turboFibra.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importamos las librerías a utilizar.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribución de Viviendas y Población en Argentina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Provincia</th>\n",
       "      <th>Viviendas</th>\n",
       "      <th>Población</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CABA</td>\n",
       "      <td>1638764</td>\n",
       "      <td>3120612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>6695273</td>\n",
       "      <td>17569053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Catamarca</td>\n",
       "      <td>158989</td>\n",
       "      <td>429556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chaco</td>\n",
       "      <td>401658</td>\n",
       "      <td>1142963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chubut</td>\n",
       "      <td>236010</td>\n",
       "      <td>603120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia  Viviendas  Población\n",
       "0          CABA    1638764    3120612\n",
       "1  Buenos Aires    6695273   17569053\n",
       "2     Catamarca     158989     429556\n",
       "3         Chaco     401658    1142963\n",
       "4        Chubut     236010     603120"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('datasets/poblacion.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizamos los nombres de las provincias\n",
    "df['Provincia'] = df['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "# Escalar las cantidades a miles\n",
    "df.loc[:, 'Viviendas'] = df['Viviendas'] / 1000000\n",
    "df.loc[:, 'Población'] = df['Población'] / 1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graficar_top_n_sin_provincia(df, n_filas, provincia_excluir):\n",
    "    # Crear una figura con dos subgráficos uno al lado del otro\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "    # Generar colores comunes para ambas gráficas\n",
    "    colors_common = plt.cm.viridis(np.linspace(0, 1, n_filas))\n",
    "\n",
    "    # Gráfico de viviendas\n",
    "    df_viviendas_sorted = df[df['Provincia'] != provincia_excluir].sort_values(by='Viviendas', ascending=False)\n",
    "    df_top_n_viviendas = df_viviendas_sorted.head(n_filas)\n",
    "    axs[0].bar(df_top_n_viviendas['Provincia'], df_top_n_viviendas['Viviendas'], color=colors_common)\n",
    "    axs[0].set_xlabel('Provincias')\n",
    "    axs[0].set_ylabel('Viviendas (millones)')\n",
    "    axs[0].set_title(f'Top {n_filas} Provincias con Mayor Cantidad de Viviendas')\n",
    "    axs[0].grid(False)\n",
    "    axs[0].invert_xaxis()\n",
    "    axs[0].tick_params(axis='x', rotation=45)  # Cambiado de ha='right' a rotation=45\n",
    "\n",
    "    # Gráfico de población\n",
    "    df_poblacion_sorted = df[df['Provincia'] != provincia_excluir].sort_values(by='Población', ascending=False)\n",
    "    df_top_n_poblacion = df_poblacion_sorted.head(n_filas)\n",
    "    axs[1].bar(df_top_n_poblacion['Provincia'], df_top_n_poblacion['Población'], color=colors_common)\n",
    "    axs[1].set_xlabel('Provincias')\n",
    "    axs[1].set_ylabel('Población (millones)')\n",
    "    axs[1].set_title(f'Top {n_filas} Provincias con Mayor Cantidad de Población')\n",
    "    axs[1].grid(False)\n",
    "    axs[1].invert_xaxis()\n",
    "    axs[1].tick_params(axis='x', rotation=45)  # Cambiado de ha='right' a rotation=45\n",
    "\n",
    "    # Ajustar el espacio entre subgráficos\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Mostrar la figura\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis de Conectividad de Internet en Argentina\n",
    "\n",
    "Este gráfico presenta información sobre las provincias argentinas con la mayor cantidad de viviendas y población. Analizar estos datos nos proporcionará un panorama más claro al evaluar la conectividad de internet en el país.\n",
    "\n",
    "### Datos Destacados:\n",
    "- **Viviendas:** Se muestra la cantidad de viviendas en cada provincia.\n",
    "- **Población:** Se destaca la cantidad de habitantes en cada provincia.\n",
    "\n",
    "La correlación entre estos dos factores nos ayudará a comprender mejor la distribución de la población y las viviendas, informando así nuestra comprensión sobre la infraestructura de conectividad en Argentina."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JPWPfm+uXr2fo+nKbjP6uJF67lqHry00y+r24eYP34r/I8PfD3MjQ9eU2mfm7nrRuY0ymbSM97NF/kuhDZTf0n7IW+k9ZC32orIP+U9aSFfpPDk1K2UN4eLhGjx6drNzPz88B0SBtPB0dAG6zQJ86OgT8P89XXnN0CLiNp+frjg4Bt/H0zPzfjkuXLtllO1kFfajsJvd8NrMD+k9ZC32orIP+U9aSFfpPDk1KFS5cWM7Ozjp9+rRN+enTp1W0aNEU2xQtWjRd9cPCwjR48GDr88TERJ0/f1733XefLBbLf3wF2U9sbKz8/Px0/PhxeXh4ODqcXI/3I+vgvchaeD+yltz+fhhjdOnSJRUvXtzRoUiyT/9Jog/1b7n9e5CV8F5kLbwfWQvvR9aR29+LtPafHJqUyps3r+rWras1a9aoU6dOkm51eNasWaP+/fun2CYgIEBr1qzRoEGDrGWRkZEKCAhIsb6rq6tcXV1tygoVKpQR4WdrHh4eufKLkVXxfmQdvBdZC+9H1pKb34+sNELKHv0niT5UanLz9yCr4b3IWng/shbej6wjN78Xaek/OfzyvcGDBys0NFT16tVT/fr1NXnyZMXFxalHjx6SpJCQEJUoUULh4eGSpIEDB6pp06Z655131K5dOy1YsEBbt27VzJkzHfkyAAAA7Ib+EwAAyAkcnpTq0qWLzp49qxEjRigmJka1a9fWqlWrrJNxRkdHy8npn5sEBgYGav78+Xrttdc0bNgwVahQQcuXL1f16tUd9RIAAADsiv4TAADICRyelJKk/v37pzrcfN26dcnKOnfurM6dO2dyVDmTq6urRo4cmWw4PhyD9yPr4L3IWng/shbej6yJ/pN98T3IOngvshbej6yF9yPr4L1IG4vJKvc3BgAAAAAAQK7hdPcqAAAAAAAAQMYiKQUAAAAAAAC7IykFAAAAAAAAuyMphQzFFGXArbteJSYmOjoMAEAWx28FgKyK/izshaQUMkx0dLQ+/fRTHT582NGhAA7z22+/qXLlylq2bJmjQwEAZFEHDx7U7t275eTkxD99ALIc+rOwJ5JSyBC7du1S69attWDBAh08eNDR4QAO8dtvvykgIEAvvviiHnvsMUeHA9hVQkKCo0MAsoWbN29q/PjxqlGjhn7//XcSU8gV+I3IPujPwt5ISuE/27t3r5o0aaKOHTvqww8/VHBwsKNDyrX4wXec3377TYGBgRo4cKDGjRtnLd+6dSuXtTrQ4cOHFRUVpZiYGJty3pOMcfLkSf3000+SJGdnZ0lcjgTcTZ48efT666/r8ccfV2BgoH777TcSUw5G/ylz8BuR/dCfzZpyen/WYnLKK4FDXLlyRV26dFG5cuU0ZcoUa7kxRmfPnlViYqKKFCli/SFC5vjzzz9VuHBh5c2b19Gh5EpHjhxR5cqVNXToUI0dO1aJiYlycnLS+PHjtWLFCi1ZskR+fn6ODjPXOXfunMqXL6969erJ2dlZISEhatu2rby9vR0dWo5w9uxZVaxYUZLUtm1bde/eXfXr15enp6e1jjFGFovFUSECWdrhw4c1dOhQrVq1Sj/99JNq1apl/f2AfdB/yjz8RmQ/9GezptzQn+VXD//ZmTNn9L///c/6/Pvvv9crr7yiSpUqKSgoSE899ZTi4+MdGGHOdvToUZUsWVKtWrXSm2++aT0jlYSzf5kvMjJS3t7eio2NlSQ5OTkpPDxcEydO1JgxY/gBd5C8efOqePHiKlOmjJ555hmNHDlSffr00dChQ3X16lXduHFDEmdt79W1a9fUsGFDzZo1S15eXpo6daoCAwP17bffWi/jTvpng/NfyO2OHz+uL774Qn/99Ze1rFy5cpo8ebKCgoLUsGFDRUVFMWLKjug/ZS5+I7If+rNZU27ozzJSCv9JbGysqlSpoieeeELDhw/X7NmzNW/ePJUvX17NmjXTlStXtGjRIj311FMaOnSoo8PNkU6ePKmgoCDVqlVL5cuX1/Tp0/Xcc8+pXr16NteBczYq4507d06enp66evWq5s2bp9mzZysoKEi+vr6aOHGiPvnkE7Vu3dqmDe+DfSSd3Vu+fLnmzZunRYsW6dChQ9qzZ4/efvttnT9/XsHBwerbt6+qVq3q6HCzrd69e+vkyZP69ttv9eeff2r27Nlat26dzp8/rx49eujhhx9WqVKlJPHZR+4VExOjWrVq6ezZsypevLhatmypqlWr6rHHHlPp0qV19uxZvfLKK1q8eLE2b96smjVrMmLKDug/ZT5+I7IH+rNZV67pzxrgP1qwYIGxWCymdOnSJn/+/GbatGlm3759xhhj4uPjTb169cxzzz3n4Chzphs3bpgbN26YV1991bz11lvGGGO+/fZb89xzz5kGDRqYdu3amRUrVphTp045ONKcZ9u2bcbLy8v89NNPxhhjLl68aCZPnmyqVq1qLBaL+f77740xxly/ft3aZsiQISY4ONgh8eZWO3bsMLVq1TJfffWVteypp54yxYoVM23atDH58uUznTt3Nl9++aUDo8x+EhISjDHG/PXXX6ZJkyZm8eLF1mVNmjQxpUqVMj4+PqZZs2ambdu25vTp044KFXC4w4cPm44dO5ratWubxo0bm+HDh5uSJUuaihUrmpo1a5p33nnHzJw507Rt29b4+PiY3bt3OzrkHI/+U+biNyL7oD+bPeT0/ixJKaTLuXPnzK5du8yiRYvMzp07rT/W+/btM99//73Nj3diYqKJj483Dz/8sAkPD7eW4b+Lj4+3eT5//nxz3333WZOBxhjz4IMPGjc3N9OkSRNTpUoVM3XqVHPo0CF7h5ojRUVFmYIFC5ohQ4YYY/75XP/9999m0qRJpnr16ub555+3aTNixAjj7u5ufv75Z7vHmxvduHHD+vcrr7xi6tevb4wxpnv37qZ48eJmx44dxhhjIiIiTMeOHc0ff/zhiDCztcTERBMXF2eeeOIJM3DgQGOMMSEhIcbX19dER0eb06dPm0mTJpmGDRuagwcPOjZYwMH27t1rQkJCTKtWrcxnn31mEhISzPbt280zzzxj2rZta/LkyWOqVKliLBaL8ff3N9evX6fPlAnoP9kPvxFZH/3ZrC+39GdJSiHNdu7caerXr28qVapk8ubNa/LmzWsqV65svvvuuxTr37x507z22mvGz8+PH/MMdPDgQfPqq6+a2NhY65koY4zp2rWrGTZsmDHm1oHKz8/PREVFmV9//dUMHDjQlCpVypw4ccJRYecYv/32m3F3dzfDhw+3KT937pwxxpgLFy6YKVOmmJo1a5pnnnnGGGPM+PHjjZubm9m6davd480toqOjzeLFi83s2bPNhQsXjDH//POxa9cu89BDD5maNWsaPz8/88svv9i0vXr1qt3jzW5OnjxpVq9ebb799ltz+fJlm2Vbt241RYoUMXXr1jVFixY127dvty5LSEhI9k8gkJskJCRY/9HbtWuXefrpp039+vXNxx9/bK1z/fp1s23bNjNr1izTtWtX6z8ZyFj0nzIPvxHZD/3ZrCm39mdJSiFNdu/ebTw9Pc3QoUPNli1bzNWrV82MGTNMo0aNjIuLi1m2bJlN/UWLFpmBAweawoUL07nKQDt37jTu7u7GYrGYkydPGmP+Oasxbdo0ExQUZNq1a2dKlCiR7EAVGxtr93hzmtOnT5syZcqYxo0b25SPHz/etGrVytoRSxr6XLduXVO2bFl+wDPZb7/9ZsqXL29q1aplihcvbipVqmT+/vtv6/LExETTpUsXY7FYbBLkjEJIm99//92UK1fO1K5d21gsFhMaGmquXbtmjLl18iE+Pt6EhISYMmXKcLwHjDHHjh0zhw8fNhcvXjTG3PqeJNmzZ495+umnTWBgoJk5c6ajQsx16D9lHn4jsh/6s1lTbu7PkpTCXV2+fNm0a9fODBgwwBhj+8HfunWradu2rXF3dze//fabMcaYX3/91bRu3do89NBDzIuQgXbs2GHc3NxMhw4dTKVKlcz+/fuNMf+8H4mJiaZ69eomf/78Zu/evdZ2ty/Hf3Pw4EHTo0cPU6NGDTN79mxjjDETJkwwXl5e1hGDSfs5NjbWTJgwwTzwwAMmKirKYTHndFFRUcbNzc0MGzbM/Pnnn+bnn3825cuXN998841Nvf3795uKFSuazz//3EGRZk87duww7u7uJiwszMTExJh169YZi8ViNm/ebFPvo48+Mh4eHubIkSPGGGMzCgHITWJiYky+fPnMQw89ZB566CGzfv16c+nSJZs6SSOmAgMDzdy5cx0TaC5C/ynz8BuRPdGfzXpye3+WpBTu6tKlS6ZatWpm0aJFxphbB6nbf0zWrVtn/Pz8zAsvvGCMMebatWvm2LFj5vz58w6JNyeKiooy+fLlM6NGjTJxcXHGw8PDLF++3Lo86SzszJkzTfPmzc2BAwccFWqOt3//fvPCCy+YqlWrmg4dOhgfHx8TGRlpU+f2H3K+B5ln//79pkCBAuaVV16xKW/QoIEZNmyY6dWrl/niiy/MiRMnTFxcnOnYsaMJDQ3lH4w02rdvn8mTJ4+ZOHGiMeaf40yTJk3MtGnTzPjx420mr23RooV57LHHuBQDudqJEyeMj4+PGTJkiJk6daopVaqU6datm3nnnXeMMf/MD/L777+bHj16mMqVK5tPPvnEkSHnaPSfMg+/Edkb/dmsg/6sMdxrFneUmJioU6dOac+ePfL09LSWOzk5yRgjSWratKkaNGigLVu2SJJcXV1VqlQpeXl5OSTmnGbHjh1q2LChXnzxRY0cOVLXr1+Xp6enLl68aK3j7OwsSfrf//6n/fv369tvv3VUuDlexYoV1a9fP7Vo0UIbNmxQ165dFRQUJOnW90WSLBaLjDEqWLAg34NMYozRtGnT5OLiokqVKln3/YQJE7Rt2zbt2bNHe/fu1SOPPKIZM2bI3d1d7du315dffmnz3UHKbty4oblz5yohIUGBgYGSbh1nwsPDtXHjRq1du1YzZsxQ//79NXz4cElSgwYNdPz4cV29etWRoQMOY4xRiRIl9MYbb+jEiRN64YUXNH/+fDVr1kzvv/++GjZsqDFjxujs2bOqUaOGRowYoWbNmqlRo0aODj1Hov+UefiNyP7oz2YN9Gf/nyMzYsi6kuZBMObWUPTSpUubZ5991qbcmH+G3/bv39+0aNHCrjHmBidOnDBFihQxL7/8sk15kyZNzNixY40x/5yZSsqWDx061Pj7+5urV6/mqAx6VrNv3z7zwgsvmMqVK1uHPhvDkHR7On/+vHnqqadMYGCgmT9/vhk3bpwpXLiwWbVqlfXWxQMHDjRubm7m2LFj5ubNm9x2Oh12795tevXqZby8vMzvv/9uPvjgA+Pl5WW93fDZs2fNY489ZipXrmz+/vtvc+nSJeulGUButn79elO7dm3z66+/WsseffRRU7x4cRMQEGC8vb3Ns88+a3bt2sVvRiah/5T5+I3IGejPOh79WWPyODophqwnJiZGPXv21EMPPaTnn39evr6+Cg4O1ieffKJWrVqpXbt2cnV1lXQrgy5JZ8+eVe3atSXdyvgmleO/uXLliubMmaOHHnpI0j/71s3NTYcPH5b0z3tgsVi0cOFClStXTj/88IPc3NwcFnduUKlSJfXr10+SNHHiRDk5OalHjx5ycmIAqr14eXlpypQpev755zVixAidPHlSS5cuVXBwsK5fvy5Jatiwob7++mslJibK2dlZPj4+Do4660tMTJSTk5OqVq2qV155RTdv3lSDBg108+ZNbdmyRbVr19bNmzdVuHBhtWjRQlu3blVcXJxKlCihAgUKODp8wK6OHz8ui8WikiVLWn+jmzRpomrVqun111/Xt99+qx49emjz5s366quvVLNmTb355ptat26d3N3d+c3IJPSfMg+/ETkL/VnHoz8rkZRCMrGxsXJ1ddXChQvl4uKiPn36aNq0adq3b5/69u2rCRMmqE2bNipWrJguX76siRMn6vvvv9dPP/0kSSSkMlCFChVUoUIFSbc6VAkJCcqTJ4+KFy+uy5cvS5L1R2PYsGGaOXOmfv31V5UsWdJhMecUCQkJ1mH9qUn6IXd2dtYrr7wiFxcXPfXUU3aKMPc5d+6cTpw4oV27dqlq1aoqVKiQypUrp/fff1+DBw/W9u3bdfLkSd28eVN58+aVJG3atEm+vr4qVKiQY4PPBm7cuCEXFxc5OTnp5s2bypMnjypUqKBhw4apQIEC+vjjj3Xp0iVJ/xzn9+zZI39/fxUsWNCRoQMOcfjwYfn7+6tatWpauXKlSpUqZf3uvPDCC3rjjTdUv359nTx5UsuXL9f9998vSRo+fLgGDRqk/PnzO/gV5Fz0nzIevxHZE/3ZrIf+bAocOUwLWdfttyxOGsoZHR1tWrdubZycnEzJkiVNYGCgadKkiSlZsqTZtm2bgyPOOc6fP28OHjxovv76a3PkyBFz7tw5Y4ztBPNjx441DzzwgHXo+YgRI0y+fPnMli1bHBZ3TnH8+HFz6dKldA3d3717txk6dKjN7VmRsX7//XdTt25dU7lyZZMvXz7j4uJiypUrZ5YuXWqMufW9eeKJJ0xAQIB5//33jTG3vicFChSw3hkUqdu9e7fp2rWreeutt1L87O/bt890797deHl5me+//94YY8xrr71mChYsyP5FrhUVFWXy589vXFxcTLVq1czRo0etyy5dumSaN29u8uTJY44dO2Yt57KwzEP/KfPwG5H90J/NmujPpoykFIwxt66rT/qBTrJ7927z9NNPm4CAAJtbFs+cOdOEhYWZnj17mpkzZ5rDhw/bOdqca+fOnaZx48amYsWKxs3Nzbi7u5sWLVqYDRs2GGP+mf9g6tSpxs/PzxhjzKhRo4ybm5vZunWrw+LOKY4dO2YsFotp0KCBGTt2rPW2uEn+/R25XdI138h4e/bsMZ6enmbo0KFmx44d5ubNm2bevHmmZcuWxsnJycyZM8cYY8y5c+fME088YZo1a2aaNWvG9yKNbt68ab0DT4sWLUzdunXNm2++aXbt2mVTb+/evaZ79+7G19fXPPLIIyZfvnzsX+RaiYmJ1n8eXn/9ddO6dWtTtmxZm8TUpk2bTNmyZZP9liDj0X/KPPxGZD/0Z7Mm+rOpIykFs3//ftOrVy/TqlUrM2DAAPPXX39ZlyWNmAoICDAzZ850YJQ5365du4yHh4cZMmSIWb9+vTl16pQZP368qVmzpsmXL5/NbVojIyNN3bp1Td++fY2rq2uOP1DZy9mzZ03t2rVNly5dzKhRo4ynp6d54YUXzKeffmpTjzPd9nPlyhXToUMH079/f2OM7b7//fffzaOPPmpcXFzMxo0bjTG3zjB17NjRFC9e3ERFRTkk5uzo448/NlWrVjXXr183H330kQkJCTFeXl5m9OjRZvXq1dZ60dHR5vHHHzdeXl6MkEWudftxaMqUKaZMmTJm586dpkWLFqZChQrWkVHR0dGmRYsWZujQoY4KNVeg/5T5+I3IXujPZj30Z++MpFQuFxUVZQoXLmwee+wx8/TTT5uCBQuazp0729S5/VK+pAwuMlZsbKwJCgoygwYNSrYsMjLSBAQEmOLFi5vff//dGGPMb7/9ZiwWi3F3dzfbt2+3d7g5zvnz563D/IcPH27eeOMNY4wxGzduNAMGDDCNGjUyzZo1M4sXL7Y5C47Md+XKFVO9enVrR+r2yzCMuTUSoVy5cqZPnz7WM3+xsbHmzz//dEi82VmbNm3MmDFjrGdJ165da/LmzWu8vb1Np06dzJo1a8zFixfNuXPnzKlTpxwcLWB/R48eNceOHTOXLl2ylsXHx5uOHTua2bNnmxMnTpj69eubihUrWhNTI0aMMD4+Puby5cuOCjtHo/9kP/xGZH30Z7Mu+rN3xrT6udjOnTvVsGFDPfvss1q8eLFmzZqlPn36aMmSJfrhhx9kjJEkValSRa+++qoqVaqkiRMn6tNPP3Vw5DnPtWvXFBMTo1atWkn6Z1JOSQoKCtJLL72kmzdv6uuvv5YkeXp66vHHH9f27dtVp04dh8WdE2zfvl2BgYE6evSoJOmBBx7QxIkTtX37djVq1EhTpkyRu7u7fv75Z02fPl0tWrRQeHi49u3b59jAc4kzZ85o79691gmBLRaLnJycrMengIAANW7cWFu2bJGzs7MSEhJUsGBBFStWzJFhZ3lnz57V1q1btW3bNmvZQw89pM2bN8vFxUWStHjxYhUvXlzz5s3TxYsXrXdlLVSokIoWLeqo0AGHOHLkiMqWLauGDRuqX79+WrZsmSQpb968qlWrlhYtWqQSJUroyy+/lJeXl4KDg3XkyBG99tpr2r59O5OaZxL6T5mD34jsh/5s1kZ/9i4cmhKDw1y6dMlUr17dlC1b1qa8d+/exsnJyXz77bdm3759xhhjzeLu3LnT9O3b1xw5csTe4eZ4SWfu1q9fb1N++9DO1q1bmxYtWlifx8XF2S2+nGrHjh0mf/78yc6w9uzZ07z00kvGGGO6d+9uSpYsaXbu3Gl+++0389prr5nSpUub48ePOyLkXCUhIcGcP3/eVKhQwYSGhpq///472XJjjBk8eLBp2rSp/QPMpnbv3m0aNmxoWrdubR555BFz48YNY8ytM6wlS5Y0s2bNMn379jXFihWzXtqSkJBgVq9ezRyCyLV+//134+3tbQoWLGheeukl4+3tbUJCQsx7771nLl++bPz9/c2CBQuMMcacPHnSVKlSxTzwwAPW7xcyB/2njMdvRPZDfzZroz97dySlcqlr166ZefPmmQIFCpgXX3zRGGPMhAkTjJubm2nTpo3p0qWLKVOmjGnevLl55ZVXrBNF0rnKeImJiebw/7V353FRlt3/wD83izCA4C6YKQi49OCTZKDivqK5ZGFpoknSS1FTMlcQUDNyzST1yX0rUUTNlEfF0rSvhFsyCKigZuSG4IIooAJzfn/4485J6ykFZoDP+x/lvm9mzgww15kz13WuX34ROzs7CQsLe+I5Lk6sfH19pW/fvoYIsUI6c+aM2NrayqxZs0Tk0YBQ/FyvWLFCOnToID179pT69evL0aNH9b6XyzDK1vjx48Xc3Fy+/vprycvLU48X/7wGDx4s48aNE51Ox/4I/0NycrJUq1ZNgoODJT09XU2Eil93/vOf/4iFhYU4Ozvrvdkgqqzu3r0r9+/fFxGRn3/+WerWrSvvv/++HD16VBYsWCAtWrSQV155RerUqSPvv/++urTp6tWr/BCvlDF/KnkcI8of5rPlB/PZP8eiVCVWWFgokZGRYm5uLq+++qrUrVtXbVaYn58v165dkwkTJoiXl5fUrVtXsrKyDBxxxebr6ys1a9aUw4cP6x0vHlx8fHwkNDRURNiY8HklJiaKnZ2dmJiYyMKFC9Xjjye0np6eYmVlJSkpKeqx4uedz3/pyMjIkJiYGAkMDJSPP/5Y1q9fr57r1auX2NnZyZIlS9Q+CLdv35bQ0FCpUaOGOrOT/tzNmzelXbt2Mm7cOL3jj/8+nzx5UmrXri3Lli0TEb7ZoMrt2rVr4ujoKBs3blQLU0ePHhVbW1t59913JTs7W0Qe7ejWr18/+eqrrwwZbqXF/KlkcIwof5jPGifms/8ci1KVXGFhoWzevFns7e31PkUqfjErfrG6fv26QeKriLKysuTQoUMyc+ZM+fLLL9VtWi9duiQtWrSQevXqSUxMjNy6dUtEHjXGCwsLk9q1a0taWpohQ68QEhISxMbGRkaNGiWffPKJNGnSRMLDw9XzxW881q1bJx07dlQHcQ7cpSslJUW8vLykdevW4ubmJg4ODqLRaOSNN96Q+/fvy/Xr18XHx0cURZE6depIy5YtpW3bttKgQQPu8PM3paSkiLOzsxw6dOipbySKf8c/+ugjeemllyQzM7OsQyQyOq+//rrUqFFDtm7dqleYql69uvTv318ePHggIo8+zKPSxfypdHGMKF+Yzxon5rPPhkWpSuTPPs24f/++bNq0SSwsLPTWIhd3/qeSk5KSIp07d5aWLVtKw4YNxcrKSuzt7dXtQbVarbRv317MzMzk3//+t3Tr1k26d+8uDg4OlfqFqqRcvXpVFEWRKVOmiMijRHbatGlPDOQiIqmpqfLiiy/Kp59+aohQKxWtVit2dnYyceJESU5OFhGRy5cvy8KFC8XW1lZ69+6tXvvVV1/JrFmzJCAgQNatW8flMf/Axo0bxczMTE1InzYm5ObmyhdffCEvv/yy2h+HqDJ6PAcaMmSIVK1aVbZt26YWn44ePSo1atSQN954Q+7cuWOoMCsN5k+lj2NE+cF81jgxn312LEpVAklJSZKQkPCX1xQv5bOwsJAJEyaUTWCVjFarlerVq8tHH32kJkgpKSkyduxYMTc3l+HDh6vXLly4UEaNGiUDBw6UiIgIOX/+vKHCrjBu374tmZmZcvDgQb3jfzWQBwUFiZOTk+Tl5fGTpVKSlJQkGo1G7YXwuLt378qaNWtEo9Gobzzo2cXFxYmlpaVs3br1T69ZtmyZdOvWTXr37s2ZBVSp6XS6pxamtm7dqs6OOnbsmNjb20v37t1ZmCpFzJ/KBseI8oH5rHFiPvt8WJSq4Ip3JXnaH8gfFRYWyqZNm0RRFAkODi6D6CqP4heqjz/++Ilz169fl7CwMLGxsZF58+YZILqK7/r169KuXTsJDw9Xe4AUFhaqA/OfDeQ///yzpKenGyTmyuDevXvSrVs3qVq1qnrsj41qb968Kb6+vtKsWTN1SQY9m8uXL0udOnWkX79+ah8DEf2p/IGBgRIeHs4+IVQppaWlyaZNm9Sv/1iYGjp0qFSvXl2vd9FPP/0kjRo14g5WpYT5U9nhGGH8mM8aJ+azz49FqQpMq9WKRqORkJCQJ879WZW8oKBAoqOj5cyZM6UdXqVx+/Zt8fDwEEdHR/VYQUGB3s/g0qVL0r59e+nUqZO6cw+VrKFDh4q7u7t89tln6lasjydVxQO5m5sbi7Jl5P79+7J582Zp2rSp3pTm4oG8+OezZ88eMTU1lXPnzhkkzopk27ZtYmFhIUOHDtVrepqbmytBQUHSoEGDSttkkyq3goICmTFjhiiKoteU9o+Fqb59+4qzs7Par0WE/aRKC/OnsscxwvgxnzU+zGefH4tSFZRWqxVra2uZOnWq3vGoqCj55ZdfDBRV5XTr1i0JDw+XV155RQICAtTjxUlu8b/r168XW1tbuXr1KqfWlqDiZRYiIqNHj5bmzZv/6UB++fJlGT9+vHh6enK3yTKSn58vO3bsEBcXF3nttdfU449/wrRo0SJp3rw5+9yVgKKiIlm2bJmYmZlJ06ZN5b333pNRo0ZJv379pE6dOnLy5ElDh0hkMFlZWTJlyhSpWrWqrF27Vj3+eGEqLS1NHBwc5MCBAwaKsvJg/lT2OEYYL+azxo357PMxAVU4GRkZaNeuHQYMGIDZs2dDp9MBAGbPno3AwEBkZ2cbNsBK4sqVK7h48SKqV6+OgIAADBkyBHFxcRg1ahQAwNTUFEVFRTA1NQUAnD9/Hs7OznBwcICiKIYMvUK4ffs2AMDMzEw9tnTpUnh5eWHt2rVYs2YNsrOzYWJiov6NvPDCC5g4cSJiYmJQq1Ytg8Rd0d27dw9XrlxBbm4udDodLC0t0aNHDyxYsABpaWno06cPgEc/t8LCQjx8+BCnT59G27ZtUVRUBBEx8CMo30xMTDBy5EjExcXBzc0NCQkJSE5ORrNmzXD48GG4u7sbOkSiMnX16lVs27YNK1euhIWFBcaNG4dRo0Zh3LhxWLdunXpd8bicm5uLmjVrombNmgaKuOJj/mQ4HCOMD/NZ48R8toQZuipGJU+r1Ur37t2lUaNG6jK88PBwqVGjhsTGxho4usohJydHXn/9dWnXrp3aZPPmzZuycOFCad68ud4nfgUFBZKbmyvvvfeeTJw48Ymp6fTPnT17VszMzKRLly4ycuRIiY+P12t2OnHiRGnWrJl89tln6rpu9kcofSkpKdKxY0dp2rSpNG7cWNatWyc5OTki8mjq89M+YQoKCpIXXnhBUlNTDRV2hcVP6qiyS0pKEnd3dxkwYICEhITIvXv3ROTRLIPJkyeLra2trFmzRu97QkJCpHXr1pKZmWmIkCs85k/Gg2OE4TGfNU7MZ0sei1IVlFarlT59+oijo6OMGzdO6tSpI3v27HniOq5pLT1r1qyR7t27S+/evf9nYhUUFCQNGzbkTiYlJCoqShRFkerVq8trr70mtWrVEldXVxk8eLBs2rRJsrOzZfjw4dKpUyf5/PPP1anPVHq0Wq3Y2trKqFGjZOvWrdKhQwext7eXo0ePqtc8PpD7+PjIzJkzRaPRcLlAKXn8zRvfyFFlc+bMGalRo4YEBwc/dXnL5cuXJSgoSBRFkfHjx8u8efMkMDBQatasKVqt1gARVx7Mn4wDxwjDYz5rfJjPlg4WpSoInU73xIBx8uRJ8fHxEUVRZPny5SIiek0gJ02aJHZ2dtzGuIQ9/nP4+uuvpXPnzn+aWI0fP15mzJghVlZWfKEqYevXrxdFUeTLL7+UkydPyvbt28Xb21uaNm0qTk5O0qdPHzExMZGaNWvKf/7zHyZcpejUqVNia2srQUFB6rG4uLin7gyal5cn3377rTg4OIiiKHLixImyDpeIKrh79+5Jnz59ZNiwYXrH/zgO5OTkyNdffy3NmzeXDh06iK+vr17zZypZzJ+InsR81ngwny09LEpVAGlpaTJ16lR56623ZM2aNXpbfp44cUL69+8vL774oiQnJ6vHQ0NDxcbGRq+qS88nIyNDLl++rNeIUORRYtWhQwfp3bu3OjPt5s2bsmjRIrG3txczMzO+UJWQP05ZXrx4sZiYmMj8+fNF5FHC+/DhQ1m7dq3MmzdPnJycpEGDBvyEtZT1799fFEWRhIQENVkKDg4WRVFk6tSpsnr1arl48aK6HKagoEB2797NTRmIqFRkZGRIs2bNZNOmTU89X7xsqfj16u7du6LT6bjLXilh/kSkj/mscWI+W3oUEXbZKs8SExPh7e2NV155Bbm5ufi///s/BAYG4pNPPoG1tbV6TUhICLRaLeLi4rBt2zYEBwfj8OHDaNmypYEfQcXwyy+/wMXFBXXq1MELL7wAf39/ODo64rXXXgMA7N69G5999hksLCwQEREBV1dX3LhxA9HR0ejevTtcXFwM/AjKt7t376Jq1arq1yKiNjtdunQpxo4di08//RSTJ0+Gicnv+ztkZWXB1NQUNWrUKPOYKxMRQatWrXD37l3s2rUL0dHRmD17Nvz9/VGvXj1ER0dDURTk5OSgf//+6NevH9q0aWPosImogkpISED79u0RExODTp06QafT6Y0NAFBUVIQvv/wSgwcPRvXq1aEoit7YQiWD+RPR75jPGjfms6WHRalyLCkpCW3btsVHH32EkJAQmJmZYfDgwdi9ezeSk5NRv3599dqEhATMnDkTO3fuhKmpKY4cOcKCVAk6evQounXrhtq1a8PFxQUPHz5EUlISnJ2d8e9//xvvv/8+YmNjkZycjMLCQixcuBBOTk5PTYTpnzl79iz69esHT09PhIaGombNmqhVq9ZTB/K5c+diwoQJfM4NQETQokULnDt3DhqNBps2bUKPHj3U80eOHMF///tf7N69G9HR0WjUqJEBoyWiiubevXuwtLSEmZkZ0tLS4Obmhjlz5uCjjz566li8a9cubN68GStXroSVlZWBoq74mD8RPcJ8tnxgPls6zP73JWSM7ty5g5dffhkdO3bE9OnT1Rcrc3Nz5Obm4saNG7C2tkb16tUBAO7u7ggLC0OtWrXw4Ycfws3NzZDhVxiXL1+Gra0tWrVqhb1792LIkCFo2rQpfHx84OTkhKioKOzduxfDhw/H7du3UVBQgFu3bsHa2hrr1q1TtzOmZ/fzzz9Do9Hg0qVLmDRpEu7evYuJEyfC09MTtWvXBgCMGTMGOp0OkyZNQl5eHqZPn27gqCu27OxsZGZm4uLFi3B2doadnR1q166NxMREdOzYEWfOnEGtWrX0tvRu3bo1WrdujeDgYGg0GgM/AiKqSC5fvgw/Pz/4+/vDx8cHjRs3xuDBgxEaGopXX30VHTp0QEFBAczNzdU3gHFxcTA3Nzd06BUW8ycifcxnjQ/z2TJkiDWD9HyuXLkiOp1OJk+eLDY2NrJ69WoREVmwYIGYmZmJq6ur+Pn5iZ2dnfj5+cnSpUvV7Scfb3ROzychIUHs7e3lm2++UY99//334ujoKIMGDdLb2TAhIUF27dol77zzjrRv316vvxc9n5MnT0qvXr0kOTlZfvnlFwkJCREnJycZNGiQLFiwQPLz89Xf+7lz50qNGjXkxo0bBo664kpKSpL27dtLkyZNpGrVqqLRaKR///7y7bffqte8/PLL0qRJE4mPj1f7JhT/ywadRFTS8vPzxcPDQ1q3bi3btm0TnU4nP/30k7Ro0UJq1qwp+/btU6+9dOmSTJkyRWrVqiWnT582YNQVF/MnoicxnzUuzGfLFotS5czJkyfFwcFB9u7dKyIiEyZMEEtLS3njjTekTp06cuDAAcnNzRURka1bt8rUqVPF0tJSPD09n7rlMT0brVYrlpaWMnXq1CfOFSdWgwcPluPHj+udu3//vty/f7+swqw03nvvPenSpYvahDY9PV2cnJxEURTp1KmTTJgwQU1kuV1u6UlOThZbW1v56KOP5NChQ5KWlibz5s0TV1dXqV+/vkRGRqrXuru7i5ubm/z4448cuImo1BQ3Lc/Pz5fu3bvLK6+8Ijt37hQRkdjYWPHy8hJFUaRjx47SqlUr6dSpkzg6OnJHt1LC/InozzGfNQ7MZ8sei1LlSEJCgmg0Gpk0aZLe8eKu/+PHj39itwYRkevXr8ulS5fKKswKT6vVipWV1RMJlVarlby8PBEROXDggDg6Ooqvr68kJCQYIMqK6/Hf8eI3GykpKdK1a1c5cuSIiIgMHz5cGjVqJPHx8TJ9+nR59dVXpX79+pKTk2OQmCuDnJwc6dKli4wdO/aJc7t375ZWrVrJSy+9pP6MREQaNWokHh4e3NGKiEpF8RuE4rEiLy9PunfvLu7u7mph6vLly7Jq1Srx8/OTwYMHy/Lly+XixYuGCrlCY/5E9Dvms8aJ+axhsChVTpw9e1bs7Oxk1qxZIvIo0SooKFDPT5kyRapUqSKrV69WB3aR31/kqGScP39erKys1MJg8YAya9Ys6dChg1y6dElNgg8cOCCurq7Sr18/OXXqlMFirkguXrwoy5cvf+onqN7e3vLhhx/KyJEjxcHBQY4ePaqez87OlitXrpR1uJXKlStXpHnz5uoymKKiIr3Xn23btom1tbV8/vnnet/HbXKJqKSlpaWpbxieVpjq0aOHtGjRQn766Sf1e572oR6VHOZPRL9jPmu8mM8aBlv2lwOJiYnw8PBATk4OHj58CABQFAWmpqYoKioCAMyZMwdjx47FqFGjEBUVhby8PABgI8gStmvXLtjY2MDCwgIFBQUwMTHB7NmzsXDhQgQFBak7HooIOnfujC+++ALp6emoWbOmgSMv/5KSkuDt7Y29e/ciMzNTPa7T6WBhYYHw8HAsXrwYO3fuRGxsLDw9PdVr7OzsUK9ePUOEXWlcv34dp0+fVneCMTExgampKeT/b/D65ptvokuXLtizZw8AqK9lTk5OhgmYiCqkBw8eYPny5WjTpg3i4+OhKApERM2ZNBoNduzYARFBcHCw+n3Fr13CTalLBfMnokeYzxo35rOGwd33jFxiYiLatGmDcePGwcPDA4MHD0ZeXh4WLFgARVFgYmKidvxfsGABTE1NMXz4cJiZmWHIkCGGDr/C+PXXX3HhwgWMGzcOeXl5+Oabb2Bubg4TExNEREQgMjISPXv2BAB1J8S8vDz07NkT7du3h7W1tSHDL/fOnj2Ljh07YuTIkRg7dqzegFw8aDRs2BC9evVCw4YN0bx5c24XXQaysrKQnp4ORVHg4uICc3NzHDt2DF27dlWf/+K/BwDq3wwAVKlSxVBhE1EFlZKSgu3btyMgIAA3btxA9+7dsW/fPnh5eamFqYKCAnUbb09PTxw9ehStWrVSb+Px1yx6fsyfiH7HfNY4MZ81AoabpEX/y/Xr18Xc3FyCg4NFRKSgoEA2bNggVapUkYkTJ6rX6XQ6vWmFISEh3DGmBF25ckVq1aolrq6u8u2330pRUZF8/PHH0rRpUzE1NZWYmBgREb3llKGhodKvXz8pLCzkkoDnlJ+fL2+99ZaMGTNG7/jDhw/l0qVLcvbsWfXY8uXLxcrKSu8YlY6UlBRp27ateHt7yxtvvCEiIv7+/lK1alV1qnnx61Lx38Fbb70lM2bMEBHuSkJEJUur1YqiKDJnzhwREcnKypIhQ4aItbW1xMXFiYj+rkhxcXHSvHlz9o8qRcyfiH7HfNY4MZ81DixKGamMjAwpKiqSEydO6B0vKCiQr7766n8Wpqjk/PDDD2JiYiIeHh7Sp08f2b59u+h0Ovn000+lefPmMnXqVL3GdmFhYWJpafnEz46eTUFBgbRv314WL16sHtu7d698+OGHYmtrK05OTtK5c2d1UPjXv/4lo0ePZjJbipKTk6VatWoSHBws6enp6huKo0ePSsuWLcXOzk5iY2Pl3r17IvKoh8v06dOldu3akpaWZsjQiagCSklJEY1GI9OnT9c7/nhh6ocfftA7FxoaKu3bt+eW6qWI+RPR75jPGh/ms8aDy/eM0J07dzBkyBBUrVoVa9euBQAUFBTA3NwcZmZmGDRoEBRFgb+/P0REbykflbxOnTrBz88PJ0+ehIWFBSIiIqAoCqZOnYrCwkLs3LkThYWFmD9/PmbPno158+bh8OHDaNmypaFDrxDy8vKQlZWFU6dOITU1Fdu3b8f69evh5uaGWbNmwcbGBrNnz8akSZOwYMEC+Pr64q233uLfQym5desWAgIC8O677yI8PFzvnKenJ+bMmYPZs2ejZ8+e8PT0hJWVFSwtLZGQkIC9e/fC1dXVQJETUUWUnJyMzp07w9HRETNmzADwe85Uq1YtLFy4EObm5ujevTvCw8NhZWWFX3/9FWvWrMHBgwfZs6gUMX8i+h3zWePCfNa4sChlhGxsbNCzZ098++23GDNmDJYuXQo7Ozu1d5SZmRkGDhwIAAgICMC9e/ewbNky9kEoBQ8ePICFhQV8fHyg0+nwzjvvYPny5ViwYAFMTEwwbdo0AMCePXvg5uaG8+fPIy4ujglVCbK1tcXSpUvh7e2Nffv24datW5g/fz66du0KFxcXFBQUICoqCtevXwcABAUFGTjiii0jIwPXrl1T/yYebw6sKAq6deuGli1bIjo6GkePHkVOTg7at2+PxYsXw9nZ2cDRE1FFkpiYCC8vL3h6eiItLQ2BgYGIiIiAubk5CgsLYWZmhtq1a2P16tVo3LgxNm7cCEtLSzg5OeHHH3+Em5uboR9ChcX8iUgf81njwnzWyBh4phb9weNbFy9ZskTatGkjvr6+kp2drR4vVlBQIKtWrZK6devK9evXDRJvRfTbb7/J9u3b9Y5lZmZK06ZNZcmSJZKZmSlvvvmmtGvXTu2REBwcLC+99JJotVoDRV3x/fbbb3LixAnJysrSO168tjskJER0Oh3XdpeyjRs3ipmZmfo8Pz6tvPj/ubm57INARKXq+PHjYm5uLjNmzJDCwkJZvny51KpVS8aNG6de83ivIpFHvTrv378vubm5ZR1upcD8ieh/Yz5rHJjPGhcWpYzEjRs35NatW3rHHj58KEuWLJFWrVr9aWGqsLBQ7ty5U6axVmS//fab1KxZUxRFkddee02ioqIkNTVVRER27twp7du3l8zMTDl9+rS8+eab0rlzZ9myZYvodDr2pTCABw8eSEhIiNSrV49ru8tIXFycWFpaytatW//0mi+++EK6d+8u9+/fL8PIiKgyOXTokF4BKjs7+6mFqcLCQvVNB9/klR7mT0TPjvls2WM+a1y4SNUInDt3DnXr1kWrVq3g7e2NqKgoHDt2DObm5hgzZgxGjRqFixcvIiAgANnZ2TA1NUVhYSEAwNTUFLa2tgZ+BBWHTqeDk5MTWrdujYyMDHz33Xfo0aMHVqxYgfz8fNjZ2eHEiRNo1qwZZs2aBVNTU6xbtw65ubnsS1HGvv76a0yaNAkrV65ETEwM13aXkYYNG8LW1hYbNmxAenq6elxE1P+np6ejZcuW3CaXiEpNhw4dEBERAeDR64+dnR0GDRqE8PBwREZGIjAwEMCjPEmn0wEA2xyUIuZPRM+G+axhMJ81Loo8/syTQezatQuvv/46XF1dUa1aNZiamiI5ORleXl7w8PDAgAEDsH37dpw8eRJ16tTB559/zkJUKTp37hymTp0KnU6Hd999F4qiICIiAtWqVcO3334LT09P/Pjjj6hSpQpSU1NhbW2N+vXrGzrsSiU1NRUBAQGoXr06wsPD0axZM0OHVKls374dgwcPxttvv42pU6fipZdeAvCoiecnn3yCyMhI7Nu3D40bNzZwpERU2eTk5GDz5s2YNm0ahg4dioULFxo6pEqD+RPRP8N81rCYzxoPFqUMKCMjA/b29gCArVu3YsKECRg6dCh69eoFS0tLbN26Ff/9738BAFlZWTAxMcG1a9cwduxYLFq0iJ/4laLU1FSMHz8eRUVFWLx4MV544QUkJSUhPDwcAwcOxJAhQ9RGeGQYmZmZsLCwgJ2dnaFDqXR0Oh1WrlyJDz74AC4uLmjTpg0sLS1x5coVHDlyBHv37oW7u7uhwySiSionJwdbtmzBiBEjMGXKFMyePdvQIVUazJ+I/hnms4bDfNZ4sChlIFqtFv3798eKFSvQo0cPAMD69esREhKC119/HWFhYahTpw4A4PDhwzhz5gy2bt2Ka9euYfPmzWoll0rPuXPn8MEHHwAAwsLC0LZtWwNHRGRcjh07hvnz5+P8+fOoWrUqvLy84O/vz6nnRGRwd+7cwY4dO9CmTRt+yl3GmD8RUXnCfNbwWJQygMTERLRu3Rrjx4/Hp59+qveJ0fr16xEcHIwBAwYgICBAbxpnbm4uFEWBlZWVoUKvdM6dO4dx48ZBRBASEoJ27doZOiQio1JUVARTU1NDh0FE9ATOyDEc5k9EVJ4wnzUsNjovY4mJiWjTpo1akAIeNd48c+YMAGDYsGGYO3cutm3bhmXLluH8+fPq91pbW7MgVcZcXV3xxRdfwNzcHJMmTcKRI0cMHRKRUTEx+X0Y4WccRGRMWJAyHOZPRFSeMJ81LBalytD58+fRunVrTJgwQZ0hBQDh4eGYNGkSMjIyAABDhgzBnDlzsHPnTsyZMwe//PKLIcOu9FxdXTF//nzUr18f9erVM3Q4REbl8Td9fANIRETFmD8RUXnBfNawzAwdQGWh0+mwZs0aVK1aVd36VlEUzJ49G/Pnz0dUVBTs7e2h0+lgYmKCIUOGIC8vDxEREbC2tjZw9NS0aVNs3LiRW4ISERER/U3Mn4iI6H9hT6kydPXqVcybNw9HjhyBn58fcnJyMG/ePGzcuBHe3t5P/Z47d+5wNwYiIiIiIiIiqnBYlCpjGRkZCA8Px3fffYcLFy4gNjYWXbp0QWFhIczMHk1cmz59Oq5cuYJVq1apM6eIiIiIiIiIiCoSLt8rY/b29ggJCYGJiQkOHjyIhIQEdOnSRa8gNW/ePBw+fBgAWJAiIiIiIiIiogqJRSkDqFu3LoKCgqDT6RAdHY3CwkJMmTIF4eHhakGqZcuWhg6TiIiIiIiIiKjUcPmeARUv5UtMTMSDBw9w6tQpFqSIiIiIiIiIqFLg2jADsre3x7Rp0+Di4oJbt24hPj6eBSkiIiIiIiIiqhQ4U8oIZGVlQafToW7duoYOhYiIiIiIiIioTHCmlBGoXbs2C1JERERERGT01q1bh2rVqpXY7f36669QFAVarbbEbpOIyg8WpYiIiIiIiMohPz8/KIoCRVFQpUoVuLi44OOPP0ZhYWGp3efAgQORlpZWYrf34osv4tq1a3Bzcyux2ySi8oO77xEREREREZVTPXv2xNq1a/HgwQPs3r0bY8aMgbm5OYKCgvSue/jwIapUqfLc96fRaKDRaJ77doqZmprC3t6+xG6PiMoXzpQiIiIiIiIqpywsLGBvb4+GDRti1KhR6NatG3bu3Ak/Pz/0798f4eHhqFevHpo0aQIASEpKQpcuXaDRaFCzZk2MGDEC9+7dAwDs27cPlpaWyM7O1ruPwMBAdOnSBcCTy/dmzJiBFi1a4KuvvoKjoyPs7OwwaNAg3L17V71Gp9Nh3rx5cHFxgYWFBRo0aIDw8HAATy7fKyoqgr+/P5ycnKDRaNCkSRNEREToxXPw4EF4enrC2toa1apVQ9u2bZGenl6STysRlREWpYiIiIiIiCoIjUaDhw8fAgD279+P1NRUfPfdd4iJiUFubi68vb1RvXp1HD9+HNHR0fj+++/xwQcfAAC6du2KatWqYdu2bertFRUVISoqCr6+vn96nxcuXMCOHTsQExODmJgYHDp0CHPmzFHPBwUFYc6cOQgNDcXp06cRGRn5pz11dTod6tevj+joaJw+fRphYWEIDg7Gli1bAACFhYXo378/OnbsiFOnTiE+Ph4jRoyAoijP/dwRUdnj8j0iIiIiIqJyTkSwf/9+xMbGYuzYscjKyoK1tTVWrVqlLttbuXIl7t+/jw0bNsDa2hoAsGTJEvTt2xdz585F3bp1MWjQIERGRsLf3x/Ao8JWdnY2fHx8/vS+dTod1q1bh6pVqwIAhg4div379yM8PBx3795FREQElixZgmHDhgEAnJ2d0a5du6felrm5OWbOnKl+7eTkhPj4eGzZsgVvv/02cnJycOfOHfTp0wfOzs4AgGbNmj3ns0dEhsKZUkREREREROVUTEwMbGxsYGlpiV69emHgwIGYMWMGAKB58+Z6faTOnDmDl19+WS1IAUDbtm2h0+mQmpoKAPD19cXBgwdx9epVAMDGjRvRu3fvv9xxz9HRUS1IAYCDgwMyMzPV+3zw4AG6du36tx/T0qVL0bJlS9SuXRs2NjZYsWIFfvvtNwBAjRo14OfnB29vb/Tt2xcRERG4du3a375tIjIuLEoRERERERGVU507d4ZWq8W5c+eQn5+P9evXq0Wnx4tPf5eHhwecnZ2xefNm5Ofn45tvvvnLpXvAo9lNj1MUBTqdDgD+cVP0zZs3Y+LEifD398e+ffug1Wrx3nvvqUsSAWDt2rWIj4+Hl5cXoqKi0LhxYxw5cuQf3Q8RGQcWpYio0vpjo87n9cdGnURERESlzdraGi4uLmjQoAHMzP66O0uzZs2QmJiI3Nxc9VhcXBxMTEzURujAo9lSGzduxK5du2BiYoLevXs/c3yurq7QaDTYv3//37o+Li4OXl5eGD16NNzd3eHi4oILFy48cZ27uzuCgoLw008/wc3NDZGRkc8cIxEZDotSRGQU/Pz8oCgKFEVBlSpV4OLigo8//hiFhYWldp8DBw5EWlpaid3eiy++iGvXrsHNza3EbpOIiIiopPj6+sLS0hLDhg1DcnIyfvjhB4wdOxZDhw7Vazzu6+uLkydPIjw8HAMGDICFhcUz36elpSWmTJmCyZMnY8OGDbhw4QKOHDmC1atXP/V6V1dXnDhxArGxsUhLS0NoaCiOHz+unr948SKCgoIQHx+P9PR07Nu3D+fOnWNfKaJyio3Oicho9OzZE2vXrsWDBw+we/dujBkzBubm5ggKCtK77uHDh3r9EZ6VRqP5x1PK/4qpqSns7e1L7PaIiIiISpKVlRViY2MRGBgIDw8PWFlZwcfHBwsXLtS7zsXFBZ6enjh27BgWLVr03PcbGhoKMzMzhIWF4erVq3BwcEBAQMBTrx05ciQSEhIwcOBAKIqCd955B6NHj8aePXvUx3D27FmsX78eN2/ehIODA8aMGYORI0c+d5xEVPYUERFDB0FE5Ofnh+zsbOzYsUM91qNHD9y9exdNmjRBdnY2PDw8sHTpUlhYWODixYtISkpCYGAg4uPj9ZIqGxsb7Nu3D/369UNGRobeEr3AwEAkJSXhwIEDWLduHT788ENkZ2cDAGbMmIEdO3ZgwoQJCA0Nxe3bt9GrVy+sXLlSbd6p0+mwYMECrFixApcuXULdunUxcuRITJs2Db/++iucnJyQkJCAFi1aoKioCCNGjMCBAweQkZGBBg0aYPTo0QgMDFTjOXjwICZPnoyUlBSYm5vjX//6FyIjI9GwYcOyeNqJiIiIiIgMhsv3iMhoaTQatanl/v37kZqaiu+++w4xMTHIzc2Ft7c3qlevjuPHjyM6Ohrff/89PvjgAwBA165dUa1aNWzbtk29vaKiIkRFRf1ls84LFy5gx44diImJQUxMDA4dOoQ5c+ao54OCgjBnzhyEhobi9OnTiIyM1Jvu/jidTof69esjOjoap0+fRlhYGIKDg7FlyxYAQGFhIfr374+OHTvi1KlTiI+Px4gRI6AoynM/d0RERERERMaOy/eIyOiICPbv34/Y2FiMHTsWWVlZsLa2xqpVq9RleytXrsT9+/exYcMGdWeZJUuWoG/fvpg7dy7q1q2LQYMGITIyEv7+/gAeFbays7Ph4+Pzp/et0+mwbt06dWbU0KFDsX//foSHh+Pu3buIiIjAkiVLMGzYMACAs7Mz2rVr99TbMjc3x8yZM9WvnZycEB8fjy1btuDtt99GTk4O7ty5gz59+sDZ2RkA2A+BiIiIiIgqDc6UIiKjERMTAxsbG1haWqJXr14YOHAgZsyYAQBo3ry5Xh+pM2fO4OWXX9bb6rht27bQ6XRITU0F8KhJ58GDB3H16lUAwMaNG9G7d++/3HHP0dFRLUgBgIODAzIzM9X7fPDgAbp27fq3H9PSpUvRsmVL1K5dGzY2NlixYgV+++03AECNGjXg5+cHb29v9O3bFxEREbh27drfvm0iIiIiIqLyjEUpIjIanTt3hlarxblz55Cfn4/169erRafHi09/l4eHB5ydnbF582bk5+fjm2+++cule8Cj2U2PUxQFOp0OAP5xU/TNmzdj4sSJ8Pf3x759+6DVavHee++pSxIBYO3atYiPj4eXlxeioqLQuHFjHDly5B/dDxERERERUXnEohQRGQ1ra2u4uLigQYMGMDP769XFzZo1Q2JiInJzc9VjcXFxMDExQZMmTdRjvr6+2LhxI3bt2gUTExP07t37meNzdXWFRqPB/v37/9b1cXFx8PLywujRo+Hu7g4XFxdcuHDhievc3d0RFBSEn376CW5uboiMjHzmGImIiIiIiMoLFqWIqFzy9fWFpaUlhg0bhuTkZPzwww8YO3Yshg4dqtd43NfXFydPnkR4eDgGDBgACwuLZ75PS0tLTJkyBZMnT8aGDRtw4cIFHDlyBKtXr37q9a6urjhx4gRiY2ORlpaG0NBQHD9+XD1/8eJFBAUFIT4+Hunp6di3bx/OnTvHvlJERERERFQpsNE5EZVLVlZWiI2NRWBgIDw8PGBlZQUfHx8sXLhQ7zoXFxd4enri2LFjWLRo0XPfb2hoKMzMzBAWFoarV6/CwcEBAQEBT7125MiRSEhIwMCBA6EoCt555x2MHj0ae/bsUR/D2bNnsX79ety8eRMODg4YM2YMRo4c+dxxEhERERERGTtFRMTQQRARERERERERUeXC5XtERERERERERFTmWJQiIiIiIiIiIqIyx6IUERERERERERGVORaliIiIiIiIiIiozLEoRUREREREREREZY5FKSIiIiIiIiIiKnMsShERERERERERUZljUYqIiIiIiIiIiMoci1JERERERERERFTmWJQiIiIiIiIiIqIyx6IUERERERERERGVORaliIiIiIiIiIiozP0/K4HW+zs/MuEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficamos!\n",
    "graficar_top_n_sin_provincia(df, n_filas=5, provincia_excluir='BUENOS AIRES')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 01 - Conectividad en Provincias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemódem</th>\n",
       "      <th>Dial Up</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>4G</th>\n",
       "      <th>3G</th>\n",
       "      <th>Telefonía Fija</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Satelital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>9 de Julio</td>\n",
       "      <td>Alfredo Demarchi (Est. Facundo Quiroga)</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>9 de Julio</td>\n",
       "      <td>Carlos María Naón</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>9 de Julio</td>\n",
       "      <td>Dudignac</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>9 de Julio</td>\n",
       "      <td>La Aurora (Est. La Niña)</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>9 de Julio</td>\n",
       "      <td>Manuel B. Gonnet (Est. French)</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia     Partido                                Localidad ADSL  \\\n",
       "0  BUENOS AIRES  9 de Julio  Alfredo Demarchi (Est. Facundo Quiroga)   SI   \n",
       "1  BUENOS AIRES  9 de Julio                        Carlos María Naón   --   \n",
       "2  BUENOS AIRES  9 de Julio                                 Dudignac   --   \n",
       "3  BUENOS AIRES  9 de Julio                 La Aurora (Est. La Niña)   --   \n",
       "4  BUENOS AIRES  9 de Julio           Manuel B. Gonnet (Est. French)   SI   \n",
       "\n",
       "  Cablemódem Dial Up Fibra óptica  4G  3G Telefonía Fija Wireless Satelital  \n",
       "0         --      --           --  SI  SI             SI       --        --  \n",
       "1         --      --           --  SI  SI             SI       --        --  \n",
       "2         --      SI           --  SI  SI             SI       SI        --  \n",
       "3         --      --           --  SI  --             --       --        --  \n",
       "4         --      --           SI  SI  SI             SI       --        --  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('datasets/listado_localidades_conConectividad_Internet.csv')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "¿Hay duplicados en el DataFrame?: True\n",
      "Filas duplicadas:\n",
      "         Provincia    Partido     Localidad ADSL Cablemódem Dial Up  \\\n",
      "353   BUENOS AIRES   La Plata  ruta del sol   SI         --      --   \n",
      "3442       TUCUMAN  Cruz Alta      Lastenia   --         --      --   \n",
      "\n",
      "     Fibra óptica  4G  3G Telefonía Fija Wireless Satelital  \n",
      "353            SI  SI  SI             SI       --        --  \n",
      "3442           --  SI  SI             --       --        --  \n",
      "Duplicados eliminados. DataFrame actualizado.\n"
     ]
    }
   ],
   "source": [
    "# Verificamos duplicados en el DataFrame\n",
    "df_duplicados = df1.duplicated().any()\n",
    "\n",
    "# Imprimir el resultado\n",
    "print(\"¿Hay duplicados en el DataFrame?:\", df_duplicados)\n",
    "\n",
    "# Si hay duplicados, mostrar las filas duplicadas y eliminarlos del DataFrame\n",
    "if df_duplicados:\n",
    "    duplicated_rows = df1[df1.duplicated()]\n",
    "    print(\"Filas duplicadas:\")\n",
    "    print(duplicated_rows)\n",
    "    \n",
    "    # Eliminamos los duplicados del DataFrame\n",
    "    df1 = df1.drop_duplicates()\n",
    "    print(\"Duplicados eliminados. DataFrame actualizado.\")\n",
    "else:\n",
    "    print(\"No hay duplicados en el DataFrame.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemódem</th>\n",
       "      <th>Dial Up</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>4G</th>\n",
       "      <th>3G</th>\n",
       "      <th>Telefonía Fija</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Satelital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2418</th>\n",
       "      <td>ENTRE RIOS</td>\n",
       "      <td>COLON</td>\n",
       "      <td>HOCKER</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2863</th>\n",
       "      <td>LA RIOJA</td>\n",
       "      <td>SANAGASTA</td>\n",
       "      <td>VILLA SANAGASTA</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3225</th>\n",
       "      <td>MENDOZA</td>\n",
       "      <td>GUAYMALLEN</td>\n",
       "      <td>COLONIA SEGOVIA</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3813</th>\n",
       "      <td>ENTRE RIOS</td>\n",
       "      <td>PARANA</td>\n",
       "      <td>EL RAMBLON</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>SI</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Provincia     Partido        Localidad ADSL Cablemódem Dial Up  \\\n",
       "2418  ENTRE RIOS       COLON           HOCKER   --         --      --   \n",
       "2863    LA RIOJA   SANAGASTA  VILLA SANAGASTA   --         SI      --   \n",
       "3225     MENDOZA  GUAYMALLEN  COLONIA SEGOVIA   --         --      --   \n",
       "3813  ENTRE RIOS      PARANA       EL RAMBLON   --         --      --   \n",
       "\n",
       "     Fibra óptica  4G  3G Telefonía Fija Wireless Satelital  \n",
       "2418           --  SI  --             --       SI        SI  \n",
       "2863           SI  SI  SI             SI       SI        SI  \n",
       "3225           SI  SI  SI             --       SI        --  \n",
       "3813           --  SI  --             --       SI        --  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificamos duplicados con respecto a las columnas 'Provincia', 'Partido' y 'Localidad'\n",
    "\n",
    "# Normalizamos las columnas\n",
    "df1['Provincia'] = df1['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "df1['Partido'] = df1['Partido'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "df1['Localidad'] = df1['Localidad'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "# Verificamos duplicados basándose en las columnas normalizadas\n",
    "duplicados = df1.duplicated(subset=['Provincia', 'Partido', 'Localidad'])\n",
    "\n",
    "# Mostramos las filas duplicadas, si es que las hay.\n",
    "filas_duplicadas = df1[duplicados]\n",
    "filas_duplicadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemódem</th>\n",
       "      <th>Dial Up</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>4G</th>\n",
       "      <th>3G</th>\n",
       "      <th>Telefonía Fija</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Satelital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2539</th>\n",
       "      <td>LA RIOJA</td>\n",
       "      <td>SANAGASTA</td>\n",
       "      <td>VILLA SANAGASTA</td>\n",
       "      <td>--</td>\n",
       "      <td>--,SI</td>\n",
       "      <td>--</td>\n",
       "      <td>--,SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>--,SI</td>\n",
       "      <td>--,SI</td>\n",
       "      <td>--,SI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Provincia    Partido        Localidad ADSL Cablemódem Dial Up  \\\n",
       "2539  LA RIOJA  SANAGASTA  VILLA SANAGASTA   --      --,SI      --   \n",
       "\n",
       "     Fibra óptica  4G  3G Telefonía Fija Wireless Satelital  \n",
       "2539        --,SI  SI  SI          --,SI    --,SI     --,SI  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hemos encontrado duplicados con respecto a las columnas señaladas.\n",
    "\n",
    "# Procedemos a consilidar las filas duplicadas\n",
    "df1 = df1.groupby(['Provincia', 'Partido', 'Localidad'], as_index=False).agg(lambda x: ','.join(x.unique()))\n",
    "\n",
    "# Verificamos lo consolidado\n",
    "df1[(df1['Provincia'] == 'LA RIOJA') & (df1['Partido'] == 'SANAGASTA') & \n",
    "               (df1['Localidad'] == 'VILLA SANAGASTA')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reemplazo de valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemódem</th>\n",
       "      <th>Dial Up</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>4G</th>\n",
       "      <th>3G</th>\n",
       "      <th>Telefonía Fija</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Satelital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>RIO NEGRO</td>\n",
       "      <td>GENERAL ROCA</td>\n",
       "      <td>PASO CORDOVA</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3407</th>\n",
       "      <td>SAN LUIS</td>\n",
       "      <td>BELGRANO</td>\n",
       "      <td>EL BARRIAL</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>COLON</td>\n",
       "      <td>COLONIA TIROLESA</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>LINCOLN</td>\n",
       "      <td>CORONEL MARTINEZ DE HOZ (AP. KILOMETRO 322)</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2637</th>\n",
       "      <td>MENDOZA</td>\n",
       "      <td>LUJAN DE CUYO</td>\n",
       "      <td>LUJAN DE CUYO</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>SI</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Provincia        Partido  \\\n",
       "3072     RIO NEGRO   GENERAL ROCA   \n",
       "3407      SAN LUIS       BELGRANO   \n",
       "1363       CORDOBA          COLON   \n",
       "508   BUENOS AIRES        LINCOLN   \n",
       "2637       MENDOZA  LUJAN DE CUYO   \n",
       "\n",
       "                                        Localidad ADSL Cablemódem Dial Up  \\\n",
       "3072                                 PASO CORDOVA   NO         NO      NO   \n",
       "3407                                   EL BARRIAL   NO         NO      NO   \n",
       "1363                             COLONIA TIROLESA   NO         NO      NO   \n",
       "508   CORONEL MARTINEZ DE HOZ (AP. KILOMETRO 322)   SI         NO      NO   \n",
       "2637                                LUJAN DE CUYO   SI         SI      SI   \n",
       "\n",
       "     Fibra óptica  4G  3G Telefonía Fija Wireless Satelital  \n",
       "3072           NO  NO  NO             NO       NO        NO  \n",
       "3407           NO  NO  NO             NO       NO        NO  \n",
       "1363           NO  SI  SI             SI       NO        NO  \n",
       "508            NO  SI  SI             SI       NO        NO  \n",
       "2637           SI  SI  SI             SI       SI        NO  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definimos lo que vamos a reemplazar\n",
    "reemplazo = {\n",
    "    '--' : 'NO',\n",
    "    '--,SI': 'SI'\n",
    "}\n",
    "cols = ['ADSL', 'Cablemódem', 'Dial Up', 'Fibra óptica', '4G', '3G', 'Telefonía Fija', 'Wireless', 'Satelital']\n",
    "df1[cols] = df1[cols].replace(reemplazo)\n",
    "\n",
    "# Verificamos \n",
    "df1.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculamos el porcentaje de conexiones por provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Provincia</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemódem</th>\n",
       "      <th>Dial Up</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>4G</th>\n",
       "      <th>3G</th>\n",
       "      <th>Telefonía Fija</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Satelital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CABA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHACO</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia  ADSL  Cablemódem  Dial Up  Fibra óptica    4G    3G  \\\n",
       "0  BUENOS AIRES  0.39        0.38     0.24          0.51  0.73  0.65   \n",
       "1          CABA  1.00        1.00     1.00          1.00  1.00  1.00   \n",
       "2     CATAMARCA  0.04        0.01     0.01          0.13  0.31  0.16   \n",
       "3         CHACO  0.30        0.13     0.01          0.49  0.82  0.48   \n",
       "4        CHUBUT  0.16        0.11     0.17          0.14  0.42  0.34   \n",
       "\n",
       "   Telefonía Fija  Wireless  Satelital  \n",
       "0            0.61      0.56       0.17  \n",
       "1            1.00      1.00       1.00  \n",
       "2            0.31      0.19       0.26  \n",
       "3            0.51      0.50       0.46  \n",
       "4            0.61      0.34       0.54  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionar las columnas relevantes para el cálculo del porcentaje de conexiones (a partir de la cuarta columna en adelante)\n",
    "columnas_conexion = df1.columns[3:]\n",
    "\n",
    "# Convertir los valores 'SI' a 1 y 'NO' a 0 en las columnas de conexión\n",
    "df1[columnas_conexion] = df1[columnas_conexion].apply(lambda x: x.map({'SI': 1, 'NO': 0}))\n",
    "\n",
    "# Agrupar el DataFrame por provincia y calcular el promedio de cada tipo de conexión (0 o 1) para cada provincia\n",
    "# El resultado es redondeado a dos decimales para representar el porcentaje\n",
    "presencia_provincia = df1.groupby('Provincia')[columnas_conexion].mean().round(2).reset_index()\n",
    "\n",
    "presencia_provincia.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorizamos la presencia de conexiones en las provincias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Provincia</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>Cablemódem</th>\n",
       "      <th>Dial Up</th>\n",
       "      <th>Fibra óptica</th>\n",
       "      <th>4G</th>\n",
       "      <th>3G</th>\n",
       "      <th>Telefonía Fija</th>\n",
       "      <th>Wireless</th>\n",
       "      <th>Satelital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Sustancial</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Sustancial</td>\n",
       "      <td>Sustancial</td>\n",
       "      <td>Sustancial</td>\n",
       "      <td>Limitado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CABA</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Ubicuo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>Escaso</td>\n",
       "      <td>Escaso</td>\n",
       "      <td>Escaso</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Limitado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHACO</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Escaso</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Ubicuo</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Sustancial</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Moderado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Limitado</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Sustancial</td>\n",
       "      <td>Moderado</td>\n",
       "      <td>Sustancial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia      ADSL Cablemódem   Dial Up Fibra óptica        4G  \\\n",
       "0  BUENOS AIRES  Moderado   Moderado  Limitado   Sustancial    Ubicuo   \n",
       "1          CABA    Ubicuo     Ubicuo    Ubicuo       Ubicuo    Ubicuo   \n",
       "2     CATAMARCA    Escaso     Escaso    Escaso     Limitado  Moderado   \n",
       "3         CHACO  Limitado   Limitado    Escaso     Moderado    Ubicuo   \n",
       "4        CHUBUT  Limitado   Limitado  Limitado     Limitado  Moderado   \n",
       "\n",
       "           3G Telefonía Fija    Wireless   Satelital  \n",
       "0  Sustancial     Sustancial  Sustancial    Limitado  \n",
       "1      Ubicuo         Ubicuo      Ubicuo      Ubicuo  \n",
       "2    Limitado       Moderado    Limitado    Limitado  \n",
       "3    Moderado     Sustancial    Moderado    Moderado  \n",
       "4    Moderado     Sustancial    Moderado  Sustancial  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definimos los rangos\n",
    "ranges = {\n",
    "    'Ausente': (0.0, 0.0),\n",
    "    'Escaso': (0.01, 0.09),\n",
    "    'Limitado': (0.1, 0.3),\n",
    "    'Moderado': (0.31, 0.5),\n",
    "    'Sustancial': (0.51, 0.7),\n",
    "    'Ubicuo': (0.71, 1.0)\n",
    "}\n",
    "\n",
    "# Categorizar los porcentajes por rango\n",
    "for col in presencia_provincia.columns[1:]:\n",
    "    presencia_provincia[col] = presencia_provincia[col] \\\n",
    "    .apply(lambda x: next((key for key, (low, high) in ranges.items() if low <= x <= high), None))\n",
    "\n",
    "presencia_provincia.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usar melt para transformar el DataFrame\n",
    "df_transformado = pd.melt(presencia_provincia, id_vars=['Provincia'], var_name='Tecnologia', value_name='Nivel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para asignar valores numéricos a las categorías\n",
    "def asignar_valor(categoria):\n",
    "    valores = {'Ausente': 0, 'Escaso': 1, 'Limitado': 2, 'Moderado': 3, 'Sustancial': 4, 'Ubicuo':5}\n",
    "    return valores[categoria]\n",
    "\n",
    "# Añadir la columna 'Valor' asignando valores numéricos\n",
    "df_transformado['Valor'] = df_transformado['Nivel'].apply(asignar_valor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graficar_conectividad_por_provincia(df_transformado, provincias_seleccionadas, tecnologias_seleccionadas):\n",
    "   \n",
    "    # Seleccionar provincias y tecnologías específicas\n",
    "    df_transformado_seleccionado = df_transformado[df_transformado['Provincia'].isin(provincias_seleccionadas) & df_transformado['Tecnologia'].isin(tecnologias_seleccionadas)]\n",
    "\n",
    "    # Crear colores para las provincias seleccionadas\n",
    "    colores_provincias = {provincia: plt.cm.viridis(i / len(provincias_seleccionadas)) for i, provincia in enumerate(provincias_seleccionadas)}\n",
    "\n",
    "    # Ancho de las barras\n",
    "    bar_width = 0.34\n",
    "\n",
    "    # Gráfico de barras horizontal desde el DataFrame transformado con colores personalizados\n",
    "    ax = df_transformado_seleccionado.pivot(index='Tecnologia', columns='Provincia', values='Valor').plot(\n",
    "        kind='barh', width=bar_width, color=[colores_provincias[col] for col in df_transformado_seleccionado['Provincia']]\n",
    "    )\n",
    "\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    \n",
    "    # Ajustar la posición de la leyenda\n",
    "    ax.legend(title='Provincia', bbox_to_anchor=(1, 1))\n",
    "    \n",
    "    plt.yticks(np.arange(len(df_transformado_seleccionado['Tecnologia'].unique())), df_transformado_seleccionado['Tecnologia'].unique())\n",
    "    plt.xticks([0, 1, 2, 3, 4, 5], ['Ausente', 'Escaso', 'Limitado', 'Moderado', 'Sustancial', 'Ubicuo'])\n",
    "    plt.title('Disponobilidad de Tecnologías en Provincias')\n",
    "    plt.xlabel('Nivel de disponibilidad')\n",
    "    plt.ylabel('Tecnologías')\n",
    "    plt.grid(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ADSL', 'Cablemódem', 'Dial Up', 'Fibra óptica', '4G', '3G',\n",
       "       'Telefonía Fija', 'Wireless', 'Satelital'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformado['Tecnologia'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis de Telefonía Fija y Tecnologías de Conectividad en Argentina\n",
    "\n",
    "El gráfico revela que las 06 provincias analizadas presentan un nivel **sustancial** y **ubicuo** en cuanto a telefonía fija. Este hallazgo indica una infraestructura extendida en todo el territorio, lo que sugiere la viabilidad de implementar tecnologías como fibra óptica, cablemódem y/o ADSL en estas áreas.\n",
    "\n",
    "Este panorama de disponibilidad generalizada también genera un entorno competitivo en la Capital Federal, donde todas las tecnologías de conectividad alcanzan un nivel de disponibilidad ubicuo.\n",
    "\n",
    "Se invita a explorar otras provincias o tipos de tecnologías ajustando los parámetros de la función para obtener una visión más completa del estado de la conectividad en Argentina."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficamos\n",
    "graficar_conectividad_por_provincia(df_transformado, ['BUENOS AIRES','CABA', 'CORDOBA','SANTA FE', 'MENDOZA', 'ENTRE RIOS'], \n",
    "                                    ['Telefonía Fija', 'ADSL', 'Cablemódem', 'Fibra óptica'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 02 - Conexiones por Provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>CABLEMODEM</th>\n",
       "      <th>DIAL UP</th>\n",
       "      <th>FIBRA OPTICA</th>\n",
       "      <th>OTROS</th>\n",
       "      <th>SATELITAL</th>\n",
       "      <th>WIMAX</th>\n",
       "      <th>WIRELESS</th>\n",
       "      <th>Total general</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>6854100</td>\n",
       "      <td>874</td>\n",
       "      <td>4,54</td>\n",
       "      <td>- 0</td>\n",
       "      <td>3</td>\n",
       "      <td>- 0</td>\n",
       "      <td>852</td>\n",
       "      <td>- 0</td>\n",
       "      <td>706</td>\n",
       "      <td>6,98</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Del Valle</td>\n",
       "      <td>6854020</td>\n",
       "      <td>181</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>192</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Gobernador Ugarte</td>\n",
       "      <td>6854040</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>181</td>\n",
       "      <td>181</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Norberto de la Riestra</td>\n",
       "      <td>6854060</td>\n",
       "      <td>- 0</td>\n",
       "      <td>782</td>\n",
       "      <td>- 0</td>\n",
       "      <td>6</td>\n",
       "      <td>167</td>\n",
       "      <td>- 0</td>\n",
       "      <td>- 0</td>\n",
       "      <td>327</td>\n",
       "      <td>1,28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Lucas Monteverde</td>\n",
       "      <td>6854050</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>6</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia     Partido               Localidad Link Indec ADSL  \\\n",
       "0  BUENOS AIRES  25 de Mayo              25 de Mayo    6854100  874   \n",
       "1  BUENOS AIRES  25 de Mayo               Del Valle    6854020  181   \n",
       "2  BUENOS AIRES  25 de Mayo       Gobernador Ugarte    6854040  - 0   \n",
       "3  BUENOS AIRES  25 de Mayo  Norberto de la Riestra    6854060  - 0   \n",
       "4  BUENOS AIRES  25 de Mayo        Lucas Monteverde    6854050  - 0   \n",
       "\n",
       "  CABLEMODEM DIAL UP FIBRA OPTICA OTROS SATELITAL WIMAX WIRELESS  \\\n",
       "0       4,54     - 0            3   - 0       852   - 0      706   \n",
       "1        - 0     - 0          - 0    10         1   - 0      - 0   \n",
       "2        - 0     - 0          - 0   - 0       - 0   - 0      181   \n",
       "3        782     - 0            6   167       - 0   - 0      327   \n",
       "4        - 0     - 0          - 0   - 0       - 0   - 0        6   \n",
       "\n",
       "  Total general  Unnamed: 13  \n",
       "0          6,98          NaN  \n",
       "1           192          NaN  \n",
       "2           181          NaN  \n",
       "3          1,28          NaN  \n",
       "4             6          NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('datasets/accesos_internetFijo_tecnologiaLocalidad.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eliminación, Limpieza y Reemplazo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Empezamos por eliminar las columnas sin informacion\n",
    "cols_drop = ['Unnamed: 13', 'Total general']\n",
    "df2 = df2.drop(columns = cols_drop)\n",
    "\n",
    "# y reemplazomos el - 0 por 0\n",
    "reemplazo = {'- 0' : 0, '-0': 0}\n",
    "cols = ['ADSL', 'CABLEMODEM', 'DIAL UP', 'FIBRA OPTICA', \n",
    "        'OTROS', 'SATELITAL', 'WIMAX', 'WIRELESS']\n",
    "df2[cols] = df2[cols].replace(reemplazo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>CABLEMODEM</th>\n",
       "      <th>DIAL UP</th>\n",
       "      <th>FIBRA OPTICA</th>\n",
       "      <th>OTROS</th>\n",
       "      <th>SATELITAL</th>\n",
       "      <th>WIMAX</th>\n",
       "      <th>WIRELESS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>6854100</td>\n",
       "      <td>874.0</td>\n",
       "      <td>4540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>706.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Del Valle</td>\n",
       "      <td>6854020</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Gobernador Ugarte</td>\n",
       "      <td>6854040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Norberto de la Riestra</td>\n",
       "      <td>6854060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>327.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Lucas Monteverde</td>\n",
       "      <td>6854050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia     Partido               Localidad Link Indec   ADSL  \\\n",
       "0  BUENOS AIRES  25 de Mayo              25 de Mayo    6854100  874.0   \n",
       "1  BUENOS AIRES  25 de Mayo               Del Valle    6854020  181.0   \n",
       "2  BUENOS AIRES  25 de Mayo       Gobernador Ugarte    6854040    0.0   \n",
       "3  BUENOS AIRES  25 de Mayo  Norberto de la Riestra    6854060    0.0   \n",
       "4  BUENOS AIRES  25 de Mayo        Lucas Monteverde    6854050    0.0   \n",
       "\n",
       "   CABLEMODEM  DIAL UP  FIBRA OPTICA  OTROS  SATELITAL  WIMAX  WIRELESS  \n",
       "0      4540.0      0.0           3.0    0.0      852.0    0.0     706.0  \n",
       "1         0.0      0.0           0.0   10.0        1.0    0.0       0.0  \n",
       "2         0.0      0.0           0.0    0.0        0.0    0.0     181.0  \n",
       "3       782.0      0.0           6.0  167.0        0.0    0.0     327.0  \n",
       "4         0.0      0.0           0.0    0.0        0.0    0.0       6.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La coma representa miles.\n",
    "# Procederemos a quitar la coma, remplazar por un punto y multiplicar por 1000.\n",
    "\n",
    "# Definimos una función personalizada para la conversión\n",
    "def convertir_valor(valor):\n",
    "    if ',' in str(valor):\n",
    "        # Si el valor contiene una coma, convierte el valor eliminando la coma y multiplicando por 10\n",
    "        return float(valor.replace(',', '.')) * 1000\n",
    "    else:\n",
    "        # Si el valor no contiene coma, déjalo como está\n",
    "        return float(valor)\n",
    "\n",
    "# Aplica la función de conversión a la columna 'columna' usando map\n",
    "df2[cols] = df2[cols].apply(lambda x: x.map(convertir_valor))\n",
    "\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "¿Hay duplicados en el DataFrame?: False\n",
      "No hay duplicados en el DataFrame.\n"
     ]
    }
   ],
   "source": [
    "# Verificamos duplicados en el DataFrame\n",
    "df_duplicados = df2.duplicated().any()\n",
    "\n",
    "# Imprimir el resultado\n",
    "print(\"¿Hay duplicados en el DataFrame?:\", df_duplicados)\n",
    "\n",
    "# Si hay duplicados, mostrar las filas duplicadas y eliminarlos del DataFrame\n",
    "if df_duplicados:\n",
    "    duplicated_rows = df2[df2.duplicated()]\n",
    "    print(\"Filas duplicadas:\")\n",
    "    print(duplicated_rows)\n",
    "    \n",
    "    # Eliminamos los duplicados del DataFrame\n",
    "    df2 = df2.drop_duplicates()\n",
    "    print(\"Duplicados eliminados. DataFrame actualizado.\")\n",
    "else:\n",
    "    print(\"No hay duplicados en el DataFrame.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>CABLEMODEM</th>\n",
       "      <th>DIAL UP</th>\n",
       "      <th>FIBRA OPTICA</th>\n",
       "      <th>OTROS</th>\n",
       "      <th>SATELITAL</th>\n",
       "      <th>WIMAX</th>\n",
       "      <th>WIRELESS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>SAN JUSTO</td>\n",
       "      <td>TRANSITO</td>\n",
       "      <td>Sin Datos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Provincia    Partido Localidad Link Indec  ADSL  CABLEMODEM  DIAL UP  \\\n",
       "1243   CORDOBA  SAN JUSTO  TRANSITO  Sin Datos   0.0       943.0      0.0   \n",
       "\n",
       "      FIBRA OPTICA  OTROS  SATELITAL  WIMAX  WIRELESS  \n",
       "1243         133.0    0.0        0.0    0.0       0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificamos duplicados con respecto a las columnas 'Provincia', 'Partido' y 'Localidad'\n",
    "\n",
    "# Normalizamos las columnas\n",
    "df2['Provincia'] = df2['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "df2['Partido'] = df2['Partido'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "df2['Localidad'] = df2['Localidad'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "# Verificamos duplicados basándose en las columnas normalizadas\n",
    "duplicados = df2.duplicated(subset=['Provincia', 'Partido', 'Localidad'])\n",
    "\n",
    "# Mostramos las filas duplicadas, si es que las hay.\n",
    "filas_duplicadas = df2[duplicados]\n",
    "filas_duplicadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>CABLEMODEM</th>\n",
       "      <th>DIAL UP</th>\n",
       "      <th>FIBRA OPTICA</th>\n",
       "      <th>OTROS</th>\n",
       "      <th>SATELITAL</th>\n",
       "      <th>WIMAX</th>\n",
       "      <th>WIRELESS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>SAN JUSTO</td>\n",
       "      <td>TRANSITO</td>\n",
       "      <td>14140400</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>SAN JUSTO</td>\n",
       "      <td>TRANSITO</td>\n",
       "      <td>Sin Datos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Provincia    Partido Localidad Link Indec  ADSL  CABLEMODEM  DIAL UP  \\\n",
       "1233   CORDOBA  SAN JUSTO  TRANSITO   14140400  71.0         0.0      0.0   \n",
       "1243   CORDOBA  SAN JUSTO  TRANSITO  Sin Datos   0.0       943.0      0.0   \n",
       "\n",
       "      FIBRA OPTICA  OTROS  SATELITAL  WIMAX  WIRELESS  \n",
       "1233           0.0    0.0        0.0    0.0     106.0  \n",
       "1243         133.0    0.0        0.0    0.0       0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[(df2['Provincia'] == 'CORDOBA') & (df2['Partido'] == 'SAN JUSTO') & (df2['Localidad'] == 'TRANSITO')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>CABLEMODEM</th>\n",
       "      <th>DIAL UP</th>\n",
       "      <th>FIBRA OPTICA</th>\n",
       "      <th>OTROS</th>\n",
       "      <th>SATELITAL</th>\n",
       "      <th>WIMAX</th>\n",
       "      <th>WIRELESS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>SAN JUSTO</td>\n",
       "      <td>TRANSITO</td>\n",
       "      <td>14140400</td>\n",
       "      <td>71.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Provincia    Partido Localidad Link Indec  ADSL  CABLEMODEM  DIAL UP  \\\n",
       "1239   CORDOBA  SAN JUSTO  TRANSITO   14140400  71.0       943.0      0.0   \n",
       "\n",
       "      FIBRA OPTICA  OTROS  SATELITAL  WIMAX  WIRELESS  \n",
       "1239         133.0    0.0        0.0    0.0     106.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupa por las columnas 'Provincia', 'Partido' y 'Localidad', y suma los valores\n",
    "df2 = df2.groupby(['Provincia', 'Partido', 'Localidad']).agg({\n",
    "    'Link Indec': 'first',  # Tomamos el primer valor de 'Link Indec' \n",
    "    'ADSL': 'sum',\n",
    "    'CABLEMODEM': 'sum',\n",
    "    'DIAL UP': 'sum',\n",
    "    'FIBRA OPTICA': 'sum',\n",
    "    'OTROS': 'sum',\n",
    "    'SATELITAL': 'sum',\n",
    "    'WIMAX': 'sum',\n",
    "    'WIRELESS': 'sum'\n",
    "}).reset_index()\n",
    "\n",
    "# Verificamos!!\n",
    "df2[(df2['Provincia'] == 'CORDOBA') & \n",
    "                (df2['Partido'] == 'SAN JUSTO') & (df2['Localidad'] == 'TRANSITO')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suma de Conexiones por Provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Provincia</th>\n",
       "      <th>ADSL</th>\n",
       "      <th>CABLEMODEM</th>\n",
       "      <th>DIAL UP</th>\n",
       "      <th>FIBRA OPTICA</th>\n",
       "      <th>OTROS</th>\n",
       "      <th>SATELITAL</th>\n",
       "      <th>WIMAX</th>\n",
       "      <th>WIRELESS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>322841.0</td>\n",
       "      <td>2766310.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>1489172.0</td>\n",
       "      <td>62349.0</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>1672.0</td>\n",
       "      <td>129885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CABA</td>\n",
       "      <td>138750.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>2150.0</td>\n",
       "      <td>138550.0</td>\n",
       "      <td>29590.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>8950.0</td>\n",
       "      <td>10994.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47346.0</td>\n",
       "      <td>2151.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHACO</td>\n",
       "      <td>24590.0</td>\n",
       "      <td>61270.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>47287.0</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>45444.0</td>\n",
       "      <td>49952.0</td>\n",
       "      <td>904.0</td>\n",
       "      <td>9819.0</td>\n",
       "      <td>7789.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30730.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia      ADSL  CABLEMODEM  DIAL UP  FIBRA OPTICA    OTROS  \\\n",
       "0  BUENOS AIRES  322841.0   2766310.0   6199.0     1489172.0  62349.0   \n",
       "1          CABA  138750.0      1240.0   2150.0      138550.0  29590.0   \n",
       "2     CATAMARCA    8950.0     10994.0      1.0       47346.0   2151.0   \n",
       "3         CHACO   24590.0     61270.0      5.0       47287.0   2490.0   \n",
       "4        CHUBUT   45444.0     49952.0    904.0        9819.0   7789.0   \n",
       "\n",
       "   SATELITAL   WIMAX  WIRELESS  \n",
       "0     1227.0  1672.0  129885.0  \n",
       "1       95.0     0.0    4600.0  \n",
       "2       90.0     0.0    1379.0  \n",
       "3       79.0     0.0    7957.0  \n",
       "4      150.0     0.0   30730.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular la suma de conexiones por cada tipo de conexión por provincia\n",
    "columnas_conexion = df2.columns[4:]\n",
    "sum_conexion_provincia = df2.groupby('Provincia')[columnas_conexion].sum().reset_index()\n",
    "sum_conexion_provincia.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformamos el dataframe\n",
    "df2_transformado = pd.melt(sum_conexion_provincia, id_vars=['Provincia'], var_name='Tecnologia', value_name='Cantidad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graficar_conexiones_por_provincia(df, provincias_seleccionadas, tecnologias_seleccionadas):\n",
    "    \"\"\"\n",
    "    Grafica la cantidad de conexiones por provincia y tecnología.\n",
    "\n",
    "    Parámetros:\n",
    "    - df: DataFrame que contiene los datos.\n",
    "    - provincias_seleccionadas: Lista de provincias que se quieren graficar.\n",
    "    - tecnologias_seleccionadas: Lista de tecnologías que se quieren graficar.\n",
    "\n",
    "    No devuelve nada, simplemente muestra el gráfico.\n",
    "    \"\"\"\n",
    "    # Filtrar las provincias y tecnologías específicas\n",
    "    df_provincias = df[df['Provincia'].isin(provincias_seleccionadas)]\n",
    "    df_seleccionado = df_provincias[df_provincias['Tecnologia'].isin(tecnologias_seleccionadas)]\n",
    "\n",
    "    # Escalar las cantidades a miles\n",
    "    df_seleccionado.loc[:, 'Cantidad'] = df_seleccionado['Cantidad'] / 1000\n",
    "\n",
    "    # Automatizar la creación de colores para cada provincia\n",
    "    colores_provincias = {provincia: plt.cm.viridis(i / len(provincias_seleccionadas)) for i, provincia in enumerate(provincias_seleccionadas)}\n",
    "\n",
    "    # Ancho de las barras\n",
    "    bar_width = 0.34\n",
    "\n",
    "    # Gráfico de barras horizontal desde el DataFrame nuevo con colores personalizados\n",
    "    ax = df_seleccionado.pivot(index='Tecnologia', columns='Provincia', values='Cantidad').plot(\n",
    "        kind='barh', width=bar_width, color=[colores_provincias[col] for col in df_seleccionado['Provincia']]\n",
    "    )\n",
    "\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    \n",
    "    # Ajustar la posición de la leyenda\n",
    "    ax.legend(title='Provincia', bbox_to_anchor=(1, 1))\n",
    "\n",
    "    plt.yticks(np.arange(len(df_seleccionado['Tecnologia'].unique())), df_seleccionado['Tecnologia'].unique())\n",
    "\n",
    "    plt.title('Cantidad de Conexiones por Provincia')\n",
    "    plt.xlabel('Cantidad (en miles)')\n",
    "    plt.ylabel('Tecnologías')\n",
    "    plt.grid(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conexiones por Tecnología en Provincias Seleccionadas\n",
    "\n",
    "Este gráfico ofrece un resumen de la cantidad de conexiones por tecnología en las provincias seleccionadas. Según el filtro aplicado a las 06 provincias principales, se destaca que la tecnología predominante es el cablemódem.\n",
    "\n",
    "Se invita a explorar el gráfico ajustando los parámetros según sea conveniente para obtener información detallada sobre otras provincias o variaciones tecnológicas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graficar_conexiones_por_provincia(df2_transformado, ['CABA', 'CORDOBA','SANTA FE', 'MENDOZA', 'ENTRE RIOS', 'TUCUMAN'], \n",
    "                                  ['ADSL', 'CABLEMODEM', 'FIBRA OPTICA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 03 - Calidad de Velocidad por Provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>Otros</th>\n",
       "      <th>0,256 Mbps</th>\n",
       "      <th>0,375 Mbps</th>\n",
       "      <th>0,5 Mbps</th>\n",
       "      <th>0,512 Mbps</th>\n",
       "      <th>0,625 Mbps</th>\n",
       "      <th>...</th>\n",
       "      <th>61 Mbps</th>\n",
       "      <th>62 Mbps</th>\n",
       "      <th>64 Mbps</th>\n",
       "      <th>65 Mbps</th>\n",
       "      <th>66 Mbps</th>\n",
       "      <th>70 Mbps</th>\n",
       "      <th>75 Mbps</th>\n",
       "      <th>78 Mbps</th>\n",
       "      <th>80 Mbps</th>\n",
       "      <th>83 Mbps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>6854100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</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>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Agustín Mosconi</td>\n",
       "      <td>6854010</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Del Valle</td>\n",
       "      <td>6854020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Ernestina</td>\n",
       "      <td>6854030</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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Gobernador Ugarte</td>\n",
       "      <td>6854040</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>1</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>5 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia     Partido          Localidad Link Indec Otros 0,256 Mbps  \\\n",
       "0  BUENOS AIRES  25 de Mayo         25 de Mayo    6854100   NaN        NaN   \n",
       "1  BUENOS AIRES  25 de Mayo    Agustín Mosconi    6854010   NaN        NaN   \n",
       "2  BUENOS AIRES  25 de Mayo          Del Valle    6854020   NaN        NaN   \n",
       "3  BUENOS AIRES  25 de Mayo          Ernestina    6854030   NaN        NaN   \n",
       "4  BUENOS AIRES  25 de Mayo  Gobernador Ugarte    6854040   NaN        NaN   \n",
       "\n",
       "  0,375 Mbps 0,5 Mbps 0,512 Mbps 0,625 Mbps  ... 61 Mbps 62 Mbps 64 Mbps  \\\n",
       "0        NaN        2        NaN        NaN  ...     NaN     NaN     NaN   \n",
       "1        NaN      NaN        NaN        NaN  ...     NaN     NaN     NaN   \n",
       "2        NaN        1        NaN        NaN  ...     NaN     NaN     NaN   \n",
       "3        NaN      NaN        NaN        NaN  ...     NaN     NaN     NaN   \n",
       "4        NaN      NaN        NaN        NaN  ...     NaN     NaN     NaN   \n",
       "\n",
       "  65 Mbps 66 Mbps 70 Mbps 75 Mbps 78 Mbps 80 Mbps 83 Mbps  \n",
       "0     NaN     NaN     NaN     NaN     NaN      12     NaN  \n",
       "1     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "2     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "3     NaN     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "4       1     NaN     NaN     NaN     NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Traemos el CSV  con los tipo de datos str.\n",
    "# Posterior, normalizaremos los numeros.\n",
    "df3 = pd.read_csv('datasets/accesosa_internetFijo_velocidadBajada_localidad.csv', dtype=str)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reemplazo y Normalizacion de Numeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se aprecio directamente en el datasets que los numeros estan separados por puntos.\n",
    "# Ejemplo: 2.343 y 1.334 cuando deben ser 2343 y 1334\n",
    "\n",
    "# Definimos una función para normalizar los números en una cadena de texto sin separadores de miles\n",
    "def normalizar_numero(numero):\n",
    "    numero_str = str(numero)\n",
    "    \n",
    "    if numero_str.endswith('.0'):\n",
    "        numero_normalizado = numero_str\n",
    "    \n",
    "    else:\n",
    "        # Si no hay parte decimal .0, eliminar todos los separadores de miles\n",
    "        numero_normalizado = numero_str.replace('.', '').replace(',', '')\n",
    "        \n",
    "    # Convertimos el número normalizado a float\n",
    "    numero_entero = float(numero_normalizado)\n",
    "    \n",
    "    return numero_entero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>Otros</th>\n",
       "      <th>0,256 Mbps</th>\n",
       "      <th>0,375 Mbps</th>\n",
       "      <th>0,5 Mbps</th>\n",
       "      <th>0,512 Mbps</th>\n",
       "      <th>0,625 Mbps</th>\n",
       "      <th>...</th>\n",
       "      <th>61 Mbps</th>\n",
       "      <th>62 Mbps</th>\n",
       "      <th>64 Mbps</th>\n",
       "      <th>65 Mbps</th>\n",
       "      <th>66 Mbps</th>\n",
       "      <th>70 Mbps</th>\n",
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       "      <th>80 Mbps</th>\n",
       "      <th>83 Mbps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
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       "    <tr>\n",
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       "      <td>BUENOS AIRES</td>\n",
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       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Del Valle</td>\n",
       "      <td>6854020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Ernestina</td>\n",
       "      <td>6854030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Gobernador Ugarte</td>\n",
       "      <td>6854040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia     Partido          Localidad Link Indec Otros 0,256 Mbps  \\\n",
       "0  BUENOS AIRES  25 de Mayo         25 de Mayo    6854100   0.0        0.0   \n",
       "1  BUENOS AIRES  25 de Mayo    Agustín Mosconi    6854010   0.0        0.0   \n",
       "2  BUENOS AIRES  25 de Mayo          Del Valle    6854020   0.0        0.0   \n",
       "3  BUENOS AIRES  25 de Mayo          Ernestina    6854030   0.0        0.0   \n",
       "4  BUENOS AIRES  25 de Mayo  Gobernador Ugarte    6854040   0.0        0.0   \n",
       "\n",
       "  0,375 Mbps 0,5 Mbps 0,512 Mbps 0,625 Mbps  ... 61 Mbps 62 Mbps 64 Mbps  \\\n",
       "0        0.0      2.0        0.0        0.0  ...     0.0     0.0     0.0   \n",
       "1        0.0      0.0        0.0        0.0  ...     0.0     0.0     0.0   \n",
       "2        0.0      1.0        0.0        0.0  ...     0.0     0.0     0.0   \n",
       "3        0.0      0.0        0.0        0.0  ...     0.0     0.0     0.0   \n",
       "4        0.0      0.0        0.0        0.0  ...     0.0     0.0     0.0   \n",
       "\n",
       "  65 Mbps 66 Mbps 70 Mbps 75 Mbps 78 Mbps 80 Mbps 83 Mbps  \n",
       "0     0.0     0.0     0.0     0.0     0.0    12.0     0.0  \n",
       "1     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "2     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "3     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "4     1.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Previo, reemplazamos los valores NaN por 0\n",
    "df3.fillna(0, inplace=True)\n",
    "\n",
    "# Aplicamos la función a cada columna del DataFrame (desde la columna 4 en adelante)\n",
    "df3.iloc[:, 4:] = df3.iloc[:, 4:].apply(lambda x: x.map(normalizar_numero))\n",
    "\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agrupación de Velocidades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para analizar la velocidad de internet, es crucial agrupar las velocidades en categorías como:\n",
    "\n",
    "- **Muy lento:** Hasta 512 kbps / 0.512 Mbps\n",
    "- **Lento:** + 512 Kbps - 1 Mbps\n",
    "- **Moderado:** + 1 Mbps - 6 Mbps\n",
    "- **Razonable:** + 6 Mbps - 10 Mbps\n",
    "- **Bueno:** + 10 Mbps - 20 Mbps\n",
    "- **Rápido:** + 20 Mbps - 30 Mbps\n",
    "- **Muy rápido:** + 30 Mbps\n",
    "\n",
    "Demasiadas columnas dificultan el análisis, por lo que se prefieren rangos consolidados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>muy_lenta</th>\n",
       "      <th>lenta</th>\n",
       "      <th>moderada</th>\n",
       "      <th>razonable</th>\n",
       "      <th>buena</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  muy_lenta lenta moderada razonable   buena rapida muy_rapida\n",
       "0       2.0  19.0    424.0     476.0  4975.0  262.0       50.0\n",
       "1       0.0   0.0     46.0      13.0     0.0    0.0        0.0\n",
       "2       1.0   0.0    181.0      10.0     0.0    0.0        0.0\n",
       "3       0.0   0.0     66.0       0.0     0.0    0.0        0.0\n",
       "4       0.0   0.0    175.0       5.0     0.0    0.0        1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1 = ['0,256 Mbps', '0,375 Mbps', '0,5 Mbps', '0,512 Mbps']\n",
    "g2 = ['0,625 Mbps','0,75 Mbps', '1 Mbps']\n",
    "g3 = ['1,25 Mbps', '1,5 Mbps', '2 Mbps', '2,2 Mbps','2,5 Mbps', '3 Mbps', '3,3 Mbps', '3,5 Mbps', '4 Mbps', '4,5 Mbps','5 Mbps', '6 Mbps']\n",
    "g4 = ['6,4 Mbps', '7 Mbps', '7,5 Mbps', '8 Mbps','9 Mbps', '10 Mbps']\n",
    "g5= ['10,2 Mbps', '11 Mbps', '12 Mbps', '12,3 Mbps','13 Mbps', '14 Mbps','15 Mbps', '16 Mbps', '17 Mbps', '18 Mbps','20 Mbps']\n",
    "g6 = ['20,5 Mbps', '21 Mbps', '22 Mbps', '23 Mbps', '24 Mbps','25 Mbps', '25,1 Mbps', '25,5 Mbps', '26 Mbps', '27 Mbps', '30 Mbps']\n",
    "g7 = ['31 Mbps', '32 Mbps', '34 Mbps', '35 Mbps', '36 Mbps', '38 Mbps','39 Mbps', '40 Mbps', '41 Mbps', '45 Mbps', '46 Mbps', '48 Mbps','49 Mbps', '50 Mbps', '50,1 Mbps', '50,25 Mbps', '51 Mbps', '55 Mbps', '58 Mbps', '59 Mbps', '60 Mbps', '61 Mbps', '62 Mbps', '64 Mbps', '65 Mbps', '66 Mbps', '70 Mbps', '75 Mbps', '78 Mbps', '80 Mbps', '83 Mbps']\n",
    "\n",
    "# Lista de grupos\n",
    "grupos = [g1, g2, g3, g4, g5, g6, g7]\n",
    "\n",
    "# Sumamos los valores en cada grupo de columnas y almacena los resultados en un nuevo DataFrame\n",
    "df_grupos = pd.DataFrame()\n",
    "for i, grupo in enumerate(grupos, start=1):\n",
    "    df_grupos[f'g{i}'] = df3[grupo].sum(axis=1)\n",
    "\n",
    "# Actualizamos los nombres\n",
    "actulizar_nombres = {\n",
    "   'g1': 'muy_lenta',\n",
    "   'g2': 'lenta',\n",
    "   'g3': 'moderada',\n",
    "   'g4': 'razonable',\n",
    "   'g5': 'buena',\n",
    "   'g6': 'rapida',\n",
    "   'g7': 'muy_rapida'\n",
    "}\n",
    "df_grupos.rename(columns = actulizar_nombres, inplace=True)\n",
    "\n",
    "df_grupos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Del Valle</td>\n",
       "      <td>6854020</td>\n",
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       "      <th>3</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 de Mayo</td>\n",
       "      <td>Ernestina</td>\n",
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       "      <td>BUENOS AIRES</td>\n",
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      ],
      "text/plain": [
       "      Provincia     Partido          Localidad Link Indec Otros muy_lenta  \\\n",
       "0  BUENOS AIRES  25 de Mayo         25 de Mayo    6854100   0.0       2.0   \n",
       "1  BUENOS AIRES  25 de Mayo    Agustín Mosconi    6854010   0.0       0.0   \n",
       "2  BUENOS AIRES  25 de Mayo          Del Valle    6854020   0.0       1.0   \n",
       "3  BUENOS AIRES  25 de Mayo          Ernestina    6854030   0.0       0.0   \n",
       "4  BUENOS AIRES  25 de Mayo  Gobernador Ugarte    6854040   0.0       0.0   \n",
       "\n",
       "  lenta moderada razonable   buena rapida muy_rapida  \n",
       "0  19.0    424.0     476.0  4975.0  262.0       50.0  \n",
       "1   0.0     46.0      13.0     0.0    0.0        0.0  \n",
       "2   0.0    181.0      10.0     0.0    0.0        0.0  \n",
       "3   0.0     66.0       0.0     0.0    0.0        0.0  \n",
       "4   0.0    175.0       5.0     0.0    0.0        1.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraemos del dataframe original, las columnas que con responda a ubicacion, incluyendo la columna 'Otros'.\n",
    "cols = ['Provincia', 'Partido', 'Localidad', 'Link Indec', 'Otros']\n",
    "df_ubicacion = df3[cols]\n",
    "\n",
    "# Concatenar los DataFrames a lo largo del eje de las columnas\n",
    "df3 = pd.concat([df_ubicacion,df_grupos ], axis=1)\n",
    "df3.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "¿Hay duplicados en el DataFrame?: False\n",
      "No hay duplicados en el DataFrame.\n"
     ]
    }
   ],
   "source": [
    "# Verificamos duplicados en el DataFrame\n",
    "df_duplicados = df3.duplicated().any()\n",
    "\n",
    "# Imprimir el resultado\n",
    "print(\"¿Hay duplicados en el DataFrame?:\", df_duplicados)\n",
    "\n",
    "# Si hay duplicados, mostrar las filas duplicadas y eliminarlos del DataFrame\n",
    "if df_duplicados:\n",
    "    duplicated_rows = df3[df3.duplicated()]\n",
    "    print(\"Filas duplicadas:\")\n",
    "    print(duplicated_rows)\n",
    "    \n",
    "    # Eliminamos los duplicados del DataFrame\n",
    "    df3 = df3.drop_duplicates()\n",
    "    print(\"Duplicados eliminados. DataFrame actualizado.\")\n",
    "else:\n",
    "    print(\"No hay duplicados en el DataFrame.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>Otros</th>\n",
       "      <th>muy_lenta</th>\n",
       "      <th>lenta</th>\n",
       "      <th>moderada</th>\n",
       "      <th>razonable</th>\n",
       "      <th>buena</th>\n",
       "      <th>rapida</th>\n",
       "      <th>muy_rapida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>SAN JUSTO</td>\n",
       "      <td>TRANSITO</td>\n",
       "      <td>14140400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Provincia    Partido Localidad Link Indec Otros muy_lenta lenta moderada  \\\n",
       "1235   CORDOBA  SAN JUSTO  TRANSITO   14140400   0.0       0.0   0.0     67.0   \n",
       "\n",
       "     razonable buena rapida muy_rapida  \n",
       "1235    1014.0  95.0    0.0       68.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificamos duplicados con respecto a las columnas 'Provincia', 'Partido' y 'Localidad'\n",
    "\n",
    "# Normalizamos las columnas\n",
    "df3['Provincia'] = df3['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "df3['Partido'] = df3['Partido'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "df3['Localidad'] = df3['Localidad'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "# Verificamos duplicados basándose en las columnas normalizadas\n",
    "duplicados = df3.duplicated(subset=['Provincia', 'Partido', 'Localidad'])\n",
    "\n",
    "# Mostramos las filas duplicadas, si es que las hay.\n",
    "filas_duplicadas = df3[duplicados]\n",
    "filas_duplicadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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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>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>Otros</th>\n",
       "      <th>muy_lenta</th>\n",
       "      <th>lenta</th>\n",
       "      <th>moderada</th>\n",
       "      <th>razonable</th>\n",
       "      <th>buena</th>\n",
       "      <th>rapida</th>\n",
       "      <th>muy_rapida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>SAN JUSTO</td>\n",
       "      <td>TRANSITO</td>\n",
       "      <td>Sin Datos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>SAN JUSTO</td>\n",
       "      <td>TRANSITO</td>\n",
       "      <td>14140400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Provincia    Partido Localidad Link Indec Otros muy_lenta lenta moderada  \\\n",
       "1234   CORDOBA  SAN JUSTO  TRANSITO  Sin Datos   0.0       0.0   0.0      0.0   \n",
       "1235   CORDOBA  SAN JUSTO  TRANSITO   14140400   0.0       0.0   0.0     67.0   \n",
       "\n",
       "     razonable buena rapida muy_rapida  \n",
       "1234       0.0   0.0    0.0       42.0  \n",
       "1235    1014.0  95.0    0.0       68.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[(df3['Provincia'] == 'CORDOBA') & (df3['Partido'] == 'SAN JUSTO') & (df3['Localidad'] == 'TRANSITO')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>Otros</th>\n",
       "      <th>muy_lenta</th>\n",
       "      <th>lenta</th>\n",
       "      <th>moderada</th>\n",
       "      <th>razonable</th>\n",
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       "      <th>muy_rapida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>CORDOBA</td>\n",
       "      <td>SAN JUSTO</td>\n",
       "      <td>TRANSITO</td>\n",
       "      <td>14140400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Provincia    Partido Localidad Link Indec Otros muy_lenta lenta moderada  \\\n",
       "1234   CORDOBA  SAN JUSTO  TRANSITO   14140400   0.0       0.0   0.0     67.0   \n",
       "\n",
       "     razonable buena rapida muy_rapida  \n",
       "1234    1014.0  95.0    0.0      110.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupa por las columnas 'Provincia', 'Partido' y 'Localidad', y suma los valores\n",
    "df3_consolidado = df3.groupby(['Provincia', 'Partido', 'Localidad']).agg({\n",
    "    'Link Indec': lambda x: x.iloc[1] if len(x) > 1 else x.iloc[0],  # Obtenemos el segundo valor de 'Link Indec' si existe, de lo contrario, toma el primer valor\n",
    "    'Otros': 'sum',\n",
    "    'muy_lenta': 'sum',\n",
    "    'lenta': 'sum',\n",
    "    'moderada': 'sum',\n",
    "    'razonable' : 'sum',\n",
    "    'buena': 'sum',\n",
    "    'rapida': 'sum',\n",
    "    'muy_rapida': 'sum'\n",
    "       \n",
    "}).reset_index()\n",
    "\n",
    "# Verificamos!!\n",
    "df3_consolidado[(df3_consolidado['Provincia'] == 'CORDOBA') & \n",
    "                (df3_consolidado['Partido'] == 'SAN JUSTO') & (df3_consolidado['Localidad'] == 'TRANSITO')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Partido</th>\n",
       "      <th>Localidad</th>\n",
       "      <th>Link Indec</th>\n",
       "      <th>muy_lenta</th>\n",
       "      <th>lenta</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 DE MAYO</td>\n",
       "      <td>25 DE MAYO</td>\n",
       "      <td>6854100</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
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       "      <td>4975</td>\n",
       "      <td>262</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 DE MAYO</td>\n",
       "      <td>AGUSTIN MOSCONI</td>\n",
       "      <td>6854010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 DE MAYO</td>\n",
       "      <td>DEL VALLE</td>\n",
       "      <td>6854020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 DE MAYO</td>\n",
       "      <td>ERNESTINA</td>\n",
       "      <td>6854030</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>25 DE MAYO</td>\n",
       "      <td>GOBERNADOR UGARTE</td>\n",
       "      <td>6854040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia     Partido          Localidad Link Indec muy_lenta lenta  \\\n",
       "0  BUENOS AIRES  25 DE MAYO         25 DE MAYO    6854100         2    19   \n",
       "1  BUENOS AIRES  25 DE MAYO    AGUSTIN MOSCONI    6854010         0     0   \n",
       "2  BUENOS AIRES  25 DE MAYO          DEL VALLE    6854020         1     0   \n",
       "3  BUENOS AIRES  25 DE MAYO          ERNESTINA    6854030         0     0   \n",
       "4  BUENOS AIRES  25 DE MAYO  GOBERNADOR UGARTE    6854040         0     0   \n",
       "\n",
       "  moderada razonable buena rapida muy_rapida  \n",
       "0      424       476  4975    262         50  \n",
       "1       46        13     0      0          0  \n",
       "2      181        10     0      0          0  \n",
       "3       66         0     0      0          0  \n",
       "4      175         5     0      0          1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminamos la columnas Otros, no aportaría valor al análisis\n",
    "df3.drop(columns='Otros', inplace=True)\n",
    "\n",
    "# Convertimos las columnas a tipo 'int'\n",
    "df3.iloc[:, 4:] = df3.iloc[:, 4:].astype(int)\n",
    "\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agrupación por Provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Provincia</th>\n",
       "      <th>muy_lenta</th>\n",
       "      <th>lenta</th>\n",
       "      <th>moderada</th>\n",
       "      <th>razonable</th>\n",
       "      <th>buena</th>\n",
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       "      <th>muy_rapida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>28934</td>\n",
       "      <td>23347</td>\n",
       "      <td>259866</td>\n",
       "      <td>281550</td>\n",
       "      <td>244555</td>\n",
       "      <td>817733</td>\n",
       "      <td>1011782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CABA</td>\n",
       "      <td>516</td>\n",
       "      <td>5096</td>\n",
       "      <td>31320</td>\n",
       "      <td>64653</td>\n",
       "      <td>36336</td>\n",
       "      <td>61172</td>\n",
       "      <td>210480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>71</td>\n",
       "      <td>315</td>\n",
       "      <td>2716</td>\n",
       "      <td>5028</td>\n",
       "      <td>4638</td>\n",
       "      <td>3122</td>\n",
       "      <td>30388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHACO</td>\n",
       "      <td>461</td>\n",
       "      <td>771</td>\n",
       "      <td>16067</td>\n",
       "      <td>17846</td>\n",
       "      <td>6704</td>\n",
       "      <td>3050</td>\n",
       "      <td>27557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>109</td>\n",
       "      <td>1614</td>\n",
       "      <td>46457</td>\n",
       "      <td>31007</td>\n",
       "      <td>34728</td>\n",
       "      <td>16759</td>\n",
       "      <td>17508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Provincia muy_lenta  lenta moderada razonable   buena  rapida muy_rapida\n",
       "0  BUENOS AIRES     28934  23347   259866    281550  244555  817733    1011782\n",
       "1          CABA       516   5096    31320     64653   36336   61172     210480\n",
       "2     CATAMARCA        71    315     2716      5028    4638    3122      30388\n",
       "3         CHACO       461    771    16067     17846    6704    3050      27557\n",
       "4        CHUBUT       109   1614    46457     31007   34728   16759      17508"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular la suma de calidad de conexiones por provincia\n",
    "cols = df3.columns[4:]\n",
    "calidad_conexion_provincia = df3.groupby('Provincia')[cols].sum().reset_index()\n",
    "\n",
    "calidad_conexion_provincia.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformamos el dataframe\n",
    "df3_transformado = pd.melt(calidad_conexion_provincia, id_vars=['Provincia'], var_name='Calidad', value_name='Cantidad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graficar_calidad_por_provincia(df, provincias_seleccionadas, calidad_seleccionadas):\n",
    "    \"\"\"\n",
    "    Grafica la calidad de conexiones por provincia.\n",
    "\n",
    "    Parámetros:\n",
    "    - df: DataFrame que contiene los datos.\n",
    "    - provincias_seleccionadas: Lista de provincias que se quieren graficar.\n",
    "    - calidad_seleccionadas: Lista de calidades que se quieren graficar.\n",
    "\n",
    "    No devuelve nada, simplemente muestra el gráfico.\n",
    "    \"\"\"\n",
    "    # Filtrar las provincias\n",
    "    df_provincias = df[df['Provincia'].isin(provincias_seleccionadas)]\n",
    "\n",
    "    # Filtrar por calidad\n",
    "    df_seleccionado = df_provincias[df_provincias['Calidad'].isin(calidad_seleccionadas)]\n",
    "\n",
    "    # Escalar las cantidades a miles\n",
    "    df_seleccionado.loc[:, 'Cantidad'] = df_seleccionado['Cantidad'] / 1000\n",
    "\n",
    "    # Automatizamos la creación de colores para cada provincia\n",
    "    colores_provincias = {provincia: plt.cm.viridis(i / len(provincias_seleccionadas)) for i, provincia in enumerate(provincias_seleccionadas)}\n",
    "\n",
    "    # Ancho de las barras\n",
    "    bar_width = 0.6\n",
    "\n",
    "    # Gráfico de barras vertical desde el DataFrame nuevo con colores personalizados\n",
    "    ax = df_seleccionado.pivot(index='Calidad', columns='Provincia', values='Cantidad').plot(\n",
    "        kind='bar', width=bar_width, color=[colores_provincias[col] for col in df_seleccionado['Provincia']]\n",
    "    )\n",
    "\n",
    "    \n",
    "    # Ajustamos la posición de la leyenda\n",
    "    ax.legend(title='Provincia', bbox_to_anchor=(1.3, 1))\n",
    "\n",
    "    # Rotar los nombres en el eje x\n",
    "    ax.set_xticklabels(df_seleccionado['Calidad'].unique(), rotation=0)\n",
    "\n",
    "    # Configurar las ubicaciones y etiquetas de las marcas en el eje x\n",
    "    plt.xticks(np.arange(len(df_seleccionado['Calidad'].unique())), df_seleccionado['Calidad'].unique())\n",
    "\n",
    "    plt.title('Calidad de Conexiones por Provincia')\n",
    "    plt.ylabel('Cantidad (en miles)')\n",
    "    plt.xlabel('Calidad')\n",
    "    plt.grid(False)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis de Velocidades de Conexión en Argentina\n",
    "\n",
    "Este gráfico destaca que la calidad de velocidad **rápida** es la predominante en comparación con otras categorías. Sin embargo, se enfatiza que la tendencia general apunta hacia la preferencia por velocidades **muy rápidas**, dado el constante deseo de adoptar tecnologías más avanzadas y rápidas.\n",
    "\n",
    "En este contexto, se sugiere promocionar la tecnología de Fibra Óptica, tanto en provincias con calidades de conexión rápidas como en aquellas con menor calidad. Esto permitirá satisfacer la demanda actual y futura de conexiones más veloces.\n",
    "\n",
    "Se invita a visualizar el gráfico para obtener una representación visual de estas tendencias y consideraciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficamos\n",
    "graficar_calidad_por_provincia(df3_transformado, ['CABA', 'CORDOBA','SANTA FE', 'MENDOZA', 'ENTRE RIOS', 'TUCUMAN'], \n",
    "                               ['moderada','buena', 'rapida', 'muy_rapida'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 04 - Historico Calidad de Conexion por Provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>HASTA 512 kbps</th>\n",
       "      <th>+ 512 Kbps - 1 Mbps</th>\n",
       "      <th>+ 1 Mbps - 6 Mbps</th>\n",
       "      <th>+ 6 Mbps - 10 Mbps</th>\n",
       "      <th>+ 10 Mbps - 20 Mbps</th>\n",
       "      <th>+ 20 Mbps - 30 Mbps</th>\n",
       "      <th>+ 30 Mbps</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>4</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>28.934</td>\n",
       "      <td>23.347</td>\n",
       "      <td>259.866</td>\n",
       "      <td>281.550</td>\n",
       "      <td>244.555</td>\n",
       "      <td>98.670</td>\n",
       "      <td>3.760.109</td>\n",
       "      <td>97.634</td>\n",
       "      <td>4.794.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>516</td>\n",
       "      <td>5.096</td>\n",
       "      <td>31.320</td>\n",
       "      <td>64.653</td>\n",
       "      <td>36.336</td>\n",
       "      <td>8.116</td>\n",
       "      <td>1.299.117</td>\n",
       "      <td>106.772</td>\n",
       "      <td>1.551.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>71</td>\n",
       "      <td>315</td>\n",
       "      <td>2.716</td>\n",
       "      <td>5.028</td>\n",
       "      <td>4.638</td>\n",
       "      <td>3.122</td>\n",
       "      <td>52.739</td>\n",
       "      <td>2.280</td>\n",
       "      <td>70.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>461</td>\n",
       "      <td>771</td>\n",
       "      <td>16.067</td>\n",
       "      <td>17.846</td>\n",
       "      <td>6.704</td>\n",
       "      <td>3.039</td>\n",
       "      <td>94.866</td>\n",
       "      <td>3.909</td>\n",
       "      <td>143.663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>109</td>\n",
       "      <td>1.614</td>\n",
       "      <td>46.457</td>\n",
       "      <td>31.007</td>\n",
       "      <td>34.728</td>\n",
       "      <td>15.770</td>\n",
       "      <td>19.048</td>\n",
       "      <td>20.026</td>\n",
       "      <td>168.759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia HASTA 512 kbps + 512 Kbps - 1 Mbps  \\\n",
       "0  2022          4     Buenos Aires         28.934              23.347   \n",
       "1  2022          4  Capital Federal            516               5.096   \n",
       "2  2022          4        Catamarca             71                 315   \n",
       "3  2022          4            Chaco            461                 771   \n",
       "4  2022          4           Chubut            109               1.614   \n",
       "\n",
       "  + 1 Mbps - 6 Mbps + 6 Mbps - 10 Mbps + 10 Mbps - 20 Mbps  \\\n",
       "0           259.866            281.550             244.555   \n",
       "1            31.320             64.653              36.336   \n",
       "2             2.716              5.028               4.638   \n",
       "3            16.067             17.846               6.704   \n",
       "4            46.457             31.007              34.728   \n",
       "\n",
       "  + 20 Mbps - 30 Mbps  + 30 Mbps    OTROS      Total  \n",
       "0              98.670  3.760.109   97.634  4.794.665  \n",
       "1               8.116  1.299.117  106.772  1.551.926  \n",
       "2               3.122     52.739    2.280     70.909  \n",
       "3               3.039     94.866    3.909    143.663  \n",
       "4              15.770     19.048   20.026    168.759  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cargamos las columnas como tipo str para evitar pérdida de datos durante la conversión automática a float.\n",
    "tipos_datos = {\n",
    "    'HASTA 512 kbps':str,\n",
    "    '+ 512 Kbps - 1 Mbps':str,\n",
    "    '+ 1 Mbps - 6 Mbps': str,\n",
    "    '+ 6 Mbps - 10 Mbps': str,\n",
    "    '+ 10 Mbps - 20 Mbps': str,\n",
    "    '+ 20 Mbps - 30 Mbps': str,\n",
    "    '+ 30 Mbps': str,\n",
    "    'OTROS': str,\n",
    "    'Total': str\n",
    "}\n",
    "\n",
    "df4 = pd.read_csv('datasets/accesos_internet_fijo_velocidadBajada_porRangos_provincia.csv', dtype=tipos_datos)\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renombramos Columnas, Normalizamos Numeros y Provincias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>muy_lenta</th>\n",
       "      <th>lenta</th>\n",
       "      <th>moderada</th>\n",
       "      <th>razonable</th>\n",
       "      <th>buena</th>\n",
       "      <th>rapida</th>\n",
       "      <th>muy_rapida</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>28934</td>\n",
       "      <td>23347</td>\n",
       "      <td>259866</td>\n",
       "      <td>281550</td>\n",
       "      <td>244555</td>\n",
       "      <td>98670</td>\n",
       "      <td>3760109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CAPITAL FEDERAL</td>\n",
       "      <td>516</td>\n",
       "      <td>5096</td>\n",
       "      <td>31320</td>\n",
       "      <td>64653</td>\n",
       "      <td>36336</td>\n",
       "      <td>8116</td>\n",
       "      <td>1299117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>71</td>\n",
       "      <td>315</td>\n",
       "      <td>2716</td>\n",
       "      <td>5028</td>\n",
       "      <td>4638</td>\n",
       "      <td>3122</td>\n",
       "      <td>52739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHACO</td>\n",
       "      <td>461</td>\n",
       "      <td>771</td>\n",
       "      <td>16067</td>\n",
       "      <td>17846</td>\n",
       "      <td>6704</td>\n",
       "      <td>3039</td>\n",
       "      <td>94866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>109</td>\n",
       "      <td>1614</td>\n",
       "      <td>46457</td>\n",
       "      <td>31007</td>\n",
       "      <td>34728</td>\n",
       "      <td>15770</td>\n",
       "      <td>19048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia  muy_lenta  lenta  moderada  razonable  \\\n",
       "0  2022          4     BUENOS AIRES      28934  23347    259866     281550   \n",
       "1  2022          4  CAPITAL FEDERAL        516   5096     31320      64653   \n",
       "2  2022          4        CATAMARCA         71    315      2716       5028   \n",
       "3  2022          4            CHACO        461    771     16067      17846   \n",
       "4  2022          4           CHUBUT        109   1614     46457      31007   \n",
       "\n",
       "    buena  rapida  muy_rapida  \n",
       "0  244555   98670     3760109  \n",
       "1   36336    8116     1299117  \n",
       "2    4638    3122       52739  \n",
       "3    6704    3039       94866  \n",
       "4   34728   15770       19048  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cambiamos el nombre de las columnas de rangos!\n",
    "actulizar_nombres = {\n",
    "    'HASTA 512 kbps':'muy_lenta',\n",
    "    '+ 512 Kbps - 1 Mbps':'lenta',\n",
    "    '+ 1 Mbps - 6 Mbps': 'moderada',\n",
    "    '+ 6 Mbps - 10 Mbps': 'razonable',\n",
    "    '+ 10 Mbps - 20 Mbps': 'buena',\n",
    "    '+ 20 Mbps - 30 Mbps': 'rapida',\n",
    "    '+ 30 Mbps': 'muy_rapida'\n",
    "}\n",
    "df4.rename(columns=actulizar_nombres, inplace=True)\n",
    "\n",
    "# Elimanmos la columna 'OTROS' y 'Total'\n",
    "df4.drop(columns=['OTROS', 'Total'], inplace=True)\n",
    "\n",
    "# Normalizamos los numeros con la funcion antes creada\n",
    "cols = ['muy_lenta', 'lenta', 'moderada', 'razonable', 'buena', 'rapida', 'muy_rapida']\n",
    "df4[cols]=df4[cols].apply(lambda x: x.map(normalizar_numero))\n",
    "\n",
    "# Convertimos los numeros a tipo int\n",
    "df4['muy_lenta'] = df4['muy_lenta'].astype(int)\n",
    "df4['lenta'] = df4['lenta'].astype(int)\n",
    "df4['moderada'] = df4['moderada'].astype(int)\n",
    "df4['razonable'] = df4['razonable'].astype(int)\n",
    "df4['buena'] = df4['buena'].astype(int)\n",
    "df4['rapida'] = df4['rapida'].astype(int)\n",
    "df4['muy_rapida'] = df4['muy_rapida'].astype(int)\n",
    "\n",
    "# Normalizamos, again, las columnas 'Provincia', 'Partido' y 'Localidad'\n",
    "df4['Provincia'] = df4['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_transformado = pd.melt(df4, id_vars=['Año','Trimestre','Provincia'], var_name='Calidad', value_name='Cantidad')\n",
    "df4_transformado['Cantidad'] = df4_transformado['Cantidad'] / 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_calidad_provincias(df, provincias, calidades=None):\n",
    "    df = df[df['Año'] >= 2018]\n",
    "    # Filtrar por provincias seleccionadas y crear una copia del DataFrame resultante\n",
    "    df_provincias = df[df['Provincia'].isin(provincias)]\n",
    "\n",
    "    orden_calidades = ['muy_rapida', 'rapida', 'buena', 'razonable', 'moderada', 'lenta', 'muy_lenta']\n",
    "\n",
    "    # Si no se proporciona la lista de calidades, mostrar todas\n",
    "    calidades = calidades or orden_calidades\n",
    "\n",
    "    # Obtener colores únicos para cada combinación de provincia y calidad\n",
    "    colores = sns.color_palette(\"viridis\", n_colors=len(provincias) * len(calidades)).as_hex()\n",
    "    colores = dict(zip([(calidad, provincia) for calidad in calidades for provincia in provincias], colores))\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    for calidad in calidades:\n",
    "        for provincia in provincias:\n",
    "            datos = df_provincias[(df_provincias['Provincia'] == provincia) & (df_provincias['Calidad'] == calidad)]\n",
    "            plt.plot(\n",
    "                datos['Año'].astype(str) + '-' + datos['Trimestre'].astype(str),\n",
    "                datos['Cantidad'],\n",
    "                label=f'{provincia} - {calidad}',\n",
    "                #marker='o',\n",
    "                linewidth=3,\n",
    "                color=colores.get((calidad, provincia), 'gray')  # Utilizar gris si no hay una asignación específica\n",
    "            )\n",
    "\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    plt.gca().invert_xaxis()\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "    plt.legend(title='Provincia - Calidad', bbox_to_anchor=(1, 1))\n",
    "\n",
    "    plt.title(f'Histórico de la Relación Provincia - Calidad')\n",
    "    plt.xlabel('Año-Trimestre')\n",
    "    plt.ylabel('Cantidad (miles)')\n",
    "    plt.grid(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis Histórico de Relación Provincia-Calidad de Velocidad\n",
    "\n",
    "Este gráfico histórico revela que a partir del año 2018 se observa un notable incremento en la calidad de velocidad, especialmente en la categoría de **muy rápida** (superior a +30 Mbps). Este cambio indica una tendencia clara de migración hacia velocidades más altas por parte de las provincias.\n",
    "\n",
    "La interpretación de estos datos sugiere que las provincias están optando por tecnologías que ofrecen velocidades superiores, y la fibra óptica emerge como una opción destacada en este escenario.\n",
    "\n",
    "Se invita a visualizar el gráfico para obtener una representación visual detallada de estas tendencias históricas, reforzando la idea de que el éxito del negocio podría estar estrechamente vinculado a la adopción de la fibra óptica.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Llamamos a la función con los datos, las provincias específicas y las calidades a mostrar en la leyenda\n",
    "plot_calidad_provincias(df4_transformado, ['CAPITAL FEDERAL', 'CORDOBA', 'MENDOZA', 'ENTRE RIOS', 'SANTA FE', 'TUCUMAN'], \n",
    "                        calidades=['muy_rapida'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 05 - Historico Velocidad Media por Provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Trimestre</th>\n",
       "      <th>Provincia</th>\n",
       "      <th>Mbps (Media de bajada)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>111.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>182.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>83.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>104.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>15.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia  Mbps (Media de bajada)\n",
       "0  2022          4     Buenos Aires                  111.00\n",
       "1  2022          4  Capital Federal                  182.86\n",
       "2  2022          4        Catamarca                   83.33\n",
       "3  2022          4            Chaco                  104.02\n",
       "4  2022          4           Chubut                   15.92"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.read_excel('datasets/historico_velocidad_media_internet_provincia.xlsx')\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 864 entries, 0 to 863\n",
      "Data columns (total 4 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Año                     864 non-null    int64  \n",
      " 1   Trimestre               864 non-null    int64  \n",
      " 2   Provincia               864 non-null    object \n",
      " 3   Mbps (Media de bajada)  864 non-null    float64\n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 27.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Mbps (Media de bajada)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>864.00</td>\n",
       "      <td>864.00</td>\n",
       "      <td>864.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2018.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>15.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.58</td>\n",
       "      <td>1.12</td>\n",
       "      <td>18.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>7.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>182.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Año  Trimestre  Mbps (Media de bajada)\n",
       "count   864.00     864.00                  864.00\n",
       "mean   2018.00       2.50                   15.38\n",
       "std       2.58       1.12                   18.32\n",
       "min    2014.00       1.00                    2.57\n",
       "25%    2016.00       1.75                    4.32\n",
       "50%    2018.00       2.50                    7.42\n",
       "75%    2020.00       3.25                   19.71\n",
       "max    2022.00       4.00                  182.86"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Mbps (Media de bajada)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>111.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CAPITAL FEDERAL</td>\n",
       "      <td>182.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>83.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHACO</td>\n",
       "      <td>104.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>15.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia  Mbps (Media de bajada)\n",
       "0  2022          4     BUENOS AIRES                  111.00\n",
       "1  2022          4  CAPITAL FEDERAL                  182.86\n",
       "2  2022          4        CATAMARCA                   83.33\n",
       "3  2022          4            CHACO                  104.02\n",
       "4  2022          4           CHUBUT                   15.92"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizamos la columna Provincia\n",
    "df5['Provincia'] = df5['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "# Redondeamos la columna de Mbps\n",
    "df5['Mbps (Media de bajada)'] = df5['Mbps (Media de bajada)'].round(2)\n",
    "\n",
    "df5.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_velocidad_provincias(df, provincias):\n",
    "    df_provincias = df[(df['Provincia'].isin(provincias)) & (df['Año'] >= 2017)]\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Utilizar la paleta de colores \"viridis\"\n",
    "    colores = sns.color_palette(\"viridis\", n_colors=len(provincias))\n",
    "\n",
    "    for i, provincia in enumerate(provincias):\n",
    "        datos_provincia = df_provincias[df_provincias['Provincia'] == provincia]\n",
    "        plt.plot(\n",
    "            datos_provincia['Año'].astype(str) + '-' + datos_provincia['Trimestre'].astype(str),\n",
    "            datos_provincia['Mbps (Media de bajada)'],\n",
    "            #marker='o',\n",
    "            linewidth = 3,\n",
    "            label=provincia,\n",
    "            color=colores[i]\n",
    "        )\n",
    "\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    plt.gca().invert_xaxis()\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "    plt.title('Velocidad Media en Provincias')\n",
    "    plt.xlabel('Año-Trimestre')\n",
    "    plt.ylabel('Velocidad Media Mbps')\n",
    "    plt.legend(title='Provincia', bbox_to_anchor=(1, 1))\n",
    "    plt.grid(False)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis Histórico de Velocidad Media en Provincias\n",
    "\n",
    "Esta gráfica histórica de velocidad media en provincias confirma de manera evidente que la migración predominante ha sido hacia la tecnología de **fibra óptica** en las principales provincias. Estos datos subrayan la importancia estratégica para la empresa de enfocarse y planificar su crecimiento en este ámbito específico.\n",
    "\n",
    "Se invita a visualizar el gráfico para obtener una representación visual detallada de estas tendencias, lo que proporcionará una perspectiva más completa y respaldará la toma de decisiones estratégicas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Llamamos a la función con los datos y la provincia específica\n",
    "plot_velocidad_provincias(df5,['BUENOS AIRES','CABA', 'CORDOBA', 'MENDOZA', 'ENTRE RIOS', 'SANTA FE', 'TUCUMAN'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 06 - Historico Conectividad en Provincias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>325908.0</td>\n",
       "      <td>2767247.0</td>\n",
       "      <td>1499149.0</td>\n",
       "      <td>129910.0</td>\n",
       "      <td>72451.0</td>\n",
       "      <td>4794665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>138753.0</td>\n",
       "      <td>1238196.0</td>\n",
       "      <td>138548.0</td>\n",
       "      <td>4603.0</td>\n",
       "      <td>31826.0</td>\n",
       "      <td>1551926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>8952.0</td>\n",
       "      <td>10998.0</td>\n",
       "      <td>47337.0</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>2243.0</td>\n",
       "      <td>70909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>24582.0</td>\n",
       "      <td>61265.0</td>\n",
       "      <td>47285.0</td>\n",
       "      <td>7957.0</td>\n",
       "      <td>2574.0</td>\n",
       "      <td>143663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>45450.0</td>\n",
       "      <td>73923.0</td>\n",
       "      <td>9815.0</td>\n",
       "      <td>30721.0</td>\n",
       "      <td>8850.0</td>\n",
       "      <td>168759.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año Trimestre        Provincia      ADSL  Cablemodem  Fibra óptica  \\\n",
       "0  2022         4     Buenos Aires  325908.0   2767247.0     1499149.0   \n",
       "1  2022         4  Capital Federal  138753.0   1238196.0      138548.0   \n",
       "2  2022         4        Catamarca    8952.0     10998.0       47337.0   \n",
       "3  2022         4            Chaco   24582.0     61265.0       47285.0   \n",
       "4  2022         4           Chubut   45450.0     73923.0        9815.0   \n",
       "\n",
       "   Wireless    Otros      Total  \n",
       "0  129910.0  72451.0  4794665.0  \n",
       "1    4603.0  31826.0  1551926.0  \n",
       "2    1379.0   2243.0    70909.0  \n",
       "3    7957.0   2574.0   143663.0  \n",
       "4   30721.0   8850.0   168759.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = pd.read_excel('datasets/accesos_internetFijo_tecnologiaProvincia.xlsx')\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>325908.0</td>\n",
       "      <td>2767247.0</td>\n",
       "      <td>1499149.0</td>\n",
       "      <td>129910.0</td>\n",
       "      <td>72451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CAPITAL FEDERAL</td>\n",
       "      <td>138753.0</td>\n",
       "      <td>1238196.0</td>\n",
       "      <td>138548.0</td>\n",
       "      <td>4603.0</td>\n",
       "      <td>31826.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>8952.0</td>\n",
       "      <td>10998.0</td>\n",
       "      <td>47337.0</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>2243.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHACO</td>\n",
       "      <td>24582.0</td>\n",
       "      <td>61265.0</td>\n",
       "      <td>47285.0</td>\n",
       "      <td>7957.0</td>\n",
       "      <td>2574.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>45450.0</td>\n",
       "      <td>73923.0</td>\n",
       "      <td>9815.0</td>\n",
       "      <td>30721.0</td>\n",
       "      <td>8850.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año Trimestre        Provincia      ADSL  Cablemodem  Fibra óptica  \\\n",
       "0  2022         4     BUENOS AIRES  325908.0   2767247.0     1499149.0   \n",
       "1  2022         4  CAPITAL FEDERAL  138753.0   1238196.0      138548.0   \n",
       "2  2022         4        CATAMARCA    8952.0     10998.0       47337.0   \n",
       "3  2022         4            CHACO   24582.0     61265.0       47285.0   \n",
       "4  2022         4           CHUBUT   45450.0     73923.0        9815.0   \n",
       "\n",
       "   Wireless    Otros  \n",
       "0  129910.0  72451.0  \n",
       "1    4603.0  31826.0  \n",
       "2    1379.0   2243.0  \n",
       "3    7957.0   2574.0  \n",
       "4   30721.0   8850.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quitamos la columna Total\n",
    "df6.drop(columns='Total', inplace=True)\n",
    "\n",
    "# Nomalizamos los nombres de las provincias\n",
    "df6['Provincia'] = df6['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>861</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>TUCUMAN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Año                                          Trimestre  \\\n",
       "861  2014                                                  1   \n",
       "862  2014                                                  1   \n",
       "863  2014                                                  1   \n",
       "864   NaN                                                NaN   \n",
       "865     *  Los datos provinciales no coinciden a nivel na...   \n",
       "\n",
       "               Provincia      ADSL  Cablemodem  Fibra óptica  Wireless   Otros  \n",
       "861  SANTIAGO DEL ESTERO   32567.0      3598.0          19.0     915.0    34.0  \n",
       "862     TIERRA DEL FUEGO   21618.0      2837.0         648.0       1.0  2934.0  \n",
       "863              TUCUMAN  129717.0        83.0         121.0      13.0    98.0  \n",
       "864                  NaN       NaN         NaN           NaN       NaN     NaN  \n",
       "865                  NaN       NaN         NaN           NaN       NaN     NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observamos algo extraño aqui!\n",
    "# Almacenemos el filtro en una variable y leamos que nos dice la descripcion\n",
    "fila_rara = df6[df6.isnull().any(axis=1)]\n",
    "fila_rara.loc[865,'Trimestre']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 866 entries, 0 to 865\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Año           865 non-null    object \n",
      " 1   Trimestre     865 non-null    object \n",
      " 2   Provincia     864 non-null    object \n",
      " 3   ADSL          864 non-null    float64\n",
      " 4   Cablemodem    864 non-null    float64\n",
      " 5   Fibra óptica  864 non-null    float64\n",
      " 6   Wireless      864 non-null    float64\n",
      " 7   Otros         864 non-null    float64\n",
      "dtypes: float64(5), object(3)\n",
      "memory usage: 54.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df6.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>325908</td>\n",
       "      <td>2767247</td>\n",
       "      <td>1499149</td>\n",
       "      <td>129910</td>\n",
       "      <td>72451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CAPITAL FEDERAL</td>\n",
       "      <td>138753</td>\n",
       "      <td>1238196</td>\n",
       "      <td>138548</td>\n",
       "      <td>4603</td>\n",
       "      <td>31826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>8952</td>\n",
       "      <td>10998</td>\n",
       "      <td>47337</td>\n",
       "      <td>1379</td>\n",
       "      <td>2243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHACO</td>\n",
       "      <td>24582</td>\n",
       "      <td>61265</td>\n",
       "      <td>47285</td>\n",
       "      <td>7957</td>\n",
       "      <td>2574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>45450</td>\n",
       "      <td>73923</td>\n",
       "      <td>9815</td>\n",
       "      <td>30721</td>\n",
       "      <td>8850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia    ADSL  Cablemodem  Fibra óptica  \\\n",
       "0  2022          4     BUENOS AIRES  325908     2767247       1499149   \n",
       "1  2022          4  CAPITAL FEDERAL  138753     1238196        138548   \n",
       "2  2022          4        CATAMARCA    8952       10998         47337   \n",
       "3  2022          4            CHACO   24582       61265         47285   \n",
       "4  2022          4           CHUBUT   45450       73923          9815   \n",
       "\n",
       "   Wireless  Otros  \n",
       "0    129910  72451  \n",
       "1      4603  31826  \n",
       "2      1379   2243  \n",
       "3      7957   2574  \n",
       "4     30721   8850  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Despues de haber encontrado el dato nulo, ahora pasamos a dropear valores nulos.\n",
    "df6.dropna(axis=0,  inplace=True)\n",
    "\n",
    "# Eliminamos el asterisco al final de cada número en las columnas\n",
    "df6['Año'] = df6['Año'].astype(str).str.rstrip('*')\n",
    "df6['Trimestre'] = df6['Trimestre'].astype(str).str.rstrip('*')\n",
    "\n",
    "# Convertimos las columnas especificas de tipo float a int\n",
    "cols = ['Año','Trimestre', 'ADSL',\t'Cablemodem', 'Fibra óptica', 'Wireless','Otros']\n",
    "df6[cols] = df6[cols].astype(int)\n",
    "\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Tecnologia</th>\n",
       "      <th>Cantidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>ADSL</td>\n",
       "      <td>325908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CAPITAL FEDERAL</td>\n",
       "      <td>ADSL</td>\n",
       "      <td>138753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>ADSL</td>\n",
       "      <td>8952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHACO</td>\n",
       "      <td>ADSL</td>\n",
       "      <td>24582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>ADSL</td>\n",
       "      <td>45450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia Tecnologia  Cantidad\n",
       "0  2022          4     BUENOS AIRES       ADSL    325908\n",
       "1  2022          4  CAPITAL FEDERAL       ADSL    138753\n",
       "2  2022          4        CATAMARCA       ADSL      8952\n",
       "3  2022          4            CHACO       ADSL     24582\n",
       "4  2022          4           CHUBUT       ADSL     45450"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df6_transformado = pd.melt(df6, id_vars=['Año','Trimestre','Provincia'], var_name='Tecnologia', value_name='Cantidad')\n",
    "df6_transformado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6_transformado['Cantidad'] = df6_transformado['Cantidad'] /1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cantidad_tecnologias_provincias(df, provincias, tecnologias):\n",
    "    df = df[df['Año'] >= 2018]\n",
    "    df_filtrado = df[(df['Provincia'].isin(provincias)) & (df['Tecnologia'].isin(tecnologias))]\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Utilizar la paleta de colores \"viridis\"\n",
    "    colores = sns.color_palette(\"viridis\", n_colors=len(provincias) * len(tecnologias))\n",
    "\n",
    "    for i, provincia in enumerate(provincias):\n",
    "        for j, tecnologia in enumerate(tecnologias):\n",
    "            datos_filtrados = df_filtrado[(df_filtrado['Provincia'] == provincia) & (df_filtrado['Tecnologia'] == tecnologia)]\n",
    "            label = f'{provincia} - {tecnologia}'\n",
    "            plt.plot(\n",
    "                datos_filtrados['Año'].astype(str) + '-Q' + datos_filtrados['Trimestre'].astype(str),\n",
    "                datos_filtrados['Cantidad'],\n",
    "                #marker='o',\n",
    "                linewidth =3,\n",
    "                label=label,\n",
    "                color=colores[i * len(tecnologias) + j]\n",
    "            )\n",
    "\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    plt.gca().invert_xaxis()\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "    plt.title(f'Tecnologías en Provincias')\n",
    "    plt.xlabel('Año-Trimestre')\n",
    "    plt.ylabel('Cantidad de Conexiones (miles)')\n",
    "    plt.legend(title='Provincia - Tecnologia', bbox_to_anchor=(1, 1))\n",
    "    plt.grid(False)\n",
    "\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis Histórico de Conexiones en Provincias\n",
    "\n",
    "Esta gráfica confirma de manera evidente que la migración predominante ha sido hacia la tecnología de **fibra óptica** en las principales provincias. Estos datos subrayan la importancia estratégica para la empresa de enfocarse y planificar su crecimiento en este ámbito específico.\n",
    "\n",
    "Se invita a visualizar el gráfico para obtener una representación visual detallada de estas tendencias, lo que proporcionará una perspectiva más completa y respaldará la toma de decisiones estratégicas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Llamamos a la función con los datos y las listas de provincias y tecnologías\n",
    "plot_cantidad_tecnologias_provincias(df6_transformado, ['CAPITAL FEDERAL', 'CORDOBA', 'MENDOZA', \n",
    "                                                        'ENTRE RIOS', 'SANTA FE', 'TUCUMAN'], \n",
    "                                     ['Fibra óptica'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 07 - Historico Banda Ancha y Angosta por Provincia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>4</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>4788466</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>4794665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>1549781</td>\n",
       "      <td>2145.0</td>\n",
       "      <td>1551926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>70908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>143658</td>\n",
       "      <td>5.0</td>\n",
       "      <td>143663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>167855</td>\n",
       "      <td>904.0</td>\n",
       "      <td>168759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia  Banda ancha fija  Dial up    Total\n",
       "0  2022          4     Buenos Aires           4788466   6199.0  4794665\n",
       "1  2022          4  Capital Federal           1549781   2145.0  1551926\n",
       "2  2022          4        Catamarca             70908      1.0    70909\n",
       "3  2022          4            Chaco            143658      5.0   143663\n",
       "4  2022          4           Chubut            167855    904.0   168759"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = pd.read_excel('datasets/Internet_BAF.xlsx')\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 864 entries, 0 to 863\n",
      "Data columns (total 4 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Año               864 non-null    int64 \n",
      " 1   Trimestre         864 non-null    int64 \n",
      " 2   Provincia         864 non-null    object\n",
      " 3   Banda ancha fija  864 non-null    int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 27.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# No usaremos Dial Up, por que representa un porcentaje minimo\n",
    "# Ademas es una tecnologia antigua\n",
    "df7.drop(columns=['Dial up', 'Total'], inplace=True)\n",
    "\n",
    "# Normalizamos los nombres de Provincia\n",
    "df7['Provincia'] = df7['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "df7.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Provincia</th>\n",
       "      <th>Banda ancha fija</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>11112.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>CAPITAL FEDERAL</td>\n",
       "      <td>5214.427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>99.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>CHACO</td>\n",
       "      <td>255.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>270.395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año        Provincia  Banda ancha fija\n",
       "0  2014     BUENOS AIRES         11112.877\n",
       "1  2014  CAPITAL FEDERAL          5214.427\n",
       "2  2014        CATAMARCA            99.006\n",
       "3  2014            CHACO           255.471\n",
       "4  2014           CHUBUT           270.395"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7_group = df7.groupby(['Año', 'Provincia'])['Banda ancha fija'].sum().reset_index()\n",
    "df7_group['Banda ancha fija'] = df7_group['Banda ancha fija']/1000\n",
    "df7_group.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graficar_banda_ancha_por_provincia(df, provincias_seleccionadas):\n",
    "\n",
    "    # Filtrar por varias provincias\n",
    "    df_filtrado = df[df['Provincia'].isin(provincias_seleccionadas)]\n",
    "\n",
    "    # Definir la paleta de colores Viridis\n",
    "    colores = sns.color_palette(\"viridis\", n_colors=len(provincias_seleccionadas))\n",
    "\n",
    "    # Graficar los datos en un gráfico de barras\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    for i, provincia in enumerate(provincias_seleccionadas):\n",
    "        df_provincia = df_filtrado[df_filtrado['Provincia'] == provincia]\n",
    "        plt.bar(df_provincia['Año'], df_provincia['Banda ancha fija'], label=provincia, color=colores[i], width=0.5)\n",
    "\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    plt.title('Banda ancha fija en varias Provincias')\n",
    "    plt.xlabel('Año')\n",
    "    plt.ylabel('Banda ancha fija (miles)')\n",
    "    plt.grid(False)\n",
    "    plt.legend(bbox_to_anchor=(1, 1))\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graficar_banda_ancha_por_provincia(df7_group, ['CAPITAL FEDERAL', 'SANTA FE', 'CHACO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 08 - Ingresos Trimestrales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>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>4</td>\n",
       "      <td>72</td>\n",
       "      <td>Oct-Dic 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>Jul-Sept 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>Abr-Jun 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>Ene-Mar 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>Oct-Dic 2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre  Ingresos (miles de pesos)        Periodo\n",
       "0  2022          4                         72   Oct-Dic 2022\n",
       "1  2022          3                         64  Jul-Sept 2022\n",
       "2  2022          2                         60   Abr-Jun 2022\n",
       "3  2022          1                         55   Ene-Mar 2022\n",
       "4  2021          4                         45   Oct-Dic 2021"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = pd.read_csv('datasets/ingresos_trimestrales.csv')\n",
    "df8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 3 columns):\n",
      " #   Column                     Non-Null Count  Dtype\n",
      "---  ------                     --------------  -----\n",
      " 0   Año                        36 non-null     int64\n",
      " 1   Trimestre                  36 non-null     int64\n",
      " 2   Ingresos (miles de pesos)  36 non-null     int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 996.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# Retiramos la columna Perido\n",
    "df8.drop(columns=['Periodo'], inplace=True)\n",
    "\n",
    "df8.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos\n",
    "### Análisis de Ingresos Trimestrales\n",
    "\n",
    "La gráfica de ingresos trimestrales revela una tendencia ascendente significativa a partir del año 2018, indicando un crecimiento más pronunciado en comparación con los años anteriores. Este patrón positivo subraya la ventaja actual para los inversores que consideran la inversión en internet a través de fibra óptica.\n",
    "\n",
    "La correlación entre el aumento de ingresos y la inversión en fibra óptica sugiere una oportunidad estratégica para capitalizar este crecimiento y garantizar una posición ventajosa en el mercado.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupar por año y sumar los ingresos por año\n",
    "df_agrupado = df8.groupby('Año')['Ingresos (miles de pesos)'].sum().reset_index()\n",
    "\n",
    "# Graficar los datos en un gráfico de líneas\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df_agrupado['Año'], df_agrupado['Ingresos (miles de pesos)'], marker='o')\n",
    "\n",
    "plt.title('Evolución de los Ingresos por Año')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Ingresos (miles de pesos)')\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 09 - Internet por cada 100 Hogares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>4</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>78,94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Capital Federal</td>\n",
       "      <td>122,73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Catamarca</td>\n",
       "      <td>65,77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chaco</td>\n",
       "      <td>43,62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>Chubut</td>\n",
       "      <td>85,57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia Accesos por cada 100 hogares\n",
       "0  2022          4     Buenos Aires                        78,94\n",
       "1  2022          4  Capital Federal                       122,73\n",
       "2  2022          4        Catamarca                        65,77\n",
       "3  2022          4            Chaco                        43,62\n",
       "4  2022          4           Chubut                        85,57"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['Año', 'Trimestre', 'Provincia', 'Accesos por cada 100 hogares']\n",
    "df9 = pd.read_csv('datasets/datasetsGraph/Internet_Penetracion_100hogares.csv', usecols=cols)\n",
    "\n",
    "df9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 864 entries, 0 to 863\n",
      "Data columns (total 4 columns):\n",
      " #   Column                        Non-Null Count  Dtype \n",
      "---  ------                        --------------  ----- \n",
      " 0   Año                           864 non-null    int64 \n",
      " 1   Trimestre                     864 non-null    int64 \n",
      " 2   Provincia                     864 non-null    object\n",
      " 3   Accesos por cada 100 hogares  864 non-null    object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 27.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df9.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>4</td>\n",
       "      <td>BUENOS AIRES</td>\n",
       "      <td>78.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CAPITAL FEDERAL</td>\n",
       "      <td>122.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CATAMARCA</td>\n",
       "      <td>65.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHACO</td>\n",
       "      <td>43.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>CHUBUT</td>\n",
       "      <td>85.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre        Provincia  Accesos por cada 100 hogares\n",
       "0  2022          4     BUENOS AIRES                         78.94\n",
       "1  2022          4  CAPITAL FEDERAL                        122.73\n",
       "2  2022          4        CATAMARCA                         65.77\n",
       "3  2022          4            CHACO                         43.62\n",
       "4  2022          4           CHUBUT                         85.57"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se observa que la columna de accesos por cada 100 hogares, es una metrica.\n",
    "# Por lo tanto, los valores almacenados son decimales.\n",
    "\n",
    "# Procedemos con el reemplazo de la coma por el punto, y de tipo float.\n",
    "df9['Accesos por cada 100 hogares'] = df9['Accesos por cada 100 hogares'].str.replace(',', '.').astype(float)\n",
    "\n",
    "# Normalizamos la columna Provincia\n",
    "df9['Provincia'] = df9['Provincia'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8').str.upper()\n",
    "\n",
    "df9.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graficar_ingresos_por_provincia(df, provincias_seleccionadas):\n",
    "  \n",
    "    # Agrupar por año y sumar los ingresos por año y provincia\n",
    "    df_agrupado = df.groupby(['Año', 'Trimestre','Provincia'])['Accesos por cada 100 hogares'].sum().reset_index()\n",
    "\n",
    "    # Filtrar las provincias seleccionadas\n",
    "    df_filtrado = df_agrupado[df_agrupado['Provincia'].isin(provincias_seleccionadas)]\n",
    "\n",
    "   # Definir la paleta de colores Viridis\n",
    "    paleta = sns.color_palette(\"viridis\", n_colors=len(provincias_seleccionadas))\n",
    "\n",
    "    # Graficar los datos en un gráfico de líneas\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    for i, provincia in enumerate(provincias_seleccionadas):\n",
    "        df_provincia = df_filtrado[df_filtrado['Provincia'] == provincia]\n",
    "        color = paleta[i]  # Seleccionar el color correspondiente a la provincia\n",
    "        plt.plot(df_provincia['Año'], df_provincia['Accesos por cada 100 hogares'], \n",
    "                 #marker='o', \n",
    "                 label=provincia, \n",
    "                 color=color)\n",
    "\n",
    "    plt.title('Conexiones por c/ 100 Hogares')\n",
    "    plt.xlabel('Año')\n",
    "    plt.ylabel('Cantidad')\n",
    "    plt.grid(False)\n",
    "    plt.legend()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis Histórico del Promedio de Conexiones por Cada 100 Hogares en Provincias Seleccionadas\n",
    "\n",
    "La presente gráfica ilustra el promedio histórico de conexiones por cada 100 hogares en las provincias seleccionadas. Se observa una tendencia al alza en este promedio, indicando un crecimiento constante en la adopción de conexiones.\n",
    "\n",
    "Este patrón ascendente refuerza la viabilidad de invertir en tecnologías como la **fibra óptica**, que puede aprovechar y potenciar esta demanda creciente.\n",
    "\n",
    "Se invita a explorar el gráfico, ajustando los parámetros según la conveniencia, para obtener una visión más detallada y adaptada a diferentes contextos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B6fHjx/j7779zvFTRmvf5vHnz8N5770EURdjb26NZs2YYP348ADx3VpiI6EVx5omIKJeqV6+OESNGYPHixfjnn3/w7rvvwt3dHbdu3cL69euRlpZmCFZlypRBp06dsHTpUqSkpODNN9/EzZs3sWzZMtStWxeNGjWy+rx9+/bFwYMH0bdvX/Tr1w/u7u44evQodu3ahQkTJgDQr862ePFivP/++3jzzTdRs2ZNHD58GBMnTsThw4fNFh1o3Lgx6tati0mTJuHx48eoUKECLl26hLVr16JTp065+k6owYMHo3v37hgwYAB69OgBpVKJnTt34uTJk1i6dCkA/T1PU6dOxbx589C4cWPEx8dj2bJlKFmyZI5fKvzll18iLS0NpUqVwu7du/HPP/9g8+bNAIDOnTtj27ZtGDJkCIYPH46AgACcPn0ae/fuxdChQ+Hi4mL16wCA4cOHY9CgQRg9ejTeffddREdHY9GiRWjRogW8vLxw/fp1k2W5X8SgQYPw4YcfYsSIEXjvvffw22+/Yf369RgzZozhUsmePXvim2++wYcffoihQ4ciNjYW8+fPR+PGjVGzZs18qcPNzQ39+/fH8uXLIZfL0axZM9y/fx9LliwxvI8B/e+md+/eGD58OLp06YKHDx9iyZIlADIvNa1WrRpWrFiBNWvWoHr16rh37x5Wr14NlUpluEcuO9a8z+vVq4eNGzfis88+Q4cOHaBWq7Fu3Tq4ubmhXr16+fL7ICKyhOGJiCgPBg0ahEqVKmHr1q2YM2cO4uLi4Ofnh6ZNm2LgwIHw8/Mz9J09ezaCgoKwd+9erF27Fj4+PggNDcXgwYMN9w1Zw9fXFzt27MDChQsxbdo0pKWloWTJkpg9eza6dOkCnU6HCRMmwNnZGZ9++ikA/b0oM2fORJcuXTBv3jzMmDHDZExBELB69WosXboUmzZtQkxMDAICAjB69GiLl989T4UKFbB161Z89dVXGDduHERRRLly5bB8+XKEhIQAALp37w61Wo0dO3Zg27ZtsLOzQ/369fHpp5/meAnctGnTsHr1akRERKBSpUrYsGEDateuDUB/P9bXX3+NhQsXYsmSJUhMTETp0qUNv5vcatasGVatWoVly5ZhyJAh8PDwQPv27TFs2DCcPHkSZcuWtepeI2vUr18fYWFhhqXBfX19MW7cOPTr18/Qx8PDA1u2bMGcOXMwduxYODo6onXr1tle2pZXw4YNg5eXF7755hvs3LkTbm5uaN26NUaOHGm4N6l27doICwvDkiVLMHjwYPj7+2Py5MkYNWqUIZwPGDAAz549w5YtW7B8+XL4+fmhY8eOhvdbfHx8toE2p/c5ADRp0gQLFizAhg0bDItE1KpVC1u2bDG594qIKL8J4vOW8CEiIrKxffv2YcKECTh16pTVS8TTy3Pq1CkUK1bMZEGHW7duoV27dlixYoUhKBMRFUaceSIiIiKrnTt3DkePHsXYsWNRqlQpPH78GCtXrkTp0qXRsGFDW5dHRPRSMTwRERGR1caPHw87OzusXLkST548gZubGxo1aoQxY8ZYXH2SiKgw4WV7REREREREVuBS5URERERERFZgeCIiIiIiIrICwxMREREREZEViuyCETqdDhqNBhKJxPClfkREREREVPSIogidTgeZTPbc72AssuFJo9Hg2rVrti6DiIiIiIgKiKpVq0KhUGS7v8iGp4xEWbVqVUilUhtXQ0REREREtqLVanHt2rXnzjoBRTg8ZVyqJ5VKGZ6IiIiIiCjH23m4YAQREREREZEVGJ6IiIiIiIiswPBERERERERkBYYnIiIiIiIiKzA8ERERERERWYHhiYiIiIiIyAoMT0RERERERFZgeCIiIiIiIrICwxMREREREZEVGJ6IiIiIiIiswPBERERERERkBYYnIiIiIiIiKzA8ERERERERWYHhiYiIiIiIyAoMT0RERERERFZgeCIiIiIiIrICwxMRUQF2+afb+OP3/2xdBhEREQGQ2boAIiKyLDEhBZ8P/RoAcPzydBtXQ0RERJx5IiIqoJ5GJdi6BCIiIjLC8ERERERERGQFhiciIiIiIiIrMDwRERERERFZgeGJiKiA0mlFw/bDiBgbVkJERJS/VCoN7t+LtnUZucbwRERUQCXEpxi2R/ZdyyXLiYio0AibewQfdQ7DzWsRti4lVxieiIheA3GxyRg/cDN+OHHd1qUQERG9sMcPY00eXxcMT0REr4F6jctDrdJgzoTd2LnxfxBFMeeDiIiIKF8xPBERvQamLOiOd7vXBQBsWHYSi2cdgkattXFVRERERQvDExFRASWTSw3bUqkEgz5ti0Fj20AiEXDswK+YNOIbJCWk2rBCIiKiooXhiYiogCpewsOs7d0e9TB1YQ8o7eT47eK/GPXRejyOjH31xRERERVBDE9ERK+Zeo3LY+G6fvDwcsa9f55gRJ+1+OuPB7Yui4iIqNBjeCIieg2VrVgcSzZ/glJlfPHsaSI+/WQjzp+5aeuyiIiICrUCEZ5UKhXatWuHixcvGtquXLmC7t27o0aNGmjVqhV2795tcsz58+fRrl07BAcHIzQ0FBERr9ca8USFxamjv+PGVf7/zxZ8irli4fp+qN2gDNLS1Jjx6U7s23qeK/ERERG9JDYPT2lpaRg9ejRu3bplaIuKisInn3yCOnXqYP/+/Rg+fDhmzpyJ77//HgDw8OFDDBkyBJ07d8aePXvg4eGBwYMH8wMD0St2/140vpy8D6M+XIe0VLWtyymSHJ3sMOOrnmj7Xm2IoojVi45j+bxwaDVciY+IiCi/2TQ83b59G++//z7+++8/k/aTJ0/Cy8sLo0ePRsmSJfHOO+/g3XffxeHDhwEAu3fvRpUqVdCvXz+ULVsWc+fOxYMHD3Dp0iVbvAyiIuvZ00TD9sDuK3Dl0r82rKboksqkGD6hHT4e0RKCIODw7p8xbfR2JCel2bo0IiKiQsWm4enSpUuoW7cudu7cadLeqFEjzJ0716x/YqL+g9rvv/+O2rVrG9rt7e1RuXJlXLly5aXWS0Sm3D2dDNsPI2IwftBmLJx+APGxyTasqmgSBAFdQ9/CxHnvQ6GU4dKPtzDm4w2Iehxn69KIiIgKDZuGp549e+Lzzz+Hvb29SXtAQACqV69ueP706VOEh4ejfv36APSX9fn4+Jgc4+npiUePHr30monInCAIaN/1TQiCgBOHfsPHXZbh9LdXeSmtDTQKqYT5qz+Em4cj/v37EUb0WYt//oq0dVlERESFgs3vecpJamoqhg0bBi8vL3Tr1g0AkJKSAoVCYdJPoVBApVLZokSiIs/RSYmhn7XDwvX9EPSGD+KeJWHepL2YOOwbPHrwzNblFTkVqgZgyeZPEFjKG0+jEjD6ow24+L+/bV0WERHRa69Ah6ekpCQMGDAAd+/exerVqw0zVEql0iwoqVQqsxksInq1KgcHYvnWAegzuDnkChku/3Qb/bsux+4tP3IBg1esWHF3fLXxI1R/sxRSU1SYNnobDu26mPOBRERElK0CG54SExPx0Ucf4datW9i8eTNKlixp2Ofr64vo6GiT/tHR0fD29n7FVRJRVnK5DD0/aoJVOwahWq2SSEtTY92SExgeuha3bj60dXlFipOzPWaFfYCWHWpApxOxfN5RrF50DFqtztalERERvZYKZHjS6XQYOnQo7t+/j6+//hply5Y12R8cHIzLly8bnqekpODGjRsIDg5+1aUSUTYCgrzw5eq+GD2lI5xc7HH7r0gMD12D1YuOISWZq8C9KnK5DKOndETfISEAgH1bf8LMT3ciNYWXORMREeVWgQxPe/bswcWLFzFr1iy4uLggKioKUVFRiI2NBQC89957+PXXX7FmzRrcunULEyZMQEBAAOrWrWvbwonIhCAIaNWxJtbtGYqmrapCpxOxb+tPGPD+Cvz8462cB6B8IQgCevRrjAlzukCukOGnH/7Ep/03IiY6wdalERERvVYKZHg6fvw4dDodBgwYgIYNGxp+hg0bBkC/Gl9YWBj27t2LLl26IDY2FsuXL4cgCDaunIgscfd0woQ5XTBr6Qfw9XPD48hYTBr+DeZ+vtvku6Lo5WraqirmrewDF1cH/H3jIUb0WYu7tx/buiwiIqLXhszWBWT466+/DNvr16/PsX+TJk3QpEmTl1kSFRIatRYyudTWZRCAN98qi9W7BmPLqjM4sP0Cvj9+Hb/89A/6j2yJlh1q8D+AvAKVqwdi8eaPMXn4Vjz47ylG9VuPSfPeR636ZWxdGhERUYFXIGeeiPLLowfP8E69GWhVaypiohP4vUMFgL2DEgNGt8bSLf1RprwfEuNTsGjGQYwbsAn370XnPAC9MP8Snli88WNUqRGE5KQ0TBqxFd/uv5zzgUREREVcgZl5InoZLv4vc0azR6sFcHBUokRJLwSU9EJAkCdKBHmhREkvFC/hAYVSbsNKi56yFYtj6ZZPsH/7BWxZdQZXL9/FwO4r0fOjxuja5y3I5fzr6WVycXPA3BWh+GrGQZz+9ioWzzqEyPsx6DskBBIJ/7saERGRJfx0QoVa6fJ+hm2JREByUhr++uMB/vrjgUk/QRDgW9xNH6yCPBEQ5IWA9GDl4eXEy8leEqlMii6930LD5pWwdO4RXP7pNjavPI3vT1zHyEkdUKlaCVuXWKgpFDKMm9kZxUt44Js132PnpnN4eP8ZPp3eCUo7/scEIiKirBieqFCTyfT/Bd3P3x1r9gzFw4gYRNyNxv170bh/Nxr37z1FxN1oJCWm4tGDZ3j04JnZKnAOjkpDoNKHKy8ElPSEfwlPfsDMJ8X83TE77AOcOXYNqxYew71/nmB0v/Vo16U2PhzSAo7OdrYusdASBAG9BzRDMX93LJ55CP87+QeiH8dh2qIecPNwsnV5REREBQrDExUZCoUMJd/wQck3fEzaRVFEbEwS7t+LRsTd6PRw9RT370Xj0YNnSE5Kw983HuLvG6Zf8CoIAnz8XPWhKsgTASW9UCJIf0mgp7czZ6tySRAENG9TDbXrl8HaJSdw4tBvOLz7Z5z//k8MGfcO3mpe0dYlFmpvt6sOn2KumDF2B25eu48Rfddh5pJeCCzFLx8nIiLKwPBERZ4gCHD3dIK7pxOq1ixpsk+l0iDyfgzu332KiLtRhlAVcTcaiQmpePwwFo8fxuLyT7dNjrN3UKRf+udpNFvlBf8SHrCzV7zCV/f6cXFzwJip7yKkTTUsmXMYDyNiMOPTHWjQtAKGjH8HXj4uti6x0AquXQpfbfwYU0ZsReSDZxj14TpMWdAdwbVL2bo0IiKiAoHhieg5FAoZgkr7IKi0D4DMmQ9RFBH3LAkR957ivmG2Sj9jFfngGVKSVbh18yFu3XxoNqavn5vpZYDp91l5+bhwtspI9TqlsWrHYGxb/wN2b/4R57//E1d+voN+Q1vgnS61uajBSxJYyhuLN3+CaaO34+bVCHw+5GuMnNwBb7erbuvSiIiIbI7hiSgPBEGAm4cT3DycULVGkMk+tVqDyPvP9KHqXuZlgBF3o5EYn4LHkbF4HBmLyxf+MTnOzl5hFKo8EVDSGyWCPOEf6FlkZ6uUdnJ8OKQFmrasiiWzD+HmtftYNi8cp769ipET26NkGV9bl1goubk7Yt7KPpg/dT/+d/IPLJi6H5H3Y9B7QDMGfCIiKtIYnojymVwuQ2Apb7N7RURRRFxscvpCFZmB6v69aDy8/wypKSrc/jMSt/+MNBvT29c1fZbKdOEKb9+iMVtVqqwvFq7/COF7f8bGZadw82oEBvdchff7NkTPjxpzmfmXQGknx+dzu2BTgDt2bjqHrWt/QOT9Zxg1pSMUCv7TQURERRP/BSR6RQRBgJu7I9zcHVElu9mq9HuqMu6run/3KeLjkhH1OA5Rj+Pw60XT2Sqlndz0vipDsCp8s1VSqQQd3q+L+k0qYPm8o/jphz+xff1ZnP3uD4yY2J735bwEEokE/Ya9Db8ADyydewSnv72KJ4/iMHVBd7i4Odi6PCIioleO4YmoAMhutgoA4mOTEZG+tLr+UT9jFXk/Bmmpavzz1yP889cjs+O8fF0MoaqE0T1WXr4ur/X9Qt6+rpi2qAd+PH0Ty+aF48F/TzFuwCa06lgDH49oCRdXfqjPb2061YKPnxtmjduJ67/dw8gP12Hm0l7wL+Fp69KIiIheKYYnogLOxc0Bld0CUTk40KRdo9bi0cNnRt9b9dQQsuJikxH9OB7Rj+Px28V/TY5TKuXwN8xWeZrMVtk7KF/lS3shbzWviOpvlsKGZSdxZM/POH7wN1z8398YOKYNmraqUiQuZ3yVatV7A19t+AiTR2zFg/+eYmSfdZi2qAcqVw/M+WAiIqJCguGJ6DUlk0sNl+plFR+XnHlPldE9Vg8jYpCWpsa/fz/Cv39bmK3ycTG7DDCgpCd8irkWyNkqR2c7DJvQDs3bVMPi2Yfw379R+GLiHpwMv4JhE9qhWHF3W5dYqJQs44slmz/B1FHb8PeNhxg/cBPGTu+Epq2q2ro0IiKiV4LhiagQcnF1QKVqDqhUrYRJu1ajxaOHsUZfBpz5hcCxMUmIfhKP6CfxuPLzHZPjlEo5igd6GGapSqSHqoIyu1O5eiBWbBuI3Zt/xLZ1P+CX87fRv+tyhA5shk496kEqk9q6xELDw8sZ89d8iC8m7sVPP/yJuZ/vwcP7MejRr3GBeT8QERG9LAxPREWIVCaFf6B++fN6jcub7EuIT8m8/C/9C4Ej7kUjMn226s6tx7hz67GNKs+ZXC5Dz4+boFGLylg65zCuXr6LtYtP4Myxaxg5qQPKVixu6xILDTt7BSbP74Z1S05g39afsHnFaUTef4bhn7eDXM5/VoiIqPDiv3I2plFr8fWaM4h6FGfrUgql2GfJti7hteHsYo+KVUugYlULs1WRsbh/92nmKoDpM1bPniYCAPwCPGxRskUlSnrhy9V9ceLQb1iz+ARu/xmJ4aFr0KlnfYQObFboViG0FalUggGjW6N4gAdWzD+KE4d+w5PIWEye3w1Ozva2Lo+IiOilYHiysetX/sOODf+zdRmFnjOXVc4zqUwK/xKe8C/hibqNypnsS0xIQeT9ZyheouCEJ0C/LHyrjjVRp2E5rFr4Lb4/fh17vzmPc6duYNjn7fBmg7K2LrHQaP9+HfgWd8OcCbtx5ec7GPXhesxc0gvF/Hm/GRERFT4MTzamVmkA6G/U79yrvo2rKZwEAajTsFzOHSnXnJztUbZiwZ1lcPd0woQ5XRHSNhhhc4/gcWQsJg37Bs1aV8XAMa3h5uFk6xILhToNy2Hhun6YMnIb/rsThRF912L6op6oUDXA1qURERHlK4anAsLd0wnvfdDA1mUQFUp1GpbDmt1DsGXVGRzYfgFnjl3Dz+dvo/+oVmjZvjoXOsgHb5T3w+JNH2PKyG349+9H+HTARoyb+R4ahVSydWlERET5puCtPUxE9BLYOygxYHRrLNn8Cd4oXwyJ8SlYNP0Axg/cjAf/PbV1eYWCt68rFq7rhzpvlYUqTYPZ43dh95YfIYqirUsjIiLKFwxPRFSklKvkj7At/fHxiJZQKuX4/Zc7GNBtBbZvOAu1WmPr8l57Do5KTFvUA+3frwNRFLFuyQksnXsEWo3W1qURERG9MIYnIipypDIpuoa+hTW7h6BW/TJQqzTYtPwUhvZajRtXI2xd3mtPKpNiyLi2GDCmNQRBwNG9v2DKqG1ISky1dWlEREQvhOGJiIqsYv7umB32AcbPeg+u7o64+88TjO63HsvmhfOD/gsSBAGde9bHlPndoFTK8cv52xjz0QY84dcyEBHRa4zhiYiKNEEQ0LxNNazbMxRvt68OURRxeNcl9O+6HOfP3LR1ea+9Bs0qYsG6D+Hh6YQ7tx9jRJ+1uHXzoa3LIiIiyhOGJyIiAC5uDhg7rRPmreyD4iU8EP0kHtPH7sCMsTsQ/STe1uW91spV8sfizZ8g6A0fxEQnYOwnG3Hh7F+2LouIiCjXGJ6IiIxUr1Maq3YMRvd+jSCVSvDjmZv4pMsyHN59CTqdztblvbZ8/dzw1fqPULPuG0hNUWH6mO04uOOCrcuil+zC2b+wb+t5W5dBRJRvGJ6IiLJQ2snx4ZAWWLZ1ACpUCUByUhqWfRGOMR9vwN1/nti6vNeWo7MdZi7phdbv1oROJ2LF/G+xcv5RaLUMpYXV1FHbsHrRcX4dABEVGgxPRETZKF22GBZt+AiDx7WFvYMCN36PwJCeq7B5xSmo0tS2Lu+1JJNLMXJSB/Qb1gIAcGDHRcwYuwMpyWk2roxepkcPntm6BCKifMHwZGMOTkoAgGP6IxEVLFKpBB271cWa3UNRr0l5aDRabFt/FoN6rMTVy3dtXd5rSRAEdOvbCBO/eB9yhQwXzv6FsZ9sxNMo3ltGREQFm8zWBRR1FasGYMzUd1GhaoCtSyGi5/Ap5oppC3vgxzM3sXzeUdy/9xSf9t+I1h1r4qMRb8PF1cHWJb52Gr9dGV6+Lpg2ejtu/xmJEX3WYcaSnihdtpitSyMiIgtEUYRGo4VapYVapYFaneVRpYVarYEq2/2Z26/rjDTDk41JJBK07FDD1mUQkRUEQUDD5pVQ483S2LDsJI7s+RnHDv6KC//7C4PGtkGTllUgCIKty3ytVKpWAks2fYxJw7fi/r1ojPloAyZ+8T5qNyhj69KIiGxOp9MZAomlwKIyCizm+y0fYxZyjPul71OpLY+rUWshimK+vkY7e0W+jveyMTwREeWSo7Mdhk1oh+ZtqmHxrEP4704U5n6+ByfDf8fQz95BseLuti7xteIX4IHFmz7GjLE7cPXyXUweuRVDx7fFO++9aevSiKgIE0URjyNjoUrT5HpWJWtIyRpEVFb2K+gL6kikEsjlUsgVMijSH+UKKeTyLI8KmaGf3KifTzE31Kr/hq1fRq4wPBER5VHl6oFYvm0gdm8+h+3rz+LnH2+hf9fl6Du4OTp2qwupTGrrEl8bzi72mLO8NxbPPIST4b9j6ZwjeBgRg4+Gvw2JhLfnEtGrt3jWIRw78KutyzCRNXxkhBNFDiHFujBjFIAsHWvhGKm06P39zPBERPQCFAoZen3SFI3froIlsw/h2q/3sHrRcZz+9hpGTuqAMhX8bF3ia0Mul2Hs9E4oXsIDW1adwZ6vz+PRw1iMm9EZSju5rcsjoiLm1s2HAAAHRyXs7BXZBoncz7iYtymsmKWRyaS8NLwAYHgiIsoHJUp64cvVfXH84G9Yt+QEbt18iGGha9C5Zz30HtDstbum21YEQUCvT5rCz98Di2YcwLlTNxD9OB7TFvWAu6eTrcsjoiJo0rz3Uas+78MkvaI310ZE9JJIJBK06VQLa/cMRZOWVaDT6rDn6/MY0G0Ffjl/29blvVaat62GuStC4exqjz+v38eIvmtx719+QTEREdkWwxMRUT7z8HLG53O7YsbiXvAp5opHD55h4rCvMW/SXsTGJNq6vNdG1ZolsXjjxyhewgOPH8Zi1Ifr8dulf21dFhERFWEMT0REL0ndRuWwZvcQdO5VHxKJgNPfXsXHXZbhxKHf8n2p18IqIMgLizd+jMrBgUhKTMXEoV/j+KHfbF0WEREVUQxPREQvkb2DEgNGt8aSzZ/gjfLFkBCXgoXTD+CzQZvxIOKprct7Lbi6O+KLlaFo2qoKtFodFk0/gE0rTjGAEhHRK8fwRET0CpSr5I+lm/vj4xFvQ6mU48rPdzCw2wrs2HAWGrXW1uUVeAqlHONnvYceHzUGAGxffxZfTNwLVZraxpUREVFRwvBERPSKyORSdA1tiNW7BqNm3TegStNg4/JTGNJrFW5ei7B1eQWeRCJB38EhGD31XUilEnx//Bo+G7QFcc+SbF0aEREVEVyqnIjoFfML8MCc5b1x5ttrWLXwW9z95wlGfbge7d9/E30Hh8DRyc7WJRZorTrUgG8xV8z4dCf++P0/jPxwHWYu6YWAIC9bl1Zo6XQ6pKaokZKchpRkFVKSVUhNUSE5SYWUFBVSk9OQnKxCavq+jH5ERIUNwxMRkQ0IgoDmbauhVv03sHbxCXx35AoO7byE82f+xJDx76BB0wq2LrFAq16nNL7a+BEmj9iKhxExGPnhOkxd2ANVawTZujSbE0URaalqpCSrkJyclhloUlRIyRJ2UtIDj6FfisoQjjIDUhrSUl/s8kgnF/t8enVERLbF8EREZEOu7o4YO70TQt4JxpLZhxF5PwbTx2xHw5BK6PlxE1uXV6AFlfbBkk2fYOqobfjrjweYMGgzRk95F83bVrN1aVYTRRGqNE16oNGHmIxAkxl20kwDTQ5hJyVZ9dIW05BIBNjZK2DvoIC9gzL9MfPHzl4BB0eloc/6pd8BAJwZnoiokGB4IiIqAGrUKY3VOwdj67ofsGfLjzh36gZ+PnfL1mUVeO6eTvhydV/Mn7of507dwLzJexH5IAY9P24CQRByPP72n5FQqzWoWLWEVedTqzWG2Rvjy9MygkxyUpohwJj+pJmEmxSj2SCdVveiv4Zs2TsoYG+vgL2jEvb2CthlCTtZ9zk4KPV9MgKSo9G2gxIKpcyq32uG7evPIjkp7aW9PiqaRFFM/wFEnQgRov5RzNyn04mACOjSn2dsI32fcV/9GMjcFjP/wwZRVgxPREQFhNJOjn5DW6BpqypYPPMQ/vrjga1Lei3Y2Ssw8Yuu2BB2Eru3/Igtq84g8v4zjJjUHnJ59v/MabU6DOm1CgDwznu1IQJm9+yYXuqmgkbz8lZGVNrJcxV2jGd/DIHHKOwo7eSQSLguVGF28X9/4/S3v0OrTQ8FGSEhPVBkbGfse17YsLQvI2wY78saSjL6AtmEkhxCinGgAdJrtxR2kF4Dv6KAbIzhiYiogCldthi+2vgxjuz5GRuXnYSPn5utSyrwJBIJPh7REn4B7lg27yi+O3IFjyNjMWVB92wvGdMazfiE7/0lV+eTK2RwMAktxoFGkSXQmIedzEvc9I929gpIpQw6lDtrvjqG+/f4fXG5JQgCBCHjMX1bIkCAoH802udb3A3lKvvbumQqQBieiIgKIKlUgo7d6qJVhxq5ukyqqHvnvTfh4+eGOZ/txtXLdzGy7zrMWtoLfgEeZn2lkszfa6uONeBTzNXsnh2L9/XYKSCTS1/lyyKyKOOysi69G8C3uJtpGEjflkgEQBAgEQRA0N+3JiBzO2Pf8wKEpX368fTjCwIsnsOacUzrBARkbmc9h2EciQD9KbMZB5mvzbxW/n1KL4bhiYioALOzV9i6hNfOmw3KYuH6fpg8Yivu34vGiL7rMP2rHs+9r+njES3h4urwCqskyj+N366C8pwdIXoleI0AEREVOqXLFsPSzZ+gTHk/xD1LwrgBm3D2uz9sXRYREb3mGJ6IiKhQ8vR2wYJ1H6Je4/JQpWkw+7Nd2Lnpf7zhnIiI8ozhiYiICi17ByWmLOiOd7vXBQBsCDuJJbMPQ6N+eavmERFR4cXwREREhZpUKsGgT9ti0Ng2kEgEfLv/MiaP2IqkRH7/EBER5Q7DExERFQnv9qiHqQt7QGknx68X/8Hoj9bbuiQiInrNcLU9IiIqMuo1Lo+F6/physhtiLgbbetyioyb1yIQ/SQeUqkEEqmgf5RIIJVKIJVJIJGkt0kl6fsEo30Ss+OIiGyF4YmIiIqUshWLY8nmTzBx2Nf4798oAOB3v7wkGb/XLyfvy9dxnxes8hrIXt5xUrN9Uomg3zYaUypNb5Oktxv3Mx7T6BgdFz8heuUYnoiIqMjxKeaKxRs/xsoF30IqkcDZxd7WJRVK3fs1wplj16DT6qDTidBqddBqddBlPOpEaDU66HQ6o336fs9bFTGjLxHRq8bwRERERZKjkx3GTutk6zIKtff7NMT7fRrm6VidTh+kLAUrnU6XHrqyD2TG/bQ6MXN/+qNWJ0Kn0UGrM2rL2J8l1GWc19pz57Xm5x1n6Jd+XAbf4m4oUdIrv/7IiCgHDE9ERERU4EgkEuhvb5LaupQCRxRF6HT6YMn7wIheLYYnIiIioteIIAiQpt93RUSvFv9fR0REREREZAWGJyIiIiIiIiswPBEREREREVmB4YmIiIiIiMgKDE9ERERERERWYHgiIiIiIiKyAsMTERERERGRFRieiIiIiIiIrMDwREREREREZAWGJyIiIiIiIiswPBEREREREVmB4YmIiIiIiMgKDE9ERERERERWYHgiIiIiIiKyAsMTERERERGRFRieiIiIiIiIrMDwREREREREZAWGJyIiIiIiIiswPBEREREREVmhQIQnlUqFdu3a4eLFi4a2iIgI9O3bF9WrV0fbtm1x7tw5k2POnz+Pdu3aITg4GKGhoYiIiHjVZRMRERERURFi8/CUlpaG0aNH49atW4Y2URQxZMgQeHl5Ye/evejYsSOGDh2Khw8fAgAePnyIIUOGoHPnztizZw88PDwwePBgiKJoq5dBRERERESFnE3D0+3bt/H+++/jv//+M2m/cOECIiIiMGPGDLzxxhsYMGAAqlevjr179wIAdu/ejSpVqqBfv34oW7Ys5s6diwcPHuDSpUu2eBlERERERFQE2DQ8Xbp0CXXr1sXOnTtN2n///XdUqlQJDg4OhrZatWrhypUrhv21a9c27LO3t0flypUN+4mIiIiIiPKbzJYn79mzp8X2qKgo+Pj4mLR5enri0aNHVu0nIiIiIiLKbza/58mSlJQUKBQKkzaFQgGVSmXVfiIiIiIiovxWIMOTUqk0C0IqlQp2dnbP3W9vb//KaiQiIiIioqKlQIYnX19fREdHm7RFR0cbLtXLbr+3t/crq5GIiIiIiIqWAhmegoOD8ccffyA1NdXQdvnyZQQHBxv2X7582bAvJSUFN27cMOwnIiIiIiLKbwUyPNWpUwd+fn6YMGECbt26hTVr1uDq1avo0qULAOC9997Dr7/+ijVr1uDWrVuYMGECAgICULduXRtXTkREREREhVWBDE9SqRQrVqxAVFQUOnfujEOHDmH58uUoXrw4ACAgIABhYWHYu3cvunTpgtjYWCxfvhyCINi4ciIiIiIiKqwEURRFWxdhC1qtFleuXEH16tUhlUptXQ4REREREdmItdmgQM48ERERERERFTQMT0RERERERFZgeCIiIiIiIrICwxMREREREZEVGJ6IiIiIiIiswPBERERERERkBYYnIiIiIiIiKzA8ERERERERWYHhiYiIiIiIyAoMT0RERERERFZgeCIiIiIiIrICwxMREREREZEVGJ6IiIiIiIiswPBERERERERkBYYnIiIiIiIiKzA8ERERERERWYHhiYiIiIiIyAoMT0RERERERFZgeCIiIiIiIrICwxMREREREZEVGJ6IiIiIiIiswPBERERERERkBYYnIiIiIiIiKzA8ERERERERWYHhiYiIiIiIyAoMT0RERERERFZgeCIiIiIiIrICwxMREREREZEVZLYuoCDTarVQq9W2LoMoR3K5HFKp1NZlEBERERVqDE8WiKKIR48eITY21talEFnNzc0NxYoVgyAIti6FiIiIqFBieLIgIzj5+PjAwcGBH0apQBNFEcnJyXjy5AkAwM/Pz8YVERERERVODE9ZaLVaQ3Dy9PS0dTlEVrG3twcAPHnyBD4+PryEj4iIiOgl4IIRWWTc4+Tg4GDjSohyJ+M9y/v0iIiIiF4Ohqds8FI9et3wPUtERET0cjE8ERERERERWYH3PBUycXFxWLlyJU6cOIGnT5+iePHi6NatG0JDQyGRmGblixcvIjQ0FIMGDcLIkSNN9oWFhWHZsmWG5xKJBC4uLmjevDlGjRoFHx8fAED58uWxZcsW+Pv7IyQkJNu66tSpg6+//hoAsG/fPkyYMAGzZs1C165dTfr17t0bderUwbBhw3J8rZ999hn2799vcd8ff/yBy5cvIzQ01OJ+f39/nD592vAaMgiCAEdHR1SrVg2jRo1CtWrVDPuaN2+OBw8eWBxvy5YtqFu3rsWaHBwcUKZMGYwbNw5vvvmmyb7k5GQ0aNAAlSpVwrZt20z27du3D8uWLTPUSURERES2xfBUiDx79gzdunWDj48PZs+ejYCAAFy7dg0zZ85EREQEJk+ebNI/PDwcgYGBOHToEEaMGGF22VeNGjUQFhYGQL+i2+PHjzFhwgSMHTsWW7ZsMenr5+eHc+fOGZ536dIF/fr1Q9u2bQHov4co63kPHjxoFp5yq02bNpg4caJZu0yW+dY2ritD1gUVwsLCUKNGDeh0Ojx9+hSbN29Gnz59sHPnTpQrV87Q7/PPPze8JmOurq7Z1vTkyRMsWrQIgwcPxpkzZ+Dk5GTYd/r0aXh7e+PXX39FREQESpQoYeUrJyIiIqJXjZftFSILFy6EQqHA+vXrUb9+fZQoUQJt27bF7NmzsXXrVty5c8fQV61W4/jx4xg0aBAiIyNx6dIls/Hkcjm8vb3h7e0NHx8fVK1aFYMGDcLFixcRFxdn0lcqlRr6ent7QyqVwtnZ2fDczc0NAPD06VP89NNPGDJkCH755RdERES80Gu2s7MzOW/GjzFL+z08PEz6uLq6wtvbG76+vqhUqRK++OILVKpUCYsWLTLpZ/yajH8UCkW2NVWuXBlz5sxBfHw8Lly4YDLekSNH0KJFC5QrVw4HDhx4od8FEREREb1cDE9WEkURqSmqV/ojiqLV9alUKoSHh6NXr15QKpUm+5o1a4ZNmzbB39/f0Pbjjz8iISEBISEhCA4OtvqDu1QqhSAIJjNJuXHs2DE4OzujQ4cO8PHxwcGDB/M0zssmCAK6du2Kc+fOITU19YXHy/h9Gc+IxcXF4dy5c6hduzaaNWuGAwcO5OrPnIiIiIheLV62ZwVRFDH6o/W48fuLzZLkVuXgQCxc38+qVdT+++8/JCcno2rVqmb7BEFAvXr1TNrCw8NRs2ZNuLq6IiQkBCtWrMDkyZOfu0T73bt3sWbNGtSvXz/PS7mHh4ejadOmkEgkaN68OQ4cOIAhQ4YUyJXiypQpA7Vajbt376JChQp5HicuLg5ffvklPD09Ubt2bUP7iRMnIJVK0aBBA3h7e2PVqlX45ZdfzO6LIiIiIqKCgTNPVhJQ8D7cG4uPjwegv6wsJ6mpqTh16hRatGgBAGjZsiWSk5Nx4sQJk36//PILatSogRo1aqBKlSpo3bo1HBwcMGvWrDzVGBkZiV9//dXkvBEREbh8+XKexgOAw4cPG2rM+Dl79qxJn6z7a9SogVWrVuU4dsbvMikpydA2depUs7HeeeedbGuqXr06GjRogAcPHmDDhg0m9zuFh4ejQYMGsLe3R9WqVVGsWLFsF8AgIiIiItvjzJMVBEHAwvX9kJb6ar98VGknt3pGJuOeoqz3Illy5swZJCUlGVbHCwoKMtxz8+677xr6ValSBQsWLACgX23Pw8MDjo6OuXsRRsLDw6FUKtGwYUMA+hX4XF1dsX//fpMZmdxo3rw5xo4da9KWsRJgBkuXJBov8JCdxMREADAJPMOHD0fLli1N+hlfimdck0ajweHDh7Fjxw4MHjzYZPYqKioKly5dwsyZMwHo32Nvv/029u3bh8mTJ8Pe3j7H+oiIiIjo1WJ4spIgCLCzV+Tc0UYCAwPh7OyMP/74w2R57QyDBg1C79690aBBA4SHhwMAWrVqZdiv0+lw+/ZtREZGws/PD4B+4YOgoKB8qzE8PBypqamoVauWoU2r1eLYsWOYPHky7Ozscj2mo6NjjjXm9TX89ddfkMvlKFmypKHN09Mzx/GMaxo5ciRiYmIwdOhQHDx4EAEBAQCAb7/9FlqtFpMnTzasgiiKInQ6Hb777jt06NAhTzUTERERvQ4Ofn8Ne0/9joWj34W3u1POBxQQvGyvkJDJZGjbti22bt0KlUplsu/06dM4ffo0fHx8kJiYiLNnz6J///44cOCA4Sdj6fGXtYDDnTt3cOPGDUyaNMnkvF999RUSExPx3XffvZTzvoh9+/ahefPmZgtw5Na4cePg4OCA6dOnG9qOHj2K+vXrm/wuDh48iMDAQK66R0RERIXe8fN/4q+7T/Dbn/dtXUqucOapEBk2bBi6du2Kjz76CMOGDUOxYsVw8eJFzJ8/H6GhoShTpgz2798PrVaL0NBQsyW9GzVqhP3792PgwIH5Xlt4eDjc3NzQrVs3k2W9y5Urh+XLl+PAgQNo3749AODevXtm9y2VK1cOxYoVy9O5o6KiLLZ7enoavjg4Li4OUVFR0Ol0iI6OxoYNG/DHH39g7969JsckJCRYHM/R0THbRTScnJwwbtw4jB07FqdPn0a5cuXw22+/YcmSJSbfIQUA3bp1w8KFC/H48WMA+vvTsv4uXF1dERwcbN2LJyIiIqJ8w/BUiHh7e2P79u0ICwvD2LFjERsbi8DAQAwfPhw9evQAoP9eocaNG5sFJwDo0aMHBg4ciCtXruR7beHh4Wjfvr1JcDI+7+zZsw2B4fDhwzh8+LBJn1mzZuX5C3Uz7rHK6ocffjAEsmHDhgHQ39vl6emJmjVrYseOHShdurTJMXPmzMGcOXPMxhoxYgQGDx6cbQ3t27fHjh07MHfuXHTs2BHu7u5o3ry5Wb/OnTtjyZIlOHjwILy8vPD06VN88sknJn1q1qyJ7du3P/9FExEREVG+E8Qi+sUyWq0WV65cQfXq1SGVSg3tqampuHPnDkqVKpWne3CIbIXvXSIiInpdDJ6zG5dvRmDm4LZoWT/vXwmTX7LLBlnxniciIiIiIiIrMDwRERERERFZgeGJiIiIiIjICgxPREREREREVmB4IiIiIiIisgLDExERERERkRUYnoiIiIiIiKzA8ERERERERGQFhiciIiIiIiIryGxdAOW/uLg4rFy5EidOnMDTp09RvHhxdOvWDaGhoZBI9Hk5ISEBK1aswLFjxxAdHY1ixYrhnXfeQf/+/eHg4GAYq3z58iZjSyQSuLm5oWHDhpg8eTJcXFwAAM2bN8eDBw8AAIIgwN7eHuXLl8eQIUPQqFEjkzGsOff9+/cREhJicl5PT0907NgRo0aNgkxm+ta9ePEiQkNDMWjQIIwcOTJ/fpFEREREREYYngqZZ8+eoVu3bvDx8cHs2bMREBCAa9euYebMmYiIiMDkyZORmJiInj17Qi6XY8aMGShVqhRu376NRYsW4ezZs/j666/h6OhoGDMsLAw1atQAAGg0Gly/fh2TJk3C3LlzMXfuXEO/zz//HG3btoVOp0NcXBwOHDiAAQMGYN26dWjQoAEA5Prcu3fvhp+fH7RaLe7cuYPPPvsMrq6u6N+/v8nrDg8PR2BgIA4dOoQRI0ZAEISX+WsmIiIiKvJUag0SktOQkJSGhOTU9Mc0JCSlWm5L75uYrN9+HTE8FTILFy6EQqHA+vXroVQqAQAlSpSAnZ0dBg8ejA8++ADbtm2DSqXCzp07DTM9AQEBqFWrFtq3b49ly5Zh/PjxhjFdXV3h7e1teO7n54fbt29jw4YNJuHJ2dnZ0M/X1xfjxo1DVFQU5s6di8OHDwMAlixZkqtze3h4GMYsVqwYevXqhW+//dYkPKnVahw/fhzjx4/HxIkTcenSJdStWzdff69EREREhY1OJyI5VZUl3GSGnPikVCRmaTPuk6bSvND5nR2UqFDSN59ezavB8GQlURSRmvZib5DcslPKcjWDolKpEB4ejnHjxhmCU4ZmzZph06ZN8Pf3x759+zB69GiTy/MAffgJDQ3F6tWrMXbsWEil0mzPpVAonrs/Q7du3dCrVy/cu3cPAQEBVp87O/b29mZtP/74IxISEhASEoJdu3bhwIEDDE9ERERUJKg1WkPIyXxMQ2L6zI9JW7JpIEpMVkEnii9cg5ODEs4OSjg7KuHsYJf+qISzo51Ju5OjEi4Zj45KuDrZQy7L+fNkQcLwZAVRFNF/5k5cvfXwlZ63WrniWDOpm9UB6r///kNycjKqVq1qtk8QBNSrVw///PMPEhMTLfYBgFq1aiE2Nhb//fcfSpUqZbHPzZs3sXXrVrRq1SrHmt544w0AwO3bt6HRaKw+t1wuN9sfGRmJ3bt3o1OnTibt4eHhqFmzJlxdXRESEoIVK1Zg8uTJZgGNiIiIqKARRRFJqSr9rE564ElMSkO80WN2l77FJ7347A8AyGVSODsq4eJol00QSn+0EI4c7RWQSorOGnQMT9Z6DW6hiY+PB6CfxclObGwsAP2leJZkLACR0Q8APvnkE8Msk1qthqOjI9q1a4dPP/00x5oyaklKSsrVuTMu1WvXrh0EQYBOp0NqaiqCgoLQsWNHwzGpqak4deoUhg8fDgBo2bIlFixYgBMnTuDdd9/NsT4iIiKiF6XWaLPc45NqFIYyQo9RCDIKQolJafky++Nor8gMP0ZBKPNRmb7PzmxWSCnP3dVORRnDkxUEQcCaSd0K/GV7bm5uAPSr7eXUJyoqCkFBQWb7nzx5YtIPAGbNmoXg4GDExMRg3rx5kMvlGDVqFOzs7HKsKTExEQDg5OSUp3OvWbMGvr6+0Ol0iI6OxsqVK9GzZ08cOnQICoUCZ86cQVJSkmFlvqCgIJQrVw4HDhxgeCIiIqI8exafjPO/30F8ooV7gdJnhTICUWo+zP7IpBI4O9oZQo6Lo+msj2kQMg1ETg7KIjX7Y0sMT1YSBAH2duaXkhUkgYGBcHZ2xh9//IFq1aqZ7R80aBB69+4NNzc3/PHHH6hdu7ZZn+vXr8PNzQ0lSpQwtPn6+iIoKAhBQUFYtWoV2rdvj08//RQrV67Msaa//voLAFC2bFn4+flZfe5Hjx4BAIoXL46AgAAAQKlSpRAUFIRGjRrhxx9/RLNmzRAeHg4AJpcQ6nQ63L59G5GRkfDz88uxRiIiIqKspq36Fheu3cvVMY72CvN7fSzM9Fi6L0ip4OzP64DhqRCRyWRo27Yttm7divfeew8KhcKw7/Tp0zh9+jTGjBmDzp07Y/369ejSpYvJsuCJiYnYuHEjOnfubPY9Shnc3NwwadIkDB8+HEePHkXbtm2fW9PevXtRuXJlQxh7kXMD+uuCAUCr1SIxMRFnz55F//790b59e0Of2NhY9OnTBwcPHsTAgQOfWx8RERGRJTHxyQCAWhVLoGRxD8PlcM7ZXPrmaK+ETMrZn8KO4amQGTZsGLp27YqPPvoIw4YNQ7FixXDx4kXMnz8foaGhKFOmDIYOHYqLFy+id+/eGDNmDEqVKoU7d+5g4cKF8Pb2xrBhw557jlatWuGtt97Cl19+iWbNmhlWwEtISEBUVBREUcSzZ8+wZ88eHD16FBs2bDAcm9tzx8TEGFYOjI2NxeLFi+Hu7o569erhu+++g1arRWhoqMlS6gDQqFEj7N+/n+GJiIiIXkho+zdRr2pJW5dBBQTDUyHj7e2N7du3IywsDGPHjkVsbCwCAwMxfPhw9OjRAwDg6OiIr7/+GmvWrMG0adPw+PFj+Pr64p133kH//v2tWqVu4sSJ6NixI1atWoVRo0YBAObMmYM5c+ZAEAR4eHigUqVK2LRpk8klerk9d9euXQ3bTk5OqFWrFjZs2AAnJyccOXIEjRs3NgtOANCjRw8MHDgQV65cQfXq1fPyqyQiIiIiMiGIYj4s7/Ea0mq1hg/Wxt9XlJqaijt37qBUqVJWLYhAVFDwvUtERJR/ek/6Gn/fi8KScZ0581QEZJcNsuKFmURERERERFaw+rK95s2bW70CyKlTp/JcEBERERERUUFkdXgyvpH/v//+w+bNm9GjRw9UrVoVcrkcN27cwDfffIM+ffq8lEKJiIiIiIhsyerw1KlTJ8N2586dMXv2bLRp08bQFhISgooVK2Lx4sUYPHhw/lZJRERERERkY3m65+nOnTsoV66cWXuJEiXw4MGDFy6KiIiIiIiooMlTeKpVqxbmzJmDx48fG9oiIiIwa9YsNGrUKN+KIyIiIiIiKijyFJ7mzJmDpKQkNG3aFPXq1UPdunXRsmVLyGQyzJgxI79rJCIiIiIisrk8fUmuj48PduzYgVu3buGff/4BAJQtWxZvvPFGvhZHRERERERUUOQpPAGARqOBi4sLqlWrBgAQRRF37tzBzZs30bZt23wrkIiIiIiIqCDI02V7J0+eRKNGjdC0aVOEhIQgJCQELVq0QNu2bfHFF1/kd41kpfLly6N8+fJ4+PCh2b7t27ejfPnyCAsLAwDs27fP0D/rT+/evQEAFy9eRPny5bFz506z8T777DN89tlnAID79++bHF+xYkXUrVsXgwcPxt27d82OvXv3LkaPHo26deuievXq6Ny5M/bs2WPSJywsLNv6Ms6bIaPOxYsX5+XXRkRERERklTyFp4ULF+Ltt99GeHg4XFxcsGPHDqxatQr+/v4YOXJkPpdIuSGXy3H69Gmz9pMnT5p9yXGxYsVw7tw5s5+MgJVh0aJFiImJyfHcu3fvxrlz53DmzBmsWbMGKpUKH3zwAZ48eWLoc/PmTXTt2hUAsHbtWhw6dAg9evTA/PnzMWXKFEO/fv36mdU1b948AEDjxo1NzhseHo7AwEAcOnQIoijmWCcRERERUV7kKTxFRETg448/RunSpVGlShVERUWhSZMmmDp1KjZu3JhvxUVGRmLAgAGoWbMmmjdvjk2bNhn23bhxA127dkVwcDDee+89XL9+Pd/O+zqrXbu2WXhKTEzEb7/9hkqVKpm0S6VSeHt7m/24ubmZ9HN0dMT8+fNzPLeHhwe8vb1RrFgxBAcHY/ny5XBwcMDq1asNfT777DM0adIEixYtQrVq1RAYGIiuXbti3bp12L17N77//nvDOY1rAoB58+ahXbt2JpeFqtVqHD9+HIMGDUJkZCQuXbqUm18XEREREZHV8hSeXFxckJKSAgAoVaoU/vzzTwBA6dKlcf/+/XwrbuTIkXBwcMC+ffvw+eefY/Hixfjuu++QnJyM/v37o3bt2ti3bx9q1KiBAQMGIDk5Od/OnZUoikhWqV/pT15mUUJCQnDp0iUkJiYa2r7//nvUrl0bjo6OeXrtEydOxP79+3H58uVcHadUKtGxY0d89913AICrV6/izz//xMCBA836Vq1aFU2aNMGuXbssjvX5559DqVRi6tSpJu0//vgjEhISEBISguDgYBw4cCBXNRIRERERWStPC0Y0adIE06dPx4wZM1C3bl18+eWXaNasGY4fPw4fH598KSwuLg5XrlzBzJkzUbJkSZQsWRKNGjXCTz/9hLi4OCiVSowbNw6CIGDixIk4e/Ysjh07hs6dO+fL+Y2JooieG3bit4jIfB/7eWqWKI6t/d43u9zuecqVKwdfX1+cPXvWMEPz3XffoUWLFjh8+HCe6ggJCUGzZs0wbdo07N+/HzKZ9W+bMmXK4PHjx0hMTMT169fh4OCQ7aqMNWvWxJYtW8zat23bhv/973/YuHEjXFxcTPaFh4ejZs2acHV1RUhICFasWIHJkyfDwcEhdy+SiIiIiCgHeZp5mjhxIoKCgnD9+nW0aNECwcHB6NKlC7Zu3Yrx48fnS2F2dnawt7fHvn37oFar8e+//+LXX39FxYoV8fvvv6NWrVqGUCEIAmrWrIkrV67ky7ktEWB9gLG1kJAQw6V7KpUKP/74I0JCQsz6PXz4EDVq1DD7OXTokFnfSZMmISIiAps3b85VLc7OzgCApKQkxMXFwdnZOdsw6OrqitjYWJO2u3fv4ssvv0SfPn1Qv359k32pqak4deoUWrRoAQBo2bIlkpOTceLEiVzVSERERERkjTzNPDk5OWHu3LmG5wsWLMC0adOgVCohl8vzpTClUokpU6Zg5syZ2LJlC7RaLTp37oyuXbvi1KlTKFOmjEl/T09P3Lp1K1/OnZUgCNja732kqDUvZfzs2MtluZp1yhASEoLhw4dDo9Hgp59+Qrly5eDp6WnWz8fHB19//bVZu6W+/v7+GDx4MJYtW4Z33nnH6loyLh90dHSEq6srYmJioNPpIJGY5/YnT56Y3G+l0Wgwbtw4BAQEYMyYMWb9z5w5g6SkJEMwDAoKQrly5XDgwAG8++67VtdIRERERGQNq8NTbu4lya8Prv/88w+aNWuGDz/8ELdu3cLMmTNRv359pKSkQKFQmPRVKBRQqVT5cl5LBEGAgyJ/guHLVqtWLQDA5cuXcfLkSbz99tsW+8lkMgQFBVk97ocffogDBw5g9uzZVt8/9ddff6F48eJwcnJCcHAw1Go1/v77b1SoUMGs7/Xr11G1alXD81WrVuHGjRvYvXu32Z83oL9kDwBatWplaNPpdLh9+zYiIyPh5+dn9WsjIiIiIsqJ1eFp6dKlJs8jIyOhUChQokQJyOVy3Lt3D2lpaahQoUK+hKeffvoJe/bswQ8//AA7OztUrVoVjx8/xsqVK1GiRAmzoKRSqWBnZ/fC5y0MZDIZmjRpgtOnT+PMmTPo379/vowrl8sxdepUhIaGwt/fH3Xq1Hluf5VKhUOHDqF169YAgMqVK6NKlSpYunQpVqxYYdL36tWr+OGHHwztV69excqVKzFixAhUrFjRbOzExEScPXsW/fv3R/v27Q3tsbGx6NOnDw4ePGhxYQoiIiIioryyOjwZL3+9cuVKXLt2DXPmzDFcZpWYmIgpU6bAy8srXwq7fv06goKCTAJRpUqVsGrVKtSuXRvR0dEm/aOjo/NtsYrCICQkBBMmTECJEiVQokQJi320Wi2ioqLM2gVByPbPsW7duujQoYPF+6JiYmKgVCqh0+nw8OFDhIWFISUlBZ988omhz9y5cxEaGooxY8agb9++cHd3x88//4z58+eja9euaN68OVQqFcaNG4cKFSqgY8eOZjXK5XKcOXMGWq0WoaGhhqXMMzRq1Aj79+9neCIiIiKifJWne57Wr1+PnTt3mtyf4uTkhKFDh6JLly74/PPPX7gwHx8f3Lt3DyqVynDJ1r///ouAgAAEBwdj7dq1EEURgiBAFEX8+uuv/LBspGHDhtBoNIbFFCx59OgRGjZsaNYulUpx48aNbI8bP3684fuYjGV8+a1UKoWPjw/q16+PGTNmwMPDw9CnXLly2L17N5YtW4ZBgwYhMTERpUuXxqhRowzHP3nyBHfu3AFg/oW4AFCnTh0oFAo0btzYLDgBQI8ePTBw4EBcuXIF1atXz/Z1EBERERHlhiDm4cuEmjVrhtGjR5tcLgUAu3fvxurVq3Hy5MkXLiwhIQFt2rRBgwYNMGjQINy5cwcTJkzAqFGj0K5dO7z99tt455130L17d+zYsQPHjh3DiRMnrF6iWqvVGj5cS6VSQ3tqairu3LmDUqVK8TJAeq3wvUtERJR/ek/6Gn/fi8KScZ1Rr2pJW5dDL1l22SCrPM08DRgwABMnTsTFixdRsWJFiKKIa9eu4dtvvzVZhe9FODs7Y9OmTZg9eza6dOkCDw8PDBo0CN26dYMgCFi9ejWmTp2KXbt2oXz58lizZg2/24eIiIiIDERRhEqthVqrhVqthUqjf1Rr9D/P2xeXmGrr8gu1VK0G158+Qm2fAFuXkit5Ck/du3eHv78/9uzZg+3btwMAypYtiw0bNqB27dr5VlyZMmWwceNGi/uqVauG/fv359u5iIiIiChvRFHUhxFNZhDRZIQTCyEl6z6NNktfjfm22T5LQUhjOr5Gq3vh16aQZT8LQXlXYesCAMDMOi3Ru0JNG1djvTyFJ0B/U36jRo3ysxYiIiIispIoioiOTYJKrbEYKrKGFIsBxJoAk01IMX6eHyHlVZBJJZDLpJDLpVDIpPptmRQKudTQLpdmPg/wdUO1ssVtXXah9mv0A/RGIQxPEyZMwMSJE+Hk5IQJEyY8t29+XbpHRERERJZNXnEU3134y9ZlWCSVSvThJEsYUcilkMkyg4vJ8+wCjaXncmnO42cZTy6TQiIRbP2roXS+9k54nJKIRn6lbF1KruR55omIiIiIbOfarYcAAKVcCqVCBrlMBrlMYhQsZJBLzZ9nBg0ZZDKJUdAwfi5LDx4Swz551ufPCTsMKZST0q4eeJySCJlEYutScsXq8GQ8m9S5c2dUr14dcrncpI9KpcLZs2fzrzoiIiIieq5Vk7qhUuliti6DqEjI08xTaGgofvzxR5Pv7wGA27dvY/To0bh69Wq+FEdERERERKZ0ogitqINOFA3bWlGEKIrQGu3T6nTQIaOPCF36c23GPlE0PNfp9GPoRB10IjLHMDmP/rnZedL369LrMO2TMWZmP62ow734WFv/GvPE6vC0bds2zJgxw/CltG+99ZbFfg0aNMi34oiIiIiIbOG3qIfY+881aHQ6QzgxDiuWgoXOpJ/OEB5Eo4BjKfhkP4bxcZkBpDBxkitsXUKuWB2eevbsibJly0Kn06FPnz5YunQpXF1dDfsFQYC9vT3KlSv3UgolIiIiInpVZv1yCpejHti6jDwRAEgFCSSCAIkgGLal6c8lggTS9HZB0PeVCgIkkvRHpB8nSR8D6WNIhMzjIEAqyRjTePyMsQUI6X2N241r8nVwQsPCvGDEm2++CQA4deoUihcvDkHgzYAFiVqtxqpVq3DgwAE8fvwYXl5eaNWqFYYNGwYnJyeTvhEREWjRogU6dOiA+fPnm+wLCwvD6tWrsX//fpQtW9ZkX/PmzTF06FDUqVMHISEh2dZSp04dfP311wCAffv2YcKECZg1axa6du2a7TFhYWFYtmyZxX0nTpyAVCp97jn/+qtgrjhEREREr58UjRoA8N4bVfCGi6dZOJBkCQoSiSQ9ZKS3S8xDiFTQ9zEJIULmtlUBx0IIyRpg+Bn95cnTPU8+Pj7Ys2cPrl27Bo1GAzHL9CGXKreNBQsW4Pz585g1axZKlCiBiIgIzJ49G/fu3cOqVatM+h49ehSBgYE4efIkkpKS4OjoaLJfrVZj+vTp+Oabbyyey8/PD+fOnTM879KlC/r164e2bdsCgMliIuHh4QgMDMTBgwefG54AoEaNGggLCzNr9/DwQGRkJABg9+7d8PPze+44RERERPmhY6nKaFz89ZodoZcnT2sDTpw4EbNnz8azZ8/MghPZzv79+zFixAjUr18fAQEBqF+/PqZNm4YzZ87gyZMnJn2PHDmCDz74AHK5HMePHzcby9fXF7/99hsOHDhg8VxSqRTe3t6GH6lUCmdnZ8NzNzc3AMDTp0/x008/YciQIfjll18QERHx3Ncgl8tNxjUeP4OHh4fFPkREREREL1OeZp6+++47LF++PNtFIwojURQN07evir1MnqtpV0EQcOHCBTRv3hyS9DXza9SogfDwcLi7uxv63b59G3///Tfq1q2Lq1evYv/+/ejcubPJWEFBQWjTpg2+/PJLNG/eHC4uLnl6DceOHYOzszM6dOiARYsW4eDBgxg6dGiexiIiIiIisqU8hSdnZ2f4+vrmdy0FliiK6HLsm1d+02Bt7wDsbt3L6gAVGhqKpUuX4uTJk2jSpAkaNGiAhg0bokyZMib9jhw5An9/f1SoUAEhISEYPXo0Hjx4AH9/f5N+w4YNw7fffouFCxdi+vTpeXoN4eHhaNq0KSQSCZo3b44DBw5gyJAhvBaXiIiIiF47ebpsb9CgQZg9ezb++ecfaDSa/K6pQBJQ8D/sDxkyBPPnz0exYsWwa9cuDB8+HI0aNcLevXtN+h09ehTNmzcHADRp0gQKhcLi5XlOTk6YMGECdu3alafv7oqMjMSvv/6KFi1aAABatmyJiIgIXL58OdtjfvnlF9SoUcPkZ+fOnSZ92rVrZ9ZnypQpua6PiIiIiCg38jTztHbtWjx58gTt2rWzuP/mzZsvVFRBIwgCdrfuVeAv2wOADh06oEOHDnj27BnOnTuHb775BhMnTkT58uVRpUoVXL16Fffu3TMEGkdHRzRo0AAHDx7EkCFDzMZr06YN9uzZg2nTpmH37t25qiU8PBxKpRINGzYEoF+Bz9XVFfv370ft2rUtHlOlShUsWLDApC3rlzGvWbPGbOYz62qCRERERET5LU/h6YsvvsjvOgo8QRDgUIC/xOvPP//EgQMH8NlnnwEA3N3d0b59e7Rq1QotW7bEhQsXUKVKFYSHhwMA+vXrZzhWp9N/C/Tly5dRq1Yts7GnTJmC9u3bY9u2bbmqKTw8HKmpqSZjarVaHDt2DJMnT4adnZ3ZMXZ2dggKCnruuMWLF0dAQECuaiEiIiLKjRvP9IttcXE0Mpan8FSnTp1s92Vd1Y1eDa1Wi40bN6JDhw6oVKmSoV2hUMDOzg4eHh7Q6XT49ttv0bFjR3z88ccmx37wwQc4cOCAxfAUFBSE/v37Y8mSJYaFKHJy584d3LhxA5MmTULdunUN7bdv38aoUaPw3XffoX379i/wiomIiIhevv3//oEm/qVtXQYVEHkKT//++y8WLFiA27dvQ6vVAtCncpVKhZiYGNy4cSNfi6ScVa5cGU2bNsXgwYMxZswY1KhRA9HR0di/fz9UKhVatmyJX375BY8fP0bv3r1Rrlw5k+M7dOiAw4cPY9KkSRbH79+/Pw4dOoR79+5ZVU94eDjc3NzQrVs3KBSZM3blypXD8uXLceDAgTyHp5iYGCiVSrN2Nzc3k++XIiIiInpR9YoF2roEKkDytGDE5MmTERMTg48++gjR0dHo168fWrdujcTERMyePTu/ayQrLV68GB07dsSyZcvQpk0bDBgwAImJifjmm2/g5OSEI0eOoHz58qhatarZsT169EBCQgJOnjxpcWyFQpGrRRnCw8PRvn17k+BkfK7z58/j8ePH1r84I127dkXDhg3Nfn7//fc8jUdERESUVSV3HwBAcce8fV0LFU6CmIcLOatVq4adO3eiYsWK6NGjB4YPH4769etj9+7dOHDgALZu3foyas1XWq0WV65cQfXq1U2+gDU1NRV37txBqVKlLN6TQ1RQ8b1LRFS0dBy5Fo+eJmDj9J6oVLqYrcspdNoe3oAbz55gS4tuaFy8lK3LoZcsu2yQVZ5mnmQyGZydnQEApUuXNqyu16BBA/z11195GZKIiIiI6JUQRRFanQ6pWg2S1CrEpaXiaWoyHicn4EFiHO4lPEOaTmvrMqkAytM9TzVq1MD69esxfvx4wwpuH374Ia5fv27xXhQiIiIiej2IogidKEIt6qDV6aDR6aARddDotEbbOtPt9P1anQ5qnQ5aMcujUV+tTge1aLmvYVydFhpRTH80PZ/pcVpodaJhPPOaMsfTiqK+f/r5iPIiT+FpwoQJGDRoEEqUKIHu3bvj66+/Rp06dZCcnIzBgwfnd41ERERElEWCswpxvhp8+ddZOEYoGCzygVQQIJNIIRMkkEkkKOHkimAvP1uXRQVIrsPTvXv3EBQUhBMnTiA1NRX29vYYN24cIiIiULVqVVSvXv0llElERERExh6USEKanQ7fR/37Ss4nFQRIBQnkEimkEkH/KEggl0ggTQ8bMonEEDz0IUQfRgz9JJn9M4+Tmh9rMp6F80kkkAv6x8z+UtPjzMazXHfGeDJBAkEQXsnvkl5fVocnURQxe/ZsbNu2DZs2bUKdOnVgb28PANi1axfOnDmDPn36IDg4mG88IiIiopdMl/5x693ilVDdv/hzQ4g+bOQ9hEgFCST8fEdkfXjasmULjh49iuXLl5t9Se6KFStw+vRpTJgwAYGBgejZs2e+F0pERERE5pp6l8a7FavYugyiIsHq1fZ27dqFyZMno1mzZhb3N2/eHGPHjsX27dvzrTgiIiIiIqKCwurw9ODBA1SrVu25ferVq4eIiIgXLoqIiIiIiKigsTo8eXp64sGDB8/t8+jRI7i5ub1oTURERERERAWO1eHp7bffRlhYGNRqtcX9Go0Gy5YtQ8OGDfOtOLLeZ599hvLly2f7s2/fPjRv3tzisc2bN8e+fftM2r7//nv07t0btWrVQr169TBkyBDcvn3bsP/ixYsoX768xfF69+6NsLAwAMC+fftQvnx5hIaGWuz7/vvvo3z58rh//75Je8b4ixcvtvha33zzTTx9+tRsX/ny5XHx4kWL5yIiIiIiehFWh6fBgwfj8ePH6Ny5M3bt2oUbN24gIiIC169fx86dO9GpUydERERg2LBhL7NeysbEiRNx7tw5nDt3Dp9//jmKFStmeH7u3LlsQ68lmzdvxsiRI9GsWTPs2rULmzZtgp2dHXr16oU7d+7kuja5XI7Lly8jPj7epP3x48e4fv26xWPCw8MRGBiIQ4cOQRRFs/3x8fGYN29ermshIiIiIsorq8OTi4sLdu3aheDgYHzxxRd477330LJlS3Tp0gULFixAzZo1sWvXLvj6+r7Meikbzs7O8Pb2hre3N5ydnSGVSg3Pvb29IZfLrRonIiIC8+fPx/Tp09GvXz+88cYbqFChAubPn48SJUpg2bJlua7Nx8cHxYsXxw8//GDSfurUKYv30anVahw/fhyDBg1CZGQkLl26ZNbH398fBw8etLiPiIiIiOhlyNWX5Lq5uWHWrFmYMmUKIiIiEB8fDzc3NwQGBkIqlb6sGgsEURSRplO90nMqJYpX/p1ZR44cgZubG9q3b2/SLpFIMG/ePCgUijyNGxISgtOnT5uMe+rUKbRo0QK//fabSd8ff/wRCQkJCAkJwa5du3DgwAHUrVvXpE+dOnWQmJiI6dOn48CBA1aHQyIiIiKivMpVeMqgUCjwxhtv5HctBZYoivj098W4GZ/7S9ZeRCWX0vgyeMQrDVB//vknqlSpAonEfFLyRf7MQ0JCMGjQIKjVasjlciQkJOC3337D+PHjMX/+fJO+4eHhqFmzJlxdXRESEoIVK1Zg8uTJcHBwMOk3ceJEtG3bFhs3bkT//v3zXBsRERERkTWsvmyvqBNQNL5VOyEhAU5OTvk+bs2aNSGVSvHzzz8D0C9I8eabb5oFotTUVMOMFAC0bNkSycnJOHHihNmYfn5+GDJkCFasWIGHDx/me81ERERERMbyNPNU1AiCgC+DR7zWl+3JZDLodDqL+3Q6HWQy/VvBzc3NbGGH7MbLODbrLJXxeBmkUimaNWuG06dPo0GDBjh58qQhIBk7c+YMkpKSEBISAgAICgpCuXLlcODAAbz77rtm/fv27YuDBw9i1qxZWLFiRY51ExER5USnE6ETReh0Ouh0IrQ6EaIoQpv+XN+mgyjCpC3jGG2W5xlj6HS69LZcjGF0TOYY+n9rLS2oREQvF8OTlQRBgJ1Uaesy8szFxQUJCQkW9yUkJMDFxQUAULlyZWzcuBGiKJoFt6NHj+J///sf5s6da+ifkJAAV1dXk37x8fGG/cZCQkIwd+5cjBs3Dj/++COmTp2K5ORkkz7h4eEAgFatWhnadDodbt++jcjISPj5+Zn0l8lkmDp1Kj744AOcOXPGml8FERG9Ilf/fojzv9+BVqd7gbCB54YYy2EDJm3Gx2SM8bxjXhe6hvpa5bLCfd85UUHC8FRElC9fHomJibh9+zbKlCljaP/nn3+QmJiIihUrAgBat26Nr776CkeOHDFZ3EGr1WLjxo0oXrw4AP2MkJ2dHa5cuYImTZoY+sXHx+POnTuG8Yy99dZbiI6OxpYtW1ChQgV4eHiYhKfExEScPXsW/fv3Nzl3bGws+vTpg4MHD2LgwIFm49auXRudOnXCzJkzX+A3RERE+e3zZUcQ9SzR1mXkO0EAJIIAiUQCiUSAVJK+LcBCmwCJREhvk0AQMvbp90sFAULGfqMxJRIBEkF/jPFziUQCqUR/zB7lDSRBDX8f15yLJqJ8wfBURPj5+SEkJARjx47FhAkT4O/vj7t372L+/Plo27atYYl5f39/DB06FBMnTsTTp0/RtGlTxMfHY9WqVfjvv/+wcOFCAPpFQ95//31Mnz4dU6dORZkyZfDw4UOEhYWhSpUqqFGjhlkNDg4OaNCgAVasWIHhw4eb7f/uu++g1WoRGhoKb29vk32NGjXC/v37LYYnAPj000/Rpk2bF/01ERFRPkpK0V/u/k6jynB1stN/6M8SCCwFiowQYtommIYSQ5gwGi9LUDFtkxid3zT4ZK3JbNysweUVr4SbnZN77yApSV1g6iEqChieipAFCxZg0aJFGDduHJ4+fQovLy+0adMGI0eONOk3cOBAFCtWDF9//TWWLl0KOzs71KpVC9u3b0dgYKCh3/jx4+Hq6opZs2bh0aNHcHV1RdOmTfHpp59m+xd5SEgIzpw5Y/F+pyNHjqBx48ZmwQkAevTogYEDB+LKlSsWx/Xw8MDo0aMxZcoU638hRET0SvTrWBcBvm62LoOI6IUJYhG921Cr1eLKlSuoXr26yXdUpaam4s6dOyhVqhTs7OxsWCFR7vC9S0QFTbNPliE5VYW9C/rlS3gSRRGi0TaAzOfpWxmfagzPMw82PM/9seb9svbJbkyzY3PxWrI8mNX5/vGteJKSiINt+yDYy/SeYCLKneyyQVaceSIiIqKXIrpMKhJ8NGj+3VqT9pzCStYwQURUUDA8ERER0UuR6K0BpIBKp7V1Ka9cxsXrGZexG56nb2Vc3W78PZJmfa04trSLB8q7eeV3+USUDYYnIiIieqmWvdkBNUr4A9AHgpzCAbIEC0v9sw0hpg/Z9hMsnB9WBh2L9XDRBqIigeGJiIiIXipvO0f4O3E5bSJ6/UlsXQAREREREdHrgOGJiIiIiIjICgxPREREREREVmB4IiIiIiIisgLDExERERERkRW42l4h0rx5czx48MDivi1btqBu3bro3bs3IiMjER4eDqVSadh///59hISE4NSpU7h06RImTJiQ7XmGDh2KTp06ISQkxKRdJpPB3d0drVq1wvjx46FQKAAA5cuXz3asU6dOISAgIMfXIggCXFxcUKtWLUyZMgV+fvpvUv/ss88AAF988YWh7927d7F06VL8+OOPSEtLQ+nSpdGzZ0906dLF5Bz37t3DggULcOHCBahUKpQpUwYffvgh2rVrl229RERERFR0MTwVMp9//jnatm1r1u7qmrlEbEREBFatWoURI0ZYHKNt27Zo1KgRACAyMhJdu3bF7t27DYHFwcEBz549AwCT9rS0NFy6dAlTp06Fu7s7hg4dahgzLCwMNWrUMDuXh4eHVa9Fp9Ph9u3bmDp1KsaPH48tW7ZYPObmzZsIDQ1Fo0aNsHbtWri5ueHixYuYP38+rl69ihkzZgAAUlJSEBoaimbNmmHr1q1QKpU4d+4cxo8fD7lcjlatWmVbFxEREREVTQxPhYyzszO8vb2f28ff3x/r1q1Dx44dUbJkSbP9dnZ2sLOzA6APRIA+5BiPmxGesrYHBATg119/xcmTJ03Ck6ura4515fRafH19MXz4cHz66adISEiAs7Oz2TGfffYZmjRpggULFhjaAgMDUaFCBbz//vto3rw5mjZtivPnzyM5ORnTpk0z9AsKCsKNGzewa9cuhiciIitpdTokqNPwLC0FsWmpiFWlIC4tFc/SUiDy5gAiKmQYnqwkiiI0YuorPadMsHsp31jesWNHnD17FjNmzMCGDRvyfXyFQgGpVJrv42aMDQASifm/yFevXsWff/6JhQsXmu2rWrUqmjRpgl27dqFp06aQSCRISkrClStXUL16dUO/MWPGGAIjEVFRotXpEK9KQ6wqPQSlpZhup6UgVpVqCEix6WEpXpUKMbtB0/+qtpPKX9XLICJ6qRierCCKIr69PxBRqdde6Xl97KqhdcDKfA9QEokE06ZNw/vvv4+jR49avMwvL0RRxKVLl3D48GF88skn+TKmsf/++w9r1qxBo0aN4OjoaLb/+vXrcHBwwBtvvGHx+Jo1axou92vQoAFKlSqF7t27o0aNGnjrrbfQqFEjBAcH53vdRESvklanQ5wqNT3o6GeBYlUphpmhuPQQ9MxoX44hyApOcgVcFXZwU9rDXWkPV4Udzl64DeGpCM9WDvn2+oiIbInhyUoC8n8G6GWYOnUqZs6cadJWvHhxhIeHm7RVrVoV3bt3x9y5c9G4ceM8n69du3aGcKdSqeDh4YHQ0FB89NFHJv0++eQTs9moWrVqYd26dVa9Fo1GA7lcjpCQEHz++ecW+8fFxcHZ2TnbsOnq6orY2FgAgFKpxLZt27Bq1SocO3YMYWFhCAsLQ6VKlfDVV19ZvJyRiOhV0uh0iE8PQfqgk2I262P2XJWCeNWLzZ47yRVwU9rDLT0IuSrs4K6017cp7UyfK+zgmt4ul5hfcdDsm2VITlW9UD1ERAUJw5MVBEFA64CVr8Vle8OHD0fLli1Nx5FZ/mMeNWoUTpw4gSVLlqBPnz55qnHNmjXw9fXFw4cPMWPGDFSoUAEDBw40C0qzZs0ym9XJuK8qOxmvJSkpCWFhYXjw4AHGjBkDd3d3i/1dXV0RExMDnU5n8bK+J0+ewM3NzaT/+PHjMX78ePz99984deoUNm7ciOHDh+PQoUNW/gaIiJ5PkzETlD7jE5dmNAuUPutj6XmC+sVCkLNcCVelHdzSw46rUSByU9qZBKTMIGQ5BBERkR7Dk5UEQYBcsLd1GTny9PREUFCQVX1dXFwwbtw4TJgwAXXr1s3T+YoXL46AgAAEBQVh9erV6NixI+bNm4dJkyaZ9PP19bW6rgzGr2XJkiXo0qULBg8ejJ07d0IuN79+Pjg4GGq1Gn///TcqVKhgtv/69euoWrUqAGDXrl1wcnIyXLJYrlw5lCtXDpUrV8Ynn3yCmJiY564ESERFT0YIMswCpQeizMvfUk1mgDK28ysEuRvP9BgCUeZlcsb7GIKIiF4OhqcirmPHjti7dy/mzJnzwmMFBgZi2LBh+PLLL9G+fft8vX9IoVBg1qxZ6NatGzZt2mTxnqrKlSujSpUqWLp0KVasWGGy7+rVq/jhhx8M7X///TcuX76M1q1bm8xSubi4QKFQwMnJKd9qJ6KC62lqMn6LemByf9Azoxki43uD8iMEPW/GJ+ulcO5Ke7golAxBREQFCMNTIZOQkICoqCizdkdHRzg4WL5hd+rUqejYsWO+nD80NBR79+7FjBkzsHv3bkMwiYuLs1iXi4uLyZf1Pk+1atXQpUsXrFixAh06dICvr69Zn7lz5yI0NBRjxoxB37594e7ujp9//hnz589H165d0bx5c0OdBw4cwNChQ/HRRx/Bx8cHt2/fxqJFi9CrVy/Dqn5EVHiJooh2RzYiMjkhV8dlhCD9ogiZl8C5K+1Mnme9b0hm4XLiF6ldq9VBrdFBo9VCrdFBrdVCm/6o1mgN+zQaHdQaLTTa9EfjY9LbNRot1Bl9LRyj1uigtXRMRp/055n99X15vxMRFTYMT4XMnDlzLM4ijRgxAoMHD7Z4zBtvvIF+/fph9erVL3x+mUyGSZMmoW/fvti7dy+6du0KABg2bJjF/l9++WWugtuoUaNw/PhxzJ8/3+S7nDKUK1cOu3fvxrJlyzBo0CAkJiaidOnSGDVqlKEWQD9Ltn37dixZsgRDhw5FQkICihcvji5dupgtdkFEhZNWFA3BqZ5vIDwU9nCR28FZpoSjTAFnqQKOUgUcJDLYCwrYS2Swgww6rWgeRNJ0UCdpodFqodKo8ECbgrvZhJfnBRG1VmceTLI55nXh7uIALzfzFVKJiF5HgiiKL7Iy6WtLq9UavuPHeHGD1NRU3LlzB6VKlcpxQQOigoTvXaLc0eh0KPPNlwAA7zNySDSvx6qq2ZFKJZBLJZDLpJBJJZDJpJDLpJDLJCbPZVn7mB0jgUya/pjlGLlMAmn6PrlUCpnRY8YxWc/v6+EMezt+zxMRFWzZZYOsOPNERERF0vNmb2RSSWbIyBIerAoihn3mYcKaIGIyttn5LfSRSl7Kl6oTEZEphiciIirytszshSAvDwYRIiJ6LoYnIiIq8hwdlHCw40IxRET0fPm39A8REREREZGVdKLG1iXkGsNTNoroOhr0GuN7loiIiF4XB+71xNe3G+Np6p+2LiVXeNleFnK5fkWg5ORk2Nvb27gaIuslJycDyHwPExEREb0IURShgwY6UQWtTgWtqIZWVEFn8qiGVkyDVlRnaVelt6mybOv7xanuAgCOPxiOnm+csO0LzQWGpyykUinc3Nzw5MkTAICDgwNvHKYCTRRFJCcn48mTJ3Bzc3vu8pr0ehNFEVqdmP6og6gDtKIOok6ETidCJ1p61GU+t9hPZ3acqMtyHhHpj8bt+n46Qy1Z9mWtNZc1mG7roBMBnaUajGoxOU+2vwv9ODqdCJVGA1Sx9Z8qEVEmURQhQmsIG/ogYho8tKIKOl1GODEOMMahxVKAyQg3pkHHbGzDtr79ZbOTur30c+QnhicLihUrBgCGAEX0OnBzczO8d6lw0OlEDP1iD678dR9aHS/LzG+iIBrCU1JyGuBh23qIqGDRiRrEpN0yChv5NftiHnSMAwxQcP++l0AGiaCAVJBDIsghNWwrIJVk02541G9LBQUkghwRSecQnfoH2pXYaOuXlSsMTxYIggA/Pz/4+PhArVbbuhyiHMnlcs44FUJxiSm4fDMiV8cIAiARBEgkEkgEQCLRL7stlQgQJAKkQvqjREhv1++XSIT044weJfpxMo4x75N5DpNjzLaNasj6KMmoARZrkVo61qh+izWY1G9cq+l+jU6Hnld2AgCcHfnF0kRk6n+PpuNu4imb1iBAaiGIyCEVlNkGFalEkbmdQ4AxOS59v8Rkv+nYgpB/yyVU8+iTb2O9SgxPzyGVSvmBlIgKhKNhA7IPA0btvMzYehqdDrii33ZyUNq0FiIqeOLV/wEAHGQ+UEpcsoQJ87AhlVgOOOYBJrugYrythESQQSLwc2hBw/BERPQa8HRztHUJRERFUgOfCfB3rGvrMqiAYHgiIqJCTyeKUGk1SNNqkabTIE2rQYqGl2UTEVHuMDwREdFLYwgtOi3StPrQotLqt1Xpbdk9N25Ls/oYLVS69Eej52qd7rl1CuDljkRElDOGJyKiQihraMkpsFhuy/0xWZ/nFFpsQQCglMqglMqgkErxVrGScFHwniciIsoZwxMRUQGWUlwLlZuIYWcP5jjrYhxqCnJoUUil+keJ1OS5UiqDMktb1j6WjjFry+EYmSDhwhpERJQnDE9ERAVUojoN8ZW0gAAcvnszz+PkGFrMwoYMSmkuQ4zEKMwwtBARUSHF8EREVECpdPrgBABT3gzJIfjoA09G8Ml4ztBCREQFlSiKr92/TwxPRESvgX4V37R1CURERPnmgwuT8EwVjzlVhyLYvZyty7Fa/n1N8EugUqkwffp0vPnmm2jQoAEWLVoEURQBADdu3EDXrl0RHByM9957D9evX7dxtUREREREZI1nqngAwMnHF21cSe4U6PA0a9YsnD9/HuvXr8fChQuxa9cu7Ny5E8nJyejfvz9q166Nffv2oUaNGhgwYACSk5NtXTIREREREeXAU+EKAKjhXt7GleROgb1sLzY2Fnv37sXGjRtRrVo1AEC/fv3w+++/QyaTQalUYty4cRAEARMnTsTZs2dx7NgxdO7c2caVExEREZGtiaIIrZgGtS45/ScJajEZGl0y1LoUQ7vGsD8ZGjFzO14VYeuXUKj52/vgqSoOUkFq61JypcCGp8uXL8PJyQl16tQxtPXv3x8AMHnyZNSqVctwg5kgCKhZsyauXLnC8ERERET0GsoadoyDjPGPRpcCtZixnWS0L0UfhMTMfiJe/GsbnOS++fDqqLAosOEpIiIC/v7+OHDgAFatWgW1Wo3OnTtj0KBBiIqKQpkyZUz6e3p64tatWzaqloiIiKho0YcdlX5GJ0vY0RjN7KjTA43GYhBKhlpMMWznR9ixRCY4QC4x/ZFJ7CEXMraN99nrnwuOcFGUgKsi8KXURK+nAhuekpOTce/ePezYsQNz585FVFQUpkyZAnt7e6SkpEChUJj0VygUUKlUNqqWiIiIqGAzDjsaMcU8xDwn6BjCUJaAJEL7UmrNDDv2psFGMAo+JkEoc1/WMCQT7CAIBfo2f3qNFNjwJJPJkJiYiIULF8Lf3x8A8PDhQ2zfvh1BQUFmQUmlUsHOzs4WpRIRERG9cvGq+3iQfB4qXVL29/GIprNALy/s2GcJLvpZHZnZbI9pCDKZBTKEHXuGHSqwCmx48vb2hlKpNAQnAChVqhQiIyNRp04dREdHm/SPjo6Gj4/Pqy6TiIiIyCZ+eDQZMWl/5elYmWBvNnuTGXYy2h1Nwo5Zf4YdKoIKbHgKDg5GWloa7ty5g1KlSgEA/v33X/j7+yM4OBhr1641fCuxKIr49ddfMXDgQBtXTURERPRqpGljAQAlHBvDWe6XHnYs3cdjn/7cMT0E2UHymq1wRlRQFNjwVLp0aTRt2hQTJkzAtGnTEBUVhTVr1mDQoEFo3bo1Fi5ciNmzZ6N79+7YsWMHUlJS0KZNG1uXTURERPRKVfMIhZddJVuXQVQkFOg51gULFiAwMBA9evTA+PHj0atXL/Tu3RtOTk5YvXo1Ll++jM6dO+P333/HmjVr4ODgYOuSiYiIiIiokCqwM08A4OzsjC+//NLivmrVqmH//v2vuCIiIiIiIiqqCvTMExEVbDqdiLq9F6H1kFW2LoWIiIjopSvQM09EVLBdun4PAPAsPtnGlRBRQfRvwgk8SPoJdlJ32Mu84CD1hL3ME/bpjwqJMwRBsHWZRERWY3giojzTaDO/Cf7YjzcBAIKQ8T+AflMwtAtI/5CU8WDUlnW/YGEMCIZDIQgCMpszdwgwahcMI+YwpnkN+teR05imNQhGJxOyeY3W/G4yjn0Smwii19mlqMWGFeEskQiK9EDlAXuplz5YpYcrB5lnepsH7KTukAj8yEJEtse/iYgoz2TSzCt/p6761oaVFE46uQg0tXUVRHmn1em/0L6EY2PIJHZI0UQjRRuDFM1TqHQJ0IkqJGoikaiJfO44AiRQSt3SA5Xx7JUX7KUecJClBy+pF2QS5at4aURURDE8UaGm0eowffW3iI5Ngo+7E3w8nOHt7gQfD6f0R2d4uDpAKuHtf3nh5+1i2K5TORBi+rYoihDTn4gwbEBMbxQB0/2i0XFGY8CoT2b/zHH0h6aPaTQ+jPoYDWO2X7QwPizVmPU4C2PAuMYsdZmOmbmNbGrMGEcr1wFQg+h1JQgARKC211C4KAJM9ml0aUjRPkWK5qnZY7ImOn07BqnaGIjQIVWr3wZuPfeccolj+syVF+ykHvrAJfMyCV0OMi9eMkiUz0RRRJI2BQnqJMSpk5CgScrcTn9uvP0gJcrWJecJwxMVan/fe4ITPz3/29elEgGebo4Wg5VP+nMvNycoFfy/S1YZl5o5OygR9lkXG1dT+DxNTUatXUttXQbRSyGTKOEsKQ5nefHn9tOJWqRpY9MDVUx6yIpGiiYGKdpoJGueGtq0ogpqXRLUuiTEq/977rgSQQ57qYchWJnOaukvF9Tv4yWDVPRoRS0S1cmI1yQjXp2I+PTAE69OMtlOUCch3rCdDB10OQ9uRCZIEehQ7CW9ipeDfxtQoabVZf6feGi3RnjyLBFPYhIQFZOIJ88S8TQ2CVqdiCcxiXgS8/z7S9yc7c3CVcbzjFktR3sF/0smEREArS7NZNY2rySC1HAv1POIogi1LikzUGXMZhmFrWTtU6RootMvGVQjSfMYSZrHOVQgwE7qZjZzZXKfVvo+ucT+hV8vUX5T69SIVyenz/wk6md+1MmI02S/naRJyfP57CQKOMsd4SJ3hIvMMX3bCS4yB7NtXztPuMgd8/HVvnwMT1Qk+Pu4one7N83aNVodYuKS8CQmEVEZwepZotnzNLUWsQkpiE1Iwa3/sp9mtlfKLYcro213ZwdIJAxYRPR60+rSkKh5hER1ZOajOhJJmkgkqh8hRfvU0PdV/EclQRCgkDpBIXWCq6Lkc/tqdarMSwRNLhmMTt+OQbImGqnaZxChRar2GVK1z/BMdfu548olDpmzVun3ZGVdYdBe5gWlxIX/oY1yTRRFpOpU+tkedZJ+RkiTnP4867Y+CCVokpCiTcvzOR1l9kYByNGqbYVEno+vuuBheKIiTSaV6C/P83DOto8oiohPTM2ctbIQrp7EJCIhOQ0paWrci3yGe5HPnntOb3enbGexMvbJZdKX8ZKJiKySm3CUHZngAD+HWnCS+b2Ciq0nlSjgJPGDk/z5dekvGYwzuRcrNT1YZb1PSyOmQq1Lhlr3X86XDEJmGqjSHx1knrDLstIgLxksnHSiDkmaVIuXwpluJyLBEIqSoBY1eTqfBAKc5Y5wlqWHnedtG8KQA6QCP4tkxf9HEuVAEAS4OtvD1dkeZQO9s+2XkqrWB6lnCdnOZD2NS4JGq0NkdDwio+Ofe153FweTSwIthS1He0V+v1wiKiLyKxw5yYvBSe4HJ5mfflumDyWOcr/XfoZFf8mgB+xlHoCybLb99JcMJpvdi5WqfWpyT1aKJgZpujjooMm8ZDCHSQGl1M1o9ipzVstB5gWtmPcZBco/WlGLBHVy+mxPTmEoYyGF3N8flEEmSNODjhOcZQ5G245wkTtY3HaU2UEicHGs/MDwRJRP7O3kCPRzR6Cfe7Z9NBotnsYl40lMQvpMViKiTMKW/lGt0eJZfDKexSfjr7tPsh3P0V6RubiFhYUuvN2d4OZs/1p/eCGivNGHo8cmgUgflDLCUXSOY8gEe30wKqThKL/oLxl0hELqCFdF0HP76i8ZjMlyT5bxJYSZKw2K0C+WkaaNxTPVP6/o1VCGZE0Kfnv2F+IMl8GlB6As26/q/iBXuROc5Y6wk/D+altieCJ6hWQyKXw9neHr+fzLBGMTUkxmr55YuFQwKUWV/hODuw9jsh1PIZfCy8141soJ3u7OJrNanm6OJt/ZREQFH8PR60l/yWAxOMmfv8KYKOqQpo0zClQxJsEq49JBR5kP3BVlXlH1RcuXf27BzzF/WN2f9wcVDQxPRAWMIAhwd3GAu4sDypf0ybZfUooq20UuMsLWs/hkqNRaPIyKw8OouOecE/B0dcx+NcH02Sw7Jf+SJ3pVGI6KNkGQwE7mDjuZO6BkOLKFp2mxAICKLqUQYO9rdn+QaRji/UFFBcMT0WvK0V4BR3sPlCzukW0ftUaLqGeJ2S5yob9HKxFarQ7RsUmIjk3CzTvZL9vr4qg0CVZc1IIo7xiOiF4PPQNbo6ZHRVuXQQUEwxNRISaXSVHc2xXFvV2z7aPTiXiWkPzc5dqfxCQiJU2N+KQ0xCel4Z/7pjeS85I/InMMR0REhQ/DE1ERJ5EI8HR1hKerIyqW8rXYRxRFJKWoLN5/9TQ2CY1r8ZISKnoYjsjWRFFEoiYZIgARIpD+pcQZX00swvR5dvszWjK+0zizPfO5+b5sxsrSz3x/djXA8KXKeanPfH+WscRsjsk8uVn7v0kPAABJ2lQQZWB4sjGVWoOvvvkej54m2LqUQikhiX/h5QdBEODkoISTgxKlA7xsXQ7RK8FwRAXdV39txaknl2xdRqF34P4ZNPKuYesyqIBgeLKxK389wL7TV21dRqHn4eJg6xKIqABSaRMRlfoHwxG9lq7G3bKqnwAh/TGzBdAvFmS836y/IJgfk/FMyOYYCMbdIRiNbmmfxfMLWc5lUoVg0ifb12hhjOzPn+U1pjdEJOvvAa7qxqsrKBPDk41ptPovSPPzcsFHnerZuJrCSYCAN6sE2roMIipgRFHE4Yi+SFQ/zLaPPhzpA5GjISQVMwQmpcSV4YhsbmH10SjnrP93ToDA92Q++fpuOHb8dxw9g9rYuhQqQBieCghXZ3u0b1zF1mUQERUZIrSG4FTcoQ6c5SUYjui1JBEESAQu3JPfepd8B71LvmPrMqiAYXgiIqIir3GxGVBKXWxdBhERFXD8zxRERERERERWYHgiIiIiIiKyAsMTERERERGRFRieiIiIiIiIrMDwRIXes9QU/BP3FE9Tk6HWaW1dTqEz9eIJ7Lr1u63LICIiInrpuNoeFWpRKUl4a+8KqIxCk5NcAVeFnf5HaQc3hT3clHZwUdjBTWkPV4Ud3JR2cFOYtjnJFVyyOIsbMY+x+a9fAQDvlw22cTVERERELxfDk435ejinPzrZuJLCKSIx1iQ4AUCiWoVEtQoPkuJzNZZUENKDlb1J8HJVKOGq1Acw4/1uCju4pPdRSKX5+bIKjMjkBMP2/x7egTT9u0YkgmDY1j8KkEokJm1Sk37ZtEtM24iIiIhsieHJxt4o4YXtc0NRzIvfL/IyBTq54cy7/RGvSkOcKhWxqhTEpaUiVpWKuLQUfVtaavpjCuJV+n2xafrnKp0WWlFETFoKYtJScn1+B5ncELgyApabYfYrc7bLeJ+L0g7OcmWBDg3GtfU+ufOlny8jSAnp4UpqIahJBQkkkqz7JRAEQCpkDXDGAU8CqUTINgCa9c0S8CQwHyO7401fg9EYWfrG5eG9RkRERC8Pw1MBUDrAy9YlFAlSiQTudvZwt7MH4J6rY1M1akPAMg5e8UYBK2N/fHo4y9gWASRr1EjWqE1maqwhEQS4yJX62ayMcKWwT5/1Mg1jxgHMVWkPO+nL/793oJObYbuCmze0oggdROh0OmhFEVpRB50o6ttFS22Z7TpRzPF8+uOL3n1rBTlAExERFSUMT0RWsJPJYSeTw9fBOVfH6UQRCRmzXWkp6TNd+nBlFrxUqYhPy5jxSkGqVgOdKOqfq1KB3OUu2EllRsFKaTLDpb/cMMtMWPq2s1wJqcS6tWQy7gFzUShxrMNHuSswCzE9QFkTtEzaLQY1y30N2zr9ow4itLoc+qY/mtWmy0Vfw3lz0ddoOySgzAv9bomIiCh/MDwRvUQSQdAHFKUdAp3dcnVs6v/bu/M4KcoDfeBPVfUx9wwMDIw4A17cgjACIsyY4JFExMSoaIwbNaskv81qPCKuFx6oAfHIeq73mY/GTQyajIsRkwiKQSQLgngAws5wzMUxw/T0VVXv74/urq7q7oGG6Zrqnnm++ZDqeuut6vd9GZl55q16W1PRYbqVsD0UD1YdltsMo2EsdhtiKABdCAQ0FQF/J5r9nYf1vhKAYo83YYYr4dbCaJkvHDqsax/0fWO3sQEA+uYzYkRERBTxf3v245PtjfjBSWPhzqFnwxmeiLJUnuJCXkERKgoObzERXQh0hoNoN4Wr9oRbCc23GUaCVxD7Q350qWEIAB2hIDpCQeDwchcRERFRWu7/ywq8/9VWDCkpQt0JxzjdnLQxPBH1MbIkoSS6zHrVYZ4b0jS0mxfRMN1mGA9e1tmwjlAA5x4z1pa+EBERUd/UGYzcvXIgkLm7WHoDwxMRGTyKgsH5hRicX+h0U4iIiIiyTnpPhRMREREREfVznHkiW8VWUFNNK4xFVkiLvU6xwpgeLzefEys3r0Sm6XrCSmWR12p0JbTtB/Y5PQRERERE1EcwPGWBz9p2Y4ev3fiBPyk4pFiKOVXA0IWAmhAwYkEisZ5mChjm8KEK3Qg2luBihB7rvnnp59h7RIJS+p/d0xs8ObSKCxERERFlJ4Ynh32xrwXnvvOS081wjCxJcEkyZEmCYmwluGQZsiRDkSTjmCJJUGTZUk+R5GiZtZ4syXDJ0a0k4/zjxjvdVSIiIiLKcQxPDmvqinzyaYHLjQnlldHQYA4B1oAQCwbmIGGuJ0eDh7leLEAospQQUmS4JAlyQn0jzCS0xXgv2bqfWB4LROb3keVYmWQJRbEPWSUiIiIiynYMT1niuNJyvP6dS5xuBhERERERdYOr7REREREREaWB4YmIiIiIiCgNvG2PiIj6NF1o0EQQqh6AKgJQdT9UEYSqdzndNCKiPiukqmj3B3EgEERHIIiOQAAdgfh+4752p5t4RBieiIjIMZHPggtHQ0083GgiiLDuh2Ypt4YfTQ9AFX6oejBhG6mn6QGERQC6CB2yHRJvxCAislA1HQeCQXT4I8EnHoJMgchvDUSR15GyoKql9T7FeR6be5JZDE9ERNSt7mdtYsHGGlgs22gIipwTMM6LHY8FIwG9l3ojwSXlwSV7oUh5cMn5cEleVBacDI9S1EttICLqHbou4AuFug05lkCUYoaoKxTucRskACV5XhTneVGSlxfZ5nuNshEDyzD92Oqed7YXMTwREeWoI5u1ic/KdDdrYw48WhqzNpkiwwWXnA9F8sIt50cDjhcuKd+0zYNLzosey4uGoUgIir1WZC/cUj4U43geXFI+FMnDj0cgopwhhIA/rEZDTSA6A9T9bXDxABQvFxloR4HHjdJY8IkFIVMAKsnLQ4npWGl+vG6hxwNZ7lv/7jI8ERFlsfV7XkCTf203szZBCKR3W0QmxIKIedYmso0HGWtgiQcXS/jppp4s8VsS2U8IAR06dCGgCx06BITQoUXLhalcF5F6IlpfsxyPXaO782KvE49b3zv2WiD1cSEEtGi5SDivU+Vze9kupGrdhJyAaQYo9W1wBwJBhPWez8x7XUo03FgDkDnkxLaJx4q9XrgU3tZsxu9UDit0eSxbIqKYoNaBdXufSauuedYmObAcZNYmYQaHszaUSava1uMfbRugCc0IFSL6g79mDgxJYSU5SBzyvGjQ0IVuCTkC0fARva7IyO/is0ue4nW6CX2Wpus4EAilCECpnvVJniEKqGqP26BIEkry8w4acoz9xBmiPC+8bv64n0kcTYfVDB6Gu6eeiZMrjna6KUSUZYSIzyp9u3Jx9Fa25NkeztpQtnps8+/QHu50uhmHTYYEWZIhRbeyJEFGZCtFXyvdHO/uPFmSjevG66QuVw5xXJZkKJIECRKqCoaiKn+I00PWJz36t4/xxAf/6HHcloDILE6KkBMJQKmPxfYLPG7+8iqL8LutwxRZxk9G1zjdDCLKctVFtU43geiwhfTIA+dzq85Cmaco/oM/5EgISQoXsfJYYIiXWwOMOVBIxnlKtFxKDC6IBg9JghILRqZyy7Uh8QdVAgD89autRnAqcLtRcpCQU5I0AxSvW+T19rnnfvozhiciIiKy1VlDp6Eyf7DTzSA6Is/8+DzUnjDC6WZQluATYERERERE3eBMJJkxPBEREREREaWB4YmIiIiIiCgNDE9ERERERERpYHgiIiIiIiJKA8MTERERERFRGhieiKhH/qfx/2Ft25NON4OIiIjIdvycJyI6Yq2BTWgJrEdLYD0UyQ1JUiBDgSS5olslvpUUSEjcuqJbuZs6Lus1Ul7TZdTncrJERERkJ4YnIjpiqu43Xq/f+7yDLYkwQlhCyDICmaRARjeB7GDhzqgnJ9dJFRRTXtOVcF6K90toX1j3OT2kREREZMLwRERHrMA12Hg9qvQ86EKDgBbZCg06ErdqZButJ4SeRp3k6wjoKdsjoEevGQZEb40CERER9RcMT0TUYx65GKdU3Nhr7yeEDgE9GrDUg4S16Fao0WCV6pjpGtAOEQDVhPOS6yeVISEIWsrUNAKnhqrCmb02tkRERNQ9hiciyjmR2+dkyJILgNfp5hAREVE/wfBEfd7Wjv9BR3hH9HkYOeE5GDm60IAS/WFcSXpuRoL5OZfIsch1XKbrdXcdF2TI8fMSrwOZixwQERER5QiGJ+rT9ge34cPmhU4346AkI3zJplXjEkJYUugy71sXMbAGOlfStZPDW+rFEA4eDCP1/No+p4ePiIiIqNcwPFGfFlutzC0X4Jji7xjPpJgXKog8b6Ibz8TEn0dJtW89L76vmxY3SD7vYKsXxM4DAC1HFzmQJMXpJhARERHZjuGJ+gWvUobpvbigQSJhCmd6ivBmXQAhObwlhrRUq9XF6x/ZdZLr6yneR4eAmnSdEcVnODa2RERERL2F4YmoF8RunYPkAudoiIiIiHKT7HQDiIiIiIiIcgHDExERERERURoYnoiIiIiIiNLA8ERERERERJQGhiciIiIiIqI0cLU96nN0oaLF/xkafCvR0PkBAECC5HCriIiIiCjXMTxRn6DqAezqWo2GzpXY4fsQQb3DOKZIXpxQcq6DrSOibPXCtrexZs/nkCUJEiRI0a2xHy2TIQHm8sR6pvqHupYEQIYcP0cCJMimepGPN5CNY5KxDyBaL1IfQLSeta2Hak/kHVP3zTieYgwO9T6J1/VrQUf+XomI7MLwRDkroO3HDt9HaOhcgV1dn0AT8W/SXrkURxfOQHVRHY4qmAqXnOdgS4koG2lCw+8blzvdjH6hiyGKiPoIhifKKQfCu9DQuQKNvpVo8a+HgG4cK3JVoqqoDtWFdajIPxGyxC9vIkotrKvoCPuM/ZtGX45CVz5E7H9CQI9uzfuAgG4qE+Z9cz0B6JErQBcCAKALPVoL0Xp69Brx/Ug9YTovdTuE6T2Neinakdz+eDuMdqVoh7EPAV0ktAt6tH/J7Ugclx3+ZgBAe+iA/X+pRES9gD9dUlYTQmBv8Gs0+laioXMF9oW2WI4P9I5EdWEdqorqMMBzHCSJzzYR9RchPYzOcBc61S50qv7o1vzaVBa2Hg/qIcu1Jg0YjWJ3gUM96btmr7jG6SYQEWVUzoSnefPmYeDAgVi0aBEAYNOmTbjjjjvw9ddf4/jjj8ddd92F8ePHO9xKygRdqGj2r0ejbwUaOlfApzYbxyQoGJI/EdVFdagqrEWRu9LBlhJRTwghENTDSaHHFws94e5CUeR1SA/3uA2FSj4mDxyNIld+BnpERER9XU6Ep/r6enzwwQc477zzAABdXV2YN28e5syZg0WLFuG1117Dz372M7z33nsoKOBvDnNRWPdjV9dqNHauxA7fR5YFH1xSHo4qOAXVRbUYVngq8pRSB1tKRGZCCAT0kGkGKBZyugs91lkgVWg9en8JEgpd+Shy5aPIVRD9E33tjr5WClDkTj5e4MqHIvETO3rDfn/A6SYQEWVE1oen/fv34/7778eJJ55olL3zzjvwer2YP38+JEnCrbfeihUrVmDZsmX44Q9/6GBr6XAE1H1o9H2EBt8K7O76BJqI30bjVcpQVTgT1YV1qCyYApfsdbClRH2bEAJ+LYBO1Y8D0VDjM9/6dpBb43yqv8cBSIZsCjwpQlD0daFlP1K3QMmDzACU9Xbs6wCGOd0KIqKey/rwtHjxYnz/+99HS0uLUbZ+/XrU1NQYz7dIkoTJkydj3bp1DE9ZriO0w3h+qTWwwbrgg/soVBfWobqoDoPzToQsKQ62lCi36EJHlxYwZnV8B7ndLXkGyG8sVnCkFEnuNvSYZ4EKTWXF0df5Sh6fVyQiopyQ1eHp448/xqeffoo//elPuPPOO43y1tZWHH/88Za65eXl2Lx5cy+3kA4lsuDDV2jwrUBj50rsC221HC/3jkZVUS2qC+tQ5jmWP0ARJdju24UvOrYdchaoS/Ubq6kdKZekWGZ1LLM8SYHItO8uQJ7s4X+/RETU52VteAoGg7jjjjuwYMEC5OVZP6PH7/fD4/FYyjweD0Ih6+pJ5IzIgg/rjCXFExd8GJo/CVVFdagqnIki91AHW0qU3YJaCNf/70NJK8MdjEd2p575ib12W8vNt8J5ZTcDEBER0UFkbXh67LHHMH78eNTW1iYd83q9SUEpFAolhSzqPWG9Czt9/0CjbyV2+FYhpMc/08Ml5WNY4TRUFdbh6MJT4VVKHGwpUe4IaEEjOJ0+ZGqKMJQwC+QugEd2O9xqIiKivitrw1N9fT3a2towadIkADDC0rvvvotzzjkHbW1tlvptbW2oqKjo9Xb2Z351Lxp9H6KxcwV2+T+FblrwIU8pQ1VhLaoK61BZcDIXfCDqoetHXep0E4iOWIHMpeCJqG/I2vD0yiuvQFVVY/+BBx4AAPzqV7/CmjVr8Mwzz0AIAUmSIITAP//5T/z85z93qrn9RkdoR/T5pRVoCWwATM9YFLuHobrwNFQX1WFQ3jgu+EBE1M/tbyxBXmkAQ48a4nRTKEcIIaDqOsKajpCqIaSp0a2GcGyraUZZqm1Y66asm2OW94i+b1hTsdfnd3o4KAtlbXgaNsy6pmlhYSEAYPjw4SgvL8eDDz6Ie++9FxdffDFef/11+P1+fO9733OiqX2aEAJ7gl+goXMlGn0rsD+0zXK83Ds6+oG1dSjzHMPnJYiIyLBnc3nkxWhn20GpCSEiYcEUTEKqagocuiVYJG7DCcEk1bGU5x0ixPRs6ZvMynO5cMygAU43g7JI1oangykqKsJTTz2FO+64A2+88QZGjRqFp59+mh+QmyGaCKO5638jM0y+lehSW41jkQUfJkcD00wUuvnbRCIiIid8sn0H3tn4FYKqmnKW5ZAhRuvZZ7T1BgmAx6XAo7iiW8XYumNbU1mqrVtJeG2p44qWy0llHkXB4OJCFOfx0QOKy5nwtGjRIsv+hAkT8Mc//tGh1vQ9Yd2Hnb7VaPCtwA7fKoT1TuOYSyrAsMJTUF1Yh6MLp8OjFDvYUiIiIgKAu/78Pra27c3Y9RRJSgod3YWNeB3ZFECSw0u6wcbdzXu4FH4INmWXnAlPlHl+dU/0A2tXYrf/U+gibBzLUwagqrAW1UV1qMyvgcIFH4iIiLKKPxz5vn3JlImoHliWEFpkeFyuFMGk+2CjyAwqRIfC8NTPtIca0Ni5Ag2+FWgNfA7zgg8l7ipUFdWhurAOg/LGcsEHSsvSHX/DNt8uRJ52kyBJgAQJxv9LUvxYdN9cI3ZcggSYX5vOTbxe7GrmfRkyIMXLzW1JvJ61babrmepH2pP6eun0TU7o5yH7JgGWtgDoUH2Z+Cuig7jomdew3x/Au9dc4XRTiI7YeSeNxYnD+LmJRL2B4amPE0JHW/BLIzC1h7Zbjg/yjo08v1RUh1L3cC74QIelJbAXz3zD22ftFgtVlFkhVcP6nU0AgCtfeRNDSoqioTcSbmXJFJKN16ZgHH2dXCd+biyAy5IpnBuvo+fHfgGQcA0p6Vzr+8hSPJRbrx//xUC8van6BEud2C8Muu9T6utby6z9ICLqaxie+iBNhNHUtRYNvpVo7FwJvxb/TCwJCioLalBVWIeqoloUugY72FLKdWE9/nECl42YA0BAxP5fRLYiOrsphOmYaR8Q0CEgRGwvWi4sZ0MIJOybXhvXi/5PoNu2xK8N89Ws7bG05eDX675vqc+1np/Yt+SxEQCmDRx/5H9JlJYPt/6f000gIqIcwPDUR4Q0H3Z2fYyGzhXY2fUxwnr8dh+3XIBhBdNRVVSLowu44ANlXqErH3Orz3S6GUSHJTYjAwD/emoNSvLzIkFWxIOvLuKBV4+FZBEP20II6LHwm+JcGHXM5+Iwrx8L/fH6B79+7HWq65vbGd/qIh7qdUsbY6+j50ffU09oo0g4N/JLCGCPrwsAMKysxO6/TiKiXsHwlMO61DY0+j5EY+cK7O76FDriswD5Srmx4MPQ/MlQZI+DLXXWmr2fY7e/DYokQ5GU6B8ZruhWkeP7srk8WteVcJ65fryeDFnig7ZEuSIQVrFpd4uxf9XMqSgryHOwRX1TzX2PwxcKQZF5Ex8R9Q0MTzmmPbQdDZ0r0OBbibbA55ZjJe5qVJsWfJD4wzx2+ltw58aneuW9ZEim8BVZtSgSvlKFsuR9ayhTDh72IMMlJ9ZL8R6yAjlFAExqU8pAGDlPjvZBkWQ+E0c56UAgiC+aWvHF7hZs2t2CTU0t+KZ1LzQRXzAnEA4DYHgiIqKDY3jKckLoaAtsQoNvBRo6V6Aj3GA5PihvHKqjM0ylnhHONDKLHQhHbl/0yh7UDBwDTWjQdB2a0KAKDbrQoQoNmoiUaeZ9XYOG6NYoj7yOPZFipkNAj14XAJD9nz142GTIplAmcyEDyjp7OruwqSkSkmJhqWFfe8q6Awrysa/LDwAYVFTYm80kIqIcxfCUhTQ9hN3+tWjsXIlG30r4tT3GMRkuVBacjKqiOlQVzkABF3xIywBPMW4d+68Zu57eXdiK7ieHMg1q7HU0lKl69+FNN4c1I+zFr5Uy7MX29YTzRTdhMUUoNF8zZb+hQxc6wppqKa8qGJKxsSVKhxACu9sPGDNJkbDUiuYDnSnrH1VajDFDKzCmcjDGVlZg7NAKDCkpMp7V4efbEBFROhieskRI68SOrlVo7FwZXfChyzgWWfDhVFQX1WFYwXR4FP6G1Gly9Bknt9MNsYkQkRXwrIEsOcDFglg1wxPZSNcFtu/dhy92t2LT7mZ80dSKz3e3oN0fSKorARhePiAakCJBaczQCgwozE95bSm2njgREVEaGJ4cFtZ9WNm0EDt9qxIWfBiEqqJaVBfW9vsFH6j3SZIEBRKU2HNz/Lxk6iUhVcPW1j3Y1NRq3Hr3ZVMrusLhpLouWcbxg8sts0mjhg5GkZf/XhIRkT0YnhzW4t+IRt8KAECpZwSqCyMfWDvIO5oLPhARNre0Id/txtEDSp1uSsb5Q2F81dwWCUlNLfh8dws2t+xBWEu+bTTP5cKoIYMiM0mVFRhbWYETBpfD6+a3MSIi6j38ruMwAR0AMNA7EnOqX3S2MUSUVTqDIcx54hUAwJd3Xudwa3qmwx/AJtOKd180teCbtn3GZxWZFXu9ltmksZUVGFE+AC6Fv1AiIiJnMTw5TIhIeNob7MBvt79jPEsjSxJkRF7HPkNIQbRcMpV3WxYpV4zXsetIpuPdXAMSZEmBYioz1yGi3tHUfsDpJhyR1gM+fBFdxCH2Z8f+jpR1BxUWWGaTxg4djKMHlHJZfCIiykoMTw5r7Ip8SOPeUAeW717mcGsOTYKUEMAkSwhTjLAlQZEUSyiLfQ6SkiKQmcsU03Ut10wV/Iz3Sh0G24L7nR4yoj5LCIGd+zuMgPRF9Dml1k5fyvrDykqMmaTYzFJFcVEvt5qIiOjIMTw5LKiHAACKJOPsypnQhQ4dOjQRWRJaF8JYHjqxLL6vQ0O0PFYWXSktscx8HQ2m68XKo7cRdkdAQBMCGnSk+KijrOWR++q6eES9Q9N1bN+zLxqUWo1b7zoCwaS6EoBjBw3EmMoKjBk6GOMqKzB6aAXKCvghtERElNsYnrKES3bh306Y63QzAKDbgBYJXPEwFgtb5oAWLzOFMkv4E6bjpuubrqsLAQ2R4Bc/nhAeTe9thETjfUztgQ4IgW8PmeL0sBLljJCqYnPLHmMmadPuFnzV3Ap/WE2q65ZlnFAxyDKbNGrIYBR4+AsLIiLqexieKEnktjiA61MT9X2+YAhfNbdi0+5W4zmlLS17ENaTZ6Hz3S6MHjoYY4ZWYFz0OaXjB5fD4+K/FURE1D8wPDmOD0UTUe/Y3xVIWshh+559Ke/ALc3zGos4xMLS8PIyKDIXjSEiov6L4clhBa5j0Rosgl+McLopRNRHCCHQcsBnPJcUe05pV3vqFe8qigsts0ljKytwVGkxV7wjIiJKwPDkMEUuwN/2jsbxRVVON4WIcpAQAo372pNWvNvj60pZv2pAqWU2aUzlYAwqKuzlVhMREeUmhiciohyhajq27dlrWfHuy6ZWHAgmr3gnSxKOGzQwuojDEIwZOhhjhg5GST5XvKPe9+Pn30C+2wWPS4FHccHtUuBWZHgUJVqmwG167XFF99M47j6MuvygZSLqKYYnIqIccNEzr+HL5lYEVS3pmFtRMGrIIGNZ8DGVFRhZMQj5XPGOHDZySDn+t3F3t5/91dtkSeomXMlGqIscl03HXYcV9MyhLt0gyGcJiXIHw5PDKvMGQYaEYfkVTjeFiLKMeRW79TubAAAFHjfGDB1sufXu2MED4Va44h1ln5cuuxDb9uxDSFUR1nSENA1hVUNY0xDSNITUyDZseh06jOPhbo/rCGkqQqpmWRBFFwIBVUVAVYHkCVvHxEJdd0HLnRT0IqFuX5ff6aYT9TsMTw4bVlCBl05ZiFI3nzkgIqsir8d4/dAFZ2PM0AoMH1gGWeZCDpQbPK7IrKhThBBQdT0SplTNCFSxIBcJdRpCmm4EMXPQSyfcpXodTiMIdhvqjkBxnjczA0ZEh8TwlAV2tnZhbUcLFEmCIsvRP9HXRpnpWEKZS5YhS1Jkm3BerIyIctvZ40c53QSinCNJUmTmRlGy6oObY6EucZYsEq70eKhLCHrx4/GgVz2wDCPKBzjdJaJ+g+HJYV81teLCZ16z/X1SBazEMldCOIuVxeqYA9vBzkuskzoMHl5ATAyD6YTI2P7zq9biQCAIlxKp41IUuGQZbmM/ujUdcyky3MYxJaGObDrfdCx6jmK8th5zyTKXfiYiIkuoI6LcwvDksOYDnQCAfLcLIysGQdV16NHfSGm6gKbr0Exlum46JqLHomWpPugyRtV1AEBIS37YnHpPLIApSQEtEtys+5F67sMMdd0FN5fpPGtwVFIEv+T3iJ2jmIKjLEsQQjAUEhERUb/A8JQljh00EL+76kc9uoZuBKp46NJE7PVByoTpWFJZZBsLdanqmEOd3k2ZNfgll8UDY/z6qc6LvefBzkusE9I0eF0K/u20U6Bqkbapmoawrpv2o9vosdg98vFjWkId3VRHS3kdXSTH2dixvmhIcdHBZxR7OOvY02unc3trujO0siTDJUuQ5cg2NtPKEElERNS3MTz1IbIsQYYCN+8CyAqR8KYlhDAd4Wg406Jl4YSAZg51WoqAFtasIS91qEs4lhAOk4NjvJ3dtUtLEQbNYrOo/ZkixQNVLGBZb2VNDl2KFDmeeJ4sy+jwB5zuEhEREZkwPDlsZMUgFHu9OHn4MKebQhkmyxI8sguePvJfWWwmzxqwNHz6fzuR53ZhSHFRtzOKesIMZk9mHTM9M5n62jpUXUDXI1vLeQcJkZoQ0DQNYd4dS0RE1Cf1kR/rctfQ0mKsmv8zPjRKWS8SBhV4YP1a7W+rwAkhoOnCCFjWYGa+RbabMpEQ5g5yu62qC9z61l9Qmp/ndLeJiIgIDE9ZgcGJKHdIkgSXIgGQ4Tlk7Z47f9K4XngXIiIiSofsdAOIiIiIiIhyAcMTERERERFRGhieiIiIiIiI0sDwRERERERElAaGJyIiIiIiojQwPBEREREREaWB4YmIiIiIiCgNDE9ERERERERpYHgiIiIiIiJKA8MTERERERFRGhieiIiIiIiI0sDwRERERERElAaGJyIiIiIiojQwPBEREREREaWB4YmIiIiIiCgNDE9ERERERERpYHgiIiIiIiJKA8MTERERERFRGlxON8ApQggAgKZpDreEiIiIiIicFMsEsYzQnX4bnnRdBwBs2LDB4ZYQEREREVE2iGWE7kjiUPGqj9J1HaqqQpZlSJLkdHOIiIiIiMghQgjoug6XywVZ7v7Jpn4bnoiIiIiIiA4HF4wgIiIiIiJKA8MTERERERFRGhieiIiIiIiI0sDwRERERERElAaGJyIiIiIiojQwPBEREREREaWB4YmIiIiIiCgNDE8Z1tzcjGuuuQZTp05FbW0tfv3rXyMYDAIAGhsbcfnll+Okk07C2WefjQ8//DDlNd5++238y7/8S7fv8eyzz2LWrFm2tD/b2TW+7e3tGDVqlOXPtGnTbO9PtrHz6/e3v/0tvvWtb2Hy5Mm45pprsH//fju7kpXsGN8dO3Ykfe3G/qxZs6ZX+pUt7Pr6DQaDWLhwIaZPn47p06djwYIF6Orqsr0/2cau8Q2Hw1iyZAlmzpyJU045BYsXL4aqqrb3J9v0ZHz/8Ic/4Lvf/S4mTZqECy+8EGvXrrUcf/HFF1FbW4tJkybhlltugd/v77V+ZQs7xzfmtttuw6OPPmp7X7KRXeMbCoWwePFi1NXVYcqUKfjFL36BpqamXu1bEkEZo+u6mDt3rrjyyivF119/LdasWSPOPPNMsWjRIqHrupgzZ4644YYbxJYtW8R//dd/iYkTJ4qdO3darvHxxx+LiRMniksvvTTlezQ0NIiJEyeKb3/7273Rpaxi5/h++umnYurUqaKlpcX409bW1pvdc5yd41tfXy8mTJggli1bJr766itxwQUXiOuuu643u+c4u8ZXVVXL121LS4u4/vrrxfnnny9CoVBvd9Mxdn79PvDAA+Kcc84Rn332mVi/fr343ve+JxYuXNib3XOc3eN76qmnir///e9i48aN4vvf/z7H9zDG94MPPhATJkwQb731lti+fbt4+OGHxeTJk0VTU5MQQohly5aJmpoa8de//lWsX79enH322eKuu+5ysru9zs7xjXn66afFyJEjxSOPPOJEFx1l5/guWbJEnHHGGWL16tVi8+bNYt68eeL8888Xuq471l+GpwzasmWLGDlypGhtbTXK/vSnP4mZM2eKVatWiZNOOkn4fD7j2GWXXWb5j+zRRx8V48ePF+ecc0634emKK64QF198cb8MT3aO7xtvvCEuuugi+zuRxewc3x/84Afi0UcfNfY/+eQTMXv2bKGqqo09yi698e+DEEKsXbtWjBs3TmzZssWejmQpO8d3zpw54pVXXjH2X375ZTF79mwbe5N97BpfXdfFpEmTxO9//3ujbN26dWLcuHGis7PT5l5lj56M77XXXisWLFhgud5ZZ50lfve73wkhhLjkkkssfxdr1qwREyZMEF1dXXZ2KavYOb4HDhwQV199tZgyZYo47bTT+mV4snN8Tz31VFFfX28ca25uFiNHjhTbtm2zsUcHx9v2Mmjw4MF49tlnMWjQIEt5Z2cn1q9fj7Fjx6KgoMAor6mpwbp164z9jz76CM899xzOOuuslNdfunQp/H4/LrjgAlvan+3sHN8tW7ZgxIgRdjU9J9g1vp2dndi0aRPOPPNMo2zKlCn485//DEVR7OlMFrL734eYBx98EHPnzsVxxx2X0fZnOzvHt6ysDO+++y7a29vR3t6Ov/zlLxgzZoxtfclGdo3v3r174fP5MHHiRKNs1KhRCIfD2Lhxoz2dyUI9Gd8rr7wSV1xxRdI1Dxw4AE3TsGHDBpx88slG+UknnYRwOIwvv/zSns5kIbvGF4jcOh0MBvHmm2+iqqrKvk5kMbvGV9d1LFmyBKeeemrK405xOfbOfVBJSQlqa2uNfV3X8eqrr+KUU05Ba2srKioqLPXLy8st922+9tprAIDVq1cnXXvv3r144IEH8MILL2DDhg029SC72Tm+W7duhaqquOCCC9Dc3IyTTz4ZN998c9I1+zK7xrexsRFA5Gv44osvxo4dOzBjxgzceuutKCkpsas7WcfOr9+YtWvXYt26dXjooYcy3PrsZ+f4zp8/H1dffbXxHOTIkSPx5JNP2tGNrGXX+JaWlsLtdqO5uRnHH388AGD37t0AgH379tnSl2zUk/EdN26c5diKFSuwfft2nHLKKejo6EAwGLSc73K5UFZW5vxzI73IrvEFgNGjR+Opp56yuQfZza7xlWU5KTi9/PLLGDBgAEaNGmVTbw6NM082WrJkCTZt2oTrrrsOfr8fHo/Hctzj8SAUCqV1rfvuuw/nnXceTjjhBDuampMyOb7ffPMNOjs7cfPNN+Phhx9GS0sLfv7zn0PTNDuanhMyNb4+nw8AcPfdd+Oqq67Cf/7nf2Lz5s2YP3++Le3OFZn8+o154403cOaZZ2LIkCGZbGpOyuT4NjQ0oLKyEi+99BKee+45BINBLFq0yI5m54xMja/L5cKZZ56Jhx56CE1NTThw4AAWL14Ml8uFcDhsV/Oz3pGOb0NDA26++WbMmTMH48aNQyAQMOqnc35/kanxpdTsGt/ly5fj+eefxw033JB0zd7E8GSTJUuW4KWXXsKSJUswcuRIeL3epC+UUCiEvLy8Q15r5cqVWLduHX7xi1/Y1dyck8nxBYD6+nq8+uqrqKmpwcknn4xHHnkEX3zxBdavX29H87NeJsfX5YpMcM+bNw+nn346ampqcO+99+Jvf/sbmpubbWl/tsv01y8AqKqK999/H+eee26mm5tzMjm+nZ2duPXWW3HTTTdh2rRpmDFjBu677z784Q9/QEtLi11dyGqZ/vq97bbbUFhYiNNOOw11dXWYPHkySktLUVRUZEfzs96Rju+2bdvwk5/8BFVVVbjnnnsAAF6v16ifeH5+fr6NvchemRxfSmbX+C5fvhzXXnstLr30Ulx44YW29uFQGJ5ssHDhQrzwwgtYsmQJvvOd7wAAhgwZgra2Nku9tra2tG4Le+edd9DU1ITp06dj0qRJuOOOO7Br1y5MmjQJn376qS19yGaZHl8AyM/Pt/yHXF5ejrKysn75w32mx3fw4MEAgGOPPdYoO+aYYwCgX902EmPH1y8ArFu3DqqqYsaMGRltb67J9Ph+88036OrqwujRo42ysWPHQtd1fv1m6Ou3vLwcL7/8MlavXo1Vq1bh0ksvxZ49ezBs2LCMtz/bHen4bt68GZdeeimGDh2KZ5991vh+VlZWBq/XazlfVVXs37/f+Le5P8n0+JKVXeNbX1+PX/7yl7joootwyy232N+RQ2B4yrDHHnsMr7/+Oh566CHMnj3bKJ84cSI+//xzYwodiDyfYH5Itju/+tWvUF9fj6VLl2Lp0qW45pprUFFRgaVLl2L8+PG29CNb2TG+nZ2dmDJlCv7xj38YZc3Nzdi3b5/lB/7+wI7xPeqoo1BRUWF5OHnr1q2QJAlHHXVUZjuQ5ewY35j169dj3Lhxxm+a+yM7xjf2DX7Lli1G2TfffAMAOProozPV9Jxg19fvjTfeiA8//BBlZWXIz8/HBx98gPLycuMZqP7iSMe3paUFP/3pTzF8+HA899xzlhk7WZZx4oknWj43Z926dXC5XJZfCPQHdowvxdk1vh9//DHmz5+PH//4x7j99tt7pzOHwPCUQVu3bsUTTzyBq666CjU1NWhtbTX+TJ06FZWVlbj55puxefNmPP300/jss8/SWjmvvLwcw4cPN/6Ul5fD5XJh+PDh/eq3H3aNb1FREWpqavDrX/8an332GT7//HNcd911qK2tdfSBxN5m1/hKkoTLL78cjzzyCD766CN8+eWXuPPOO3HGGWf0q9982jW+MZs3b+53K+yZ2TW+Q4cORW1tLW6//XZs3LgRGzZswO23347Zs2dj4MCBvdCz7GDn129ZWRkefvhhfP3111i9ejUWLlyIefPmQZb7z48oPRnfxYsXQ9d13Hvvvejq6jLOiz1veskll+C5557D8uXL8dlnn+HOO+/E3Llz+9Vte3aOL9k3vqqq4pZbbsGUKVNw1VVXWa7r5DN7XG0vg95//31omoYnn3wyaSWmr776Ck888QRuvfVW/PCHP8Tw4cPx+OOP97vfvPeEneO7ePFiLFq0CPPmzUMoFMLpp5+O2267zY5uZC07x/enP/0pgsEg5s+fj66uLsyaNQt33nmnDb3IXnb/+9DW1tbvls82s3N8H3zwQePfB0mScPrpp+Omm26yoxtZy87xvfbaa3HXXXfhkksuQUFBAS6//HJcfvnlNvQiex3p+AohsHz5cgQCAXz3u9+1nPfv//7vuPrqqzF79mzs3LkTCxYsQCgUwllnnYUbb7yxN7vnODvHl+wb39raWuzatQu7du3CzJkzLcdffvllYwXU3iYJIYQj70xERERERJRD+s+cOBERERERUQ8wPBEREREREaWB4YmIiIiIiCgNDE9ERERERERpYHgiIiIiIiJKA8MTERERERFRGhieiIiIiIiI0sDwRERERERElAaGJyIi6tPefPNNjBo1Cv/93/+ddOzpp5/GjBkz8O677+L555/HM88840ALiYgoVzA8ERFRn1ZfX4/q6mq89dZbScdeffVVPPbYY3jxxRfx+OOPY9asWQ60kIiIcoUkhBBON4KIiMgOe/bsQW1tLe677z78x3/8B9577z1UVVUZx1taWlBRUQEA8Pv9yM/Pd6qpRESUAzjzREREfdayZctQXFyMc889FxUVFZbZp1mzZuG9997D3LlzceKJJ+Liiy/Gxo0bjeNNTU345S9/ialTp2LatGm45557EAqFnOgGERFlCYYnIiLqs+rr6/Gtb30Lsixj1qxZWLp0Kcw3XDz66KOYN28e3n77bRQXF+Oee+4BAIRCIVx22WXw+/145ZVX8Jvf/AZ///vfcf/99zvVFSIiygIMT0RE1Cft3r0b//znP3HGGWcAAM466yw0NjZi7dq1Rp3zzjsPZ5xxBo455hhcccUVxszTypUr0dzcjCVLlmDUqFGYPn06FixYgNdeew0+n8+R/hARkfMYnoiIqE+qr6+H1+vFzJkzAQBTp05FaWkp/vjHPxp1RowYYbwuKipCOBwGAGzduhUjRoxAaWmpcXzy5MlQVRUNDQ290wEiIso6DE9ERNQn1dfXIxAIoKamBmPHjsWECRPQ3t6OZcuWIRAIAADcbnfKc71eb1KZpmmWLRER9T8upxtARESUadu2bcOmTZtw2223Ydq0aUb5li1bcN111+G999476PnHHHMMtm/fjv3796OsrAwAsG7dOrhcLlRXV9vZdCIiymIMT0RE1OfU19ejrKwMF110ETwej1E+cuRIPP7441i6dOlBz58xYwaqqqowf/583HDDDdi3bx8WLlyIc845ByUlJTa3noiIshVv2yMioj6nvr4ec+bMsQSnmB/96EdYtWoVmpubuz1fURQ88cQTAIC5c+fi+uuvx+mnn467777btjYTEVH244fkEhERERERpYEzT0RERERERGlgeCIiIiIiIkoDwxMREREREVEaGJ6IiIiIiIjSwPBERERERESUBoYnIiIiIiKiNDA8ERERERERpYHhiYiIiIiIKA0MT0RERERERGlgeCIiIiIiIkoDwxMREREREVEa/j9wNIRT20F4uAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Llamamos a la funcion\n",
    "graficar_ingresos_por_provincia(df9, ['CAPITAL FEDERAL','CORDOBA', 'MENDOZA', 'SANTA FE', 'TUCUMAN', 'ENTRE RIOS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10 - Acceso por cada 100 Habitantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
       "      <th>Accesos por cada 100 hab</th>\n",
       "      <th>Periodo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>77,21</td>\n",
       "      <td>24,15</td>\n",
       "      <td>Oct-Dic 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>76,64</td>\n",
       "      <td>23,95</td>\n",
       "      <td>Jul-Sept 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>75,97</td>\n",
       "      <td>23,72</td>\n",
       "      <td>Abr-Jun 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>73,88</td>\n",
       "      <td>23,05</td>\n",
       "      <td>Ene-Mar 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>73,18</td>\n",
       "      <td>22,81</td>\n",
       "      <td>Oct-Dic 2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre Accesos por cada 100 hogares Accesos por cada 100 hab  \\\n",
       "0  2022          4                        77,21                    24,15   \n",
       "1  2022          3                        76,64                    23,95   \n",
       "2  2022          2                        75,97                    23,72   \n",
       "3  2022          1                        73,88                    23,05   \n",
       "4  2021          4                        73,18                    22,81   \n",
       "\n",
       "         Periodo  \n",
       "0   Oct-Dic 2022  \n",
       "1  Jul-Sept 2022  \n",
       "2   Abr-Jun 2022  \n",
       "3   Ene-Mar 2022  \n",
       "4   Oct-Dic 2021  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = pd.read_csv('datasets/datasetsGraph/Internet_Penetracion.csv')\n",
    "\n",
    "df10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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",
       "      <th>Accesos por cada 100 hab</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>77.21</td>\n",
       "      <td>24.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>3</td>\n",
       "      <td>76.64</td>\n",
       "      <td>23.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>75.97</td>\n",
       "      <td>23.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>73.88</td>\n",
       "      <td>23.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>73.18</td>\n",
       "      <td>22.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Trimestre  Accesos por cada 100 hogares  Accesos por cada 100 hab\n",
       "0  2022          4                         77.21                     24.15\n",
       "1  2022          3                         76.64                     23.95\n",
       "2  2022          2                         75.97                     23.72\n",
       "3  2022          1                         73.88                     23.05\n",
       "4  2021          4                         73.18                     22.81"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se observa que la columna de accesos por cada 100 hogares, es una metrica.\n",
    "# Por lo tanto, los valores almacenados son decimales.\n",
    "\n",
    "# Procedemos con el reemplazo de la coma por el punto, y de tipo float.\n",
    "df10['Accesos por cada 100 hogares'] = df10['Accesos por cada 100 hogares'].str.replace(',', '.').astype(float)\n",
    "df10['Accesos por cada 100 hab'] = df10['Accesos por cada 100 hab'].str.replace(',', '.').astype(float)\n",
    "\n",
    "# Eliminamos la columna Periodo\n",
    "df10.drop(columns='Periodo', inplace=True)\n",
    "\n",
    "\n",
    "df10.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graficamos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El gráfico histórico presenta la cantidad de accesos por cada 100 habitantes a lo largo del tiempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupar por año y sumar los ingresos por año\n",
    "df_agrupado = df10.groupby('Año')['Accesos por cada 100 hab'].mean().reset_index()\n",
    "\n",
    "# Graficar los datos en un gráfico de líneas\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df_agrupado['Año'], df_agrupado['Accesos por cada 100 hab'], marker='o')\n",
    "\n",
    "plt.title('Accesos por cada 100 hab')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Cantidad de accesos')\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
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