EDA.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importo las librerias utilizadas en el EDA."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brindar acceso a internet es la principal actividad de la empresa:\n",
    "\n",
    "Brindar un buen servicio (buena calidad).\n",
    "\n",
    "Identificar oportunidades de crecimiento.\n",
    "\n",
    "Soluciones personalizadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicción de número de usuarios de internet en Argentina de 2019 a 2028."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predic_internet_2028 = pd.read_csv('internet_users_argentina_2019_2028.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Internet users in Argentina 2019-2028</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Number of internet users in Argentina from 201...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>33.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>34.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>36.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022</td>\n",
       "      <td>37.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023</td>\n",
       "      <td>38.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024</td>\n",
       "      <td>39.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2025</td>\n",
       "      <td>40.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2026</td>\n",
       "      <td>40.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2027</td>\n",
       "      <td>41.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2028</td>\n",
       "      <td>42.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Internet users in Argentina 2019-2028  Unnamed: 1\n",
       "0   Number of internet users in Argentina from 201...         NaN\n",
       "1                                                 NaN         NaN\n",
       "2                                                2019       33.60\n",
       "3                                                2020       34.87\n",
       "4                                                2021       36.06\n",
       "5                                                2022       37.18\n",
       "6                                                2023       38.22\n",
       "7                                                2024       39.20\n",
       "8                                                2025       40.12\n",
       "9                                                2026       40.99\n",
       "10                                               2027       41.80\n",
       "11                                               2028       42.57"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predic_internet_2028"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Number of internet users in Argentina from 2019 to 2028 (in millions)'"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predic_internet_2028.loc[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predic_internet_2028.rename(columns={'Internet users in Argentina 2019-2028' : 'Año',\n",
    "    'Unnamed: 1' : 'Número de usuarios de internet en Argentina'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predic_internet_2028.drop(index=[0,1], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predic_internet_2028.sort_values(by='Año', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predic_internet_2028.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predic_internet_2028['Número de usuarios de internet en Argentina'] = df_predic_internet_2028['Número de usuarios de internet en Argentina'] * 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predic_internet_2028['Número de usuarios de internet en Argentina'] = df_predic_internet_2028['Número de usuarios de internet en Argentina'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Año</th>\n",
       "      <th>Número de usuarios de internet en Argentina</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
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       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>36060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>34870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>33600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   Año  Número de usuarios de internet en Argentina\n",
       "0     11  2028                                     42570000\n",
       "1     10  2027                                     41800000\n",
       "2      9  2026                                     40990000\n",
       "3      8  2025                                     40120000\n",
       "4      7  2024                                     39200000\n",
       "5      6  2023                                     38220000\n",
       "6      5  2022                                     37180000\n",
       "7      4  2021                                     36060000\n",
       "8      3  2020                                     34870000\n",
       "9      2  2019                                     33600000"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predic_internet_2028"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculo la variación porcentual de usuarios en internet, por año."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_predic_internet_2028['Número de usuarios de internet en Argentina']\n",
    "df_predic_internet_2028['Variación porcentual'] = round((x - x.shift(-1)) / x.shift(-1) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df_predic_internet_2028.sort_values(by='Año', ascending=True)\n",
    "df_predic_internet_2028['Suma variación porcentual'] = round(a['Variación porcentual'].cumsum(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Año</th>\n",
       "      <th>Número de usuarios de internet en Argentina</th>\n",
       "      <th>Variación porcentual</th>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2023</td>\n",
       "      <td>38220000</td>\n",
       "      <td>2.80</td>\n",
       "      <td>13.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>2022</td>\n",
       "      <td>37180000</td>\n",
       "      <td>3.11</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>36060000</td>\n",
       "      <td>3.41</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>34870000</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>33600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   Año  Número de usuarios de internet en Argentina   \n",
       "0     11  2028                                     42570000  \\\n",
       "1     10  2027                                     41800000   \n",
       "2      9  2026                                     40990000   \n",
       "3      8  2025                                     40120000   \n",
       "4      7  2024                                     39200000   \n",
       "5      6  2023                                     38220000   \n",
       "6      5  2022                                     37180000   \n",
       "7      4  2021                                     36060000   \n",
       "8      3  2020                                     34870000   \n",
       "9      2  2019                                     33600000   \n",
       "\n",
       "   Variación porcentual  Suma variación porcentual  \n",
       "0                  1.84                      24.00  \n",
       "1                  1.98                      22.16  \n",
       "2                  2.17                      20.18  \n",
       "3                  2.35                      18.01  \n",
       "4                  2.56                      15.66  \n",
       "5                  2.80                      13.10  \n",
       "6                  3.11                      10.30  \n",
       "7                  3.41                       7.19  \n",
       "8                  3.78                       3.78  \n",
       "9                   NaN                        NaN  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predic_internet_2028"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Año</th>\n",
       "      <th>Número de usuarios de internet en Argentina</th>\n",
       "      <th>Variación porcentual</th>\n",
       "      <th>Suma variación porcentual</th>\n",
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       "      <td>11</td>\n",
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       "      <td>42570000</td>\n",
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       "      <td>20.18</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>2025</td>\n",
       "      <td>40120000</td>\n",
       "      <td>2.35</td>\n",
       "      <td>18.01</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2024</td>\n",
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       "      <td>2.56</td>\n",
       "      <td>15.66</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2023</td>\n",
       "      <td>38220000</td>\n",
       "      <td>2.80</td>\n",
       "      <td>13.10</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>2022</td>\n",
       "      <td>37180000</td>\n",
       "      <td>3.11</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>2021</td>\n",
       "      <td>36060000</td>\n",
       "      <td>3.41</td>\n",
       "      <td>7.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>2020</td>\n",
       "      <td>34870000</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>33600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   Año  Número de usuarios de internet en Argentina   \n",
       "0     11  2028                                     42570000  \\\n",
       "1     10  2027                                     41800000   \n",
       "2      9  2026                                     40990000   \n",
       "3      8  2025                                     40120000   \n",
       "4      7  2024                                     39200000   \n",
       "5      6  2023                                     38220000   \n",
       "6      5  2022                                     37180000   \n",
       "7      4  2021                                     36060000   \n",
       "8      3  2020                                     34870000   \n",
       "9      2  2019                                     33600000   \n",
       "\n",
       "   Variación porcentual  Suma variación porcentual  \n",
       "0                  1.84                      24.00  \n",
       "1                  1.98                      22.16  \n",
       "2                  2.17                      20.18  \n",
       "3                  2.35                      18.01  \n",
       "4                  2.56                      15.66  \n",
       "5                  2.80                      13.10  \n",
       "6                  3.11                      10.30  \n",
       "7                  3.41                       7.19  \n",
       "8                  3.78                       3.78  \n",
       "9                   NaN                        NaN  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predic_internet_2028"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predic_internet_2028.to_csv('df_predic_internet_2028.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Penetración de Internet fijo (accesos por cada 100 hogares)\n",
    "\n",
    "Número de accesos al servicio de Internet fijo por cada 100 hogares por provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_penetracion = pd.read_csv('Internet_Penetracion.csv', decimal=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Accesos por cada 100 hogares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>78.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>122.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>65.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>43.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>84.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>28.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>47.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Santiago Del Estero</td>\n",
       "      <td>16.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "      <td>63.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>33.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>840 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Año  Trimestre            Provincia  Accesos por cada 100 hogares\n",
       "0    2022          3         Buenos Aires                         78.11\n",
       "1    2022          3      Capital Federal                        122.28\n",
       "2    2022          3            Catamarca                         65.33\n",
       "3    2022          3                Chaco                         43.86\n",
       "4    2022          3               Chubut                         84.38\n",
       "..    ...        ...                  ...                           ...\n",
       "835  2014          1           Santa Cruz                         28.94\n",
       "836  2014          1             Santa Fe                         47.85\n",
       "837  2014          1  Santiago Del Estero                         16.31\n",
       "838  2014          1     Tierra Del Fuego                         63.97\n",
       "839  2014          1              Tucumán                         33.42\n",
       "\n",
       "[840 rows x 4 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_internet_penetracion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 840 entries, 0 to 839\n",
      "Data columns (total 4 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Año                           840 non-null    int64  \n",
      " 1   Trimestre                     840 non-null    int64  \n",
      " 2   Provincia                     840 non-null    object \n",
      " 3   Accesos por cada 100 hogares  840 non-null    float64\n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 26.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_internet_penetracion.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elimino duplicados si los hay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(840, 4)\n",
      "(840, 4)\n"
     ]
    }
   ],
   "source": [
    "print(df_internet_penetracion.shape)\n",
    "df_internet_penetracion.drop_duplicates(inplace=True)\n",
    "print(df_internet_penetracion.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Año</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>96.0</td>\n",
       "      <td>36.892083</td>\n",
       "      <td>21.789081</td>\n",
       "      <td>9.35</td>\n",
       "      <td>22.2075</td>\n",
       "      <td>30.720</td>\n",
       "      <td>49.3275</td>\n",
       "      <td>114.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>96.0</td>\n",
       "      <td>39.198333</td>\n",
       "      <td>22.587402</td>\n",
       "      <td>9.71</td>\n",
       "      <td>26.2925</td>\n",
       "      <td>31.980</td>\n",
       "      <td>52.2650</td>\n",
       "      <td>118.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>96.0</td>\n",
       "      <td>39.889792</td>\n",
       "      <td>22.997631</td>\n",
       "      <td>9.59</td>\n",
       "      <td>26.6825</td>\n",
       "      <td>32.115</td>\n",
       "      <td>53.3225</td>\n",
       "      <td>120.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>96.0</td>\n",
       "      <td>42.525729</td>\n",
       "      <td>22.634444</td>\n",
       "      <td>9.51</td>\n",
       "      <td>28.8375</td>\n",
       "      <td>35.605</td>\n",
       "      <td>56.6050</td>\n",
       "      <td>124.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>96.0</td>\n",
       "      <td>48.477500</td>\n",
       "      <td>20.551914</td>\n",
       "      <td>15.15</td>\n",
       "      <td>32.9050</td>\n",
       "      <td>41.720</td>\n",
       "      <td>62.3525</td>\n",
       "      <td>108.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>96.0</td>\n",
       "      <td>53.666667</td>\n",
       "      <td>20.455662</td>\n",
       "      <td>23.67</td>\n",
       "      <td>37.6250</td>\n",
       "      <td>47.610</td>\n",
       "      <td>67.0625</td>\n",
       "      <td>112.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>96.0</td>\n",
       "      <td>56.130625</td>\n",
       "      <td>18.206331</td>\n",
       "      <td>30.23</td>\n",
       "      <td>41.5850</td>\n",
       "      <td>52.775</td>\n",
       "      <td>69.1150</td>\n",
       "      <td>108.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>96.0</td>\n",
       "      <td>61.978437</td>\n",
       "      <td>18.908952</td>\n",
       "      <td>32.73</td>\n",
       "      <td>45.5400</td>\n",
       "      <td>56.865</td>\n",
       "      <td>77.0075</td>\n",
       "      <td>111.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>72.0</td>\n",
       "      <td>67.421528</td>\n",
       "      <td>20.212262</td>\n",
       "      <td>34.96</td>\n",
       "      <td>49.3025</td>\n",
       "      <td>63.685</td>\n",
       "      <td>80.6750</td>\n",
       "      <td>122.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count       mean        std    min      25%     50%      75%     max\n",
       "Año                                                                       \n",
       "2014   96.0  36.892083  21.789081   9.35  22.2075  30.720  49.3275  114.90\n",
       "2015   96.0  39.198333  22.587402   9.71  26.2925  31.980  52.2650  118.29\n",
       "2016   96.0  39.889792  22.997631   9.59  26.6825  32.115  53.3225  120.77\n",
       "2017   96.0  42.525729  22.634444   9.51  28.8375  35.605  56.6050  124.06\n",
       "2018   96.0  48.477500  20.551914  15.15  32.9050  41.720  62.3525  108.45\n",
       "2019   96.0  53.666667  20.455662  23.67  37.6250  47.610  67.0625  112.65\n",
       "2020   96.0  56.130625  18.206331  30.23  41.5850  52.775  69.1150  108.31\n",
       "2021   96.0  61.978437  18.908952  32.73  45.5400  56.865  77.0075  111.27\n",
       "2022   72.0  67.421528  20.212262  34.96  49.3025  63.685  80.6750  122.28"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_internet_penetracion.groupby('Año')['Accesos por cada 100 hogares'].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boxplot para verificar posibles outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df_internet_penetracion, y='Provincia', x='Accesos por cada 100 hogares')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_2022_3 = df_internet_penetracion.loc[(df_internet_penetracion['Año'] == 2022) & (df_internet_penetracion['Trimestre'] == 3)]\n",
    "df_2022_3['Accesos por cada 100 hogares'].plot.box(grid=True)\n",
    "plt.title('Accesos por cada 100 hogares, por provincia, trimestre 3, año 2022.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Accesos por cada 100 hogares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>43.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Año  Trimestre   Provincia  Accesos por cada 100 hogares\n",
       "19  2022          3  Santa Cruz                         43.19"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2022_3.loc[df_2022_3['Accesos por cada 100 hogares'] == df_2022_3['Accesos por cada 100 hogares'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "santa_cruz = df_internet_penetracion.loc[df_internet_penetracion['Provincia'] == 'Santa Cruz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Accesos por cada 100 hogares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.885714</td>\n",
       "      <td>2.457143</td>\n",
       "      <td>32.635143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.564135</td>\n",
       "      <td>1.120474</td>\n",
       "      <td>6.476454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>27.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>36.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Año  Trimestre  Accesos por cada 100 hogares\n",
       "count    35.000000  35.000000                     35.000000\n",
       "mean   2017.885714   2.457143                     32.635143\n",
       "std       2.564135   1.120474                      6.476454\n",
       "min    2014.000000   1.000000                     23.670000\n",
       "25%    2016.000000   1.500000                     27.340000\n",
       "50%    2018.000000   2.000000                     29.380000\n",
       "75%    2020.000000   3.000000                     36.340000\n",
       "max    2022.000000   4.000000                     44.010000"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "santa_cruz.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupo los datos por provincia y obtengo los valores de la columna \"Accesos por cada 100 hogares\" de un trimestre anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_anterior = df_internet_penetracion.groupby(['Provincia'])['Accesos por cada 100 hogares'].shift(-1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculo el aumento del 2% del mismo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "aumento_2 = tr_anterior * 1.02"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genero una nueva columna con la tasa de penetración esperada del 2% redondeada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_penetracion.loc[:, 'Tasa de penetración esperada'] = round(aumento_2, 2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el caso del último trimestre, normalizo llenando los NaN con el valor de 'Accesos por cada 100 hogares' del mismo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_penetracion.loc[:, 'Tasa de penetración esperada'] = df_internet_penetracion.loc[:, 'Tasa de penetración esperada'].fillna(df_internet_penetracion['Accesos por cada 100 hogares'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtengo la diferencia entre el indicador de aumento del 2% con el valor de Accesos por cada 100 hogares del trimestre en cuestión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_penetracion['Desviación de penetración'] = round(df_internet_penetracion['Tasa de penetración esperada'] - df_internet_penetracion['Accesos por cada 100 hogares'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Accesos por cada 100 hogares</th>\n",
       "      <th>Tasa de penetración esperada</th>\n",
       "      <th>Desviación de penetración</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>78.11</td>\n",
       "      <td>79.13</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>77.58</td>\n",
       "      <td>77.60</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>76.08</td>\n",
       "      <td>77.17</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>75.66</td>\n",
       "      <td>73.11</td>\n",
       "      <td>-2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>71.68</td>\n",
       "      <td>71.40</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Año  Trimestre     Provincia  Accesos por cada 100 hogares   \n",
       "0   2022          3  Buenos Aires                         78.11  \\\n",
       "24  2022          2  Buenos Aires                         77.58   \n",
       "48  2022          1  Buenos Aires                         76.08   \n",
       "72  2021          4  Buenos Aires                         75.66   \n",
       "96  2021          3  Buenos Aires                         71.68   \n",
       "\n",
       "    Tasa de penetración esperada  Desviación de penetración  \n",
       "0                          79.13                       1.02  \n",
       "24                         77.60                       0.02  \n",
       "48                         77.17                       1.09  \n",
       "72                         73.11                      -2.55  \n",
       "96                         71.40                      -0.28  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_internet_penetracion.loc[df_internet_penetracion['Provincia'] == 'Buenos Aires'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_penetracion.to_csv('df_internet_penetracion.csv', index= False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculo la variación porcentual, por trimestre, por provincia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "variacion_pct = df_internet_penetracion.groupby(['Provincia'])['Accesos por cada 100 hogares'].apply(lambda x: (x - x.shift(-1)) / x.shift(-1) * 100).rename(\"Variación porcentual\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "variacion_pct = variacion_pct.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "variacion_pct.set_index('level_1', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "variacion_pct.drop(columns='Provincia', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "variacion_pct = variacion_pct.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>Variación porcentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level_1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.683166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.799604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.695513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.159854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.430460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Variación porcentual\n",
       "level_1                      \n",
       "0                    0.683166\n",
       "1                    0.799604\n",
       "2                    4.695513\n",
       "3                    0.159854\n",
       "4                    1.430460"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variacion_pct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_penetracion = df_internet_penetracion.merge(variacion_pct, how='inner', left_index=True, right_index=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Promedio de porcentaje de aumento en el último año, cada 100 hogares, por provincia."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupo filas por provincia, me quedo con el último año."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_provincia_2022 = df_internet_penetracion.groupby(['Provincia']).apply(lambda x: x[x['Año'] == 2022])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uso rolling con ventana de tamaño 3 tomando el promedio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_provincia_2022['Promedio variación porcentual'] = df_internet_provincia_2022['Variación porcentual'].rolling(3).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Accesos por cada 100 hogares</th>\n",
       "      <th>Tasa de penetración esperada</th>\n",
       "      <th>Desviación de penetración</th>\n",
       "      <th>Variación porcentual</th>\n",
       "      <th>Promedio variación porcentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Provincia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Buenos Aires</th>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>78.11</td>\n",
       "      <td>79.13</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.683166</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>77.58</td>\n",
       "      <td>77.60</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.971609</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>76.08</td>\n",
       "      <td>77.17</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.555115</td>\n",
       "      <td>1.069963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Capital Federal</th>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>122.28</td>\n",
       "      <td>123.74</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.799604</td>\n",
       "      <td>1.108776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>121.31</td>\n",
       "      <td>114.04</td>\n",
       "      <td>-7.27</td>\n",
       "      <td>8.506261</td>\n",
       "      <td>3.286993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tierra Del Fuego</th>\n",
       "      <th>46</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "      <td>99.86</td>\n",
       "      <td>100.26</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.597314</td>\n",
       "      <td>0.500624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "      <td>98.29</td>\n",
       "      <td>100.37</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.111789</td>\n",
       "      <td>0.378345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Tucumán</th>\n",
       "      <th>23</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>61.14</td>\n",
       "      <td>60.50</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>3.085483</td>\n",
       "      <td>1.523670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>59.31</td>\n",
       "      <td>58.54</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>3.345531</td>\n",
       "      <td>2.106408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>57.39</td>\n",
       "      <td>57.05</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>2.610406</td>\n",
       "      <td>3.013806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Año  Trimestre         Provincia   \n",
       "Provincia                                                \n",
       "Buenos Aires     0   2022          3      Buenos Aires  \\\n",
       "                 24  2022          2      Buenos Aires   \n",
       "                 48  2022          1      Buenos Aires   \n",
       "Capital Federal  1   2022          3   Capital Federal   \n",
       "                 25  2022          2   Capital Federal   \n",
       "...                   ...        ...               ...   \n",
       "Tierra Del Fuego 46  2022          2  Tierra Del Fuego   \n",
       "                 70  2022          1  Tierra Del Fuego   \n",
       "Tucumán          23  2022          3           Tucumán   \n",
       "                 47  2022          2           Tucumán   \n",
       "                 71  2022          1           Tucumán   \n",
       "\n",
       "                     Accesos por cada 100 hogares   \n",
       "Provincia                                           \n",
       "Buenos Aires     0                          78.11  \\\n",
       "                 24                         77.58   \n",
       "                 48                         76.08   \n",
       "Capital Federal  1                         122.28   \n",
       "                 25                        121.31   \n",
       "...                                           ...   \n",
       "Tierra Del Fuego 46                         99.86   \n",
       "                 70                         98.29   \n",
       "Tucumán          23                         61.14   \n",
       "                 47                         59.31   \n",
       "                 71                         57.39   \n",
       "\n",
       "                     Tasa de penetración esperada  Desviación de penetración   \n",
       "Provincia                                                                      \n",
       "Buenos Aires     0                          79.13                       1.02  \\\n",
       "                 24                         77.60                       0.02   \n",
       "                 48                         77.17                       1.09   \n",
       "Capital Federal  1                         123.74                       1.46   \n",
       "                 25                        114.04                      -7.27   \n",
       "...                                           ...                        ...   \n",
       "Tierra Del Fuego 46                        100.26                       0.40   \n",
       "                 70                        100.37                       2.08   \n",
       "Tucumán          23                         60.50                      -0.64   \n",
       "                 47                         58.54                      -0.77   \n",
       "                 71                         57.05                      -0.34   \n",
       "\n",
       "                     Variación porcentual  Promedio variación porcentual  \n",
       "Provincia                                                                 \n",
       "Buenos Aires     0               0.683166                            NaN  \n",
       "                 24              1.971609                            NaN  \n",
       "                 48              0.555115                       1.069963  \n",
       "Capital Federal  1               0.799604                       1.108776  \n",
       "                 25              8.506261                       3.286993  \n",
       "...                                   ...                            ...  \n",
       "Tierra Del Fuego 46              1.597314                       0.500624  \n",
       "                 70             -0.111789                       0.378345  \n",
       "Tucumán          23              3.085483                       1.523670  \n",
       "                 47              3.345531                       2.106408  \n",
       "                 71              2.610406                       3.013806  \n",
       "\n",
       "[72 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_internet_provincia_2022)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creo un datasets con el Promedio variación porcentual de cada provincia en el último año."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_index = df_internet_provincia_2022.columns.get_loc(\"Promedio variación porcentual\")\n",
    "df_prom_var_pct_2022 = df_internet_provincia_2022.iloc[2::3, col_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prom_var_pct_2022 = df_prom_var_pct_2022.reset_index()\n",
    "df_prom_var_pct_2022.drop(columns='level_1', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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",
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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>Provincia</th>\n",
       "      <th>Promedio variación porcentual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>1.069963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>3.260728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Catamarca</td>\n",
       "      <td>5.750352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chaco</td>\n",
       "      <td>-0.143415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chubut</td>\n",
       "      <td>0.849221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Corrientes</td>\n",
       "      <td>1.960360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Córdoba</td>\n",
       "      <td>1.477833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Entre Ríos</td>\n",
       "      <td>0.269669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Formosa</td>\n",
       "      <td>8.820847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jujuy</td>\n",
       "      <td>1.167389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>La Pampa</td>\n",
       "      <td>2.552010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>La Rioja</td>\n",
       "      <td>1.159656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mendoza</td>\n",
       "      <td>2.414349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Misiones</td>\n",
       "      <td>1.854605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Neuquén</td>\n",
       "      <td>3.170527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Río Negro</td>\n",
       "      <td>1.151647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Salta</td>\n",
       "      <td>0.566244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>San Juan</td>\n",
       "      <td>8.760798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>San Luis</td>\n",
       "      <td>0.595660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>0.109314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>0.627670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Santiago Del Estero</td>\n",
       "      <td>0.655215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "      <td>0.378345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tucumán</td>\n",
       "      <td>3.013806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Provincia  Promedio variación porcentual\n",
       "0          Buenos Aires                       1.069963\n",
       "1       Capital Federal                       3.260728\n",
       "2             Catamarca                       5.750352\n",
       "3                 Chaco                      -0.143415\n",
       "4                Chubut                       0.849221\n",
       "5            Corrientes                       1.960360\n",
       "6               Córdoba                       1.477833\n",
       "7            Entre Ríos                       0.269669\n",
       "8               Formosa                       8.820847\n",
       "9                 Jujuy                       1.167389\n",
       "10             La Pampa                       2.552010\n",
       "11             La Rioja                       1.159656\n",
       "12              Mendoza                       2.414349\n",
       "13             Misiones                       1.854605\n",
       "14              Neuquén                       3.170527\n",
       "15            Río Negro                       1.151647\n",
       "16                Salta                       0.566244\n",
       "17             San Juan                       8.760798\n",
       "18             San Luis                       0.595660\n",
       "19           Santa Cruz                       0.109314\n",
       "20             Santa Fe                       0.627670\n",
       "21  Santiago Del Estero                       0.655215\n",
       "22     Tierra Del Fuego                       0.378345\n",
       "23              Tucumán                       3.013806"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prom_var_pct_2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prom_var_pct_2022.to_csv('df_prom_var_pct_2022.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Promedio de porcentaje de aumento en los últimos dos años, cada 100 hogares, por provincia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_provincia_2021_2022 = df_internet_penetracion.groupby(['Provincia']).apply(lambda x: x[(x['Año'] == 2021) | (x['Año'] == 2022)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_provincia_2021_2022['Promedio variación porcentual'] = df_internet_provincia_2021_2022['Variación porcentual'].rolling(7).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Accesos por cada 100 hogares</th>\n",
       "      <th>Tasa de penetración esperada</th>\n",
       "      <th>Desviación de penetración</th>\n",
       "      <th>Variación porcentual</th>\n",
       "      <th>Promedio variación porcentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Provincia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Buenos Aires</th>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>78.11</td>\n",
       "      <td>79.13</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.683166</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>77.58</td>\n",
       "      <td>77.60</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.971609</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>76.08</td>\n",
       "      <td>77.17</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.555115</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>75.66</td>\n",
       "      <td>73.11</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>5.552455</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>71.68</td>\n",
       "      <td>71.40</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>70.00</td>\n",
       "      <td>70.49</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.287802</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>69.11</td>\n",
       "      <td>70.36</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.188460</td>\n",
       "      <td>1.805515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">Capital Federal</th>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>122.28</td>\n",
       "      <td>123.74</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.799604</td>\n",
       "      <td>1.822149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>121.31</td>\n",
       "      <td>114.04</td>\n",
       "      <td>-7.27</td>\n",
       "      <td>8.506261</td>\n",
       "      <td>2.755671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>111.80</td>\n",
       "      <td>113.50</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.476319</td>\n",
       "      <td>2.744415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>111.27</td>\n",
       "      <td>113.50</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.951207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>111.27</td>\n",
       "      <td>112.34</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.025967</td>\n",
       "      <td>1.754916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>110.14</td>\n",
       "      <td>110.59</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.586423</td>\n",
       "      <td>1.797576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>108.42</td>\n",
       "      <td>110.48</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.101560</td>\n",
       "      <td>1.785162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Año  Trimestre        Provincia   \n",
       "Provincia                                               \n",
       "Buenos Aires    0    2022          3     Buenos Aires  \\\n",
       "                24   2022          2     Buenos Aires   \n",
       "                48   2022          1     Buenos Aires   \n",
       "                72   2021          4     Buenos Aires   \n",
       "                96   2021          3     Buenos Aires   \n",
       "                120  2021          2     Buenos Aires   \n",
       "                144  2021          1     Buenos Aires   \n",
       "Capital Federal 1    2022          3  Capital Federal   \n",
       "                25   2022          2  Capital Federal   \n",
       "                49   2022          1  Capital Federal   \n",
       "                73   2021          4  Capital Federal   \n",
       "                97   2021          3  Capital Federal   \n",
       "                121  2021          2  Capital Federal   \n",
       "                145  2021          1  Capital Federal   \n",
       "\n",
       "                     Accesos por cada 100 hogares   \n",
       "Provincia                                           \n",
       "Buenos Aires    0                           78.11  \\\n",
       "                24                          77.58   \n",
       "                48                          76.08   \n",
       "                72                          75.66   \n",
       "                96                          71.68   \n",
       "                120                         70.00   \n",
       "                144                         69.11   \n",
       "Capital Federal 1                          122.28   \n",
       "                25                         121.31   \n",
       "                49                         111.80   \n",
       "                73                         111.27   \n",
       "                97                         111.27   \n",
       "                121                        110.14   \n",
       "                145                        108.42   \n",
       "\n",
       "                     Tasa de penetración esperada  Desviación de penetración   \n",
       "Provincia                                                                      \n",
       "Buenos Aires    0                           79.13                       1.02  \\\n",
       "                24                          77.60                       0.02   \n",
       "                48                          77.17                       1.09   \n",
       "                72                          73.11                      -2.55   \n",
       "                96                          71.40                      -0.28   \n",
       "                120                         70.49                       0.49   \n",
       "                144                         70.36                       1.25   \n",
       "Capital Federal 1                          123.74                       1.46   \n",
       "                25                         114.04                      -7.27   \n",
       "                49                         113.50                       1.70   \n",
       "                73                         113.50                       2.23   \n",
       "                97                         112.34                       1.07   \n",
       "                121                        110.59                       0.45   \n",
       "                145                        110.48                       2.06   \n",
       "\n",
       "                     Variación porcentual  Promedio variación porcentual  \n",
       "Provincia                                                                 \n",
       "Buenos Aires    0                0.683166                            NaN  \n",
       "                24               1.971609                            NaN  \n",
       "                48               0.555115                            NaN  \n",
       "                72               5.552455                            NaN  \n",
       "                96               2.400000                            NaN  \n",
       "                120              1.287802                            NaN  \n",
       "                144              0.188460                       1.805515  \n",
       "Capital Federal 1                0.799604                       1.822149  \n",
       "                25               8.506261                       2.755671  \n",
       "                49               0.476319                       2.744415  \n",
       "                73               0.000000                       1.951207  \n",
       "                97               1.025967                       1.754916  \n",
       "                121              1.586423                       1.797576  \n",
       "                145              0.101560                       1.785162  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_internet_provincia_2021_2022.head(14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_index = df_internet_provincia_2021_2022.columns.get_loc(\"Promedio variación porcentual\")\n",
    "df_prom_var_pct_2021_2022 = df_internet_provincia_2021_2022.iloc[6::7, col_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prom_var_pct_2021_2022 = df_prom_var_pct_2021_2022.reset_index()\n",
    "df_prom_var_pct_2021_2022.drop(columns='level_1', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prom_var_pct_2021_2022.to_csv('df_prom_var_pct_2021_2022.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ingresos trimestrales por la prestación del servicio de Internet fijo\n",
    "\n",
    "Ingresos trimestrales de los operadores por el servicio de Internet fijo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ing_internet = pd.read_csv('Internet_Ingresos.csv', thousands='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Ingresos (miles de pesos)</th>\n",
       "      <th>Periodo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>67055930</td>\n",
       "      <td>Jul-Sept 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>60335724</td>\n",
       "      <td>Abr-Jun 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>55589997</td>\n",
       "      <td>Ene-Mar 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>45467887</td>\n",
       "      <td>Oct-Dic 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>42999944</td>\n",
       "      <td>Jul-Sept 2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre  Ingresos (miles de pesos)        Periodo\n",
       "0  2022          3                   67055930  Jul-Sept 2022\n",
       "1  2022          2                   60335724   Abr-Jun 2022\n",
       "2  2022          1                   55589997   Ene-Mar 2022\n",
       "3  2021          4                   45467887   Oct-Dic 2021\n",
       "4  2021          3                   42999944  Jul-Sept 2021"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ing_internet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ing_internet.drop(columns='Periodo', inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estos datos no me indican mas que la importancia que tiene la conectividad en el siglo 21, sería mas preciso obtener los datos por compañia de telecomunicaciones."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acceso a Internet fijo por tecnología y provincia\n",
    "\n",
    "Número de accesos al servicio de Internet fijo por tipo de tecnología en cada provincia (trimestral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_fijo_tec = pd.read_csv('Internet_Accesos-por-tecnologia.csv', thousands='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemodem</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Otros</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>339648.0</td>\n",
       "      <td>2748325.0</td>\n",
       "      <td>1436433.0</td>\n",
       "      <td>126846.0</td>\n",
       "      <td>70416.0</td>\n",
       "      <td>4721668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>140791.0</td>\n",
       "      <td>1240125.0</td>\n",
       "      <td>129218.0</td>\n",
       "      <td>5758.0</td>\n",
       "      <td>31787.0</td>\n",
       "      <td>1547679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>10010.0</td>\n",
       "      <td>10495.0</td>\n",
       "      <td>46224.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>2235.0</td>\n",
       "      <td>70293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>27164.0</td>\n",
       "      <td>61800.0</td>\n",
       "      <td>44645.0</td>\n",
       "      <td>8178.0</td>\n",
       "      <td>2359.0</td>\n",
       "      <td>144146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>45377.0</td>\n",
       "      <td>72212.0</td>\n",
       "      <td>9574.0</td>\n",
       "      <td>29784.0</td>\n",
       "      <td>8831.0</td>\n",
       "      <td>165778.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>322662.0</td>\n",
       "      <td>174296.0</td>\n",
       "      <td>3059.0</td>\n",
       "      <td>5951.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>506612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Santiago Del Estero</td>\n",
       "      <td>32567.0</td>\n",
       "      <td>3598.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>37133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "      <td>21618.0</td>\n",
       "      <td>2837.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2934.0</td>\n",
       "      <td>28038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>129717.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>130032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>*</td>\n",
       "      <td>Los datos provinciales no coinciden a nivel na...</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>841 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Año                                          Trimestre   \n",
       "0    2022                                                  3  \\\n",
       "1    2022                                                  3   \n",
       "2    2022                                                  3   \n",
       "3    2022                                                  3   \n",
       "4    2022                                                  3   \n",
       "..    ...                                                ...   \n",
       "836  2014                                                  1   \n",
       "837  2014                                                  1   \n",
       "838  2014                                                  1   \n",
       "839  2014                                                  1   \n",
       "840     *  Los datos provinciales no coinciden a nivel na...   \n",
       "\n",
       "               Provincia      ADSL  Cablemodem  Fibra óptica  Wireless   \n",
       "0           Buenos Aires  339648.0   2748325.0     1436433.0  126846.0  \\\n",
       "1        Capital Federal  140791.0   1240125.0      129218.0    5758.0   \n",
       "2              Catamarca   10010.0     10495.0       46224.0    1329.0   \n",
       "3                  Chaco   27164.0     61800.0       44645.0    8178.0   \n",
       "4                 Chubut   45377.0     72212.0        9574.0   29784.0   \n",
       "..                   ...       ...         ...           ...       ...   \n",
       "836             Santa Fe  322662.0    174296.0        3059.0    5951.0   \n",
       "837  Santiago Del Estero   32567.0      3598.0          19.0     915.0   \n",
       "838     Tierra Del Fuego   21618.0      2837.0         648.0       1.0   \n",
       "839              Tucumán  129717.0        83.0         121.0      13.0   \n",
       "840                  NaN       NaN         NaN           NaN       NaN   \n",
       "\n",
       "       Otros      Total  \n",
       "0    70416.0  4721668.0  \n",
       "1    31787.0  1547679.0  \n",
       "2     2235.0    70293.0  \n",
       "3     2359.0   144146.0  \n",
       "4     8831.0   165778.0  \n",
       "..       ...        ...  \n",
       "836    644.0   506612.0  \n",
       "837     34.0    37133.0  \n",
       "838   2934.0    28038.0  \n",
       "839     98.0   130032.0  \n",
       "840      NaN        NaN  \n",
       "\n",
       "[841 rows x 9 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_internet_fijo_tec"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " La fibra óptica y wireless se consideran tecnologías clave para el futuro de las telecomunicaciones, ya que ofrecen velocidades de conexión de alta velocidad y confiables y son cada vez más accesibles para una amplia gama de usuarios finales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 841 entries, 0 to 840\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Año           841 non-null    object \n",
      " 1   Trimestre     841 non-null    object \n",
      " 2   Provincia     840 non-null    object \n",
      " 3   ADSL          840 non-null    float64\n",
      " 4   Cablemodem    840 non-null    float64\n",
      " 5   Fibra óptica  840 non-null    float64\n",
      " 6   Wireless      840 non-null    float64\n",
      " 7   Otros         840 non-null    float64\n",
      " 8   Total         840 non-null    float64\n",
      "dtypes: float64(6), object(3)\n",
      "memory usage: 59.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_internet_fijo_tec.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "string = df_internet_fijo_tec.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
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       "      <th>288</th>\n",
       "      <td>2019 *</td>\n",
       "      <td>3 *</td>\n",
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       "      <td>1277249.0</td>\n",
       "      <td>2144956.0</td>\n",
       "      <td>425386.0</td>\n",
       "      <td>85572.0</td>\n",
       "      <td>27070.0</td>\n",
       "      <td>3960233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2019 *</td>\n",
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       "      <td>Capital Federal</td>\n",
       "      <td>300288.0</td>\n",
       "      <td>1080973.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>3895.0</td>\n",
       "      <td>31155.0</td>\n",
       "      <td>1441211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2019 *</td>\n",
       "      <td>3 *</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>24349.0</td>\n",
       "      <td>5543.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>2251.0</td>\n",
       "      <td>32400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>2019 *</td>\n",
       "      <td>3 *</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>50416.0</td>\n",
       "      <td>49156.0</td>\n",
       "      <td>8063.0</td>\n",
       "      <td>5224.0</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>114451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>2019 *</td>\n",
       "      <td>3 *</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>37298.0</td>\n",
       "      <td>27090.0</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>15149.0</td>\n",
       "      <td>7551.0</td>\n",
       "      <td>89005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2019 *</td>\n",
       "      <td>1 *</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>340038.0</td>\n",
       "      <td>340344.0</td>\n",
       "      <td>15621.0</td>\n",
       "      <td>23215.0</td>\n",
       "      <td>17860.0</td>\n",
       "      <td>737078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2019 *</td>\n",
       "      <td>1 *</td>\n",
       "      <td>Santiago Del Estero</td>\n",
       "      <td>25337.0</td>\n",
       "      <td>61547.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2847.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>89853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2019 *</td>\n",
       "      <td>1 *</td>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "      <td>19789.0</td>\n",
       "      <td>23549.0</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>46447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2019 *</td>\n",
       "      <td>1 *</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>159215.0</td>\n",
       "      <td>11670.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>172002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>*</td>\n",
       "      <td>Los datos provinciales no coinciden a nivel na...</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>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Año                                          Trimestre   \n",
       "288  2019 *                                                3 *  \\\n",
       "289  2019 *                                                3 *   \n",
       "290  2019 *                                                3 *   \n",
       "291  2019 *                                                3 *   \n",
       "292  2019 *                                                3 *   \n",
       "..      ...                                                ...   \n",
       "356  2019 *                                                1 *   \n",
       "357  2019 *                                                1 *   \n",
       "358  2019 *                                                1 *   \n",
       "359  2019 *                                                1 *   \n",
       "840       *  Los datos provinciales no coinciden a nivel na...   \n",
       "\n",
       "               Provincia       ADSL Cablemodem Fibra óptica Wireless    Otros   \n",
       "288         Buenos Aires  1277249.0  2144956.0     425386.0  85572.0  27070.0  \\\n",
       "289      Capital Federal   300288.0  1080973.0      24900.0   3895.0  31155.0   \n",
       "290            Catamarca    24349.0     5543.0         13.0    244.0   2251.0   \n",
       "291                Chaco    50416.0    49156.0       8063.0   5224.0   1592.0   \n",
       "292               Chubut    37298.0    27090.0       1917.0  15149.0   7551.0   \n",
       "..                   ...        ...        ...          ...      ...      ...   \n",
       "356             Santa Fe   340038.0   340344.0      15621.0  23215.0  17860.0   \n",
       "357  Santiago Del Estero    25337.0    61547.0         23.0   2847.0     99.0   \n",
       "358     Tierra Del Fuego    19789.0    23549.0       1623.0      0.0   1486.0   \n",
       "359              Tucumán   159215.0    11670.0        118.0    678.0    321.0   \n",
       "840                  nan        nan        nan          nan      nan      nan   \n",
       "\n",
       "         Total  \n",
       "288  3960233.0  \n",
       "289  1441211.0  \n",
       "290    32400.0  \n",
       "291   114451.0  \n",
       "292    89005.0  \n",
       "..         ...  \n",
       "356   737078.0  \n",
       "357    89853.0  \n",
       "358    46447.0  \n",
       "359   172002.0  \n",
       "840        nan  \n",
       "\n",
       "[73 rows x 9 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string[string.apply(lambda x: x.str.contains('\\*', na=False)).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_fijo_tec.loc[:, 'Año'] = df_internet_fijo_tec.loc[:, 'Año'].replace('2019 *', '2019')\n",
    "df_internet_fijo_tec.loc[:,'Trimestre'] = df_internet_fijo_tec.loc[:,'Trimestre'].str.replace('1 *', '1')\n",
    "df_internet_fijo_tec.loc[:,'Trimestre'] = df_internet_fijo_tec.loc[:,'Trimestre'].str.replace('2 *', '2')\n",
    "df_internet_fijo_tec.loc[:,'Trimestre'] = df_internet_fijo_tec.loc[:,'Trimestre'].str.replace('3 *', '3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemodem</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Otros</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>720011.0</td>\n",
       "      <td>2191672.0</td>\n",
       "      <td>669194.0</td>\n",
       "      <td>87111.0</td>\n",
       "      <td>18266.0</td>\n",
       "      <td>3686254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>196938.0</td>\n",
       "      <td>1093143.0</td>\n",
       "      <td>35534.0</td>\n",
       "      <td>3914.0</td>\n",
       "      <td>28576.0</td>\n",
       "      <td>1358105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>22999.0</td>\n",
       "      <td>3318.0</td>\n",
       "      <td>9506.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>2227.0</td>\n",
       "      <td>38311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>48661.0</td>\n",
       "      <td>53602.0</td>\n",
       "      <td>8831.0</td>\n",
       "      <td>5229.0</td>\n",
       "      <td>1580.0</td>\n",
       "      <td>117903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>47045.0</td>\n",
       "      <td>26977.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>15267.0</td>\n",
       "      <td>5489.0</td>\n",
       "      <td>96102.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>10659.0</td>\n",
       "      <td>4615.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>2735.0</td>\n",
       "      <td>24905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>340038.0</td>\n",
       "      <td>340344.0</td>\n",
       "      <td>15621.0</td>\n",
       "      <td>23215.0</td>\n",
       "      <td>17860.0</td>\n",
       "      <td>737078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>Santiago Del Estero</td>\n",
       "      <td>25337.0</td>\n",
       "      <td>61547.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2847.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>89853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "      <td>19789.0</td>\n",
       "      <td>23549.0</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>46447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>159215.0</td>\n",
       "      <td>11670.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>172002.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Año Trimestre            Provincia      ADSL  Cablemodem  Fibra óptica   \n",
       "264  2019         4         Buenos Aires  720011.0   2191672.0      669194.0  \\\n",
       "265  2019         4      Capital Federal  196938.0   1093143.0       35534.0   \n",
       "266  2019         4            Catamarca   22999.0      3318.0        9506.0   \n",
       "267  2019         4                Chaco   48661.0     53602.0        8831.0   \n",
       "268  2019         4               Chubut   47045.0     26977.0        1324.0   \n",
       "..    ...       ...                  ...       ...         ...           ...   \n",
       "355  2019         1           Santa Cruz   10659.0      4615.0         382.0   \n",
       "356  2019         1             Santa Fe  340038.0    340344.0       15621.0   \n",
       "357  2019         1  Santiago Del Estero   25337.0     61547.0          23.0   \n",
       "358  2019         1     Tierra Del Fuego   19789.0     23549.0        1623.0   \n",
       "359  2019         1              Tucumán  159215.0     11670.0         118.0   \n",
       "\n",
       "     Wireless    Otros      Total  \n",
       "264   87111.0  18266.0  3686254.0  \n",
       "265    3914.0  28576.0  1358105.0  \n",
       "266     261.0   2227.0    38311.0  \n",
       "267    5229.0   1580.0   117903.0  \n",
       "268   15267.0   5489.0    96102.0  \n",
       "..        ...      ...        ...  \n",
       "355    6514.0   2735.0    24905.0  \n",
       "356   23215.0  17860.0   737078.0  \n",
       "357    2847.0     99.0    89853.0  \n",
       "358       0.0   1486.0    46447.0  \n",
       "359     678.0    321.0   172002.0  \n",
       "\n",
       "[96 rows x 9 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_internet_fijo_tec.loc[df_internet_fijo_tec['Año'] == '2019']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Los datos provinciales no coinciden a nivel nacional, ya que se rincorporó información que no contien apertuta a nivel geográfico.'"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_internet_fijo_tec.iloc[-1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_fijo_tec.drop(index=[840], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_fijo_tec.loc[:, ['Año', 'Trimestre', 'ADSL', 'Cablemodem', 'Fibra óptica', 'Wireless', 'Otros', 'Total']] = df_internet_fijo_tec.loc[:, ['Año', 'Trimestre', 'ADSL', 'Cablemodem', 'Fibra óptica', 'Wireless', 'Otros', 'Total']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_fijo_tec.loc[:, ['Año', 'Trimestre', 'ADSL', 'Cablemodem', 'Fibra óptica', 'Wireless', 'Otros', 'Total']] = df_internet_fijo_tec.loc[:, ['Año', 'Trimestre', 'ADSL', 'Cablemodem', 'Fibra óptica', 'Wireless', 'Otros', 'Total']].apply(lambda x: x.astype(str)\n",
    "                                                                                                                                                                                                                                                            .str.strip()\n",
    "                                                                                                                                                                                                                                                            .str.replace(',', '')\n",
    "                                                                                                                                                                                                                                                            .str.replace('- ', '')\n",
    "                                                                                                                                                                                                                                                            .str.replace('.0', '')\n",
    "                                                                                                                                                                                                                                                            .astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemodem</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Otros</th>\n",
       "      <th>Total</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>339648.0</td>\n",
       "      <td>2748325.0</td>\n",
       "      <td>1436433.0</td>\n",
       "      <td>126846.0</td>\n",
       "      <td>70416.0</td>\n",
       "      <td>4721668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>140791.0</td>\n",
       "      <td>1240125.0</td>\n",
       "      <td>129218.0</td>\n",
       "      <td>5758.0</td>\n",
       "      <td>31787.0</td>\n",
       "      <td>1547679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>10010.0</td>\n",
       "      <td>10495.0</td>\n",
       "      <td>46224.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>2235.0</td>\n",
       "      <td>70293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>27164.0</td>\n",
       "      <td>61800.0</td>\n",
       "      <td>44645.0</td>\n",
       "      <td>8178.0</td>\n",
       "      <td>2359.0</td>\n",
       "      <td>144146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>45377.0</td>\n",
       "      <td>72212.0</td>\n",
       "      <td>9574.0</td>\n",
       "      <td>29784.0</td>\n",
       "      <td>8831.0</td>\n",
       "      <td>165778.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>18927.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>4092.0</td>\n",
       "      <td>3264.0</td>\n",
       "      <td>26764.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>322662.0</td>\n",
       "      <td>174296.0</td>\n",
       "      <td>3059.0</td>\n",
       "      <td>5951.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>506612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Santiago Del Estero</td>\n",
       "      <td>32567.0</td>\n",
       "      <td>3598.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>37133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Tierra Del Fuego</td>\n",
       "      <td>21618.0</td>\n",
       "      <td>2837.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2934.0</td>\n",
       "      <td>28038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>129717.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>130032.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>840 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Año Trimestre            Provincia      ADSL  Cablemodem  Fibra óptica   \n",
       "0    2022         3         Buenos Aires  339648.0   2748325.0     1436433.0  \\\n",
       "1    2022         3      Capital Federal  140791.0   1240125.0      129218.0   \n",
       "2    2022         3            Catamarca   10010.0     10495.0       46224.0   \n",
       "3    2022         3                Chaco   27164.0     61800.0       44645.0   \n",
       "4    2022         3               Chubut   45377.0     72212.0        9574.0   \n",
       "..    ...       ...                  ...       ...         ...           ...   \n",
       "835  2014         1           Santa Cruz   18927.0       100.0         381.0   \n",
       "836  2014         1             Santa Fe  322662.0    174296.0        3059.0   \n",
       "837  2014         1  Santiago Del Estero   32567.0      3598.0          19.0   \n",
       "838  2014         1     Tierra Del Fuego   21618.0      2837.0         648.0   \n",
       "839  2014         1              Tucumán  129717.0        83.0         121.0   \n",
       "\n",
       "     Wireless    Otros      Total  \n",
       "0    126846.0  70416.0  4721668.0  \n",
       "1      5758.0  31787.0  1547679.0  \n",
       "2      1329.0   2235.0    70293.0  \n",
       "3      8178.0   2359.0   144146.0  \n",
       "4     29784.0   8831.0   165778.0  \n",
       "..        ...      ...        ...  \n",
       "835    4092.0   3264.0    26764.0  \n",
       "836    5951.0    644.0   506612.0  \n",
       "837     915.0     34.0    37133.0  \n",
       "838       1.0   2934.0    28038.0  \n",
       "839      13.0     98.0   130032.0  \n",
       "\n",
       "[840 rows x 9 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_internet_fijo_tec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_fijo_tec.to_csv('df_internet_fijo_tec.csv', index= False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accesos a banda ancha y banda angosta por provincia\n",
    "\n",
    "Número de accesos al servicio de Internet fijo por banda ancha y banda angosta en cada provincia (trimestral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_internet_BAF = pd.read_csv('Internet_BAF.csv', thousands='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 840 entries, 0 to 839\n",
      "Data columns (total 6 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Año               840 non-null    int64 \n",
      " 1   Trimestre         840 non-null    int64 \n",
      " 2   Provincia         840 non-null    object\n",
      " 3   Banda ancha fija  840 non-null    int64 \n",
      " 4   Dial up           838 non-null    object\n",
      " 5   Total             840 non-null    int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 39.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_internet_BAF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Banda ancha fija</th>\n",
       "      <th>Dial up</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>4715469</td>\n",
       "      <td>6.199</td>\n",
       "      <td>4721668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>1545534</td>\n",
       "      <td>2.145</td>\n",
       "      <td>1547679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>70292</td>\n",
       "      <td>1</td>\n",
       "      <td>70293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>144141</td>\n",
       "      <td>5</td>\n",
       "      <td>144146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>164874</td>\n",
       "      <td>904</td>\n",
       "      <td>165778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia  Banda ancha fija Dial up    Total\n",
       "0  2022          3     Buenos Aires           4715469   6.199  4721668\n",
       "1  2022          3  Capital Federal           1545534   2.145  1547679\n",
       "2  2022          3        Catamarca             70292       1    70293\n",
       "3  2022          3            Chaco            144141       5   144146\n",
       "4  2022          3           Chubut            164874     904   165778"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_internet_BAF.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La información obtenida de este csv es redundante con el csv de accesos por tecnologia, por ende lo descarto."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuadros de resultados provisionales del CENSO 2022. Del informe general solo me quedo con el primer cuadro del excel.\n",
    "\n",
    "Cuadro 1. Total del país. Viviendas particulares, viviendas colectivas, población en viviendas particulares, población en viviendas colectivas y población en situación de calle (vía pública), por jurisdicción. Año 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indec = pd.read_csv('indec_total_pais.csv', thousands='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>Jurisdicción</th>\n",
       "      <th>Total de viviendas particulares</th>\n",
       "      <th>Total de viviendas colectivas</th>\n",
       "      <th>Total de población</th>\n",
       "      <th>Población en viviendas particulares</th>\n",
       "      <th>Población en viviendas colectivas (1)</th>\n",
       "      <th>Población en situación de calle (vía pública)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total</td>\n",
       "      <td>17780210</td>\n",
       "      <td>25501</td>\n",
       "      <td>46044703</td>\n",
       "      <td>45767858</td>\n",
       "      <td>273883</td>\n",
       "      <td>2.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ciudad Autónoma de Buenos Aires</td>\n",
       "      <td>1638764</td>\n",
       "      <td>1946</td>\n",
       "      <td>3120612</td>\n",
       "      <td>3092942</td>\n",
       "      <td>26767</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>6695273</td>\n",
       "      <td>6535</td>\n",
       "      <td>17569053</td>\n",
       "      <td>17450899</td>\n",
       "      <td>117193</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24 Partidos del Gran Buenos Aires</td>\n",
       "      <td>3714607</td>\n",
       "      <td>1383</td>\n",
       "      <td>10865182</td>\n",
       "      <td>10817508</td>\n",
       "      <td>46877</td>\n",
       "      <td>797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resto de partidos de la Provincia\\nde Buenos A...</td>\n",
       "      <td>2980666</td>\n",
       "      <td>5152</td>\n",
       "      <td>6703871</td>\n",
       "      <td>6633391</td>\n",
       "      <td>70316</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Jurisdicción   \n",
       "0                                              Total  \\\n",
       "1                    Ciudad Autónoma de Buenos Aires   \n",
       "2                                       Buenos Aires   \n",
       "3                  24 Partidos del Gran Buenos Aires   \n",
       "4  Resto de partidos de la Provincia\\nde Buenos A...   \n",
       "\n",
       "   Total de viviendas particulares  Total de viviendas colectivas   \n",
       "0                         17780210                          25501  \\\n",
       "1                          1638764                           1946   \n",
       "2                          6695273                           6535   \n",
       "3                          3714607                           1383   \n",
       "4                          2980666                           5152   \n",
       "\n",
       "   Total de población  Población en viviendas particulares   \n",
       "0            46044703                             45767858  \\\n",
       "1             3120612                              3092942   \n",
       "2            17569053                             17450899   \n",
       "3            10865182                             10817508   \n",
       "4             6703871                              6633391   \n",
       "\n",
       "   Población en viviendas colectivas (1)   \n",
       "0                                 273883  \\\n",
       "1                                  26767   \n",
       "2                                 117193   \n",
       "3                                  46877   \n",
       "4                                  70316   \n",
       "\n",
       "  Población en situación de calle (vía pública)  \n",
       "0                                         2.962  \n",
       "1                                           903  \n",
       "2                                           961  \n",
       "3                                           797  \n",
       "4                                           164  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indec.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descarto las columnas de población, solo me quedo con la información de viviendad para obtener la tasa de tecnología por provincia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indec.drop(columns=['Total de población', 'Población en viviendas particulares', 'Población en viviendas colectivas (1)', 'Población en situación de calle (vía pública)'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Total', 'Ciudad Autónoma de Buenos Aires', 'Buenos Aires',\n",
       "       '24 Partidos del Gran Buenos Aires',\n",
       "       'Resto de partidos de la Provincia\\nde Buenos Aires', 'Catamarca',\n",
       "       'Chaco', 'Chubut', 'Córdoba', 'Corrientes', 'Entre Ríos',\n",
       "       'Formosa', 'Jujuy', 'La Pampa', 'La Rioja', 'Mendoza', 'Misiones',\n",
       "       'Neuquén', 'Río Negro', 'Salta', 'San Juan', 'San Luis',\n",
       "       'Santa Cruz', 'Santa Fe', 'Santiago del Estero',\n",
       "       'Tierra del Fuego, Antártida e Islas\\ndel Atlántico Sur (2)',\n",
       "       'Tucumán'], dtype=object)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indec[\"Jurisdicción\"].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizo las provincias, teniendo en cuenta los anteriores datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indec[\"Jurisdicción\"] = df_indec[\"Jurisdicción\"].replace(\"Ciudad Autónoma de Buenos Aires\", \"Capital Federal\")\n",
    "df_indec[\"Jurisdicción\"] = df_indec[\"Jurisdicción\"].replace(\"Tierra del Fuego, Antártida e Islas\\ndel Atlántico Sur (2)\", \"Tierra del Fuego\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indec.rename(columns={'Jurisdicción': 'Provincia'}, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elimino los datos del total y las subregiones de Buenos Aires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indec.drop(index=[0, 3, 4], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Provincia</th>\n",
       "      <th>Total de viviendas particulares</th>\n",
       "      <th>Total de viviendas colectivas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>1638764</td>\n",
       "      <td>1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>6695273</td>\n",
       "      <td>6535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Catamarca</td>\n",
       "      <td>158989</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chaco</td>\n",
       "      <td>401658</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chubut</td>\n",
       "      <td>236010</td>\n",
       "      <td>1123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Córdoba</td>\n",
       "      <td>1630356</td>\n",
       "      <td>3548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Corrientes</td>\n",
       "      <td>406480</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Entre Ríos</td>\n",
       "      <td>559398</td>\n",
       "      <td>1159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Formosa</td>\n",
       "      <td>212941</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jujuy</td>\n",
       "      <td>276222</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>La Pampa</td>\n",
       "      <td>163805</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>La Rioja</td>\n",
       "      <td>149910</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mendoza</td>\n",
       "      <td>708354</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Misiones</td>\n",
       "      <td>466850</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Neuquén</td>\n",
       "      <td>273685</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Río Negro</td>\n",
       "      <td>295502</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Salta</td>\n",
       "      <td>478433</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>San Juan</td>\n",
       "      <td>265047</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>San Luis</td>\n",
       "      <td>201271</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Santa Cruz</td>\n",
       "      <td>131395</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>1458652</td>\n",
       "      <td>1928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Santiago del Estero</td>\n",
       "      <td>348705</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tierra del Fuego</td>\n",
       "      <td>67091</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Tucumán</td>\n",
       "      <td>555419</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Provincia  Total de viviendas particulares   \n",
       "1       Capital Federal                          1638764  \\\n",
       "2          Buenos Aires                          6695273   \n",
       "5             Catamarca                           158989   \n",
       "6                 Chaco                           401658   \n",
       "7                Chubut                           236010   \n",
       "8               Córdoba                          1630356   \n",
       "9            Corrientes                           406480   \n",
       "10           Entre Ríos                           559398   \n",
       "11              Formosa                           212941   \n",
       "12                Jujuy                           276222   \n",
       "13             La Pampa                           163805   \n",
       "14             La Rioja                           149910   \n",
       "15              Mendoza                           708354   \n",
       "16             Misiones                           466850   \n",
       "17              Neuquén                           273685   \n",
       "18            Río Negro                           295502   \n",
       "19                Salta                           478433   \n",
       "20             San Juan                           265047   \n",
       "21             San Luis                           201271   \n",
       "22           Santa Cruz                           131395   \n",
       "23             Santa Fe                          1458652   \n",
       "24  Santiago del Estero                           348705   \n",
       "25     Tierra del Fuego                            67091   \n",
       "26              Tucumán                           555419   \n",
       "\n",
       "    Total de viviendas colectivas  \n",
       "1                            1946  \n",
       "2                            6535  \n",
       "5                             449  \n",
       "6                             443  \n",
       "7                            1123  \n",
       "8                            3548  \n",
       "9                             555  \n",
       "10                           1159  \n",
       "11                            232  \n",
       "12                            326  \n",
       "13                            437  \n",
       "14                            272  \n",
       "15                           1744  \n",
       "16                            652  \n",
       "17                            392  \n",
       "18                            910  \n",
       "19                            766  \n",
       "20                            387  \n",
       "21                            339  \n",
       "22                            486  \n",
       "23                           1928  \n",
       "24                            353  \n",
       "25                            137  \n",
       "26                            382  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indec"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creo una columna con la suma de las viviendas por provincia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indec['Total de viviendas'] = df_indec['Total de viviendas colectivas'] + df_indec['Total de viviendas particulares']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_indec.to_csv('df_indec.csv', index= False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obtengo la tasa de fibra óptica y de wireless en el 2022, por provincia.\n",
    "\n",
    "Son las dos tecnologías mas significativas a la hora de obtener conectividad total del país."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "tec_2022_q3 = df_internet_fijo_tec[(df_internet_fijo_tec['Año'] == 2022) & (df_internet_fijo_tec['Trimestre'] == 3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\AppData\\Local\\Temp\\ipykernel_32072\\1884646700.py:1: 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",
      "  tec_2022_q3.drop(columns=['ADSL', 'Cablemodem', 'Otros', 'Total'], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "tec_2022_q3.drop(columns=['ADSL', 'Cablemodem', 'Otros', 'Total'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "tec_2022_q3 = tec_2022_q3.merge(df_indec[['Provincia', 'Total de viviendas']], on='Provincia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "tec_2022_q3.to_csv('tec_2022_q3.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accesos a Internet fijo por velocidad bajada y provincia\n",
    "\n",
    "\n",
    "Número de accesos al servicio de Internet fijo por velocidad de bajada en cada provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_velocidad_internet = pd.read_csv('Accesos_Internet_fijo_velocidad_bajada_provincia.csv', thousands=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
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      "---  ------      --------------  -----  \n",
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      " 2   Provincia   480 non-null    object \n",
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      " 7   0,5 Mbps    480 non-null    float64\n",
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      " 10  1 Mbps      480 non-null    object \n",
      " 11  1,2 Mbps    480 non-null    object \n",
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      " 18  3 Mbps      480 non-null    float64\n",
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      " 48  25,1 Mbps   480 non-null    object \n",
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      " 75  56 Mbps     480 non-null    object \n",
      " 76  58 Mbps     480 non-null    object \n",
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      " 78  60 Mbps     480 non-null    object \n",
      " 79  61 Mbps     480 non-null    object \n",
      " 80  62 Mbps     480 non-null    object \n",
      " 81  63 Mbps     480 non-null    object \n",
      " 82  64 Mbps     480 non-null    object \n",
      " 83  65 Mbps     480 non-null    object \n",
      " 84  66 Mbps     480 non-null    object \n",
      " 85  67 Mbps     480 non-null    object \n",
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      " 88  71 Mbps     480 non-null    object \n",
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      " 94  83 Mbps     480 non-null    object \n",
      " 95  82 Mbps     480 non-null    object \n",
      " 96  86 Mbps     480 non-null    object \n",
      "dtypes: float64(6), object(91)\n",
      "memory usage: 364.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_velocidad_internet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
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       "      <th>Otros</th>\n",
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       "      <td>2,208</td>\n",
       "      <td>- 0</td>\n",
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       "      <td>2022.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Chaco</td>\n",
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       "      <td>2017.0</td>\n",
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       "    <tr>\n",
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       "      <td>2017.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Tucumán</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>481 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Año  Trimestre            Provincia    Otros 0,256 Mbps 0,375 Mbps   \n",
       "0    2022.0        3.0         Buenos Aires   65,821         19        - 0  \\\n",
       "1    2022.0        3.0      Capital Federal  105,477         31        - 0   \n",
       "2    2022.0        3.0            Catamarca    2,208        - 0        - 0   \n",
       "3    2022.0        3.0                Chaco    3,711        - 0        225   \n",
       "4    2022.0        3.0               Chubut   20,024          8        - 0   \n",
       "..      ...        ...                  ...      ...        ...        ...   \n",
       "476  2017.0        4.0             Santa Fe   14,119        - 0        - 0   \n",
       "477  2017.0        4.0  Santiago Del Estero      - 0        - 0        - 0   \n",
       "478  2017.0        4.0     Tierra Del Fuego      - 0        - 0        - 0   \n",
       "479  2017.0        4.0              Tucumán      - 0        - 0        - 0   \n",
       "480     NaN        NaN                  NaN      NaN        NaN        NaN   \n",
       "\n",
       "    0,625 Mbps  0,5 Mbps 0,512 Mbps 0,75 Mbps  ... 70 Mbps 71 Mbps 75 Mbps   \n",
       "0          - 0   29774.0        192     4,611  ...   3,163     - 0  98,387  \\\n",
       "1          - 0     451.0         35     1,307  ...       3     - 0  30,482   \n",
       "2          - 0      34.0         37       - 0  ...     - 0     - 0     - 0   \n",
       "3          111     234.0          2       - 0  ...     - 0     - 0     - 0   \n",
       "4          - 0      69.0         32       836  ...     - 0     - 0     - 0   \n",
       "..         ...       ...        ...       ...  ...     ...     ...     ...   \n",
       "476        - 0    3109.0        - 0     7,637  ...     - 0     - 0     - 0   \n",
       "477        - 0      41.0        - 0       909  ...     - 0     - 0     - 0   \n",
       "478        - 0       6.0        - 0       282  ...     - 0     - 0     - 0   \n",
       "479        - 0     105.0        - 0         1  ...     - 0     - 0     - 0   \n",
       "480        NaN       NaN        NaN       NaN  ...     NaN     NaN     NaN   \n",
       "\n",
       "    77 Mbps 78 Mbps 80 Mbps 81 Mbps 83 Mbps  82 Mbps 86 Mbps  \n",
       "0       - 0       2     223     - 0       1      - 0     - 0  \n",
       "1       - 0     - 0       8     - 0     - 0      - 0     - 0  \n",
       "2       - 0     - 0     - 0     - 0     - 0      - 0     - 0  \n",
       "3       - 0     - 0     - 0     - 0     - 0      - 0     - 0  \n",
       "4       - 0     - 0     - 0     - 0     - 0      - 0     - 0  \n",
       "..      ...     ...     ...     ...     ...      ...     ...  \n",
       "476     - 0     - 0     - 0     - 0     - 0      - 0     - 0  \n",
       "477     - 0     - 0     - 0     - 0     - 0      - 0     - 0  \n",
       "478     - 0     - 0     - 0     - 0     - 0      - 0     - 0  \n",
       "479     - 0     - 0     - 0     - 0     - 0      - 0     - 0  \n",
       "480     NaN     NaN     NaN     NaN     NaN      NaN     NaN  \n",
       "\n",
       "[481 rows x 97 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_velocidad_internet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_velocidad_internet.drop(index=[480], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_velocidad_internet = df_velocidad_internet.apply(lambda x: x.astype(str)\n",
    "                                                    .str.replace(',', '')\n",
    "                                                    .str.replace('- ', '')\n",
    "                                                    .str.replace('.0', '')\n",
    "                                                    .astype(int) if x.name != 'Provincia' else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Año', 'Trimestre', 'Provincia', 'Otros', '0,256 Mbps', '0,375 Mbps',\n",
       "       '0,625 Mbps', '0,5 Mbps', '0,512 Mbps', '0,75 Mbps', '1 Mbps',\n",
       "       '1,2 Mbps', '1,25 Mbps', '1,5 Mbps', '2 Mbps', '2,2 Mbps', '2,5 Mbps',\n",
       "       '6,7 Mbps', '3 Mbps', '3,2 Mbps', '3,3 Mbps', '3,5 Mbps', '4 Mbps',\n",
       "       '4,5 Mbps', '5 Mbps', '6 Mbps', '6,4 Mbps', '7 Mbps', '7,5 Mbps',\n",
       "       '8 Mbps', '9 Mbps', '10 Mbps', '10,6 Mbps', '11 Mbps', '12 Mbps',\n",
       "       '13 Mbps', '14 Mbps', '15 Mbps', '16 Mbps', '17 Mbps', '18 Mbps',\n",
       "       '19 Mbps', '20 Mbps', '21 Mbps', '22 Mbps', '23 Mbps', '24 Mbps',\n",
       "       '25 Mbps', '25,1 Mbps', '25,11 Mbps', '25,5 Mbps', '26 Mbps', '28 Mbps',\n",
       "       '29 Mbps', '30 Mbps', '31 Mbps', '32 Mbps', '34 Mbps', '35 Mbps',\n",
       "       '36 Mbps', '37 Mbps', '38 Mbps', '39 Mbps', '40 Mbps', '41 Mbps',\n",
       "       '45 Mbps', '46 Mbps', '47 Mbps', '48 Mbps', '49 Mbps', '50 Mbps',\n",
       "       '51 Mbps', '52 Mbps', '54 Mbps', '55 Mbps', '56 Mbps', '58 Mbps',\n",
       "       '59 Mbps', '60 Mbps', '61 Mbps', '62 Mbps', '63 Mbps', '64 Mbps',\n",
       "       '65 Mbps', '66 Mbps', '67 Mbps', '68 Mbps', '70 Mbps', '71 Mbps',\n",
       "       '75 Mbps', '77 Mbps', '78 Mbps', '80 Mbps', '81 Mbps', '83 Mbps',\n",
       "       '82 Mbps', '86 Mbps'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_velocidad_internet.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizo la información creando grupos con rangos de velocidad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_velocidad_internet['<10 Mbps'] = df_velocidad_internet.loc[:,'0,256 Mbps':'9 Mbps'].sum(axis=1)\n",
    "df_velocidad_internet['10-50 Mbps'] = df_velocidad_internet.loc[:,'10 Mbps':'49 Mbps'].sum(axis=1)\n",
    "df_velocidad_internet['>50 Mbps'] = df_velocidad_internet.loc[:,'50 Mbps':].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_velocidad_internet.drop(columns=['0,256 Mbps', '0,375 Mbps',\n",
    "       '0,625 Mbps', '0,5 Mbps', '0,512 Mbps', '0,75 Mbps', '1 Mbps',\n",
    "       '1,2 Mbps', '1,25 Mbps', '1,5 Mbps', '2 Mbps', '2,2 Mbps', '2,5 Mbps',\n",
    "       '6,7 Mbps', '3 Mbps', '3,2 Mbps', '3,3 Mbps', '3,5 Mbps', '4 Mbps',\n",
    "       '4,5 Mbps', '5 Mbps', '6 Mbps', '6,4 Mbps', '7 Mbps', '7,5 Mbps',\n",
    "       '8 Mbps', '9 Mbps', '10 Mbps', '10,6 Mbps', '11 Mbps', '12 Mbps',\n",
    "       '13 Mbps', '14 Mbps', '15 Mbps', '16 Mbps', '17 Mbps', '18 Mbps',\n",
    "       '19 Mbps', '20 Mbps', '21 Mbps', '22 Mbps', '23 Mbps', '24 Mbps',\n",
    "       '25 Mbps', '25,1 Mbps', '25,11 Mbps', '25,5 Mbps', '26 Mbps', '28 Mbps',\n",
    "       '29 Mbps', '30 Mbps', '31 Mbps', '32 Mbps', '34 Mbps', '35 Mbps',\n",
    "       '36 Mbps', '37 Mbps', '38 Mbps', '39 Mbps', '40 Mbps', '41 Mbps',\n",
    "       '45 Mbps', '46 Mbps', '47 Mbps', '48 Mbps', '49 Mbps', '50 Mbps',\n",
    "       '51 Mbps', '52 Mbps', '54 Mbps', '55 Mbps', '56 Mbps', '58 Mbps',\n",
    "       '59 Mbps', '60 Mbps', '61 Mbps', '62 Mbps', '63 Mbps', '64 Mbps',\n",
    "       '65 Mbps', '66 Mbps', '67 Mbps', '68 Mbps', '70 Mbps', '71 Mbps',\n",
    "       '75 Mbps', '77 Mbps', '78 Mbps', '80 Mbps', '81 Mbps', '83 Mbps',\n",
    "       '82 Mbps', '86 Mbps'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_velocidad_internet.to_csv('df_velocidad_internet.csv', index=False)"
   ]
  }
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