdn7HD3BrevulAKuLCqt0D9A1es08LztSEkPpqUNHT/V7UKUp01BB5VWfUADz6OCxSVpwInZex9VqHcyV/3ApW4XoFKfBp53HQDkZIycvI/bhSgFnFRUWjHY7SJUatPA866vB/KH48sepNdBqf4gDfim20Wo1KaB50FOZ5V9sooPG6udVVQ/8m23C1CpTQPPm6YCpBfutb/bhSjVylE68orqTRp4HhMsLhHguLTCCX5/Vu5wt+tRqhUB/tvtIlTq0sDznkJgXFbRQa7eEUGpDpzqdgEqdWngec80wEobNGqi24Uo1Y7DikorCtwuQqUmDTzvOcyXkd3syy4Y43YhSrXDDxzvdhEqNWngeUiwuCQXGJ9ZNG2oiE9fe9Vf6agrqlfom563TARIL9xLD2eq/uxEtwtQqUkDz1sOAGKB/OEaeKo/G1NUWqEjAKmk08DzCOdyhBn+3CFhX0ZwkNv1KNWJErcLUKlHA887hgPZGSMnD3O7EKW6YIbbBajUo4HnHaMBAoNGj3K7EKW6QANPJZ0GnndMAKxA7lANPDUQHFhUWqG3rVJJpYHnHfsADb7sAg08NRCkAwe6XYRKLRp4HhAsLgkA4wP5w8UXSM92ux6lumia2wWo1KKB5w0jAAkMHqO9M9VAMsntAlRq0cDzhuEAgdwhGnhqINHrRVVSaeB5w1AAf7BAA08NJNrCU0mlgecNY4CILyt3sNuFKNUN2sJTSaWB5w2jgGZfZo628NRAklNUWlHodhEqdWjgpThnSLGRQFjSs/LdrkepbtIbFauk0cBLfVnOV0z8gSy3i1Gqm/QwvEoaDbzUlwskJCM7Xe+BpwYgDTyVNPoGmPoyAfxZeZluF6JUD2jgqaTRwEt9WQC+rFw9nKkGIu1opZJGAy/1ZQLiy8jWFp4aiLSFp5JGAy/1ZQHiS8/McLsQpXpAx35VSaOBl/qyAAFxuw6lesLvdgEqdWjgpb4MwALjdh1K9YQGnkqagNsFKDXQXRu+veainMrhbteRSiyDL9NvwgkJRGC72+WoFKGBl/oMgDHaxOstZ6QvGpkdsPQcafJlpBHTo1AqafSPyRv0BF4vmd68sGF0MKZh13tibhegUocGXuqzW3ZWPOFyHSnpIt8LO9yuIcVF3S5ApQ4NvNRnAKxIU9jtQlKNmDjHBlcPcbuOFKctPJU0GnipLwqI1dzQ7HYhqea4yBvbctJMjtt1pLgmtwtQqUMDL/U1A1YiVKeBl2QX+uboPu19a90uQKUODbzU1wwYq7k+bIzRnppJkmaFrcOyN+ilCL1vjdsFqNShgZf6vmiFJGLaIkmSUyMvbcnwo70ze58GnkoaDbzUF2p5YMWjej4kSc4PzNVer73MGGMB692uQ6UODbzU14xzHZ6JNO10uZaUkJ2oj0/L3j7C7TpSnYhs4vo67aWpkkYDL/U14rzOiXDjLndLSQ3nRss3B3w6SlEf0MOZKqk08FJcqLoyCtQD6VaoTi+SToJz0+fr/03f0B6aKqn0H9cbtgCZ8fpttW4XMtANjW+OTAnW6+HMvqEtPJVUGnjesB4Ixnds0GHn99C3Y89u8Yno/03f0MBTSaX/uN6wBkiP7dxQZxJxHZtwD5yV8W6m2zV4yGduF6BSiwaeN9QCFsaYRKhuo9vFDFTjoqtC47KaC92uwwucSxLecbsOlVo08LxhMy09NRu2b3C5lgFrtvXsNhG901IfWc71dbvcLkKlFg08b6jFvh4vLbZzowZeD52SuSTX7Rq8QkTedLsGlXo08DwgVF1pgE+BvOjmlRp4PbB/ZEn98Mz4YLfr8JB5bhegUo8Gnnd8BGTHd22qt2KRRreLGWhmm3IdpaZvaeCppNPA8451ODeDTTRsr3G3lIHnhGDVILdr8ApjTBXX1211uw6VejTwvGMDzpia0W01K12uZUA5Ijy/Nj/dynO7Dq/Q83eqt2jgeUSourIB+wL03PCaxSv11nhdN1te1EPAfUsPZ6peoYHnLe8CBYmG2iaruWGT28UMBD4TN0dn1wxzuw6P0Rae6hUaeN7yccuD2M4NelizC06MvLI1GCDodh1eYRmzmuvr9B54qldo4HnLGiAGBKIbq6rdLmYg+I7vlYjbNXiJT+RRt2tQqUsDz0NC1ZVxYDEwOLx26Tor2lznckn9WobVZM3I3qx3RuhbD7pdgEpdGnje8z6QBRDbvnaZy7X0a2dGXtic7ifd7Tq8Ipow73J93Sq361CpSwPPez4CEoC/efWipW4X05/9d9ob2pW1DwV83Ot2DSq1aeB5TKi6sglYBAyNblm1LdFcv9ntmvqj/HhtbP/gDj2c2UcsY8I+kSfcrkOlNg08b3oLyACIblmtrbx2fCv23Ga/T/xu1+EVcYtyrq+rd7sOldo08LypCggD6aFPFyw1lpVwu6D+5pz0hQG3a/CSdL/c53YNKvVp4HlQqLoyhj2axbBEw/am+M4NK9yuqT8ZHtsQnhhs1MOZfSRumc3Aa27XoVKfBp53LQQCAKHqd/TO0q1cGH96q0/v9NqXHuT6OsvtIlTq08DzrrVANTA4suHjTfHGHWvdLqi/OD3zgyy3a/AKY4wV8OnhTNU3NPA8yrkpbAWQCxBes0RbecDE6CeNY7IiOnZmHwnFeFavvVN9RQPP21YAO4DsUNVbn1iRkOdvcnqx9Vyt2zV4hWWMyQjwK7frUN6hgedhzlBjFcAQjDHhNYs9f1uWk7KW9/l978Jxw6H3NnLg3Y3s99dGfvN6GIAdzYZvPNxE8e2NfOPhJnY2d3wdfMIyHPS3Rk55NPT5c794JczUuxq58Jnmz597eEmUW9/pH8ODNkR4KXBj/Sdu16G8QwNPVQJRIL1x+dwlVrhxu9sFueXg8Hu7hmQk+vzO5hl+mDs7myWX5rD4h9m8tCrOO+vjlL0V4esTAlRfkcPXJwQoe6vjoLq1Mso+Q7/4d64LGxasT7D0shwSxrBsS4LmmOHBJTEun+H+aGnGGJOdzrVu16G8RQPP45yRV/4DjMBYJrR60etu1+SW2fK8K4Npiwg56Xan0JgFsYR9a/rnquLMPjDNru3ANJ6tirc7//p6i4rqON87+Isg8wlEEwZjDM0xSPPDzQuiXHloOml+9zug1kV4IXBjvV4Oo/qUBp4CeB37QvTM0MfzPkqE6r13c1hjcVxw5RC3Vp+wDNPubqTw5ga+sVeAkjEBtjRajMy1/0VH5vrY2tR+z/2fvBTmpuMz8bXKsdwM4ex90jjob01MKPCRnyG8tzHB6Xun9cXm7FbCMokMP1e6XYfyHg08Rai6MgT8Gxhu/7xwrrsV9b1jIm9uz00zOW6t3+8TFl+aw/qf5fLuxgTLt3Zt8JvnP41RmC1MH/XVUdB+fmQGiy/N4X9PzOS61yPceEwG930Q5dwnQ/xunnvn8WqbzaNZv69f7VoByrM08FSLt4B6ILt5ZeXKeN1WT90R/ULfi6HOp+p9BZnCMeMDvLQyzvAcH5sa7FbdpgaLwuyv/ru+vTZBeVWcov9r4FtPNTP3szjfebr5S9N8uMkOz8lDfPxjSYwnvhlk+dYE1bV9P6JcNGHCueny0z5fsVJo4ClHqLoyAjwJDANo+LDiBWMl2j9plGICVsQcEVxf6Nb6tzVZ7ArbPTCbY4ZXP4uz91Afp00O8NCSGAAPLYlx+pSvDu/5x+MzWf+zXGp+kstj52Rx3IQA/zzry9fNX/d6hBuPzSBmQcLp6OkTCMV6d7vas6PZ3Jr1+3q99EO5QgNPtfYOsA4YEqtdtzOy4eP5bhfUF2ZF5mzJDJDp1vo3NRqOfaiJqXc1MuPeJr6xV4BTJqdRelQ6r6yOU3x7I6+sjlN6VAYAGxssTn6kaw3SZz+JMWOUn1G5PgoyhcPH+DngrkZE4MARfXsziJ3NZs2IHN+v+3SlSrUixug9LtUXgsUlE4HrgLXiT5MhJ115mS8j27XOHH3hidhV6w7N3TbW7TpSWdwyiUUbE8cedl+TJz5Eqf5JW3jqS0LVlauAucBok4glmj56s8LtmnpTMNGQOCh7m94ZoZdVbbfu07BTbtPAU+15BvsyhWDz6kWfRbfVvO92Qb3lnGj55jQf7vfVT2FbGq2aN2oSP3a7DqU08NRXhKorG4B/4lymUPfOk3OscFNKdjQ4L00bHb0pmjDxT7Zb5/7ohWZPdIBS/ZsGnupIJfAhMMpEm2MNi1/4tzFWSt2zbHBsa3TvYN1It+tIZZ9st27/2oNN77ldh1Kggac6EKqutIAHscfZzIls+HhTZN3ylBp27Pz4s1v8PtH/gV6yscH69MXq+DVu16FUC/1nVx0KVVfuAv4GFAK++kXPvR1v3LHG3aqS5+yMSvdHUU5RkbiJfrLdOucXr4ZT6qiAGtg08NRuhaorlwEvAWMwxtQtfPwpKxZucLuuPTU2WhMqygoNd7uOVGQZYz7YlPjNcQ81LXO7FqVa08BTXfE0sBkYlqjf1tjw4YtPGMvq+3Gpkmi29cw2EffvGpCK3lqbeOaPb0X/5HYdSrWlgac6FaquDAN3AGlAdmTdsvXNKyufd7msPXJK5mLXBopOZe9vTHzw5wXRS8qrYjqihep3NPBUl4SqKzcCd2Kfzws0LntlcWTTpwtcLqtH9o0sqx+RGUvp0WPcUF2bqLnj3ehp5VUxV+4rqFRnNPBUl4WqK5dgDzA9DpC6hY+/Gtu56WOXy+q22aZ8h9s1pJpNDdb2e96PnfbA4ugGt2tRqiMaeKq7XsC+ldA4jDG75j3073jD9gF1b7MTgh8PcruGVFIXNk3/WBK74OYFEe2kovo1DTzVLc71eQ8B1cBoE48mdr7x4GOJpp3rXS6tS0rCC3cMSrfy3a4jVYTjJvrPpbGrf/Fq+CW3a1GqMxp4qtuce+fdBmwERppoKLbzzYceSTTXb3G5tE7NlucH/CUV/UXcMtaTK2K3zFkVv8ftWpTqCg081SOh6spG4C/ATqDQaq4P75r/8MP9ecxNn4mbrwVrhrpdRyqIJkz80WWxe5/8KH6d9shUA4UGnuoxZySWP2PfWWFooqG2aee8hx7ory29b4Rf25adZrLdrmOga46ZyG2V0Yee+ij+s/KqmA4KrQYMDTy1R0LVlduBmwALGJpo2N608/X7H0g07ljrcmlf8R3/y2G3axjo6sKm8Q/zI/fMW5O4urwq1rXbrivVT+gdz1VSBItLRgLXAEFgi6RlBgZ97aJzA/mFxS6XBkCG1WQty/h+LN1Phtu1DFRbm6ydf5wfvX3VTutPGnZqINIWnkqKUHXlJuAP2Of0RplYOL7j9fsei+1Y3y+6qp8aeWmLhl3Pra2ztlw3N3LDqp3W7zXs1EClgaeSJlRdWQuUAeuBMSTi1s7X//50eN3y190+knB+2usDeuxPN32yPbHuV6+Ff76p0dxRXhWLul2PUj2lhzRV0gWLS4LA5cD+wFrACk4+Ykr2vsecJf5An9+SJze+K/5h8HICPgJ9ve6B7r0Nieqb3o78LJKgQntjqoFOA0/1imBxSTrwLeB4YAMQSS/ca2jeoWd+y5eR3afjWH6v+aH1vx40Z0xfrnOgi1sm/szH8UUPL41dU14Ve9vtepRKBg081WuCxSUCzAQuAnYBdb6s3Iz8I84/M61g+JS+qmNO4gebpmQ3juyr9Q10O5rNzr8sjLy+ZIt1Q3lVbKnb9SiVLBp4qtcFi0uKgSuBALAFIOfAE6dn7TX9RPEF0npz3YWxjeF3cq7O8OnN77rkw02J6j8viDzfEOXW8qpYytzdXinQwFN9JFhcMgT4EbAXdqeWeNrQ8YPzZpx+lj9YMLq31nt1+M61VxS8Pa63lp8qwnHT/MjS2HvPVcX/CTyiPTFVKtLAU30mWFySBpwMnAHUAbvw+X15M86cmTF6n5nSC62w+ebibWOzIsOSvdxUUl2b+OzmBdEFmxvNXcAC7ZyiUpUGnupzweKSScClwCDsDi1W+qgpI3KnnnCyP3vQ2GStZ0K0uvH1vN/onc07EE2YyBMrYu89sSL+CnBfeVVso9s1KdWb9Do81edC1ZUrgf8B5gHjgfzoxqrNtS/d/vfQpwufM/FoUg6nXWQ9uz0Zy0k1CctY726IL/tRRfiZJ1bE/wj8obOwE5GEiCwWkeUi8qSIBLuzThEpEpHz96jwry6zRkS+Mhi4iJwmIqWd1LK8B+trdL6PEpGnOpjmDRE5pJPlXC8iFzmPLxKRUR1Md6OIHN/dOp152903u5m+2/ukZX8ki4icISL7tvq5x9vfEb0uSbkiVF0ZChaX/AN4H5iNHXwbG5e9sji06t1P8qaf9vW0YUXT9+Qw58mZy/S+d60YY/i01vrkb+/HPlq5w/oIuLe8KtbVMU+bjTHTAETkEewW+i3dWH0RcD7waDfm6RFjTDlQ3ovL3wick6TFXQQsx77VVtv1/E+S1jFQnAE8D3wEvbP92sJTrglVV5pQdeVy4NfAU8BwYIQVqovsmv9wRd2Cx+6O7dr8SU+WPTXyQd2wzLje2dyxod5a89t5kf9c+0rk9ZU7rDuB33Yj7NqaD0wSkcEi8qyILBWRd0RkKoCIfM1pDS4WkQ9FJBd7BJ6jned+KiL7ici7zs9LReRLY66KyLkicovz+CoRWe08nigib7Wa9AoR+UBElonI3s40F4nIHc7j4SLyjIgscb6OcObzi8i9IrJCRF4Wkay2GykiE0RkoYi8JyK/bfX8560hEckSkcecbXgcyGo1XaOI/N5Z7zsiMtz5VSPQLCLnAIcAjzj74Us1iMiDzjQtLbYb2tnWHBF5wHluqYic3WYZX2q5icg1InK983i6U9tC7A5lLdP4ReRmZ7uXisgP2+6bNusQZ/rlTh3ntfrdz53nlohImfPc951lLxGRf4tI0HldTgNudvbFxDbb/3Xnb2mZiPxdRDJ2t186ooGnXBeqroyEqisrgF8CH2O3BgZFN1dv3fnaPY/XvfPkPfG6rSu7s8yL+c/OXih1wNnRbG29893onMsqwq8s2mjdD1xbXhWb29MhwkQkAJwELANuAD40xkzFfu3+4Ux2DfAjp0V4NNAMlALzjTHTjDF/wW4h3upMcwh2z93W5jnz4nyvFZHRwFHYgdtiuzHmYOAuZ71t3Qa8aYw5EDgYWOE8XwzcaYzZD/sa0bPbmfdW4C5jzAxgcwe75DIg5OyD3wPTW/0uG3jHWfc84PsAxpg/G2MeN8Y8BSwCvu3sl+YO1rG7bb0OqDPGHODUMLeTZbT2AHClMebwNs9/11nmDGAG8H0RmbCb5ZwFTAMOxB5o4mYRGSkiJ2G32kqcfXCTM/3TxpgZznMfA981xizAbpVf6+yLVS0LF5FM4EHgPGPMAdhHJi9rtf7O/gY+p4Gn+o1QdeVW7Deom7DfhIqAgsiGjzftePXuR+rfe+b+eMP21Z0uyFh8PevTPh3Npb9pjJr6fy2LvXHJc+EX5qyKPwr8orwq9lx5Vayph4vMEpHF2G/Qa4H7scPnYQBjzFxgiIjkA28Dt4jIlUCBMaa9e+YtBH4pIr8Axrd9szfGbAZynNbhWOxDoTOxw6914D3tfH8f+++lreOw3wgxxiSMMXXO858ZYxZ3Mu+RwL+cxw+383ucmv7pLH8p0PpC/Sj2IbrdraM72tvW44E7WyYwxnTpg57zOhUYY950nmq9fScAFzqvdyUwBPsDQkeOAv7l7N8twJvYQXk88IAxJuTUtsOZfn8RmS8iy4BvA/t1Uu4U7NfrU+fnh7D3e4vO/gY+p+fwVL8Sqq40wIpgcckNwAHAecAEoDa8dtn68NplD6ePKC4MTj6iJG3ImKni83/lb3hmZH5tXoHxXOBZxph1dWblK6vj1S9Ux7fHLRYBT5dXxTYkYfGfn8NrIdLu+VVjjCkTkQrsS1DekXY6HhhjHhWRSmAWMEdEvueEZmsLgYuBKuyQuwQ4HLi61TQR53uC7r2fRVo9TtDqUGTbUruwrI6miZkvusF3t772tLetspv1A8T5csMmswvzCXCFMWZOF+vq6Dx7R+t4EDjDGLNE7M47x/Rw+S26/DegLTzVL4WqK61QdeUS7EM2twEx7E9vQ6Obq7ftmvfQf2rn3HlL85olr1rR5rrW817oezGpvcf6u6aoqX9rbXzeT14KP3rFi+EF5VXxhXGL3wF3JCnsOjIP+xM6InIM9qGlehGZaIxZZoz5E3aLcG+gAchtmVFE9gJWG2Nuwz6UNbWD5V/jfP8QOBaItGqldcVrOIe/nHNTed2Y923s8WDB2c4OamzZB/vT/nbszpf2Sw+8DPy45QcRaXveegtQKCJDnPNepwAYY3YBdSJylDNd6+2bA1wmImnOMieLSPZuapgHnOfs32HYra93ndouEadHr4gMdqbPBTY5y2+93o72xSdAkYhMcn6+ALsV2W3awlP9Wqi6MgG8HywuWQzsg33+aF8gboV2bWlY9NzbDeJbkDXp0El5hWOODwwdN/jI4NpCN2vuC5YxZm2dqX5lVXzFC9XxuoQhgf0m8yZQXV4Vs/qgjOuBB0RkKRDC7m0L8BMRORb7E/dHwIuABcRFZAn2J/xM4DsiEsM+P3ZjO8ufj304c54xJiEi67Df/LrjKuAeEfmuU89lwKZuzPuoiFwF/LuDae7ii32wGPs16I4HgbtFpBk4vAvn8dr6HXCn0zElgX1eteUQH8aYmIjciH1o8jO+vP8uBv4uIiHskGtxH/aHyw+cVvw27HNxHXkGu+W9BLtF93PnkPRLIjINWCQiUeAF7HO91zn1rME+F9wSco8B9zqHwj/vBWuMCYvIxcCTzjnk94C7u7Jz2tILz9WAEywuGY19GOQYwA/UCVbjAfLZWYUZsbkX7hMbPX2kf9+x+TIx4JOU+lC3s9lsW7ol8dETK2Jr19WbBLAV+81qUXlVrN7l8pTq1zTw1IAVLC7Jwe4Vd1wuoan7yprxudL8GtAEMCiT9JOL0yYfMsq/7+g8KcoMfLXreX/XFDUNa+us1Su2Watf/yy+dV29ycD+JL8QuzW3uo9ac0oNeBp4asALFpdIHk3jp8rq6ZkSmwEUYh9a2Yl9zRMAU4f7hhw80j9m0mDf2NG5MmZQlhT2t7soRBMmsqHe1FTVJla/vTaxbskWq6VDhcE+9DcH+KC8KtbgaqFKDUAaeCqlnDYlTYCR2J0HZgIjsMPCYJ8Ub8A+n0R+BumHjw2M3m+Yb8z4At/YETkypi9bgZG4ad4ZNtu3h8y2TQ1m+5ItiQ1vr000JQwtHQSasbu5LwZWA9t1YGelek4DT6UsJ/zysTs+TAD2x749kThfYaDe+Q7A8GzJGpsvucOzfTnDsiV3UKbkFmRKbm6G5OSkk5udJrlZaeS0nBu0jDHGYFmGhNXO94Qh3hg19XVhU1fbbOq2NZldGxusuqpaq3a9fQ4uCJ8HnIV9Ie77wCpgox6uVCp5NPCUp5w2JS0NuwU4Bru35z7Yd22w+CIIE9jX9kRbfX0peLIC+CMJLMvs9hoowe6NmNHqu4Xd2vRhH3JdB3zqfK3t6QgoSqnOaeApT3NagXnY43jmYHeRHup8DXG+8un8At+W30urny1gB3ZPyk3Y5+B2YHfz3l5eFQu3sxylVC/RwFOqE04oBrHDMIgdbBZ2SzDR6nHb72E9JKlU/6GBp5RSyhN0aDGllFKeoIGnlFLKEzTwlFJKeYIGnlJKKU/QwFNKKeUJGnhKKaU8QQNPKaWUJ2jgKaWU8gQNPKWUUp6ggaeUUsoTNPCUUkp5ggaeUkopT9DAU0op5QkaeEoppTxBA08ppZQnaOAppZTyBA08pZRSnqCBp5RSyhM08JRSSnmCBp5SSilP0MBTSinlCRp4SimlPEEDTymllCdo4CmllPIEDTyllFKe8P8B6D01g0C/eOEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie([df['location_id'].count(), df['location_id'].isna().sum()], explode=None, \n",
    "        labels= ['Posts which included location', 'Posts which didn\\'t include location'], colors=None, \n",
    "autopct='%1.1f%%', shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e6a978-403f-4552-a5f3-4bf6f41faa93",
   "metadata": {},
   "source": [
    "69.6% of posts include a tag to the location, while 30.4% do not. This implies that a large majority of posts include a location tag. Only 3 out of 10 posts don't have a location tag. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179503a6-c553-48fe-80ed-c101f850fa9c",
   "metadata": {},
   "source": [
    "### How many posts include only photos? How many also have videos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fc9cf04d-e49c-4d52-9bf7-d6d5a7b3b9d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('instagram_posts.csv', sep='\\t', usecols = ['profile_id', 'post_id', 'post_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d5a2022e-85ca-47a4-ba8a-07a5b0772c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    41534486\n",
       "2     1134768\n",
       "3       40943\n",
       "Name: post_type, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['post_type'].value_counts()   #1: only photos. 3:also videos    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce629b5b-0b6f-40a2-a15b-b606f209b4da",
   "metadata": {},
   "source": [
    "41534486 posts include only photos, while only 40943 include also videos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d5912b-3c8d-477e-8f1b-66e850d0675e",
   "metadata": {},
   "source": [
    "### What's the percentage of business accounts vs non-business? What can you interpret regarding that percentage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "815d252f-3fd3-409a-adc2-8d7b08dae731",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('instagram_profiles.csv', sep='\\t', usecols = ['profile_id', 'profile_name', 'is_business_account'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3f55b1fb-38d2-4d41-bf1d-a64fc6e4b730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.751764\n",
       "True     0.248236\n",
       "Name: is_business_account, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['is_business_account'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fbc513-f146-4649-ba0a-c4d7c308e410",
   "metadata": {},
   "source": [
    "The majority of the accounts are non-business accounts. About 75% of Instagram accounts are personal accounts, while slightly less that 25% of them is made up of business accounts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c41cbe-c61e-4b53-a1e8-e5e60e536604",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# RQ 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "77e09ae2-8d8a-43bb-aeeb-0e9c584bcf23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\f",
      "\n"
     ]
    }
   ],
   "source": [
    "clear(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e61517b-b036-4fec-8a4a-335f9f191991",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('instagram_posts.csv', sep='\\t', usecols = ['cts'], parse_dates=['cts'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14bf6db1-1e0b-4943-98f0-c0478497e960",
   "metadata": {
    "tags": []
   },
   "source": [
    "## What is the most common time in which users publish their posts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e186e918-d45a-4874-ad21-3bf2087135d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cts\n",
       "21:00:09    928\n",
       "21:00:04    910\n",
       "20:00:12    906\n",
       "18:00:29    895\n",
       "20:00:06    894\n",
       "17:00:25    880\n",
       "20:00:18    879\n",
       "21:00:02    878\n",
       "18:00:06    877\n",
       "21:00:11    872\n",
       "19:00:27    870\n",
       "21:00:19    870\n",
       "21:00:05    869\n",
       "18:00:11    869\n",
       "20:00:13    866\n",
       "18:00:02    865\n",
       "19:00:07    864\n",
       "20:00:03    863\n",
       "19:00:06    863\n",
       "19:00:08    862\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df['cts'].dt.time).size().sort_values(ascending = False).head(20)   #Grouping by time and sorting in descending order"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d32fb9-34b2-4258-a119-12fd80cf6232",
   "metadata": {},
   "source": [
    "The most common time in which users publish their posts is 21:00:09. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ebee0862-7b88-4674-9c58-ff37ddd211e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cts\n",
       "19.0    2499758\n",
       "20.0    2495269\n",
       "18.0    2486381\n",
       "17.0    2423768\n",
       "21.0    2414932\n",
       "16.0    2341052\n",
       "15.0    2263009\n",
       "22.0    2167378\n",
       "14.0    2158976\n",
       "13.0    2025569\n",
       "12.0    1868544\n",
       "23.0    1838215\n",
       "11.0    1720602\n",
       "10.0    1571222\n",
       "0.0     1547036\n",
       "9.0     1434724\n",
       "1.0     1376147\n",
       "2.0     1290434\n",
       "8.0     1267423\n",
       "3.0     1231644\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df['cts'].dt.hour).size().sort_values(ascending = False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e96cf3-4d95-46e7-a92a-cea30e6b5b96",
   "metadata": {},
   "source": [
    "The most common hour of the day in which users publish their posts is 19. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82cbdf0c-31c6-4be5-ae48-3b1f9bbbc6ea",
   "metadata": {},
   "source": [
    "## Create a function that receives a time intervals list as a parameter and returns a plot with the number of posts for each given interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "6aa357e0-375f-492c-ac17-c42d576f4a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_number_of_posts(slots):       #slots is a list of time intervals \n",
    "    \n",
    "    time_results = {}                  #time_results is an empty dictionary\n",
    "    labels = []                        #labels is an empty list, for the plot\n",
    "\n",
    "    \n",
    "    #iterate through the time intervals and update the time_results dictionary with the number of posts published\n",
    "    #in the time interval. Also update the labels list with the time intervals after converting them to strings.\n",
    "    \n",
    "    for slot in slots:\n",
    "        labels.append((slot[0].strftime(\"%H:%M:%S\"), slot[1].strftime(\"%H:%M:%S\")))\n",
    "        result= len( df[(df.cts.dt.time >= slot[0]) & (df.cts.dt.time <= slot[1])])\n",
    "        time_results[slot] = result   \n",
    "         \n",
    "    plt.figure(figsize=(8,6))\n",
    "    ax1 = plt.subplot()\n",
    "    plt.yscale(\"log\")                        #set log scale on y axis to better visualize high frequencies\n",
    "    ax1.set_xticks(range(len(time_results)))\n",
    "    plt.bar(range(len(time_results)), list(time_results.values()))\n",
    "    ax1.set_xticklabels(labels)\n",
    "    plt.xticks(rotation = 'vertical')\n",
    "    plt.show()\n",
    "    \n",
    "    return \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "81b372b0-f4fc-4064-9d30-3345a456d24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "initial_time = ['06:00:00', '11:00:00', '14:00:00', '17:00:00', '20:00:00', '00:00:00', '03:00:00']\n",
    "final_time = ['10:59:59', '13:59:59', '16:59:59', '19:59:59', '23:59:59', '02:59:59', '05:59:59']\n",
    "\n",
    "#first convert the string lists to datetime objects \n",
    "\n",
    "i_time = []\n",
    "f_time = []\n",
    "\n",
    "for i in range(len(initial_time)):\n",
    "    i_time.append(datetime.strptime(initial_time[i], '%H:%M:%S').time())\n",
    "    f_time.append(datetime.strptime(final_time[i], '%H:%M:%S').time())\n",
    "\n",
    "#then create the slots list\n",
    "slots = zip(i_time, f_time)\n",
    "#and finally call the function\n",
    "plot_number_of_posts(slots)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e4319d-457c-4e43-b612-5beb5b5e6542",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# [RQ4] \n",
    "In most cases, we will not have a consistent dataset, and the one we are dealing\n",
    "with is not an exception (ex. in the given datasets, you may not find the information of\n",
    "the profiles for some of the posts). So let’s enhance our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fb9f76-e4fe-4efa-8f47-ce7066681025",
   "metadata": {
    "tags": []
   },
   "source": [
    "### RQ4.1\n",
    "Get a profile from the dataset of instagram profiles and search for the posts published by that profile_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8da8c8fb-9a38-4e56-80d3-511000dcab79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that given a profile_id it gives back the posts of that profile\n",
    "def PostsFromID(ID, df_names):\n",
    "    post = []\n",
    "    # Check the posts dataset dividing it in chunks of 500000 element\n",
    "    for chunk in pd.read_csv(df_names[0], delimiter='\\t', on_bad_lines='skip', chunksize=500000):\n",
    "        chunk = clean_df(chunk, ['profile_id'])\n",
    "        # If the profile_id is equal to the one in input I add the post row to my list\n",
    "        post.append(chunk[chunk.profile_id == ID])\n",
    "        \n",
    "    # return the dataframe with all the post made by the wanted profile\n",
    "    post = pd.concat(post)\n",
    "    return post\n",
    "\n",
    "\n",
    "# Function to print the results from the PostsFromID function\n",
    "def ResultsPostFromId (Profile, posts):\n",
    "    print('While looking for the posts of the profile:\\n\\n')\n",
    "    # The profile we were working with\n",
    "    display(Profile)\n",
    "    if posts.empty:\n",
    "        # Couldn't find any posts linked to the ID\n",
    "        print('We found a inconsistency in our datasets. There was no match between the profile with the',\n",
    "              Profile['profile_id'], 'ID and the dataset of posts.')\n",
    "    else:\n",
    "        print('We found', len(posts), 'different posts:\\n\\n')\n",
    "        # Show the posts found\n",
    "        display(posts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5dc9d574-80c1-4299-9d67-ba02bdb95e9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "While looking for the posts of the profile:\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>profile_id</th>\n",
       "      <th>profile_name</th>\n",
       "      <th>firstname_lastname</th>\n",
       "      <th>description</th>\n",
       "      <th>following</th>\n",
       "      <th>followers</th>\n",
       "      <th>n_posts</th>\n",
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       "    </tr>\n",
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       "      <th>2446216</th>\n",
       "      <td>2209710</td>\n",
       "      <td>1.009423e+10</td>\n",
       "      <td>aristeraphos</td>\n",
       "      <td>Emanuele Musella</td>\n",
       "      <td>Writer-bassistsinger-musicmaker-philosophy and...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>www.facebook.com/aristeraphos</td>\n",
       "      <td>2019-04-06 01:18:02.598 -0400</td>\n",
       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "             sid    profile_id  profile_name firstname_lastname  \\\n",
       "2446216  2209710  1.009423e+10  aristeraphos   Emanuele Musella   \n",
       "\n",
       "                                               description following  \\\n",
       "2446216  Writer-bassistsinger-musicmaker-philosophy and...      85.0   \n",
       "\n",
       "        followers n_posts                            url  \\\n",
       "2446216      94.0    11.0  www.facebook.com/aristeraphos   \n",
       "\n",
       "                                   cts is_business_account  \n",
       "2446216  2019-04-06 01:18:02.598 -0400               False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 11 different posts:\n",
      "\n",
      "\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>sid_profile</th>\n",
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       "      <th>profile_id</th>\n",
       "      <th>location_id</th>\n",
       "      <th>cts</th>\n",
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       "      <td>2019-03-07 09:30:31.000</td>\n",
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       "      <td>2019-03-06 15:31:23.000</td>\n",
       "      <td>1</td>\n",
       "      <td>Irrealt6</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>28797387</th>\n",
       "      <td>17515524</td>\n",
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       "      <td>BurdiOYH_st</td>\n",
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       "      <td>2019-03-06 14:45:48.000</td>\n",
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       "      <td>Colonne san Lorenzo</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td></td>\n",
       "      <td>2019-03-01 13:22:47.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#spirits #twinpeaks #madness #shadows #blacklo...</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>2019-02-28 06:59:08.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#mysculpture #myart #goldenagegods #andogynous...</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2209710</td>\n",
       "      <td>BubJ7ttHS6k</td>\n",
       "      <td>1.009423e+10</td>\n",
       "      <td>213658970.0</td>\n",
       "      <td>2019-02-28 06:46:40.000</td>\n",
       "      <td>1</td>\n",
       "      <td>Moondog / Odin sculpture - One of my artworks ...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>17515565</td>\n",
       "      <td>2209710</td>\n",
       "      <td>BuY8R12HF-M</td>\n",
       "      <td>1.009423e+10</td>\n",
       "      <td></td>\n",
       "      <td>2019-02-27 10:08:52.000</td>\n",
       "      <td>1</td>\n",
       "      <td>Middle Point ○●&gt;}☆{&lt;●○ #embracingtheyin #outsi...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>28798197</th>\n",
       "      <td>17515574</td>\n",
       "      <td>2209710</td>\n",
       "      <td>Bto7EdDi8mf</td>\n",
       "      <td>1.009423e+10</td>\n",
       "      <td></td>\n",
       "      <td>2019-02-08 18:34:46.000</td>\n",
       "      <td>1</td>\n",
       "      <td>Nocturn punk</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>28798198</th>\n",
       "      <td>17515586</td>\n",
       "      <td>2209710</td>\n",
       "      <td>BtY6kcSHDBy</td>\n",
       "      <td>1.009423e+10</td>\n",
       "      <td></td>\n",
       "      <td>2019-02-02 13:22:32.000</td>\n",
       "      <td>1</td>\n",
       "      <td>The mountain temple #mountainvirgins #sagliuta...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>#insomnia</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "               sid  sid_profile      post_id    profile_id  location_id  \\\n",
       "28794842  17515504      2209710  ButeP9mnqFp  1.009423e+10                \n",
       "28797386  17515511      2209710  BuriwJ_nWkP  1.009423e+10                \n",
       "28797387  17515524      2209710  BurdiOYH_st  1.009423e+10                \n",
       "28797396  17515531      2209710  BuecD37nM5n  1.009423e+10                \n",
       "28797652  17515543      2209710  BubLXFaHZLq  1.009423e+10  213658970.0   \n",
       "28797653  17515552      2209710  BubJ7ttHS6k  1.009423e+10  213658970.0   \n",
       "28797654  17515565      2209710  BuY8R12HF-M  1.009423e+10                \n",
       "28798197  17515574      2209710  Bto7EdDi8mf  1.009423e+10                \n",
       "28798198  17515586      2209710  BtY6kcSHDBy  1.009423e+10                \n",
       "28798200  17515598      2209710  BsYyDavnu2F  1.009423e+10                \n",
       "28798201  17515611      2209710  BsU-DShnk_v  1.009423e+10  471064366.0   \n",
       "\n",
       "                              cts  post_type  \\\n",
       "28794842  2019-03-07 09:30:31.000          1   \n",
       "28797386  2019-03-06 15:31:23.000          1   \n",
       "28797387  2019-03-06 14:45:48.000          1   \n",
       "28797396  2019-03-01 13:22:47.000          1   \n",
       "28797652  2019-02-28 06:59:08.000          1   \n",
       "28797653  2019-02-28 06:46:40.000          1   \n",
       "28797654  2019-02-27 10:08:52.000          1   \n",
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       "28798200  2019-01-08 15:36:44.000          1   \n",
       "28798201  2019-01-07 04:04:37.000          1   \n",
       "\n",
       "                                                description  numbr_likes  \\\n",
       "28794842                                             Martyr         12.0   \n",
       "28797386                                           Irrealt6         11.0   \n",
       "28797387                                Colonne san Lorenzo         10.0   \n",
       "28797396  #spirits #twinpeaks #madness #shadows #blacklo...         30.0   \n",
       "28797652  #mysculpture #myart #goldenagegods #andogynous...         36.0   \n",
       "28797653  Moondog / Odin sculpture - One of my artworks ...         19.0   \n",
       "28797654  Middle Point ○●>}☆{<●○ #embracingtheyin #outsi...         17.0   \n",
       "28798197                                       Nocturn punk         16.0   \n",
       "28798198  The mountain temple #mountainvirgins #sagliuta...         11.0   \n",
       "28798200                                          #insomnia         10.0   \n",
       "28798201                     2019 fears #2019 #fears #metro         12.0   \n",
       "\n",
       "          number_comments  \n",
       "28794842              2.0  \n",
       "28797386              0.0  \n",
       "28797387              1.0  \n",
       "28797396              1.0  \n",
       "28797652              2.0  \n",
       "28797653              4.0  \n",
       "28797654              0.0  \n",
       "28798197              0.0  \n",
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "#Testing previous function with a sample form the profile.csv\n",
    "df = pd.read_csv(df_names[1], delimiter='\\t', on_bad_lines='skip')\n",
    "df = clean_df(df, 'profile_id')\n",
    "Profile = df.sample()\n",
    "Posts = PostsFromID(Profile.iloc[0].profile_id, df_names)\n",
    "ResultsPostFromId(Profile, Posts)\n",
    "del df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2e26d6-3856-4676-b3e1-0e9011226dc0",
   "metadata": {},
   "source": [
    "### RQ4.2 \n",
    "Find the posts of the top n posted profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd61ce02-b9b4-4271-a905-7a9b37464e72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that given a number in input gives back the n profile that has posted more (Top Posted Profiles)\n",
    "def FindTopPP(df_names, n):\n",
    "    \n",
    "    # Read the profiles dataset and return the top n profiles sorted by number of posts\n",
    "    df = pd.read_csv(df_names[1], delimiter='\\t', on_bad_lines='skip')\n",
    "    df = clean_df(df, ['profile_id','n_posts'])\n",
    "    \n",
    "    # return only the top n profiles after sorting\n",
    "    return df.sort_values(by = 'n_posts', ascending=False).head(n)\n",
    "\n",
    "\n",
    "# Modified version of the PostsFromID function that is now given n profiles \n",
    "# It gives back a list of lenght n with all their post\n",
    "def PostsFromID_2 (profiles, df_names):\n",
    "    # Initialize the list of lists were I can store the posts while working with the chunks\n",
    "    post = []\n",
    "    for i in range(len(profiles)):\n",
    "        post.append([])\n",
    "        \n",
    "    # Taking the posts df in chucks\n",
    "    for chunk in pd.read_csv(df_names[0], delimiter='\\t', on_bad_lines='skip', chunksize=500000):\n",
    "        \n",
    "        #clean the dataframe\n",
    "        chunk = clean_df(chunk, ['profile_id'])\n",
    "        chunk['numbr_likes'] = pd.to_numeric(chunk['numbr_likes'], errors='coerce')\n",
    "        chunk['number_comments'] = pd.to_numeric(chunk['number_comments'], errors='coerce')\n",
    "        \n",
    "        # If the profile_id is equal to one of the i-th profile in input \n",
    "        # I add it to the i-th element of the posts list\n",
    "        for i in range(len(profiles)):\n",
    "            post[i].append(chunk[chunk.profile_id == profiles.iloc[i]['profile_id']])\n",
    "    \n",
    "    # put together all the posts from the same ID\n",
    "    for i in range(len(profiles)):\n",
    "        post[i] = pd.concat(post[i])\n",
    "        \n",
    "    return post\n",
    "\n",
    "\n",
    "# Function that given an integer n gives back a df of the n Top Posted Profiles and the list of their post\n",
    "def TopPostedProfile (df_names):\n",
    "    n = int(input())\n",
    "    \n",
    "    # Find the top n posted profiles\n",
    "    topPP = FindTopPP(df_names, n)\n",
    "    \n",
    "    # Find all their post that are saved in the post.csv\n",
    "    posts = PostsFromID_2(topPP, df_names)\n",
    "        \n",
    "    return topPP, posts\n",
    "\n",
    "\n",
    "# Function that print the result for the posts of the Top Posted Profiles\n",
    "def ResultsTPP (topPP, posts, n):\n",
    "    print('These are the top', n, 'posted profiles in descending order and their posts.')\n",
    "    \n",
    "    # Print the profile and their posts for each of the Top Posted Profiles\n",
    "    for i in range(n):\n",
    "        print('The profile #'+ str(i+1) +' with', topPP.iloc[i]['n_posts'], 'posts is: \\n\\n')\n",
    "        display(topPP.iloc[i])\n",
    "        print(\"\\n While searching for this profile's posts we found\", len(posts[i]), 'different posts: \\n\\n')\n",
    "        display(posts[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7fc724f9-e337-4dac-9e6f-f362ad5b089f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " 10\n"
     ]
    }
   ],
   "source": [
    "TPP, PostsTPP = TopPostedProfile(df_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "500bd684-a9b7-49a6-b9b5-64cbd35621a0",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the top 10 posted profiles in descending order and their posts.\n",
      "The profile #1 with 183730.0 posts is: \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sid                                          3661552\n",
       "profile_id                               199664772.0\n",
       "profile_name                              jasem33312\n",
       "firstname_lastname                        jasem33312\n",
       "description                                         \n",
       "following                                        2.0\n",
       "followers                                    82049.0\n",
       "n_posts                                     183730.0\n",
       "url                                                 \n",
       "cts                    2019-06-30 19:14:19.780 -0400\n",
       "is_business_account                            False\n",
       "Name: 3990313, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " While searching for this profile's posts we found 21 different posts: \n",
      "\n",
      "\n"
     ]
    },
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       "              sid  sid_profile      post_id   profile_id   location_id  \\\n",
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       "9592953  21205482      3661552  Bq3hN53hUsD  199664772.0  1029593228.0   \n",
       "9592954  24906254      3661552  BCqCe9LypeF  199664772.0  1029569178.0   \n",
       "9592955  25468626      3661552   20Wu4zypd2  199664772.0   237398449.0   \n",
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       "9593160  37701797      3661552  BzRkmb3JXea  199664772.0                 \n",
       "\n",
       "                             cts  post_type description  numbr_likes  \\\n",
       "9592952  2018-12-02 03:03:33.000          1                      0.0   \n",
       "9592953  2018-12-02 03:03:17.000          1                      0.0   \n",
       "9592954  2016-03-07 16:28:50.000          1     #تصويري         27.0   \n",
       "9592955  2015-05-18 11:21:27.000          1     #تصويري         28.0   \n",
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       "9592960  2015-05-31 11:49:04.000          1     #تصويري         19.0   \n",
       "9592961  2019-06-30 07:19:05.000          1                     11.0   \n",
       "9592962  2019-06-30 07:18:55.000          1                      6.0   \n",
       "9592963  2019-06-30 07:18:46.000          1                      2.0   \n",
       "9592964  2019-06-30 07:18:40.000          1                      4.0   \n",
       "9592965  2019-06-30 07:18:35.000          1                      2.0   \n",
       "9592966  2019-06-30 07:18:30.000          1                      6.0   \n",
       "9592967  2019-06-30 07:18:23.000          1                      2.0   \n",
       "9592968  2019-06-30 07:18:15.000          1                      3.0   \n",
       "9592969  2019-06-30 07:18:05.000          1                      3.0   \n",
       "9593158  2019-06-29 11:11:09.000          1                     17.0   \n",
       "9593159  2019-06-29 03:04:33.000          1                     10.0   \n",
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       "\n",
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    {
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     "output_type": "stream",
     "text": [
      "The profile #2 with 142428.0 posts is: \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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       "sid                                          3288809\n",
       "profile_id                              8563366588.0\n",
       "profile_name                               koenigpic\n",
       "firstname_lastname                 Helperwait Studio\n",
       "description             Веб сайты от идеи до запуска\n",
       "following                                     4538.0\n",
       "followers                                      675.0\n",
       "n_posts                                     142428.0\n",
       "url                             vk.com/ru.helperwait\n",
       "cts                    2019-05-07 00:14:06.799 -0400\n",
       "is_business_account                             True\n",
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     "text": [
      "\n",
      " While searching for this profile's posts we found 15 different posts: \n",
      "\n",
      "\n"
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      "The profile #3 with 126340.0 posts is: \n",
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       "sid                                          3970193\n",
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       "12373607                                                             2.0   \n",
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       "12373793                                                             2.0   \n",
       "12373794                                                             3.0   \n",
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       "34500138                                                             5.0   \n",
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       "sid                                                              3621669\n",
       "profile_id                                                     9813508.0\n",
       "profile_name                                                 hometown_ni\n",
       "firstname_lastname                                   AngelsCowboysLakers\n",
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       "sid                                                              3851869\n",
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     "text": [
      "The profile #6 with 117099.0 posts is: \n",
      "\n",
      "\n"
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       "sid                             345591\n",
       "profile_id                1087613619.0\n",
       "profile_name                waxking911\n",
       "firstname_lastname     Frank Hernandez\n",
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       "url                                   \n",
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     "text": [
      "\n",
      " While searching for this profile's posts we found 38 different posts: \n",
      "\n",
      "\n"
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      "The profile #7 with 113780.0 posts is: \n",
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       "sid                                                              3944512\n",
       "profile_id                                                  1603889832.0\n",
       "profile_name                                               gamt_official\n",
       "firstname_lastname                          Official IG Of GAMT Thailand\n",
       "description            ❥ แว่น gamt พร้อมส่งของแท้💯 มีโปร1290 พร้อมซอง...\n",
       "following                                                         7470.0\n",
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       "url                                                       bit.ly/2GdIYJl\n",
       "cts                                        2019-07-14 21:10:30.785 -0400\n",
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      " While searching for this profile's posts we found 67 different posts: \n",
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       "               sid  sid_profile      post_id    profile_id        location_id  \\\n",
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       "4701724   33216456      3944512  BwIx0JsBwV4  1.603890e+09       1018683106.0   \n",
       "7295774   35977485      3944512  Bvk-3r5BSzz  1.603890e+09        235582752.0   \n",
       "...            ...          ...          ...           ...                ...   \n",
       "30301859  14893051      3944512  BwobrRLhPn9  1.603890e+09       1020186939.0   \n",
       "31758941  16344061      3944512  BvwMZZXBawf  1.603890e+09       1012809343.0   \n",
       "37713201  23336033      3944512  BvtdiYNhwwF  1.603890e+09        445477176.0   \n",
       "42154726  27798703      3944512  BxtyMobBsXy  1.603890e+09       1014532695.0   \n",
       "42240454  27889914      3944512  Bwldv-OhvYh  1.603890e+09  289760735000057.0   \n",
       "\n",
       "                              cts  post_type  \\\n",
       "494943    2019-05-17 10:40:38.000          1   \n",
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       "7295774   2019-03-29 04:53:48.000          1   \n",
       "...                           ...        ...   \n",
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       "31758941  2019-04-02 14:23:38.000          1   \n",
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       "42154726  2019-05-21 08:59:01.000          1   \n",
       "42240454  2019-04-23 06:55:01.000          1   \n",
       "\n",
       "                                                description  numbr_likes  \\\n",
       "494943    #gamt 1,290 free ems #gamtthailand #gamtsungla...          4.0   \n",
       "637087    📞 สนใจตัวช่วย#dwfifteen🔥เพิ่มการเผาผลาญไขมัน\\n...          1.0   \n",
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       "4701724   เคล็ดลับผิวสวย เรียนเนียน ออร่ากระจาย‼️\\nด้วยส...          3.0   \n",
       "7295774   😊อยากถอดหุ่นเก่าออกมั้ย\\n😊ไม่ใช่เรื่องยาก ทักม...          2.0   \n",
       "...                                                     ...          ...   \n",
       "30301859  #gamt 1,290 free ems #เครื่องเขียนน่ารัก #บรา ...          7.0   \n",
       "31758941  😊อยากถอดหุ่นเก่าออกมั้ย\\n😊ไม่ใช่เรื่องยาก ทักม...          5.0   \n",
       "37713201  😊อยากถอดหุ่นเก่าออกมั้ย\\n😊ไม่ใช่เรื่องยาก ทักม...          2.0   \n",
       "42154726  #gamt 1,290 free ems #gamtthailand #รัดส้น #คิ...         36.0   \n",
       "42240454  #gamt 1,290 free ems #แปรงแต่งหน้า #แปรงแต่งหน...          4.0   \n",
       "\n",
       "          number_comments  \n",
       "494943                0.0  \n",
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       "7295774               0.0  \n",
       "...                   ...  \n",
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       "\n",
       "[67 rows x 10 columns]"
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     "text": [
      "The profile #8 with 113182.0 posts is: \n",
      "\n",
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       "sid                                                              3851808\n",
       "profile_id                                                   218172998.0\n",
       "profile_name                                        collarosedw_official\n",
       "firstname_lastname                                   grapeseed+collarose\n",
       "description            🍇 สินค้าแบรนด์ dwplus\\n💓 collarose+grapeseed+f...\n",
       "following                                                         7496.0\n",
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       "n_posts                                                         113182.0\n",
       "url                                                       bit.ly/2asesw7\n",
       "cts                                        2019-07-13 02:08:17.407 -0400\n",
       "is_business_account                                                 True\n",
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     "text": [
      "\n",
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       "\n",
       "                              cts  post_type  \\\n",
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       "\n",
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     "text": [
      "The profile #9 with 112777.0 posts is: \n",
      "\n",
      "\n"
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       "sid                                                              3868432\n",
       "profile_id                                                   285549744.0\n",
       "profile_name                                              gamt_authentic\n",
       "firstname_lastname                          Official IG Of GAMT Thailand\n",
       "description            ❥ แว่น gamt พร้อมส่ง มีโปร1290 พร้อมซองแว่น+ผ้...\n",
       "following                                                         7480.0\n",
       "followers                                                        27511.0\n",
       "n_posts                                                         112777.0\n",
       "url                                                       bit.ly/2T2RIqG\n",
       "cts                                        2019-07-13 09:28:30.025 -0400\n",
       "is_business_account                                                 True\n",
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      "\n",
      " While searching for this profile's posts we found 64 different posts: \n",
      "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>sid_profile</th>\n",
       "      <th>post_id</th>\n",
       "      <th>profile_id</th>\n",
       "      <th>location_id</th>\n",
       "      <th>cts</th>\n",
       "      <th>post_type</th>\n",
       "      <th>description</th>\n",
       "      <th>numbr_likes</th>\n",
       "      <th>number_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11565095</th>\n",
       "      <td>12683706</td>\n",
       "      <td>3868432</td>\n",
       "      <td>Bv9WuDjA_my</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>633832676958395.0</td>\n",
       "      <td>2019-04-07 17:03:58.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#gamt 1290.- #รับตัวแทนจำหน่ายรายได้ดี  #รับหิ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11565096</th>\n",
       "      <td>14718062</td>\n",
       "      <td>3868432</td>\n",
       "      <td>BwyQXF_ASSO</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>351493443.0</td>\n",
       "      <td>2019-04-28 06:08:09.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#gamt 1290.- #เช่าชุดราตรีรามอินทรา #ศูนย์การค...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11565097</th>\n",
       "      <td>16787955</td>\n",
       "      <td>3868432</td>\n",
       "      <td>BxMDBXTgUEj</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>215680072.0</td>\n",
       "      <td>2019-05-08 06:31:51.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#gamt 1290.- #ไปตำ #ผ่อนไปใช้ไป #ไปให้สุด #ไปใ...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11565098</th>\n",
       "      <td>18107669</td>\n",
       "      <td>3868432</td>\n",
       "      <td>BxQ0vapg8o1</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>2091863157805662.0</td>\n",
       "      <td>2019-05-10 03:03:16.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#gamt 1290.- #บ้านนี้มีรัก #อยากกลับบ้าน #บ้าน...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11565099</th>\n",
       "      <td>18754428</td>\n",
       "      <td>3868432</td>\n",
       "      <td>BxHK3IAAvgm</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>597152288.0</td>\n",
       "      <td>2019-05-06 09:04:09.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#gamt 1290.- #กางเกงใส่เที่ยว #ใส่ไปทะเล #ใส่ไ...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11565172</th>\n",
       "      <td>39183860</td>\n",
       "      <td>3868432</td>\n",
       "      <td>Bz2KZMWAzaS</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>261807660848564.0</td>\n",
       "      <td>2019-07-13 08:07:09.000</td>\n",
       "      <td>1</td>\n",
       "      <td>พร้อมส่ง 2,490- ว่าง 2\\nCoach F73011 ACCORDION...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11565173</th>\n",
       "      <td>39183862</td>\n",
       "      <td>3868432</td>\n",
       "      <td>Bz2KIsGguxY</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>5229182.0</td>\n",
       "      <td>2019-07-13 08:04:54.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2290-(ป้าย 4,200)\\nWMNS NIKE FREE RN CMTR 2017...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11565174</th>\n",
       "      <td>39183865</td>\n",
       "      <td>3868432</td>\n",
       "      <td>Bz2JMmhA1Bv</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>783465844.0</td>\n",
       "      <td>2019-07-13 07:56:42.000</td>\n",
       "      <td>1</td>\n",
       "      <td>***SALE*** พร้อมส่ง ราคา 750.- พร้อมส่ง สร้อยข...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27484079</th>\n",
       "      <td>11825625</td>\n",
       "      <td>3868432</td>\n",
       "      <td>BxR5tuQAfxT</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>821943618003726.0</td>\n",
       "      <td>2019-05-10 13:05:58.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#gamt 1290.-</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39195221</th>\n",
       "      <td>24787801</td>\n",
       "      <td>3868432</td>\n",
       "      <td>Bxrfj8-pq-V</td>\n",
       "      <td>285549744.0</td>\n",
       "      <td>321867414948473.0</td>\n",
       "      <td>2019-05-20 05:37:41.000</td>\n",
       "      <td>1</td>\n",
       "      <td>#gamt 1290.- #เป้ยปานวาด #เป้ย #peipanward #pp...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               sid  sid_profile      post_id   profile_id         location_id  \\\n",
       "11565095  12683706      3868432  Bv9WuDjA_my  285549744.0   633832676958395.0   \n",
       "11565096  14718062      3868432  BwyQXF_ASSO  285549744.0         351493443.0   \n",
       "11565097  16787955      3868432  BxMDBXTgUEj  285549744.0         215680072.0   \n",
       "11565098  18107669      3868432  BxQ0vapg8o1  285549744.0  2091863157805662.0   \n",
       "11565099  18754428      3868432  BxHK3IAAvgm  285549744.0         597152288.0   \n",
       "...            ...          ...          ...          ...                 ...   \n",
       "11565172  39183860      3868432  Bz2KZMWAzaS  285549744.0   261807660848564.0   \n",
       "11565173  39183862      3868432  Bz2KIsGguxY  285549744.0           5229182.0   \n",
       "11565174  39183865      3868432  Bz2JMmhA1Bv  285549744.0         783465844.0   \n",
       "27484079  11825625      3868432  BxR5tuQAfxT  285549744.0   821943618003726.0   \n",
       "39195221  24787801      3868432  Bxrfj8-pq-V  285549744.0   321867414948473.0   \n",
       "\n",
       "                              cts  post_type  \\\n",
       "11565095  2019-04-07 17:03:58.000          1   \n",
       "11565096  2019-04-28 06:08:09.000          1   \n",
       "11565097  2019-05-08 06:31:51.000          1   \n",
       "11565098  2019-05-10 03:03:16.000          1   \n",
       "11565099  2019-05-06 09:04:09.000          1   \n",
       "...                           ...        ...   \n",
       "11565172  2019-07-13 08:07:09.000          1   \n",
       "11565173  2019-07-13 08:04:54.000          1   \n",
       "11565174  2019-07-13 07:56:42.000          1   \n",
       "27484079  2019-05-10 13:05:58.000          1   \n",
       "39195221  2019-05-20 05:37:41.000          1   \n",
       "\n",
       "                                                description  numbr_likes  \\\n",
       "11565095  #gamt 1290.- #รับตัวแทนจำหน่ายรายได้ดี  #รับหิ...          5.0   \n",
       "11565096  #gamt 1290.- #เช่าชุดราตรีรามอินทรา #ศูนย์การค...          0.0   \n",
       "11565097  #gamt 1290.- #ไปตำ #ผ่อนไปใช้ไป #ไปให้สุด #ไปใ...          4.0   \n",
       "11565098  #gamt 1290.- #บ้านนี้มีรัก #อยากกลับบ้าน #บ้าน...          1.0   \n",
       "11565099  #gamt 1290.- #กางเกงใส่เที่ยว #ใส่ไปทะเล #ใส่ไ...          8.0   \n",
       "...                                                     ...          ...   \n",
       "11565172  พร้อมส่ง 2,490- ว่าง 2\\nCoach F73011 ACCORDION...          0.0   \n",
       "11565173  2290-(ป้าย 4,200)\\nWMNS NIKE FREE RN CMTR 2017...          0.0   \n",
       "11565174  ***SALE*** พร้อมส่ง ราคา 750.- พร้อมส่ง สร้อยข...          0.0   \n",
       "27484079                                       #gamt 1290.-          0.0   \n",
       "39195221  #gamt 1290.- #เป้ยปานวาด #เป้ย #peipanward #pp...          6.0   \n",
       "\n",
       "          number_comments  \n",
       "11565095              0.0  \n",
       "11565096              0.0  \n",
       "11565097              0.0  \n",
       "11565098              0.0  \n",
       "11565099              0.0  \n",
       "...                   ...  \n",
       "11565172              0.0  \n",
       "11565173              0.0  \n",
       "11565174              0.0  \n",
       "27484079              0.0  \n",
       "39195221              0.0  \n",
       "\n",
       "[64 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The profile #10 with 112723.0 posts is: \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sid                                          1312725\n",
       "profile_id                              8312603412.0\n",
       "profile_name                          ivankkkka.trmp\n",
       "firstname_lastname            IVANKA DARCEY K. TRUMP\n",
       "description                           DORA. 👄 #Mango\n",
       "following                                     7497.0\n",
       "followers                                        2.0\n",
       "n_posts                                     112723.0\n",
       "url                         www.flightclub.com/yeezy\n",
       "cts                    2019-04-01 02:41:26.980 -0400\n",
       "is_business_account                            False\n",
       "Name: 1631526, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " While searching for this profile's posts we found 0 different posts: \n",
      "\n",
      "\n"
     ]
    },
    {
     "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>sid</th>\n",
       "      <th>sid_profile</th>\n",
       "      <th>post_id</th>\n",
       "      <th>profile_id</th>\n",
       "      <th>location_id</th>\n",
       "      <th>cts</th>\n",
       "      <th>post_type</th>\n",
       "      <th>description</th>\n",
       "      <th>numbr_likes</th>\n",
       "      <th>number_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sid, sid_profile, post_id, profile_id, location_id, cts, post_type, description, numbr_likes, number_comments]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ResultsTPP(TPP, PostsTPP, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88830f5d-2847-439a-b439-9b3f4dc2614f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Considerations RQ4.2:\n",
    "By looking at the result from the search of the Top Posted Profiles's posts we notice a big inconsistency in out datasets. We were able to find only a handfull of posts that are liked to the profiles that are supposed to have te most posts published, in fact we found only 341 out of the 1.264.541 posts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d52e8a-bd89-481f-b39e-53e94a6f3e26",
   "metadata": {},
   "source": [
    "### RQ4.3\n",
    "What is the average number of \"likes\" and comments of the top 10 profiles with the highest number of posts which their information is available in profile.csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "19ead994-04ca-483d-83e2-49bfec005b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that compute the average of likes and comments only of the TPP\n",
    "def Like_Comment_TPP (df_names, post):\n",
    "    likes = 0\n",
    "    comments = 0\n",
    "    \n",
    "    # Sum together all the likes and comments from the posts of the TPP\n",
    "    for i in range(len(post)):\n",
    "        likes += post[i].numbr_likes.sum()\n",
    "        comments += post[i].number_comments.sum() \n",
    "    \n",
    "    print('Average number of likes of the top 10 Posted Profiles: '+ str(likes/float(len(post))) + \n",
    "          '\\nAverage number of comments: '+ str(comments/float(len(post))))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0276fd26-43e0-4bbe-b28e-6f7eb1e7d485",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of likes of the top 10 Posted Profiles: 117.4\n",
      "Average number of comments: 5.7\n"
     ]
    }
   ],
   "source": [
    "# Using PostTPP computed in RQ4.2 that contain the posts of the Top Posted Profiles \n",
    "Like_Comment_TPP(df_names, PostsTPP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b71c3c-1791-428f-ad82-31006773a85a",
   "metadata": {},
   "source": [
    "### RQ4.4 \n",
    "Plot the number of posts that these top 10 profiles have sent on Instagram in the given interval in question RQ3. Interpret the resulting chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d5a3a74-54c7-4251-ab02-7b6bb48a6a8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function the find in which intervals are the posts from the Top Posted Profiles published\n",
    "def TimeTPP(Post):\n",
    "    Time_intervals = [(6,11),(11,14),(14,17),(17,20),(20,24),(0,3),(3,6)]\n",
    "    # Inizializing the Post_time array \n",
    "    Post_time = np.zeros(len(Time_intervals))\n",
    "\n",
    "    for i in range(len(Post)):\n",
    "        Post[i].cts = pd.to_datetime(Post[i].cts)\n",
    "        for j in range(len(Time_intervals)):\n",
    "            # Assign each post to a Time interval\n",
    "            a = Post[i][(Post[i].cts.dt.hour>=Time_intervals[j][0])]\n",
    "            b = Post[i][(Post[i].cts.dt.hour<Time_intervals[j][1])]\n",
    "            \n",
    "            # Intersection of a that contains the posts published afted Time_intervals[j][0]\n",
    "            # and b that contains the posts published before Time_intervals[j][1]\n",
    "            Post_time[j] += len(pd.merge(a,b, how='inner'))\n",
    "    return Post_time\n",
    "\n",
    "\n",
    "\n",
    "# The box plot of the result of the previous function\n",
    "def plotTimeTPP(Post_time):\n",
    "    barx =  ['[6,11)','[11,14)','[14,17)','[17,20)','[20,24)','[0,3)','[3,6)']\n",
    "    \n",
    "    plt.bar(barx, Post_time)\n",
    "    plt.title(\"Number of posts for each time frame\", fontsize=15, y=1.03)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7b5ec56a-773e-4a51-95de-e52f297772dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Times = TimeTPP(PostsTPP)\n",
    "plotTimeTPP(Times)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c84d73-2f4c-4a90-8785-d8290c300843",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Analysis RQ4.4:\n",
    "From the bar plot we can see that most of the post are published in the time frame form $6$ to $11$.\n",
    "\n",
    "But since we noticed in the RQ4.2 that there are some big inconsistency in out datasets (our sample is really small, there is less than 0.027% out of all the posts the Top Posted Profiles published), those result may not be accurate when looking at all the actual posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5763a0c4-bc8c-4d60-9b89-cb2b11c036bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Used to free the memory\n",
    "del TPP, Times, PostsTPP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecab005c-1300-4939-b9ab-922a4f04e6bc",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Q5\n",
    "\n",
    "## 1\n",
    "\n",
    "##### Who are the 10 users with most followers? Write their names and number of posts.\n",
    "We are only importing these columns since these are the ones we require."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8388d11-04a5-4973-8de4-e58b64b1b964",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_profiles = pd.read_csv('~/instagram_profiles.zip', delimiter='\\t',usecols=['profile_id','profile_name','followers','n_posts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13065a56-a5a8-4bb2-a090-19d4b1aff7b2",
   "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>profile_id</th>\n",
       "      <th>profile_name</th>\n",
       "      <th>followers</th>\n",
       "      <th>n_posts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.721050e+09</td>\n",
       "      <td>jphillip033</td>\n",
       "      <td>146.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.905830e+08</td>\n",
       "      <td>mama_haas</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>2878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.074147e+09</td>\n",
       "      <td>yellowlightbreen</td>\n",
       "      <td>324.0</td>\n",
       "      <td>431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.472039e+09</td>\n",
       "      <td>tec1025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.531421e+09</td>\n",
       "      <td>luckyfluff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.450642e+08</td>\n",
       "      <td>sabahlke</td>\n",
       "      <td>192.0</td>\n",
       "      <td>590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.061868e+09</td>\n",
       "      <td>masslivehs</td>\n",
       "      <td>4137.0</td>\n",
       "      <td>753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.446651e+09</td>\n",
       "      <td>hvcanes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.743726e+09</td>\n",
       "      <td>will_jay_k</td>\n",
       "      <td>183.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.455198e+09</td>\n",
       "      <td>ashley_downing722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     profile_id       profile_name  followers  n_posts\n",
       "0  4.721050e+09        jphillip033      146.0    170.0\n",
       "1  5.905830e+08          mama_haas     1145.0   2878.0\n",
       "2  1.074147e+09   yellowlightbreen      324.0    431.0\n",
       "3  1.472039e+09            tec1025        NaN      NaN\n",
       "4  3.531421e+09         luckyfluff        NaN      NaN\n",
       "5  1.450642e+08           sabahlke      192.0    590.0\n",
       "6  2.061868e+09         masslivehs     4137.0    753.0\n",
       "7  1.446651e+09            hvcanes        NaN      NaN\n",
       "8  1.743726e+09         will_jay_k      183.0     37.0\n",
       "9  5.455198e+09  ashley_downing722        NaN      NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_profiles.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd449fe-63cc-4355-bee1-e851ebbb96a5",
   "metadata": {},
   "source": [
    "We clean all the data, which in this case is changing all the NaN in followers for $0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b304d6c-b3ce-4018-9464-6dddb052c2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_profiles['followers'].fillna(0,inplace=True)\n",
    "data_profiles['n_posts'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a323d9e-0467-4891-b843-52193eb6cd92",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_data_profiles=data_profiles.sort_values(by='followers',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2df0c3e7-76e9-45d9-aa0c-675915f10e01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            profile_name  n_posts\n",
      "2880218        instagram   5760.0\n",
      "2582507  victoriassecret   7355.0\n",
      "3820939              nba  28816.0\n",
      "701965      louisvuitton   3352.0\n",
      "2414951    buzzfeedtasty   2791.0\n",
      "2785241   andresiniesta8    990.0\n",
      "333193                hm   4566.0\n",
      "3934581  princessyahrini   4719.0\n",
      "1311970     danbilzerian   1311.0\n",
      "1703764      akshaykumar   1173.0\n"
     ]
    }
   ],
   "source": [
    "top10=s_data_profiles.head(10)\n",
    "print(top10.loc[:,('profile_name','n_posts')])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b857549-9b23-4863-8b5e-c7135ee8c5d9",
   "metadata": {},
   "source": [
    "And these are the 10 users with more followers with their respective number of posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f696a7ab-3991-4c84-b423-25237335cabc",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data_profiles,s_data_profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b9ad22-6d8b-4ae0-88fd-39ba62c18155",
   "metadata": {},
   "source": [
    "## 2\n",
    "\n",
    "#### Who is the user with more followers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1760ccbe-d0d2-48fc-95c2-cf2ea4ab897a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        profile_name  n_posts\n",
      "2880218    instagram   5760.0\n"
     ]
    }
   ],
   "source": [
    "top=top10.head(1)\n",
    "print(top.loc[:,('profile_name','n_posts')])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e358dc7-8b3f-4c9e-a5ee-f3446115ca34",
   "metadata": {},
   "source": [
    "The Instagram account of **Instagram** is the profile with more followers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85bfb8c9-1491-4075-a664-854c883fc3d9",
   "metadata": {},
   "source": [
    "## 3\n",
    "\n",
    "#### Have they posted anything with tagged locations? Extract the most frequent areas on their posts and plot the number of times each city has been visited."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4362196-558e-441b-ba0d-8eb4b0d5b616",
   "metadata": {},
   "source": [
    "Here we require `instagram_posts.csv` and `instagram_locations.csv`, so we upload the columns that we need from the files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a35d78e-b54b-4b2a-877c-ea401500bde0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loc=pd.read_csv('~/instagram_locations.zip', delimiter='\\t',usecols=['id','name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bbea273-a71a-459b-9c98-885e597fdbb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids=top10['profile_id'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "deba85fe-8909-4474-9f25-22d3728788e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_posts=pd.read_csv('~/instagram_posts.zip', delimiter='\\t',usecols=['profile_id','location_id','post_type','numbr_likes','number_comments'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cce59312-94e0-4d39-afcf-6c86a02b9a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Next we have to choose only the posts that have the same profile_id as one of the top10 influencers from before\n",
    "top10_posts=[]\n",
    "for i in range(10):\n",
    "    top10_posts.append(data_posts[data_posts.profile_id==ids[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22d37716-1ce5-4650-9e73-ebf2c3a57395",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.8/site-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return self._update_inplace(result)\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    top10_posts[i].location_id.fillna(0,inplace=True) # Posts of the 10 more popular users "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a148ea4-8ff1-4b27-887a-5a2797504f94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we create a list of lists where the list 'i' inside the main one corresponds to the locations visited by \n",
    "# by the i-th user among the 10 we are dealing with.\n",
    "\n",
    "Loc=[]\n",
    "\n",
    "for k in range(10):\n",
    "    l=[]\n",
    "    for i in range(len(top10_posts[k]['location_id'])):\n",
    "        for j in range(len(data_loc['id'])):\n",
    "            if top10_posts[k]['location_id'].iloc[i] == data_loc['id'][j]:\n",
    "                l.append(data_loc['name'][j])\n",
    "                \n",
    "    Loc.append(l)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2a7e6bc-c65e-4a0a-8c4c-024b72db7623",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we count how many times has each user visited each location with the function counter\n",
    "from collections import Counter\n",
    "C=[]\n",
    "for i in range(10):\n",
    "    C.append(Counter(Loc[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7386a25e-2411-49e1-a911-718c2c5a82dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of posts where they have tagged a location corresponds to the 74.46808510638297 %.\n"
     ]
    }
   ],
   "source": [
    "num_posts,loc_tagged=0,0\n",
    "for i in range(10):\n",
    "    num_posts+=len(top10_posts[i])\n",
    "    loc_tagged+=len(Loc[i])\n",
    "print(f'The number of posts where they have tagged a location corresponds to the {100*loc_tagged/num_posts} %.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "329a74cc-949f-4381-ae7b-0f5d892ef8d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The influencer number 1:\n",
      "The influencer number 1 has not revisited any place.\n",
      "The influencer number 2:\n",
      "Victoria's Secret has been visied 4 times.\n",
      "The influencer number 3:\n",
      "Wells Fargo Center has been visied 2 times.\n",
      "The influencer number 4:\n",
      "The influencer number 4 has not revisited any place.\n",
      "The influencer number 5:\n",
      "The influencer number 5 has not revisited any place.\n",
      "The influencer number 6:\n",
      "Kobe-shi, Hyogo, Japan has been visied 3 times.\n",
      "The influencer number 7:\n",
      "The influencer number 7 has not revisited any place.\n",
      "The influencer number 8:\n",
      "Sydney, Australia has been visied 9 times.\n",
      "Bora Bora, French Polynesia has been visied 2 times.\n",
      "The influencer number 9:\n",
      "Bel-Air Estate has been visied 2 times.\n",
      "The influencer number 10:\n",
      "The influencer number 10 has not revisited any place.\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    k=0\n",
    "    print(f'The influencer number {i+1}:')\n",
    "    #If the city has been revisited, we print it.\n",
    "    for key in C[i].keys():\n",
    "        if C[i][key]>1:\n",
    "            print(key,'has been visied',C[i][key],'times.')\n",
    "            k+=1\n",
    "    #If the user has not visited any city\n",
    "    if k==0:\n",
    "        print(f'The influencer number {i+1} has not revisited any place.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9407759d-a880-4663-97da-7494d92ee2c0",
   "metadata": {},
   "source": [
    "## 4\n",
    "\n",
    "How many pictures-only posts have they published? How many reels? (only videos) and how many with both contents? Provide the number as percentages and interpret those figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "45b6dcde-824c-40ff-ac83-1c601406e686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pics,vid,both=0,0,0\n",
    "for i in range(10):\n",
    "    for j in range(len(top10_posts[i])):\n",
    "        aux=top10_posts[i]['post_type'].iloc[j]\n",
    "        if aux==1:\n",
    "            pics+=1\n",
    "        elif aux==2:\n",
    "            vid+=1\n",
    "        elif aux==3:\n",
    "            both+=1\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "v=[pics,vid,both]\n",
    "\n",
    "plt.pie(v,labels=['Pictures','Reels','Both'])\n",
    "plt.show()\n",
    "\n",
    "pic=100*pics/(pics+vid+both)\n",
    "vids=100*vid/(pics+vid+both)\n",
    "b=100*both/(pics+vid+both)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3491706e-4728-47be-a051-8c27848e38a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observe that 90.78014184397163% of the posts onyl contain pictures, \n",
      " 9.21985815602837% contain reels and the remaining 0.0% contains both types of media.\n"
     ]
    }
   ],
   "source": [
    "print(f'Observe that {pic}% of the posts onyl contain pictures, \\n {vids}% contain reels and the remaining {b}% contains both types of media.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "302ca8d4-7ec9-4fad-85a2-9ddcb4a9377d",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data_loc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80318aaf-be69-45e6-a949-386515334312",
   "metadata": {},
   "source": [
    "## 5\n",
    "\n",
    "How many \"likes\" and comments did posts with only pictures receive? How about videos and mixed posts? Try to provide the average numbers and confront them with their followers amount, explaining what you can say from that comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "827a7181-ea7d-4b04-8483-ca45d1c0d0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "posts_photo=[]\n",
    "posts_vid=[]\n",
    "likes,comments=[[0,0] for _ in range(10)],[[0,0] for _ in range(10)]\n",
    "posts=[0 for _ in range(10)]\n",
    "for i in range(10):\n",
    "    #We split the posts between the ones with pictures only and the ones with reels or mixed\n",
    "    posts_photo=top10_posts[i][top10_posts[i]['post_type']==1]\n",
    "    posts_vid=top10_posts[i][top10_posts[i]['post_type']!=1]\n",
    "    \n",
    "    #For each, we add up all the likes and comments\n",
    "    for k in range(len(posts_photo['numbr_likes'])):\n",
    "        likes[i][0]+=posts_photo['numbr_likes'].iloc[k]\n",
    "        comments[i][0]+=posts_photo['number_comments'].iloc[k]\n",
    "        posts[i]+=1\n",
    "\n",
    "    for k in range(len(posts_vid['numbr_likes'])):\n",
    "        likes[i][1]+=posts_vid['numbr_likes'].iloc[k]\n",
    "        comments[i][1]+=posts_vid['number_comments'].iloc[k]\n",
    "        posts[i]+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c25be5b1-91a0-4f82-aac2-cde28ac08d5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The user instagram  has an average number of 825998.1470588235 likes in its posts with photos, \n",
      " an average number of 52359.67647058824  likes in its posts with videos \n",
      " with a total of 285457645.0 followers. \n",
      "\n",
      "The user victoriassecret  has an average number of 164821.2857142857 likes in its posts with photos, \n",
      " an average number of 74688.0  likes in its posts with videos \n",
      " with a total of 65269440.0 followers. \n",
      "\n",
      "The user nba  has an average number of 174948.0 likes in its posts with photos, \n",
      " an average number of 57582.11111111111  likes in its posts with videos \n",
      " with a total of 35339708.0 followers. \n",
      "\n",
      "The user louisvuitton  has an average number of 186857.0 likes in its posts with photos, \n",
      " an average number of 0.0  likes in its posts with videos \n",
      " with a total of 30689316.0 followers. \n",
      "\n",
      "The user buzzfeedtasty  has an average number of 100862.92307692308 likes in its posts with photos, \n",
      " an average number of 0.0  likes in its posts with videos \n",
      " with a total of 30246783.0 followers. \n",
      "\n",
      "The user andresiniesta8  has an average number of 473022.6666666667 likes in its posts with photos, \n",
      " an average number of 0.0  likes in its posts with videos \n",
      " with a total of 29542143.0 followers. \n",
      "\n",
      "The user hm  has an average number of 81213.5 likes in its posts with photos, \n",
      " an average number of 0.0  likes in its posts with videos \n",
      " with a total of 29176804.0 followers. \n",
      "\n",
      "The user princessyahrini  has an average number of 125503.34615384616 likes in its posts with photos, \n",
      " an average number of 4818.884615384615  likes in its posts with videos \n",
      " with a total of 29042823.0 followers. \n",
      "\n",
      "The user danbilzerian  has an average number of 863113.2666666667 likes in its posts with photos, \n",
      " an average number of 0.0  likes in its posts with videos \n",
      " with a total of 26476004.0 followers. \n",
      "\n",
      "The user akshaykumar  has an average number of 1027101.8461538461 likes in its posts with photos, \n",
      " an average number of 0.0  likes in its posts with videos \n",
      " with a total of 26252169.0 followers. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#We were going to divide the likes of the post with photos with the number of posts with photos and\n",
    "#likewise for the ones with videos but we met some 0 division, so we chose to divide by the total amount\n",
    "#of posts for that profile.\n",
    "\n",
    "for i in range(10):\n",
    "    print('The user',top10['profile_name'].iloc[i],' has an average number of', likes[i][0]/posts[i],'likes in its posts with photos, \\n an average number of', likes[i][1]/posts[i],' likes in its posts with videos \\n with a total of',top10['followers'].iloc[i], 'followers. \\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a36956-b3a8-463a-8007-212b071c4882",
   "metadata": {},
   "source": [
    "We are now going to comment this last piece of data:\n",
    "\n",
    "- **Instagram**: Has 16 times more likes in posts with photos rather than videos but still the number of likes is not even a $1$ % of the amount of followers.\n",
    "- **Victoria's Secret**: Has twice more likes in posts with photos rather than videos but still the number of likes is not even a $1$ % of the amount of followers.\n",
    "- **NBA**:  Has three times more likes in posts with photos rather than videos but still the number of likes is $0.5$ % of the amount of followers.\n",
    "- **Louis Vuitton**: The number of likes in posts with pictures is slightly above the $0.5$ % of the amount of followers. On average, they recieve no likes in posts with only videos or both videos and pictures.\n",
    "- **buzzfeedtasty**: The number of likes in posts with pictures is slightly above the $0.3$ % of the amount of followers. On average, they recieve no likes in posts with only videos or both videos and pictures.\n",
    "- **Andrés Iniesta**: The number of likes in posts with pictures is around the $1.6$ % of the amount of followers. On average, he recieves no likes in posts with only videos or both videos and pictures.\n",
    "- **H & M**: The number of likes in posts with pictures is slightly under the $0.3$ % of the amount of followers. On average, they recieve no likes in posts with only videos or both videos and pictures.\n",
    "- **Pricess Yahrini**: The number of likes in posts with pictures is slightly above the $0.4$ % of the amount of followers. The number of likes in posts with videos is slightly above the $0.016$ % of the amount of followers. She recieves 26 times more likes in posts with pictures than in ones with videos.\n",
    "- **Dan Bilzerian**: The number of likes in posts with pictures is slightly above the $3.3$ % of the amount of followers. On average, he recieves no likes in posts with only videos or both videos and pictures.\n",
    "- **Aksha Ykumar**: The number of likes in posts with pictures is slightly under the $4$ % of the amount of followers. On average, they recieve no likes in posts with only videos or both videos and pictures."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7fcdc4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Q6\n",
    "\n",
    "## 1\n",
    "\n",
    "What is the average time (days and minutes) a user lets pass before publishing another post? Plot the top 3 users that publish posts more frequently (calculate the average time that passes between posts), including their amount of followers and following. Provide insights from that chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d9f36d",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"profile_id\", \"profile_name\"]\n",
    "profiles = pd.read_csv('~/instagram_profiles.zip', delimiter='\\t')\n",
    "columns = [\"profile_id\", \"cts\", \"numbr_likes\", \"number_comments\"]\n",
    "\n",
    "posts = pd.read_csv('~/instagram_posts.zip', delimiter='\\t', usecols = columns)\n",
    "posts = posts.sort_values(by = [\"profile_id\", \"cts\"])\n",
    "#drop where either id or timestamp is null\n",
    "posts = posts.drop(posts[(posts.profile_id.isnull()) | (posts.cts.isnull())].index).reset_index()\n",
    "#create a second table from posts without the first line, then concat the two\n",
    "merge = posts.drop(index = posts.index[0]).reset_index()\n",
    "posts[\"profile_id2\"] = merge[\"profile_id\"]\n",
    "posts[\"cts2\"] = merge[\"cts\"]\n",
    "posts[\"numbr_likes2\"] = merge[\"numbr_likes\"]\n",
    "posts[\"number_comments2\"] = merge[\"number_comments\"]\n",
    "#delete rows where profile_ids don't match\n",
    "posts = posts.drop(posts[(posts.profile_id != posts.profile_id2)].index)\n",
    "#calculate time differences, the groupby according to profile id\n",
    "posts['diff'] = pd.to_datetime(posts['cts2']) - pd.to_datetime(posts['cts'])\n",
    "\n",
    "mean = posts.groupby('profile_id')['diff'].mean()\n",
    "mean = mean.sort_values()\n",
    "mean = mean.drop(mean[(mean == '0 days 00:00:00')].index)\n",
    "ids = mean.head(3).keys()\n",
    "for i in mean.head(3).keys():\n",
    "    p = profiles[profiles[\"profile_id\"] == i]\n",
    "    print(p[\"profile_name\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a69743",
   "metadata": {},
   "source": [
    "## 2\n",
    "Using the function you previously coded, plot the time intervals with the highest average number of “likes” and the ones with the highest average number of comments on posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77fb1f99",
   "metadata": {},
   "outputs": [],
   "source": [
    "posts[\"diff_like\"] = posts[\"numbr_likes\"] - posts[\"numbr_likes2\"]\n",
    "posts[\"diff_comment\"] = posts[\"number_comments\"] - posts[\"number_comments2\"]\n",
    "\n",
    "like = posts.groupby(\"diff\")[\"diff_like\"].mean().sort_values(ascending = False)\n",
    "comment = posts.groupby(\"diff\")[\"diff_comment\"].mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26bc053c-322e-4f1d-bde8-6d7d8c9fa5f4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Q7\n",
    "\n",
    "## 1\n",
    "What's the probability that a post receives more than 20% \"likes\" of the number of followers a user has?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8b875d75-ecb5-4e92-985e-ba61bd0a8043",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prof=pd.read_csv('~/instagram_profiles.csv', delimiter='\\t',usecols=['profile_id','followers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e854ce58-d422-4c49-a780-fc747cfa5b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_prof.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "418b2dfd-f7b2-4c3b-b4e3-e3672caf624b",
   "metadata": {},
   "outputs": [],
   "source": [
    "posts=pd.DataFrame(columns=['profile_id','numbr_likes'])\n",
    "for chunk in pd.read_csv('~/instagram_posts.csv',delimiter='\\t',usecols=['profile_id','numbr_likes'],chunksize=500000):\n",
    "    posts=pd.concat([posts,chunk.dropna()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "57390dd7-4e43-4e76-8143-e008a808dab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We put all the data of posts and and profiles together\n",
    "dataframe=pd.merge(posts,data_prof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f482493f-e8d6-4686-93ec-a176ecdbc6d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a new column with the 20% of the amount of followers to later compare it with the likes\n",
    "dataframe['perc']=0.2*dataframe['followers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "11db5691-19b1-4b02-8573-312e0bdb25a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "res=dataframe.loc[dataframe['numbr_likes']>dataframe['perc']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "feaf785d-24ab-4eb3-b591-69b0dab51990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that a post receives more than 20% \"likes\" of the number of followers that the user has is 15.72 %\n"
     ]
    }
   ],
   "source": [
    "result=len(res['profile_id'])\n",
    "total=len(dataframe['profile_id'])\n",
    "print('The probability that a post receives more than 20% \"likes\" of the number of followers that the user has is',round(100*result/total,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8bc43b02-b98d-438b-96ee-b9b0eea96b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "del dataframe,data_prof,posts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e576cfc8-7b90-4e94-ae41-304bbc78cb08",
   "metadata": {},
   "source": [
    "## 2\n",
    "\n",
    "Do users usually return to locations? Extract the probability that a user returns to a site after having posted it in the past. Does that probability make sense to you? Explain why or why not.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118263ac-ea35-4695-9a3f-eff721424e92",
   "metadata": {},
   "source": [
    "We will count the number of locations visited once and the number of locations revisited and compute the probability in an empirical way, just like we have done in the previous part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "20c1e884-f20c-41ee-a732-413640c966e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "posts=pd.DataFrame(columns=['profile_id','location_id'])\n",
    "for chunk in pd.read_csv('~/instagram_posts.csv',delimiter='\\t',usecols=['profile_id','location_id'],chunksize=500000):\n",
    "    posts=pd.concat([posts,chunk.dropna()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ad29e2-0de9-4e2e-843b-f2b5199e31cc",
   "metadata": {},
   "source": [
    "We sort all the posts by their location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c68b7348-3bcc-47b9-b68c-2dc53729f806",
   "metadata": {},
   "outputs": [],
   "source": [
    "posts.sort_values(by='location_id',ascending=False,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8658d9a0-a212-488b-86cf-60398a248130",
   "metadata": {},
   "source": [
    "We count how many posts there are for each location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "33736b00-c3c0-469a-972e-68964aec5a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "w=Counter(posts['location_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb19b4c-25dc-4acb-8f9c-c5be1ff3c7bb",
   "metadata": {},
   "source": [
    "Then we make a vector such that all the posts between the elements `i` and `i+1` belong to the same location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ecc83382-2324-44ce-bf54-9d6619d3c3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "aux=[w[i] for i in w.keys()]\n",
    "for i in range(1,len(w)):\n",
    "    aux[i]+=aux[i-1]\n",
    "posts_per_loc=[0]\n",
    "for i in range(len(w)):\n",
    "    posts_per_loc.append(aux[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c00f17-d856-42a9-8316-8991746ef7ed",
   "metadata": {},
   "source": [
    "Finally, for every location, we take all the profiles that have posted something in that location. Then we count how many times each has done it and we add up all the users that have been in some location more than once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "084ca180-a7d9-4044-8caa-147cc1942098",
   "metadata": {},
   "outputs": [],
   "source": [
    "total=len(posts['location_id'])\n",
    "k=0\n",
    "for i in range(len(w)):\n",
    "    v=posts['profile_id'][posts_per_loc[i]:posts_per_loc[i+1]].values\n",
    "    D=Counter(v)\n",
    "    for j in D.keys():\n",
    "        if D[j]>1:\n",
    "            k+=1\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3233cea0-6317-4013-a40b-6b86a12b07f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability that a user returns to a location is 10.35 %\n"
     ]
    }
   ],
   "source": [
    "print(f'The probability that a user returns to a location is {round(100*(k/total),2)} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaaa197a-9f3e-4e88-bcf4-fc7cec7bca73",
   "metadata": {},
   "source": [
    "We obtain that the probability of a user returning to a site is around the $10$ %. \n",
    "This means that once a user posts something in a locations, it is not very likely to post something there again. \n",
    "This makes sense since people, once they have visited a place, they are more likely to go somewhere else rather than repeating the same place, since it would be very similar to what they saw when they went the last time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f994fbb-c996-406a-b29c-dc66a682dda6",
   "metadata": {
    "tags": []
   },
   "source": [
    "# [RQ8] \n",
    "Every decision you take in a data-based environment should be reinforced with\n",
    "charts, statistical tests and analysis methods to check whether a hypothesis is correct or\n",
    "not.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20b3ef0-0d09-4584-bf8d-8aa9bebad8cb",
   "metadata": {},
   "source": [
    "### RQ8.1 \n",
    "Does more “likes” also mean more comments? Plot a scatter plot of “likes” vs\n",
    "comments for posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3fd68758-9708-475a-90c4-9c97500fa9d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function that find all the number of comments and likes for each post\n",
    "def FindLC(df_names):\n",
    "    posts = []\n",
    "    \n",
    "    # Read in chunks only the col from the post df that I need \n",
    "    for chunk in pd.read_csv(df_names[0], delimiter='\\t', usecols = ['numbr_likes', 'number_comments'], \n",
    "                             on_bad_lines='skip', chunksize=500000):\n",
    "        \n",
    "        # clean the df\n",
    "        chunk = clean_df(chunk, ['numbr_likes', 'number_comments'])\n",
    "        posts.append(chunk[['numbr_likes', 'number_comments']])\n",
    "    \n",
    "    # Put everything together\n",
    "    posts = pd.concat(posts)\n",
    "    \n",
    "    # Since I have to plot the points, I just need one for each 'couple' of (n_comments, n_likes) and \n",
    "    # I can drop the duplicates\n",
    "    posts = posts.drop_duplicates()\n",
    "    return posts \n",
    "\n",
    "\n",
    "#plot the scatter plot of the likes and comment of every posts in the posts dataset\n",
    "def plotLC(post):\n",
    "    sns.regplot(x=post.numbr_likes, y=post.number_comments, color='green')\n",
    "    #plt.scatter(post.numbr_likes, post.number_comments, s=7, c='green', alpha = 0.5)\n",
    "    plt.xlabel('number of likes')\n",
    "    plt.ylabel('number of comments')\n",
    "    plt.title(\"Number of likes and comments from the posts dataset\", fontsize=15, y=1.03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0d35b26a-81c0-45c7-9eab-240c401f773d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "post = FindLC(df_names)\n",
    "plotLC(post)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d92f59-e275-43fd-a0dc-7193a7d3cd2e",
   "metadata": {},
   "source": [
    "#### Analysis RQ8.1:\n",
    "From the plot we can't find a clear connection between the two values. What we can clearly see is a tendency of the number of likes to be higher that the number of comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bf832b3-63db-40dc-8697-3df46ed9b1ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# free up memory\n",
    "del post"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1361cbb1-14a8-421b-a47e-20db64be3d90",
   "metadata": {},
   "source": [
    "### RQ8.2 \n",
    "Can you find any significant relationship between the time a user publishes a post and the number of comments and “likes”? Use an appropriate statistical test or technique and support your choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "924b4e25-6eb0-45b4-a8d2-07bef339b341",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function that find all the number of likes for each post and at what time they were published \n",
    "#FindLikesCommentsTime\n",
    "def FindLT(df_names):\n",
    "    posts = []\n",
    "    \n",
    "    # Read in chunks only the col from the post df that I need \n",
    "    for chunk in pd.read_csv(df_names[0], delimiter='\\t', usecols = ['numbr_likes', 'number_comments', 'cts'], \n",
    "                             on_bad_lines='skip', chunksize=500000):\n",
    "        \n",
    "        # clean the df\n",
    "        chunk = clean_df(chunk, ['numbr_likes', 'number_comments', 'cts'])\n",
    "        posts.append(chunk[['numbr_likes', 'number_comments', 'cts']])\n",
    "        \n",
    "    # Put everything together\n",
    "    posts = pd.concat(posts)\n",
    "    return posts \n",
    "\n",
    "\n",
    "# Function that using the time intervals from the RQ3 devide the posts in time of publishment \n",
    "# and count the number of likes and comments in each time frame\n",
    "def TimeAllPost(Post):\n",
    "    Time_intervals = [(6,11),(11,14),(14,17),(17,20),(20,24),(0,3),(3,6)]\n",
    "    \n",
    "    # Inizializing the Post_time array\n",
    "    Post_time = np.zeros(shape = (len(Time_intervals), 2))\n",
    "    Post.cts = pd.to_datetime(Post.cts)\n",
    "    \n",
    "    for j in range(len(Time_intervals)):\n",
    "        # Find to which Time interval each post was published\n",
    "        a = Post[(Post.cts.dt.hour >= Time_intervals[j][0])]\n",
    "        b = Post[(Post.cts.dt.hour < Time_intervals[j][1])]\n",
    "        \n",
    "        # Sum together all the numeber of posts that were published in the j Time interval\n",
    "        Post_time[j][0] += pd.merge(a,b, how='inner').numbr_likes.sum()\n",
    "        Post_time[j][1] += pd.merge(a,b, how='inner').number_comments.sum()\n",
    "        \n",
    "    return Post_time\n",
    "\n",
    "\n",
    "# Function that plot in the same bar plot the number of comments and like in each time frame\n",
    "# plotLikesCommentsTime\n",
    "def plotLCT (Times):\n",
    "    barx =  ['[6,11)','[11,14)','[14,17)','[17,20)','[20,24)','[0,3)','[3,6)']\n",
    "    X_axis = np.arange(len(barx))\n",
    "    \n",
    "    # bar plot of both number of comments and number of likes\n",
    "    plt.subplot(3,1,1)\n",
    "    plt.bar(X_axis - 0.2, Times[:,0], 0.4, label = 'Likes', color = 'purple')\n",
    "    plt.bar(X_axis + 0.2, Times[:,1], 0.4, label = 'Comments', color = 'orange')\n",
    "    plt.xticks(X_axis, barx)\n",
    "    plt.xlabel(\"Time frames\")\n",
    "    plt.title(\"Likes and comments\", fontsize=15, y=1.03)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    # bar plot of just the number of likes\n",
    "    plt.subplot(3,1,2)\n",
    "    plt.bar(X_axis, Times[:,0], label = 'Likes', color = 'purple')\n",
    "    plt.xticks(X_axis, barx)\n",
    "    plt.xlabel(\"Time frames\")\n",
    "    plt.title(\"Likes\", fontsize=15, y=1.03)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    # bar plot of just the number of comments\n",
    "    plt.subplot(3,1,3)\n",
    "    plt.bar(X_axis, Times[:,1], label = 'Comments', color = 'orange')\n",
    "    plt.xticks(X_axis, barx)\n",
    "    plt.xlabel(\"Time frames\")\n",
    "    plt.title(\"Comments\", fontsize=15, y=1.03)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1466bdc6-f141-4cdb-99d4-61b4b2d4536b",
   "metadata": {},
   "outputs": [],
   "source": [
    "post = FindLT(df_names)\n",
    "Times = TimeAllPost(post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3c0eee2e-2523-45d2-ab05-342c427d9f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotLCT(Times)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6318cad-edf9-4845-a7b8-6bb3fe3f24cf",
   "metadata": {},
   "source": [
    "#### Analysis RQ8.2:\n",
    "As noticed in the RQ8.1 the number of likes is, more or less, 100 times higher then the number of comments. From the bar plots can be clearly noticed that: \n",
    "1. The trend is the same for both the number of comments and the number of likes.\n",
    "2. The posts published during the $[20,24)$ time frame are the ones that receive more attention when it comes to numbers. That can probably suggest that that's the time frame where we can find the higher number of active users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971bc065-f59e-4a18-8754-bc957d412b56",
   "metadata": {},
   "source": [
    "### RQ8.3\n",
    "What’s the distribution of followers? Plot the empirical distribution of followers amongst all users and extract the mean, mode, and quantiles. Interpret those figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ebd719d-cb60-4645-8de4-2c276b66e9b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that find the number of followers for each profile and compute the mean, mode, and quantiles\n",
    "def FollowersPlots(df_names):\n",
    "    follow = pd.read_csv(df_names[1], delimiter='\\t', usecols = ['followers'], on_bad_lines='skip')\n",
    "    follow = clean_df(follow, 'followers')\n",
    "    return follow\n",
    "\n",
    "# plot distribution of followers\n",
    "def followersDist(Followers):\n",
    "    plt.subplot(3,1,1)\n",
    "    Followers.followers.plot(kind = 'kde', c = 'black') \n",
    "    plt.title(\"distribution, kde pandas function\", fontsize=15, y=1.03)\n",
    "    plt.show()\n",
    "    plt.subplot(3,1,2)\n",
    "    plt.hist(Followers.followers, color = 'red', bins = 200, density=True )  \n",
    "    plt.title(\"distribution, histogram\", fontsize=15, y=1.03)\n",
    "    plt.show()\n",
    "    plt.subplot(3,1,2)\n",
    "    plt.hist(Followers.followers, color = 'red', bins = 200, density=True) \n",
    "    plt.ylim((0,10**(-12)))\n",
    "    plt.title(\"Zoom near zero, histogram\", fontsize=15, y=1.03)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8981c3e1-2bb8-4bd5-8de3-3dea7881b83d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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Z86BxN+MDTmb+qRq+8rmUoQefykTzhDUow0DuAbwjIl7QQ7XuVPRy4qP+G+fmFadQrhZuRGly8AVKh/BB6Pb7qv9//IkyZOpU/HHqxZm2bp/jX4BXVY9vA75IuXBwIeUk6Y5WgBIRH6JLm/AR0HQ8/A/KaEpQOtF+ntL5/1LglqwNlRkRJzLRLO1eqprhN0fEYZQ+U0+jdKRu1R5vTQls3xER+2Tm0ZNmtGoa1ePPdMo1qp9FA2TwoFFSv2rW61XNmV79BO65avfj6kZEPJQSMOxNGZN9Dcof47cpbVdHXS/bpZ5msiuW9/wpREQ0tGceRqfJmyhXT1ejXD1rqn3YsPZ4VNobT8eFlHHef0X5TG+OiLsy820N7+v376t+lfKOzPxkj3n20wYRsVoPtQ/1q6Pt3/1bq/u/A7tn5u+75POAqRZwBqb0fVU1KOt3Wb4xZWx9KCPm7NTQ0bynz5qZf6F0rj+4ahK1K+VCzD8z0W/kfyJihyrtINWPUb1cWJrN41b99zJK7func1y4rxxLNU02W9IoWULphAewbTXxVUcRMZeJGoK+yswrM/ObmfliytW31oRWT42I7bq8dVTs0EOax9QeT9ZufFnt8boNeU02Usyg1UdX2rGH9PU03UZmGnmZ+QfKld7WCclbI+IzDe9ZzkQb5PUi4mE9rOoxXZZdykSzhEdXo7bMtrVZuc12J5Pu69XJbmsSs/9rCBxg+hOeTUf9N9nL73kbuvd32IWJ//yvdwscImJNptEPKjNvz8xfZeZ+lJqHU6tFawH7TjW/aZjKMQtm97hVnwTw8bO43iZTOo5WE6u2Bmq4HehnTbzGhMFDDyJi54g4ICJ+EBFXRkRGxEjOhBgRa0fE+yLinIhYFhF3RMSFEfG5aibYkVWd3JxZPZ0DPLvhLS9gFvbhzDyViSEVoXRYrbuz9ngQ/SKm4/ER8ZCGNHvVHp86yfL67MRNJxLP6qFM/d5Ov6k93rtbwuqq7ItqL53Sh/UPVWaeS5mFvHXl750R8YmGt9U/9ws7pgIi4ol07izd+r2eXD1dk+EN99v0OeYz0b7/0rZmh/V+BTc25LMxzUP89k016lvrNzgvIpoCiK6/AabwWSk1B/drSNNVNaznYbWX2o+bg9DzMavanrP5n3gCEzUjz4uI6fYz6/dx9HRKrRvAbj30UXkxE/+7p3cZaUn3YQYPvTkY+Bjl4LzpkMvSUUSsTRl+8v8Bm1GGCfwJ5U/gbcBZ1TCEo+xbtccHd+qkXF0Z67WjaL+1j6O/tPa4qSPubFmdLuP0V/vBguppUjo5tju79rjjiUlE7EQJ5Jr0ezt9s/b49RGxZceUpV17a/kllHbeY68aj/8ZlEntAPaLiG7jqtd/X/tVtXeddJwnpOYLtccLq7HsG1WjG/XLuyKiWxObhbXH32xbdhMTJ3Q7NpTrUBpGMhqA+ve1sFOi6kT0nQ151QOGnTolioj7MYUhX6eg2/wjfZGZlzNRG7fHFPaLgavmjTm2eroOZQLA6ejrcbSa6+WY6ukadN/P1qF0tm/5Rqe0um8zeOjNqcCHKbOzjvLV+3+hdKD8LbBlZj4/M19EqT7+HqXsvZwQDNMioDU/wS7A16qg6B7VCc+3KE0IZtRZKyK+XdXUdDzpiYgXUE7QoEx60z7M3kWUyXWgjHs9oyt2fZKUtvDval8QEVtQJrtqbdejMnOyZkvHMLF93xkR96pqj4hdKPNt9HIFrN7eecYThGXmH5mYrO1+wE+q+Q/ay7g3K18B/UiPI/TMSETsXtVS5jSGMe1Z1dTmmUyM3LV/RHykQ/JjmZhrYXPg+xGxfj1BRKwZEZ+j7PNN5T6GibkMNgZOjIh/6JQ4IjaIiDdSZo/vh6Qc146OiJVOpCJijYj4FBM1mEtZOdhpTfzWqu18OHBIVUtVz2dO1SRsAbPfOfSzTDTF2TsiPtp+QaUaQekYyvbvVr7TmLhq/ZqIeG57gqp2+ljgUd3yioh/iogjI+JJnQKuKq/6BZ6TJ0s3AK0T4bnAFyNipb6d1fd5GM01NYNwEBNNYPeNiMM7NfeLiK0i4sBJFg3i/+ajTNQ+vKlqabHS91oFqD9goqnwhZSJKbUKssN0DzLz0Prz/l4066vWyBifqYYgBUrzgoj4KKWz3Ci1tbyXavzwf6H8AaxOGXv9HyPih5Sx2R9KaX6yCRPVrbtNnltPHgK8AvhIRJxDOam5ghIkPJjSTOFxtfSfbJ/cJzP/FhEnUNqgbwicGhFHU670tf6Aj8/MbhNK9ds3KM2SPh0R+wA/A26ljDW+NxPDZF4NvGOyDDLzsoj4b8osonOB30TEDyjD2s6hzGz89Cr5ocD+DWX6CWV8eSjbezvgPCZOaJZm5n9N4TNC6Ti8M2WYyO2AP0bEjygj+cyhBCn1GXKPysypjgo08jLzdxHxLMooQesB74/SifqDbenuiojXUmpe1qE0N7uw+l4vpYzg9ALKCcISSu3TXl3WmxHx8iq/bav3nRwRv6ecLF5Lae++MaU99eMpTZyWTZrh1F1Gmb9jr9rnuITS8fMFrDyfxzs6jJT278B3qsfvB14QEb8GrqMEWHtSapyvowTKr+9T2Rtl5qURsR/wpeqlA4EXRcSPKceXrSnHw/UpgfROwKR9WTLzpoj4KmU41dWBYyPieMoxbxklYNiT8lv/PyaGrZ7MmpQRql4FXB0Rp1EuDtxSlWVbStOnVr+1iygXhmbDJyhN6NaqyrdzRBxDqZ3bnLJfPJTyGVcHOga7/ZaZ50TEvwJfoXTofiPwsoj4KWWejjursu1K+S4vpbR6qOfR9/+bzDwzIg5iYjbojwH7RMRPqny3pPxvtDpU3w78c310Lq1iMtPbFG+UH+gdDWnWpQzp9wfKD20ppUnRXgMs15FV2V4+ybIdq2UnD3v79fhZ/qnabtnhdg7lhPGE1msd8plXe8+iSZb/oss66re7gE8Cq3VYzy6UYR47vX/BJPtQUmaO7XWbLOn2HiaujCal6vlpTPyhTHa7BHhUwzofQKl565TH3ygB3u71dXfJ7ztd8lrS5fMs6JLnZpTaoG7f392UMenX6JLPCbX08xq2S+PnpYzWdc/+M8PfQ31999qPa+meSDnWtNIe3CFdq7N1x++C0kl3US/bpNpPun237bfTZrg96uXcoO27a7+tAN7ekN9HG8p7GaVWd2Httd2bytbD52jMr0r3XsqJZafy/Ypy0r6k27opNXTHN3zW/6MEe/ds00ny+acpfNdnAFvM8Puub6eOx4K28v294TNu2O0zTuV3V0vfNb9auhdR5npo2nYXdnj/VP9vuu4XtXTvpBzTm34Lu3bJYyDbzNto3Wy2NABVh6PfUpoIbUA5Qf0t5Qrp0RFxwIBW/Yvq/p1RRkRolWctoDUD7Vi0UczMIykTNn0eWEwZ1eUGynZ8N/DEzLysD6t6HqXJx6GU5heXV+v6O6U99O+ATwM7ZOZ+2aG5S2aeQamh+CLlanrrwD40mflLStD4ScpIH7dRah/OouwPj87M8xvyuIVy9f7tlCDiFsr2WUw5Gd8pM6eyT70SeB1lX72WcmI3I5l5BeXE7hWUavXLKU0DbqVcDT0ceHxmvilnt3Pfo2qPvzcbK8zSuf85TMza+uHJmj5U+8YjKfv9nyiB+i2UmoYPUL7XXmY1buV3S2a+nLK/fYpywvhXyu9oGeXk5aeUZhs7ZuauU/90Hdd9E6UG7F8oJ4XXUb7/SylzT+ycmZ9ryON9VR4/pOyXd1b5/BY4oCrz6f0q81Rl5icotTaLKCdvK6rynQi8AXhGdh92tZXP3yi1Ta+nbKubq7yuoNROLgD2yMzrGvI5krJ/vws4irIP3Uq5yLKMUtPwHeAlwBP6dKzuWVW+HYH/YmJ7XU+pIXsD8I+ZOaMJRmdYvlbzn3dSjoVXV2X8G+W3cgxl7pYndHj/QP5vMvMwSg3uxynN+W6i/BauoQSdbwe2zczTZroujbfIHOr5zViq2i8vz8xJZxitqvqeQ6kOPygz76xe34rSrGAeU/xz7rFcq1PmIXgp5YrzKZQf/hOA+wOHZuYgOsJJahMR3wFeRvlTf2yW4VXVB7U+JJdm5rxhlkWSVjXWPPRZlJmKn0M5cT+gFTgAZBl67z2UdpZ9bzebmXdRrux+kjIKw/Mp7RQfSrmiOFsd1iRNDOt5jIGDJOm+wg7T/dcaleeYnLxap3UCv1LH5Yg4itJMZyr2qVelV6MhHF3l/Q7g+5TmCE8B/gP4ZUS8NDN/OMX1SJqCiHg4pTM+QKeRjyRJGjsGD/03r7o/NCIO7ZJuw7bn85j6JDrrtD3/DKV9+jvb2vgeExFXUtrvfjYifjzLbb+lVU2r1uGn2TxrsSRJY8Pgof9aY3CfBFzcJd1KnbUyc5eZrLTq7/DK6ulR7csz84yIuIQytN9WlGHhJA1AZn4d+PqwyyFJUr8ZPPTfFdX9UU0jfPTZxkyMqb20Q5rW66MyC7IkSZLGiB2m++/46n6vWV7vjUwMe3mvWoxq6NZWs6hLZ6tQkiRJuu8weOizavzjXwJ7RMRnImLd+vKIWC0inhkRnWbunO56lwPHVU8/HRGtzppExNqU8aDXAX6Tk8+yKkljITOjus0bdlkkaVXjPA89iIjnAQfXXppPGbu9PmnQIZl5bJX+wZT5HHag1AicTZkw6aGUq/8bAe+qJmTpZzm3Bn4DPJgyYc+plElnHg9sWpXlqZn5x36uV5IkSasG+zz0ZiNKwFAXba9t1HqQmddGxK7Am4CXU07e16LMInkWZfbI7/a7kJm5OCIeC+xPmWviKVU5Lwe+AHy8mo1XkiRJmjJrHiRJkiT1xD4PkiRJknpi8CBJkiSpJwYPkiRJknpi8CBJkiSpJ4621EFEXEOZF+HyYZdFkiRJ6qPNgdszc5OpvtHRljqIiKVz5sy5/9Zbbz3sokiSJEl9s3jxYpYvX35rZq431fda89DZ5VtvvfWjzjvvvGGXQ5IkSeqb7bffnvPPP39arWvs8yBJkiSpJwYPkiRJknpi8CDpHhdddBELFy7krLPOGnZRJEnSCLLPgyQAMpPnPOc5XHTRRRx22GFcd911rLXWWsMuliRJGiHWPEgCYMWKFVx00UUA3HLLLfz+978fcokkSdKoMXiQJEmS1BODB0lAabYkSZLUjcGDJMDgQZIkNTN4kATcO3gwmJAkSe0MHiQBBguSJKmZwYMkAO6+++5hF0GSJI04gwdJgDUPkiSpmcGDJMDgQZIkNRvL4CEi1omIvSLiaxFxbkQsjYhlEXFORHwgItYddhmlcWOzJUmS1GQsgwfgVcDRwGspn+E44CRgS+BDwO8iYuPhFU8aP9Y8SJKkJuMaPKwAvgRsk5mPzsyXZeazgW2Bs4DtgMOGWD5p7Bg8SJKkJmMZPGTmf2XmWzLzwrbXrwb+tXr6oohYa/ZLJ40nmy1JkqQmYxk8NDinup8DPGiYBZHGiTUPkiSpyX0xeNiqur8TuHGYBZHGicGDJElqssawCzAA76juj8vM5U2JI+K8Dou27l+RpNFnsyVJktTkPlXzEBHPBV5HqXU4eMjFkcZKe82DNRGSJKndfabmISIeCXwTCOC9mXlOw1sAyMztO+R3HvCo/pVQGm0GC5Ikqcl9ouYhIjajzPWwAfDpzPzskIskjR2bLUmSpCZjHzxExIbAL4AtgK8D+w23RNJ4suZBkiQ1GevgISLuD/yUMincD4A3pGdA0rT405EkSU3GNniIiDnAMcAuwM+AV2bmXcMtlTS+bLYkSZKajGXwEBGrA98G9gBOAl6UmSuGWyppvLXXPBhMSJKkduM62tJbgb2rx9cDX4yIydLtl5nXz1qppDHmUK2SJKnJuAYPG9Qe790xFSykBBeSGrTXNFjzIEmS2vW92VJE7NbvPNtl5sLMjB5uSwZdFum+wpoHSZLUZBB9Hk6OiD9GxDsi4oEDyF/SANjnQZIkNRlE8HAi8Ejg08CVEXFkROw+gPVI6qP2YMGaB0mS1K7vwUNm7gFsA3wCuAl4JfDLiLggIv4tIh7c73VKmjmbLUmSpCYDGao1Mxdn5gHA5sCLKfMwbAV8HLgsIo6KiGdHhyGSJM0+my1JkqQmA53nITPvysyjM/O5wDzgQ8DVlBGSjgWWRMTBEbHJIMshqZnNliRJUpNZmyQuM68AfgOcAUR125wSUFwSEZ+IiDVnqzySVmbNgyRJajLweR4i4iHAa6vbPErQcA7wZeCXwEspk769uyrPuwZdJkn3Zp8HSZLUZCA1DxGxWkTsGRHHAJcChwCbAN8AnpiZj8vMwzPzwsz8KLAdsITSuVrSEDhJnCRJatL3moeIOATYF3gIpZbhfOA/gW9k5i2TvSczl0bEScCr+10eSb2x5kGSJDUZRLOl9wPLgW8DX87Mk3p83wmAZyvSkBg8SJKkJoMIHv4N+Hpm3jCVN2XmImDRAMojqQc2W5IkSU0G0efhdOBBTYki4hER8ZQBrF/SNFjzIEmSmgwiePg1sH8P6f6tSitpBDhUqyRJajKI4KE1h0Mv6SSNCCeJkyRJTWZtkrhJbArcNsT1S6qx5kGSJDXpS4fpiNin7aWHT/JafZ3bAk8HTuvH+iXNnH0eJElSk36NtrSIiWFWE/iH6tZJAHcAH+7T+iXNkM2WJElSk34FDx+mBA0BfAA4GzimQ9oVwFXAzzPz6j6tX9IM2WxJkiQ16UvwkJkLW48jYgFwfGZ+qB95S5odNluSJElN+j5JXGbO63eekgbPSeIkSVKTYY62JGmEWPMgSZKazLjmISKOoPR3eF9mXls971Vm5utmWgZJM2efB0mS1KQfzZYWUIKHQ4Frq+e9SsDgQRoBjrYkSZKa9CN42KO6v6ztuaQxYrMlSZLUZMbBQ2ae2O25pPFgsyVJktTEDtOSAJstSZKkZn0PHiJinYjYIiLmtr3+gIj4WET8OCK+EBFb9nvdkqbPmgdJktSk7/M8AAcB+wPzgTMAImIt4FRgW8os1AAvjojHZua1AyiDpCmyz4MkSWoyiGZLTwMuycwzaq+9CtgO+DXwLOAwYGPgXQNYv6RpcJI4SZLUZBDBwxbABW2v7QXcDSzIzF9k5ruBvwDPG8D6JU2DNQ+SJKnJIIKHDYCb2l7bDfhDZl5Re+1cYPMBrF/SNBg8SJKkJoMIHq4BNm09iYjtgQ2B9iFcPTORRojNliRJUpNBBA9nAf8QETtWz99FCRR+3JbuEcBVA1i/pGmw5kGSJDUZRPDw8SrfMyLiBuC1wDnAr1oJImJj4LHA7wewfknT4FCtkiSpSd+Dh8z8LfBC4GRKE6ZvAi/IzPqZyKuAW4Hj+r1+SdPjJHGSJKnJIOZ5IDOPBY7tsvwwynCt0xYROwPPAJ5AmVNiU2B5Zq49k3ylVZU1D5IkqclAgodZcjClhkNSH9jnQZIkNRlo8BARawAPAuZ0SpOZl00z+1MpfSl+V92umWY+krDZkiRJajaQ4CEing4cBOwKrNklaU63DJl5aNs6p5ONpIrNliRJUpO+Bw8R8XzgaGB1ymRxFwO39Xs9kvrLmgdJktRkEDUPH6SM4vRO4AuZedcA1iGpz6x5kCRJTQYRPGwPnJqZnxtA3pIGxA7TkiSpySCCh9uAaweQ70BExHkdFm09qwWRhqy9psGaB0mS1G4QM0wfD+wUEYPIW9KAWPMgSZKaDKLmYX/K0Kmfioj9Rr3PQ2ZuP9nrVY3Eo2a5ONLQGDxIkqQmgwge9gV+CrwdeH5EnABcQRmWtV1m5iEDKIOkKbLZkiRJajKI4GEhJVAISr+Bbn0HEjB4kEaANQ+SJKnJoGoeJI0Zh2qVJElN+h48ZOY3+p2npMFzkjhJktRkEDUPsyIingcc3PbyWhFxWu35IZl57CwWSxpb1jxIkqQmAwseImIN4PnA44ENgd9m5hHVsk2r187PzL9PcxUbAfPbV9v22kbTzFta5djnQZIkNRlI8BARTwX+G3go5YQ+gTWBI6okTwMWAS8Dvj+ddWTmoioPSX1gsyVJktSk7xO5RcRjgJ8AGwOfBV5KCSDqvg/cDry43+uXND02W5IkSU0GUfPwAWAO8MzM/BVAxMqxQ2beHhF/Ah43gPVLmgabLUmSpCZ9r3kAngqc1gocurgM2HQA65c0DU4SJ0mSmgwieFgPuLKHdHOA1QewfknTYM2DJElqMojg4WrgkT2kezRw6QDWL2kaDB4kSVKTQQQPPwe2j4i9OyWIiAXAwwDnYJBGhM2WJElSk0EEDx8FbgO+HRGHRMQu1evrRMSjI+Ig4IvADcCnB7B+SdNgzYMkSWrS9+AhMy8FngfcBLwf+C1lnoeXAucAHwZuBV6Ymdf0e/2SpsehWiVJUpOBTBKXmSdHxDbA64CnA/MonaOvAI4HvpyZNw9i3ZKmx0niJElSk4EEDwCZeStwWHWTNOKseZAkSU0G0edB0hiyz4MkSWrS95qHiNgN2IMyXOsGlP4ONwLnA7/OzN/2e52SZs5mS5IkqUnfgoeI2AE4Anhc66W2JFmlOx14XWae3691S5o5my1JkqQmfQkeIuLxwK+AucAy4KfA2cD1lCBiQ2BH4NnAfODUiNg9M8/qx/olzZzNliRJUpMZBw8RsTpwJCVw+Brwnsxc2iHtepS5HV4LfCsits9ML29KI8BJ4iRJUpN+dJh+IfBw4DuZ+YZOgQNAZi7NzNcD3wO2Afbsw/ol9YE1D5IkqUk/goc9gbuB903hPQdW9y/sw/ol9YF9HiRJUpN+BA87A3/JzEt6fUNmXgz8uXqvpBHgaEuSJKlJP4KHhwAXTON9FwCb9mH9kvrAZkuSJKlJP4KHBwC3TON9S4H1+rB+SX1gsyVJktSkH8HDGpQ+D1N1NwOYpE7S9NhsSZIkNelH8CDpPsCaB0mS1KRfV/5fExGv6VNekobAPg+SJKlJv4KHmOb7PDuRRoSTxEmSpCYzDh4y06ZP0n2ANQ+SJKmJJ/6SAIMHSZLUzOBBEmCzJUmS1MzgQRJgzYMkSWpm8CAJcKhWSZLUzOBBEuAkcZIkqZnBgyTAmgdJktTM4EESYJ8HSZLUzOBBEmCzJUmS1MzgQRJgsyVJktTM4EESYLMlSZLUzOBBEuAkcZIkqdlYBw8RsXZEfCgiLoiIOyLiqog4IiI2G3bZpHHTXtOwbNmyIZVEkiSNqrENHiJibeCXwAeAdYFjgMuBfYEzI2LrIRZPGjvtwcOSJUuGUxBJkjSyxjZ4AN4H7AacCmyTmS/PzPnAe4CNgCOGWThp3LQ3U/rrX//KrbfeOqTSSJKkUTSWwUNErAm8rXr6r5l5W2tZZn4aOBd4SkTsPIzySeNosg7SF1988RBKIkmSRtVYBg/Ak4D1gcWZedYky4+q7vectRJJY26y4OHwww8fQkkkSdKoWmPYBZimx1b3Z3ZYfmZburFy9NFHs3jx4mEXQ6uYP/7xj/d67fDDD+f0009n/vz5zJ07l7///e+sWLGCTTbZhIi457baaqsREfe8r/W4/V6SJE2YP38+T37yk4ddjCkZ1+Bhi+r+ig7Lr2hL11FEnNdh0dA6XC9atIgf/ehHw1q9tJIzzzyTM8/sFKdLkqTpOvDAA8cueBjXZkvrVve3d1i+rC2dpCnYd999mTt37rCLIUmSRsy41jy02kB0mgK35zYSmbn9pBmUGolHTbFcfbHbbrux5pprDmPVEjvuuCMHHHAABx10EMcddxxXXXUVy5YtY8WKFdx55533NF3KzHturZGa6v0mWo+dqVqSpMltv/2kp6EjbVyDh9b4kZ0uja5T3d/WYflI23///YddBImtttqKt7zlLcMuhiRJGiHj2mzpsuq+00zSm7WlkyRJkjRD4xo8nFPd79Rheev1c2ehLJIkSdIqIcaxPXJErAVcBzwA2Kl9roeIOAfYAXhCZv5umutYOmfOnPtvvfXQBl2SJEmS+m7x4sUsX7781sxcb6rvHcvgASAiPgK8HzgFeGZmLqtefzfwKeDkzJz22FcRcQ2l78TlfSjuqqYVcTlZxfjxuxtffnfjze9vfPndja9V+bvbHLg9MzeZ6hvHOXhYGzgBmA9cDZwEPKx6fgOwa2ZeNLQCrsJac2d0GslKo8vvbnz53Y03v7/x5Xc3vvzupmdc+zyQmXcAewCHUOZ72AuYB3wDeJyBgyRJktRf4zpUKwCZ+TfgA9VNkiRJ0gCNbc2DJEmSpNll8CBJkiSpJwYPkiRJknoytqMtSZIkSZpd1jxIkiRJ6onBgyRJkqSeGDxIkiRJ6onBgyRJkqSeGDxIkiRJ6onBgyRJkqSeGDxIkiRJ6onBgwYuIuZGxKsj4j8i4vSIWB4RGREHDLtsKiJi7Yj4UERcEBF3RMRVEXFERGw27LKps4jYOSIOiIgfRMSV1e/qjmGXS91FxDoRsVdEfC0izo2IpRGxLCLOiYgPRMS6wy6juouId1e/uwsj4pbqf+3SiPhGRGw/7PKpNxHxwIi4rjp2/nnY5RkXThKngYuIHYGzJll0YGZ+fJaLozYRsTbwS2A34GrgJGAe8ATgr8ATM3Px0AqojiLih8AL215enplrD6E46lFEvB74SvX0POB8YD3Kb/D+wJ+Bp2bmdcMpoZpExPXAXOBc4Mrq5e2BbYAVwF6Z+dMhFU89iohFwD5AAH/JzO2GW6LxYM2DZsOtwNeANwI7Af9vuMVRm/dRTlpOBbbJzJdn5nzgPcBGwBHDLJy6OhX4MLAnsMmQy6LerQC+RPm9PTozX5aZzwa2pVxo2Q44bIjlU7MXAhtk5vzMfFF12xZ4C7AW8NWIWH24RVQ3EfE04DVMBPLqkTUPmnURsRD4INY8DF1ErAlcB6wP7JSZZ7UtPwfYAdglM38/+yXUVEREYs3DWIuIJwKnAMuB9TJzxZCLpCmKiAuBhwPbZ+b5wy6P7i0i7kepNVoB7AVcgDUPPbPmQVq1PYkSOCxuDxwqR1X3e85aiaRV2znV/RzgQcMsiKbtrurewG90fRDYGngzcOeQyzJ2DB6kVdtjq/szOyw/sy2dpMHaqrq/E7hxmAXR1EXEPpTmZxcAFw+5OJpEROxAaZb79cz8v2GXZxytMewCSBqqLar7Kzosv6ItnaTBekd1f1xmLh9qSdQoIt5L6Sg9F3hk9fgq4FWZefcwy6Z7i4jVKH0cbgb+bbilGV8GD9KqrTUk5O0dli9rSydpQCLiucDrKLUOBw+5OOrNs4Cn1Z5fDrzaPmIj622UkQT3zcwbhl2YcWXwoEYRcRTw6Cm+bZ/MPH0Q5VFfRXXfaeSE6PC6pD6KiEcC36T85t6bmec0vEUjIDOfDhAR6wOPAT4AnBARB2WmIwuOkIjYHPgIcGJmLhpyccaawYN6MY/ShnMq1hlAOdR/t1b3czssb32Pt81CWaRVUjUZ43HABsCnM/OzQy6SpigzbwZOqmqPTgUOiYifZ+bvhlsy1XyRMozum4ddkHFn8KBGmbnLsMuggbmsuu80k/Rmbekk9VFEbAj8gtKv6OvAfsMtkWYiM++MiO8AO1NGqTN4GB3Pp/R1+FLESpXqraGtt4iIE1ppM9OLZh0YPEirtlbTiJ06LG+9fu4slEVapUTE/YGfUiaF+wHwhnTypfuC66v7jYZaCk1mfeCpHZbdr7bM8+MuHKpVWrX9BrgF2DoiHjfJ8pdU9z+evSJJ930RMQc4BtgF+Bnwysy8q/u7NCZaJ6CLh1oKrSQzY7IbsGWV5C+1128eYlFHnsGDtAqrZq/9fPX08xFxT9+HiHg3ZXbpk223K/VPRKwOfBvYAzgJeJEzSY+PiHhyRLw8ItZoe33NiHgb8Grgb8B3hlJAacCsltGsiIijgYdUT1vt6N8SEXtVj6/OzL1nvWCCMvrE04HdgAsj4iTgYcB84AZg3yGWTV1ExPO495Cea0XEabXnh2TmsbNYLDV7K9A63l0PfLGtDXbLfpl5/WQLNFRbU/qnXB8Rv6ccJzekjLb0EOAOYEFmXj68IkqDY/Cg2fI4yglp3ebVDeDS2S2OWjLzjojYAzgQeBWwF3AT8A3gYP8AR9pGlCCvLtpes9316Nmg9rjbRZOFTLSf1+g4EfgopXnSDpTAYQWwBDgK+FxmXjS00kkDFvbNkiRJktQL+zxIkiRJ6onBgyRJkqSeGDxIkiRJ6onBgyRJkqSeGDxIkiRJ6onBgyRJkqSeGDxIkiRJ6onBgyRJkqSeGDxIkiRJ6onBgyRJkqSeGDxIkiRJAxQRO0fEARHxg4i4MiIyIu4YwHp2jYjvR8Q1EXFnRNwYEb+MiJf0bR2Z2a+8JEmSJLWJiB8CL2x7eXlmrt3HdbwU+B9K5cAZwGJgU+AfqtcOzcwDZrwegwdJkiRpcCJif2Ad4HfV7Rr6GDxExBrAVcBGwCsy8zu1ZU8EfgXMAR6RmYtntC6DB0mSJGn2RETS3+Dh0cAfgD9n5iMnWf5DSs3HyzPzuzNZl30eJEmSpBETEetGxAci4g8RcXtELI2IEyNir0mSL+8x2xtnXC5rHiRJkqTZ01TzEBEPpjQ1ehRwJfB7SrOnJwJzgQMz8+O19KsDFwBb0Va7UGu2dDWwXWaumFHZDR4kSZKk2dND8PAT4DnAvwMHZead1etbAT8H5gE7Zea5tfc8Gfhf4AFMdJh+CPAk4HTg1Zl50UzLbrMlSZIkaURExI6UwOEU4IBW4ACQmRcD7wFWB15ff19mngQ8FbgE2AV4OfAUYBlwPKVD9YwZPEiSJEmj4xnV/TE5eROhk6v7x9dfjIhXAr8FLgPmA+sC2wDfBg4Cjo+INWdaOJstSZIkSbOoW7OliPgC8JYesrkoMx9RvecRwHnAtZR+Dcva8vwRsCfwxsz8z5mUfY2ZvFmSJElSX61e3Z8EXNwl3fW1x68A1gSOaw8cKt+lBA+7AwYPkiRJ0n3EFdX9UZn5uR7fs1l1v7TD8tbrD5x2qSr2eZAkSZJGx/HV/V5TeM811f0uHZa3+kcsmUZ5VmLwIEmSJI2IzDwN+CWwR0R8JiLWrS+PiNUi4pkR8aTay8dU90+JiDe3pd8VeFf19KiZls8O05IkSdIARcTzgINrL80HkjL/QsshmXlslf7BlPkcdqDMCn028FfgocC2wEbAuzLzsNo6PgHsVz09Dzgf2JQysdxqwH9m5htn+lns8yBJkiQN1kaUgKEu2l7bqPUgM6+tagzeRJmv4fHAWpRZos+i1DR8t/ZeMvO9EXFK9Z6dKUHGrcCJwFcz81v9+CDWPEiSJEnqiX0eJEmSJPXE4EGSJElSTwweJEmSJPXE4EGSJElSTwweJEmSJPXE4EGSJElSTwweJEmSJPXE4EGSJElSTwweJEmSJPXE4EGSJElSTwweJEmSJPXE4EGSJElSTwweJEmSJPXE4EGSJElSTwweJEmSJPXE4EGSJElSTwweJEmSJPXk/wM0bdoiKjYvfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Followers = FollowersPlots(df_names)\n",
    "followersDist(Followers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5e288d03-8d0e-4955-9772-0995ecbac830",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that campute mean, mode and quantiles of the number of followers\n",
    "def FollowersInfo(Followers):\n",
    "    print(\"Other than the distribution some other usefull informations of are it'summaries like:\\n-The mean:\", \n",
    "          Followers.followers.mean(),\"\\n-The mode:\", str(float(Followers.followers.mode())))\n",
    "    print('We can also be interested in the quantiles, a couple can be visualized with the box plot:\\n')\n",
    "    \n",
    "    plt.subplot(2,1,1)\n",
    "    plt.boxplot(Followers.followers, vert=False)  \n",
    "    plt.title(\"boxplot of the followers\", fontsize=15, y=1.03)\n",
    "    plt.show()\n",
    "    \n",
    "    plt.subplot(2,1,2)\n",
    "    plt.boxplot(Followers.followers,vert=False)  \n",
    "    plt.xlim((0,1500))\n",
    "    plt.title(\"Zoom of the boxplot\", fontsize=15, y=1.03)\n",
    "    plt.show()\n",
    "    \n",
    "    print('\\nFrom what we can get from the box plot is the first quartile:', float(Followers.quantile(.25)), \n",
    "          'and the third:', str(float(Followers.quantile(.75))) + '. \\nBut we can also plot the quantiles:\\n')\n",
    "    \n",
    "    \n",
    "    plt.subplot(2,1,1)\n",
    "    Followers.followers.quantile(np.linspace(0, 1, 100)).plot(color = 'orange')\n",
    "    plt.title(\"Quantiles\", fontsize=15, y=1.03)\n",
    "    plt.show()\n",
    "    \n",
    "    plt.subplot(2,1,2)\n",
    "    Followers.followers.quantile(np.linspace(0, 1, 100)).plot(color = 'orange')  \n",
    "    plt.xlim((0,0.9))\n",
    "    plt.ylim((0,3000))\n",
    "    plt.title(\"Zoom of the quantiles\", fontsize=15, y=1.03)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "790b2abc-9e89-4781-9251-b96a7bc2bc8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Other than the distribution some other usefull informations of are it'summaries like:\n",
      "-The mean: 4739.899180397426 \n",
      "-The mode: 0.0\n",
      "We can also be interested in the quantiles, a couple can be visualized with the box plot:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "From what we can get from the box plot is the first quartile: 159.0 and the third: 974.0. \n",
      "But we can also plot the quantiles:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8WmTfE2Z2egwci+UdSHgQaC5ofiQ5oYGIdJ0G2IvIRmZ2BvDRxKb7gZO68wVjkYGs3yTcprQH4Zadh8zsZsIHAiPca/9O8l+izKD0079/Rrj95mjCPf63mNldsb6twO6EWXpyH+pnA5/r8klkgLu/amafAv5EaKdPE3qn/k54ovjmhEkQtohZ2ggzEHVnIoTu1G+tmU0nDC4eB8wws2sJgW7ug/Dt7v5ciSLSOP7xhCm1JxF6E54ys/sJA8wXE6aX3Szu24NwjVX6QNNix1xhZp8gfAgeSHjexmFmdh3hZ7IF8L5Yn4cJD7/s7IGkPyA/U9UvzOz9wPSYdyfgWMK1fgFlekTc/Wkzu4n878bDZvYPwqxgbYTzP4rwu/EnwkxWnQVAabqZ0OswADgzPg/mURK9D+7+8zQPGJ8/dAohkNuBMIboPjN7lHCL2AJCe0wgjA3bh3DLWLGHjO5O+GLgUjN7iPAAyTcJ18HmwHvI/y468K00z0WkX3J3LVq0aMHdIXxj7GksJcofRfiw0ln+a4FhndS1iXCrU2dl3QtMLFPOeYm0p3dyzKqkrfBncwqwrJNzXQy8tws/40NTum72JjwMsFS9Ti9In9s+t4Kyz6ukvoQPiHd04Rq9MoXz/iCwpswxZhKmBZ5e7vciUd4lndT554QP+bn300uUM55wa1m5sq4hPENkbrmfBXB6Is95FbRJ2fJimh+Vq1tB2kM7O37BNb11meOOBK7swjXyYJEyWivMuxw4NY3fLy1a+vuinhURqRl3X0a4lekowge9A4GJcffrhG85f+/ud1ZQVgtwmpldSugNOoTwzWYD4ZvOB4G/uPu1aZ9HPbj7lWZ2B2H80LsJT7cfSQhgngduBC6NbVzruv3LzPYAPk/4OWxF+CBcs+eLuPtrwOFm9nbC9LEHEZ41MhJYS7i+ngXuAf7h7i+kcMw/mdkM4AuEnowtCN/GzyZ8KP6Vu6+ptGfS3c+KvSKfIny7P4rwrf8jwC/d/TaAzspz94VxXNiZhCD3LYSegzcIY9B+n/u9qM0EapvU76uxV+J0Qm/XOMKXD9U+7nLC808uAD5M/lodDbQQHvT4b0IP7Q3uPrNIMeMJPVMHx7pvE/M7YVrrZ4FbgMu8Rr2bIn2duXu96yAiItJnxdvkDgFw99pHByIivZgG2IuIiIiISCYpWBERERERkUxSsCIiIiIiIpmkYEVERERERDJJwYqIiIiIiGSSghUREREREckkTV0sIiIiIiKZpJ4VERERERHJJAUrIiIiIiKSSQpWREREREQkk1ILVszsHDO7xsxeMLPlZtZiZi+b2RVmtnOZfB8xs4fNbJWZLTGzf5rZAZ0c64CYbknM97CZndZJnslm9jszm29m68xslpl9z8yau3vOIiIiIiJSPakNsDezRcBQ4Engtbh5Z2B7YD1wvLvfVJDnQuALwFrgVqAZOBww4GR3v7bIcU4A/koItO4BFsU8o4D/dfdziuSZCswAxgNPA88CewPbxu3vcPeW7p+9iIiIiIikLc1g5UDgUXdfV7D9TOASYD4wxd3b4vbDgDuAxcD+7v5C3L4/MJ0QwGzj7ksTZY0GXgJGAie6+zVx+0TgPmAacJi731VQh7uBg4GL3f3suK0BuAo4Afieu38nlYYQEREREZFUpHYbmLvfXxioxO2/BGYDmwM7JHZ9Ma7PzwUqMf0M4FJCQHJGQXEfj9uvzwUqMc8C4CvxbYeeFTPbhxCovJlIg7tvAM4EWoHPmlljxScrIiIiIiJVV6sB9m1xvR4gjhM5PG67ukj63LZjC7YfUybPjcA64IiCcSi5PP8ovNUrBjn3AqOBAzs5BxERERERqaGqBytm9hFCj8os4MW4eUegCVjo7q8WyfZYXO9WsH23gv0buft6wniUZjr24OxeKk/B9t1L7BcRERERkTpoSLtAM/syYWD9UOAt8fV84FR3b4/JpsR1sUAFd19tZsuA0WY23N1XmtkIwiD6kvni9r1j+TMrOVZi+5QS+0VEREREpA5SD1aAd5G/xQtgHvBhd380sW1YXK8pU85qQnAyDFiZyFMu3+qC8is5VrE8JZnZMyV2bU+YFGBeJeWIiIiIiPQCWwJr3H1SPQ6eerDi7kcAmNkoYFfg28B0MzvX3X8Qk1kueZmirJP3leSp5FiVlFuJAU1NTcOnTp26U0rliYiIiIjU1Zw5c2hpqd8TPqrRswKAuy8D7jWzdxOeZfJ9M7vV3R8h9JRAuFWslCFxvSquVxbsW1FBnmS+Uscqlqckdy/6gEsze2bq1Kk7PfNMqY4XEREREZHeZeedd+bZZ5+t251DVR9g7+6twJWEHozc7F6vxPXkYnnMbCjhFrBl7r4ylrMCWF4uX2L7K4ltZY9VIo+IiIiIiNRZraYuXhTX4+P6eaAFGG9mxYKIPeP6yYLtMwv2bxSfk7JLLPf5SvJ0ciwREREREamjWgUrh8T1HAB3XwvcGbedVCR9btsNBdtvLJPnGMK0xXcUPJwyl+dYM2tKZrDw5Pu3E3ps7uvkHEREREREpIZSCVbM7O1mdoqZNRRsbzSzzwIfJsyUdWVi94Vxfa6ZbZfIsz/wScKYlN8WHOo3cftxZva+RJ4JwH8XlAuAuz8M3A9MAH6UyNMAXAI0Aj+Lt6uJiIiIiEhGpDXAfipwGbDIzB4FFgPjCLOBbUZ4svzp7r5xcI67325mFwFnA0+Y2W3AIOBIQhD1QXdfkjyIuy8xszOAq4Crzexuwi1mRxDGuFzs7ncUqd9HCYP8zzazw4BngX2AbYGHgB8UySMiIiIiInWU1m1gdwMXEMaK7AacDBwILAF+Buzq7lcVZnL3zxMCiX8TgpQDgDuAQ9z9b8UOFLcfDNwCvBV4N+H2sjPc/ewSeV4A9gAuJ4ybOYEwlfH5wDsKbhsTEREREZEMSKVnxd1fAr7ZzbyXE4KIruS5Hzi6i3nmEQIjERERERHpBWo1wF5ERERERKRLFKyIiIiIiEgmKVgREREREZFMUrAiIiIiIiKZpGBFREREREQyScGKiIiIiIhkkoIVERERERHJJAUrIiIiIiKSSQpWREREREQkkxSsiIiIiIhIJilYERERERGRTFKwIiIiIiIimaRgRUREREREMknBioiIiIiIZJKCFRERERERySQFKyIiIiIikkkKVkREREREJJMUrIiIiIiISCYpWBERERERkUxSsCIiIiIiIsV5e10Pr2BFRERERESK27CqrodXsCIiIiIiIsW1rqjr4RWsiIiIiIjIptpa1LMiIiIiIiIZtOBOjVkREREREZEMmndNvWugYEVERERERAq0t8Gr19e7FgpWRERERESkwII7oWVhvWuhYEVERERERArM+lm9awAoWBERERERkaSVc+C1G+pdC0DBioiIiIiIJM36OeDh9YBBda2KghUREREREQlaV8KLv8u/bxpTv7qgYEVERERERHJe+n3+qfWNI6BxVF2ro2BFRERERETCAyCTA+u3/RhYfcMFBSsiIiIiIgKv3wYrno9vDHb4TF2rAwpWREREREQE4PmL8q+3OBaGbVu/ukQKVkRERERE+rsVs+D1m/Lvd/hc/eqSoGBFRERERKS/m/Xz/OuRO8HEw+pXlwQFKyIiIiIi/VnrCnjxsvz77T8HZvWrT4KCFRERERGR/mzOZbBhVXg9aDRs86H61idBwYqIiIiISH9VOF3x1I9Dw9D61aeAghURERERkf5q/k2wak54bQNgu7PqW58CClZERERERPqr5y/Ov97iOBi2dd2qUoyCFRERERGR/mj5v+GNW/PvMzJdcZKCFRERERGR/ig5VmXUbjDhkPrVpQQFKyIiIiIi/c3aBfDiFfn3O2RnuuIkBSsiIiIiIv3NU+dB25rwumkcbHVqXatTioIVEREREZH+ZPmzMOf/8u93+RY0DK5ffcpQsCIiIiIi0p88/lXwtvB62DSY9qn61qcMBSsiIiIiIv3Fgrtg/g3593v8CAYOql99OqFgRURERESkP/B2eOxL+ffjD4TJJ9SvPhVQsCIiIiIi0h/M/TMsfSz/fo8fZ3IGsKQeBytmNsTMjjez35rZk2a2wsxWm9lMM/u2mQ0rk/cjZvawma0ysyVm9k8zO6CT4x0Q0y2J+R42s9M6yTPZzH5nZvPNbJ2ZzTKz75lZc3fPW0RERESk19iwFmZ+I/9+yvth3NvqV58KpdGzcipwLXBGLO9m4F5gG+C7wCNmNqEwk5ldCFwB7ALcDjwMHAncY2ZF+6Pi9nuAo4An47G2Ay6P5RXLMxV4DPgosBi4HhgIfAu408yaunXWIiIiIiK9xayLYc288HpAI7z1h/WtT4XSCFbWA78Etnf3Xdz9/e5+FLAD8DiwI/DTZAYzOwz4AiF42N3dj495DgbagMvMbHRBntHAZYRA4yR3P9TdT4rlzwa+YGbvKFK/3wHjgYvdfVd3PyXW7Vpgf+AbRfKIiIiIiPQN6xbCMxfk32/3GRi2bf3q0wU9Dlbc/ffufpa7v1Cw/XXg0/Ht+8wsOc3AF+P6/GQ+d58BXAqMJPTUJH08br/e3a9J5FkAfCW+PSeZwcz2IQRAbybS4O4bgDOBVuCzZtZY+RmLiIiIiPQiT38PWleE142jYJdz61qdrqj2APuZcd0EjAWI40QOj9uvLpInt+3Ygu3HlMlzI7AOOKJgHEouzz/cvSWZIQY59wKjgQPLn4aIiIiISC+0Yha8cGn+/S7fgqYx9atPF1U7WMn1L7UCS+LrHQnBy0J3f7VIntwUBbsVbN+tYP9G7r4eeBpoJtzilbN7qTwF23cvsV9EREREpPd64mvgG8LrodvA9p8unz5jqh2snB3XNyd6NqbEdbFABXdfDSwDRpvZcAAzGwGMKpcvsX1KYlvZY5XIIyIiIiLS+715L7x6bf79W38IA3vX3FIN1SrYzN4NfIzQq/KtxK7cVMZrymRfTQhOhgErE3nK5VtdUH4lxyqWpywze6bErqmVliEiIiIiUlXtG+CxL+Tfj90vTFfcy1SlZ8XM3gL8ETDgy+4+M7k7rr1cEZ28ryRPJcfK9lNwRERERES64/mLYMmj+fe94AGQxaTes2JmkwnPPxkNXOjuFxUkWRnXQ8sUMySuVxXkye1bUUGeSo5VLE9Z7r5zse2xx2WnSssREREREamKlbPhycSNTducBhMOql99eiDVnhUzGwfcRhgDchnwpSLJXonrySXKGEq4BWyZu68EcPcVwPJy+RLbX0lsK3usEnlERERERHond3joP6FtbXjfPAH2LPrs9F4htWAlDoa/iTDb1zXAf7p7sduvngdagPGxF6bQnnH9ZMH2mQX7k8duBHaJ5T5fSZ5OjiUiIiIi0vvM+Q28OT3/fu9f9KqpigulEqyYWRNwPbA3cAvwAXdvK5bW3dcCd8a3JxVJktt2Q8H2G8vkOYYwbfEd7r6uSJ5jYx2TdZ4IvJ3QY3NfsbqKiIiIiPQaa16DxxM3Nk0+AbY8sX71SUGPgxUzGwj8P+AdhIcsvi8+96ScXF/UuWa2XaKs/YFPEsak/LYgz2/i9uPM7H2JPBOA/y4oFwB3fxi4H5gA/CiRpwG4BGgEfuburZ2fqYiIiIhIRrnDI2cmnlQ/Evb5Ra8cVJ+UxgD7zwAnxNeLgEuseKN8yd0XAbj77WZ2EeE5LE+Y2W3AIOBIQgD1QXdfkszs7kvM7AzgKuBqM7s7Hu8IwhiXi939jiLH/SgwAzjbzA4DngX2ITyw8iHgB909cRERERGRTHjlKnjtH/n3e14IgzerX31SkkawMjrx+oSSqeA8QnABgLt/3syeIAQ7RxKex3IHcL67F70ty93/ZmYHA+cCbyMEOP8GfuHul5XI84KZ7QF8Dzgq1nEecD5wQcFtYyIiIiIivUvLYvjXZ/PvJx4O2360fvVJUY+DFXc/jxCIdCfv5cDlXcxzP3B0F/PMI/SwiIiIiIj0LY9+AVoWhtcDB8N+v+71t3/lVOWhkCIiIiIiUgPzb4K5f8i/3/0HMGzb+tUnZQpWRERERER6o9aV8PAn8+/H7gvbf65+9akCBSsiIiIiIr2NOzx6NqyZF94PaIT9fgsDBta3XilTsCIiIiIi0tvM/hW8mJhfaudvwqhd6lefKlGwIiIiIiLSmyy8Hx5N3O41bn/Y6ev1q08VKVgREREREekt1syHe0+C9vhM8+ZJcNDVMHBQfetVJQpWRERERER6g7YWuPdEWPdGeD+gEd7+NxiyeX3rVUUKVkREREREeoNHPweLH8y/3+tnMP6A+tWnBhSsiIiIiIhk3exfhyVn6sdh2ifqV58aUbAiIiIiIpJlC2fAvz6Tfz92P9j7533mKfXlKFgREREREcmqta/DfScmBtRPDONUBjbVt141omBFRERERCSL2tbDfSeHgAXAGsLMX0O2qG+9akjBioiIiIhI1rRvgBkfDs9UydnrIphwUP3qVAcKVkREREREsqS9DR48HV65Kr9t2zNguzPrVqV6UbAiIiIiIpIV3g4P/yfM/VN+22ZHwT6X9IsB9YUUrIiIiIiIZIE7PHIWvHhZftvEw+Ht1/SbAfWFFKyIiIiIiNSbOzx6Nsz+VX7bhIPhkOuhYXD96lVnClZEREREROrJHR7/Msz6WX7buP3hkBugYWj96pUBClZEREREROrFHWZ+E577SX7bmH3g0JugcXj96pURClZEREREROrl6e/Bsz/Mvx+9Bxx2CwwaWb86ZUhDvSsgIiIiItLvtLfB41+E5y/Kbxu1K7zjVhg0un71yhgFKyIiIiIitbRhDTzwQXj1uvy2EW+Bw26H5nF1q1YWKVgREREREamVtQvg7mNhySP5baP3hENvhOYJ9atXRilYERERERGpheX/hunvhtVz89s2fw8c+BdoHFa3amWZBtiLiIiIiFTbgulw6wEdA5XtzoKDr1OgUoZ6VkREREREqumlP8JDZ0B7a9xgsMePYccvgFldq5Z1ClZERERERKrB2+Hp8+Gp7+S3DWyG/f8IU06sX716EQUrIiIiIiJpa1kCMz4C82/Mb2saB4f8A8a9rX716mUUrIiIiIiIpGnRw3D/+2H1y/ltw7eHQ/8Jw6fWr169kIIVEREREZE0uMOsX8Dj5yTGpwBbHAv7X6GHPXaDghURERERkZ5qXQkP/Se8cmV+mw2E3S+At3wJTJPwdoeCFRERERGRnlj2FNx7Eqycld82eLPw/JQJB9evXn2AghURERERke5whzm/hUc/B21r89snHgYH/BkGT6xf3foIBSsiIiIiIl21+mV46BPwxq0dt+98Lux6HgwYWJdq9TUKVkREREREKuXtMPtX8PhXYMOq/PamseH5KZsfVb+69UEKVkREREREKrFyDjz0cXhzesftm78b9v0VDJlcl2r1ZQpWRERERETKaW+DWT+Hmd+AtjX57YNGw14XwdYfArP61a8PU7AiIiIiIlLK8mfDlMSLHui4ffIJsM8lMHhSferVTyhYEREREREptH4pPHkevPAL8Lb89qbxsPfPYcrJ6k2pAQUrIiIiIiI57Rtgzv/Bk9+ClsUd9231gXDbV/P4+tStH1KwIiIiIiICsOAuePTzsOzJjtuHbQt7/hQmH1uPWvVrClZEREREpH9b9RI8/mWY97eO2xuGwS7nwg6fh4FNdalaf6dgRURERET6p7UL4NkfwQuXQHtLx33bng67XwCDN6tL1SRQsCIiIiIi/cu6hfDv/wnTEbet7bhv7H6w18Uwbt/61E06ULAiIiIiIv1Dy2L4909g1sWwYXXHfYM3h7f+CLY+FWxAfeonm1CwIiIiIiJ92/pl8NyF8NxPYcPKjvuaJ8BOX4Npn4KGwfWonZShYEVERERE+qY18+H5i2D2pdC6ouO+prHwlq/C9mdBw9D61E86pWBFRERERPqWZU/Dcz+BuX+C9taO+waNhrd8Gbb/DDQOr0/9pGIKVkRERESk93OHN6fDs/8Dr9+06f7GkbDjObDD2TBoZM2rJ92jYEVEREREeq+2Fph3TehJWfLopvuHbBmekzLt49A4oubVk55RsCIiIiIivc/K2TD71/DiZdCyaNP9o3aHt3wJtjoFBjTWvn6SCgUrIiIiItI7tLfCq9fDC5fCgjuKp5l0ZBiTMukIMKtt/SR1qUwibWZ7mdnXzOwaM3vNzNzM1lWQ7yNm9rCZrTKzJWb2TzM7oJM8B8R0S2K+h83stE7yTDaz35nZfDNbZ2azzOx7Ztbc1XMVERERkRpbOQee+AZctyXcd/KmgcrAweGJ80c/DofdCpsdqUClj0irZ+VbwHFdyWBmFwJfANYCtwLNwJHAO83sZHe/tkieE4C/EoKse4BFwOHA5Wa2u7ufUyTPVGAGMB54GrgX2DvW+Qgze4e7t3Sl7iIiIiJSZesWwStXwdw/wqIZxdOM3AmmfRK2+XCY5Uv6nLSClRnATOCRuLxRLrGZHUYIVBYD+7v7C3H7/sB04DIzm+7uSxN5RgOXAQOBE939mrh9InAf8AUz+4e731VwuN8RApWL3f3smKcBuAo4AfgG8J3un7qIiIiIpGLDWnjtHyFAmX8T+IZN0wxogiknhyBl/IHqQenjUglW3P1HyffW+UXzxbg+PxeoxHJmmNmlwOeAM4CfJPJ8HBgJXJ8LVGKeBWb2FeAa4BxgY7BiZvsABwNvAl9J5NlgZmcCxwCfNbPz3b1gEm4RERERqbr2VnjjTnjlSnjl6k2fMJ8zcifY9mOw7WnhgY7SL9R8gH0cJ3J4fHt1kSRXE4KVY+kYrBxTJs+NwDrCbV3N7r6uIM8/Cm/1ikHOvcBhwIGEHh0RERERqba2dfD6rTDvb/Dq36F1WfF0gzeDrU6FbT4UZvdSL0q/U4/ZwHYEmoCF7v5qkf2PxfVuBdt3K9i/kbuvN7OnCWNRdiDckgawe6k8ie2HxXTTK6m8iIiIiHTDhtXh1q5Xrob5N8KGVcXTNQyDLU8MAcqEd8CAgbWtp2RKPYKVKXFdLFDB3Veb2TJgtJkNd/eVZjYCGFUuX9y+dyw/F6yUPVZi+5QS+0VERESkuzashfn/hJf/EgKUtrXF0w1shs3eBVNOgcnHQcOQ2tZTMqsewcqwuF5TJs1qQnAyDFiZyFMu3+qC8is5VrE8ZZnZMyV2Ta20DBEREZE+q60l3OL18l/gtb+X6UEZCpu/J/SibP5uaKz445j0I/UIVnI3G3oFaUq9ryRPJcfSjY8iIiIiPdW2HhbcFQbJz7sGWpcXT9c4ErY4NgQom70LGgbXtp7S69QjWMlN8TC0TJpc318uFF9ZsG9FBXkqOVaxPGW5+87Ftscel50qLUdERESkV2tdBa/fDK9eB6/dUDpAaRgGk4+HrU4JT5cf2FTLWkovV49g5ZW4nlxsp5kNJdwCtszdVwK4+wozW06Yungy8GyRrLnyXklsewXYo9SxSuQRERERkWLWLQrPQXn1WnjjtjCrVzEDB4celK1Ogc2OVg+KdFs9gpXngRZgvJlNLjIj2J5x/WTB9pmEZ6bsSUGwYmaNwC6x3OcL8hyXKLNQqWOJiIiICMCqF+HV68Oy8F7w9uLpNg6S/w/Y4hiNQZFU1DxYcfe1ZnYncDRwEvDTgiQnxfUNBdtvJAQrJwF/LNh3DNAM/DPxjJVcnm8Dx5pZU/JZKxaefP92YDlwX7dPSERERKQv8XZY/EgYHP/q9bC81NxCQOOoEJhseUIcg1LuLn+RrqtHzwrAhYRg5VwzuzH3FHsz2x/4JGFMym8L8vwG+CZwnJm9L/cUezObAPx3otyN3P1hM7uf8NDHHwGfj3kagEuARuBnenq9iIiI9Gsb1sKCO0Nw8to/YN0bpdMO3iyMQZl8Akw8FAY01qqW0g+lEqyY2XuAbxVsHmRmDybef9/dbwRw99vN7CLgbOAJM7sNGAQcCQwAPujuS5KFufsSMzsDuAq42szuBhYBRxDGuFzs7ncUqd5HgRnA2WZ2GOEWsn2AbYGHgB90/8xFREREeql1i2D+DeEJ8q/fAm1lnioxcifY4jiY/F4Yuy/YgNrVU/q1tHpWxgP7FWyzgm3jkzvd/fNm9gTwGUKQ0grcAZzv7kVvy3L3v5nZwcC5wNsIAc6/gV+4+2Ul8rxgZnsA3wOOAk4A5gHnAxcU3DYmIiIi0neteAFeuz4EKIvuLz3+xAbA+LfDFu8NAcrwabWtp0iUSrDi7pcDl9cin7vfT7iFrCt55hF6WERERET6jw1rYMH00HPy+s2wclbptA1DYbOjwhPkN383NI2tWTVFSqnXmBURERERSZs7rPg3zL85BCdv3gPtLaXTD94s9p4cBxPfEWb0EskQBSsiIiIivVnLYnjjDnjjVnj9Vlgzr3z6Ubvmx5+M2UvjTyTTFKyIiIiI9CZtLbDoAXj9thCgLHkM8NLpG4bDpCNg86PC9MJDt6pZVUV6SsGKiIiISJZ5Oyx7Kvae3A5v3l1+5i6A0Xvmg5Nx+2t6Yem1FKyIiIiIZM2ql0Jg8sYd4fknLQvLp2+eBJOOhM2OhEnvhMETa1NPkSpTsCIiIiJSb6vnhR6TN+8OAcrql8qnHzgYJhySD1BG7gJmtamrSA0pWBERERGpJXdYPTcfnCy4u/PgxAbA6L1g0uFh/Mn4AzVzl/QLClZEREREqqm9DZY/DQvvDwPj37yn8xm7AEbsABOPCAHKxENh0OiqV1UkaxSsiIiIiKRp/XJY/BAsfCA8JX7Rg7BhVef5hm0LEw6GCYeGAGXI5KpXVSTrFKyIiIiIdJd7uIVr4f0xOHkgzNxVbirhnBE7wPiDw9iTCQfD0C2rXl2R3kbBioiIiEil2tbBksdDUJK7rWvdgs7z2UAYvUcYazLugBCcDJ5U/fqK9HIKVkRERESK8XZY8Twsfji/LJsJ7a2d5x00OgQl4w8I67H7QMPQ6tdZpI9RsCIiIiLiDqtfhiX/CsviR8KyYWVl+UfsEIKSXIAyYscwg5eI9IiCFREREelf3MNsXEv+BUsehcX/gqWPQsviyvIPbIax++aDk3H7Q/O46tZZpJ9SsCIiIiJ9l7fDyjmw9HFY+lgYb7L0MWhZVFl+GwAjdw7BSW4ZuTMMaKxuvUUEULAiIiIifcWGtbDi2TAb15LHY4DyROW3cgEMmwpj9gpjTMbuC6P3hMZhVauyiJSnYEVERER6l/Y2WDUnBCXLnoLlT8Gyp2HV7NCTUqmhW8OYvWHs3iFAGb0nNI2pWrVFpOsUrIiIiEh2ta6ApU+GWbiWzgzrZU9B29ouFGIwfDsYs2eYPji3aJyJSOYpWBEREZH6a2uBlbNg+bOw/JkQkCydGR642BUDm8OYklG7hYBkzJ4wanfdyiXSSylYERERkdppWx+DkmfiEoOTlS+At3WhIIPh02DUrjBy17AetWsYczJgYNWqLyK1pWBFRERE0lfYU9LtoARoHBF6SkbtDqN3D+tRu0DDkOrUXUQyQ8GKiIiIdI+3w9r54SnvK2fBiln59eqXuh6UWEN4uOLIneKtXDE4Gbo1mFXlFEQk2xSsiIiISHnrlyYCkWRg8gK0rel6edYAI7YPAcnInfPByfDt9PwSEelAwYqIiIjA+uVh6t+VcVk1Ox+gVPoAxUIDGmH49vlgJBeYKCgRkQopWBEREekP3KFlcT4gWTWnY2DS3YAEYPBmISgZvn3oMcm9Hj5VQYmI9IiCFRERkb7CHda9UTwYWTkbWpd3v+yG4WE8STIgya0bh6d3DiIiCQpWREREepO2dbD6FVj9chjEvnJOx9u3ujOGJKdhKAybFqYEHj4tTAM8YgcYvgM0T9AgdxGpOQUrIiIiWdLWEgKRVS+GYGTV3BiYxPW6N3pWfuOIMGZk2LRwm9bG19OgeaICEhHJFAUrIiIitdTWAmtfC70ja+bBqpdiUPJiWNa8BnjPjtE0Lh+MdOgpmQZNYxWQiEivoWBFREQkLblB7GtezveIrIlBSS44Wbeg58exATB4Cxi6VT4ISd66NWhUz48hIpIBClZEREQq1d4aej7WvAKr54XgY80rHW/T2rC658exATBkSxi6DQzbJgQlQ7cKD0ccuhUMmaxZtkSkX1CwIiIiAvFp7G/EAGRex96Q3LL2DXp8ixYABoMnxYBkKxi2bViGbhPXUxSMiIigYEVERPqDthZY+3oYK7LmtYL1qzEYeQ18QzrHaxiW6AmZAkOmxMBky7AevAUMHJTOsURE+jAFKyIi0nu5w/olRQKQuF47P7xuWZjucZsnhqBjyJYhGEneojV0axg0WoPYRURSoGBFRESyqa0lH2xsEojMzwcjbevSPW7jqHwPSDIg2fh+MgxsSveYIiJSlIIVERGpHfcwAH3dAlj3Zli3vBlu0SoMSloWpX/85gkwePNwG9aQLfLrZGDSOCz944qISLcoWBERkZ5rWx8Cj7Wvh4cWrn294+t1b+SDk7a16R9/YPOmAUjhunkzjRMREellFKyIiEhx7rB+ab4XpGQg8np4tki1lOoNSa41RkREpE9SsCIi0p+0rQ+DzTfehpW8FSuuk/vSmh1rExaest48MQQjzRPVGyIiIptQsCIi0pu5w4aV+aCj1DoXjLQuq259Bo2G5kkweLOw5F43T4LBE0NQ0jQhBCoDBla3LiIi0uspWBERyRJ32LAqDC5vWQTrFuZftywsHoSkPRtWIWuIvR9FgpAO7yeFsSMiIiIpUbAiIlJNbS2JYKNYALKoYzDSsgja11e/Xg3DE7dgxduwmsaH9eDY+5Hbr/EgIiJSJwpWREQq4e2wflkYSL5+SVha4nr90vz7wgBkw8ra1M8G5MeAJAON5LppfD4QaRhcm3qJiIj0gIIVEel/2tYXCThyrxcX7wVZvzgELLU0aEwIQJrGQfN4GDS2eBDSPCHs0xgQERHpYxSsiEjv5e2hV2OTwKJUIBKXDatrX9eGYdA0NvRuNI3Lr5vH5QOS5PZBo2GA/kSLiEj/pv+EIlJf7tC2Jt5KlVyWbfq6dVnH4GT9ktr3dtiA0OMxaEwIKAaNgaYx+W1NYxKBSC7wGKvbrkRERLpBwYqIpKNtHaxfHgOKZeV7NlqWQGsiMGlvrU+dBwwKvR3JQGPQ6ETvx7iOr5vHQePIELCIiIhI1SlYEZFgYw9HDDg2CTSWFgQby6B1eQxMlkF7S/3qbg0dx3XkApDCHo9cb0hu/8DBmuVKREQkwxSsiPQVbS3QuiLMPtW6Iiy5QCIXVLR28r5qTyuv0MDB8daqUWHdODq+T2wbNGrTMR6NIxR0iIiI9EEKVkSyoK2lYy9F6/JNA4nCIKN1ObSuzAco9bqVqpA1xMCisEejoHdj0JhEABKXgU31rr2IiIhkiIIVke7y9jCrVDJgaF0R3idfF+5Lvs8FHtV+AnlXDWjqJMhI9HY0jozrUWGtW6tEREQkJQpWpH9qb41jM5YXDyZy23LjN4r2biwHvK6nUVLD8HBr1KBR+YCiMfF60Mh4m9WoIgHHSBjYXL+6i4iIiET9Jlgxs2bg68AHgCnAEuBm4Nvu/mo96yZd4B57MxIBQzK42KQnY0XHGapy+drW1Pc8SrGBicChoMciF0hsfD8ivzQMh8YYoDQM1WxVIiIi0if0i2AlBip3AAcArwPXA1sDHwWOMbP93X1O/WrYD3g7bFhV2a1RHd6v3HQMh7fV+WSKsIZE8DC8Y/DQOAIaRiTeD8+/LwxKGobqFioRERGRqF8EK8A3CIHKDOCd7r4KwMzOAX4C/A44pH7Vy6iNAUbBrVGVBBibjOFYVe+zKcHyAcQmPRSJW6lyvRkdgouRcRkexngoyBARERFJVZ8PVsysEfhsfPvpXKAC4O4XmtlpwMFmtpe7P1qXSlZD+4aC6WlLzS61LHEr1cpeEmBEA5tjsDCiY3DRoUdjePFbqHLBRsMwBRkiIiIiGdXngxXgIGAUMMfdHy+y/2pgN+BYIDvBStu64tPWbnw6+DI2mcY2GYBsWF2nildgQNOmvRcbb50q0stRaoC4BoGLiIiI9Gn9IVjZPa4fK7H/sYJ03dfeAkufBG8NPRvtreEhe+2tYV/RmaWWFfR6xPf1fBp4MQMGle+96ErwoWdpiIiIiEgF+kOwMiWuS8349WpBurLM7JkSu3acM3sOO+/a85gndTYgzDK1cR1fM7DIvgFxX+H75K1SrYTJ1JbU42xEREREpEbmzJkDsGW9jt8fgpVhcV1qrtrVBem6a0DLBtqffY3nelhOFbTHJXOmxrVmYusatVvXqc26R+3WdWqz7lG7dZ3arHvUbl23IzC4XgfvD8FKrkug1NP7ujS62t13LlpI7HEptV82pTbrHrVb16nNukft1nVqs+5Ru3Wd2qx71G5dV+auoproD0+OWxnXQ0vsHxLXGZ/6SkRERESkf+kPwcorcT25xP7JBelERERERCQD+kOwMjOu9yyxP7f9yRrURUREREREKtQfgpX7geXAVDPbo8j+k+L6htpVSUREREREOtPngxV3Xw/8PL79uZltHLtiZucQHgh5n7s/Uo/6iYiIiIhIceZeapKsvsPMmoHpwH7A68C9wFbx/WLgbe4+u24VFBERERGRTfSLYAXAzAYDXwdOJTzYZilwM/Atd59Xz7qJiIiIiMim+k2wIiIiIiIivUufH7MiIiIiIiK9k4IVERERERHJJAUrIiIiIiKSSQpWREREREQkkxSsiIiIiIhIJilYKcLMms3su2Y2y8zWmdl8M/udmU3uRlmjzOynZvaymbXE9UVmNqoKVa+rtNrNzA4xs++Y2Y1mttDM3Myeq1a96ymNNovX2Klm9mcze9bMVpvZSjN7yMzONrPGap5DPaTUbg1mdl68zl6MbbbOzF4ws1+Y2ZRqnkOtpfl3raDc7cxsbfw9vTmt+mZFin/X5sY2KrXsWK1zqLW0rzUzm2Zm/xfbcF38v/CAmX057brXU0p/107v5DrLLR+p5rnUSprXmpkdZWY3mdkiM2s1szfN7AYzO7wada+nlNvtaDO7zcyWmdkaM3vKzL5sZg2p1VdTF3dk4QGSdwAHkH+A5NbAvsBCYH93n1NhWWOBGcB2wIvAv4Cd4zKb8DDKxSmfQl2k3G5PALsXbH7e3fvMP3NIr83M7Hzgm0A78Djh2hoPHAg0AfcB73L3NemfRe2l2G7DgJXAKuBJYD4wCHgrMAVYDhzm7o+lfhI1lubvZ5Gy7wQOBQy4xd2PSqHKmZDy37W5hIcRX1Eiydfd/fUeVrnu0r7WzOwE4M+Ev2WPA7OAscCuwGp3n5Zm/eslxb9rBwEfL7F7JHB8fD3V3V/sWa3rK+Xfz3OAnwAO3A+8BmwL7BOTnOnul6ZZ/3pJud2+CvwX4fPHQzH/24AJwK3Ae9x9Q48r7e5aEgvwPcLF+gAwLLH9nLj97i6U9fuY529AQ2L7xXH7FfU+34y2238D3wCOBPaI+Z+r9zlmtc2ArwE/ALYo2L4d8HIs64J6n28G262BENA1FGwfCFwQy3qw3uebpTYrUu7HYv5fxfXN9T7XrLYbMDf8y63/efWiNtsdaAEWAQcV7BsA7F3v881iu5U5xpmxrPvqfb5ZajPCl3stcSm8zk4kfBBfnTxGb15SbLd9YtusB96Z2D4SuDuW9dVU6lzvRsvSAjQSnmzvwB5F9s+M+/aqoKxJQFv8IU4s2NcEvAlsKNzXG5c0261I3q3pg8FKNdusoJwPxHJeqvc597J2awDWxrKG1vu8s9hmhG/OlgC3EXpW+lSwkna70Q+ClSq02T0x/TH1Prfe1G5ljnN/LOeT9T7nLLUZcExMe1OJ/U/E/fvW+7wz1m6/iWl/XWTfznHfm8DAntZbY1Y6OggYBcxx98eL7L86ro+toKyjCd/83OPuC5I73L0F+AfhG9yju13b7Eiz3fqLWrXZzLjevIflZEWt2s0J3xi1E75U6M2q1WYXA4MJ39b2Rfq71nWptZmZvQV4OzDL3W9IrYbZVPVrzcy2Idz2sx64qrvlZEiabdZS4TGXVJguy9Jst73ienrhDnd/htAjOp5w3fVIaoNf+ojd47rUPeqPFaTraVlnVFhW1qXZbv1Frdps27h+o4flZEXV283MjHBr3RDg9vjlQm+WepuZ2buBU4Bvu/vsng7Sz6iqXGtxUPhUwgekZ4Br3X1ht2qYPWm2WW5Q823xHvtTgL0JXyQ8CVzl7iu6W9GMqcX/gw/F9Y3uvrQH5WRFmm32CHGMopkd5O735XaY2fuA3YAH3H12dyubIWm229C4LnU9LQHGxbLurah2JShY6Sg3+8+rJfa/WpCuVmVlXX8617TUqs3Ojuvre1hOVlSl3czsR8BEYAThH9NU4DngE92oY9ak2mZmNhS4BHge+FHPqpZp1fod/e+C9/9rZp9z9992sZwsSrPNdo7rtYTbcHYo2P9DMzvR3e/pUg2zqRb/Dz4Y13/oQRlZklqbufsyM/s48CfgHjPLDbDfhjAu42bg9B7VNjvSvNYWEsbGblW4w8wGAFvGt1t3oX5F6TawjobFdalZk1YXpKtVWVnXn841LVVvMzP7FHAEsIwwW0dfUK12OxE4DTiBEKg8DZzi7i91uYbZk3abnU/453Smu6/vScUyLu12+zvwPkLbDQF2AS4kjGH8jZkd371qZkqabTY6rj8PjCG03ShC0PJnwje215nZZt2paMZU9f+Bme1LaLelwI3dKSODUm0zd7+acFv+YsKtUqcQZsd6E7gzbu8L0my3u+P6tCL7TiHcJgwwvLKqlaZgpSOLa+9kf63Lyrr+dK5pqWqbmdkhwEWx/DPcfX5PysuQqrSbu09zdyPcX3sU4RadR82s2B/h3ia1NjOzvYHPAr9397t6WrGMS/Vac/fPufu17v6Ku69192fc/YvAWTFJX+ilSrPNBsZ1A/Ch2HbL3X2Wu3+QcOvOaODT3atqplT7f2juFrAr+9AXDKm2mZl9kTBZyD2E3vVhcT0D+B/gyu5VM3PSbLdfEG6fe5uZXR6fhzTKzE6J+3LjPdu7V9U8BSsdrYzroSX2D4nrVTUuK+v607mmpWptZma7AdcRnhlytrtf2+XaZVdVrzV3X+TutxDul58P/NLMtuwkW9al0mbxAV//R/jn9KV0qpZptfq79hvCt7fbx0HQvVk1/oe+5u63Ftl/WVwfWlnVMq2a/w8aCN9yQ9+5BQxSbLP45d6PCbcbnuzuT7n7and/CjiJ8HyfE83snT2rciak1m7u/hrhboQlhN6VFwi9d38B5gG/i0l7PEZKwUpHr8R1qcGikwvS1aqsrOtP55qWqrSZmU0FbiHcLnGeu/+sW7XLrppca+6+HLiB0I19ZE/KyoC02mwy4YGZ64G/mtn03AL8NKbZN27rC7M31epaawdyD2Dr7bc0pdlmc+P65U72T6igrKyr5rX2TkIbvejuD3Qjf1al2WYfietr4u/jRu7eBlwT3x7alQpmVKrXWuxhnwp8ijCW8efAhwm30I2KyZ7pTkWTNMC+o9w0r3uW2J/b/mSNy8q6/nSuaUm9zcxsc0I39iTgInf/bverl1m1vNYWxfX4FMqqp7TbbFJcihkNHELofentanmt5cZn9Pbe5zTbLDet6pgS+8fGdW9vM6jutZa7BeyP3cibZWm2We4DeqnZ5XLbS12LvUnq15q7LyM8GHij2KN3COEWsB5PgqGelY7uJ/yTnWpmexTZf1JcV/Kt4c2EH9LbzazDNz9m1kSYw7oduKn71c2MNNutv0i1zcxsNKFHZRvC7RFfSKOSGVTLa+2QuJ5TNlX2pdJm7j7X3a3YArwjJrslbhuVWu3rpybXmpntTBj8vIYwA11vlmab3UEY7Du1xK2Yh8Z1qSlYe5OqXGtmNgw4Lr7ta8FKmm2Wm9p/7xL794nruRXXLrtq9T/0g4QZNm9293k9LEtPsC9cCDPdOOEHOjSx/Zy4/d6C9J8h/IP5YZGy/hjzXA00JLbnBj7/od7nm8V2K0i3NX3wCfZpthnhHtMZMc+VpPC02CwvKbbbewmzv1iR9vxBLOt1YFi9zzkrbVam/EPpY0+wT/laexdFnghNGMD7bCzronqfb5baLO77YcxzQ0FZRwGthC/8ev1TxdNut0Saj8S8M+p9flluM8K4CycMCD+2YN9xQFtcdqj3OWep3eK+vYr8Dz2S0Bu1Nq02021gmzqfMN3rAcALZnYvYarJ/QhT1320IP04wrdixe41/jzwNsK0qM+Z2b8Ic8fvQvi2ti99+51au8X5zj8e3zbF9VZm9mAi2Vnu3tu/UUurzX5AuM7aCH9sfxuea9iRu5+eYt3rKa122xP4DjDfzB4nfNs0iTAuY0x8/3537wu3maT5d60/Savd9ge+Y2YvE/72LyT0gu5JuB37buDrVTqHWkvzWvsu4Sn274llPUQYf/E2wp0h33T3h6txEnVQjd/R3C1gfWlgfVJabXYd8FfgZODv8bPaS4Tf0Vxvyzfd/fkqnEM9pHmt/Q0YaGZPEf5n7gDsQQhUTkqrzXQbWAF3X0e4peH7hG754wnf7l8B7OFdeIKpuy8idB/+jDAz0wnASMIApH3j/j4hzXYj3D+6X1zeGrc1J7btR3h4X6+WYpvl7ncfCJxKmJWj2NInpNhu1xCec/Ea4ff0/XH9MuEb3be4e4+eupsVKf9+9hspttsthJlxVhCe5nwiMA24D/hP4HB3L/Xcg14l5f+h64DDgG8Snhd1NOELv7uAY9z9ghSrXldp/47G588cRuiB6ivT7naQVpt56A44BfgYYXzFNMLnta2BfwJH61or6VLC/9D9CM9CGgP8GtjF3VN7po/FLhsREREREZFMUc+KiIiIiIhkkoIVERERERHJJAUrIiIiIiKSSQpWREREREQkkxSsiIiIiIhIJilYERERERGRTFKwIiIiIiIimaRgRUREREREMknBioiIiIiIZJKCFRERERERySQFKyIiIiIikkkKVkREREREJJMUrIiIiIiISCYpWBERERERkUxSsCIiIiIiIpmkYEVERERERDJJwYqIiIiIiGSSghUREREREcmk/w9LvJecNIObIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FollowersInfo(Followers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6978cbc5-d5ff-4b91-a34d-e2257c6fe253",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### RQ8.4\n",
    "What are histograms, bar plots, scatterplots and pie charts used for?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce736cb-b2fa-48bd-8719-001447a737ba",
   "metadata": {},
   "source": [
    "Every kind of plot has it's best use:\n",
    "\n",
    "\n",
    "| Plot | Use | Used in |\n",
    "| --- | :-: | :-: | \n",
    "| Histograms | Display a variable distribution | RQ8.3 |\n",
    "| Bar plot | Compare Data divided in subgroups | RQ8.2 |\n",
    "| Scatterplot | Search and Find relationship between variables | RQ8.1 |\n",
    "| Pie charts | Display and Find how Data is devided as a percentage of a whole | RQ2.4 RQ5.4 |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a20cae-7790-4018-ad82-0b41bab836a8",
   "metadata": {},
   "source": [
    "#### RQ8.5\n",
    "What insights can you extract from a Box Plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfbf9d75-9e16-4df7-81f7-fb8f0a602e46",
   "metadata": {},
   "source": [
    "When working with a set of data it may worth to plot a box plot, the reason for that is that it gives multiple informations about the population. In particolar, it shows 5 different summaries:\n",
    "1. The minimum, the lower element of the set.\n",
    "2. The first quartile, given a sorted set, it's the value that leave to it's left 25% of the population. \n",
    "3. The median, also known as second quartile, the value that leave to it's left 50% of the population.\n",
    "4. The third quartile, the value that leave to it's left 75% of the population.\n",
    "5. The maximum, the higher element of the set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09579cc6",
   "metadata": {},
   "source": [
    "# Command Line Question\n",
    "\n",
    "Using the instagram_posts.csv, retrieve the first ten posts with descriptions longer than 100 characters and output the profiles that posted them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "596bf246",
   "metadata": {},
   "source": [
    "profiles=`cat instagram_posts.csv | awk '{ if (length($8) > 100) {print $4} }' | uniq -c | head -n 10`\n",
    "for i in `echo $profiles`; do echo $i; solution=`cat instagram_profiles.csv | awk -v var=$i '{ if ($2 == var) { print $3 }}'`; echo $solution; done"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2dd250",
   "metadata": {
    "tags": []
   },
   "source": [
    "# AQ1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b69ae970",
   "metadata": {},
   "source": [
    "Given the following pseudocode, answer the questions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d722384-207f-456f-8070-c763b1557fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#do not run this\n",
    "Input:\n",
    "    N : an integer\n",
    "    List : array of characters of length N without repetition\n",
    "        \n",
    "function f1(sequence, end):\n",
    "    For i=0 To end:\n",
    "        OUTPUT sequence[i]\n",
    "    EndFor\n",
    "    Output \"\\n\"\n",
    "    \n",
    "function f2(sequence, start, end):\n",
    "    If start = end:\n",
    "        f1(sequence, end)\n",
    "    Else\n",
    "        For i=start To end:\n",
    "        temp <-- sequence[start]\n",
    "        sequence[start] <-- sequence[i]\n",
    "        sequence[i] <-- temp\n",
    "        f2(sequence, start+1, end)\n",
    "        temp <-- sequence[start]\n",
    "        sequence[start] <-- sequence[i]\n",
    "        sequence[i] <-- temp\n",
    "        \n",
    "f2(List, 0, N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927ae13d",
   "metadata": {},
   "source": [
    "#### AQ1.1:\n",
    "What is the output of this algorithm? Describe the mechanism of the algorithm in\n",
    "detail . We do not want to know only its final result. (Describe one example on your\n",
    "own)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698fa9d2",
   "metadata": {},
   "source": [
    "##### Explanation of the algorithm:\n",
    "\n",
    "The goal of the algorithm is to print every permutation of the characters that are contained in the list given in input.\n",
    "\n",
    "To obtain this the function $\\textbf{f2}$ is called recursively, on the $list(start+1:end$), until it get to the point were the $start$ is equal to $end$. When this happen the function $\\textbf{f2}$ calls the function $\\textbf{f1}$ and the elements of the list get printed.\n",
    "\n",
    "The key to this algorithm is the function $\\textbf{f2}$. In general during the $i^{th}$ call of $\\textbf{f2}$ what happen is:\n",
    "- If $start$, that is equal to $i$, is also equal to $end$ $\\textbf{f1}$ is called.\n",
    "- The $i^{th}$ element of the list get swapped iteratively with the element $j$, with $ j \\in{\\{i, i+1,...,end\\}} $. \n",
    "- The $(i+1)^{th}$ iteration of $\\textbf{f2}$ gets called on $(list, start + 1, end)$.\n",
    "- The $i^{th}$ element of the list get swapped back and the new iteration start."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b830243",
   "metadata": {},
   "source": [
    "##### Example:\n",
    "\n",
    "Let's make and example to explain it more clearly. \n",
    "\n",
    "We get a list: $List = [t,r,e]$, that has length 3 and we call the function $\\textbf{f2}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71043764-e90a-4735-86ff-ac7e95572ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do not run this\n",
    "f2([t,r,e], 1, 3):\n",
    "    if: the check is false\n",
    "    - i = 1\n",
    "    swap -> [t,r,e]\n",
    "    f2([t,r,e], 2, 3):\n",
    "        if: the check is false\n",
    "        - i = 2\n",
    "        swap -> [t,r,e]\n",
    "        f2([t,r,e], 3, 3):\n",
    "            if: the check is true\n",
    "                f1([t,r,e], 3):\n",
    "                            output:\n",
    "                                t\n",
    "                                r\n",
    "                                e\n",
    "        swap back -> [t,r,e]\n",
    "        \n",
    "        - i = 3\n",
    "        swap -> [t,e,r]\n",
    "        f2([t,e,r], 3, 3):\n",
    "            if: the check is true\n",
    "                f1([t,e,r], 3):\n",
    "                            output:\n",
    "                                t\n",
    "                                e\n",
    "                                r\n",
    "        swap back -> [t,r,e]\n",
    "    swap back -> [t,r,e]\n",
    "    \n",
    "    - i = 2\n",
    "    swap -> [r,t,e]\n",
    "    f2([r,t,e], 2, 3):\n",
    "        if: the check is false\n",
    "        - i = 2\n",
    "        swap -> [r,t,e]\n",
    "        f2([r,t,e], 3, 3):\n",
    "            if: the check is true\n",
    "                f1([r,t,e], 3):\n",
    "                            output:\n",
    "                                r\n",
    "                                t\n",
    "                                e\n",
    "        swap back -> [r,t,e]\n",
    "        \n",
    "        - i = 3\n",
    "        swap -> [r,e,t]\n",
    "        f2([r,e,t], 3, 3):\n",
    "            if: the check is true\n",
    "                f1([r,e,t], 3):\n",
    "                            output:\n",
    "                                r\n",
    "                                e\n",
    "                                t\n",
    "        swap back -> [r,t,e]\n",
    "    swap back -> [t,r,e]\n",
    "    \n",
    "    - i = 3\n",
    "    swap -> [e,r,t]\n",
    "    f2([e,r,t], 2, 3):\n",
    "        if: the check is false\n",
    "        - i = 2\n",
    "        swap -> [e,r,t]\n",
    "        f2([e,r,t], 3, 3):\n",
    "            if: the check is true\n",
    "                f1([r,t,e], 3):\n",
    "                            output:\n",
    "                                e\n",
    "                                r\n",
    "                                t\n",
    "        swap back -> [e,r,t]\n",
    "        \n",
    "        - i = 3\n",
    "        swap -> [e,t,r]\n",
    "        f2([e,t,r], 3, 3):\n",
    "            if: the check is true\n",
    "                f1([e,t,r], 3):\n",
    "                            output:\n",
    "                                e\n",
    "                                t\n",
    "                                r\n",
    "        swap back -> [e,r,t]\n",
    "    swap back -> [t,r,e] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2ba68d7-555c-41f7-bde1-eb5cff867ed3",
   "metadata": {},
   "source": [
    "#### AQ1.2\n",
    "What is asymptotically (i.e., we are asking for big-O complexity) the algorithm's\n",
    "running time as a function of N?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d7efff-b565-40f0-96fb-3f21a5359a2a",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Complexity of $\\textbf{f1}$\n",
    "As for $\\textbf{f1}$, we enter the function only when $start$ reaches $end$ and what it does is print iteratively all the elements of my list.  \n",
    " So if we think of it's complexity, and the list is of length N, we have to compute N times the print operation on a single element and that has a constant time. \n",
    "\n",
    "$T(\\textbf{f1})=N\\cdot \\mathcal{O}(1) = \\mathcal{O}(N)$\n",
    "\n",
    "The function $\\textbf{f1}$ has a linear complexity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31780c2f-b3b4-4238-928d-0eb399076650",
   "metadata": {},
   "source": [
    "##### Complexity of $\\textbf{f2}$\n",
    "$\\textbf{f2}$ is main function of the algorithm and we can define it's complexity recursively usind the length of the list $N$.\n",
    "\n",
    "For each call of the function $\\textbf{f2}$, if $start \\neq end$, the algorithm is going to call $N$ times itself on the $list = list[1:N]$ that has length $N-1$ and and compute some linear operations like swapping the elements.\n",
    "\n",
    "$$ T(N)= N\\cdot T(N-1) + \\mathcal{O}(1)=N\\cdot \\left[(N-1)\\cdot T(N-2) + \\mathcal{O}(1)\\right] + \\mathcal{O}(1)=...= N!\\cdot T(0) + \\mathcal{O}(1)\\cdot \\sum_{i=0}^{N}\\frac{N!}{N-i!}$$\n",
    "\n",
    "Now we got to the point In which $N=0$, that means that $start = end$ and $\\textbf{f1}$ gets called, $T(0)=T(\\textbf{f1})$.\n",
    "\n",
    "$$T(N) = N!\\cdot T(0) + \\mathcal{O}(1)\\cdot \\sum_{i=0}^{N}\\frac{N!}{N-i!} = N!\\cdot T(\\textbf{f1}) + \\mathcal{O}(1)\\cdot \\sum_{i=0}^{N}\\frac{N!}{N-i!} = \\mathcal{O}(N\\cdot N!) + \\mathcal{O}(1)\\cdot \\sum_{i=0}^{N}\\frac{N!}{N-i!}$$.\n",
    "\n",
    "Now it's easy to conclude that the dominant factor is $T(N)= \\mathcal{O}(N\\cdot N!)$ by noticing that $\\frac{N!}{N-i!}<N!$  and, as a consequence, $\\sum_{i=0}^{N}\\frac{N!}{N-i!} < N\\cdot N!$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46870a2d-8108-45c2-95e3-04cecd78f7a0",
   "metadata": {},
   "source": [
    "#### AQ1.3\n",
    "Is this algorithm the optimal one to produce this output? If not, can you suggest a\n",
    "better algorithm to perform the same task?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af97a029-ca1f-4775-b163-967d5bf49e23",
   "metadata": {},
   "source": [
    "Yes the algorithm is optimal. That's because, given a list of length N, we have to find all of it's permutations and print them.\n",
    " Just the print, since the number of permutation is $N!$ and the print of each of their elements (as we saw with $\\textbf{f1}$) has a complexity of $\\mathcal{O}(N)$, has a complexity of $\\Omega(N\\cdot N!)$. \n",
    " \n",
    "That means that every algorithm that compute and print all of the permutations has to be an $\\Omega(N\\cdot N!)$ and since we saw already that our is a $\\mathcal{O}(N\\cdot N!)$, what we get is: $T(N)=\\Theta(N\\cdot N!)$ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "929c9383-24a5-42d6-ba46-130fe448986c",
   "metadata": {},
   "source": [
    "# AQ2: Ribbon Maximum Product"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1907a549-f820-4e63-9117-0678df35ef48",
   "metadata": {},
   "source": [
    "# 1\n",
    "If the length of the ribbon is 0 or 1 and we must make a cut, then $res=0$.\n",
    "\n",
    "Otherwise, we can cut the ribbon at different integer lengths and then we check which quantity is bigger:\n",
    "\n",
    "- The first part before the cut times the remaining part, or\n",
    "- We repeat this procedure with the remaining part of the ribbon and then multiply byy the very first part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bbb3bde3-8fd7-4f52-8a8d-dba724d00ffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxProd(n):\n",
    "    if n==0 or n==1:\n",
    "        return(0)\n",
    "    res=0 #Initialize the result\n",
    "    for i in range(1,n-1): #Because cutting at the beginning or at the very end makes no sense\n",
    "        res=max(res,i*(n-i),i*maxProd(n-i))\n",
    "    return(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "048a8d97-1404-45de-801b-61e22d01cd85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum product with a ribbon of length 0 is 0\n",
      "The maximum product with a ribbon of length 5 is 6\n",
      "The maximum product with a ribbon of length 10 is 36\n",
      "The maximum product with a ribbon of length 15 is 243\n",
      "The maximum product with a ribbon of length 20 is 1458\n"
     ]
    }
   ],
   "source": [
    "#Let's try it out:\n",
    "for i in range(0,21,5):\n",
    "    print(f'The maximum product with a ribbon of length {i} is {maxProd(i)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d399f86a-95a5-4fe1-a6ee-2074e5185a1a",
   "metadata": {},
   "source": [
    "But we can see that is we try it for higher values, it takes some time. How much in terms of $n$?\n",
    "\n",
    "# 2\n",
    "\n",
    "Observe that,\n",
    "\n",
    "$$T(n)=c+ \\sum_{i=1}^{n-1}T(i)$$\n",
    "\n",
    "from that we can deduce that\n",
    "\n",
    "$$T(n)=2c+2\\sum_{i=1}^{n-2}T(n)=\\dots=2^kc+2^kT(n-k)$$\n",
    "\n",
    "and when $k=n-1$ we obtain that\n",
    "\n",
    "$$T(n)=2^{n-1}c+2^{n-1}c=2^nc \\Rightarrow T(n)=\\Theta(2^n)$$\n",
    "\n",
    "It is an exponential time algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3de5595-e732-4fc0-a556-a99d4471e7b5",
   "metadata": {},
   "source": [
    "# 3\n",
    "\n",
    "We used a recursive method and we are going to use its structure, the maximum inside the loop in dynammic programming in order not to compute the same value several times. To do so, we will create a vector where we will save the previous results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e91c3479-f436-426e-b817-67ac5fc8c800",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxProdDP(n):\n",
    "    res=[0 for _ in range(n+1)] #We make a list/vector to save the values from 0 to the n-th, the one we want.\n",
    "    \n",
    "    for i in range(1,n+1):\n",
    "        aux=0  \n",
    "        for j in range(1,i):  \n",
    "            aux=max(aux,(i-j)*j,j*res[i-j]) #We call the vector with the previous values instead of the function    \n",
    "        res[i]=aux\n",
    "        \n",
    "    return(res[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4db3e72b-184e-47ce-92d8-a8a1eb0bc33e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum product with a ribbon of length 0 is 0\n",
      "The maximum product with a ribbon of length 5 is 6\n",
      "The maximum product with a ribbon of length 10 is 36\n",
      "The maximum product with a ribbon of length 15 is 243\n",
      "The maximum product with a ribbon of length 20 is 1458\n",
      "The maximum product with a ribbon of length 25 is 8748\n",
      "The maximum product with a ribbon of length 30 is 59049\n",
      "The maximum product with a ribbon of length 35 is 354294\n",
      "The maximum product with a ribbon of length 40 is 2125764\n"
     ]
    }
   ],
   "source": [
    "#Let's try it out:\n",
    "for i in range(0,41,5):\n",
    "    print(f'The maximum product with a ribbon of length {i} is {maxProdDP(i)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c951da-ebd1-4304-962c-1b1afbeed3c8",
   "metadata": {},
   "source": [
    "Here we can clearly observe that\n",
    "\n",
    "$$T(n)=\\sum_{i=1}^{n}i=\\frac{n(n+1)}{2}\\Rightarrow T(n)=\\Theta(n^2)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fee894c-e61e-4e59-ae3c-2b8dd6f4d4ec",
   "metadata": {},
   "source": [
    "# 4\n",
    "\n",
    "We are going to evaluate the running time of the algorithm for different values of $n\\in[2,25]$ and plot it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "131a07c8-efbc-4ed3-bf2c-df009675d42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time of execution / T(n) (s)')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "t=[0 for _ in range(26)]\n",
    "\n",
    "for i in range(2,26):\n",
    "    b=time.time()\n",
    "    maxProdDP(i)\n",
    "    t[i]=time.time()-b\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(t)\n",
    "plt.title('T(n) of maxProdDP \\n')\n",
    "plt.xlabel('Value of n (length of the ribbon)')\n",
    "plt.ylabel('Time of execution / T(n) (s)')\n",
    "\n"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}