{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hrIjW1WdwZBK"
   },
   "source": [
    "# Trabajemos en algunos gráficos\n",
    "\n",
    "Usaremos el datased [ifood](https://github.com/Izainea/seminario-de-programacion/Datos/ifood_df.csv) para evaluar la eficiencia de unas campañas publicitarias aplicadas en un restaurante.\n",
    "\n",
    "## Carga de datos\n",
    "\n",
    "Para cargar los datos debe hacer clic en el panel izquierdo en el botón de exploración de archivos. En ese menu tendra la opción de subir algunos datos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "id": "uPV77Vux0beE",
    "outputId": "0f08fa26-fb96-462c-f934-3b62a3d804c4"
   },
   "outputs": [
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       "      <th>Recency</th>\n",
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       "      <td>0</td>\n",
       "      <td>734</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26646.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
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       "      <td>0</td>\n",
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       "    Income  Kidhome  Teenhome  Recency  MntWines  MntFruits  MntMeatProducts  \\\n",
       "0  58138.0        0         0       58       635         88              546   \n",
       "1  46344.0        1         1       38        11          1                6   \n",
       "2  71613.0        0         0       26       426         49              127   \n",
       "3  26646.0        1         0       26        11          4               20   \n",
       "4  58293.0        1         0       94       173         43              118   \n",
       "\n",
       "   MntFishProducts  MntSweetProducts  MntGoldProds  ...  marital_Together  \\\n",
       "0              172                88            88  ...                 0   \n",
       "1                2                 1             6  ...                 0   \n",
       "2              111                21            42  ...                 1   \n",
       "3               10                 3             5  ...                 1   \n",
       "4               46                27            15  ...                 0   \n",
       "\n",
       "   marital_Widow  education_2n Cycle  education_Basic  education_Graduation  \\\n",
       "0              0                   0                0                     1   \n",
       "1              0                   0                0                     1   \n",
       "2              0                   0                0                     1   \n",
       "3              0                   0                0                     1   \n",
       "4              0                   0                0                     0   \n",
       "\n",
       "   education_Master  education_PhD  MntTotal  MntRegularProds  \\\n",
       "0                 0              0      1529             1441   \n",
       "1                 0              0        21               15   \n",
       "2                 0              0       734              692   \n",
       "3                 0              0        48               43   \n",
       "4                 0              1       407              392   \n",
       "\n",
       "   AcceptedCmpOverall  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "\n",
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      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset from the user's Google Drive\n",
    "df = pd.read_csv(\"ifood_df.csv\")\n",
    "\n",
    "# Show DataFrame\n",
    "df.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de datos\n",
    "\n",
    "Vamos a explorar los datos para entender su estructura y contenido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 461
    },
    "id": "HJQrma1Q2b01",
    "outputId": "b30fe287-f5d0-47b6-b539-1043f47aa89e"
   },
   "outputs": [
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>684</td>\n",
       "      <td>100</td>\n",
       "      <td>801</td>\n",
       "      <td>21</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1672</td>\n",
       "      <td>1672</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10979.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <th>29</th>\n",
       "      <td>38620.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>112</td>\n",
       "      <td>17</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>22</td>\n",
       "      <td>89</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>229</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>40548.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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      ],
      "text/plain": [
       "     Income  Kidhome  Teenhome  Recency  MntWines  MntFruits  MntMeatProducts  \\\n",
       "20   2447.0        1         0       42         1          1             1725   \n",
       "21  58607.0        0         1       63       867          0               86   \n",
       "22  65324.0        0         1        0       384          0              102   \n",
       "23  40689.0        0         1       69       270          3               27   \n",
       "24  18589.0        0         0       89         6          4               25   \n",
       "25  53359.0        1         1        4       173          4               30   \n",
       "26  38360.0        1         0       26        36          2               42   \n",
       "27  84618.0        0         0       96       684        100              801   \n",
       "28  10979.0        0         0       34         8          4               10   \n",
       "29  38620.0        0         0       56       112         17               44   \n",
       "30  40548.0        0         1       31       110          0                5   \n",
       "\n",
       "    MntFishProducts  MntSweetProducts  MntGoldProds  ...  marital_Together  \\\n",
       "20                1                 1             1  ...                 0   \n",
       "21                0                 0            19  ...                 0   \n",
       "22               21                32             5  ...                 0   \n",
       "23               39                 6            99  ...                 1   \n",
       "24               15                12            13  ...                 0   \n",
       "25                3                 6            41  ...                 0   \n",
       "26               20                21            10  ...                 0   \n",
       "27               21                66             0  ...                 0   \n",
       "28                2                 2             4  ...                 0   \n",
       "29               34                22            89  ...                 1   \n",
       "30                2                 0             3  ...                 1   \n",
       "\n",
       "    marital_Widow  education_2n Cycle  education_Basic  education_Graduation  \\\n",
       "20              0                   0                0                     1   \n",
       "21              0                   0                0                     0   \n",
       "22              0                   0                0                     0   \n",
       "23              0                   0                0                     1   \n",
       "24              0                   0                0                     1   \n",
       "25              0                   0                0                     1   \n",
       "26              0                   0                0                     1   \n",
       "27              0                   0                0                     0   \n",
       "28              0                   0                0                     0   \n",
       "29              0                   0                0                     0   \n",
       "30              0                   0                0                     1   \n",
       "\n",
       "    education_Master  education_PhD  MntTotal  MntRegularProds  \\\n",
       "20                 0              0      1729             1728   \n",
       "21                 0              1       953              934   \n",
       "22                 0              1       539              534   \n",
       "23                 0              0       345              246   \n",
       "24                 0              0        62               49   \n",
       "25                 0              0       216              175   \n",
       "26                 0              0       121              111   \n",
       "27                 0              1      1672             1672   \n",
       "28                 1              0        26               22   \n",
       "29                 1              0       229              140   \n",
       "30                 0              0       117              114   \n",
       "\n",
       "    AcceptedCmpOverall  \n",
       "20                   0  \n",
       "21                   1  \n",
       "22                   0  \n",
       "23                   0  \n",
       "24                   0  \n",
       "25                   0  \n",
       "26                   0  \n",
       "27                   1  \n",
       "28                   0  \n",
       "29                   0  \n",
       "30                   1  \n",
       "\n",
       "[11 rows x 39 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ayudame a ver las filas de la 20 a la 30 de df\n",
    "\n",
    "df.iloc[20:31]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hagamos una descripicón de los datos interesnates, en este caso, la descripción de las columnas: ***Income***, ***Recency***, ***Kidhome*** y ***Teenhome***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2205.000000</td>\n",
       "      <td>2205.000000</td>\n",
       "      <td>2205.000000</td>\n",
       "      <td>2205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51622.094785</td>\n",
       "      <td>49.009070</td>\n",
       "      <td>0.442177</td>\n",
       "      <td>0.506576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20713.063826</td>\n",
       "      <td>28.932111</td>\n",
       "      <td>0.537132</td>\n",
       "      <td>0.544380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1730.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35196.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>51287.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68281.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>113734.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Income      Recency      Kidhome     Teenhome\n",
       "count    2205.000000  2205.000000  2205.000000  2205.000000\n",
       "mean    51622.094785    49.009070     0.442177     0.506576\n",
       "std     20713.063826    28.932111     0.537132     0.544380\n",
       "min      1730.000000     0.000000     0.000000     0.000000\n",
       "25%     35196.000000    24.000000     0.000000     0.000000\n",
       "50%     51287.000000    49.000000     0.000000     0.000000\n",
       "75%     68281.000000    74.000000     1.000000     1.000000\n",
       "max    113734.000000    99.000000     2.000000     2.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columnas = ['Income','Recency','Kidhome','Teenhome']\n",
    "\n",
    "# Veamos información de las columnas seleccionadas\n",
    "\n",
    "df[columnas].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráficos de distribución\n",
    "\n",
    "Sabemos por nuestros cursos de estadística que los histogramas son una buena forma de entender la distribución de los datos. Hagamos un histograma para cada una de las variables mencionadas.\n",
    "\n",
    "\n",
    "**Income:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "R0DbMtlU5o3x",
    "outputId": "cee160c3-f3b1-4b72-9955-0af0784af36f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      2205.000000\n",
      "mean      51622.094785\n",
      "std       20713.063826\n",
      "min        1730.000000\n",
      "25%       35196.000000\n",
      "50%       51287.000000\n",
      "75%       68281.000000\n",
      "max      113734.000000\n",
      "Name: Income, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Descripción de la variable 'Income'\n",
    "print(df['Income'].describe())\n",
    "\n",
    "# Histograma de la variable 'Income'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Income'], kde=True)\n",
    "plt.title('Distribución de Income')\n",
    "plt.show()\n",
    "\n",
    "# Boxplot de la variable 'Income'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df['Income'])\n",
    "plt.title('Boxplot de Income')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recency:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    2205.000000\n",
      "mean       49.009070\n",
      "std        28.932111\n",
      "min         0.000000\n",
      "25%        24.000000\n",
      "50%        49.000000\n",
      "75%        74.000000\n",
      "max        99.000000\n",
      "Name: Recency, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Describe la variable 'Recency'\n",
    "print(df['Recency'].describe())\n",
    "\n",
    "# Histograma de la variable 'Recency'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Recency'], kde=True)\n",
    "plt.title('Distribución de Recency')\n",
    "plt.show()\n",
    "\n",
    "# Boxplot de la variable 'Recency'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df['Recency'])\n",
    "plt.title('Boxplot de Recency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "Observe que la sintáxis para hacer un histograma es la siguiente:\n",
    "\n",
    "```python\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(dataframe['columna'], bins=30)\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "La columna ***Recency***  se puede cambiar por cualquier otra columna que se desee analizar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>222.0</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>212.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>213.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0    Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1    Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2    Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3    Adelie  Torgersen             NaN            NaN                NaN   \n",
       "4    Adelie  Torgersen            36.7           19.3              193.0   \n",
       "..      ...        ...             ...            ...                ...   \n",
       "339  Gentoo     Biscoe             NaN            NaN                NaN   \n",
       "340  Gentoo     Biscoe            46.8           14.3              215.0   \n",
       "341  Gentoo     Biscoe            50.4           15.7              222.0   \n",
       "342  Gentoo     Biscoe            45.2           14.8              212.0   \n",
       "343  Gentoo     Biscoe            49.9           16.1              213.0   \n",
       "\n",
       "     body_mass_g     sex  \n",
       "0         3750.0    Male  \n",
       "1         3800.0  Female  \n",
       "2         3250.0  Female  \n",
       "3            NaN     NaN  \n",
       "4         3450.0  Female  \n",
       "..           ...     ...  \n",
       "339          NaN     NaN  \n",
       "340       4850.0  Female  \n",
       "341       5750.0    Male  \n",
       "342       5200.0  Female  \n",
       "343       5400.0    Male  \n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "penguins = sns.load_dataset(\"penguins\")\n",
    "penguins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kidhome\n",
       "0    1276\n",
       "1     883\n",
       "2      46\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Kidhome'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    2205.000000\n",
      "mean        0.442177\n",
      "std         0.537132\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         1.000000\n",
      "max         2.000000\n",
      "Name: Kidhome, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Describe la variable 'Kidhome'\n",
    "print(df['Kidhome'].describe())\n",
    "\n",
    "# Histograma de la variable 'Kidhome'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.catplot(df['Kidhome'].value_counts(),kind=\"bar\")\n",
    "plt.title('Distribución de Kidhome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Kidhome:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    2205.000000\n",
      "mean        0.442177\n",
      "std         0.537132\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         1.000000\n",
      "max         2.000000\n",
      "Name: Kidhome, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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Kzc3VoEGD9O9//1v+/v6uNkOGDNFzzz2nWbNmadKkSerZs6e+/PJL/e1vf3Nbls1m09dff62nnnpK7777rv773/+qYcOG6tu3rzp37nzGGjp27KjvvvtOU6ZM0YwZM+R0OtW7d2+98847p9xj63wMHTpUH330kR555BFNmTJFLVu21Lx58/T5559rxYoVbm3nzp2rHj166JVXXtHDDz8su92uZs2a6ZZbblGfPn2qtP5p06a5Al5oaKhb71UFq9WqL774QpMmTdI777wji8Wiq6++Ws8995y6d+9epfVWLHPq1Kn64IMPNG/ePDVr1kzPPPPMKf+G52vAgAFKTEzUE088oZdeekl5eXmKiopS7969deedd1brugCgOlmM6rryFwAAAADgwjVbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAP4D5b58DpdOrIkSMKCgqSxWIxuxwAAAAAJjEMQ7m5uYqJiTntjeR/jbB1Do4cOaLY2FizywAAAABQQ6SkpKhJkyZnbUPYOgdBQUGSyndocHCwydUAAAAAMEtOTo5iY2NdGeFsCFvnoOLUweDgYMIWAAAAgHO6vIgBMgAAAADAAwhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABdrMLQNUkJycrIyPD7DLOW3h4uJo2bWp2GQAAAEC1I2zVQsnJyWrXvr0KCwrMLuW8+fn7a+eOHQQuAAAA1DmErVooIyNDhQUFGvXgM4ps2tLscqosLXmvFjx9vzIyMghbAAAAqHMIW7VYZNOWatK6o9llAAAAADgNBsgAAAAAAA8gbAEAAACABxC2AAAAAMADCFsAAAAA4AGELQAAAADwAMIWAAAAAHgAYQsAAAAAPICwBQAAAAAeQNgCAAAAAA8gbAEAAACABxC2AAAAAMADCFsAAAAA4AGELQAAAADwAMIWAAAAAHgAYQsAAAAAPICwBQAAAAAeQNgCAAAAAA8gbAEAAACABxC2AAAAAMADTA1bq1at0lVXXaWYmBhZLBZ99tlnrnmlpaV68MEH1blzZwUEBCgmJka33nqrjhw54raMzMxMjRo1SsHBwQoNDdW4ceOUl5fn1mbz5s3q16+ffH19FRsbq5kzZ16IzQMAAABQj5katvLz89W1a1fNmTPnlHkFBQXasGGDHn30UW3YsEGffPKJdu3apauvvtqt3ahRo7Rt2zYtXrxYX375pVatWqU77rjDNT8nJ0eDBw9WXFyckpKS9Mwzz2jatGl69dVXPb59AAAAAOovu5krHzZsmIYNG3baeSEhIVq8eLHbtJdeekkXX3yxkpOT1bRpU+3YsUMLFy7U+vXr1bNnT0nSiy++qOHDh+vZZ59VTEyMFixYoJKSEr355pvy9vZWx44dtWnTJs2aNcstlAEAAABAdapV12xlZ2fLYrEoNDRUkpSYmKjQ0FBX0JKkhIQEWa1WrV271tXm0ksvlbe3t6vNkCFDtGvXLp04ceK06ykuLlZOTo7bAwAAAAAqo9aEraKiIj344IO6+eabFRwcLElKTU1VRESEWzu73a6wsDClpqa62kRGRrq1qXhe0ea3ZsyYoZCQENcjNja2ujcHAAAAQB1XK8JWaWmpbrzxRhmGoZdfftnj65syZYqys7Ndj5SUFI+vEwAAAEDdYuo1W+eiImgdPHhQy5Ytc/VqSVJUVJTS09Pd2peVlSkzM1NRUVGuNmlpaW5tKp5XtPktHx8f+fj4VOdmAAAAAKhnanTPVkXQ2r17t5YsWaKGDRu6zY+Pj1dWVpaSkpJc05YtWyan06nevXu72qxatUqlpaWuNosXL1bbtm3VoEGDC7MhAAAAAOodU8NWXl6eNm3apE2bNkmS9u/fr02bNik5OVmlpaW64YYb9OOPP2rBggVyOBxKTU1VamqqSkpKJEnt27fX0KFDNX78eK1bt06rV6/WxIkTNXLkSMXExEiS/vSnP8nb21vjxo3Ttm3b9MEHH+hf//qXJk+ebNZmAwAAAKgHTD2N8Mcff9TAgQNdzysC0JgxYzRt2jR98cUXkqRu3bq5vW758uUaMGCAJGnBggWaOHGiBg0aJKvVquuvv14vvPCCq21ISIgWLVqkCRMmqEePHgoPD9fUqVMZ9h0AAACAR5katgYMGCDDMM44/2zzKoSFhendd989a5suXbrou+++q3R9AAAAAFBVNfqaLQAAAACorQhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABhC0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABpoatVatW6aqrrlJMTIwsFos+++wzt/mGYWjq1KmKjo6Wn5+fEhIStHv3brc2mZmZGjVqlIKDgxUaGqpx48YpLy/Prc3mzZvVr18/+fr6KjY2VjNnzvT0pgEAAACo50wNW/n5+eratavmzJlz2vkzZ87UCy+8oLlz52rt2rUKCAjQkCFDVFRU5GozatQobdu2TYsXL9aXX36pVatW6Y477nDNz8nJ0eDBgxUXF6ekpCQ988wzmjZtml599VWPbx8AAACA+stu5sqHDRumYcOGnXaeYRiaPXu2HnnkEV1zzTWSpLfffluRkZH67LPPNHLkSO3YsUMLFy7U+vXr1bNnT0nSiy++qOHDh+vZZ59VTEyMFixYoJKSEr355pvy9vZWx44dtWnTJs2aNcstlAEAAABAdaqx12zt379fqampSkhIcE0LCQlR7969lZiYKElKTExUaGioK2hJUkJCgqxWq9auXetqc+mll8rb29vVZsiQIdq1a5dOnDhx2nUXFxcrJyfH7QEAAAAAlVFjw1ZqaqokKTIy0m16ZGSka15qaqoiIiLc5tvtdoWFhbm1Od0yfr2O35oxY4ZCQkJcj9jY2PPfIAAAAAD1So0NW2aaMmWKsrOzXY+UlBSzSwIAAABQy9TYsBUVFSVJSktLc5uelpbmmhcVFaX09HS3+WVlZcrMzHRrc7pl/Hodv+Xj46Pg4GC3BwAAAABURo0NW82bN1dUVJSWLl3qmpaTk6O1a9cqPj5ekhQfH6+srCwlJSW52ixbtkxOp1O9e/d2tVm1apVKS0tdbRYvXqy2bduqQYMGF2hrAAAAANQ3poatvLw8bdq0SZs2bZJUPijGpk2blJycLIvFokmTJunJJ5/UF198oS1btujWW29VTEyMRowYIUlq3769hg4dqvHjx2vdunVavXq1Jk6cqJEjRyomJkaS9Kc//Une3t4aN26ctm3bpg8++ED/+te/NHnyZJO2GgAAAEB9YOrQ7z/++KMGDhzoel4RgMaMGaP58+frgQceUH5+vu644w5lZWWpb9++WrhwoXx9fV2vWbBggSZOnKhBgwbJarXq+uuv1wsvvOCaHxISokWLFmnChAnq0aOHwsPDNXXqVIZ9BwAAAOBRpoatAQMGyDCMM863WCyaPn26pk+ffsY2YWFhevfdd8+6ni5duui7776rcp0AAAAAUFk19potAAAAAKjNCFsAAAAA4AGELQAAAADwAMIWAAAAAHgAYQsAAAAAPICwBQAAAAAeQNgCAAAAAA8gbAEAAACABxC2AAAAAMADCFsAAAAA4AGELQAAAADwAMIWAAAAAHgAYQsAAAAAPICwBQAAAAAeQNgCAAAAAA8gbAEAAACABxC2AAAAAMADCFsAAAAA4AGELQAAAADwAMIWAAAAAHgAYQsAAAAAPICwBQAAAAAeQNgCAAAAAA8gbAEAAACABxC2AAAAAMADCFsAAAAA4AGELQAAAADwAMIWAAAAAHgAYQsAAAAAPICwBQAAAAAeQNgCAAAAAA8gbAEAAACAB9jNLgAAgNokOTlZGRkZZpdx3sLDw9W0aVOzywCAOo2wBQDAOUpOTla79u1VWFBgdinnzc/fXzt37CBwAYAHEbYAADhHGRkZKiwo0KgHn1Fk05Zml1Nlacl7teDp+5WRkUHYAgAPImwBAFBJkU1bqknrjmaXAQCo4RggAwAAAAA8gLAFAAAAAB5A2AIAAAAADyBsAQAAAIAHELYAAAAAwAMIWwAAAADgAYQtAAAAAPAAwhYAAAAAeABhCwAAAAA8gLAFAAAAAB5A2AIAAAAADyBsAQAAAIAHELYAAAAAwAMIWwAAAADgAYQtAAAAAPAAwhYAAAAAeABhCwAAAAA8gLAFAAAAAB5A2AIAAAAADyBsAQAAAIAHELYAAAAAwAMIWwAAAADgATU6bDkcDj366KNq3ry5/Pz81LJlSz3xxBMyDMPVxjAMTZ06VdHR0fLz81NCQoJ2797ttpzMzEyNGjVKwcHBCg0N1bhx45SXl3ehNwcAAABAPVKjw9bTTz+tl19+WS+99JJ27Nihp59+WjNnztSLL77oajNz5ky98MILmjt3rtauXauAgAANGTJERUVFrjajRo3Stm3btHjxYn355ZdatWqV7rjjDjM2CQAAAEA9YTe7gLP54YcfdM011+iKK66QJDVr1kzvvfee1q1bJ6m8V2v27Nl65JFHdM0110iS3n77bUVGRuqzzz7TyJEjtWPHDi1cuFDr169Xz549JUkvvviihg8frmeffVYxMTHmbBwAAACAOq1G92xdcsklWrp0qX7++WdJ0k8//aTvv/9ew4YNkyTt379fqampSkhIcL0mJCREvXv3VmJioiQpMTFRoaGhrqAlSQkJCbJarVq7du1p11tcXKycnBy3BwAAAABURo3u2XrooYeUk5Ojdu3ayWazyeFw6KmnntKoUaMkSampqZKkyMhIt9dFRka65qWmpioiIsJtvt1uV1hYmKvNb82YMUOPP/54dW8OAAAAgHqkRvdsffjhh1qwYIHeffddbdiwQW+99ZaeffZZvfXWWx5d75QpU5Sdne16pKSkeHR9AAAAAOqeGt2zdf/99+uhhx7SyJEjJUmdO3fWwYMHNWPGDI0ZM0ZRUVGSpLS0NEVHR7tel5aWpm7dukmSoqKilJ6e7rbcsrIyZWZmul7/Wz4+PvLx8fHAFgEAAACoL2p0z1ZBQYGsVvcSbTabnE6nJKl58+aKiorS0qVLXfNzcnK0du1axcfHS5Li4+OVlZWlpKQkV5tly5bJ6XSqd+/eF2ArAAAAANRHNbpn66qrrtJTTz2lpk2bqmPHjtq4caNmzZqlP//5z5Iki8WiSZMm6cknn1Tr1q3VvHlzPfroo4qJidGIESMkSe3bt9fQoUM1fvx4zZ07V6WlpZo4caJGjhzJSIQAAAAAPKZGh60XX3xRjz76qO6++26lp6crJiZGd955p6ZOnepq88ADDyg/P1933HGHsrKy1LdvXy1cuFC+vr6uNgsWLNDEiRM1aNAgWa1WXX/99XrhhRfM2CQAAAAA9USNDltBQUGaPXu2Zs+efcY2FotF06dP1/Tp08/YJiwsTO+++64HKgQAAACA06vR12wBAAAAQG1F2AIAAAAADyBsAQAAAIAHELYAAAAAwAMIWwAAAADgAYQtAAAAAPAAwhYAAAAAeABhCwAAAAA8gLAFAAAAAB5A2AIAAAAADyBsAQAAAIAHELYAAAAAwAMIWwAAAADgAYQtAAAAAPAAwhYAAAAAeABhCwAAAAA8gLAFAAAAAB5A2AIAAAAADyBsAQAAAIAHELYAAAAAwAOqFLZatGih48ePnzI9KytLLVq0OO+iAAAAAKC2q1LYOnDggBwOxynTi4uLdfjw4fMuCgAAAABqO3tlGn/xxReuv3/77bcKCQlxPXc4HFq6dKmaNWtWbcUBAAAAQG1VqbA1YsQISZLFYtGYMWPc5nl5ealZs2Z67rnnqq04AAAAAKitKhW2nE6nJKl58+Zav369wsPDPVIUAAAAANR2lQpbFfbv31/ddQAAAABAnVKlsCVJS5cu1dKlS5Wenu7q8arw5ptvnndhAAAAAFCbVSlsPf7445o+fbp69uyp6OhoWSyW6q4LAAAAAGq1KoWtuXPnav78+Ro9enR11wMAAAAAdUKV7rNVUlKiSy65pLprAQAAAIA6o0ph6/bbb9e7775b3bUAAAAAQJ1RpdMIi4qK9Oqrr2rJkiXq0qWLvLy83ObPmjWrWooDAAAAgNqqSmFr8+bN6tatmyRp69atbvMYLAMAAAAAqhi2li9fXt11AAAAAECdUqVrtgAAAAAAZ1elnq2BAwee9XTBZcuWVbkgAAAAAKgLqhS2Kq7XqlBaWqpNmzZp69atGjNmTHXUBQAAAAC1WpXC1vPPP3/a6dOmTVNeXt55FQQAAAAAdUG1XrN1yy236M0336zORQIAAABArVStYSsxMVG+vr7VuUgAAAAAqJWqdBrhdddd5/bcMAwdPXpUP/74ox599NFqKQwAAAAAarMqha2QkBC351arVW3bttX06dM1ePDgaikMAAAAAGqzKoWtefPmVXcdAAAAAFCnVClsVUhKStKOHTskSR07dlT37t2rpSgAAAAAqO2qFLbS09M1cuRIrVixQqGhoZKkrKwsDRw4UO+//74aNWpUnTUCAAAAQK1TpdEI77nnHuXm5mrbtm3KzMxUZmamtm7dqpycHN17773VXSMAAAAA1DpV6tlauHChlixZovbt27umdejQQXPmzGGADAAAAABQFXu2nE6nvLy8Tpnu5eUlp9N53kUBAAAAQG1XpbB12WWX6a9//auOHDnimnb48GHdd999GjRoULUVBwAAAAC1VZXC1ksvvaScnBw1a9ZMLVu2VMuWLdW8eXPl5OToxRdfrO4a8SuFJQ5lFTnMLgMAAADA76jSNVuxsbHasGGDlixZop07d0qS2rdvr4SEhGotDqda+XO6/vJFumLGv6Ifj9uUHZCt2Ab+CvY79bROAAAAAOapVNhatmyZJk6cqDVr1ig4OFiXX365Lr/8cklSdna2OnbsqLlz56pfv34eKRbSvox8SZJXWGMdzJcO7kiXxSJd3CxMvZqFyWa1mFwhAAAAAKmSpxHOnj1b48ePV3Bw8CnzQkJCdOedd2rWrFnVVhxOdfeAVnp7RKTSP5qmtsEORQX7yjCktfsz9VFSik7kl5hdIgAAAABVMmz99NNPGjp06BnnDx48WElJSeddFM4u0Nuqwn0/qlOoQzf1itXQjlHysVuVllOsd9cla2dqjtklAgAAAPVepcJWWlraaYd8r2C323Xs2LHzLgqV0zYqSKN6N1VsAz+VOQ0t2p6mAydPNwQAAABgjkqFrcaNG2vr1q1nnL9582ZFR0efd1GovCBfL13bvbHaRwXJMKSvtx5Vek6R2WUBAAAA9Valwtbw4cP16KOPqqjo1C/xhYWFeuyxx3TllVdWW3GoHIvFokHtIxXbwE+lDkOf/3REOYWlZpcFAAAA1EuVCluPPPKIMjMz1aZNG82cOVOff/65Pv/8cz399NNq27atMjMz9fe//91TteIc2KwWXdElWg0DvFVQ4tDnPx1RcRn35QIAAAAutEqFrcjISP3www/q1KmTpkyZomuvvVbXXnutHn74YXXq1Enff/+9IiMjq7XAw4cP65ZbblHDhg3l5+enzp0768cff3TNNwxDU6dOVXR0tPz8/JSQkKDdu3e7LSMzM1OjRo1ScHCwQkNDNW7cOOXl5VVrnTWJj92ma7rFKNDHrsz8Ev2w57jZJQEAAAD1TqXCliTFxcXp66+/VkZGhtauXas1a9YoIyNDX3/9tZo3b16txZ04cUJ9+vSRl5eXvvnmG23fvl3PPfecGjRo4Gozc+ZMvfDCC5o7d67Wrl2rgIAADRkyxO1Ux1GjRmnbtm1avHixvvzyS61atUp33HFHtdZa0wT5emlwh/Lgu/lwto5kFZpcEQAAAFC/VOqmxr/WoEED9erVqzprOcXTTz+t2NhYzZs3zzXt14HOMAzNnj1bjzzyiK655hpJ0ttvv63IyEh99tlnGjlypHbs2KGFCxdq/fr16tmzpyTpxRdf1PDhw/Xss88qJibGo9tgptgwf3WMCda2IzlasiNNf+rdVHZrpfM1AAAAgCqo0d+8v/jiC/Xs2VN//OMfFRERoe7du+u1115zzd+/f79SU1OVkJDgmhYSEqLevXsrMTFRkpSYmKjQ0FBX0JKkhIQEWa1WrV279rTrLS4uVk5OjtujturbKlz+3jadKCjVjwdOmF0OAAAAUG/U6LC1b98+vfzyy2rdurW+/fZb3XXXXbr33nv11ltvSZJSU1Ml6ZTrxCIjI13zUlNTFRER4TbfbrcrLCzM1ea3ZsyYoZCQENcjNja2ujftgvH1smlAm0aSpPUHMnU8r9jkigAAAID6oUaHLafTqYsuukj/+Mc/1L17d91xxx0aP3685s6d69H1TpkyRdnZ2a5HSkqKR9fnaa0iAtUiPEBOQ1q2K12GYZhdEgAAAFDn1eiwFR0drQ4dOrhNa9++vZKTkyVJUVFRkqS0tDS3Nmlpaa55UVFRSk9Pd5tfVlamzMxMV5vf8vHxUXBwsNujNrNYLBrQtpFsVouOZBUpObPA7JIAAACAOq9Gh60+ffpo165dbtN+/vlnxcXFSSofLCMqKkpLly51zc/JydHatWsVHx8vSYqPj1dWVpaSkpJcbZYtWyan06nevXtfgK2oGYJ8vdSlSYgk6Ye9x+ndAgAAADysRoet++67T2vWrNE//vEP7dmzR++++65effVVTZgwQVJ5j82kSZP05JNP6osvvtCWLVt06623KiYmRiNGjJBU3hM2dOhQjR8/XuvWrdPq1as1ceJEjRw5sk6PRHg6PeMayMtmUXpusfZn5JtdDgAAAFCn1eiw1atXL3366ad677331KlTJz3xxBOaPXu2Ro0a5WrzwAMP6J577tEdd9yhXr16KS8vTwsXLpSvr6+rzYIFC9SuXTsNGjRIw4cPV9++ffXqq6+asUmm8ve2q2uTUElS4j56twAAAABPqvJ9ti6UK6+8UldeeeUZ51ssFk2fPl3Tp08/Y5uwsDC9++67niiv1ukR10CbD2UrI69Ee9Lz1DoyyOySAAAAgDqpRvdsofr5etnUvWmoJGnNvkw56d0CAAAAPIKwVQ91bxoqH7tVmQUl2p2WZ3Y5AAAAQJ1E2KqHfOw2dYsNlSRtSskytRYAAACgriJs1VNdmoTIZrEoNadIR7MLzS4HAAAAqHMIW/WUv7ddbaICJdG7BQAAAHgCYase6x7bQJK0Oz1PuUWlJlcDAAAA1C2ErXqsUZCPmoT6yTCkzYeyzS4HAAAAqFMIW/Vct5PDwG89nK1Sh9PcYgAAAIA6hLBVzzUPD1Cwr11FZU7tTM01uxwAAACgziBs1XNWi8VtGHiDmxwDAAAA1YKwBXWICZbdalFmfolSc4rMLgcAAACoEwhbkI/dptYR5cPAbzuSY3I1AAAAQN1A2IIkqWNMiCTp57RclZQxUAYAAABwvghbkCTFhPoq1M9LpQ5Du9MZKAMAAAA4X4QtSJIsFos6xARL4lRCAAAAoDoQtuDSITpYFot0NLtImfklZpcDAAAA1GqELbgE+NjVrGGAJGk7vVsAAADAeSFswU3Hk6cSbj+aI4eTe24BAAAAVUXYgptmDQPk721TYalDB47nm10OAAAAUGsRtuDGZrWofVR579bOo4xKCAAAAFQVYQunaBsVJEnafzxfxWUOk6sBAAAAaifCFk4RHuitsABvOZyG9qZzKiEAAABQFYQtnMJisahtZHnv1s40RiUEAAAAqoKwhdOqOJXwUGah8ovLTK4GAAAAqH0IWzitED8vRYf4ypD0cxoDZQAAAACVRdjCGVWcSriLsAUAAABUGmELZ9Q6MlAWi5SWU6wTBSVmlwMAAADUKoQtnJG/t11Nw/wlSbtS6d0CAAAAKoOwhbNq96tTCQ3DMLkaAAAAoPYgbOGsWjQKlM1qUVZBqTLyOJUQAAAAOFeELZyVt92qZg3LTyXcnc6phAAAAMC5Imzhd7WOKD+VcHd6HqcSAgAAAOeIsIXf1Tw8gFMJAQAAgEoibOF3edutijs5KuGe9DyTqwEAAABqB8IWzknryEBJ5ddtcSohAAAA8PsIWzgnFacSnigo1fF8TiUEAAAAfg9hC+fEx25znUq4m1MJAQAAgN9F2MI5ax1RfirhnjTCFgAAAPB7CFs4Z80bBchmsSizoETH84rNLgcAAACo0QhbOGc+dpuaNuRUQgAAAOBcELZQKa0alZ9KuO9YvsmVAAAAADUbYQuV0rxRgCySjuUVK6ew1OxyAAAAgBqLsIVK8fOyqXGonyRp7zFOJQQAAADOhLCFSmvRKECStJdTCQEAAIAzImyh0lqevG7rSFahCkscJlcDAAAA1EyELVRasJ+XGgX5yJC0P4PeLQAAAOB0CFuokpbhFacSct0WAAAAcDqELVRJi5OnEh7MLFCpw2lyNQAAAEDNYze7ANRO4YHeCva1K6eoTAePF6hVRKDZJQFAtcsuLNWynWn6dmua9mXkqbCwSNF/nqPFR+1qUpqmVhGBig3zk93Kb5cAgFMRtlAlFotFLSMCtTE5S/uO5RG2ANQpmw9l6fnFP+v7PRkqdRhu87wbxSmnVNp+NEfbj+bI22ZVy0YBim/ZUEG+XiZVDACoiQhbqLKW4SfDVka+HE5DNqvF7JIA4LwUlJRp1qKf9ebq/XKezFitIwI1rFOUejUP0769e3TnnXfqununK98nXHuO5Sm/2KEdqbnacyxPl7QMV5cmIbJaeD8EABC2cB6iQ33l52VTYalDR7IKFRvmb3ZJAFBlq/dk6MH/btahE4WSpKu7xujeQa3deu4DclNUnLxF0X6GmrSOUP82jXQ0u0jf78nQ0ewirfz5mHal5uryDpEKC/A2a1MAADUEJ5mjyqwWi5ozKiGAOuDjpEMa/cZaHTpRqMahfpo3tpdeuLn7754ibbFYFBPqpz/2aKKBbRvJ22ZVak6RPvwxRUeyCi9Q9QCAmoqwhfPSMqIibOXLMIzfaQ0ANc+81fv1fx/9JKchXde9sRbdd6kGto2o1DIsFou6NAnV6D/EKTrEV8VlTn268bAOHudehABQnxG2cF6aNvCXl82ivOIypecWm10OAJwzwzD0wtLdevx/2yVJf+7TXM/+sasCfKp+hn2gr13Xdm+suDB/lTkN/e+no9qTTs8/ANRXhC2cF7vNqriw8t6tfcf4BRdA7fHvFXs1a/HPkqT7Etro0Svby1oNA/142ay6smu0WjUKlMMw9PWWo9qfwfsjANRHhC2ct5aNuG4LQO3y7bZUPfPtLknS34e3118TWstSjSMI2q1WDesUpXZRQTIkLdyaquN59P4DQH1D2MJ5axYeIKtFOp5foqyCErPLAYCz2pmao/s+2CRJGhMfp/GXtvDIeqxWixLaR6pxqJ9KHE79b/NRFZU6PLIuAEDNVKvC1j//+U9ZLBZNmjTJNa2oqEgTJkxQw4YNFRgYqOuvv15paWlur0tOTtYVV1whf39/RURE6P7771dZWdkFrr7u8vWyqXEDP0nlA2UAQE2VmV+i29/6UQUlDvVp1VCPXtnBo+uzWS0a3jlKwb52ZReW6ustR+V0MpgQANQXtSZsrV+/Xq+88oq6dOniNv2+++7T//73P3300UdauXKljhw5ouuuu8413+Fw6IorrlBJSYl++OEHvfXWW5o/f76mTp16oTehTmsZXj48MqcSAqipHE5Ddy9I0qEThYpr6K+Xbr5IdpvnPwb9ve26skuMvGwWpZwo1Hd7Mjy+TgBAzVArwlZeXp5GjRql1157TQ0aNHBNz87O1htvvKFZs2bpsssuU48ePTRv3jz98MMPWrNmjSRp0aJF2r59u9555x1169ZNw4YN0xNPPKE5c+aopIRT3qpLi5PXbR3NLlJ+Mb2GAGqeV1ft05p9mQrwtum1W3uqwQW86XCjIB8N7hAlSdqUksWQ8ABQT9SKsDVhwgRdccUVSkhIcJuelJSk0tJSt+nt2rVT06ZNlZiYKElKTExU586dFRkZ6WozZMgQ5eTkaNu2baddX3FxsXJyctweOLsgXy9FBvtIEqNuAahxdqXm6vmTIw9Ou7qj2kQGXfAaWkUEqmuTEEnSkh3pKub6LQCo82p82Hr//fe1YcMGzZgx45R5qamp8vb2VmhoqNv0yMhIpaamutr8OmhVzK+YdzozZsxQSEiI6xEbG1sNW1L3tWjEqYQAap5Sh1OTP9ykEodTCe0jdEOPJqbV0qdVuEL8vJRXXKaVu4+ZVgcA4MKo0WErJSVFf/3rX7VgwQL5+vpesPVOmTJF2dnZrkdKSsoFW3dt1jK8/FTClBOFKilzmlwNAJR7adkebTuSo1B/L/3jus7VOsR7ZXnZrBrcIVIWSTuO5mofP04BQJ1Wo8NWUlKS0tPTddFFF8lut8tut2vlypV64YUXZLfbFRkZqZKSEmVlZbm9Li0tTVFR5efGR0VFnTI6YcXzija/5ePjo+DgYLcHfl9YgLdC/LzkcBo6mMmphADMt+VQtl5avkeS9OSITooIunA/3J1JTKifLoorv/546c50FZZwOiEA1FU1OmwNGjRIW7Zs0aZNm1yPnj17atSoUa6/e3l5aenSpa7X7Nq1S8nJyYqPj5ckxcfHa8uWLUpPT3e1Wbx4sYKDg9Whg2eH/K1vLBaLa6CMfQwBD8BkDqehKZ9ulsNp6Mou0bqyS4zZJbn8oXmYGgZ4q6DEodV7GZ0QAOoqu9kFnE1QUJA6derkNi0gIEANGzZ0TR83bpwmT56ssLAwBQcH65577lF8fLz+8Ic/SJIGDx6sDh06aPTo0Zo5c6ZSU1P1yCOPaMKECfLx8bng21TXtQwP1MbkLB3IyJfTachqNe90HQD12wfrU7T1cI6CfO2adnVHs8txY7dZdVm7CH2UdEjbjuSoU+MQRQWb3+sGAKheNbpn61w8//zzuvLKK3X99dfr0ksvVVRUlD755BPXfJvNpi+//FI2m03x8fG65ZZbdOutt2r69OkmVl13RYf4ytfLqqIyp45kF5pdDoB6KqugRM98u1OSNPnyNgoPrHk/rsWE+ql9VPmoiCt2pcswuNkxANQ1Nbpn63RWrFjh9tzX11dz5szRnDlzzviauLg4ff311x6uDJJktVrUPDxAO47mau+xfDVp4G92SQDqoecW/awTBaVqGxmk0X+IM7ucM+rTKlx7j+UrLadY24/mqGNMiNklAQCqUa3v2ULN0yK8fAj4fcfy+KUWwAW3/UiOFqw9KKn8nlp2W839qAvwsat38zBJ0uo9x7n3FgDUMTX3Ewi1VlxDf9msFuUUlel4fonZ5QCoRwzD0LQvtslpSFd0iVZ8y4Zml/S7usaGqoG/lwpLHVqzP9PscgAA1YiwhWrnZbOqaVj56YOMSgjgQvp2W6rWHciUn5dNfx/e3uxyzonNalH/No0kSZsPZSmrgB+pAKCuIGzBI1qcvMHxXm7YCeACKXM49cy3uyRJ4/s1V0yon8kVnbu4hgFq1tBfTkNK3Hfc7HIAANWEsAWPaH4ybKXnFiuvqMzkagDUB59sOKy9x/LVwN9Lt1/awuxyKu2SluGSpJ/T8pSeW2RyNQCA6kDYgkcE+NgVHVJ+z5h9GfRuAfCsolKHnl/ysyRpwsBWCvb1MrmiymsU5KO2keVDwf+wl94tAKgLCFvwmIpTCbluC4CnvbPmoI5mFyk6xFe31OCh3n/PH1qEyWqRDh4v0KETBWaXAwA4T4QteEyLRuVDwKecKFBxGcMZA/CMnKJSzVm+R5J0X0Ib+XrZTK6o6kL9vdXp5L22Vu85zu0zAKCWI2zBY8ICvBXq7yWnUf4rLQB4wuur9ulEQalaNgrQdRc1Nruc83Zx8zDZrRal5hRpXwZnBgBAbUbYgke1dN3gmC8MAKpfdkGp3lx9QJL0f4Pb1ugbGJ+rAB+7usWGSpLW7sukdwsAarHa/6mEGq1Fo/Lrtg4cz5fDyRcGANXrzdX7lVdcpnZRQRrSMcrscqrNRXEN5GWz6FhesfbTuwUAtRZhCx4VFeIrPy+bisucOpxVaHY5AOqQnKJSvbl6vyTpnstay2q1mFxR9fHzsqlLk1BJ0tr99G4BQG1F2IJHWS0W1z239nGDYwDV6K3VB5RbVKbWEYEa1qnu9GpVuKhpqOxWi9Jzi3WA614BoFYibMHjWp48lXBfRj6/zgKoFnnFZXr9+5O9WoPqVq9WBX9vu7o0KR+ZcB29WwBQKxG24HGxYf6yWy3KLSpTRl6J2eUAqAPeTjyg7MJStWgUoCs6R5tdjsdc1LSBbCdHJkzOpHcLAGobwhY8zstmVdMwf0nSXk4lBHCe8ovL9Pp3FddqtZKtDvZqVQjwsatz4/LeLa7dAoDah7CFC6JiVEKGgAdwvt5fn6LM/BLFNfTXVV1izC7H43rElfduHc0u0pGsIrPLAQBUAmELF0SL8EBZJB3LK1Z2YanZ5QCopUrKnHr9u32SpL/0b1kn7qv1ewJ97GofFSRJSko+YXI1AIDKqPufUqgR/LxtahzqJ4lTCQFU3eebDutodpEignx03UWNzS7ngrkoroEkaX9Gvo7nFZtcDQDgXBG2cMG0jAiURNgCUDVOp6FXVpX3av25b3P52G0mV3ThNPD3do3suiE5y9xiAADnjLCFC6biuq0jWUXKLy4zuRoAtc2SHWnak56nIF+7RvVuanY5F1yPk71bO1NzlMd7KADUCoQtXDDBvl6KCPKRVH4qDACcK8Mw9PLKvZKk0X+IU5Cvl8kVXXjRIX6KCfGV05A2pWSZXQ4A4BwQtnBBtWxUfirhHk4lBFAJ6/ZnamNylrztVo3t09zsckxT0bu15VC2isscJlcDAPg9hC1cUBXXHBzKLFSp0+RiANQac0/2av2xRxM1OtlDXh81Dw9QmL+3ShxObT2cY3Y5AIDfQdjCBRUW4K1Qfy85DEOphRx+AH7f7rRcLd91TBaLNL5fC7PLMZXFYtFFcaGSpI0pJ+RwcpNjAKjJ+LaLC8pisbhOJTxSaDG5GgC1wRvf75ckDe4QqWbhASZXY762UUEK8LYpv9ihXam5ZpcDADgLwhYuuFYnw1ZqoVWy1b+L3AGcu4y8Yn2y8bAkerUq2K1WdWsaKknakHxChkHvFgDUVIQtXHCRwT4K9LGrzLDIr1l3s8sBUIP9J/GgSsqc6hob6hocAlLnxiHytll1PL9EB44XmF0OAOAMCFu44MpPJSw/Fci/7SUmVwOgpioqdeg/aw5Kksb3ay6LhVOPK/jYberUOFiSlHTwhMnVAADOhLAFU7SOCJIk+bX+g0odnAID4FSfbjyszPwSNQ7109COUWaXU+N0iw2V1SIdzipUanaR2eUAAE6DsAVTRIf6ysdqyOYbqC3pxWaXA6CGcToNvf7dPknS2D7NZLfxcfVbQb5eahtV/sMVvVsAUDPx6QVTWC0WNfYvv9FW4iF+kQXgbuXPx7T3WL6CfOy6qVes2eXUWBc1Lb+Obc+xPGUVlJhcDQDgtwhbME1F2Fp7uEilDu5wDOAXr53s1Rp5cayCfBm19EzCA30U19BfkrQpJcvcYgAApyBswTThPoYcBdnKKzG0dl+m2eUAqCG2HcnWD3uPy2a16LY+zc0up8ar6N3adiRHRaUOk6sBAPwaYQumsVqkgp8TJUlfbz1qcjUAaoo3viu/ifHwztFqHOpncjU1X2wDP4UHeqvMaWjL4WyzywEA/AphC6Yq2PW9JOnbralyOBmVEKjvUrOL9MVPRyRJt/elV+tcWCwWV+/WT4eyeC8FgBqEsAVTFSVvUaC3RcfzS7RuP6cSAvXdW4kHVOY0dHGzMHWNDTW7nFqjTWSQArxtyi926Oe0XLPLAQCcRNiCuZwO9W7sK0n6hlMJgXotv7hMC07exHhcP3q1KsNmtbjC6YbkEzIMercAoCYgbMF08U3Kw9bXWziVEKjPPk46pJyiMjVr6K+E9pFml1PrdG4cIrvVooy8Eh06UWh2OQAAEbZQA3SO8FGov5cy8oq1dt9xs8sBYAKH09Cbq8sHxvhz3+ayWS0mV1T7+HrZ1CEmWFJ57xYAwHyELZjOy2bRsE5RkqT/bT5icjUAzLB4e5oOHi9QiJ+XbujRxOxyaq3uJ08lPHC8QJn53OQYAMxG2EKNcFWXGEnSN1tTVVLGDY6B+uaN78tvYjyqd1P5e9tNrqb2CvX3VstGAZKkjfRuAYDpCFuoEXq3aKhGQT7KKijV6j0ZZpcD4ALalJKl9QdOyMtm0ZhLmpldTq3X/eQw8DtSc1VQUmZyNQBQvxG2UCPYrBZd0TlakvS/nziVEKhPXv+uvFfrqq4xigz2Nbma2i8mxFeRwT5yOA1tPsRNjgHATIQt1BhXdS0/lXDR9jQVlTpMrgbAhXDoRIG+2ZoqSbq9bwuTq6kbfn2T482HslXm4NRsADALYQs1xkVNQ9U41E95xWVasSvd7HIAXADzVx+Qw2moT6uGrpH0cP5aNQpUkK9dhaUO7UzlJscAYBbCFmoMi8WiK7tWnErIDY6Bui6nqFTvr0+RJN3ej16t6mS1WtTt5MiEG5OzuMkxAJiEsIUapWJUwiU70pRXzIXdQF32wboU5RWXqXVEoAa0aWR2OXVOx5hgedusyiwo0cHjBWaXAwD1EmELNUrHmGC1CA9QcZlTi7alml0OAA8pKXPqje/Lb2J8e7/msli4iXF187Hb1LExNzkGADMRtlCjWCwWXdOtsSTp042HTa4GgKf876cjSs0pUqMgH43o3tjscuqsbk1CZbFIKScKdSy32OxyAKDeIWyhxrn25Bev1XsylJpdZHI1AKqbYRh6dVX5cO9j+zSTj91mckV1V7Cfl1o3CpTETY4BwAyELdQ4TRv6q1ezBnIa0ueb6N0C6poVPx/TrrRcBXjbNKp3nNnl1HkVNznelZbLtbAAcIERtlAjXXdRE0nSJxsOM4oWUMe8urK8V+vmi5sqxM/L5GrqvqgQX8WE+MppSD+lZJldDgDUK4Qt1EjDO0fL227VrrRcbT+aY3Y5AKrJ5kNZStx3XHarRX/u29zscuqNi+LKe7e2HM5WKTc5BoALhrCFGinEz0sJ7SMklfduAagbXjl5rdbVXWMUE+pncjX1R/PwAIX4eam4zKntR/gBCwAuFMIWaqzrupefSvj5piMq45dYoNZLPl6gb7aU37B8/KXcxPhCslos6l5xk+OULDk5OxsALgjCFmqs/m0bKSzAWxl5xfpuT4bZ5QA4T69/v09OQ+rfppHaRwebXU690yEmWL5eVmUXlupwAR//AHAh8G6LGsvLZtXVXWMkcSohUNtl5pfowx9TJEl30qtlCi+bVV2bhEqSfs7h4x8ALoQa/W47Y8YM9erVS0FBQYqIiNCIESO0a9cutzZFRUWaMGGCGjZsqMDAQF1//fVKS0tza5OcnKwrrrhC/v7+ioiI0P3336+yMoa/rQ2uu6j8nluLtqUqu6DU5GoAVNXbiQdUVOpU58Yhim/Z0Oxy6q2usaGyWy3KKrXKt1k3s8sBgDqvRoetlStXasKECVqzZo0WL16s0tJSDR48WPn5+a429913n/73v//po48+0sqVK3XkyBFdd911rvkOh0NXXHGFSkpK9MMPP+itt97S/PnzNXXqVDM2CZXUuXGI2kUFqbjMqc+45xZQKxWWOPR24kFJ0h2XtpDFYjG5ovrLz8umTjEhkqTg3jeYXA0A1H01OmwtXLhQt912mzp27KiuXbtq/vz5Sk5OVlJSkiQpOztbb7zxhmbNmqXLLrtMPXr00Lx58/TDDz9ozZo1kqRFixZp+/bteuedd9StWzcNGzZMTzzxhObMmaOSkhIzNw/nwGKxaGSvWEnSe+uSuecWUAt9nJSizPwSNWngp2Gdoswup97r3jRUFhnya9ZNezM5YwAAPKlGh63fys7OliSFhYVJkpKSklRaWqqEhARXm3bt2qlp06ZKTEyUJCUmJqpz586KjIx0tRkyZIhycnK0bdu2066nuLhYOTk5bg+YZ0T3xvK2W7UzNVebD2WbXQ6ASnA4Db3+/X5J0vh+LWS31aqPnTop2M9Lsf7lI7x+ujPP5GoAoG6rNZ96TqdTkyZNUp8+fdSpUydJUmpqqry9vRUaGurWNjIyUqmpqa42vw5aFfMr5p3OjBkzFBIS4nrExsZW89agMkL9vTX85K/h769PMbkaAJWxcGuqDh4vUKi/l/7Ys4nZ5eCkNsHlYWvN4SLtz8j/ndYAgKqqNWFrwoQJ2rp1q95//32Pr2vKlCnKzs52PVJS+IJvtpt6NZUkfbHpsPKLGdwEqA0Mw9Cc5XskSbfGN5O/t93kilAhxNtQwZ51chrSyyv2mF0OANRZtSJsTZw4UV9++aWWL1+uJk1++WU0KipKJSUlysrKcmuflpamqKgoV5vfjk5Y8byizW/5+PgoODjY7QFz/aFFmJo19Fd+iUNfnbwpKoCabdnOdG0/mqMAb5vGXtLM7HLwG9mJH0oqv7XGoRMFJlcDAHVTjQ5bhmFo4sSJ+vTTT7Vs2TI1b97cbX6PHj3k5eWlpUuXuqbt2rVLycnJio+PlyTFx8dry5YtSk9Pd7VZvHixgoOD1aFDhwuzIThvFovF1bv1/rpkk6sB8HsMw9CLy8p7TG6Jj1ODAG+TK8JvlRzZqS4R3ipzGnpl5T6zywGAOqlGh60JEybonXfe0bvvvqugoCClpqYqNTVVhYWFkqSQkBCNGzdOkydP1vLly5WUlKSxY8cqPj5ef/jDHyRJgwcPVocOHTR69Gj99NNP+vbbb/XII49owoQJ8vHxMXPzUEnX92gsm9WiDclZ+jkt1+xyAJzF6j3HtSklSz52q27vy02Ma6obOgRKkj74MUVpOUUmVwMAdU+NDlsvv/yysrOzNWDAAEVHR7seH3zwgavN888/ryuvvFLXX3+9Lr30UkVFRemTTz5xzbfZbPryyy9ls9kUHx+vW265RbfeequmT59uxibhPEQE+WpQuwhJ0oI1B02uBsDZvLhstyTp5oubqlEQP2zVVB0beatnXAOVlDn12ip6twCgutXoq5XP5Z5Kvr6+mjNnjubMmXPGNnFxcfr666+rszSY5Nb4Zlq0PU0fJx3S/w1pqyBfL7NLAvAb6/Znau3+THnbrLqzP71aNZnFYtHEy1rptnnrtWBtsu4a0FINAwnHAFBdanTPFvBbfVo1VMtGAcovcei/SYfMLgfAabx0cgTCG3o2UXSIn8nV4Pf0b9NIXZqEqLDUoTdO3hMNAFA9CFuoVSwWi8acHNXs7cSDcjp/v/cTwIWTdPCEVv18TDarRXf1b2l2OTgHFotFEwe2kiS99cMBZeaXmFwRANQdhC3UOtdd1ESBPnbty8jXd3syzC4HwK/MWrxLknTDRU0UG+ZvcjU4V5d3iFTHmGDllzj0yqq9ZpcDAHUGYQu1TqCPXTf0KL/f2ts/HDC3GAAua/Yd1+o9x+Vls+ieQa3MLgeVYLFYNPnyNpKkt384qGO5xSZXBAB1A2ELtdKt8XGSpGW70pV8nJtxAmYzDEOzFv8sSbqpV6yaNKBXq7a5rF2EusWGqrDUoZdX0LsFANWBsIVaqUWjQF3appEMQ3o78YDZ5QD13uo9x7Vuf6a87VZNHNja7HJQBb/u3Xpn7UGlZnPfLQA4X4Qt1Fq3XVLeu/XBjynKLSo1uRqg/jIMQ8+dvFZrVO+migrxNbkiVFW/1uHq1az8vltzTo4qCQCoOsIWaq0BbSLUolGAcovK9N66ZLPLAeqtFbuOaWNylny9rLprACMQ1mYWi0X3nezden99sg6d4DRtADgfhC3UWlarRX+5tPyL3Rvf71dxmcPkioD6x+E09PTCnZLKbzoeEUSvVm13SctwXdKyoUodv1yHBwCoGsIWarVruscoMthHaTnF+nzjEbPLAeqdTzce1s7UXAX72nU3vVp1xgND20kq//fdfiTH5GoAoPYibKFW87HbNK5vc0nS3FV7uckxcAEVlTr03KLya7UmDGylUH9vkytCdekWG6oru0TLMKR/nuy5BABUHmELtd7NFzdVkK9d+47la9H2NLPLAeqN+T8c0NHsIsWE+GrMJc3MLgfV7P4hbeVls2jVz8f0/W5uIA8AVUHYQq0X5Ovluu/W3JV7ZRj0bgGediK/xDVa3d8Gt5Wvl83kilDd4hoGaFTv8vfWGd/s4MwBAKgCwhbqhNsuaS5vu1WbUrK0Zl+m2eUAdd5Ly/cot6hM7aODNaJ7Y7PLgYfcc1krBfnYte1Ijr74ietiAaCyCFuoExoF+ejGnk0kSbOX/EzvFuBBBzLy9Z/Eg5KkKcPayWa1mFwRPKVhoI/+cnLgk2e+3aXCEkZ9BYDKIGyhzrh7QCt526xauz9Tq/ccN7scoM564svtKnE4dWmbRrq0TSOzy4GH/blPc8WE+OpwVqHmrtxrdjkAUKsQtlBnxIT66U+9m0qSnl20i94twAOW70zX0p3pslstmnplB7PLwQXg523T368o/7eeu3KvUjK50TEAnCvCFuqUuwe2lJ+XTZtSsrRsZ7rZ5QB1SnGZQ4//b5sk6c99m6tVRKDJFeFCGd45SvEtGqq4zKknv9pudjkAUGsQtlCnRAT9MgT1c4t+ZvQsoBq9+f0BHTheoEZBPrrnslZml4MLyGKxaNrVHWWzWvTttjR9t/uY2SUBQK1A2EKdc+elLRToY9f2ozlauC3V7HKAOiE1u0gvLtstSXpoaDsF+XqZXBEutLZRQRr9h/Kh4B//33aVOpwmVwQANR9hC3VOgwBvjevbXJL03KJdKuMLAXDenvhquwpKHLqoaaiuZaj3euu+y9uoYYC39qTn6Y3v95tdDgDUeIQt1Enj+jVXA38v7T2Wr/fWJZtdDlCrLdmepq82H5XNatH0azrJylDv9VaIn5ceGtZOkvT84p91ICPf5IoAoGYjbKFOCvb10uTL20iSnlv8s7IKSkyuCKidcotK9chnWyVJt/drrk6NQ0yuCGa7oUcT9WlVPljGlE+2MPIrAJwFYQt11s0XN1XbyCBlFZRq9pLdZpcD1EozF+5Sak6R4hr6a9KgNmaXgxrAYrFoxrVd5OtlVeK+4/rox0NmlwQANRZhC3WW3WbV1KvK7w3znzUHtTst1+SKgNpl/YFM/WfNQUnSjOs6y8/bZnJFqCmaNvTX3y5vK0l68qvtSs8pMrkiAKiZCFuo0/q0CtfgDpFyOA1N/3I7p7sA56io1KGH/rtZknRTz1hd0jLc5IpQ04zt00ydG4cop6hMj32xjfdXADgNwhbqvL9f0V7eNqu+252hJTu40TFwLp5euFN7j+WrUZCPHh7e3uxyUAPZbVY9fX0X2a0WfbM1VZ9uPGx2SQBQ4xC2UOfFNQzQuH7lQ8FP/XyrcotKTa4IqNlW/XxM81YfkCTNvL6LQvy5pxZOr0NMsP46qLUkaern25SSWWByRQBQsxC2UC/ce1lrxTX019HsIv3zm51mlwPUWJn5JfrbRz9Jkm6Nj9PAdhEmV4Sa7u6BrdSrWQPlFZfpvg82cW9DAPgVwhbqBT9vm/55XRdJ0oK1yUrce9zkioCaxzAMPfjfzTqWW6xWEYGcPohzYrNaNOvGbgrysevHgyf07xV7zS4JAGoMwhbqjfiWDfWn3k0lSQ99slmFJQ6TKwJqlvfWpWjx9jR52Sz618hu8vVi9EGcm9gwf00f0VGS9K+lu7Uh+YTJFQFAzUDYQr0yZVg7RYf46uDxAs1avMvscoAaY/OhLE373zZJ0v8NbquOMdy8GJUzoltjXd01Rg6noQkLNigjr9jskgDAdIQt1CtBvl566tpOkqTXv9+vH/ZmmFwRYL7M/BLd9c4GlZQ5ldA+QuP7tTC7JNRCFotFT13bSS0aBehodpHueXcj128BqPcIW6h3LmsXqRt7NpFhSJPe38Svr6jXHE5D9763UYezCtWsob+eu7GbrFaL2WWhlgry9dIrt/RQgLdNifuO65lvOYMAQP1G2EK9NO3qjmodEaj03GLd98EmOZ3cjBP107OLdun7PRny87LpldE9FeLHMO84P60jg/TMH7tKkl5ZtU9fbT5qckUAYB7CFuolf2+75oy6SL5e5Tc7nruK0bNQ/3y28bBePjly3MwbuqhtVJDJFaGuGN45Wnf2Lz8d9f6Pf9LmQ1nmFgQAJiFsod5qExmkx68uHz3ruUU/a/2BTJMrAi6c1XsydP/H5ffTuvPSFrqqa4zJFaGuuX9wW/VrHa6CEof+PH+9ko9zw2MA9Q9hC/XajT1jNaJb+ehZf/lPklIy+TKAum/7kRzd+Z8klToMXdU1Rg8ObWd2SaiD7DarXr6lhzpEBysjr0Rj5q1TZn6J2WUBwAVF2EK9Vj56Vmd1jAnW8fwSjZ2/XtmFpWaXBXjM4axCjZ2/TnnFZerdPEzP/rELA2LAYwJ97Jo/tpcah/ppf0a+xr21nnscAqhXCFuo9wJ87HpjTC9FBftqT3qe7l6QpFKGK0YdlJZTpNGvr1VaTrHaRAbq1Vt7ysfOjYvhWRHBvnrrz70U4ueljclZuvOdJBWVErgA1A+ELUBSVIiv3ritp/y9bVq957ge+XSrDIMRClF3pGYXaeSra7QvI1+NQ/00f+zFjDyIC6ZVRJBeH9NTfl42rfr5mMa//SOBC0C9QNgCTuoYE6KX/tRdVov0wY8pevKrHQQu1Amp2UW6+bU12n8yaL1/xx8UE+pndlmoZ3o1C9O8sb3k52XTd7szCFwA6gXCFvArl7WL1D+u7SxJeuP7/XqKwIVa7tCJAo18NVH7M/LVpEF50IoN8ze7LNRTf2jRUPPH9pK/d3nguv2tH5VXXGZ2WQDgMYQt4DdGXtxUT13bSZL0+vf79Y+vCVyonTYfytKIOT/owPECNWngp/fGE7Rgvt4tGmr+2Ivl723T93sydNMriUrLKTK7LADwCMIWcBqjesfpyRHlgeu17/br8f9tl8NJ4ELtsWR7mm56ZY0y8orVLipIH/0lnqCFGuPi5mF6d/wfFB7orW1HcjRizmrtTM0xuywAqHZ2swsAaqpb/hAnQ9Kjn23V/B8O6NCJQv1rZDcF+PDfBjWXYRiat/qAnvxqu5yGdGmbRprzp+4K8mUwDNQs3WJD9cldfXTb/HXadyxff3w5US/8qbsGto0wuzSgzklOTlZGRobZZZy38PBwNW3a1OwyKoVvjcBZjP5DnEL9vPS3j37Skh1puvGVRL15Wy9FBvuaXRpwipyiUj348WZ9szVVkjSyV6yeGNFJXjZOYkDN1LShvz656xLd+Z8krd2fqbHz1mvCwJa6L6GN7By3QLVITk5Wu/btVVhQYHYp583P3187d+yoVYGLsAX8jqu6xigm1Ffj307StiM5uual1XrpT93Vs1mY2aUBLlsOZWvCuxuUnFkgL5tFU4a119g+zWSxcMNi1Gyh/t56e9zFevLLHfrPmoOas3yv1u8/oRdu7q6oEH7YAs5XRkaGCgsKNOrBZxTZtKXZ5VRZWvJeLXj6fmVkZBC2gLqmR1yYPru7j8bOX6e9x/J14yuJmpTQRhMGtpLNypdZmKfU4dSrq/bpX0t2q8ThVONQP80ZdZG6xYaaXRpwznzsNj0xopN6twjTQ//donUHMjXsX6s07eqOurprDD8aANUgsmlLNWnd0ewy6h366IFz1LShvz6b0EfXdm8spyHNWvyzbn5tjQ5nFZpdGuqpzYeydNWL3+uZb3epxOHU5R0i9fW9/QhaqLWu7BKjL+/pq44xwTpRUKq/vr9Jf56/nvdZALUWYQuohCBfLz1/UzfNurGrArxtWrc/U5fPWqlXV+1VqcNpdnmoJ7IKSjT9f9tPjuCWq1B/L826sateHd1DIf4MhIHarVl4gD69u48mX95G3jarlu86psGzVur17/apuIybIAOoXQhbQBVcd1ETfXVvP/WIa6CCEof+8fVOXfHCd1qz77jZpaEOKyp16JWVe3XpzOV6c/V+OQ3p6q4xWjK5v667qAmnWqHO8LZbde+g1vrq3r7qEddA+SUOPfnVDg16bqU+33RYTm7FAaCW4JotoIqahQfoozvj9fGGQ/rnNzv1c1qeRr66Rpe1i9CkhNbq0iTU7BJRRxSWOPRRUormrtirI9nlN39tFxWkh4e316VtGplcHeA5rSOD9NGd8fooKUWzFv+sQycK9df3N+m17/ZpwoBWGtwxiutmgTrEMAw5nIbKTj4cTkNOw5BhSDmlkld4nIrLatePLYQt4DxYrRbd2DNWgztE6plvd+n99SlatjNdy3ama1C7CE24rJW6x4bS44AqOZ5XrLcTD+rtxAM6UVAqSYoJ8dXkwW11bffGfMlEvWC1WnRTr6a6umtjvbl6v15esVdbD+forgUb1Kyhv8b1a6EbLmoiP2+b2aUCOKnU4VRecZnyisqUX1ymvJIyFZc6VVzmVHGZo/zP0l/+XuY05HAYchhnC1Leihk3R8nZpYq/YFty/ghbQDUI9ffWU9d21vh+LfTCst36bONhLd2ZrqU709WpcbBG/yFOV3dtzJcB/C6H09B3u4/pox8PafH2NJWcvBYwNsxP4/u10I09Y+XrxXGE+sfP26YJA1tpZK9YvfXDAb2VeFAHjhfo0c+2aubCnbq6a4xu7BmrLk1C+IEL8BDDMFRc5lRuRYj67aOo/M/isvO/jt1ikexWiywWiyySDKdDhblZslganv+GXECELaAaNQsP0Kwbu2niwFb694q9+uKnI9p6OEcP/neLnvxqh4Z2jNKVXWN0ScuG3GgWLg6noR8PZOrbbWn6estRpeYUueZ1aRKiOy5toaEdo7jJKyCpYaCPJg9uq78MaKkP16fojdX7lZJZqAVrk7VgbbLaRAZqeOdoDekYpXZRQQQv4Bw5DUMFxY4zBqiKh+Mcr5n0slkU6GNXgI9dgT52+XrZ5GO3nnzY5OP1y9/tNotsVovsVovsVqvsVousvzl749DubZo1YbRajU3yxOZ7DGEL8IAWjQL17B+76uHh7fXRjyl6Z+1BpWQW6qOkQ/oo6ZAa+HtpUPtIXdqmkfq2CldYgLfZJeMCS8spUuLe41q9J0PLdqbreH6Ja16ov5dGdGusP/Zsoo4xISZWCdRc/t523danuW6Nb6bEfcf14Y8p+mZrqn5Oy9PPabs1e8luxTX018C2EerTKly9W4Qp2JfROlE/WezeyiuVDp0oOG2Iyi92KL+4TOd6NZSfl00BPjYFngxSgb72X/5+8rm3zcqPHSJsAR4VFuCtO/u31Ph+LbR2f6a+2nJE32xJ1fH8En2cdEgfJx2SxSJ1jAlWz7gwdW8aqouaNlCTBn68QdUhZQ6ndqXl6qeUbP2UkqWk5BPak57n1ibEz0uD2kdocIcoDWzXSD52ThUEzoXValGfVuHq0ypc0wtK9e32VC3alqpVuzN08HiB5v9wQPN/OCCrRercOETdmzZQt9hQdY0NVbOG/rzXolYrdTh1LLdY6bnFSs8pUlpusY7lFCk9t1ipOUVKzS7Socw8Nf3bJ/r2qKSjh8+6PItFCvC2nzVEBXjbONOiEupV2JozZ46eeeYZpaamqmvXrnrxxRd18cUXm10W6gGr1aL4lg0V37Khpl3VUev2Z2rlz8e0aneGdhzN0dbD5Y/5P5S3D/X3UtvIILWLClKbqCA1DfNXkwb+ig7x5XqdGsowDB3LK9bhE4U6nFWog8cL9HNarn5Oy9PeY3kq+c356xaL1CkmRJe0bKhL2zTSxc3DOLUUOE8h/l66sWesbuwZq/ziMn23+5i+252hH/Ye1/6MfP10KFs/Hcp2tQ/0sat1ZKDaRASpdWSgYsP81TjUT00a+CnEz4sghgvO6TSUXViqzIISZRWUKDO/VCfyS5RZUKIT+SU6nl/iClbHcovdzor4PTaLoSBf719Ck49dQRWn+Z0MVf7eNlk57qtVvQlbH3zwgSZPnqy5c+eqd+/emj17toYMGaJdu3YpIiLC7PJQj9htVl3SKlyXtArXFEnpueWnk21MztLGlCxtP5KtrIJSrd2fqbX7M095faMgH9eXgcYN/BQe4KNQfy+F+nurwa/+DPHz4pen8+RwGsorKtOJgpJfHvmlOlFQoqyCUh3LLdbhrELX47eB6teCfOzqEhuirk3Kf1Hv3TxMof6cPgp4SoCPXUM7RWtop2hJ0pGsQq3bn6mfDmXpp5QsbT2So7zisvL33uSsU1/vbVPjBn5qHFr+XtswwEcN/L3UIMDb9T7bwN9bof5eCvC2n3J9CeoPp7N80IiiUoeKyhwqLnWqqMyhwhKHcot+OWUv13XqXqnyisuUU/TLqXxZBSU6UVCqrIISVfY2cnarRY2CfBQR7KuIIJ+TD19FBvsoKsRXJw7v1w3DB+mvz/1HsW3aeGYn4IzqTdiaNWuWxo8fr7Fjx0qS5s6dq6+++kpvvvmmHnroIZOrQ30WEeSra7o11jXdGksqv3HtnvQ87UrN1a60XP2clqtDJwp1+EShCksdOpZbrGO5xdqUkvW7y/bzssnf2yY/74o/7fI/Oc3Hyyq71Sovm1VeNovsNsvJv5dfmPrLdKusFskiiywWyWKxyGqRrJZfnlv0y/PTtbWcnGao/F4ZhiHXeeGGYZT/3dAv813zfjOtom3FvJPPK+YbhqFShyGHs3wY2TJHxX06nCenlz8vdZz8UCx1qLC0/AOxsLR8WmGJQwUlZSoqdbpGAjxXFosUFezrCsOtI4PUJjJIbSOD1KSBH1/GABPFhPppRPfGGtG9/L221OHUgYz8k++zedqTnuvqmc7IK1F+iePk9V95v7Pkcv4n32f9ve2u990Ab7u87eXvpV42q7wr3mMrnp+cV/5eXD4ggEXu77HWivdca/l7bfn76qltfv1nbVXxnu90/vKe7ppmyO3vMiqmnfzzLO0rPi8cTqns5OeD4+RnhMPpVOnJYcddnxe/eV7icKro5DDlRaUVnx8nhy2vwmfFuQjytSvsZLAPOxnyw/y91SDAW42CfBT5q2DVwN/7rJ8vG/IPyVmcX6uPjdqsXoStkpISJSUlacqUKa5pVqtVCQkJSkxMPKV9cXGxiouLXc+zs8tPOcjJyfF8secgL6/8jf/Q7m0qLiwwuZqqO3ZovyQpKSnJtU21ldVqldNZvW+2jSQ1CpT6BkqSRYbhp7xSQxn5Th0rLNPxfIcyCh3KLXYqr9RQXolTecVO5ZYaKiwtjyT5xVJ+tVZVP/nYLQrysijQx6JAb6uCvKwK9LEq2MeqcH+bwv1sCve3qoGfTXarRVKZpNzyR4Z0IEM6UMV1e+LYMktd2JZdu3ZJ4v23pjmfYytYUk8fqWespFhJ8lFJmbeOFzqUUehUxsn32or32JzS8j/zSgzllTpV6ihfTl6xVPv3JM6XzVI+Cp+3zSJvq+TnZZG/l0V+Xlb52S3ys1vl52Upf9is8ve2yNduUeDJz5Ug7/K/220Vychx8vHLKLUqKH8cS5WOnUNNde19Ky8vz/Tv5BXrN856X7ByFuNcWtVyR44cUePGjfXDDz8oPv6X26A98MADWrlypdauXevWftq0aXr88ccvdJkAAAAAaomUlBQ1adLkrG3qRc9WZU2ZMkWTJ092PXc6ncrMzFTDhg1rxMWyOTk5io2NVUpKioKDg80up85h/3oW+9ez2L+exf71LPavZ7F/PYv961k1af8ahqHc3FzFxMT8btt6EbbCw8Nls9mUlpbmNj0tLU1RUVGntPfx8ZGPj4/btNDQUE+WWCXBwcGmH2x1GfvXs9i/nsX+9Sz2r2exfz2L/etZ7F/Pqin7NyTk3O6DWS+GKvP29laPHj20dOlS1zSn06mlS5e6nVYIAAAAANWlXvRsSdLkyZM1ZswY9ezZUxdffLFmz56t/Px81+iEAAAAAFCd6k3Yuummm3Ts2DFNnTpVqamp6tatmxYuXKjIyEizS6s0Hx8fPfbYY6ec6ojqwf71LPavZ7F/PYv961nsX89i/3oW+9ezauv+rRejEQIAAADAhVYvrtkCAAAAgAuNsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtmqAOXPmqFmzZvL19VXv3r21bt26s7b/6KOP1K5dO/n6+qpz5876+uuv3eYbhqGpU6cqOjpafn5+SkhI0O7duz25CTVaZfbva6+9pn79+qlBgwZq0KCBEhISTml/2223yWKxuD2GDh3q6c2osSqzf+fPn3/KvvP19XVrw/HrrjL7d8CAAafsX4vFoiuuuMLVhuP3F6tWrdJVV12lmJgYWSwWffbZZ7/7mhUrVuiiiy6Sj4+PWrVqpfnz55/SprLv6XVVZffvJ598ossvv1yNGjVScHCw4uPj9e2337q1mTZt2inHb7t27Ty4FTVXZffvihUrTvv+kJqa6taO47dcZffv6d5bLRaLOnbs6GrD8fuLGTNmqFevXgoKClJERIRGjBihXbt2/e7rauN3YMKWyT744ANNnjxZjz32mDZs2KCuXbtqyJAhSk9PP237H374QTfffLPGjRunjRs3asSIERoxYoS2bt3qajNz5ky98MILmjt3rtauXauAgAANGTJERUVFF2qzaozK7t8VK1bo5ptv1vLly5WYmKjY2FgNHjxYhw8fdms3dOhQHT161PV47733LsTm1DiV3b9S+Z3ff73vDh486Daf4/cXld2/n3zyidu+3bp1q2w2m/74xz+6teP4LZefn6+uXbtqzpw559R+//79uuKKKzRw4EBt2rRJkyZN0u233+4WCKryf6Kuquz+XbVqlS6//HJ9/fXXSkpK0sCBA3XVVVdp48aNbu06duzodvx+//33nii/xqvs/q2wa9cut/0XERHhmsfx+4vK7t9//etfbvs1JSVFYWFhp7z/cvyWW7lypSZMmKA1a9Zo8eLFKi0t1eDBg5Wfn3/G19Ta78AGTHXxxRcbEyZMcD13OBxGTEyMMWPGjNO2v/HGG40rrrjCbVrv3r2NO++80zAMw3A6nUZUVJTxzDPPuOZnZWUZPj4+xnvvveeBLajZKrt/f6usrMwICgoy3nrrLde0MWPGGNdcc011l1orVXb/zps3zwgJCTnj8jh+3Z3v8fv8888bQUFBRl5enmsax+/pSTI+/fTTs7Z54IEHjI4dO7pNu+mmm4whQ4a4np/vv1lddS7793Q6dOhgPP74467njz32mNG1a9fqK6yOOJf9u3z5ckOSceLEiTO24fg9vaocv59++qlhsViMAwcOuKZx/J5Zenq6IclYuXLlGdvU1u/A9GyZqKSkRElJSUpISHBNs1qtSkhIUGJi4mlfk5iY6NZekoYMGeJqv3//fqWmprq1CQkJUe/evc+4zLqqKvv3twoKClRaWqqwsDC36StWrFBERITatm2ru+66S8ePH6/W2muDqu7fvLw8xcXFKTY2Vtdcc422bdvmmsfx+4vqOH7feOMNjRw5UgEBAW7TOX6r5vfef6vj3wy/cDqdys3NPeX9d/fu3YqJiVGLFi00atQoJScnm1Rh7dStWzdFR0fr8ssv1+rVq13TOX6r1xtvvKGEhATFxcW5Tef4Pb3s7GxJOuX/+6/V1u/AhC0TZWRkyOFwKDIy0m16ZGTkKedQV0hNTT1r+4o/K7PMuqoq+/e3HnzwQcXExLj9xx06dKjefvttLV26VE8//bRWrlypYcOGyeFwVGv9NV1V9m/btm315ptv6vPPP9c777wjp9OpSy65RIcOHZLE8ftr53v8rlu3Tlu3btXtt9/uNp3jt+rO9P6bk5OjwsLCannPwS+effZZ5eXl6cYbb3RN6927t+bPn6+FCxfq5Zdf1v79+9WvXz/l5uaaWGntEB0drblz5+q///2v/vvf/yo2NlYDBgzQhg0bJFXPZybKHTlyRN98880p778cv6fndDo1adIk9enTR506dTpju9r6Hdhu2pqBGu6f//yn3n//fa1YscJtEIeRI0e6/t65c2d16dJFLVu21IoVKzRo0CAzSq014uPjFR8f73p+ySWXqH379nrllVf0xBNPmFhZ3fPGG2+oc+fOuvjii92mc/yiNnj33Xf1+OOP6/PPP3e7pmjYsGGuv3fp0kW9e/dWXFycPvzwQ40bN86MUmuNtm3bqm3btq7nl1xyifbu3avnn39e//nPf0ysrO556623FBoaqhEjRrhN5/g9vQkTJmjr1q119vo1erZMFB4eLpvNprS0NLfpaWlpioqKOu1roqKiztq+4s/KLLOuqsr+rfDss8/qn//8pxYtWqQuXbqctW2LFi0UHh6uPXv2nHfNtcn57N8KXl5e6t69u2vfcfz+4nz2b35+vt5///1z+vCur8dvVZzp/Tc4OFh+fn7V8n8C0vvvv6/bb79dH3744SmnDP1WaGio2rRpw/FbRRdffLFr33H8Vg/DMPTmm29q9OjR8vb2Pmtbjl9p4sSJ+vLLL7V8+XI1adLkrG1r63dgwpaJvL291aNHDy1dutQ1zel0aunSpW6//v9afHy8W3tJWrx4sat98+bNFRUV5dYmJydHa9euPeMy66qq7F+pfCSbJ554QgsXLlTPnj1/dz2HDh3S8ePHFR0dXS111xZV3b+/5nA4tGXLFte+4/j9xfns348++kjFxcW65ZZbfnc99fX4rYrfe/+tjv8T9d17772nsWPH6r333nO7ZcGZ5OXlae/evRy/VbRp0ybXvuP4rR4rV67Unj17zunHrvp8/BqGoYkTJ+rTTz/VsmXL1Lx58999Ta39Dmza0BwwDMMw3n//fcPHx8eYP3++sX37duOOO+4wQkNDjdTUVMMwDGP06NHGQw895Gq/evVqw263G88++6yxY8cO47HHHjO8vLyMLVu2uNr885//NEJDQ43PP//c2Lx5s3HNNdcYzZs3NwoLCy/49pmtsvv3n//8p+Ht7W18/PHHxtGjR12P3NxcwzAMIzc31/i///s/IzEx0di/f7+xZMkS46KLLjJat25tFBUVmbKNZqrs/n388ceNb7/91ti7d6+RlJRkjBw50vD19TW2bdvmasPx+4vK7t8Kffv2NW666aZTpnP8usvNzTU2btxobNy40ZBkzJo1y9i4caNx8OBBwzAM46GHHjJGjx7tar9v3z7D39/fuP/++40dO3YYc+bMMWw2m7Fw4UJXm9/7N6tPKrt/FyxYYNjtdmPOnDlu779ZWVmuNn/729+MFStWGPv37zdWr15tJCQkGOHh4UZ6evoF3z6zVXb/Pv/888Znn31m7N6929iyZYvx17/+1bBarcaSJUtcbTh+f1HZ/VvhlltuMXr37n3aZXL8/uKuu+4yQkJCjBUrVrj9fy8oKHC1qSvfgQlbNcCLL75oNG3a1PD29jYuvvhiY82aNa55/fv3N8aMGePW/sMPPzTatGljeHt7Gx07djS++uort/lOp9N49NFHjcjISMPHx8cYNGiQsWvXrguxKTVSZfZvXFycIemUx2OPPWYYhmEUFBQYgwcPNho1amR4eXkZcXFxxvjx4+vlB1GFyuzfSZMmudpGRkYaw4cPNzZs2OC2PI5fd5V9f9i5c6chyVi0aNEpy+L4dVcxFPZvHxX7dMyYMUb//v1PeU23bt0Mb29vo0WLFsa8efNOWe7Z/s3qk8ru3/79+5+1vWGUD7UfHR1teHt7G40bNzZuuukmY8+ePRd2w2qIyu7fp59+2mjZsqXh6+trhIWFGQMGDDCWLVt2ynI5fstV5f0hKyvL8PPzM1599dXTLpPj9xen27eS3N5T68p3YIthGIbHus0AAAAAoJ7imi0AAAAA8ADCFgAAAAB4AGELAAAAADyAsAUAAAAAHkDYAgAAAAAPIGwBAAAAgAcQtgAAAADAAwhbAAAAAOABhC0AQJ01YMAATZo06axtmjVrptmzZ7ueWywWffbZZx6tCwBQP9jNLgAAgKq67bbblJWV5RaOPv74Y91yyy166qmn9Mknn8jLy8u8AgEA9RphCwBQZ7z++uuaMGGC5s6dq7Fjx5pdDgCgnuM0QgBAnTBz5kzdc889ev/9911B67enEaanp+uqq66Sn5+fmjdvrgULFpx2WRkZGbr22mvl7++v1q1b64svvnCbv3LlSl188cXy8fFRdHS0HnroIZWVlbnmDxgwQPfcc48mTZqkBg0aKDIyUq+99pry8/M1duxYBQUFqVWrVvrmm2/clrt161YNGzZMgYGBioyM1OjRo5WRkVFNewgAcKERtgAAtd6DDz6oJ554Ql9++aWuvfbaM7a77bbblJKSouXLl+vjjz/Wv//9b6Wnp5/S7vHHH9eNN96ozZs3a/jw4Ro1apQyMzMlSYcPH9bw4cPVq1cv/fTTT3r55Zf1xhtv6Mknn3RbxltvvaXw8HCtW7dO99xzj+666y798Y9/1CWXXKINGzZo8ODBGj16tAoKCiRJWVlZuuyyy9S9e3f9+OOPWrhwodLS0nTjjTdW454CAFxQBgAAtdSYMWMMb29vQ5KxdOnSU+b379/f+Otf/2oYhmHs2rXLkGSsW7fONX/Hjh2GJOP55593TZNkPPLII67neXl5hiTjm2++MQzDMB5++GGjbdu2htPpdLWZM2eOERgYaDgcDtd6+/bt65pfVlZmBAQEGKNHj3ZNO3r0qCHJSExMNAzDMJ544glj8ODBbvWnpKQYkoxdu3ZVdtcAAGoAerYAALValy5d1KxZMz322GPKy8s7Y7sdO3bIbrerR48ermnt2rVTaGjoaZdZISAgQMHBwa4esB07dig+Pl4Wi8XVpk+fPsrLy9OhQ4dOuwybzaaGDRuqc+fOrmmRkZGS5FruTz/9pOXLlyswMND1aNeunSRp796957QvAAA1CwNkAABqtcaNG+vjjz/WwIEDNXToUH3zzTcKCgo6r2X+dgRDi8Uip9N53sv49bSKsFax3Ly8PF111VV6+umnT1lWdHR0pdYNAKgZ6NkCANR6cXFxWrlypVJTUzV06FDl5uae0qZdu3YqKytTUlKSa9quXbuUlZVVqXW1b99eiYmJMgzDNW316tUKCgpSkyZNqrwNF110kbZt26ZmzZqpVatWbo+AgIAqLxcAYB7CFgCgToiNjdWKFSuUnp6uIUOGKCcnx21+27ZtNXToUN15551au3atkpKSdPvtt8vPz69S67n77ruVkpKie+65Rzt37tTnn3+uxx57TJMnT5bVWvWP1QkTJigzM1M333yz1q9fr7179+rbb7/V2LFj5XA4qrxcAIB5CFsAgDqjSZMmWrFihTIyMk4buObNm6eYmBj1799f1113ne644w5FRERUah2NGzfW119/rXXr1qlr1676y1/+onHjxumRRx45r9pjYmK0evVqORwODR48WJ07d9akSZMUGhp6XiEOAGAei/Hr8yAAAAAAANWCn8oAAAAAwAMIWwAAAADgAYQtAAAAAPAAwhYAAAAAeABhCwAAAAA8gLAFAAAAAB5A2AIAAAAADyBsAQAAAIAHELYAAAAAwAMIWwAAAADgAYQtAAAAAPCA/weOdtmBqDfHwwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot de Kidhome')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Describe la variable 'Kidhome'\n",
    "print(df['Kidhome'].describe())\n",
    "\n",
    "# Histograma de la variable 'Kidhome'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Kidhome'], kde=True)\n",
    "plt.title('Distribución de Kidhome')\n",
    "plt.show()\n",
    "\n",
    "# Boxplot de la variable 'Kidhome'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df['Kidhome'])\n",
    "plt.title('Boxplot de Kidhome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Teenhome:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "3gGVzJvyF2F-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    2205.000000\n",
      "mean        0.506576\n",
      "std         0.544380\n",
      "min         0.000000\n",
      "25%         0.000000\n",
      "50%         0.000000\n",
      "75%         1.000000\n",
      "max         2.000000\n",
      "Name: Teenhome, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Describe la variable 'Teenhome'\n",
    "print(df['Teenhome'].describe())\n",
    "\n",
    "# Histograma de la variable 'Teenhome'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Teenhome'], kde=True)\n",
    "plt.title('Distribución de Teenhome')\n",
    "plt.show()\n",
    "\n",
    "# Boxplot de la variable 'Teenhome'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df['Teenhome'])\n",
    "plt.title('Boxplot de Teenhome')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}