final-cleaning-and-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime as dt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Germany"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numerical Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_germany = pd.read_csv('./data/final_data/germany_final.csv', sep=';')\n",
    "df_germany_long = pd.read_csv('./data/final_data/germany_final_long.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>tests</th>\n",
       "      <th>devices</th>\n",
       "      <th>quarter</th>\n",
       "      <th>category</th>\n",
       "      <th>country</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "      <th>federal_state</th>\n",
       "      <th>county</th>\n",
       "      <th>municipality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>Germany</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>List auf Sylt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>Germany</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>List auf Sylt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>Germany</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>List auf Sylt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>Germany</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>List auf Sylt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>Germany</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>List auf Sylt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_lat_ms  tests  devices     quarter category  country  avg_d_mbps  \\\n",
       "0          43      4        2  2019-01-01    fixed  Germany           8   \n",
       "1          34      1        1  2019-01-01    fixed  Germany          33   \n",
       "2          29      1        1  2019-01-01    fixed  Germany          24   \n",
       "3          40      2        1  2019-01-01    fixed  Germany          35   \n",
       "4          33      4        3  2019-01-01    fixed  Germany           7   \n",
       "\n",
       "   avg_u_mbps       federal_state         county   municipality  \n",
       "0           1  Schleswig-Holstein  Nordfriesland  List auf Sylt  \n",
       "1           9  Schleswig-Holstein  Nordfriesland  List auf Sylt  \n",
       "2          11  Schleswig-Holstein  Nordfriesland  List auf Sylt  \n",
       "3           7  Schleswig-Holstein  Nordfriesland  List auf Sylt  \n",
       "4           2  Schleswig-Holstein  Nordfriesland  List auf Sylt  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_germany.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>federal_state</th>\n",
       "      <th>county</th>\n",
       "      <th>category</th>\n",
       "      <th>quarter</th>\n",
       "      <th>avg_d_mbps_copy</th>\n",
       "      <th>avg_u_mbps_copy</th>\n",
       "      <th>avg_lat_ms_copy</th>\n",
       "      <th>measure</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>fixed</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>Download Speed</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>fixed</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>Download Speed</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>fixed</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>Download Speed</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>fixed</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>Download Speed</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schleswig-Holstein</td>\n",
       "      <td>Nordfriesland</td>\n",
       "      <td>fixed</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>Download Speed</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        federal_state         county category     quarter  avg_d_mbps_copy  \\\n",
       "0  Schleswig-Holstein  Nordfriesland    fixed  2019-01-01                8   \n",
       "1  Schleswig-Holstein  Nordfriesland    fixed  2019-01-01               33   \n",
       "2  Schleswig-Holstein  Nordfriesland    fixed  2019-01-01               24   \n",
       "3  Schleswig-Holstein  Nordfriesland    fixed  2019-01-01               35   \n",
       "4  Schleswig-Holstein  Nordfriesland    fixed  2019-01-01                7   \n",
       "\n",
       "   avg_u_mbps_copy  avg_lat_ms_copy         measure  value  \n",
       "0                1               43  Download Speed      8  \n",
       "1                9               34  Download Speed     33  \n",
       "2               11               29  Download Speed     24  \n",
       "3                7               40  Download Speed     35  \n",
       "4                2               33  Download Speed      7  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_germany_long.drop(columns=['municipality'], inplace=True)\n",
    "df_germany_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5347564, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_germany.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>tests</th>\n",
       "      <th>devices</th>\n",
       "      <th>quarter</th>\n",
       "      <th>category</th>\n",
       "      <th>country</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "      <th>federal_state</th>\n",
       "      <th>county</th>\n",
       "      <th>municipality</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5347559</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Germany</td>\n",
       "      <td>122</td>\n",
       "      <td>23</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>Berchtesgadener Land</td>\n",
       "      <td>Eck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5347560</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>Berchtesgadener Land</td>\n",
       "      <td>Berchtesgaden</td>\n",
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       "    <tr>\n",
       "      <th>5347561</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Germany</td>\n",
       "      <td>75</td>\n",
       "      <td>9</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>Berchtesgadener Land</td>\n",
       "      <td>Eck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5347562</th>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>Berchtesgadener Land</td>\n",
       "      <td>Schönau a. Königssee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5347563</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Germany</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Bayern</td>\n",
       "      <td>Berchtesgadener Land</td>\n",
       "      <td>Schönau a. Königssee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         avg_lat_ms  tests  devices     quarter category  country  avg_d_mbps  \\\n",
       "5347559          36      2        2  2022-07-01   mobile  Germany         122   \n",
       "5347560          26      1        1  2022-07-01   mobile  Germany          48   \n",
       "5347561          50      1        1  2022-07-01   mobile  Germany          75   \n",
       "5347562         113      1        1  2022-07-01   mobile  Germany           0   \n",
       "5347563          62      1        1  2022-07-01   mobile  Germany          21   \n",
       "\n",
       "         avg_u_mbps federal_state                county          municipality  \n",
       "5347559          23        Bayern  Berchtesgadener Land                   Eck  \n",
       "5347560           4        Bayern  Berchtesgadener Land         Berchtesgaden  \n",
       "5347561           9        Bayern  Berchtesgadener Land                   Eck  \n",
       "5347562           0        Bayern  Berchtesgadener Land  Schönau a. Königssee  \n",
       "5347563           1        Bayern  Berchtesgadener Land  Schönau a. Königssee  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_germany.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5347564 entries, 0 to 5347563\n",
      "Data columns (total 11 columns):\n",
      " #   Column         Dtype \n",
      "---  ------         ----- \n",
      " 0   avg_lat_ms     int64 \n",
      " 1   tests          int64 \n",
      " 2   devices        int64 \n",
      " 3   quarter        object\n",
      " 4   category       object\n",
      " 5   country        object\n",
      " 6   avg_d_mbps     int64 \n",
      " 7   avg_u_mbps     int64 \n",
      " 8   federal_state  object\n",
      " 9   county         object\n",
      " 10  municipality   object\n",
      "dtypes: int64(5), object(6)\n",
      "memory usage: 448.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_germany.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>tests</th>\n",
       "      <th>devices</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.347564e+06</td>\n",
       "      <td>5.347564e+06</td>\n",
       "      <td>5.347564e+06</td>\n",
       "      <td>5.347564e+06</td>\n",
       "      <td>5.347564e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.614932e+01</td>\n",
       "      <td>9.455708e+00</td>\n",
       "      <td>2.921295e+00</td>\n",
       "      <td>6.339939e+01</td>\n",
       "      <td>1.869401e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.398488e+01</td>\n",
       "      <td>2.178635e+01</td>\n",
       "      <td>4.111197e+00</td>\n",
       "      <td>6.810599e+01</td>\n",
       "      <td>2.373100e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>8.300000e+01</td>\n",
       "      <td>2.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.187100e+04</td>\n",
       "      <td>3.987000e+03</td>\n",
       "      <td>5.170000e+02</td>\n",
       "      <td>1.616000e+03</td>\n",
       "      <td>9.120000e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_lat_ms         tests       devices    avg_d_mbps    avg_u_mbps\n",
       "count  5.347564e+06  5.347564e+06  5.347564e+06  5.347564e+06  5.347564e+06\n",
       "mean   2.614932e+01  9.455708e+00  2.921295e+00  6.339939e+01  1.869401e+01\n",
       "std    4.398488e+01  2.178635e+01  4.111197e+00  6.810599e+01  2.373100e+01\n",
       "min    0.000000e+00  1.000000e+00  1.000000e+00  0.000000e+00  0.000000e+00\n",
       "25%    1.400000e+01  1.000000e+00  1.000000e+00  2.000000e+01  6.000000e+00\n",
       "50%    2.100000e+01  3.000000e+00  2.000000e+00  4.400000e+01  1.200000e+01\n",
       "75%    3.000000e+01  9.000000e+00  3.000000e+00  8.300000e+01  2.400000e+01\n",
       "max    3.187100e+04  3.987000e+03  5.170000e+02  1.616000e+03  9.120000e+02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_germany.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visual Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1178588e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot count broadband types fixed and mobile\n",
    "plt = sns.catplot(data=df_germany,\n",
    "\t\t\tx=\"category\",\n",
    "\t\t\tkind=\"count\"\n",
    "\t\t    )\n",
    "\n",
    "plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1178588e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots\n",
    "sns.catplot(data=df_germany,\n",
    "            x=\"avg_d_mbps\", y=\"category\",\n",
    "            kind=\"box\"\n",
    "            )\n",
    "\n",
    "plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Whole World"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_world = pd.read_csv('./data/final_data/world_aggregated.csv', sep=';')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numerical Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>country</th>\n",
       "      <th>quarter</th>\n",
       "      <th>category</th>\n",
       "      <th>devices</th>\n",
       "      <th>tests</th>\n",
       "      <th>row_count</th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>2884</td>\n",
       "      <td>13839</td>\n",
       "      <td>819</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>23259</td>\n",
       "      <td>73138</td>\n",
       "      <td>6259</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>9787</td>\n",
       "      <td>42838</td>\n",
       "      <td>4774</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>161</td>\n",
       "      <td>618</td>\n",
       "      <td>72</td>\n",
       "      <td>115</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>2132</td>\n",
       "      <td>4574</td>\n",
       "      <td>235</td>\n",
       "      <td>21</td>\n",
       "      <td>69</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country     quarter category  devices  tests  row_count  avg_lat_ms  \\\n",
       "0     Afghanistan  2019-01-01    fixed     2884  13839        819         103   \n",
       "1         Albania  2019-01-01    fixed    23259  73138       6259          33   \n",
       "2         Algeria  2019-01-01    fixed     9787  42838       4774          89   \n",
       "3  American Samoa  2019-01-01    fixed      161    618         72         115   \n",
       "4         Andorra  2019-01-01    fixed     2132   4574        235          21   \n",
       "\n",
       "   avg_d_mbps  avg_u_mbps  \n",
       "0           3           3  \n",
       "1           9           6  \n",
       "2           3           2  \n",
       "3          15           6  \n",
       "4          69          64  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6995, 9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_world.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6995 entries, 0 to 6994\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   country     6995 non-null   object\n",
      " 1   quarter     6995 non-null   object\n",
      " 2   category    6995 non-null   object\n",
      " 3   devices     6995 non-null   int64 \n",
      " 4   tests       6995 non-null   int64 \n",
      " 5   row_count   6995 non-null   int64 \n",
      " 6   avg_lat_ms  6995 non-null   int64 \n",
      " 7   avg_d_mbps  6995 non-null   int64 \n",
      " 8   avg_u_mbps  6995 non-null   int64 \n",
      "dtypes: int64(6), object(3)\n",
      "memory usage: 492.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>devices</th>\n",
       "      <th>tests</th>\n",
       "      <th>row_count</th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.995000e+03</td>\n",
       "      <td>6.995000e+03</td>\n",
       "      <td>6.995000e+03</td>\n",
       "      <td>6995.000000</td>\n",
       "      <td>6995.000000</td>\n",
       "      <td>6995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.031419e+05</td>\n",
       "      <td>3.448508e+05</td>\n",
       "      <td>2.168110e+04</td>\n",
       "      <td>62.865046</td>\n",
       "      <td>35.768978</td>\n",
       "      <td>16.738813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.124471e+05</td>\n",
       "      <td>1.495993e+06</td>\n",
       "      <td>9.145172e+04</td>\n",
       "      <td>107.451315</td>\n",
       "      <td>35.153482</td>\n",
       "      <td>22.563189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.685000e+02</td>\n",
       "      <td>9.295000e+02</td>\n",
       "      <td>1.590000e+02</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.480000e+03</td>\n",
       "      <td>9.831000e+03</td>\n",
       "      <td>1.025000e+03</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.426600e+04</td>\n",
       "      <td>1.092515e+05</td>\n",
       "      <td>1.035050e+04</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.892389e+06</td>\n",
       "      <td>3.220960e+07</td>\n",
       "      <td>1.706847e+06</td>\n",
       "      <td>2135.000000</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            devices         tests     row_count   avg_lat_ms   avg_d_mbps  \\\n",
       "count  6.995000e+03  6.995000e+03  6.995000e+03  6995.000000  6995.000000   \n",
       "mean   1.031419e+05  3.448508e+05  2.168110e+04    62.865046    35.768978   \n",
       "std    4.124471e+05  1.495993e+06  9.145172e+04   107.451315    35.153482   \n",
       "min    1.000000e+00  1.000000e+00  1.000000e+00     3.000000     0.000000   \n",
       "25%    3.685000e+02  9.295000e+02  1.590000e+02    26.000000    13.000000   \n",
       "50%    3.480000e+03  9.831000e+03  1.025000e+03    37.000000    24.000000   \n",
       "75%    4.426600e+04  1.092515e+05  1.035050e+04    54.000000    44.000000   \n",
       "max    7.892389e+06  3.220960e+07  1.706847e+06  2135.000000   376.000000   \n",
       "\n",
       "        avg_u_mbps  \n",
       "count  6995.000000  \n",
       "mean     16.738813  \n",
       "std      22.563189  \n",
       "min       0.000000  \n",
       "25%       7.000000  \n",
       "50%      10.000000  \n",
       "75%      16.000000  \n",
       "max     198.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_world.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visual Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1178ac910>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot count tests per country\n",
    "plt = sns.catplot(data=df_world,\n",
    "\t\t\tx=\"category\",\n",
    "\t\t\tkind=\"count\"\n",
    "\t\t    )\n",
    "\n",
    "plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/fx/lflfvn0n10963h3v1rgc6nvc0000gn/T/ipykernel_95512/298329116.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df_top_10_d = df_world.sort_values(by=['avg_d_mbps'], ascending=False)[(df_world['quarter'] == '2022-07-01') & (df_world['tests'] > 10000)].iloc[:10]\n"
     ]
    }
   ],
   "source": [
    "# countries with highest avg download speeds in some quarter\n",
    "df_top_10_d = df_world.sort_values(by=['avg_d_mbps'], ascending=False)[(df_world['quarter'] == '2022-07-01') & (df_world['tests'] > 10000)].iloc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>quarter</th>\n",
       "      <th>category</th>\n",
       "      <th>devices</th>\n",
       "      <th>tests</th>\n",
       "      <th>row_count</th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6934</th>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17258</td>\n",
       "      <td>34154</td>\n",
       "      <td>9840</td>\n",
       "      <td>33</td>\n",
       "      <td>249</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3512</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>83530</td>\n",
       "      <td>321013</td>\n",
       "      <td>982</td>\n",
       "      <td>11</td>\n",
       "      <td>227</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3363</th>\n",
       "      <td>China</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>1356979</td>\n",
       "      <td>4921189</td>\n",
       "      <td>461226</td>\n",
       "      <td>16</td>\n",
       "      <td>212</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6981</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>18244</td>\n",
       "      <td>49308</td>\n",
       "      <td>5307</td>\n",
       "      <td>34</td>\n",
       "      <td>211</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3530</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>676030</td>\n",
       "      <td>2164643</td>\n",
       "      <td>117756</td>\n",
       "      <td>8</td>\n",
       "      <td>197</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6933</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>7358</td>\n",
       "      <td>14668</td>\n",
       "      <td>2027</td>\n",
       "      <td>28</td>\n",
       "      <td>195</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6874</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17231</td>\n",
       "      <td>36070</td>\n",
       "      <td>2482</td>\n",
       "      <td>21</td>\n",
       "      <td>195</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3417</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>102499</td>\n",
       "      <td>404698</td>\n",
       "      <td>1324</td>\n",
       "      <td>10</td>\n",
       "      <td>194</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>241093</td>\n",
       "      <td>694784</td>\n",
       "      <td>10508</td>\n",
       "      <td>10</td>\n",
       "      <td>181</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>fixed</td>\n",
       "      <td>5818788</td>\n",
       "      <td>19823785</td>\n",
       "      <td>1601486</td>\n",
       "      <td>33</td>\n",
       "      <td>181</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       country     quarter category  devices     tests  \\\n",
       "6934         Republic of Korea  2022-07-01   mobile    17258     34154   \n",
       "3512                 Singapore  2022-07-01    fixed    83530    321013   \n",
       "3363                     China  2022-07-01    fixed  1356979   4921189   \n",
       "6981      United Arab Emirates  2022-07-01   mobile    18244     49308   \n",
       "3530                  Thailand  2022-07-01    fixed   676030   2164643   \n",
       "6933                     Qatar  2022-07-01   mobile     7358     14668   \n",
       "6874                    Kuwait  2022-07-01   mobile    17231     36070   \n",
       "3417                 Hong Kong  2022-07-01    fixed   102499    404698   \n",
       "3543      United Arab Emirates  2022-07-01    fixed   241093    694784   \n",
       "3546  United States of America  2022-07-01    fixed  5818788  19823785   \n",
       "\n",
       "      row_count  avg_lat_ms  avg_d_mbps  avg_u_mbps  \n",
       "6934       9840          33         249          27  \n",
       "3512        982          11         227         198  \n",
       "3363     461226          16         212          53  \n",
       "6981       5307          34         211          27  \n",
       "3530     117756           8         197         171  \n",
       "6933       2027          28         195          26  \n",
       "6874       2482          21         195          26  \n",
       "3417       1324          10         194         149  \n",
       "3543      10508          10         181         103  \n",
       "3546    1601486          33         181          55  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_10_d"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# aggregate the df_world once more (weighted average on number of rows)\n",
    "# reasons: 1) check which countries to remove (see below)\n",
    "#          2) later, we will compare this weighted average calculation to simple mean\n",
    "#          3) based on our analysis in 2), we will come to the conclusion that this fully aggregated version of the dataset has to be the one we load into PowerBI\n",
    "\n",
    "# this logic uses the same workaround for the weighted average as in combined_preprocessed_files\n",
    "def weighted_average_calculation(df_world):\n",
    "\tdf_world['product1'] = df_world['avg_d_mbps'] * df_world['row_count']\n",
    "\tdf_world['product2'] = df_world['avg_u_mbps'] * df_world['row_count']\n",
    "\tdf_world['product3'] = df_world['avg_lat_ms'] * df_world['row_count']\n",
    "\n",
    "\tdf_world_agg = df_world.groupby('country', as_index=False).agg({'product1': sum,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'product2': sum,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'product3': sum,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'row_count': sum,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'devices': sum,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'tests': sum})\n",
    "\n",
    "\tdf_world_agg['avg_d_mbps'] = df_world_agg['product1'] / df_world_agg['row_count']\n",
    "\tdf_world_agg['avg_u_mbps'] = df_world_agg['product2'] / df_world_agg['row_count']\n",
    "\tdf_world_agg['avg_lat_ms'] = df_world_agg['product3'] / df_world_agg['row_count']\n",
    "\n",
    "\tdf_world_agg = df_world_agg.drop(columns=['product1', 'product2', 'product3'])\n",
    "\n",
    "\tdf_world_agg = df_world_agg.sort_values('avg_d_mbps')\n",
    "\treturn df_world_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_world_agg = weighted_average_calculation(df_world)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# there are small territories (which are not even countries) that falsify the result. They should not be in the country ranking and most of them even have so little amount of tests that we can consider them insignificant. Thus, we drop them from both dataframes.\n",
    "territory_drop_list = ['South Georgia & the South Sandwich Islands',\n",
    "\t\t\t\t\t   'Reunion',\n",
    "\t\t\t\t\t   'Jersey',\n",
    "\t\t\t\t\t   'Macao',\n",
    "\t\t\t\t\t   'Azores Islands',\n",
    "\t\t\t\t\t   'Kuril Islands',\n",
    "\t\t\t\t\t   'Madeira Islands',\n",
    "\t\t\t\t\t   'Gibraltar',\n",
    "\t\t\t\t\t   'Bermuda',\n",
    "\t\t\t\t\t   'Holy See',\n",
    "\t\t\t\t\t   'French Guiana',\n",
    "\t\t\t\t\t   'Aruba',\n",
    "\t\t\t\t\t   'Puerto Rico',\n",
    "\t\t\t\t\t   'Guadeloupe',\n",
    "\t\t\t\t\t   'Svalbard and Jan Mayen Islands',\n",
    "\t\t\t\t\t   'Martinique',\n",
    "\t\t\t\t\t   'Aksai Chin',\n",
    "\t\t\t\t\t   'West Bank',\n",
    "\t\t\t\t\t   'Faroe Islands',\n",
    "\t\t\t\t\t   'Isle of Man',\n",
    "\t\t\t\t\t   'Cayman Islands',\n",
    "\t\t\t\t\t   'British Virgin Islands',\n",
    "\t\t\t\t\t   'Netherlands Artilles',\n",
    "\t\t\t\t\t   'New Caledonia',\n",
    "\t\t\t\t\t   'Guam',\n",
    "\t\t\t\t\t   'Northern Mariana Islands',\n",
    "\t\t\t\t\t   'Jammu-Kashmir',\n",
    "\t\t\t\t\t   'Montserrat',\n",
    "\t\t\t\t\t   'Guernsey',\n",
    "\t\t\t\t\t   'United States Virgin Islands',\n",
    "\t\t\t\t\t   'Midway Is.',\n",
    "\t\t\t\t\t   'Heard Island and McDonald Islands',\n",
    "\t\t\t\t\t   'Greenland',\n",
    "\t\t\t\t\t   'American Samoa',\n",
    "\t\t\t\t\t   'Arunachal Pradesh',\n",
    "\t\t\t\t\t   'Anguilla',\n",
    "\t\t\t\t\t   'Guantanamo',\n",
    "\t\t\t\t\t   'Norfolk Island',\n",
    "\t\t\t\t\t   'French Polynesia',\n",
    "\t\t\t\t\t   'Turks and Caicos Islands',\n",
    "\t\t\t\t\t   'Gaza Strip',\n",
    "\t\t\t\t\t   'Mayotte',\n",
    "\t\t\t\t\t   'French Southern and Antarctic Territories',\n",
    "\t\t\t\t\t   'Cocos (Keeling) Islands',\n",
    "\t\t\t\t\t   'Christmas Island',\n",
    "\t\t\t\t\t   'Niue',\n",
    "\t\t\t\t\t   \"Hala'ib Triangle\",\n",
    "\t\t\t\t\t   'British Indian Ocean Territory',\n",
    "\t\t\t\t\t   'Falkland Islands (Malvinas)',\n",
    "\t\t\t\t\t   'Abyei']\n",
    "df_world_agg = df_world_agg[df_world_agg['country'].isin(territory_drop_list) == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# comparison of weighted average with standard mean:\n",
    "\n",
    "# As a reminder: weighted average is computationally correct, the mean would be easy to implement in PowerBI\n",
    "# Purpose of this comparison is to find out how far they are apart, to check if it makes sense to implement the mean as a proxy for the weighted average\n",
    "\n",
    "# 1) calculate aggregation with mean\n",
    "world_grouped = df_world.groupby('country',\n",
    "\t\t\t\t\t\t\t\t as_index=False).agg({'avg_d_mbps': np.average,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t \t  'avg_u_mbps': np.average,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t  'avg_lat_ms': np.average,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t  'devices': sum,\n",
    "\t\t\t\t\t\t\t\t\t\t\t\t\t  'tests': sum})\n",
    "\n",
    "# 2) merge mean results with weighted average results\n",
    "merged = df_world_agg.merge(world_grouped, on='country', how='left')\n",
    "\n",
    "# 3) create comparison columns\n",
    "merged['diff_download'] = abs((merged['avg_d_mbps_x'] - merged['avg_d_mbps_y']) / merged['avg_d_mbps_x'])\n",
    "merged['diff_upload'] = abs((merged['avg_u_mbps_x'] - merged['avg_u_mbps_y']) / merged['avg_u_mbps_x'])\n",
    "merged['diff_latency'] = abs((merged['avg_lat_ms_x'] - merged['avg_lat_ms_y']) / merged['avg_lat_ms_x'])\n",
    "print()\n",
    "# when you look at the difference measures for the most important countries (the ones at the beginning of the list, you can see that the differences are very high\n",
    "# thus, mean is no good proxy for weighted average\n",
    "# we tried to implement the weighted average as aggregation measure in PowerBI, but did not manage to do so (we are not sure if it is even possible). Thus, we decided to load the fully aggregated version of the world dataset into PowerBI (which was already correctly aggregated in Python). This means that unfortunately, no filtering of the \"International Comparison\" table and the \"Germany World Rank\" are possible. However, our approach ensures that the presented figures are correct. Moreover, if you wanted to, you can load in different data from Python to analyze the world ranking for a specific category or time period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/fx/lflfvn0n10963h3v1rgc6nvc0000gn/T/ipykernel_95512/1579269596.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_world['quarter'] = pd.to_datetime(df_world['quarter']).dt.date\n",
      "/var/folders/fx/lflfvn0n10963h3v1rgc6nvc0000gn/T/ipykernel_95512/1729803941.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_world['product1'] = df_world['avg_d_mbps'] * df_world['row_count']\n",
      "/var/folders/fx/lflfvn0n10963h3v1rgc6nvc0000gn/T/ipykernel_95512/1729803941.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_world['product2'] = df_world['avg_u_mbps'] * df_world['row_count']\n",
      "/var/folders/fx/lflfvn0n10963h3v1rgc6nvc0000gn/T/ipykernel_95512/1729803941.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df_world['product3'] = df_world['avg_lat_ms'] * df_world['row_count']\n"
     ]
    }
   ],
   "source": [
    "# last transformations for world dataset before reading it into PowerBI:\n",
    "\n",
    "# drop territories that aren't countries\n",
    "df_world = df_world[df_world['country'].isin(territory_drop_list) == False]\n",
    "\n",
    "# filter for last 4 quarters\n",
    "df_world['quarter'] = pd.to_datetime(df_world['quarter']).dt.date\n",
    "df_world_final = df_world[df_world['quarter'] >= dt.date(2021, 9, 30)]\n",
    "\n",
    "\n",
    "# redo weighted average calculation\n",
    "df_world_final = weighted_average_calculation(df_world_final)\n",
    "\n",
    "# drop unnecessary countries that have too little datapoints for 2022 / are too small for comparison to Germany\n",
    "df_world_final = df_world_final[df_world_final['row_count'] >= 10000]\n",
    "\n",
    "# add ranking columns to df_world_agg (necessary for world rankings)\n",
    "df_world_final['rank_download'] = df_world_final['avg_d_mbps'].rank(ascending=False)\n",
    "df_world_final['rank_upload'] = df_world_final['avg_u_mbps'].rank(ascending=False)\n",
    "df_world_final['rank_latency'] = df_world_final['avg_lat_ms'].rank(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>row_count</th>\n",
       "      <th>devices</th>\n",
       "      <th>tests</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>rank_download</th>\n",
       "      <th>rank_upload</th>\n",
       "      <th>rank_latency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1435746</td>\n",
       "      <td>4273683</td>\n",
       "      <td>13496240</td>\n",
       "      <td>85.753601</td>\n",
       "      <td>23.514811</td>\n",
       "      <td>23.041944</td>\n",
       "      <td>30.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  row_count  devices     tests  avg_d_mbps  avg_u_mbps  avg_lat_ms  \\\n",
       "65  Germany    1435746  4273683  13496240   85.753601   23.514811   23.041944   \n",
       "\n",
       "    rank_download  rank_upload  rank_latency  \n",
       "65           30.0         46.0          43.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_world_final[df_world_final['country'] == 'Germany']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>row_count</th>\n",
       "      <th>devices</th>\n",
       "      <th>tests</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>rank_download</th>\n",
       "      <th>rank_upload</th>\n",
       "      <th>rank_latency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>12102</td>\n",
       "      <td>40746</td>\n",
       "      <td>163045</td>\n",
       "      <td>6.706247</td>\n",
       "      <td>2.797802</td>\n",
       "      <td>86.922988</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>53669</td>\n",
       "      <td>255368</td>\n",
       "      <td>1020466</td>\n",
       "      <td>10.733403</td>\n",
       "      <td>9.053923</td>\n",
       "      <td>44.812853</td>\n",
       "      <td>110.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>72141</td>\n",
       "      <td>533846</td>\n",
       "      <td>2560333</td>\n",
       "      <td>13.286273</td>\n",
       "      <td>10.302089</td>\n",
       "      <td>67.577522</td>\n",
       "      <td>109.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>325715</td>\n",
       "      <td>2403531</td>\n",
       "      <td>8470990</td>\n",
       "      <td>13.403485</td>\n",
       "      <td>8.767066</td>\n",
       "      <td>39.249915</td>\n",
       "      <td>108.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>132359</td>\n",
       "      <td>473200</td>\n",
       "      <td>1776364</td>\n",
       "      <td>13.700897</td>\n",
       "      <td>6.448870</td>\n",
       "      <td>43.175009</td>\n",
       "      <td>107.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  row_count  devices    tests  avg_d_mbps  \\\n",
       "193                 Yemen      12102    40746   163045    6.706247   \n",
       "169  Syrian Arab Republic      53669   255368  1020466   10.733403   \n",
       "190             Venezuela      72141   533846  2560333   13.286273   \n",
       "130              Pakistan     325715  2403531  8470990   13.403485   \n",
       "2                 Algeria     132359   473200  1776364   13.700897   \n",
       "\n",
       "     avg_u_mbps  avg_lat_ms  rank_download  rank_upload  rank_latency  \n",
       "193    2.797802   86.922988          111.0        111.0         111.0  \n",
       "169    9.053923   44.812853          110.0         98.0         104.0  \n",
       "190   10.302089   67.577522          109.0         93.0         110.0  \n",
       "130    8.767066   39.249915          108.0        100.0          99.0  \n",
       "2      6.448870   43.175009          107.0        110.0         101.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_world_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unneeded cols\n",
    "df_world_final.drop(columns=['row_count', 'devices', 'tests'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>avg_d_mbps</th>\n",
       "      <th>avg_u_mbps</th>\n",
       "      <th>avg_lat_ms</th>\n",
       "      <th>rank_download</th>\n",
       "      <th>rank_upload</th>\n",
       "      <th>rank_latency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6.706247</td>\n",
       "      <td>2.797802</td>\n",
       "      <td>86.922988</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>10.733403</td>\n",
       "      <td>9.053923</td>\n",
       "      <td>44.812853</td>\n",
       "      <td>110.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>13.286273</td>\n",
       "      <td>10.302089</td>\n",
       "      <td>67.577522</td>\n",
       "      <td>109.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>13.403485</td>\n",
       "      <td>8.767066</td>\n",
       "      <td>39.249915</td>\n",
       "      <td>108.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>13.700897</td>\n",
       "      <td>6.448870</td>\n",
       "      <td>43.175009</td>\n",
       "      <td>107.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  avg_d_mbps  avg_u_mbps  avg_lat_ms  rank_download  \\\n",
       "193                 Yemen    6.706247    2.797802   86.922988          111.0   \n",
       "169  Syrian Arab Republic   10.733403    9.053923   44.812853          110.0   \n",
       "190             Venezuela   13.286273   10.302089   67.577522          109.0   \n",
       "130              Pakistan   13.403485    8.767066   39.249915          108.0   \n",
       "2                 Algeria   13.700897    6.448870   43.175009          107.0   \n",
       "\n",
       "     rank_upload  rank_latency  \n",
       "193        111.0         111.0  \n",
       "169         98.0         104.0  \n",
       "190         93.0         110.0  \n",
       "130        100.0          99.0  \n",
       "2          110.0         101.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_world_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_world_final.to_excel('./data/final_data/world_aggregated_final.xlsx', sheet_name='world_aggregated_final')\n",
    "df_world_final.to_csv('./data/final_data/world_aggregated_final.csv', sep=';', index=False)"
   ]
  }
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