notebooks/2_eda_enrichment_and_preprosesing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-LplsHfRNCLg"
   },
   "source": [
    "# 1. General Code\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ## 1.1 Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "id": "BMMOXa7PNCL9"
   },
   "outputs": [],
   "source": [
    "# Libraries for data manipulation and analysis\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from packages.common_functions import decompress_file, load_csv_into_dataframe, load_dataframe_to_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries for data visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries for machine learning\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, OrdinalEncoder\n",
    "import joblib\n",
    "from packages.machine_learning_class import ModeloML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ## 1.2 Data Visualization Parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "id": "4YQt1CbNNCMM"
   },
   "outputs": [],
   "source": [
    "# Configuration of initial parameters for the Matplotlib library\n",
    "params = {\n",
    "    'figure.figsize': (16, 10),  # Size of the figure (width, height)\n",
    "    'figure.dpi': 80,  # Figure resolution in dots per inch\n",
    "    'figure.facecolor': 'white',  # Background color of the figure\n",
    "    'figure.edgecolor': 'white',  # Edge color of the figure\n",
    "    'figure.frameon': True,  # Show or hide the frame of the figure\n",
    "    'figure.constrained_layout.use': False,  # Enable constrained layout for figures\n",
    "    'figure.constrained_layout.h_pad': 0.04167,  # Horizontal spacing in constrained layout\n",
    "    'figure.constrained_layout.w_pad': 0.04167,  # Vertical spacing in constrained layout\n",
    "    'axes.labelsize': 12,  # Size of the axis labels\n",
    "    'axes.titlesize': 14,  # Size of the title\n",
    "    'axes.labelweight': 'normal',  # Font weight of the labels\n",
    "    'xtick.labelsize': 10,  # Size of the x-axis labels\n",
    "    'ytick.labelsize': 10,  # Size of the y-axis labels\n",
    "    'xtick.color': 'black',  # Color of the x-axis labels\n",
    "    'ytick.color': 'black',  # Color of the y-axis labels\n",
    "    'axes.spines.top': True,  # Show or hide top border\n",
    "    'axes.spines.right': True,  # Show or hide right border\n",
    "    'legend.frameon': True,  # Show or hide legend frame\n",
    "    'grid.linestyle': '-',  # Style of grid lines\n",
    "    'grid.linewidth': 0.8,  # Width of grid lines\n",
    "    'grid.color': 'gray',  # Color of grid lines\n",
    "    'legend.fontsize': 10,  # Font size of the legend\n",
    "    'axes.titleweight': 'normal',  # Font weight of the title\n",
    "    'axes.titlecolor': 'black',  # Color of the title\n",
    "    'axes.titlelocation': 'center',  # Location of the title (left, center, right)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying the initial parameters\n",
    "plt.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the 'tableau-colorblind10' style\n",
    "plt.style.use('tableau-colorblind10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Activating inline visualization of plots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ## 1.3 EDA Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is not need"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ## 1.4 Preprocessing Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.1 Encoding DataFrame function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_encoder(dataframe, columns, encoding=False, decoding=False, le_dict=None):\n",
    "    \"\"\"\n",
    "    Encodes or decodes labels in the specified columns of the DataFrame.\n",
    "\n",
    "    Args:\n",
    "        dataframe (pd.DataFrame): Original DataFrame.\n",
    "        columns (list): List of column names to encode or decode.\n",
    "        encoding (bool): If True, performs label encoding.\n",
    "        decoding (bool): If True, performs label decoding.\n",
    "        le_dict (dict): Dictionary to store LabelEncoder instances.\n",
    "\n",
    "    Returns:\n",
    "        pd.DataFrame: DataFrame with encoded or decoded columns.\n",
    "        dict: Dictionary with LabelEncoder instances.\n",
    "    \"\"\"\n",
    "    if le_dict is None:\n",
    "        le_dict = {}\n",
    "\n",
    "    if encoding:\n",
    "        encoded_df = dataframe.copy()\n",
    "        for col in columns:\n",
    "            le = LabelEncoder()\n",
    "            encoded_df[col] = le.fit_transform(encoded_df[col])\n",
    "            le_dict[col] = le\n",
    "        return encoded_df, le_dict\n",
    "\n",
    "    elif decoding:\n",
    "        decode_df = dataframe.copy()\n",
    "        for col in columns:\n",
    "            if col in le_dict:\n",
    "                decode_df[col] = le_dict[col].inverse_transform(decode_df[col])\n",
    "            else:\n",
    "                raise ValueError(f\"No LabelEncoder instance found for column {col}\")\n",
    "        return decode_df\n",
    "\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4.2 Imputation null values function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.2.1 Imputation by dispersion measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_null_values_by_mode(df, column_to_impute, grouping_column):\n",
    "    \"\"\"\n",
    "    Imputes null values in a column based on the mode of another column.\n",
    "\n",
    "    Parameters:\n",
    "    - df (pd.DataFrame): DataFrame containing the data.\n",
    "    - column_to_impute (str): Name of the column to be imputed.\n",
    "    - grouping_column (str): Name of the column used for grouping and mode calculation.\n",
    "    \"\"\"\n",
    "    # Calculate the mode value of the column grouped by the grouping column\n",
    "    mode_df = df.groupby(grouping_column)[column_to_impute].apply(lambda x: x.mode().iat[0] if not x.mode().empty else None).reset_index()\n",
    "\n",
    "    # Create a dictionary to map values of the grouping column to their respective mode values\n",
    "    mode_mapping = dict(zip(mode_df[grouping_column], mode_df[column_to_impute]))\n",
    "\n",
    "    # Impute the specified column based on the mode mapping\n",
    "    df[column_to_impute] = df.apply(lambda row: mode_mapping[row[grouping_column]] if pd.isnull(row[column_to_impute]) else row[column_to_impute], axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.2.2 Imputation by ml model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_null_values_by_ml_model(codec_df, objetive_column, specific_null_value, ml_model_path):\n",
    "    \"\"\"\n",
    "    This function imputes null values in a specific column based on a specific machine learning model.\n",
    "\n",
    "    Params:\n",
    "    - codec_df (pd.DataFrame): Data frame with encoded data.\n",
    "    - objetive_column (str): Name of the column in which null values are imputed.\n",
    "    - specific_null_value: Value that represents null values in the column.\n",
    "    - ml_model_path (str): Path that contains the machine learning model previously trained.\n",
    "    \"\"\"\n",
    "\n",
    "    # Load pre-trained ml model\n",
    "    modelo_ml = joblib.load(ml_model_path)\n",
    "\n",
    "    # Filter columns that contain null values\n",
    "    missing_data = codec_df[codec_df[objetive_column] == specific_null_value]\n",
    "\n",
    "    # Select features to predict without deleting rows with missing values\n",
    "    X = codec_df.drop(objetive_column, axis=1).copy()\n",
    "\n",
    "    # Filter prediction features based on rows with missing values\n",
    "    caracteristicas_prediccion = X[X.index.isin(missing_data.index)]\n",
    "\n",
    "    if not caracteristicas_prediccion.empty:\n",
    "        # Predict null values\n",
    "        predicts = modelo_ml.predict(caracteristicas_prediccion)\n",
    "\n",
    "        # Fill missing values ​​with predictions in the original DataFrame\n",
    "        codec_df.loc[codec_df[objetive_column] == specific_null_value, objetive_column] = predicts\n",
    "\n",
    "    else:\n",
    "        print(f\"No hay muestras para predecir en la columna {objetive_column}.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OTYcQXfTNCMR"
   },
   "source": [
    "# 2. Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xodg9CAONCMS"
   },
   "source": [
    "   ## 2.1 Import CSV File\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLEAN_CENSUS_PATH = '../data/processed_csv/2014_census_clean.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "id": "jaroSyajNCMY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSV file successfully load in DataFrame.\n"
     ]
    }
   ],
   "source": [
    "# Reading the clean census data into a pandas DataFrame\n",
    "# In my analysis, I'm loading the data from the specified file path into a DataFrame\n",
    "df = load_csv_into_dataframe(CLEAN_CENSUS_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 541
    },
    "id": "l9sfoKU5NCMZ",
    "outputId": "ab5343a2-e511-42a7-9d0d-2a48ed72c35a"
   },
   "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>ID_SECTOR</th>\n",
       "      <th>AREA_2000</th>\n",
       "      <th>PROVINCIA</th>\n",
       "      <th>ZONA_PLANIFICACION</th>\n",
       "      <th>REGION_NATURAL</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>ULTIMO_NIVEL_EDUCATIVO</th>\n",
       "      <th>GRADO_CURSO</th>\n",
       "      <th>ANIOS_TRABAJO</th>\n",
       "      <th>MES</th>\n",
       "      <th>ANIO</th>\n",
       "      <th>CIUDAD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38902</th>\n",
       "      <td>90150098001</td>\n",
       "      <td>Urbano</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>8</td>\n",
       "      <td>Costa</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>38</td>\n",
       "      <td>Secundaria</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>Guayaquil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8624</th>\n",
       "      <td>20153999004</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Bolivar</td>\n",
       "      <td>5</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>49</td>\n",
       "      <td>Ninguno</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103618</th>\n",
       "      <td>230150060008</td>\n",
       "      <td>Urbano</td>\n",
       "      <td>Santo Domingo de los Tsachilas</td>\n",
       "      <td>4</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>65</td>\n",
       "      <td>Primaria</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89540</th>\n",
       "      <td>180168999017</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Tungurahua</td>\n",
       "      <td>3</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>59</td>\n",
       "      <td>Alfabetización</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50383</th>\n",
       "      <td>100157001006</td>\n",
       "      <td>Urbano</td>\n",
       "      <td>Imbabura</td>\n",
       "      <td>1</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53559</th>\n",
       "      <td>110150024010</td>\n",
       "      <td>Urbano</td>\n",
       "      <td>Loja</td>\n",
       "      <td>7</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55787</th>\n",
       "      <td>110756999003</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Loja</td>\n",
       "      <td>7</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>6</td>\n",
       "      <td>Educación básica</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID_SECTOR AREA_2000                       PROVINCIA  \\\n",
       "38902    90150098001    Urbano                          Guayas   \n",
       "8624     20153999004     Rural                         Bolivar   \n",
       "103618  230150060008    Urbano  Santo Domingo de los Tsachilas   \n",
       "89540   180168999017     Rural                      Tungurahua   \n",
       "50383   100157001006    Urbano                        Imbabura   \n",
       "53559   110150024010    Urbano                            Loja   \n",
       "55787   110756999003     Rural                            Loja   \n",
       "\n",
       "        ZONA_PLANIFICACION REGION_NATURAL    SEXO  EDAD  \\\n",
       "38902                    8          Costa  Hombre    38   \n",
       "8624                     5         Sierra   Mujer    49   \n",
       "103618                   4         Sierra   Mujer    65   \n",
       "89540                    3         Sierra  Hombre    59   \n",
       "50383                    1         Sierra   Mujer     4   \n",
       "53559                    7         Sierra   Mujer     2   \n",
       "55787                    7         Sierra  Hombre     6   \n",
       "\n",
       "       ULTIMO_NIVEL_EDUCATIVO  GRADO_CURSO  ANIOS_TRABAJO  MES  ANIO  \\\n",
       "38902              Secundaria            2              0    1  2014   \n",
       "8624                  Ninguno            0              0    2  2014   \n",
       "103618               Primaria            3              0   11  2013   \n",
       "89540          Alfabetización            1             30    1  2014   \n",
       "50383                     NaN            0              0    9  2014   \n",
       "53559                     NaN            0              0    5  2014   \n",
       "55787        Educación básica            1              0    5  2014   \n",
       "\n",
       "           CIUDAD  \n",
       "38902   Guayaquil  \n",
       "8624          NaN  \n",
       "103618        NaN  \n",
       "89540         NaN  \n",
       "50383         NaN  \n",
       "53559         NaN  \n",
       "55787         NaN  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sampling 7 random rows from the clean census DataFrame with a specified random state\n",
    "# In my analysis, I'm randomly selecting 7 rows from the DataFrame using a fixed seed for reproducibility\n",
    "sampled_data = df.sample(7, random_state=5)\n",
    "sampled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Azuay', 'Bolivar', 'Cañar', 'Carchi', 'Cotopaxi', 'Chimborazo',\n",
       "       'El Oro', 'Esmeraldas', 'Guayas', 'Imbabura', 'Loja', 'Los Rios',\n",
       "       'Manabi', 'Morona Santiago', 'Napo', 'Pastaza', 'Pichincha',\n",
       "       'Tungurahua', 'Zamora Chinchipe', 'Galápagos', 'Sucumbios',\n",
       "       'Orellana', 'Santo Domingo de los Tsachilas', 'Santa Elena'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting unique values from the 'PROVINCIA' column in the clean census DataFrame\n",
    "# In my analysis, I'm retrieving the unique values present in the 'PROVINCIA' column\n",
    "df['PROVINCIA'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ## 2.2 Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x1400 with 35 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a pair plot for exploratory data analysis using Seaborn\n",
    "# Assuming that the DataFrame 'df' contains relevant numerical columns\n",
    "\n",
    "# Creating the pair plot with the mask\n",
    "sns.pairplot(df, corner=True)\n",
    "\n",
    "# Displaying the pair plot for a comprehensive view of variable interactions\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the age threshold based on the year 2023\n",
    "# In my analysis, I'm determining the age threshold for individuals of 50 years or more in 2023\n",
    "calculate_age = 50 - (2023 - 2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new DataFrame with rows of individuals aged 50 or more in the year 2023 in the province of Pichincha\n",
    "# In my analysis, I'm filtering the original DataFrame for individuals aged 50 or more in 2023 and residing in Pichincha\n",
    "df_age_fifty_pichincha_quito = df.loc[(df['EDAD'] >= calculate_age) & (df['PROVINCIA'] == 'Pichincha') & (df['CIUDAD'] == 'Quito')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'df' is my DataFrame\n",
    "# Creating a histogram for individuals aged 50 or more in the province of Pichincha\n",
    "# In my analysis, I'm visualizing the age distribution for this specific group\n",
    "\n",
    "df_age_fifty_pichincha_quito['EDAD'].hist()\n",
    "\n",
    "# Adding labels to the plot for clarity\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Age Histogram')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'df' is my DataFrame\n",
    "# Creating a histogram for cities of individuals aged 50 or more in the province of Pichincha\n",
    "# In my analysis, I'm visualizing the distribution of cities for this specific group\n",
    "\n",
    "df_age_fifty_pichincha_quito['CIUDAD'].hist()\n",
    "\n",
    "# Adding labels to the plot for clarity\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('City Histogram')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABB4AAAOCCAYAAADQpRGFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAxOAAAMTgF/d4wjAAB2I0lEQVR4nOzdeZid48E/8O/JnoaItYmGBIlQRIRYqkQoQVu1xB7ES6hW0zbaatVelLZCab1iS+xL7bToYl9iT0QtQXgJ0hARspFMzu8PV+ZnmqB533nOk8x8Ptc1lzn3c+bMd+Y8MjPfcz/3XalWq9UAAAAAFKBF2QEAAACApkvxAAAAABRG8QAAAAAURvEAAAAAFEbxAAAAABRG8QAAAAAURvEAAAAAFEbxAMBS5cQTT0y7du0WeWz06NGpVCqZPHly/Vj37t3z3e9+9z9+/HvvvTennHLK/zlnUzRnzpwMGTIkK6+8ciqVSk488cRF3m/B87Cot29961uf+zkmT56cSqWS0aNHN/4X8B868cQT8+CDDy40vrjnUhGGDBmSddZZ53Pvs6j/D8q2zTbbZMcddyw7BgAlaVV2AAAo0k033ZROnTr9x/e/9957c/rpp+fYY48tLtRS6uKLL84VV1yR0aNHp0ePHunatevn3v/Pf/5zVlhhhQZjyy+/fJERG8VJJ52Udu3a5etf/3qD8cU9lwCATygeAGjSNtpoo7Ij/Ec++uijtG3btuwYn+v555/PqquumsGDB/9H9+/bt286d+5ccKraWVrOJQBY0rjUAoAm7d+nxz///PP59re/nZVWWint27fPmmuumR/+8IdJPplif9JJJ+Wjjz6qvzRgm222qf/YBx54IFtttVXat2+fTp06Za+99sobb7zR4PN98MEHOfjgg9OxY8essMIK+e53v5tRo0alUqnktddeS5K89tprqVQqueSSS/L9738/K6+8crp3754kueOOO7Ljjjvmy1/+cpZZZpn06dMnl112WYPPce+996ZSqeSOO+7Ifvvtl2WXXTarrrpqzj///CTJRRddlDXXXDPLLbdc9t5773zwwQdf+H169tlns9NOO2WZZZbJMsssk5122inPPvtsg+/jH//4x7zxxhv135sFX8//xemnn56vfOUr+dKXvpTtt98+r7766kL3WdQlDmPGjEmlUsm9995bP1atVnPWWWdlvfXWS9u2bbPKKqtk1113zb/+9a8kyYQJE7L//vunW7duad++fdZee+0cc8wxmTNnTv1jVCqVJMkvfvGL+q9zwedYVI6bb745ffv2Tbt27bLyyitn6NChmTZtWv3xBc/1ZZddluHDh2ellVbKKquskqFDh2bWrFn195s9e3aGDRuWddddN1/60pfStWvX7Lfffnnrrbf+d9/YL/DBBx9k2LBh6dq1a9q2bZv1118/1113Xf3xK6+8Mi1atMjrr7/e4OPmzZuXlVdeOT/72c/qx1588cXsscceWX755dO+ffsMGDAgY8eOLSQ3AEsnxQMAS6V58+Yt9DZ//vwv/Lhvfetbeeedd3LhhRfmjjvuyPHHH5958+YlSQ499NAccsghadOmTR555JE88sgjOe+885IkTz75ZL7xjW+kZcuWufbaa3Puuefm4YcfTv/+/fPhhx/WP/6hhx6a66+/PqecckquuuqqzJw5M8cff/wisxx77LGZMWNGLr/88lxwwQVJPvlDdccdd8yll16aW265JbvuumsOOeSQXHLJJQt9/Pe+97307NkzN954YwYMGJAjjjgiP/3pT3PNNdfknHPOye9+97vcdtttn/n5F3jjjTey1VZb5c0338zo0aMzevToTJo0KVtvvXV9sXLTTTdlt912S+fOneu/N126dPncx62rq1voOapWq/XHzzvvvPziF7/IHnvskZtvvjlbbrll9tlnn899zM/z/e9/Pz/5yU+y/fbb55ZbbsnIkSPTuXPn+iLgzTffzJprrplzzz03d955Z4YPH57Ro0fnsMMOq3+MRx55JMkn39sFX2ffvn0X+fluvfXW7L777lljjTVy44035uSTT86f/vSn7LTTTqmrq2tw3+OOOy6zZs3KVVddlaOPPjqXXnppg7VEZs+enY8++ignn3xy7rjjjowYMSKvvPJKtt5668ydO/d//T1ZlLlz52aHHXbIDTfckOOOOy633357+vfvn3322Sd//etfkyS77rpr2rdvn6uvvrrBx95111159913s//++yf55Hz92te+ljfffDMXXHBBrr/++rRo0SLbbLNN3n333UbNDcBSrAoAS5ETTjihmuRz395+++36+3fr1q16+OGHV6vVavWdd96pJqnecsstn/v4bdu2XWh8t912q3bp0qU6e/bs+rEnn3yymqR6zjnnVKvVavWf//xntVKpVC+88MIGH7v55ptXk1RfffXVarVarb766qvVJNUBAwZ87tdaV1dXnTt3bnXo0KHVfv361Y/fc8891STVH/3oR/VjH374YbVt27bVVVZZpTpz5sz68UMOOaT6la985XM/z49//ONqu3btqpMnT64fe+utt6pt27atDh8+vH7s8MMPr3br1u1zH6tarVZHjRr1mc/Nr3/962q1Wq3Omzevuuqqq1b32muvBh/705/+tJqkOmrUqPqxTz+HCzzyyCPVJNV77rmnWq1Wqy+88EK1UqlUTzzxxC/MV61Wq/Pnz6/OnTu3esUVV1RbtmxZnT59ev2xT+f8tH/PsdFGG1X79u1bnT9/fv3YjTfeWE1SvfXWW6vV6v9/rr/zne80eKy999672qNHj8/MN2/evOobb7xRTVL985//XD9+0EEHVXv16vW5X9uC7/+n/z/4tNGjR1dbtGhRffrppxuMf+c736luttlm9bf32Wefau/evRvcZ//9969+9atfrb89ZMiQ6mqrrVadMWNG/disWbOqXbp0qR599NH1Y/37968OHDjwc3MD0HSZ8QDAUqdNmzZ5/PHHF3o74YQTPvfjVlxxxXTv3j0///nPc8kllyzWpQIPPPBAvvOd7zTYUaNv377p1atXHnjggSTJ448/nmq1ml133bXBx+6+++6LfMxddtllobG33norhxxySFZbbbW0adMmrVu3zoUXXpgJEyYsdN8ddtih/v1lllkmq666arbYYot86Utfqh9fe+218/bbby/0Cvy/f23bbLNNvvzlL9ePdenSJdtss0391/a/ceeddy70HA0ZMiRJMmnSpLz11lsZNGhQg4/599v/qXvuuSfVajWHHnroZ95nwYyCtddeO+3bt0/r1q0zePDg1NXVZeLEiYv1+WbMmJGxY8dmr732qr88I/n/MwX+/fs2cODABre/+tWvLnSZztVXX51NNtkkHTt2TKtWrbLaaqslySKf+/+Lv/71r/nqV7+a9ddfv8FslO233z5PPfVU/bmy33775Zlnnslzzz2XJJk1a1ZuueWW+tkOCx5rl112Sdu2besfp3Xr1vn617+exx57rFFzA7D0srgkAEudSqWSTTbZZKHxT69J8Fkf97e//S0nnHBCjjrqqLz//vv56le/mlNOOSW77bbb537stGnTFrlQ4qen8r/99ttp0aJFVlxxxQb3WWWVVRb5mJ/+Qz9J5s+fn29/+9t5991388tf/jK9evXKsssum//+7//OlVdeudDH//sOEW3atFlo14U2bdpk/vz5mTt3blq2bPmZX9v666+/yK/tlVdeWeTH/Cc23HDDz1xc8u23306y8Pfm378n/6mpU6emUql87uUfP//5z3Peeefl2GOPzeabb55OnTrl8ccfz/e///0G6zz8J95///1Uq9WFvr5KpZJVVlmlwToPyaKfq48++qj+9i233JL99tsvBx10UE488cSstNJKqVQq2XzzzRc72xeZMmVKnn322bRu3XqRx99+++107do1O+64Y1ZcccVceeWVOfXUU3Pbbbdl5syZ2W+//Ro81h//+Mf88Y9/XOhx1lprrUbNDcDSS/EAQLPSo0ePXHnllamrq8uTTz6ZU089NXvuuWdeeOGF9OjR4zM/bvnll69fpPDTJk+enN69eyf5ZJbA/PnzM3Xq1Ky00kr195kyZcoiH/PTr5QnySuvvJKnnnoqf/rTnxq88r9gDYqifN7XVtT2lwsKgn//3iwqR7t27fLxxx83GHvvvfca3F5ppZVSrVbz9ttv5ytf+coiP+d1112XoUOH5rjjjqsf+6Ky6rN06tQplUplobzVajVTpkxZ7O/bddddl/XXXz+jR4+uH1vUQpuNYYUVVsh6663X4HN92oIyqHXr1hk0aFCuvvrqnHrqqbnqqquyxRZb1C+EuuCxdtxxx/zgBz9Y6HGW9F1aAKgdl1oA0Cy1bNkym266aX71q1+lrq6ufjp7mzZtMnfu3IUWqtxqq61yyy23NHiVeuzYsXnxxRez1VZbJUn69euXSqWSm2++ucHH3njjjf9RpgW7HLRp06Z+7P3338+tt9662F/f4thqq61y7733NigBJk+enPvuu6/+a2tsq622WlZdddVcf/31Dcb//faC+y6Y7r/AXXfd1eD2tttum0qlkosvvvgzP+esWbMafG+T5PLLL1/ofq1bt/7CWQYLdhz505/+1GD81ltvzezZsxf7+zZr1qyFZiD8+24mjWWHHXbIq6++mi9/+cvZZJNNFnr79Pdov/32y6uvvpq//OUvufPOOxtcZrHgsZ555pn06dNnocfZYIMNCskPwNLHjAcAmo1nnnkmP/7xj7P33ntnrbXWypw5c3LOOeekU6dO6devX5Jk3XXXzfz583P22Wdnq622SseOHdOrV6/88pe/zNe+9rXsvPPO+dGPfpTp06fnmGOOyRprrFG/bsFXv/rVDBo0KD/+8Y8ze/bsrL322rniiisyadKkJEmLFp/f96+77rpZbbXVcvTRR2f+/PmZN29eTjvttKywwgp58803C/u+/PjHP86oUaOyww475Ljjjku1Ws3JJ5+cdu3a5Uc/+tH/+nGfeuqprLDCCg3G2rdvnw033DAtWrTIL3/5y3z/+99P586d881vfjOPPPJIgy0dF9hrr71y2GGH5dhjj80222yTe+65J7fffnuD+/Ts2TNHHnlkTjrppLz//vsZOHBg5syZk7/+9a/5wQ9+kHXWWScDBw7MRRddlHXXXTddu3bNpZdemv/5n/9Z6POtu+66ufXWWzNgwIB06NCh/pKXf3fiiSdm1113zV577ZWDDz44r7/+en7+859ns802y84777xY36uBAwfmiCOOyDHHHJNtt902999/f6655prFeox/d/vtty906c1aa62VAw44IBdffHG22WabHHXUUVl33XXz4Ycf5tlnn81rr71Wv8NK8kkptdpqq2Xo0KGZP39+9tprrwaPd/LJJ2fTTTfNdtttl+9+97tZddVVM2XKlIwZMybdunXLsGHD/k9fAwBNRJkrWwLA4vqsXSeq1UWv5v/pnQj+9a9/VQ844IDqWmutVW3Xrl11xRVXrO60007VJ554ov7+c+fOrX7ve9+rfvnLX65WKpVq//7964/df//91S233LLarl276nLLLVfdc889q6+//nqDDNOnT68edNBB1WWXXbbaqVOn6qGHHlodMWJENUn1/fffr1ar/3+ng6uvvnqhr+HJJ5+sbr755tX27dtXu3XrVj3zzDMX+poX7GrxyCOPNPjYXr16VQ866KAGY2eddVY1SYPdOBblmWeeqQ4cOLDaoUOHaocOHao77rhjdfz48Q3u0xi7Wvz7jgynnXZatUuXLtX27dtXv/GNb1QffvjhhXa1mDdvXvUXv/hFddVVV60us8wy1cGDB1fvvPPOBrtaVKuf7FRx5plnVtdee+1q69atq6usskp1t912q/7rX/+qVqvV6pQpU6qDBg2qduzYsbriiitWDz/88Optt9220PfygQceqPbt27favn37Bp9jUbtr3HTTTdWNNtqo2qZNm+pKK61UPeSQQ6rvvfde/fHPeq5//etfVz/9a9i8efOqRx99dLVz587VDh06VAcOHFh96aWXFtphY3F2tVjU24L8M2bMqB599NHVNdZYo/57td1221WvuOKKhR7vZz/7WTVJdeedd17k55s4cWJ1//33r66yyirVNm3aVFdfffXqXnvt1eB7alcLgOatUq1+akNtAKDR7b333nnssccKu2YfAGBJ5lILAGhEN910UyZOnJjevXvn448/zm233Zbrrrsuv//978uOBgBQCjMeAKAR/e1vf8svfvGLTJgwIXPmzMlaa62V73//+znyyCPLjgYAUArFAwAAAFAY22kCAAAAhVE8AAAAAIVRPAAAAACFabK7WrRt2zYrr7xy2TEWy8yZM9OhQ4eyY1Ai5wDOAZwDOAdwDjRvnn+WxnPgnXfeyUcfffSZx5ts8bDyyitn0qRJZcdYLCNGjMjw4cPLjkGJnAM4B3AO4BzAOdC8ef5ZGs+Brl27fu5xl1oAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFaVV2AChD5cgry46wSMNXmLXEZvvfqP5h/7IjAAAAJTPjAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAoTKuyA/D/vfn+rFSOvLLsGAAAANBozHgAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKo3gAAAAACqN4AAAAAAqjeAAAAAAKU0rxMGrUqFQqldx8881JkilTpmTHHXdMz549s/766+f++++vv+/nHQMAAACWbDUvHl577bVceOGF2XzzzevHfv7zn2fzzTfPSy+9lFGjRmW//fbL3Llzv/AYAAAAsGSrafEwf/78HHrooTn33HPTtm3b+vHrrrsu3/3ud5Mk/fr1y6qrrpr77rvvC48BAAAAS7ZKtVqt1uqT/e53v8uHH36Yk046Kdtss01+9KMfZauttsqqq66ajz76qP5+e+21V3bcccd85zvf+cxj//Vf/9XgsUeMGJERI0bU3546dWpOPfXU4r+oRjT9gw8zY37LsmNQomVa1DWpc+Arnb5UdoSlzowZM7LMMsuUHYMSOQdwDuAcaN48/yyN58CIESMyadKkzzzeqlZBnn322dxwww2FrdEwfPjwDB8+vP52165dG9xeGhx1/CkZ8d4aZcegRMNXeLVJnQPVk/cvO8JSZ8SIEUvdv100LucAzgGcA82b55+l8Rz49CSARanZpRYPPPBAXnvttfTs2TPdu3fPmDFjcthhh+W6665Lq1atMnny5Pr7vvbaa1l99dWz4oorfuYxAAAAYMlXs+LhiCOOyNtvv53XXnstr732WjbffPNccMEFOeKII7Lnnnvm/PPPT5I8/vjjefPNN9O/f/8k+dxjAAAAwJKtZpdafJ4zzjgjBxxwQHr27Jk2bdrkiiuuSOvWrb/wGAAAALBkK614uPfee+vf//KXv5y//vWvi7zf5x0DAAAAlmw13U4TAAAAaF4UDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGFqWjzssMMO6d27d/r06ZOtttoqTz/9dJLkpZdeyte+9rWsvfba6devX/75z3/Wf8znHQMAAACWbDUtHq677ro888wzGTt2bIYPH54hQ4YkSQ4//PAcdthhmTBhQo4++uj68S86BgAAACzZalo8dOrUqf796dOnp1KpZMqUKXniiScyePDgJMkee+yRN954Iy+//PLnHgMAAACWfK1q/QkPPPDA3HPPPUmSv/zlL3njjTfSpUuXtGr1SZRKpZLVV189r7/+epZbbrnPPNajR49aRwcAAAAWU6VarVbL+MSXXnpprr322vzqV7/KfvvtlxdffLH+2KabbprTTz89yy233Gce23bbbRs83ogRIzJixIj621OnTs2pp55a/BfSiKZ/8GFmzG9ZdgxKtEyLuiZ1Dnyl05fKjrDUmTFjRpZZZpmyY1Ai5wDOAZwDzZvnn6XxHBgxYkQmTZr0mcdLKx6SpH379nnttdfSs2fPvPfee2nVqlWq1Wq6dOmSBx98MB07dkyPHj0WeeyLZjx07dr1c7/wJdFRx5+SEe+tUXYMSjR8hVeb1DlQ/cP+ZUdY6owYMSLDhw8vOwYlcg7gHMA50Lx5/lkaz4Ev+vu7Zms8vP/++3nrrbfqb998881ZccUVs8oqq6Rv37654oorkiQ33HBDunbtmh49enzuMQAAAGDJV7M1HqZPn54999wzs2fPTosWLbLyyivn9ttvT6VSyciRIzNkyJCcdtpp6dixY0aNGlX/cZ93DAAAAFiy1ax46NatWx577LFFHuvVq1ceeeSRxT4GAAAALNlqup0mAAAA0LwoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMIoHgAAAIDCKB4AAACAwigeAAAAgMK0KjsAAMAClSOvLDtC6YavMKsm34fqH/Yv/HMAQGLGAwAAAFAgxQMAAABQGMUDAAAAUBjFAwAAAFAYxQMAAABQGMUDAAAAUJjFKh523333/O1vfysqCwAAANDELFbxsOqqq2avvfZKjx498pvf/CbvvPNOUbkAAACAJmCxioc//OEPeeutt3LMMcfkxhtvzGqrrZZ99tkn9913X1H5AAAAgKXYYq/x0L59+/zXf/1XxowZkzFjxuTll1/Otttum3XWWScXXHBB5s+fX0ROAAAAYCnU6n/zQdOmTcull16aCy+8MFOmTMlRRx2VNddcM2eddVbuvvvuXHPNNY2dEwAAAFgKLVbx8MADD+SCCy7IDTfckI022ig///nPs9dee6Vt27ZJkr322iurrbZaIUEBAACApc9iFQ8777xz9t9//zz88MPp06fPQsdXWGGF/PCHP2ysbAAAAMBSbrGKh7feeivLLrvs597ntNNO+z8FAgAAAJqOxVpc8uabb87YsWMbjD399NO58sorGzMTAAAA0EQsVvFw0kknpXPnzg3GunTpkhNOOKFRQwEAAABNw2IVD1OmTFmoeOjcuXP+9a9/NWooAAAAoGlYrOKhS5cumTBhQoOxCRMmZJVVVmnUUAAAAEDTsFjFw5577pkDDzwwTz/9dGbOnJmnn346Q4YMyV577VVUPgAAAGAptli7Whx77LF57bXXsvHGG6dSqSRJ9t133xx//PGFhAMAAACWbotVPLRr1y5XXHFFzj777Lz22mvp3r17VlpppaKyAQAAAEu5xSoeFlhppZUUDgAAAMAXWqzi4e23384xxxyTxx57LB9++GGDY6+//nqjBgMAAACWfotVPBx00EGZNWtWvv/976dDhw5FZQIAAACaiMUqHh599NFMmjQpyy67bFF5AAAAgCZksbbT7Ny5c/1uFgAAAABfZLGKh9NOOy1HHHFE3nzzzcyfP7/BGwAAAMC/W6xLLfbcc88kyVVXXbXQsbq6usZJBAAAADQZi1U83HPPPUXlAAAAAJqgxSoe+vfvX1QOAAAAoAlarDUekuThhx/OYYcdlm9/+9tJkqeeeioPPvhgowcDAAAAln6LVTxce+212WmnnZIk9913X5Jk/vz5Of744xs/GQAAALDUW6zi4ZRTTskdd9yRCy64IC1btkySbLDBBnn22WcLCQcAAAAs3RareHjjjTfyta99LUlSqVSSJG3atMm8efMaPxkAAACw1Fus4qF79+55+umnG4w9+eSTWXPNNRs1FAAAANA0LFbxMHz48Oy222658MILM2/evFxxxRXZf//9c9RRRxWVDwAAAFiKLdZ2mgceeGDmz5+fs846K/PmzcsJJ5yQH/7wh9l3332LygcAAAAsxRareEiSIUOGZMiQIQVEAQAAAJqaxSoe5s+f/5nHWrRYrKs2AAAAgGZgsYqHVq1a1e9m8e/q6uoaJRAAAADQdCxW8XDPPfc0uP3mm2/mzDPPzNChQxs1FAAAANA0LFbx0L9//4XGNt988xx44IH57ne/22ihAAAAgKbh/7wwQ7du3fLMM880RhYAAACgiVmsGQ8TJ05scHvmzJm56KKL0r1798bMBAAAADQRi1U89OjRo8HiktVqNWuuuWYuu+yyRg8GAAAALP0Wq3h49dVXG9xedtlls8IKKzRqIAAAAKDpWKzioVu3bkXlAAAAAJqgxSoeTj755P/ofscff/z/KgwAAADQtCxW8fC3v/0tjz76aJZffvmsvvrqeeONNzJt2rRsttlmqVarSZJKpaJ4AAAAAJIsZvGw1VZbZcCAATnxxBPTokWLzJ8/PyeddFLmzZuXU089taiMAAAAwFJqsYqHiy++OJMmTUqLFi2SJC1atMgvf/nLrLbaaooHAAAAYCEtFuvOLVpk4sSJDcZeffXVBltsAgAAACywWDMeDjjggOy00075yU9+ku7du+e1117LmWeemQMOOKCofAAAAMBSbLGKh1//+tfp1KlTzj777EyaNCldu3bNf/3Xf+Xoo48uKh8AAACwFFus4qFly5Y55phjcswxxxSVBwAAAGhCFmuNhyT54IMPctVVV+W3v/1tkuRf//pXJk+e3OjBAAAAgKXfYhUPY8eOTc+ePXPiiSfmpJNOSpI8/fTTOfLIIwsJBwAAACzdFqt4+NGPfpQTTjghEyZMSOvWrZMkW265ZcaMGVNIOAAAAGDptlhrPIwfPz533313ktRvobnsssvmww8/bPxkAAAAwFJvsWY8LL/88vnXv/7VYOz1119P586dGzUUAAAA0DQsVvGw11575aCDDspLL72UJJk0aVJ+8IMfZP/99y8kHAAAALB0W6zi4YQTTsiqq66aXr165f3330+3bt3SqlWrHH300UXlAwAAAJZi/3HxUFdXl3HjxuWCCy7Iu+++mzFjxuTVV1/NDTfckLZt2xaZEQAAAFhK/ceLS7Zs2TIDBgzIjBkzssIKK2TTTTctMhcAAADQBCzWpRY9e/bM22+/XVQWAAAAoIlZrO00hw0blr333jsnnHBCunfvnhYt/n9vseaaazZ6OAAAAGDptljFw6GHHpokGThwYP1YtVpNpVJJXV1d4yYDAAAAlnr/UfGwzTbb5N57782rr76aJLn88stzwAEHFBoMAAAAWPr9R8XDU089lSTp1q1bkmTEiBE59thji0sFAAAANAmLtbjkAtVqtbFzAAAAAE3Q/6p4qFQqjZ0DAAAAaIL+o0stPvrooxx//PH1t2fPnt3gdpKcfPLJjZsMAAAAWOr9R8XDFltskQceeKD+9uabb97gthkQAAAAwKL8R8XDvffeW3AMAAAAoCn6X63xAAAAAPCfUDwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFqVnxMGfOnOy6665Ze+21s+GGG2b77bfPyy+/nCSZMmVKdtxxx/Ts2TPrr79+7r///vqP+7xjAAAAwJKtpjMeDjvssLz44osZN25cvvOd7+TQQw9Nkvz85z/P5ptvnpdeeimjRo3Kfvvtl7lz537hMQAAAGDJVrPioV27dtl5551TqVSSJJtvvnlee+21JMl1112X7373u0mSfv36ZdVVV8199933hccAAACAJVulWq1Wy/jEBxxwQFZYYYUcf/zxWXXVVfPRRx/VH9trr72y44475jvf+c5nHvuv//qvBo83YsSIjBgxov721KlTc+qppxb/hTSi6R98mBnzW5YdgxIt06KuSZ0DX+n0pbIjLHVmzJiRZZZZpuwYlKi5nwNvvj+r7Ailq9XPAv9GL7ma+78DzZ3nn6XxHBgxYkQmTZr0mcdb1TBLvdNOOy0vv/xy/vGPf2T27NmN8pjDhw/P8OHD62937dq1we2lwVHHn5IR761RdgxKNHyFV5vUOVA9ef+yIyx1RowYsdT920Xjau7nQOXIK8uOULpa/Szwb/SSq7n/O9Dcef5ZGs+BT08CWJSa72rxu9/9LjfeeGPuuOOOfOlLX8qKK66YVq1aZfLkyfX3ee2117L66qt/7jEAAABgyVfT4mHEiBG5+uqr87e//S2dOnWqH99zzz1z/vnnJ0kef/zxvPnmm+nfv/8XHgMAAACWbDW71GLSpEk56qijsuaaa2bAgAFJkrZt2+bRRx/NGWeckQMOOCA9e/ZMmzZtcsUVV6R169ZJ8rnHAAAAgCVbzYqHrl275rPWsfzyl7+cv/71r4t9DAAAAFiy1XyNBwAAAKD5UDwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFqWnxMGzYsHTv3j2VSiVjx46tH3/ppZfyta99LWuvvXb69euXf/7zn//RMQAAAGDJVtPiYdCgQXnwwQfTrVu3BuOHH354DjvssEyYMCFHH310hgwZ8h8dAwAAAJZsNS0ett5663Tt2rXB2JQpU/LEE09k8ODBSZI99tgjb7zxRl5++eXPPQYAAAAs+Upf4+GNN95Ily5d0qpVqyRJpVLJ6quvntdff/1zjwEAAABLvlZlB2gsI0aMyIgRI+pvT506tcHtpcEyLeoyfIVXy45BiZraObC0/T+4JJgxY4bvWzPX3M+B4SvMKjtC6Wr1s6A5n2dLuub+70Bz5/mnKZ4DlWq1Wq31J+3evXtuvvnm9OnTJ1OmTEmPHj3y3nvvpVWrVqlWq+nSpUsefPDBdOzY8TOP9ejR43M/R9euXTNp0qQafUWN46jjT8mI99YoOwYlGr7Cq03qHKj+Yf+yIyx1RowYkeHDh5cdgxI193OgcuSVZUcoXa1+Fvg3esnV3P8daO48/yyN58AX/f1d+qUWq6yySvr27ZsrrrgiSXLDDTeka9eu6dGjx+ceAwAAAJZ8Nb3U4vDDD8+f//znTJ48OQMHDsyyyy6bl19+OSNHjsyQIUNy2mmnpWPHjhk1alT9x3zeMQAAAGDJVtPiYeTIkYsc79WrVx555JHFPgYAAAAs2Uq/1AIAAABouhQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhFA8AAABAYRQPAAAAQGEUDwAAAEBhWpUdAACWBpUjr6zJ5xm+wqyafS4AgFow4wEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAojOIBAAAAKIziAQAAACiM4gEAAAAoTKuyAwAAQFNVOfLKxf6Y4SvM+l99XHNW/cP+ZUdoFpyXtXHmmmUnaHxmPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVRPAAAAACFUTwAAAAAhVE8AAAAAIVpVXYAoOmqHHll2RGWOsNXmLXY37fqH/YvKA0AAPzfmfEAAAAAFEbxAAAAABRG8QAAAAAURvEAAAAAFGapWFzypZdeykEHHZR33303yy23XEaPHp311luv7FgAAMASoCktaP2/WWgalnRLxYyHww8/PIcddlgmTJiQo48+OkOGDCk7EgAAAPAfWOKLhylTpuSJJ57I4MGDkyR77LFH3njjjbz88sslJwMAAAC+yBJfPLzxxhvp0qVLWrX65KqQSqWS1VdfPa+//nrJyQAAAIAvUqlWq9WyQ3yeJ598Mvvtt19efPHF+rFNN900p59+erbddtv6sREjRmTEiBH1tydPnpzOnTvXNOv/1YwZM7LMMsuUHYMSOQdwDuAcwDmAc6B58/yzNJ4D77zzTj766KPPPL7EFw9TpkxJjx498t5776VVq1apVqvp0qVLHnzwwfTo0aPseI2qa9eumTRpUtkxKJFzAOcAzgGcAzgHmjfPP03xHFjiL7VYZZVV0rdv31xxxRVJkhtuuCFdu3ZtcqUDAAAANEVLxXaaI0eOzJAhQ3LaaaelY8eOGTVqVNmRAAAAgP/AUlE89OrVK4888kjZMQo3fPjwsiNQMucAzgGcAzgHcA40b55/muI5sMSv8QAAAAAsvZb4NR4AAACApZfiAQAAACjMUrHGA0BTNmvWrIwdOzZJ0qdPn3zpS18qNxAAADQixQNAiR5++OHsscce6dy5c5LkX//6V2644YZsscUWJScDamX+/Pl5/PHHs9lmm5UdhRL96U9/yt/+9rckyQ477JBBgwaVnAig8VhcEkr20ksvZdiwYRk3blzmzJlTP/7ee++VmIpa2XzzzXPmmWdmyy23TPJJETF8+PCMGTOm5GSU4a233kqSrLrqqiUnodY23HDDjBs3ruwYlOTkk0/OzTffnAMPPDCVSiWXX355dt111xx77LFlR6NGnnrqqRxzzDGZOHFi5s2bVz8+ceLEElNRhqb6u4AZD0uI6667LmPHjm3wh+eIESNKTEStDB06NEcccUROPvnkXHPNNTn33HPTvXv3smNRI7Nnz64vHZLka1/7WoN/B2genn/++QwaNKj+l42uXbvmT3/6U9ZZZ52Sk1ErPXv2zMsvv5wePXqUHYUSXH/99RkzZkz9pXaHHnpotthiC8VDM3LQQQflyCOPzBZbbJGWLVuWHYcSNPXfBSwuuQQYNmxYLr/88owePTqVSiXXX399pk+fXnYsauSDDz7I3nvvnRYtWmSDDTbIyJEjc/PNN5cdixpZZpll8ve//73+9j/+8Y906NChxESU4Xvf+15++ctfZtq0aZk2bVp++ctf5ogjjig7FjX03nvvpU+fPtlhhx2y++6717/RPFSr1Qbr+3To0CEmJTcvLVu2zOGHH57evXtnvfXWq3+j+WjqvwuY8bAEuOeeezJu3LhstNFGOfPMM/PTn/40Bx10UNmxqJHWrVsnSZZddtm89tpr6dy5c959992SU1Erv//977PHHnvUv7oxf/783HDDDSWnotamTZuW/fbbr/72Pvvsk9NPP73ERNTaQQcd5Gd/M7bpppvmgAMOyNChQ5MkF198cTbddNOSU1FLW265ZZ544olssskmZUehJE39dwHFwxKgXbt2adGiRSqVSubOnZvOnTvXT7Gh6dt6660zderUHHnkkdl4443Tpk2b7LPPPmXHokY22WSTvPzyy3nxxReTJL169aovo2g+WrZsmeeeey5f/epXkyTPPfecqbbNzILSYcGr3JVKpcw41Ng555yTk08+OcOHD0+SfOMb38hxxx1Xcipq6f7778+FF16YHj16pF27dvXjTz31VImpqKWm/ruAxSWXANtuu21uv/32/OxnP8s777yTzp07Z8yYMXn00UfLjkaNvfHGG5k+fXrWX3/9sqNQI7vuuutCl9Ysaoym7a677sr++++f3r17J0nGjx+fK6+8MjvssEPJyaiVt99+O4ccckjuueeeJMl2222XCy+8MF26dCk5GVAL99133yLH+/fvX+MklKWp/y6geFgC/Otf/0qnTp0yf/78jBgxItOmTcsPf/jDrLbaamVHo0AzZ85Mhw4d8sEHHyzyeMeOHWuciDL07dt3oVczNthgg4wfP76kRJRlypQpeeyxx5J8stvJSiutVHIiaunb3/52tthii3zve99Lkpx//vl56KGHctttt5WcjCJdffXV2XfffXPOOecs8viwYcNqnIiyNdUdDfjPNOXfBRQPUJIFf3AuuMymWq02+G9dXV3ZESnQyJEjc/7552fChAnp1atX/fj06dOz3nrr5dZbby0xHVBrffr0ydixY79wjKblhBNOyEknnZSDDz54oWOVSiWXXHJJCamolbq6uvqp9E19RwNQPJToqKOOyplnnpnddtttkddy3njjjSWkAmrhf/7nf/Lqq6/miCOOyPnnn18/3rFjx/Tu3btJXdPHF7vzzjvzox/9KBMnTkxdXZ0CshnacMMNc9ddd6Vz585JksmTJ2fgwIEZN25cyckoWl1dXW6++ebsscceZUehxs4555xst912WW+99TJgwIAMHTq0fnHBa665JiNHjqy//Iqma8GLkJ+lqfwuoHgo0W233ZZvf/vbufTSSxd53OrWTV9dXV022GCDPPfcc2VHAUq09tpr59xzz11o/3ZbqzYfl19+eX72s59lp512SvJJGfXb3/42+++/f8nJqIVFXXZH0/fBBx9k6NChufbaa7PhhhsuVDSa9dQ8zJw5M9VqNWeffXZmz55dv4Xm+eefn/bt2+eXv/xlyQkbh+KhZHV1dTnmmGNyxhlnlB2FkvTv3z933HFHg/27aT5mz56dc889N2PHjs2cOXPqx814al422WSTPPHEE2XHoGTPPvts7r333iTJgAEDst5665UbiJo59NBDM2TIkHz9618vOwo1Nn369Cy33HLZeOONc/nllzfY0eCAAw7Ik08+WXJCamXjjTde6Ple1NjSynaaJWvZsqUpVM1cjx49suWWW2bPPffMMsssUz9uQanmYejQoenYsWMefvjhHHXUURk9enS23nrrsmNRY9/61rdy8803Z9dddy07CiVaf/317WrUTI0ZMyajR4/Ommuu2eB3AbMgmr7lllsuSXLaaadl6623XmhHA5qPDz/8MFOmTMkqq6yS5JOFJj/88MOSUzUeMx6WACeeeGJat26dgw8+uMEPG7saNA8WlGreFuxg0bt37zzzzDP58MMP881vfjP3339/2dGooeWXXz7Tp09P+/bt07Zt2/o1Ht57772yo1GwfffdN1dffXU22mijRV7j6w/P5sFWiiRNe0cDvtjIkSNz4oknZuedd07yySV3J554YoYOHVpyssaheFgCtGjRov59uxpA87LpppvmscceS9++ffPAAw+kQ4cOWXfddfP888+XHY0a+p//+Z9Fjnfr1q3GSai1J598MhtvvLE/PAHIs88+Wz8bftttt21Sl9y51GIJMH/+/LIjULK33norzz77bINr/HfZZZcSE1ErK6ywQqZNm5add945AwcOzEorrZSuXbuWHYsaUzA0XxtvvHHq6upy0UUX5fLLLy87DiWx3k/z1b9//9x3331ZfvnlG8x6MvOteWrKl9yZ8QAlu+SSS3LyySfnvffeS8+ePTNu3LhsvvnmefDBB8uORg0s2MO7Wq3mqquuyrRp03LggQe61KqZWWONNRY5zX7ixIklpKEMm222WR599NGyY1CSwYMHp2PHjvnLX/7SYL2fs846q+xoFOztt99Oly5dzHwjU6ZMyQknnJBx48Y1KCCbyiV3ZjwsAV566aUMGzZsoZNMw9k8nHXWWXn66aez7bbb5sknn8z999+f0aNHlx2LGqirq8vAgQPz97//PZVKxbZ5zdjtt99e//6cOXNy+eWXZ8UVVywxEbU2YMCAHHbYYRkyZEiD9Z4WLDRH0zZu3Lj69X5+8IMfZMiQIfnmN79ZdixqoEuXLkk+udx6lVVWSbt27ZJ88rPgnXfeKTMaNXbIIYfk61//ev7xj3/kzDPPzMiRI7PRRhuVHavRtPjiu1C0oUOHZsiQIVl++eVz3333ZdCgQfnJT35SdixqpE2bNll++eUzb968JMnWW29tz+ZmomXLlpk1a5bLrch6661X/7bxxhvn7LPPzh133FF2LGro2muvzd/+9rfsv//++c53vpPvfOc7djlpRtq3b58kadWqVWbOnJlll13WH53NzKBBgxrcrlarC43RtL3xxhs5+uij07Zt23z729/OjTfemL///e9lx2o0ZjwsAT744IPsvffeOeWUU7LBBhtk5MiR2WyzzXLMMceUHY0aWLCC/dprr52zzz473bp1y4wZM8qORY3069cv3/rWtzJ48OAGr3Ja46N5mzp1aiZPnlx2DGro1VdfLTsCJbLeDx9//HH9bIfkkzLqo48+KjERtdamTZskSbt27TJ16tQsv/zyeffdd0tO1XgUD0uA1q1bJ0mWXXbZvPbaa+ncuXOTOsn4fKeccko++OCD/OY3v8l3v/vdvP/++znvvPPKjkWNPPPMM0mSCy+8sH6sUqkoHpqZT2+lWFdXl//5n//Jz372s5JTUWs33HBDXnzxxRxzzDF56623MnXq1GywwQZlx6IG/vznP6dly5b51a9+Vb/ez0EHHVR2LGqoUqlkypQpWWWVVZIkkydPjqX4mpe11147U6dOzeDBg7PZZpulY8eO2XjjjcuO1WgsLrkE+OlPf5qf//znueuuu/KDH/wgbdq0yT777GNBIYBm4tNbKbZq1Sprrrlm/XW/NA/HH398Hn/88bzyyiuZMGFC3n777eyxxx55+OGHy45GDZxyyik59thjv3CMpmvUqFE55ZRTcsABByRJrrjiipxwwgn1t2leHnrooUybNi077rhjWrVqGnMFFA9LmDfeeCPTp09vstuo8P+dfPLJn3v8+OOPr1ESyjRv3rz8/ve/zyuvvJLzzjsvr7zySv7nf/4n2267bdnRgBracMMN89RTT2WTTTbJ008/neSThSUXzIqiaevbt+9CK9cvaoym7d57781f/vKXJMm3v/3tbLXVViUnolbq6uqywQYb5Lnnnis7SmGaRn3SBHzwwQd57bXX6hcYfOqpp9K3b9+SU1GkDz/8MEkyadKk/OMf/8guu+ySSqWSW2+9Ndttt13J6aiVI488MnV1dfXbp6644orZe++988QTT5ScjFoaMGDAQttpdurUKVtssUWGDRuWtm3blpSMWmnfvn1atmzZYMxrQ03fXXfdlTvvvDNvvvlmhg8fXj8+ffr0ElNRlm222SbbbLNN2TEoQcuWLbPyyitn1qxZ+dKXvlR2nEIoHpYAZ511Vo4//visvPLK9b90VCqVTJgwoeRkFOm3v/1tkmSHHXbI2LFjs+qqqyb5ZCbEkCFDSkxGLY0ZMyZjx46t3y6pU6dOmTt3bsmpqLWNN944zzzzTA466KBUKpVcdtllWXXVVfPEE0/kBz/4QS644IKyI1Kwbt265YEHHkilUsncuXNz2mmnpU+fPmXHomDt2rVLp06d0qJFiyy33HL146uttlqOO+64EpNRa7Nnz865556bsWPHZs6cOfXjN954Y4mpqKUePXpkyy23zJ577tlgwfFhw4aVmKrxKB6WAOeee25efPHF+j88aV7eeuutBs99ly5d8uabb5aYiFr69ArWySdT7Wyv2fw8/PDDeeCBB+rL5z333DNbbbVVHnzwQYsLNhPnnHNODjrooIwfPz4dOnTIgAEDcuWVV5Ydi4L1798//fv3z6677poNN9yw7DiUaOjQoenYsWMefvjhHHXUURk9enS23nrrsmNRQ/Pnz0+fPn3y0ksv1Y/9+2zIpZniYQnwla98RenQjHXt2jUnnHBCDj300CTJxRdfbAutZqR379654oorMn/+/Lz88ss544wzTLNshqZOndrgl4tKpZJp06alVatWC5VTNE1f/vKXc+edd2bWrFmpVqvp0KFD2ZGogauvvjr77rtv7rvvvgaLzC7QVF7p5IuNGzcu48ePT+/evfODH/wgQ4YMyTe/+c2yY1FDo0aNKjtCoRQPS4Bjjjkmw4YNy7e+9a0Gv2BqOZuH0aNHZ9iwYfVTarfffvuMHj261EzUzogRI3LUUUdl8uTJ2XLLLbPrrrvm9NNPLzsWNbbddttlp512yuDBg5MkV111VbbddtvMmDHD+g7NyDXXXJO///3vqVQq2X777bPXXnuVHYmCvfDCC0lSv6DopzWlVzr5Yu3bt0/yyc5GM2fOzLLLLpt33nmn5FTU0mWXXbbQWKdOnbLxxhvnK1/5SgmJGpddLZYAxx9/fM4555ysueaaDdZ4eOyxx0pOBkAtzJs3LyNHjszdd9+dJNl2221z+OGHN5kttPhiP/nJT3Lfffc1KJ/69++f3/zmNyUnA2phxx13zNVXX50zzzwz9957b1ZaaaXMnDkzf/vb38qORo0MHDgw999/f77+9a+nUqnkwQcfzKabbpoJEybk7LPPXurLaMXDEqB79+4ZO3ZsOnXqVHYUSvLoo4/mlVdeqd/VJEkOPPDAEhNRtFtvvfVzj++yyy41SgIsCdZee+2MGzeu/lXP2bNnZ8MNN7TQdDMxcuTI7LPPPlluueVy5JFHZsyYMRkxYoTZr81IXV1dWrZsmWq1mquuuirTpk3LgQcemI4dO5YdjRrZY489cuqpp2adddZJkrz44ov5xS9+kREjRmSXXXZZ6rdX9lLKEqBbt25Kh2bsiCOOyF133ZU+ffo0mPGieGjazjrrrM88VqlUFA/NzPvvv5+RI0cuVEBecsklJaailjp16tTgsprWrVtn+eWXLzERtfTHP/4xhx9+eB566KGMHz8+p556an7yk5+Y/dqMXHXVVdl9993ToUOH7L///kmS22+/Pd/61rdKTkatTJgwob50SJJevXrl5ZdfTvfu3dOiRYsSkzUOxcMSoF+/ftlrr70yaNCgBms8+MOjefj73/+e5557zgJyzcw999xTdgSWIIMGDcrKK6+cLbbYor6ApHnZfPPNM3DgwPrS+YorrsgWW2xRPzvK7wRN24LLqu6+++4ceOCBGThwYH7xi1+UnIpaOuyww3L22WfnL3/5S7785S8n+eRybMVD87HsssvmsssuywEHHJAkufzyyxtsq7m0UzwsAZ588skkyX//93/Xj3nFs/no0qWLxeOaoS+aLte7d+8aJWFJ8Pbbb+fvf/972TEo0fjx45M0nOUybty4jBs3zu8EzUCLFi1y7bXX5tprr82f//znJMnHH39ccipqaZ111snw4cOz1VZb5dZbb80666wTV8Q3L5dcckkOPPDADB06NEmy4YYb5tJLL83MmTPz29/+tuR0/3eKhyWAVz6bt8022yyDBg3K3nvvbcZLM/Kd73znM49VKpVMnDixhmko21prrZX333/fZXfNmN8Fmrc//vGP+fWvf52hQ4emW7dumTBhQrbddtuyY1FDlUol+++/f7p06ZKdd945o0aNsrNJM7POOuvksccey4cffpjkkxkQC2y//fZlxWo0Fpcs0UsvvZSePXt+5iufXvFsHgYMGLDQWKVSqV/dHmj69tlnnzz22GPZcccdGxSQI0aMKDEVtXT//fcvctzigk1fXV1djjnmmJxxxhllR6FEG220Uf22qs8++2x22223vPvuu5k2bVrJyaiVTTfddKF1XRY1trQy46FEP/7xj3P77bcv8pVPr3g2H17lap5mzpyZDh065IMPPljkcatYNy/rrrtu1l133bJjUKKjjjqq/v05c+bkxRdfzPrrr5+nnnqqxFTUQsuWLf0uQE477bT699dff/3ce++9ufjii0tMRK19enHpJJk7d2797IemwIyHklWr1bz99ttZddVVy45CSbzK1Tz17ds3Tz31VFq0aJFKpdLgOs5KpZK6uroS0wFle+yxxzJ69Oicd955ZUehBk488cS0bt06Bx98cIPF5JTQ0PSdccYZOf300zNjxowGl1fMnj07Bx54YEaOHFliusajeChZtVrNBhtskGeffbbsKJSkX79+9e97lQual6uvvjr77rtvzjnnnEUeHzZsWI0TsST59NRrmrZPb5W3oIxWQjcP/fv3z3333Zfll1++wZoOC86B9957r8R01ML06dMzbdq0HHHEETn//PPrxzt27NiktlV2qUXJKpVKunbtmnfffTcrrbRS2XEoweOPP97g9oJXuYCm74UXXkiSRf5xaVGx5uXT6z3V1dVlzJgxmTt3bomJqKX58+eXHYGSXHPNNUmSsWPHlhuE0iy33HJZbrnlcsstt6RNmzZJkokTJ+ahhx7KTjvt1GS22TbjYQkwaNCgjBkzJjvvvHOD6XUWFWu+vMrVfNxxxx358Y9/nIkTJ6aurs6rXM3MZ11qtYBLrpqPNdZYo/791q1bp0ePHvnVr36VjTfeuMRU1NKTTz6Z5557LgcccEDef//9zJ49O126dCk7FjVyyimn5Nhjj/3CMZqufv365e67787HH3+c3r17p3v37tlggw0azIJYmpnxULJnn302L774YgYMGJCvfOUrZcehBP/+Ktejjz7qVa5m5Ic//GHOPffcbLHFFk2m0eY/9+kFBReoVCp566238vbbbyugmpFXX301SfLKK6/k1ltvTY8ePZQOzch5552XkSNHZsaMGTnggAMyderUHHrooRadbEZuvPHGhUqGRY3RdM2dOzfLLrtsLrvsshx00EE57bTTssEGG5Qdq9EoHkp03nnn5ZhjjkmvXr1y22235ZJLLsnuu+9edixq7NO7mrRq1So9e/bMpZdeWmIiaqljx44ZOHBg2TEoyb9favXee+/llFNOyRVXXJGTTjqppFTU0je+8Y387ne/S58+ffLWW2+lX79+2WyzzXLBBRfkueeey9FHH112RGrgggsuyJgxY/K1r30tSbLWWmvlnXfeKTkVtXDXXXflzjvvzJtvvpnhw4fXj0+fPr3EVJRhwQuP9957b/bbb78kaVIvSikeSnTeeeflmWeeyeqrr57x48fniCOOUDw0Qwte5aJ5+ta3vpWbb745u+66a9lRKNGcOXNy1lln5fe//33222+/PP/881lxxRXLjkUNvPnmm+nTp0+S5Kqrrkr//v1z0003Zdq0aenfv7/ioZlo27Zt2rdv32CsVSu/pjcH7dq1S6dOndKiRYsst9xy9eOrrbZajjvuuBKTUWsDBgzIV7/61dTV1WXkyJGZNm1ak/p3oOl8JUuh1q1bZ/XVV0+SbLDBBpk5c2bJiSjDbbfdlq222iqdOnVKkkybNi0PP/xwvvnNb5YbjEItWL26Wq1m+vTpad++fdq2bWsV62Zm/vz5ueiii/KrX/0q2267bR599NF069at7FjU0Kf/2Hz44Yez8847J/nk34im9Asnn2/llVfOhAkT6heVHT16dP3viDRt/fv3T//+/bPrrrtmww03LDsOJTr33HMzbty4rLnmmmndunXq6upy4YUXlh2r0fiJVqI5c+Zk/PjxWbC+57/f7t27d5nxqJHjjjuuwUrGnTp1ynHHHad4aOKsXk2SrL/++vnoo49y2mmnZcMNN8z06dMbrPvi50DT16JFi0yaNCmdOnXKfffdl9NPP73+2KxZs0pMRi2dffbZ2XffffPCCy9ktdVWS8eOHXP77beXHYsaWmGFFfKtb30rkyZNytixYzN27Njcc889+fGPf1x2NGqkUqnk448/zh/+8Ickyfbbb59+/fqVnKrx2NWiRN27d//M7dIqlUomTpxY40SUoU+fPgv9Ebrhhhtm3Lhx5QSipl5//fWsssoqadeuXZJPCsh33nknq622WsnJqAU/B7jxxhtz+OGHp1WrVtlyyy1z/fXXJ/lk9sPJJ5+cO++8s+SE1Mr8+fPz4osvplqtplevXk3q2m6+2M4775z99tsvv/3tbzNu3LjMmzcvG220UcaPH192NGrkggsuyCmnnJLdd989lUolN954Y4477rgceuihZUdrFIoHKNlWW22VM844o35BqYceeihHH310HnzwwZKTUQubbrpp7r///vriYfbs2dlmm23y6KOPlpwMqJXJkyfnX//6V3r37l1fRL311luZN2+e6fbNxFFHHZVDDjkkX/3qV8uOQkk22WSTPPHEEw22VLe9evPSu3fv/OMf/8jKK6+cJHnnnXey3XbbNZgJuTRzqQWU7De/+U122223rLPOOkmSl156KTfddFPJqaiVjz/+uL50SD653vujjz4qMRFQa507d07nzp0bjK266qolpaEMHTt2zLe+9a2stNJKOfjgg7Pffvs1WGiQpq9Vq1b59OvB06ZNi9eHm58FpcO/v98UtCg7ADR3W2yxRZ5//vn87Gc/y89+9rP885//zKabblp2LGqkUqlkypQp9bcnT57sFw2AZuaEE07IxIkTc8YZZ+TRRx9Nz549s//++5cdixrac889c/jhh+eDDz7IRRddlO23377JTLHnP9OzZ8/88pe/zOuvv57XX389xx13XHr27Fl2rEZjxgMsASZOnJipU6fmgAMOyPvvv5+33347Xbp0KTsWNTBs2LBsscUWOeCAA5IkV1xxRU444YSSUwFQhgEDBmSZZZbJ/Pnzc+211+bKK68sOxI1ctRRR+Xqq6/O9OnT89e//jXDhw/PfvvtV3YsauTZZ5/NPvvsk2uvvTZ9+/ZNpVLJN77xjfz3f/932dEajTUeoGTnnXdeRo4cmRkzZuSVV17JK6+8kkMPPTT33HNP2dGokXvvvTd/+ctfkiTf/va3s9VWW5WcCIBamjJlSi6//PKMGjUq1Wo1Bx98cAYPHrzQJTg0fQv+NPushYdpes4777wcc8wx6dWrV1544YWMGjUqu+++e9mxGp3iAUrWp0+fPPLII/na175Wv4DQ+uuvn2effbbkZABALay00koZNGhQhgwZks0337zsOJTg9ddfz9ChQ3Pfffcl+WT2y8iRIy0w2wysv/76+ctf/pLVV18948ePzxFHHNEkF5l3qQWUrG3btmnfvn2DsVat/K/ZXMyePTvnnntuxo4dmzlz5tSP33jjjSWmAqCW3njjjYV+F6B5OfDAA/PNb34z1113XarVai666KIceOCBuffee8uORsFat25dXzBtsMEGmTlzZsmJiuGvGyjZyiuvnAkTJtRPqRs9erR2uxkZOnRoOnbsmIcffjhHHXVURo8ena233rrsWADUwNVXX5199903F1544SKPDxs2rMaJKMs777yTn/70p/W3f/KTn2T06NHlBaJm5syZk/Hjx9dfZvPvt3v37l1mvEajeICSnX322dl3333zwgsvZLXVVkvHjh1z++23lx2LGhk3blzGjx+f3r175wc/+EGGDBmSb37zm2XHAqAGXnjhhSSpv9Ty01zj37z06NEjEyZMyNprr50kmTBhQpPa0YDPNnv27Oyyyy4NxhbcrlQqmThxYhmxGp3iAUrWo0ePPProo/nnP/+Z2267LRtssEHWWGONsmNRIwum1rZq1SozZ87Msssum3feeafkVADUwkknnZQkGTVqVMlJKMtuu+2WSqWSGTNmZMMNN8zXvva1JKlf/4um77XXXis7Qk0oHqAk3/jGN/K73/0uffr0yeTJk9O/f/9sttlmufzyy/Pcc8/l6KOPLjsiNbDCCitk2rRp2XnnnTNw4MCstNJK6dq1a9mxAKixRx99NK+88krmzZtXP3bggQeWmIha2HXXXevf//Tz7bmnqbGrBZRk3XXXzfPPP58k+d3vfpeHHnooN910U6ZNm5b+/fvnmWeeKTkhtVBXV5eWLVumWq3mqquuyrRp03LggQemY8eOZUcDoEaOOOKI3HXXXenTp09atmyZ5JMp1tddd13JyQAahxkPUJJPr1798MMPZ+edd06SLL/88na1aEY+/QvmjjvumBVXXLHkRADU2t///vc899xzadeuXdlRKNF111230C5XI0aMKDERNJ4WZQeA5qpFixaZNGlSZsyYkfvuuy/9+/evPzZr1qwSk1ELEyZMyN57753vfve7ef3119OvX7+svPLK+fKXv9wk924G4LN16dIlbdu2LTsGJRo2bFguv/zyjB49OpVKJddff32mT59edixoNIoHKMkxxxyTjTbaKD179syAAQPqVzF++OGH071793LDUbjDDjssffv2zYorrpitt9463//+9zNz5sxcdNFF+clPflJ2PABqaLPNNsugQYNy3XXX5dZbb61/o/m45557csstt2TllVfOmWeemcceeyyTJk0qOxY0Gms8QIkmT56cf/3rX+ndu3f9tllvvfVW5s2bl9VXX73kdBSpd+/eeeaZZ1KtVrPaaqs1+OWiT58+GTt2bHnhAKipAQMGLDRWqVRy9913l5CGMvTr1y+PP/54+vTpk8cffzytW7fOBhtskPHjx5cdDRqFC8mhRJ07d07nzp0bjK266qolpaGWFqzjUalUssoqqzQ41qKFyWgAzck999xTdgRKtuyyy2bWrFn5+te/nsGDB6dz58750pe+VHYsaDSKB4ASTJkyJeecc85C7yfJO++8U1YsAGro3y+nWFBG9+7du8Ei1DR9V199dVq1apXf/va3GTFiRKZNm5brr7++7FjQaFxqAVCCgw8++HOPjxo1qkZJACjLoi6xePfddzNt2rTcfPPN2WSTTUpIRS1tscUWeeSRR/KjH/0oZ599dtlxoDCKBwAAWILcd999OeGEE3LvvfeWHYWCrbvuurn33nuzww475MEHH8y//2nWsWPHkpJB43KpBQAALEH69++fDz74oOwY1MBee+2VNdZYIx999FGWW265BscqlUrq6upKSgaNywpmAACwBKmrq/MHZzNx0kknZdasWdl8880zf/78Bm/OAZoSMx4AAKAEzzzzzEJjU6dOzcUXX5z+/fuXkIiyPPTQQ2VHgEJZ4wFgCfDWW28lsZ0qQHOyxhprNLhdqVSy8sorZ+DAgTnmmGPSrl27kpJRK7vttlsqlcpnHr/xxhtrmAaKY8YDQImef/75DBo0qL546Nq1a/70pz9lnXXWKTkZAEV79dVXy45AyXbddddFjs+dOzczZsyobRgokBkPACUaMGBAhg4dmv322y9Jcs0112TkyJG55557Sk4GANTaiy++mIsuuiiXX355unbtmieeeKLsSNAoFA8AJerTp0/Gjh37hWMAQNM0a9asXHvttbn44oszceLEzJ49O4888ojZjzQpdrUAKFHLli3z3HPP1d9+7rnn0rJlyxITAQC1MnTo0Ky22mq59dZbc/TRR+f1119Pp06dlA40OdZ4ACjRaaedlq233jq9e/dOkowfPz5XXnllyakAKMOCicift9ggTcs111yTTTbZJIcffngGDhyYSqXi+adJcqkFQMneeeedPProo0mSzTffPCuttFLJiQCopbfffjuHHHJI7r777lQqlWy33Xa58MIL06VLl7KjUbAZM2bUX2YxadKkHHjggbn00kvzxhtvlB0NGpXiAQAASvTtb387W2yxRb73ve8lSc4///w89NBDue2220pORi0999xzueSSS3L55ZdnrbXWyuDBg+vPCVjaKR4AAKBEFhrm0+bNm5dbbrkll1xySf785z+XHQcahcUlAQCgRNVqNZMnT66/PXny5HhtsPlq1apV9thjD6UDTYrFJQEAoEQ/+clPstFGG2WnnXZKktx555357W9/W3IqgMbjUguAkj322GMZO3Zs5syZUz82bNiwEhMBUGvPPvts7r333iTJgAEDst5665UbCKARKR4ASnTaaafl+uuvz+uvv57+/fvnb3/7W7bbbrvcdNNNZUcDAIBG4VILgBJdddVVeeKJJ7L55pvnhhtuyIsvvphjjjmm7FgA1MCAAQNSqVQWeaxSqeQf//hHjRMBFEPxAFCidu3apV27dpk/f36q1Wp69eqVV155pexYANTAT37yk4XG3n777Zx22mn58MMPS0gEUAzFA0CJ2rdvn7lz56ZPnz75yU9+kq5du6aurq7sWADUwDe/+c3692fMmJHTTz89F1xwQYYOHZqjjz66xGQAjct2mgAl+u///u98/PHHOfPMM/PBBx/koYceyuWXX152LABqZN68eTn77LPTs2fPvPvuuxk3blxOPfXUdOzYsexoAI3G4pIAAFCCK6+8Mscff3z69u2b0047LT179iw7EkAhFA8AJTjzzDNz1FFH5cc//vEiFxYbMWJECakAqKUWLVqkW7du6dOnzyJ/Ftx4440lpAJofNZ4ACjBMssskyTp1KlTuUEAKM2oUaPKjgBQE2Y8AAAAAIUx4wGgBCeffPLnHj/++ONrlAQAAIqleAAowYL92SdNmpR//OMf2WWXXVKpVHLrrbdmu+22KzkdAAA0HpdaAJRohx12yOjRo7PqqqsmSd5+++0MGTIkd911V8nJAACgcbQoOwBAc/bWW2/Vlw5J0qVLl7z55pslJgIAgMaleAAoUdeuXXPCCSfkjTfeyBtvvJETTzwxXbt2LTsWACU77LDDyo4A0GgUDwAlGj16dJ5//vn06dMnG220UV544YWMHj267FgAlOzb3/522REAGo01HgAAAIDCmPEAUKLp06fnyCOPrH9l67nnnsvVV19dcioAaumjjz7K6aefnm984xvZfvvtc8YZZ+Sjjz4qOxZAozHjAaBE++yzT9Zff/1cc801efbZZzN79uxsscUWGTt2bNnRAKiRgw8+OFOnTs0hhxySJBk1alSWX375jBo1quRkAI2jVdkBAJqzCRMm5JprrskNN9yQJGnfvn30wQDNyyOPPJLnn38+lUolSfKtb30r6623XsmpABqPSy0AStSmTZsGt2fPnq14AGhmVlxxxcyePbv+9kcffZSVVlqpxEQAjcuMB4ASDRgwIKeeemrmzJmTv//97znrrLOy++67lx0LgBpaZ511stlmm2WvvfZKklx//fXp169fzjnnnCTJsGHDyowH8H9mjQeAEs2bNy+//e1vc/PNN6darWbXXXfN0UcfnZYtW5YdDYAaOfjggz/zWKVSySWXXFLDNACNT/EAAAAAFMalFgAlWDB99rOYVgvQfFx22WWLHD/wwANrnASgGGY8AJSgRYsW6devX9Zbb72FFpM0rRagedlzzz3r358zZ04efPDBbL755rnjjjtKTAXQeBQPACUYNWpURo0alblz5+a//uu/su+++2aZZZYpOxYAS4BXX301v/zlL3PVVVeVHQWgUSgeAEr00ksv5ZJLLsn111+fLbfcMj//+c+zzjrrlB0LgJJtuOGGGTduXNkxABqFNR4AStSzZ8+cdNJJWW+99TJ8+PBsvPHGigeAZubWW2+tf7+uri6PPvpo2rZtW2IigMaleAAoydNPP52LL744d9xxRwYOHJjbb789m266admxAKixs846q/79Vq1apUePHrn22mtLTATQuFxqAVCCvn37plWrVjn44IOz++67p3379g2Od+zYsaRkAADQuBQPACVo0aJF/fuVSqX+/Wq1mkqlkrq6ujJiAVBD77zzTjp06JAvfelLSZKHH3441113XXr06JHvfe97DX5WACzN/GsGUIL58+fXv9XV1dW/LbgNQNO3++67580330zyyWLDAwcOzEcffZTrr78+Rx99dMnpABqPGQ8AAFCC9dZbL//85z+TJKecckomTJiQyy67LLNnz06/fv3y7LPPlpwQoHGY8QAAACX49M4VjzzySL7xjW8kSdq3b5/WrVuXFQug0SkeAACgBO3atcu4ceMyefLkPPDAAxkwYED9sdmzZ5eYDKBx2U4TAABK8Otf/zrbbrttPvzww3z/+9/PaqutliS58847s84665ScDqDxWOMBoGSPPvpoXnnllcybN69+7MADDywxEQC1UldXlw8//DCdOnWqH5s5c2aq1WqWWWaZ8oIBNCLFA0CJjjjiiNx1113p06dPWrZsmeST7TWvu+66kpMBAEDjUDwAlKhnz54ZP3582rVrV3YUAAAohMUlAUrUpUuXBquaAwBAU2NxSYASbbbZZhk0aFD23nvvBrMedtlllxJTAVALv/3tb7PrrrumZ8+eZUcBKJRLLQBK9Omt0xaoVCq5++67S0gDQC2deeaZufnmmzN9+vTssssu2W233bLxxhuXHQug0SkeAACgRFOmTMktt9ySm2++ORMmTMhOO+2UXXfdNdtss01atHBlNLD0UzwAlOxPf/pT/va3vyVJBg4cmD322KPkRACUZcaMGfnLX/6Sm266KY8++mgmTpxYdiSA/zPFA0CNPfbYY9l0002TJCeffHJuvvnmHHjggalUKrn88suz66675thjjy05JQBlmzt3blq3bl12DID/M8UDQI39+c9/zgMPPJDTTz89vXv3zpgxY/KlL30pSTJz5sxsscUWeeaZZ0pOCQAAjcOuFgA19s1vfjPTpk1LklSr1frSIUk6dOgQfTAAAE2JGQ8AJTrkkEPy8ccfZ+jQoUmSiy++OK1atcrFF19ccjIAAGgcigeAEs2cOTMnn3xy/vGPfyRJvvGNb+S4445Lhw4dSk4GQK3MmzcvZ511Vv1CwzvssEN+9KMfpVUrk5OBpkHxAAAAJRo2bFheeeWVDB06NJVKJRdddFHWWGONnHPOOWVHA2gUigeAElx99dXZd999P/OXymHDhtU4EQBl6d27d8aOHZsWLVok+WQGRN++fS00DDQZ5m8BlOCFF15Ikjz99NMlJwGgbNVqNfPnz68vHqrVqoWGgSbFjAcAACjRT3/60zz55JMZMmRIkuSyyy7LRhttlN/+9rflBgNoJIoHgBKdcMIJGTZsWFZcccUkybvvvps//vGPOeGEE0pOBkCtzJ8/PxdccEH+/ve/J/lkoeHDDz88lUql5GQAjUPxAFCiPn36ZOzYsQ3G+vbtm6eeeqqcQADU3J133pkdd9zxC8cAllYtyg4A0JzNnz9/obGPP/64hCQAlOWYY475j8YAllYWlwQoUa9evfKb3/wmRx11VKrVas4888yss846ZccCoAYmTJiQF154IdOnT8+tt95aPz59+vTMmjWrxGQAjculFgAleuuttzJ48OA8+OCDqVQq2XrrrXPZZZelS5cuZUcDoGCXXnppRo8enSeeeCKbbLJJ/XjHjh1z2GGH5Zvf/GaJ6QAaj+IBYAkwc+bMJEmHDh1KTgJArV188cU55JBDyo4BUBjFA0DJ5s6dm1dffTVz5sypH+vdu3eJiQCoNT8LgKbMGg8AJbr99tszdOjQTJs2LR06dMi0adPSrVu3vPrqq2VHA6BG/CwAmjq7WgCU6LjjjsuYMWOy7rrrZurUqbnssssyaNCgsmMBUEN+FgBNneIBoEQtWrRIt27dMm/evCTJ4MGDc/fdd5ecCoBa8rMAaOpcagFQotatWydJunbtmptuuindu3fPtGnTSk4FQC35WQA0dYoHgBL98Ic/zLRp03LKKadkn332yfvvv5+zzz677FgA1NCifhb8/ve/LzsWQKOxqwUAAJSkrq4uN998c/bYY4+yowAUxhoPACU64YQTMnXq1Prb7777bk466aQSEwFQSy1btsypp55adgyAQikeAEp0yy23ZMUVV6y/vdJKK+WWW24pMREAtda3b988+OCDZccAKIw1HgBKNH/+/IXGPv744xKSAFCWMWPGZPTo0VlzzTWzzDLL1I8/9dRTJaYCaDyKB4AS9erVK7/5zW9y1FFHpVqt5swzz8w666xTdiwAauiPf/xj2REACmVxSYASvfXWWxk8eHAefPDBVCqVbL311rnsssvSpUuXsqMBUENz587N66+/nrXWWqvsKACNTvEAsASYOXNmkqRDhw4lJwGg1u69997st99+adWqVV5//fU8/vjj+f3vf58rrrii7GgAjcKlFgAluv/++xc5vvXWW9c4CQBl+fnPf54HHngggwYNSpL069cvTz/9dMmpABqP4gGgREcddVT9+3PmzMmLL76Y9ddf34JiAM1IXV3dQpdYtGnTpqQ0AI1P8QBQoscff7zB7cceeyyjR48uJwwApWjXrl1mzJiRSqWSJBk/fnzat29fciqAxtOi7AAA/H+bbrppHnnkkbJjAFBDxx13XHbYYYe8+eabGTx4cLbffvuccsopZccCaDRmPACU6Jlnnql/v66uLo8++mjmzp1bYiIAam2HHXZIz549c+edd6Zareakk06yuwXQpNjVAqBEa6yxRv37rVq1Ss+ePfOrX/0qG2+8cYmpAACg8SgeAACgBBtttFH9ug6LYqFhoKlwqQVACV5//fXPPb766qvXKAkAZTn77LPLjgBQE2Y8AJRg5ZVXrn+Va+rUqWndunWSZO7cuVlxxRUzZcqUMuMBAECjMeMBoATvvPNOkuToo49Ojx49csghhyRJLrnkkrzyyitlRgOgRnbbbbfPvdTixhtvrGEagOKY8QBQoj59+mTs2LENxjbaaKM8/fTT5QQCoGYuvfTSzz1+0EEH1SgJQLHMeAAo0ccff5wXX3wxvXr1SpJMmDAhH330UcmpAKgFxQLQXCgeAEp0+umnZ8stt8yGG26YJHnmmWdyySWXlJwKgFo488wzc9RRR2X48OGLPD5ixIgaJwIohuIBoES77LJLnn/++YwZMyZJssUWW2SllVYqORUAtbDMMsskSZZbbrmSkwAUyxoPACX45z//mfXWWy9J8tFHH6Vt27b1x+67777079+/rGgAANCoFA8AJejbt2+eeuqphd5f1G0AmrZ58+blhhtuyCuvvJJ58+bVjx9//PElpgJoPC61ACjBpzvff+9/9cEAzcs+++yTyZMnZ9NNN03Lli3LjgPQ6BQPACX49L7t/76H++ft6Q5A0zN+/Pi88MIL/v0HmizFA0AJZs+enfHjx6darTZ4f8ExAJqP1VZbLR9//HGD9X4AmhJrPACUoHv37p/5ylalUsnEiRNrnAiAWjvnnHOSJM8991zGjRuX3XbbLe3atas/PmzYsLKiATQqMx4ASvDaa6+VHQGAkj399NP176+zzjp5/vnn62+77AJoSsx4AAAAAArTouwAAADQnJ1wwgmZOnVq/e133303J510UomJABqX4gEAAEp0yy23ZMUVV6y/vdJKK+WWW24pMRFA41I8AABAiebPn7/Q2Mcff1xCEoBiKB4AAKBEvXr1ym9+85vU1dVl3rx5OeOMM7LOOuuUHQug0VhcEgAASvTWW29l8ODBefDBB1OpVLL11lvnsssuS5cuXcqOBtAoFA8AALAEmDlzZpKkQ4cOJScBaFytyg4AAADN3dy5c/Pmm29mzpw59WO9e/cuMRFA41E8AABAiW6//fYMHTo006ZNS4cOHTJt2rR069Ytr776atnRABqFxSUBAKBExx13XMaMGZN11103U6dOzWWXXZZBgwaVHQug0SgeAACgRC1atEi3bt0yb968JMngwYNz9913l5wKoPG41AIAAErUunXrJEnXrl1z0003pXv37pk2bVrJqQAaj+IBAABK9MMf/jDTpk3LKaeckn322Sfvv/9+fv/735cdC6DR2E4TAAAAKIw1HgAAoCTPP/983nzzzSSf7G7xgx/8IH/84x9TV1dXcjKAxmPGAwAAlODUU0/NH/7whyTJYYcdlttuuy3bb799HnrooWy00UY599xzS04I0Dis8QAAACW48sorM2HChHz44YdZa6218vbbb6dTp0756KOP0rdv37LjATQaxQMAAJSgbdu2WXbZZbPsssumZ8+e6dSpU/14mzZtyg0H0Iis8QAAACWoVCr177dq1eozjwEs7azxAAAAJWjVqlU6duyYJPnggw/q369Wq5kxY0bmzp1bZjyARuNSCwAAKMErr7xSdgSAmjDjAQAAACiMNR4AAACAwigeAAAAgMIoHgAAAIDCKB4AAKBEm2666X80BrC0UjwAAECJ5s2bt9DtDz/8sKQ0AI1P8QAAACU444wzsvzyy2f8+PFZYYUV6t+WXXbZbL311mXHA2g0ttMEAIASTJ8+PdOmTcsRRxyR888/v368Y8eOWX755UtMBtC4FA8AAFCijz/+OG3atEmSTJw4Mc8991x22mmntGzZsuRkAI1D8QAAACXq169f7r777nz88cfp3bt3unfvng022KDBLAiApZk1HgAAoERz587Nsssumz//+c856KCD8tBDD+Whhx4qOxZAo1E8AABAiebOnZskuffee7PtttsmicssgCalVdkBAACgORswYEC++tWvpq6uLiNHjsy0adPSqpVf04GmwxoPAABQomq1mnHjxmXNNddMx44d8+677+aNN97IRhttVHY0gEahSgUAgBJVKpV8/PHH+cMf/pAk2X777dOvX7+SUwE0Hms8AABAiS644IIMGjQoU6ZMyTvvvJNBgwbloosuKjsWQKNxqQUAAJSod+/e+cc//pGVV145SfLOO+9ku+22yzPPPFNyMoDGYcYDAACUbEHp8O/vAzQFigcAAChRz54988tf/jKvv/56Xn/99Rx33HHp2bNn2bEAGo3iAQAASnT++efnlVdeSd++fbPxxhvn5Zdfzn//93+XHQug0VjjAQAASvLss89mwoQJ2XDDDbPWWmuVHQegEGY8AABACc4777x8/etfzxlnnJGNN944N910U9mRAAphxgMAAJRg/fXXz1/+8pesvvrqGT9+fI444og8+OCDZccCaHRmPAAAQAlat26d1VdfPUmywQYbZNasWSUnAihGq7IDAABAczRnzpyMHz8+CyYgz549u8Ht3r17lxkPoNG41AIAAErQvXv3VCqVRR6rVCqZOHFijRMBFEPxAAAAABTGGg8AAABAYRQPAAAAQGEUDwAAAEBhFA8A0EQNHjw4Q4YMKTXDlVdemV69etX0cw4ZMiSDBw+u6ef8T7322mupVCp5+eWXy44CADWjeACApcA222yTNm3aZJlllmnwdu2115Ydrd4222yTY489tsHY/vvvnxdffLGkRIu2JBcTANAUtSo7AADwn/nZ/2vv7kKabMM4gP+3VJSMOZ3LSGWW62R9QIhJJCQrj1coQTasNWxSYbC0yLDCD2R+ZVk4cm1pNSHEk1qRipBFmhQFRZZpEVazrPyqltl8D8KHnqhe5XW8KP8f3Af3c93X7bUduWv3sycnBwUFBf93GURERETTwhMPREREc0RJSQmio6MREhICo9GIsbExUVwikaC5uVmY/+7Y/5UrV5CQkAC5XI6wsDCkpKQIsYyMDKhUKgQHByMmJgZHjhyB1+sFAJhMJrS1tcFisQinMQDA4XAgMjJS2MPj8eDAgQOIiYmBXC5HYmIiOjo6hPjkeqvVCpVKBZlMhtTUVAwPDwtr8vLysGzZMixYsABRUVHYu3cvPn/+PEPvIlBbW4tVq1ZBJpNBo9Ggvr4eAOD1ehEVFYW6ujrR+vLycqxcuVKYu1wurFmzBnK5HGq1GidOnJix2oiIiGYjNh6IiIjmgIsXL6KoqAj19fV49+4d4uPj0djYOK09mpqakJKSArPZjP7+frx69QqZmZlCPD4+Hh0dHRgZGYHT6URVVRXOnDkDAKiurkZiYiJycnIwOjqK0dHR3/6N7OxsuFwuNDU1ob+/HzqdDhs2bEBfX5+wxu12o6urC48fP0ZXVxfu37+PsrIyIa5Wq9Hc3Izh4WFcu3YNV69eRX5+/rRe6584HA4cPnwYNpsNHz9+hNVqRUZGBm7evAmpVIodO3bAZrOJcmpqamA0GgEAra2t2Lp1K4qKivD+/Xs0NjaipKQEFy5cmJH6iIiIZiM2HoiIiGaJ0tJShISEiEZ3dzcAwG63w2AwYO3atfD390dGRoboW/ipqKysxM6dO5GamoqAgAAEBgZCq9UKcaPRiIULF0IikSAhIQHbtm3D9evXp7y/1+uFzWZDQUEBYmNjERAQALPZjCVLluD8+fPCOj8/P1gsFgQFBWHRokXQ6XS4c+eOENfr9YiOjoZEIoFGo8Hu3bunVcfflJeXIzc3F3FxcZBKpVi3bh22bNkCh8MBADAYDGhraxNOidy6dQu9vb3Cb0ZUVFQgMzMTWq0WUqkUy5cvh8lkgt1un5H6iIiIZiM2HoiIiGaJ/fv3Y3BwUDTUajUAoK+vDzExMaL1v87/zfPnz//4BIqJiQkUFhZCo9FALpcjJCQEVqsVb9++nfL+AwMD+PLlC5YuXSq6Hhsbi5cvXwpzhUIBf39/YT5//nyMjIwIc6vVitWrVyMsLAwymQy5ubnTquNvuru7YTabRc0dp9OJ169fAwBUKhW0Wq1w6qGmpgabN29GaGiokF9ZWSnKLy4uxps3b2akPiIiotmIjQciIqI5IDIyEi9evBBd+3UeHByMT58+CfPJD9OTVCoVnj59+tv96+vrcfz4cdTW1mJgYACDg4PYtWsXJiYmhDVS6d//rVAoFAgMDERPT4/oek9PD6Kjo/+aO+n27dvYs2cPysrK4Ha7MTQ0hMLCQlEd/0VERAROnz4tau6Mjo7C5XIJa4xGI86dO4cPHz7g0qVLwm0Wk/kHDx4U5Y+MjODRo0czUh8REdFsxMYDERHRHJCeno6zZ8+ivb0d4+PjqKmpwYMHD0Rr4uLi4HA44PF40N/fj2PHjoniWVlZsNlsaGhowNjYGDweD1paWgAAQ0ND8PPzg1KphEQiQWtrq+j2CODHh+4/NS6AH40Jg8GAvLw89Pb2YmxsDBUVFXj27BnS0tKm9DqHhoYwb948hIeHw9/fH/fu3UNVVdWUcn/m9Xrh8XhE4/v379i3bx/y8/PR2dkJr9eLr1+/orOzE3fv3hVydTodvn37hvT0dERERCApKUmIZWVl4eTJk2hpacH4+DjGx8fx8OFD3LhxY9o1EhERzRVsPBAREc0SPz8xYnJYLBYAQFpaGnJycpCamgqFQoH29nZs2rRJlH/q1Cm43W4oFAps3LgRer1eFE9OTobT6URxcTHCw8OFp0sAwPbt26HVarFixQooFApUV1cLv2swyWw248mTJ8KtGL9TWlqK5ORkJCUlQalUoqGhAU1NTYiKiprSe5CcnAyTyYT169dDJpPh0KFDSE9Pn1Luz5xOJ4KCgkTDbrcjKysLR48ehclkQmhoKBYvXozs7GzRSZGAgADo9XpcvnwZBoMBEolEiOl0OtTV1SEvLw9KpRJKpRJGoxEDAwPTrpGIiGiukEzM1NlEIiIiIiIiIqJf8MQDEREREREREfkMGw9ERERERERE5DNsPBARERERERGRz7DxQEREREREREQ+w8YDEREREREREfkMGw9ERERERERE5DNsPBARERERERGRz7DxQEREREREREQ+w8YDEREREREREfkMGw9ERERERERE5DP/AAZmJVV9mBvXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'df' is my DataFrame\n",
    "# Creating a histogram for the level of higher education of individuals aged 50 or more in the province of Pichincha\n",
    "# In my analysis, I'm visualizing the distribution of education levels for this specific group\n",
    "\n",
    "df_age_fifty_pichincha_quito['ULTIMO_NIVEL_EDUCATIVO'].hist()\n",
    "\n",
    "# Adding labels to the plot for clarity\n",
    "plt.xlabel('Educational Level')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Educational Level')\n",
    "plt.xticks(rotation=90)  # Rotating x-axis labels for better readability\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'df' is my DataFrame\n",
    "# Creating a histogram for the natural region of individuals aged 50 or more in the province of Pichincha\n",
    "# In my analysis, I'm visualizing the distribution of natural regions for this specific group\n",
    "\n",
    "df_age_fifty_pichincha_quito['REGION_NATURAL'].hist()\n",
    "\n",
    "# Adding labels to the plot for clarity\n",
    "plt.xlabel('Natural Region')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Natural Region')\n",
    "plt.xticks(rotation=90)  # Rotating x-axis labels for better readability\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming 'df' is my DataFrame\n",
    "# Creating a histogram for the AREA_2000 variable of individuals aged 50 or more in the province of Pichincha\n",
    "# In my analysis, I'm visualizing the distribution of the AREA_2000 variable for this specific group\n",
    "\n",
    "df_age_fifty_pichincha_quito['AREA_2000'].hist()\n",
    "\n",
    "# Adding labels to the plot for clarity\n",
    "plt.xlabel('Area')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Area')\n",
    "plt.xticks(rotation=90)  # Rotating x-axis labels for better readability\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 Text Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "#there is not need"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e6iCRwVnNCMi"
   },
   "source": [
    "### 2.2.2 Number of Variables per Data Types\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "id": "h0alsXSgNCMl",
    "outputId": "2ab3da8d-fa77-4a62-9aa4-836d4dc213a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID_SECTOR                  int64\n",
      "AREA_2000                 object\n",
      "PROVINCIA                 object\n",
      "ZONA_PLANIFICACION         int64\n",
      "REGION_NATURAL            object\n",
      "SEXO                      object\n",
      "EDAD                       int64\n",
      "ULTIMO_NIVEL_EDUCATIVO    object\n",
      "GRADO_CURSO                int64\n",
      "ANIOS_TRABAJO              int64\n",
      "MES                        int64\n",
      "ANIO                       int64\n",
      "CIUDAD                    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Checking the data types of columns in the DataFrame 'df'\n",
    "# In my analysis, I'm inspecting the data types of each column\n",
    "\n",
    "print(df_age_fifty_pichincha_quito.dtypes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2bMT7tRQNCNK"
   },
   "source": [
    "### 2.2.3 Proportion Between Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "#there is not need"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6aotS7e1NCNT"
   },
   "source": [
    "### 2.2.4 Basic Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "id": "lRK-6Wd1NCNU",
    "outputId": "bd1221de-6966-4622-faed-bea1e4338332"
   },
   "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>ID_SECTOR</th>\n",
       "      <th>AREA_2000</th>\n",
       "      <th>PROVINCIA</th>\n",
       "      <th>ZONA_PLANIFICACION</th>\n",
       "      <th>REGION_NATURAL</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>ULTIMO_NIVEL_EDUCATIVO</th>\n",
       "      <th>GRADO_CURSO</th>\n",
       "      <th>ANIOS_TRABAJO</th>\n",
       "      <th>MES</th>\n",
       "      <th>ANIO</th>\n",
       "      <th>CIUDAD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.096940e+05</td>\n",
       "      <td>109694</td>\n",
       "      <td>109694</td>\n",
       "      <td>109694.000000</td>\n",
       "      <td>109694</td>\n",
       "      <td>109694</td>\n",
       "      <td>109694.000000</td>\n",
       "      <td>98221</td>\n",
       "      <td>109694.000000</td>\n",
       "      <td>109694.000000</td>\n",
       "      <td>109694.00000</td>\n",
       "      <td>109694.000000</td>\n",
       "      <td>16354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Urbano</td>\n",
       "      <td>Guayas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Educación básica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Guayaquil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>55059</td>\n",
       "      <td>11334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52326</td>\n",
       "      <td>55397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.172315e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.701433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.589968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.879337</td>\n",
       "      <td>1.154831</td>\n",
       "      <td>6.46977</td>\n",
       "      <td>2013.835178</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.600366e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.406265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.344113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.703888</td>\n",
       "      <td>23.132177</td>\n",
       "      <td>3.44737</td>\n",
       "      <td>0.371022</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.015000e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.015003e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.102510e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.701640e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.403520e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5610.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID_SECTOR AREA_2000 PROVINCIA  ZONA_PLANIFICACION REGION_NATURAL  \\\n",
       "count   1.096940e+05    109694    109694       109694.000000         109694   \n",
       "unique           NaN         2        24                 NaN              4   \n",
       "top              NaN    Urbano    Guayas                 NaN         Sierra   \n",
       "freq             NaN     55059     11334                 NaN          52326   \n",
       "mean    1.172315e+11       NaN       NaN            4.701433            NaN   \n",
       "std     6.600366e+10       NaN       NaN            2.406265            NaN   \n",
       "min     1.015000e+10       NaN       NaN            1.000000            NaN   \n",
       "25%     7.015003e+10       NaN       NaN            3.000000            NaN   \n",
       "50%     1.102510e+11       NaN       NaN            5.000000            NaN   \n",
       "75%     1.701640e+11       NaN       NaN            7.000000            NaN   \n",
       "max     2.403520e+11       NaN       NaN            9.000000            NaN   \n",
       "\n",
       "          SEXO           EDAD ULTIMO_NIVEL_EDUCATIVO    GRADO_CURSO  \\\n",
       "count   109694  109694.000000                  98221  109694.000000   \n",
       "unique       2            NaN                     10            NaN   \n",
       "top      Mujer            NaN       Educación básica            NaN   \n",
       "freq     55397            NaN                  30298            NaN   \n",
       "mean       NaN      28.589968                    NaN       3.879337   \n",
       "std        NaN      21.344113                    NaN       2.703888   \n",
       "min        NaN       0.000000                    NaN       0.000000   \n",
       "25%        NaN      11.000000                    NaN       2.000000   \n",
       "50%        NaN      24.000000                    NaN       4.000000   \n",
       "75%        NaN      43.000000                    NaN       6.000000   \n",
       "max        NaN     100.000000                    NaN      10.000000   \n",
       "\n",
       "        ANIOS_TRABAJO           MES           ANIO     CIUDAD  \n",
       "count   109694.000000  109694.00000  109694.000000      16354  \n",
       "unique            NaN           NaN            NaN          4  \n",
       "top               NaN           NaN            NaN  Guayaquil  \n",
       "freq              NaN           NaN            NaN       5307  \n",
       "mean         1.154831       6.46977    2013.835178        NaN  \n",
       "std         23.132177       3.44737       0.371022        NaN  \n",
       "min          0.000000       1.00000    2013.000000        NaN  \n",
       "25%          0.000000       3.00000    2014.000000        NaN  \n",
       "50%          0.000000       6.00000    2014.000000        NaN  \n",
       "75%          0.000000       9.00000    2014.000000        NaN  \n",
       "max       5610.000000      12.00000    2014.000000        NaN  "
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating descriptive statistics for different types of columns in the DataFrame 'df'.\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b6vH15xcNCNZ"
   },
   "source": [
    " ### 2.2.5 Linear Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.5.1 Correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 863
    },
    "id": "NKnurhodNCNf",
    "outputId": "e2c173ba-875b-4f7c-c4ec-15ca3d1ddf3c"
   },
   "outputs": [],
   "source": [
    "# Calculating the correlation matrix of the scaled data.\n",
    "matriz_correlacion_df = scaled_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an upper triangular mask to hide the redundant half of the matrix.\n",
    "mask = np.triu(np.ones_like(matriz_correlacion_df, dtype=bool))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a heatmap to visualize the correlation matrix.\n",
    "plt.figure(figsize=(18, 15))\n",
    "sns.heatmap(matriz_correlacion_df, mask=mask, annot=True, cmap='coolwarm')\n",
    "\n",
    "# Displaying the heatmap.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.5.2 Pairs of columns greater than 70% of correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the correlation threshold (35% or -35% in absolute value).\n",
    "# In your analysis, the correlation threshold is set at 0.70.\n",
    "threshold = 0.70\n",
    "\n",
    "# Find correlations that meet the threshold and store them in a list of tuples.\n",
    "unique_tuples_result = [\n",
    "    (col1, col2, value)\n",
    "    for col1 in matriz_correlacion_df.columns\n",
    "    for col2, value in matriz_correlacion_df[col1].items()\n",
    "    if (col1 < col2) and abs(value) >= threshold and not np.isnan(value)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of labels for column pairs\n",
    "pares_columnas = [f'{col1} - {col2}' for col1, col2, correlation in unique_tuples_result]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of correlation values\n",
    "correlation_values = [correlation for col1, col2, correlation in unique_tuples_result]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Seaborn to create a bar plot with specified column pairs and correlation values\n",
    "ax = sns.barplot(x=pares_columnas, y=correlation_values)\n",
    "\n",
    "# Configuring the plot title\n",
    "plt.title(f'Correlations Above +/- {threshold}%')\n",
    "plt.xlabel('Column Pair')\n",
    "plt.ylabel('Correlation Percentage')\n",
    "\n",
    "# Adjusting the x-axis labels for better readability\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')\n",
    "\n",
    "# Removing y-axis labels for better visualization\n",
    "ax.set_yticklabels([])\n",
    "\n",
    "# Adjusting the layout for a cleaner presentation\n",
    "plt.tight_layout()\n",
    "\n",
    "# Displaying the bar plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ## 2.3 Preprocessing and Enrichment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "THyPA5HgNCM1"
   },
   "source": [
    "### 2.3.1 Coding Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "codec_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "id": "TJGwNMUHNCM2"
   },
   "outputs": [],
   "source": [
    "# Encoding nominal categorical columns\n",
    "# Selecting columns of type 'object' for encoding\n",
    "nominal_columns = ['AREA_2000', 'PROVINCIA', 'CIUDAD', 'REGION_NATURAL', 'SEXO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "codec_df, le_dict = label_encoder(dataframe=codec_df, columns=nominal_columns, encoding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary with ordered values\n",
    "educational_categories_ordered = {\n",
    "    'Ninguno': 0,\n",
    "    'Alfabetización': 1,\n",
    "    'Educación inicial': 2,\n",
    "    'Primaria': 3,\n",
    "    'Educación básica': 4,\n",
    "    'Educación Media o Bachillerato': 5,\n",
    "    'Secundaria': 6,\n",
    "    'Post Bachillerato / Sup. No Universitario': 7,\n",
    "    'Superior': 8,\n",
    "    'Postgrado': 9\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply encoding using the map method and assign a default value for NaN\n",
    "codec_df['ULTIMO_NIVEL_EDUCATIVO'] = codec_df['ULTIMO_NIVEL_EDUCATIVO'].map(educational_categories_ordered).fillna(-1, downcast='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure that the column is of integer type\n",
    "codec_df['ULTIMO_NIVEL_EDUCATIVO'] = codec_df['ULTIMO_NIVEL_EDUCATIVO'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C6jwJMAbNCNG"
   },
   "source": [
    "### 2.3.2 Scaling Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "id": "6JSdeuJDNCNJ"
   },
   "outputs": [],
   "source": [
    "# Instantiate MinMaxScaler for feature scaling.\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy the encoded data for preservation.\n",
    "scaled_df = codec_df.copy()\n",
    "\n",
    "# Apply scaler to normalize features using fit_transform.\n",
    "scaled_df = scaler.fit_transform(scaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a DataFrame from the scaled data using pandas.\n",
    "# In my analysis, I'm constructing a DataFrame to preserve the data structure and column names.\n",
    "\n",
    "scaled_df = pd.DataFrame(scaled_df, columns=codec_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ID_SECTOR  AREA_2000  PROVINCIA  ZONA_PLANIFICACION  REGION_NATURAL  \\\n",
      "0             0.0        1.0   0.000000               0.625        1.000000   \n",
      "1             0.0        1.0   0.000000               0.625        1.000000   \n",
      "2             0.0        1.0   0.000000               0.625        1.000000   \n",
      "3             0.0        1.0   0.000000               0.625        1.000000   \n",
      "4             0.0        1.0   0.000000               0.625        1.000000   \n",
      "...           ...        ...        ...                 ...             ...   \n",
      "109689        1.0        1.0   0.826087               0.500        0.333333   \n",
      "109690        1.0        1.0   0.826087               0.500        0.333333   \n",
      "109691        1.0        1.0   0.826087               0.500        0.333333   \n",
      "109692        1.0        1.0   0.826087               0.500        0.333333   \n",
      "109693        1.0        1.0   0.826087               0.500        0.333333   \n",
      "\n",
      "        SEXO  EDAD  ULTIMO_NIVEL_EDUCATIVO  GRADO_CURSO  ANIOS_TRABAJO  \\\n",
      "0        1.0  0.39                     0.4          0.6            0.0   \n",
      "1        0.0  0.13                     0.5          0.8            0.0   \n",
      "2        0.0  0.36                     0.4          0.6            0.0   \n",
      "3        1.0  0.36                     0.4          0.6            0.0   \n",
      "4        0.0  0.18                     0.9          0.1            0.0   \n",
      "...      ...   ...                     ...          ...            ...   \n",
      "109689   0.0  0.51                     0.7          0.4            0.0   \n",
      "109690   1.0  0.50                     0.7          0.4            0.0   \n",
      "109691   1.0  0.30                     0.4          0.6            0.0   \n",
      "109692   0.0  0.23                     0.1          0.0            0.0   \n",
      "109693   1.0  0.17                     0.6          0.2            0.0   \n",
      "\n",
      "             MES  ANIO  CIUDAD  \n",
      "0       0.909091   0.0     0.0  \n",
      "1       0.909091   0.0     0.0  \n",
      "2       0.909091   0.0     0.0  \n",
      "3       0.909091   0.0     0.0  \n",
      "4       0.909091   0.0     0.0  \n",
      "...          ...   ...     ...  \n",
      "109689  0.818182   1.0     1.0  \n",
      "109690  0.818182   1.0     1.0  \n",
      "109691  0.818182   1.0     1.0  \n",
      "109692  0.818182   1.0     1.0  \n",
      "109693  0.818182   1.0     1.0  \n",
      "\n",
      "[109694 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "# Displaying the scaled DataFrame 'scaled_df'\n",
    "# In my analysis, I'm showing the content of the scaled data\n",
    "print(scaled_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.3 Null Values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.3.1 Impute Null Values\n",
    "\n",
    "<span style=\"color:red\"> Execute on second iteration, later execute machine learning stage. </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the original DataFrame for imputation\n",
    "imputed_df = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.3.1.1 Imputation by Dispersion Measures\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'CIUDAD' Column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "codec_df_ciudad = codec_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_to_input = 'CIUDAD'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decode values equivalent to before codec in column 'CIUDAD'\n",
    "column_to_decode = [col_to_input]\n",
    "\n",
    "codec_df_ciudad = label_encoder(dataframe=codec_df_ciudad, columns=column_to_decode, decoding=True, le_dict=le_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Null values pre imputation\n",
    "total_nulls_pre_imputation = codec_df_ciudad[col_to_input].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example usage for 'CIUDAD' column\n",
    "codec_df_ciudad = impute_null_values_by_mode(codec_df_ciudad, col_to_input, 'PROVINCIA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sum of null values post imputation in 'CIUDAD' column\n",
    "total_nulls_post_imputation = codec_df_ciudad[col_to_input].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Quantity of null values in CIUDAD column before imputation of null values: 93340\n",
      "    Quantity of null values in CIUDAD column after imputation of null values: 73605\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f'''\n",
    "    Quantity of null values in {col_to_input} column before imputation of null values: {total_nulls_pre_imputation}\n",
    "    Quantity of null values in {col_to_input} column after imputation of null values: {total_nulls_post_imputation}\n",
    "    '''\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputed_df[col_to_input] = codec_df_ciudad[col_to_input]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.3.3.1.2 Imputation by Machine Learning Models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"CIUDAD\" Column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in it case i do not use ml logistig regression becouse it is low acuracy percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"ULTIMO_NIVEL_EDUCATIVO\" Column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "codec_df_ultimo_educativo = codec_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list with destination ml model path and destination decompressing folder path\n",
    "model_paths = [\n",
    "    ('..data/ml_best_model/ultimo_nivel_educativo_random_forest.pkl.zip', '../data/ml_best_model/')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error decompressing the file: [Errno 2] No such file or directory: '..data/ml_best_model/ultimo_nivel_educativo_random_forest.pkl.zip'\n"
     ]
    }
   ],
   "source": [
    "# Iterate over each file and destination pair, calling the decompress_file function\n",
    "for file_path, destination in model_paths:\n",
    "    decompress_file(file_path, destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Column to imputation values\n",
    "col_to_input = 'ULTIMO_NIVEL_EDUCATIVO'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to ml model for imputation of null values\n",
    "ML_MODEL_PATH_TO_ULTIMO_NIVEL_EDUCATIVO_IMPUTATION = '../data/ml_best_model/ultimo_nivel_educativo_random_forest.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the sum of null values in the \"ULTIMO_NIVEL_EDUCATIVO\" column before imputation\n",
    "total_nulls_pre_imputation = codec_df_ultimo_educativo[codec_df_ultimo_educativo[col_to_input] == -1].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute missing values in the \"ULTIMO_NIVEL_EDUCATIVO\" column\n",
    "impute_null_values_by_ml_model(codec_df_ultimo_educativo, col_to_input, -1, ML_MODEL_PATH_TO_ULTIMO_NIVEL_EDUCATIVO_IMPUTATION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map numeric values to specific educational categories\n",
    "educational_categories_ordered = {\n",
    "    0: 'Ninguno',\n",
    "    1: 'Alfabetización',\n",
    "    2: 'Educación inicial',\n",
    "    3: 'Primaria',\n",
    "    4: 'Educación básica',\n",
    "    5: 'Educación Media o Bachillerato',\n",
    "    6: 'Secundaria',\n",
    "    7: 'Post Bachillerato / Sup. No Universitario',\n",
    "    8: 'Superior',\n",
    "    9: 'Postgrado'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the dictionary mapping to the specific column\n",
    "codec_df_ultimo_educativo[col_to_input] = codec_df_ultimo_educativo[col_to_input].map(educational_categories_ordered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the sum of null values in the \"ULTIMO_NIVEL_EDUCATIVO\" column after imputation\n",
    "total_nulls_post_imputation = codec_df_ultimo_educativo[codec_df_ultimo_educativo[col_to_input] == -1].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Quantity of null values in ULTIMO_NIVEL_EDUCATIVO column before imputation of null values: 149149\n",
      "    Quantity of null values in ULTIMO_NIVEL_EDUCATIVO column after imputation of null values: 0\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "# Print the quantity of null values in the specific column before and after null values imputation.\n",
    "print(\n",
    "    f'''\n",
    "    Quantity of null values in {col_to_input} column before imputation of null values: {total_nulls_pre_imputation}\n",
    "    Quantity of null values in {col_to_input} column after imputation of null values: {total_nulls_post_imputation}\n",
    "    '''\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy the mapped column to another DataFrame (e.g., codec_df)\n",
    "imputed_df[col_to_input] = codec_df_ultimo_educativo[col_to_input].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GBNTg_lecbIp"
   },
   "source": [
    "## 2.4 Saved Processed CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the path for the cleaned data of individuals aged 50 or more in the province of Pichincha\n",
    "CLEAN_PICHINCHA_50_PLUS_DF_PATH = '../data/processed_csv/2014_census_clean.csv'\n",
    "CODEC_PICHINCHA_50_PLUS_DF_PATH = '../data/processed_csv/2014_census_codec.csv'\n",
    "SCALED_PICHINCHA_50_PLUS_DF_PATH = '../data/processed_csv/2014_census_scaled.csv'\n",
    "IMPUTED_PICHINCHA_50_PLUS_DF_PATH = '../data/processed_csv/2014_census_imputed.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of processed DataFrames with corresponding paths\n",
    "df_to_save = [\n",
    "    (df, CLEAN_PICHINCHA_50_PLUS_DF_PATH),\n",
    "    (codec_df, CODEC_PICHINCHA_50_PLUS_DF_PATH),\n",
    "    (scaled_df, SCALED_PICHINCHA_50_PLUS_DF_PATH),\n",
    "    (imputed_df, IMPUTED_PICHINCHA_50_PLUS_DF_PATH)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "id": "TUQu1q6bcd-v"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame successfully saved to: ../data/processed_csv/2014_census_clean.csv\n",
      "DataFrame successfully saved to: ../data/processed_csv/2014_census_codec.csv\n",
      "DataFrame successfully saved to: ../data/processed_csv/2014_census_scaled.csv\n",
      "DataFrame successfully saved to: ../data/processed_csv/2014_census_imputed.csv\n"
     ]
    }
   ],
   "source": [
    "# Save each processed DataFrame to its respective path\n",
    "for processed_df, path_processed_df in df_to_save:\n",
    "    load_dataframe_to_csv(processed_df, path_processed_df)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}