NetflixData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Available Globally?</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Hours Viewed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Night Agent: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-03-23</td>\n",
       "      <td>812100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ginny &amp; Georgia: Season 2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>665100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Glory: Season 1 // 더 글로리: 시즌 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>622800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wednesday: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-11-23</td>\n",
       "      <td>507700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-05-04</td>\n",
       "      <td>503000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Title Available Globally? Release Date  \\\n",
       "0            The Night Agent: Season 1                 Yes   2023-03-23   \n",
       "1            Ginny & Georgia: Season 2                 Yes   2023-01-05   \n",
       "2   The Glory: Season 1 // 더 글로리: 시즌 1                 Yes   2022-12-30   \n",
       "3                  Wednesday: Season 1                 Yes   2022-11-23   \n",
       "4  Queen Charlotte: A Bridgerton Story                 Yes   2023-05-04   \n",
       "\n",
       "   Hours Viewed  \n",
       "0     812100000  \n",
       "1     665100000  \n",
       "2     622800000  \n",
       "3     507700000  \n",
       "4     503000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read 'data/What_We_Watched_A_Netflix_Engagement_Report_2023Jan-Jun.xlsx' into a DataFrame: netflix\n",
    "netflix = pd.read_excel('data/What_We_Watched_A_Netflix_Engagement_Report_2023Jan-Jun.xlsx', header=5, usecols='B:E')\n",
    "netflix.head()"
   ]
  },
  {
   "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>Title</th>\n",
       "      <th>Available Globally?</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Hours Viewed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18209</th>\n",
       "      <td>راس السنة</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18210</th>\n",
       "      <td>心が叫びたがってるんだ。</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18211</th>\n",
       "      <td>두근두근 내 인생</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18212</th>\n",
       "      <td>라디오 스타</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18213</th>\n",
       "      <td>선생 김봉두</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Title Available Globally? Release Date  Hours Viewed\n",
       "18209     راس السنة                  No          NaT        100000\n",
       "18210  心が叫びたがってるんだ。                  No          NaT        100000\n",
       "18211     두근두근 내 인생                  No          NaT        100000\n",
       "18212        라디오 스타                  No          NaT        100000\n",
       "18213        선생 김봉두                  No          NaT        100000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of the 'Hours Viewed' column\n",
    "netflix['Hours Viewed'].iloc[:100].plot(kind='hist')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the log-normal distribution to the data\n",
    "shape, loc, scale = stats.lognorm.fit(netflix['Hours Viewed'], floc=0)\n",
    "\n",
    "# Generate a log-normal distribution with the fitted parameters\n",
    "fitted_distribution = stats.lognorm(shape, loc, scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the Sum of Squared Errors (SSE) between the observed data and the fitted distribution\n",
    "observed_frequencies, bin_edges = np.histogram(netflix['Hours Viewed'], bins=30, density=True)\n",
    "bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
    "expected_frequencies = fitted_distribution.pdf(bin_centers)\n",
    "\n",
    "sse = np.sum((observed_frequencies - expected_frequencies) ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Sum of Squared Errors (SSE) test, when applied to compare the observed Hours Viewed data with a fitted Log-Normal distribution, results in an SSE value of approximately  9.177635477248848e-16\n"
     ]
    }
   ],
   "source": [
    "print(\"The Sum of Squared Errors (SSE) test, when applied to compare the observed Hours Viewed data with a fitted Log-Normal distribution, results in an SSE value of approximately \", sse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This very small SSE value suggests a good fit of the Log-Normal distribution to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To improve the visualization, we can use a logarithmic scale for the x-axis\n",
    "# This will help in better visualizing the distribution of data over a wide range of values\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Creating the histogram for the observed data with a log scale\n",
    "sns.histplot(netflix['Hours Viewed'], bins=30, kde=False, stat=\"density\", color=\"blue\", log_scale=True, label=\"Observed Data\")\n",
    "\n",
    "# Adding labels, title and setting the x-axis to log scale\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Hours Viewed (Log Scale)')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Comparison of Hours Viewed with Fitted Log-Normal Distribution (Log Scale)')\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Plotting the fitted Log-Normal distribution on a log scale\n",
    "log_space = np.logspace(np.log10(min(netflix['Hours Viewed'])), np.log10(max(netflix['Hours Viewed'])), base=10, num=100)\n",
    "pdf_log = fitted_distribution.pdf(log_space)\n",
    "plt.plot(log_space, pdf_log, 'r', label=\"Fitted Log-Normal Distribution\")\n",
    "# Adding labels, title and setting the x-axis to log scale\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Hours Viewed (Log Scale)')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Comparison of Hours Viewed with Fitted Log-Normal Distribution (Log Scale)')\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4473661.235904143"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the mean of the log-normal distribution using the fitted parameters\n",
    "\n",
    "# The mean of the log-normal distribution is given by e^(mu_ln + sigma_ln^2 / 2)\n",
    "# where mu_ln and sigma_ln are the parameters of the underlying normal distribution\n",
    "# In our case, shape parameter is sigma_ln and scale parameter is e^mu_ln in the log-normal distribution\n",
    "\n",
    "mu_ln = np.log(scale)  # since scale = e^mu_ln\n",
    "sigma_ln = shape\n",
    "\n",
    "mean_log_normal = np.exp(mu_ln + (sigma_ln ** 2) / 2)\n",
    "mean_log_normal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5130954.211046448"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix['Hours Viewed'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 13700 entries, 6 to 18213\n",
      "Data columns (total 4 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Title                13700 non-null  object        \n",
      " 1   Available Globally?  13700 non-null  object        \n",
      " 2   Release Date         940 non-null    datetime64[ns]\n",
      " 3   Hours Viewed         13700 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(2)\n",
      "memory usage: 535.2+ KB\n"
     ]
    }
   ],
   "source": [
    "netflix[netflix['Available Globally?'] == 'No'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Sum of Squared Errors (SSE) test, when applied to compare the observed Hours Viewed data with a fitted Log-Normal distribution, results in an SSE value of approximately  2.989728706262658e-15\n"
     ]
    }
   ],
   "source": [
    "hours_viewed_non_global = netflix[netflix['Available Globally?'] == 'No']['Hours Viewed']\n",
    "\n",
    "# Fit the log-normal distribution to the data\n",
    "shape, loc, scale = stats.lognorm.fit(hours_viewed_non_global, floc=0)\n",
    "\n",
    "# Generate a log-normal distribution with the fitted parameters\n",
    "fitted_distribution = stats.lognorm(shape, loc, scale)\n",
    "\n",
    "# Calculate the Sum of Squared Errors (SSE) between the observed data and the fitted distribution\n",
    "observed_frequencies, bin_edges = np.histogram(hours_viewed_non_global, bins=30, density=True)\n",
    "bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
    "expected_frequencies = fitted_distribution.pdf(bin_centers)\n",
    "\n",
    "sse = np.sum((observed_frequencies - expected_frequencies) ** 2)\n",
    "\n",
    "print(\"The Sum of Squared Errors (SSE) test, when applied to compare the observed Hours Viewed data with a fitted Log-Normal distribution, results in an SSE value of approximately \", sse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To improve the visualization, we can use a logarithmic scale for the x-axis\n",
    "# This will help in better visualizing the distribution of data over a wide range of values\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Creating the histogram for the observed data with a log scale\n",
    "sns.histplot(hours_viewed_non_global, bins=30, kde=False, stat=\"density\", color=\"blue\", log_scale=True, label=\"Observed Data\")\n",
    "\n",
    "# Adding labels, title and setting the x-axis to log scale\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Hours Viewed (Log Scale)')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Comparison of Hours Viewed with Fitted Log-Normal Distribution (Log Scale)')\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2610237.1573635084"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the mean of the log-normal distribution using the fitted parameters\n",
    "\n",
    "# The mean of the log-normal distribution is given by e^(mu_ln + sigma_ln^2 / 2)\n",
    "# where mu_ln and sigma_ln are the parameters of the underlying normal distribution\n",
    "# In our case, shape parameter is sigma_ln and scale parameter is e^mu_ln in the log-normal distribution\n",
    "\n",
    "mu_ln = np.log(scale)  # since scale = e^mu_ln\n",
    "sigma_ln = shape\n",
    "\n",
    "mean_log_normal = np.exp(mu_ln + (sigma_ln ** 2) / 2)\n",
    "mean_log_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/8y/0ldkfh015gjc7lvpvxd7pr380000gn/T/ipykernel_71767/4015402496.py:2: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  imdb_title_basics = pd.read_csv('data/imdb/title.basics.tsv', sep='\\t')\n"
     ]
    },
    {
     "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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>short</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>0</td>\n",
       "      <td>1894</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1</td>\n",
       "      <td>Documentary,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>short</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>5</td>\n",
       "      <td>Animation,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>short</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "      <td>Animation,Comedy,Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>short</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>12</td>\n",
       "      <td>Animation,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>short</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>0</td>\n",
       "      <td>1893</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy,Short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst titleType            primaryTitle           originalTitle  \\\n",
       "0  tt0000001     short              Carmencita              Carmencita   \n",
       "1  tt0000002     short  Le clown et ses chiens  Le clown et ses chiens   \n",
       "2  tt0000003     short          Pauvre Pierrot          Pauvre Pierrot   \n",
       "3  tt0000004     short             Un bon bock             Un bon bock   \n",
       "4  tt0000005     short        Blacksmith Scene        Blacksmith Scene   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes                    genres  \n",
       "0       0      1894      \\N              1         Documentary,Short  \n",
       "1       0      1892      \\N              5           Animation,Short  \n",
       "2       0      1892      \\N              4  Animation,Comedy,Romance  \n",
       "3       0      1892      \\N             12           Animation,Short  \n",
       "4       0      1893      \\N              1              Comedy,Short  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the tab separated file 'data/imdb/title.basics.tsv' into a DataFrame: imdb_title_basics\n",
    "imdb_title_basics = pd.read_csv('data/imdb/title.basics.tsv', sep='\\t')\n",
    "\n",
    "# Print the first five rows of imdb_title_basics\n",
    "imdb_title_basics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Title</th>\n",
       "      <th>Available Globally?</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Hours Viewed</th>\n",
       "      <th>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Night Agent: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-03-23</td>\n",
       "      <td>812100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ginny &amp; Georgia: Season 2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>665100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Glory: Season 1 // 더 글로리: 시즌 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>622800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wednesday: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-11-23</td>\n",
       "      <td>507700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-05-04</td>\n",
       "      <td>503000000</td>\n",
       "      <td>tt14661396</td>\n",
       "      <td>tvMiniSeries</td>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023</td>\n",
       "      <td>347</td>\n",
       "      <td>Drama,History,Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Title Available Globally? Release Date  \\\n",
       "0            The Night Agent: Season 1                 Yes   2023-03-23   \n",
       "1            Ginny & Georgia: Season 2                 Yes   2023-01-05   \n",
       "2   The Glory: Season 1 // 더 글로리: 시즌 1                 Yes   2022-12-30   \n",
       "3                  Wednesday: Season 1                 Yes   2022-11-23   \n",
       "4  Queen Charlotte: A Bridgerton Story                 Yes   2023-05-04   \n",
       "\n",
       "   Hours Viewed      tconst     titleType  \\\n",
       "0     812100000         NaN           NaN   \n",
       "1     665100000         NaN           NaN   \n",
       "2     622800000         NaN           NaN   \n",
       "3     507700000         NaN           NaN   \n",
       "4     503000000  tt14661396  tvMiniSeries   \n",
       "\n",
       "                          primaryTitle                        originalTitle  \\\n",
       "0                                  NaN                                  NaN   \n",
       "1                                  NaN                                  NaN   \n",
       "2                                  NaN                                  NaN   \n",
       "3                                  NaN                                  NaN   \n",
       "4  Queen Charlotte: A Bridgerton Story  Queen Charlotte: A Bridgerton Story   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes                 genres  \n",
       "0     NaN       NaN     NaN            NaN                    NaN  \n",
       "1     NaN       NaN     NaN            NaN                    NaN  \n",
       "2     NaN       NaN     NaN            NaN                    NaN  \n",
       "3     NaN       NaN     NaN            NaN                    NaN  \n",
       "4       0      2023    2023            347  Drama,History,Romance  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform an left join on the netflix and imdb_title_basics DataFrames using the netflix 'Title' column and the 'originalTitle' column of imdb_title_basics\n",
    "netflix_imdb = netflix.merge(imdb_title_basics, left_on='Title', right_on='primaryTitle', how='left')\n",
    "\n",
    "# Print the first five rows of netflix_imdb\n",
    "netflix_imdb.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42069 entries, 0 to 42068\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Title                42069 non-null  object        \n",
      " 1   Available Globally?  42069 non-null  object        \n",
      " 2   Release Date         14431 non-null  datetime64[ns]\n",
      " 3   Hours Viewed         42069 non-null  int64         \n",
      " 4   tconst               28680 non-null  object        \n",
      " 5   titleType            28680 non-null  object        \n",
      " 6   primaryTitle         28680 non-null  object        \n",
      " 7   originalTitle        28680 non-null  object        \n",
      " 8   isAdult              28680 non-null  object        \n",
      " 9   startYear            28680 non-null  object        \n",
      " 10  endYear              28680 non-null  object        \n",
      " 11  runtimeMinutes       28680 non-null  object        \n",
      " 12  genres               28680 non-null  object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(11)\n",
      "memory usage: 4.2+ MB\n"
     ]
    }
   ],
   "source": [
    "netflix_imdb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Available Globally?</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Hours Viewed</th>\n",
       "      <th>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Night Agent: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-03-23</td>\n",
       "      <td>812100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ginny &amp; Georgia: Season 2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>665100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Glory: Season 1 // 더 글로리: 시즌 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>622800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wednesday: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-11-23</td>\n",
       "      <td>507700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-05-04</td>\n",
       "      <td>503000000</td>\n",
       "      <td>tt14661396</td>\n",
       "      <td>tvMiniSeries</td>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023</td>\n",
       "      <td>347</td>\n",
       "      <td>Drama,History,Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Title Available Globally? Release Date  \\\n",
       "0            The Night Agent: Season 1                 Yes   2023-03-23   \n",
       "1            Ginny & Georgia: Season 2                 Yes   2023-01-05   \n",
       "2   The Glory: Season 1 // 더 글로리: 시즌 1                 Yes   2022-12-30   \n",
       "3                  Wednesday: Season 1                 Yes   2022-11-23   \n",
       "4  Queen Charlotte: A Bridgerton Story                 Yes   2023-05-04   \n",
       "\n",
       "   Hours Viewed      tconst     titleType  \\\n",
       "0     812100000         NaN           NaN   \n",
       "1     665100000         NaN           NaN   \n",
       "2     622800000         NaN           NaN   \n",
       "3     507700000         NaN           NaN   \n",
       "4     503000000  tt14661396  tvMiniSeries   \n",
       "\n",
       "                          primaryTitle                        originalTitle  \\\n",
       "0                                  NaN                                  NaN   \n",
       "1                                  NaN                                  NaN   \n",
       "2                                  NaN                                  NaN   \n",
       "3                                  NaN                                  NaN   \n",
       "4  Queen Charlotte: A Bridgerton Story  Queen Charlotte: A Bridgerton Story   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes                 genres  \n",
       "0     NaN       NaN     NaN            NaN                    NaN  \n",
       "1     NaN       NaN     NaN            NaN                    NaN  \n",
       "2     NaN       NaN     NaN            NaN                    NaN  \n",
       "3     NaN       NaN     NaN            NaN                    NaN  \n",
       "4       0      2023    2023            347  Drama,History,Romance  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the rows where it didn not find a match, the 'tconst' column will have NaN values\n",
    "# Try to match the rows where 'tconst' is NaN with the 'originalTitle' column of imdb_title_basics\n",
    "# Perform an left join on the netflix and imdb_title_basics DataFrames using the netflix 'Title' column and the 'originalTitle' column of imdb_title_basics\n",
    "netflix_imdb = netflix.merge(imdb_title_basics, left_on='Title', right_on='originalTitle', how='left')\n",
    "\n",
    "# Print the first five rows of netflix_imdb\n",
    "netflix_imdb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41660 entries, 0 to 41659\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Title                41660 non-null  object        \n",
      " 1   Available Globally?  41660 non-null  object        \n",
      " 2   Release Date         14133 non-null  datetime64[ns]\n",
      " 3   Hours Viewed         41660 non-null  int64         \n",
      " 4   tconst               28376 non-null  object        \n",
      " 5   titleType            28376 non-null  object        \n",
      " 6   primaryTitle         28376 non-null  object        \n",
      " 7   originalTitle        28376 non-null  object        \n",
      " 8   isAdult              28376 non-null  object        \n",
      " 9   startYear            28376 non-null  object        \n",
      " 10  endYear              28376 non-null  object        \n",
      " 11  runtimeMinutes       28376 non-null  object        \n",
      " 12  genres               28376 non-null  object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(11)\n",
      "memory usage: 4.1+ MB\n"
     ]
    }
   ],
   "source": [
    "netflix_imdb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Title</th>\n",
       "      <th>Available Globally?</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Hours Viewed</th>\n",
       "      <th>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>genre_0</th>\n",
       "      <th>genre_1</th>\n",
       "      <th>genre_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Night Agent: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-03-23</td>\n",
       "      <td>812100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ginny &amp; Georgia: Season 2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>665100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Glory: Season 1 // 더 글로리: 시즌 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>622800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wednesday: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-11-23</td>\n",
       "      <td>507700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-05-04</td>\n",
       "      <td>503000000</td>\n",
       "      <td>tt14661396</td>\n",
       "      <td>tvMiniSeries</td>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023</td>\n",
       "      <td>347</td>\n",
       "      <td>Drama,History,Romance</td>\n",
       "      <td>Drama</td>\n",
       "      <td>History</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Title Available Globally? Release Date  \\\n",
       "0            The Night Agent: Season 1                 Yes   2023-03-23   \n",
       "1            Ginny & Georgia: Season 2                 Yes   2023-01-05   \n",
       "2   The Glory: Season 1 // 더 글로리: 시즌 1                 Yes   2022-12-30   \n",
       "3                  Wednesday: Season 1                 Yes   2022-11-23   \n",
       "4  Queen Charlotte: A Bridgerton Story                 Yes   2023-05-04   \n",
       "\n",
       "   Hours Viewed      tconst     titleType  \\\n",
       "0     812100000         NaN           NaN   \n",
       "1     665100000         NaN           NaN   \n",
       "2     622800000         NaN           NaN   \n",
       "3     507700000         NaN           NaN   \n",
       "4     503000000  tt14661396  tvMiniSeries   \n",
       "\n",
       "                          primaryTitle                        originalTitle  \\\n",
       "0                                  NaN                                  NaN   \n",
       "1                                  NaN                                  NaN   \n",
       "2                                  NaN                                  NaN   \n",
       "3                                  NaN                                  NaN   \n",
       "4  Queen Charlotte: A Bridgerton Story  Queen Charlotte: A Bridgerton Story   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes                 genres genre_0  \\\n",
       "0     NaN       NaN     NaN            NaN                    NaN     NaN   \n",
       "1     NaN       NaN     NaN            NaN                    NaN     NaN   \n",
       "2     NaN       NaN     NaN            NaN                    NaN     NaN   \n",
       "3     NaN       NaN     NaN            NaN                    NaN     NaN   \n",
       "4       0      2023    2023            347  Drama,History,Romance   Drama   \n",
       "\n",
       "   genre_1  genre_2  \n",
       "0      NaN      NaN  \n",
       "1      NaN      NaN  \n",
       "2      NaN      NaN  \n",
       "3      NaN      NaN  \n",
       "4  History  Romance  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The genre column contains a comma separated list of genres\n",
    "# We will split the genre column into multiple columns, one for each genre\n",
    "# To do this, we will use the str.split() method on the 'genre' column\n",
    "# We will also use the expand=True argument to split the column into multiple columns\n",
    "# We will then use the add_prefix() method to add the prefix 'genre_' to the column names\n",
    "netflix_imdb = netflix_imdb.join(netflix_imdb['genres'].str.split(',', expand=True).add_prefix('genre_'))\n",
    "\n",
    "# Print the first five rows of netflix_imdb\n",
    "netflix_imdb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>Title</th>\n",
       "      <th>Available Globally?</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Hours Viewed</th>\n",
       "      <th>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>genre_0</th>\n",
       "      <th>genre_1</th>\n",
       "      <th>genre_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Night Agent: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-03-23</td>\n",
       "      <td>812100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ginny &amp; Georgia: Season 2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>665100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Glory: Season 1 // 더 글로리: 시즌 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>622800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wednesday: Season 1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2022-11-23</td>\n",
       "      <td>507700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2023-05-04</td>\n",
       "      <td>503000000</td>\n",
       "      <td>tt14661396</td>\n",
       "      <td>tvMiniSeries</td>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>Queen Charlotte: A Bridgerton Story</td>\n",
       "      <td>0</td>\n",
       "      <td>2023</td>\n",
       "      <td>2023</td>\n",
       "      <td>347</td>\n",
       "      <td>Drama,History,Romance</td>\n",
       "      <td>Drama</td>\n",
       "      <td>History</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>41655</th>\n",
       "      <td>راس السنة</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41656</th>\n",
       "      <td>心が叫びたがってるんだ。</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41657</th>\n",
       "      <td>두근두근 내 인생</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41658</th>\n",
       "      <td>라디오 스타</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41659</th>\n",
       "      <td>선생 김봉두</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41660 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Title Available Globally? Release Date  \\\n",
       "0                The Night Agent: Season 1                 Yes   2023-03-23   \n",
       "1                Ginny & Georgia: Season 2                 Yes   2023-01-05   \n",
       "2       The Glory: Season 1 // 더 글로리: 시즌 1                 Yes   2022-12-30   \n",
       "3                      Wednesday: Season 1                 Yes   2022-11-23   \n",
       "4      Queen Charlotte: A Bridgerton Story                 Yes   2023-05-04   \n",
       "...                                    ...                 ...          ...   \n",
       "41655                            راس السنة                  No          NaT   \n",
       "41656                         心が叫びたがってるんだ。                  No          NaT   \n",
       "41657                            두근두근 내 인생                  No          NaT   \n",
       "41658                               라디오 스타                  No          NaT   \n",
       "41659                               선생 김봉두                  No          NaT   \n",
       "\n",
       "       Hours Viewed      tconst     titleType  \\\n",
       "0         812100000         NaN           NaN   \n",
       "1         665100000         NaN           NaN   \n",
       "2         622800000         NaN           NaN   \n",
       "3         507700000         NaN           NaN   \n",
       "4         503000000  tt14661396  tvMiniSeries   \n",
       "...             ...         ...           ...   \n",
       "41655        100000         NaN           NaN   \n",
       "41656        100000         NaN           NaN   \n",
       "41657        100000         NaN           NaN   \n",
       "41658        100000         NaN           NaN   \n",
       "41659        100000         NaN           NaN   \n",
       "\n",
       "                              primaryTitle  \\\n",
       "0                                      NaN   \n",
       "1                                      NaN   \n",
       "2                                      NaN   \n",
       "3                                      NaN   \n",
       "4      Queen Charlotte: A Bridgerton Story   \n",
       "...                                    ...   \n",
       "41655                                  NaN   \n",
       "41656                                  NaN   \n",
       "41657                                  NaN   \n",
       "41658                                  NaN   \n",
       "41659                                  NaN   \n",
       "\n",
       "                             originalTitle isAdult startYear endYear  \\\n",
       "0                                      NaN     NaN       NaN     NaN   \n",
       "1                                      NaN     NaN       NaN     NaN   \n",
       "2                                      NaN     NaN       NaN     NaN   \n",
       "3                                      NaN     NaN       NaN     NaN   \n",
       "4      Queen Charlotte: A Bridgerton Story       0      2023    2023   \n",
       "...                                    ...     ...       ...     ...   \n",
       "41655                                  NaN     NaN       NaN     NaN   \n",
       "41656                                  NaN     NaN       NaN     NaN   \n",
       "41657                                  NaN     NaN       NaN     NaN   \n",
       "41658                                  NaN     NaN       NaN     NaN   \n",
       "41659                                  NaN     NaN       NaN     NaN   \n",
       "\n",
       "      runtimeMinutes                 genres genre_0  genre_1  genre_2  \n",
       "0                NaN                    NaN     NaN      NaN      NaN  \n",
       "1                NaN                    NaN     NaN      NaN      NaN  \n",
       "2                NaN                    NaN     NaN      NaN      NaN  \n",
       "3                NaN                    NaN     NaN      NaN      NaN  \n",
       "4                347  Drama,History,Romance   Drama  History  Romance  \n",
       "...              ...                    ...     ...      ...      ...  \n",
       "41655            NaN                    NaN     NaN      NaN      NaN  \n",
       "41656            NaN                    NaN     NaN      NaN      NaN  \n",
       "41657            NaN                    NaN     NaN      NaN      NaN  \n",
       "41658            NaN                    NaN     NaN      NaN      NaN  \n",
       "41659            NaN                    NaN     NaN      NaN      NaN  \n",
       "\n",
       "[41660 rows x 16 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_imdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GDdSnTx+VLVtWiYmJ2rBhgwIDA822gbdo0ULLly+XwWAwbX+3trZW8+bNtWXLFrVu3drsPumqVatq8uTJGjdunM6dO6fu3bvLxcVFCQkJWrt2rZ555hlFRETI1tZWkydP1rPPPqs2bdqod+/eSkhIUFRUVJ7uSa9Vq5bat2+vF154QXZ2dpo/f74kadKkSTn6hoaG6oknnpAkvfHGG7mKX6pUKa1du1ZdunRR3bp11adPHzVu3Fi2trb66aef9Mknn0gyv698+vTp2rlzp5o2barBgwerRo0a+uOPP/TNN99o27Zt+uOPP3J9fXfy9NNPa9WqVRoyZIh27typwMBAZWVl6dSpU1q1apW2bNmiRo0aWWw+AEDBQpF+CydOnFBWVlaObX3p6ekqXbp0PmUFALgbH374oapUqaLo6GitXbtWHh4eGjdunCZOnGjWb/DgwUpISNCiRYu0efNmtWjRQlu3br3tk9X/zdHRUUOHDtWXX35peiK6r6+v5s+fr+eeey7X+YaEhGj//v1q0qTJHR+CdsNTTz2lChUqaPr06Xr77beVnp6uihUrqkWLFjmeHn5j9dzf39/s77QWLVpoy5YtpvP/NnbsWPn5+WnWrFmmYtnLy0uPPfaYunbtaur3zDPPKCsrS2+//bZefvll1a5dW59//rnGjx+f6+tv1aqVmjVrpkmTJikxMVE1atRQdHT0Te8Z79Kli0qWLGn6bvHcatasmb799lvNnDlTGzZs0MqVK5Wdna2KFSvqkUce0fvvv2/2PpQvX14HDx7U66+/rjVr1mj+/PkqXbq0atasqTfffDPX8+aGlZWV1q1bp1mzZmnJkiVau3atHB0dVaVKFb344ou3veUAAFD4GYz/3dNVTBkMBq1du9b0hPaVK1cqJCRE3333XY6Hvzg7O+d4ME9YWJiuXLmS4wnxLVu2VIMGDcy2WUZFRWnEiBE5tkQCAIC8yczMVIUKFdSlSxctWrQov9MBAOCesZJ+C/Xr11dWVpYuXbp00xWF3GrWrJnpO3pv2Lp1q5o1a3avKQIAUOytW7dOly9fVmhoaH6nAgCARRTrIj01NVVnzpwxHSckJCguLk6lSpWSn5+fQkJCFBoaqhkzZqh+/fq6fPmytm/frjp16pgeNHTy5Eldv35df/zxh/766y/FxcVJ+ucBPJI0ZMgQzZ07V6NHj9bAgQO1Y8cOrVq1Shs2bHjQlwsAQJHx9ddf6/jx43rjjTdUv359tWrVKr9TAgDAIor1dvfY2FizBwbd0L9/f0VHRysjI0OTJ0/WkiVLdOHCBZUpU0YPP/ywJk2apNq1a0uSvL29df78+Rwx/v22xsbG6qWXXtLJkydVqVIljR8/XmFhYfftugAAKOrCwsK0bNky1atXT9HR0apVq1Z+pwQAgEUU6yIdAAAAAICChO9JBwAAAACggKBIBwAAAACggCh2D47Lzs7WL7/8IhcXFxkMhvxOBwAAAABQxBmNRv3111+qUKGCrKxuv1Ze7Ir0X375RV5eXvmdBgAAAACgmPnpp59UqVKl2/YpdkW6i4uLpH/eHFdX13zOBgAAAABQ1KWkpMjLy8tUj95OsSvSb2xxd3V1pUgHAAAAADwwubnlmgfHAQAAAABQQFCkAwAAAABQQFCkAwAAAABQQBS7e9IBAMiNrKwsZWRk5HcaKERsbW1lbW2d32kAAAo5inQAAP7FaDTq4sWLunLlSn6ngkLI3d1dHh4euXowEAAAN0ORDgDAv9wo0MuVKydHR0eKLeSK0WjUtWvXdOnSJUmSp6dnPmcEACisKNIBAPg/WVlZpgK9dOnS+Z0OChkHBwdJ0qVLl1SuXDm2vgMA7goPjgMA4P/cuAfd0dExnzNBYXXjs8PzDAAAd4siHQCA/2CLO+4Wnx0AwL2iSAcAAAAAoICgSAcAAAAAoICgSAcAAPnmu+++U8+ePeXt7S2DwaDZs2fnd0oAAOQrinQAAHBXrl+/fs8xrl27pipVqmj69Ony8PCwQFYAABRuFOkAABQBf/31l0JCQuTk5CRPT0/NmjVLrVu31ogRIyRJ6enpioiIUMWKFeXk5KSmTZsqNjbWND46Olru7u7asmWLAgIC5OzsrODgYCUlJZn6hIWFqXv37poyZYoqVKig6tWrS5J++ukn9erVS+7u7ipVqpS6deumc+fO5Srvxo0b6+2331afPn1kZ2dnqbcDAIBCiyIdAIAiYOTIkdq3b58+//xzbd26VXv27NE333xjOj98+HAdOHBAMTExOn78uJ588kkFBwfrhx9+MPW5du2a3nnnHS1dulS7d+9WYmKiIiIizObZvn27Tp8+ra1bt2r9+vXKyMhQ+/bt5eLioj179mjfvn2mAt8SK+0AABQ3NvmdAAAAuDd//fWXFi9erBUrVqht27aSpKioKFWoUEGSlJiYqKioKCUmJpraIiIitHnzZkVFRWnq1KmS/vlu74ULF6pq1aqS/insX3/9dbO5nJyc9OGHH6pEiRKSpGXLlik7O1sffvih6evHoqKi5O7urtjYWD322GP3/w0AAKAIoUgHAKCQ+/HHH5WRkaEmTZqY2tzc3Ezb0U+cOKGsrCz5+fmZjUtPT1fp0qVNx46OjqYCXZI8PT116dIlszG1a9c2FeiSdOzYMZ05c0YuLi5m/dLS0nT27Nl7vzgAAIoZinQAAIq41NRUWVtb68iRI7K2tjY75+zsbPrZ1tbW7JzBYJDRaDRrc3JyyhG7YcOGWr58eY55y5Yte6+pAwBQ7FCkAwBQyFWpUkW2trY6dOiQKleuLElKTk7W999/r5YtW6p+/frKysrSpUuX1KJFC4vO3aBBA61cuVLlypWTq6urRWMDAFAc8eA4AAAKORcXF/Xv318vv/yydu7cqe+++07h4eGysrKSwWCQn5+fQkJCFBoaqjVr1ighIUEHDx7UtGnTtGHDhnuaOyQkRGXKlFG3bt20Z88eJSQkKDY2Vi+88IJ+/vnnO46/fv264uLiFBcXp+vXr+vChQuKi4vTmTNn7ikvAAAKK4p0AACKgJkzZ6pZs2bq3Lmz2rVrp8DAQAUEBMje3l7SPw9zCw0N1ahRo1S9enV1797dbOX9bjk6Omr37t2qXLmyevTooYCAAIWHhystLS1XK+u//PKL6tevr/r16yspKUnvvPOO6tevr0GDBt1TXgAAFFYG439vNiviUlJS5ObmpuTkZLblAQDMpKWlKSEhQT4+PqbitrC6evWqKlasqBkzZig8PDy/0yk2itJnCABgOXmpQ7knHQCAIuDo0aM6deqUmjRpouTkZNNXp3Xr1i2fMwMAAHlBkQ4AQBHxzjvv6PTp0ypRooQaNmyoPXv2qEyZMvma07+fHv9fmzZtsviD7AAAKOwo0gEAKALq16+vI0eO5HcaOcTFxd3yXMWKFR9cIgAAFBIU6QAA4L7x9fXN7xQAAChUeLo7AAAAAAAFBCvpAAAAQDEyb8gOi8YbtrCNReMBxR1FOgAAAFCMtIkdZuGI8RaOBxRvbHcHAAAAAKCAoEgHAAAAAKCAoEgHAKCYioyMVL169fI0xtvbW7Nnz74v+QAAAO5JBwAgV7zHbnhgc52b3umuxx44cECPPPKIgoODtWHD/c/ZYDBo7dq16t69+32fCwCA4oCVdAAAipBFixbp+eef1+7du/XLL7/kdzoAACCPKNIBACgiUlNTtXLlSj333HPq1KmToqOjzc5Pnz5d5cuXl4uLi8LDw5WWlmZ2vnXr1hoxYoRZW/fu3RUWFnbT+by9vSVJjz/+uAwGg+kYAADcPYp0AACKiFWrVsnf31/Vq1dXv3799NFHH8loNJrORUZGaurUqTp8+LA8PT01f/78e5rv0KFDkqSoqCglJSWZjgEAwN2jSAcAoIhYtGiR+vXrJ0kKDg5WcnKydu3aJUmaPXu2wsPDFR4erurVq2vy5MmqUaPGPc1XtmxZSZK7u7s8PDxMxwAA4O5RpAMAUAScPn1aBw8eVN++fSVJNjY26t27txYtWiRJio+PV9OmTc3GNGvW7IHnCQAAbo+nuwMAUAQsWrRImZmZqlChgqnNaDTKzs5Oc+fOzVUMKysr0/b4GzIyMiyaJwAAuD1W0gEAKOQyMzO1ZMkSzZgxQ3FxcabXsWPHVKFCBX388ccKCAjQ119/bTbuq6++MjsuW7askpKSTMdZWVn69ttvbzu3ra2tsrKyLHcxAAAUc6ykAwBQyK1fv15//vmnwsPD5ebmZnauZ8+eWrRokSIiIhQWFqZGjRopMDBQy5cv13fffacqVaqY+rZp00YjR47Uhg0bVLVqVc2cOVNXrly57dze3t7avn27AgMDZWdnp5IlS96PSwQAoNhgJR0AgEJu0aJFateuXY4CXfqnSD98+LACAgI0fvx4jR49Wg0bNtT58+f13HPPmfUdOHCg+vfvr9DQULVq1UpVqlRRUFDQbeeeMWOGtm7dKi8vL9WvX9+i1wUAQHFkMP735rMiLiUlRW5ubkpOTparq2t+pwMAKEDS0tKUkJAgHx8f2dvb53c6KIT4DKEwiPcPsGi8gFPxFo0HFEV5qUNZSQcAAAAAoICgSAcAAAAAoICgSAcAAAAAoICgSAcAAAAAoICgSAcAAAAAoICgSAcAAAAAoICgSAcAAAAAoICgSAcAAAAAoICgSAcAAAAAoICgSAcAoJiKjIxUvXr18jsNAADwLzb5nQAAAIVCpNsDnCv5roceOHBAjzzyiIKDg7VhwwYLJmUZ586dk4+Pj44ePcovCAAAuAlW0gEAKEIWLVqk559/Xrt379Yvv/yS3+ncV9evX8/vFAAAsDiKdAAAiojU1FStXLlSzz33nDp16qTo6Giz89OnT1f58uXl4uKi8PBwpaWlmc59+eWXsre315UrV8zGvPjii2rTpo3peO/evWrRooUcHBzk5eWlF154QVevXjWd9/b21tSpUzVw4EC5uLiocuXKev/9903nfXx8JEn169eXwWBQ69atJUmtW7fWiBEjzObu3r27wsLCzGK/8cYbCg0Nlaurq5555plc5QQAQGFCkQ4AQBGxatUq+fv7q3r16urXr58++ugjGY1G07nIyEhNnTpVhw8flqenp+bPn28a27ZtW7m7u2v16tWmtqysLK1cuVIhISGSpLNnzyo4OFg9e/bU8ePHtXLlSu3du1fDhw83y2PGjBlq1KiRjh49qqFDh+q5557T6dOnJUkHDx6UJG3btk1JSUlas2ZNnq7xnXfeUd26dXX06FGNHz8+1zkBAFBYUKQDAFBELFq0SP369ZMkBQcHKzk5Wbt27ZIkzZ49W+Hh4QoPD1f16tU1efJk1ahRwzTW2tpaffr00YoVK0xt27dv15UrV9SzZ09J0rRp0xQSEqIRI0aoWrVqat68uebMmaMlS5aYrcp37NhRQ4cOla+vr8aMGaMyZcpo586dkqSyZctKkkqXLi0PDw+VKlUqT9fYpk0bjRo1SlWrVlXVqlVznRMAAIUFRToAAEXA6dOndfDgQfXt21eSZGNjo969e2vRokWSpPj4eDVt2tRsTLNmzcyOQ0JCFBsba7qXffny5erUqZPc3d0lSceOHVN0dLScnZ1Nr/bt2ys7O1sJCQmmOHXq1DH9bDAY5OHhoUuXLlnkOhs1amR2nNucAAAoLHi6OwAARcCiRYuUmZmpChUqmNqMRqPs7Ow0d+7cXMVo3LixqlatqpiYGD333HNau3at2X3tqampevbZZ/XCCy/kGFu5cmXTz7a2tmbnDAaDsrOzbzu3lZWVaWv+DRkZGTn6OTk5mR3nNicAAAoLinQAAAq5zMxMLVmyRDNmzNBjjz1mdq579+76+OOPFRAQoK+//lqhoaGmc1999VWOWCEhIVq+fLkqVaokKysrderUyXSuQYMGOnnypHx9fe861xIlSkj65373fytbtqySkpJMx1lZWfr2228VFBR023iWyAkAgIKE7e4AABRy69ev159//qnw8HDVqlXL7NWzZ08tWrRIL774oj766CNFRUXp+++/18SJE/Xdd9/liBUSEqJvvvlGU6ZM0RNPPCE7OzvTuTFjxmj//v0aPny44uLi9MMPP+izzz7L00PaypUrJwcHB23evFm//vqrkpP/+U74Nm3aaMOGDdqwYYNOnTql5557LseT5m/GEjkBAFCQUKQDAFDILVq0SO3atZObm1uOcz179tThw4cVEBCg8ePHa/To0WrYsKHOnz+v5557Lkd/X19fNWnSRMePHzc91f2GOnXqaNeuXfr+++/VokUL1a9fXxMmTDDbYn8nNjY2mjNnjv73v/+pQoUK6tatmyRp4MCB6t+/v0JDQ9WqVStVqVLljqvolsoJAICCxGD87w1gRVxKSorc3NyUnJwsV1fX/E4HAFCApKWlKSEhQT4+PrK3t8/vdFAI8RlCYRDvH2DReAGn4i0aDyiK8lKHspIOAAAAAEABQZEOAAAAAEABQZEOAAAAAEABQZEOAAAAAEABQZEOAAAAAEABQZEOAAAAAEABka9F+rRp09S4cWO5uLioXLly6t69u06fPn3HcZ988on8/f1lb2+v2rVra+PGjQ8gWwAAAAAA7q98LdJ37dqlYcOG6auvvtLWrVuVkZGhxx57TFevXr3lmP3796tv374KDw/X0aNH1b17d3Xv3l3ffvvtA8wcAAAAAADLMxiNRmN+J3HD5cuXVa5cOe3atUstW7a8aZ/evXvr6tWrWr9+vant4YcfVr169bRw4cI7zpGXL5EHABQvaWlpSkhIkI+Pj+zt7fM7HRRCfIZQGMT7B1g0XsCpeIvGA4qivNShBeqe9OTkZElSqVKlbtnnwIEDateunVlb+/btdeDAgZv2T09PV0pKitkLAADkFBkZqXr16uXb/AaDQevWrcu3+QEAKAhs8juBG7KzszVixAgFBgaqVq1at+x38eJFlS9f3qytfPnyunjx4k37T5s2TZMmTbJorgCA4qf24toPbK4T/U/c9dgDBw7okUceUXBwsDZs2JCnsREREXr++efveu7cioyM1Lp16xQXF2fWnpSUpJIlS973+QEAKMgKzEr6sGHD9O233yomJsaicceNG6fk5GTT66effrJofAAACpJFixbp+eef1+7du/XLL7/kaayzs7NKly59nzK7Mw8PD9nZ2eXb/AAAFAQFokgfPny41q9fr507d6pSpUq37evh4aFff/3VrO3XX3+Vh4fHTfvb2dnJ1dXV7AUAQFGUmpqqlStX6rnnnlOnTp0UHR1tOhcbGyuDwaDt27erUaNGcnR0VPPmzc2+VeW/293DwsLUvXt3TZ06VeXLl5e7u7tef/11ZWZm6uWXX1apUqVUqVIlRUVFmeUxZswY+fn5ydHRUVWqVNH48eOVkZEhSYqOjtakSZN07NgxGQwGGQwGU57/3e5+4sQJtWnTRg4ODipdurSeeeYZpaam5sjvnXfekaenp0qXLq1hw4aZ5gIAoDDK1yLdaDRq+PDhWrt2rXbs2CEfH587jmnWrJm2b99u1rZ161Y1a9bsfqUJAEChsGrVKvn7+6t69erq16+fPvroI/33+bCvvvqqZsyYocOHD8vGxkYDBw68bcwdO3bol19+0e7duzVz5kxNnDhRnTt3VsmSJfX1119ryJAhevbZZ/Xzzz+bxri4uCg6OlonT57Uu+++qw8++ECzZs2S9M8DYEeNGqWaNWsqKSlJSUlJ6t27d455r169qvbt26tkyZI6dOiQPvnkE23btk3Dhw8367dz506dPXtWO3fu1OLFixUdHW32ywkAAAqbfC3Shw0bpmXLlmnFihVycXHRxYsXdfHiRf3999+mPqGhoRo3bpzp+MUXX9TmzZs1Y8YMnTp1SpGRkTp8+HCOv7QBAChuFi1apH79+kmSgoODlZycrF27dpn1mTJlilq1aqUaNWpo7Nix2r9/v9LS0m4Zs1SpUpozZ46qV6+ugQMHqnr16rp27ZpeeeUVVatWTePGjVOJEiW0d+9e05jXXntNzZs3l7e3t7p06aKIiAitWrVKkuTg4CBnZ2fZ2NjIw8NDHh4ecnBwyDHvihUrlJaWpiVLlqhWrVpq06aN5s6dq6VLl5rtqCtZsqTmzp0rf39/de7cWZ06dcrxy3wAAAqTfC3SFyxYoOTkZLVu3Vqenp6m18qVK019EhMTlZSUZDpu3ry5VqxYoffff19169bVp59+qnXr1t32YXMAABR1p0+f1sGDB9W3b19Jko2NjXr37q1FixaZ9atTp47pZ09PT0nSpUuXbhm3Zs2asrL6//9cKF++vGrX/v8P0bO2tlbp0qXNYqxcuVKBgYHy8PCQs7OzXnvtNSUmJubpeuLj41W3bl05OTmZ2gIDA5WdnW22Rb9mzZqytrY2u6bbXQ8AAAVdvj7dPTdf0R4bG5uj7cknn9STTz55HzICAKBwWrRokTIzM1WhQgVTm9FolJ2dnebOnWtqs7W1Nf1sMBgk/fMNK7fy7/43xtys7UaMAwcOKCQkRJMmTVL79u3l5uammJgYzZgx4+4v7jZulwsAAIVRgfkKNgAAcHcyMzO1ZMkSzZgxQ4899pjZue7du+vjjz+Wv7//A8ll//79euihh/Tqq6+a2s6fP2/Wp0SJEsrKyrptnICAAEVHR+vq1aum1fR9+/bJyspK1atXt3ziAAAUEAXi6e4AAODurV+/Xn/++afCw8NVq1Yts1fPnj1zbHm/n6pVq6bExETFxMTo7NmzmjNnjtauXWvWx9vbWwkJCYqLi9Nvv/2m9PT0HHFCQkJkb2+v/v3769tvv9XOnTv1/PPP6+mnn1b58uUf1OUAAPDAUaQDAFDILVq0SO3atZObm1uOcz179tThw4d1/PjxB5JL165d9dJLL2n48OGqV6+e9u/fr/Hjx+fIKTg4WEFBQSpbtqw+/vjjHHEcHR21ZcsW/fHHH2rcuLGeeOIJtW3b1mzrPgAARZHBmJsbw4uQlJQUubm5KTk5me9MBwCYSUtLU0JCgnx8fGRvb5/f6aAQ4jOEwiDeP8Ci8QJOxVs0HlAU5aUOZSUdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAIBi4Ny5czIYDIqLi8vvVHItLCxM3bt3z+80AAB4oGzyOwEAAAqDeP+ABzZXwKn4PI8JCwvT4sWLTcelSpVS48aN9dZbb6lOnTry8vJSUlKSypQpY8lUAQCAhbGSDgBAEREcHKykpCQlJSVp+/btsrGxUefOnSVJ1tbW8vDwkI3N/f39/PXr1+9rfAAAijqKdAAAigg7Ozt5eHjIw8ND9erV09ixY/XTTz/p8uXLN93uvmvXLjVp0kR2dnby9PTU2LFjlZmZaTr/119/KSQkRE5OTvL09NSsWbPUunVrjRgxwtTH29tbb7zxhkJDQ+Xq6qpnnnlGkjRmzBj5+fnJ0dFRVapU0fjx45WRkWEaFxkZqXr16ul///ufvLy85OjoqF69eik5OTnHdb3zzjvy9PRU6dKlNWzYMFOc119/XbVq1crRv169eho/fvy9vp0AAOQLtrsDAFAEpaamatmyZfL19VXp0qV19epVs/MXLlxQx44dFRYWpiVLlujUqVMaPHiw7O3tFRkZKUkaOXKk9u3bp88//1zly5fXhAkT9M0336hevXpmsd555x1NmDBBEydONLW5uLgoOjpaFSpU0IkTJzR48GC5uLho9OjRpj5nzpzRqlWr9MUXXyglJUXh4eEaOnSoli9fbuqzc+dOeXp6aufOnTpz5ox69+6tevXqafDgwRo4cKAmTZqkQ4cOqXHjxpKko0eP6vjx41qzZo2F39HibUbvzhaNN2rleovGA4CihCIdAIAiYv369XJ2dpYkXb16VZ6enlq/fr2srHJunJs/f768vLw0d+5cGQwG+fv765dfftGYMWM0YcIEXb16VYsXL9aKFSvUtm1bSVJUVJQqVKiQI1abNm00atQos7bXXnvN9LO3t7ciIiIUExNjVqSnpaVpyZIlqlixoiTpvffeU6dOnTRjxgx5eHhIkkqWLKm5c+fK2tpa/v7+6tSpk7Zv367BgwerUqVKat++vaKiokxFelRUlFq1aqUqVarcy1sJAEC+Ybs7AABFRFBQkOLi4hQXF6eDBw+qffv26tChg86fP5+jb3x8vJo1ayaDwWBqCwwMVGpqqn7++Wf9+OOPysjIUJMmTUzn3dzcVL169RyxGjVqlKNt5cqVCgwMlIeHh5ydnfXaa68pMTHRrE/lypVNBbokNWvWTNnZ2Tp9+rSprWbNmrK2tjYde3p66tKlS6bjwYMH6+OPP1ZaWpquX7+uFStWaODAgXd6qwAAKLBYSQcAoIhwcnKSr6+v6fjDDz+Um5ubPvjgAw0aNOi+zvtvBw4cUEhIiCZNmqT27dvLzc1NMTExmjFjRp5j29ramh0bDAZlZ2ebjrt06SI7OzutXbtWJUqUUEZGhp544om7uxDckn3JkfmdAgAUGxTpAAAUUQaDQVZWVvr7779znAsICNDq1atlNBpNq+n79u2Ti4uLKlWqpJIlS8rW1laHDh1S5cqVJUnJycn6/vvv1bJly9vOu3//fj300EN69dVXTW03W81PTEzUL7/8YtpC/9VXX8nKyuqmq/W3YmNjo/79+ysqKkolSpRQnz595ODgkOvxAAAUNBTpAAAUEenp6bp48aIk6c8//9TcuXOVmpqqLl265Og7dOhQzZ49W88//7yGDx+u06dPa+LEiRo5cqSsrKzk4uKi/v376+WXX1apUqVUrlw5TZw4UVZWVmZb5G+mWrVqSkxMVExMjBo3bqwNGzZo7dq1OfrZ29urf//+euedd5SSkqIXXnhBvXr1Mt2PnluDBg1SQMA/32O/b9++PI0FAKCg4Z50AACKiM2bN8vT01Oenp5q2rSpDh06pE8++UStW7fO0bdixYrauHGjDh48qLp162rIkCEKDw83e+DbzJkz1axZM3Xu3Fnt2rVTYGCgAgICZG9vf9s8unbtqpdeeknDhw9XvXr1tH///pt+JZqvr6969Oihjh076rHHHlOdOnU0f/78PF93tWrV1Lx5c/n7+6tp06Z5Hg8AQEFiMBqNxvxO4kFKSUmRm5ubkpOT5erqmt/pAAAKkLS0NCUkJMjHx+eOhWhxdPXqVVWsWFEzZsxQeHj4PcWKjIzUunXrzL63/W4ZjUZVq1ZNQ4cO1ciR+XvvdFH9DM0bssOi8YYtbGPReMibeP8Ai8YLOBVv0XhAUZSXOpTt7gAA4KaOHj2qU6dOqUmTJkpOTtbrr78uSerWrVs+Z/b/Xb58WTExMbp48aIGDBiQ3+kAAHDPKNIBAMAtvfPOOzp9+rRKlCihhg0bas+ePSpTpkx+p2VSrlw5lSlTRu+//75KliyZ3+kAAHDP2O4OAMD/KapblfHgFNXPUO3FtS0a70T/ExaNh7xhuzvw4OWlDuXBcQAAAAAAFBBsdwcAAMBtnUhIzO8UAKDYYCUdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAAAAAIACgiIdAAA8cJGRkapXr15+pwEAQIHD090BAMiFeUN2PLC5hi1sc1fjLl68qClTpmjDhg26cOGCypUrp3r16mnEiBFq27athbMEAAD3A0U6AABFwLlz5xQYGCh3d3e9/fbbql27tjIyMrRlyxYNGzZMp06dyu8UAQBALrDdHQCAImDo0KEyGAw6ePCgevbsKT8/P9WsWVMjR47UV199JUlKTExUt27d5OzsLFdXV/Xq1Uu//vqrKcaNLegfffSRKleuLGdnZw0dOlRZWVl666235OHhoXLlymnKlClmc1+5ckWDBg1S2bJl5erqqjZt2ujYsWNmfaZPn67y5cvLxcVF4eHhSktLM53bvXu3bG1tdfHiRbMxI0aMUIsWLSz9VgEAUKBRpAMAUMj98ccf2rx5s4YNGyYnJ6cc593d3ZWdna1u3brpjz/+0K5du7R161b9+OOP6t27t1nfs2fPatOmTdq8ebM+/vhjLVq0SJ06ddLPP/+sXbt26c0339Rrr72mr7/+2jTmySef1KVLl7Rp0yYdOXJEDRo0UNu2bfXHH39IklatWqXIyEhNnTpVhw8flqenp+bPn28a37JlS1WpUkVLly41tWVkZGj58uUaOHCgpd8uAAAKNLa7AwBQyJ05c0ZGo1H+/v637LN9+3adOHFCCQkJ8vLykiQtWbJENWvW1KFDh9S4cWNJUnZ2tj766CO5uLioRo0aCgoK0unTp7Vx40ZZWVmpevXqevPNN7Vz5041bdpUe/fu1cGDB3Xp0iXZ2dlJkt555x2tW7dOn376qZ555hnNnj1b4eHhCg8PlyRNnjxZ27ZtM1tNDw8PV1RUlF5++WVJ0hdffKG0tDT16tXrvrxnAAAUVKykAwBQyBmNxjv2iY+Pl5eXl6lAl6QaNWrI3d1d8fHxpjZvb2+5uLiYjsuXL68aNWrIysrKrO3SpUuSpGPHjik1NVWlS5eWs7Oz6ZWQkKCzZ8+a5m7atKlZPs2aNTM7DgsL05kzZ0xb86Ojo9WrV6+b7gwAAKAoYyUdAIBCrlq1ajIYDBZ5OJytra3ZscFguGlbdna2JCk1NVWenp6KjY3NEcvd3T3X85YrV05dunRRVFSUfHx8tGnTppvGBACgqGMlHQCAQq5UqVJq37695s2bp6tXr+Y4f+XKFQUEBOinn37STz/9ZGo/efKkrly5oho1atz13A0aNNDFixdlY2MjX19fs1eZMmUkSQEBAWb3sEsyrZj/26BBg7Ry5Uq9//77qlq1qgIDA+86LwAACiuKdAAAioB58+YpKytLTZo00erVq/XDDz8oPj5ec+bMUbNmzdSuXTvVrl1bISEh+uabb3Tw4EGFhoaqVatWatSo0V3P265dOzVr1kzdu3fXl19+qXPnzmn//v169dVXdfjwYUnSiy++qI8++khRUVH6/vvvNXHiRH333Xc5YrVv316urq6aPHmyBgwYcNc5AQBQmFGkAwBQBFSpUkXffPONgoKCNGrUKNWqVUuPPvqotm/frgULFshgMOizzz5TyZIl1bJlS7Vr105VqlTRypUr72leg8GgjRs3qmXLlhowYID8/PzUp08fnT9/XuXLl5ck9e7dW+PHj9fo0aPVsGFDnT9/Xs8991yOWFZWVgoLC1NWVpZCQ0PvKS8AAAorgzE3T5spQlJSUuTm5qbk5GS5urrmdzoAgAIkLS1NCQkJ8vHxkb29fX6nUyyFh4fr8uXL+vzzz/M7lbtSZD9DkW4Wjpds2XjIk3j/AIvGCzgVf+dOQDGXlzqUB8cBAIB8l5ycrBMnTmjFihWFtkAHAMASKNIBAEC+69atmw4ePKghQ4bo0Ucfze90AADINxTpAAAg3/F1awAA/IMHxwEAAAAAUEBQpAMAAAAAUEBQpAMAAAAAUEBQpAMAAAAAUEBQpAMAAAAAUEBQpAMAAAAAUEBQpAMAUIzFxsbKYDDoypUr+Z0KAAAQ35MOAECuzOjd+YHNNWrl+rsad/HiRU2ZMkUbNmzQhQsXVK5cOdWrV08jRoxQ27ZtbzqmefPmSkpKkpub272kDAAALIQiHQCAIuDcuXMKDAyUu7u73n77bdWuXVsZGRnasmWLhg0bplOnTuUYk5GRoRIlSsjDwyMfMgYAADfDdncAAIqAoUOHymAw6ODBg+rZs6f8/PxUs2ZNjRw5Ul999ZUkyWAwaMGCBerataucnJw0ZcqUHNvdo6Oj5e7urvXr16t69epydHTUE088oWvXrmnx4sXy9vZWyZIl9cILLygrK8s0f3p6uiIiIlSxYkU5OTmpadOmio2NzYd3AgCAwo2VdAAACrk//vhDmzdv1pQpU+Tk5JTjvLu7u+nnyMhITZ8+XbNnz5aNjY1+/PHHHP2vXbumOXPmKCYmRn/99Zd69Oihxx9/XO7u7tq4caN+/PFH9ezZU4GBgerdu7ckafjw4Tp58qRiYmJUoUIFrV27VsHBwTpx4oSqVat2364dAICihiIdAIBC7syZMzIajfL3979j36eeekoDBgwwHd+sSM/IyNCCBQtUtWpVSdITTzyhpUuX6tdff5Wzs7Nq1KihoKAg7dy5U71791ZiYqKioqKUmJioChUqSJIiIiK0efNmRUVFaerUqRa6UgAAij6KdAAACjmj0Zjrvo0aNbpjH0dHR1OBLknly5eXt7e3nJ2dzdouXbokSTpx4oSysrLk5+dnFic9PV2lS5fOdW4AAIAiHQCAQq9atWoyGAw3fTjcf91sO/x/2dramh0bDIabtmVnZ0uSUlNTZW1trSNHjsja2tqs378LewAAcGc8OA4AgEKuVKlSat++vebNm6erV6/mOH+/vwO9fv36ysrK0qVLl+Tr62v24snxAADkDUU6AABFwLx585SVlaUmTZpo9erV+uGHHxQfH685c+aoWbNm93VuPz8/hYSEKDQ0VGvWrFFCQoIOHjyoadOmacOGDfd1bgAAihq2uwMAUARUqVJF33zzjaZMmaJRo0YpKSlJZcuWVcOGDbVgwYL7Pn9UVJQmT56sUaNG6cKFCypTpowefvhhde7c+b7PDQBAUWIw5uVpM0VASkqK3NzclJycLFdX1/xOBwBQgKSlpSkhIUE+Pj6yt7fP73RQCBXZz1Ckm4XjJVs2HvIk3j/AovECTsVbNB5QFOWlDmW7OwAAAAAABQRFOgAAAAAABQRFOgAAAAAABQRFOgAAAAAABQRFOgAAAAAABQRFOgAAAAAABQRFOgAAAAAABQRFOgAAAAAABQRFOgAAAAAABQRFOgAAAAAABYRNficAAEBh8PPYPQ9srkrTW+R5TFhYmBYvXixJsrGxUalSpVSnTh317dtXYWFhsrIq+r+Xj46O1ogRI3TlypX8TgUAgLtW9P/GBgCgmAgODlZSUpLOnTunTZs2KSgoSC+++KI6d+6szMzM/E6v0DAajbxfAIB8Q5EOAEARYWdnJw8PD1WsWFENGjTQK6+8os8++0ybNm1SdHS0JCkxMVHdunWTs7OzXF1d1atXL/36669mcb744gs1btxY9vb2KlOmjB5//HHTOYPBoHXr1pn1d3d3N8U/d+6cDAaDVq1apRYtWsjBwUGNGzfW999/r0OHDqlRo0ZydnZWhw4ddPnyZbM4H374oQICAmRvby9/f3/Nnz/fdO5G3DVr1igoKEiOjo6qW7euDhw4IEmKjY3VgAEDlJycLIPBIIPBoMjISEnS0qVL1ahRI7m4uMjDw0NPPfWULl26ZIodGxsrg8GgTZs2qWHDhrKzs9OyZctkZWWlw4cPm+U4e/ZsPfTQQ8rOzs7znw8AALlBkQ4AQBHWpk0b1a1bV2vWrFF2dra6deumP/74Q7t27dLWrVv1448/qnfv3qb+GzZs0OOPP66OHTvq6NGj2r59u5o0aZLneSdOnKjXXntN33zzjWxsbPTUU09p9OjRevfdd7Vnzx6dOXNGEyZMMPVfvny5JkyYoClTpig+Pl5Tp07V+PHjTVv4b3j11VcVERGhuLg4+fn5qW/fvsrMzFTz5s01e/Zsubq6KikpSUlJSYqIiJAkZWRk6I033tCxY8e0bt06nTt3TmFhYTlyHjt2rKZPn674+Hh17dpV7dq1U1RUlFmfqKioYnP7AAAgf3BPOgAARZy/v7+OHz+u7du368SJE0pISJCXl5ckacmSJapZs6YOHTqkxo0ba8qUKerTp48mTZpkGl+3bt08zxkREaH27dtLkl588UX17dtX27dvV2BgoCQpPDzctPou/VPUz5gxQz169JAk+fj46OTJk/rf//6n/v37m8Xt1KmTJGnSpEmqWbOmzpw5I39/f7m5uclgMMjDw8Msl4EDB5p+rlKliubMmaPGjRsrNTVVzs7OpnOvv/66Hn30UdPxoEGDNGTIEM2cOVN2dnb65ptvdOLECX322Wd5fj8AAMgtfg0MAEARZzQaZTAYFB8fLy8vL1OBLkk1atSQu7u74uPjJUlxcXFq27btPc9Zp04d08/ly5eXJNWuXdus7caW86tXr+rs2bMKDw+Xs7Oz6TV58mSdPXv2lnE9PT0lyWzr+s0cOXJEXbp0UeXKleXi4qJWrVpJ+mfr/781atTI7Lh79+6ytrbW2rVrJf3zYLqgoCB5e3vf8foBALhbrKQDAFDExcfHy8fHJ1d9HRwcbnveYDDIaDSatWVkZOToZ2trazbmZm037utOTU2VJH3wwQdq2rSpWRxra+s7xr3d/eFXr15V+/bt1b59ey1fvlxly5ZVYmKi2rdvr+vXr5v1dXJyMjsuUaKEQkNDFRUVpR49emjFihV69913bzkXAACWQJEOAEARtmPHDp04cUIvvfSSKlWqpJ9++kk//fSTaTX95MmTunLlimrUqCHpn5Xq7du3a8CAATeNV7ZsWSUlJZmOf/jhB127du2ecixfvrwqVKigH3/8USEhIXcdp0SJEsrKyjJrO3XqlH7//XdNnz7ddM3/fRjc7QwaNEi1atXS/PnzlZmZadqODwDA/UKRDgBAEZGenq6LFy8qKytLv/76qzZv3qxp06apc+fOCg0NlZWVlWrXrq2QkBDNnj1bmZmZGjp0qFq1amXa6j1x4kS1bdtWVatWVZ8+fZSZmamNGzdqzJgxkv55EN3cuXPVrFkzZWVlacyYMWar23dr0qRJeuGFF+Tm5qbg4GClp6fr8OHD+vPPPzVy5MhcxfD29lZqaqq2b9+uunXrytHRUZUrV1aJEiX03nvvaciQIfr222/1xhtv5DqvgIAAPfzwwxozZowGDhx4x50GAADcK4p0AAByodL0Fvmdwh1t3rxZnp6esrGxUcmSJVW3bl3NmTNH/fv3Nz2N/LPPPtPzzz+vli1bysrKSsHBwXrvvfdMMVq3bq1PPvlEb7zxhqZPny5XV1e1bNnSdH7GjBkaMGCAWrRooQoVKujdd9/VkSNH7jn3QYMGydHRUW+//bZefvllOTk5qXbt2hoxYkSuYzRv3lxDhgxR79699fvvv2vixImKjIxUdHS0XnnlFc2ZM0cNGjTQO++8o65du+Y6bnh4uPbv32/2ADoAAO4Xg/G/N5YVcSkpKXJzc1NycrJcXV3zOx0AQAGSlpamhIQE+fj4yN7ePr/TQQHxxhtv6JNPPtHx48fv2LfIfoYi3SwcL9my8ZAn8f4BFo0XcCreovGAoigvdShPdwcAALiJ1NRUffvtt5o7d66ef/75/E4HAFBMUKQDAADcxPDhw9WwYUO1bt2are4AgAeGe9IBAABuIjo6WtHR0fmdBgCgmGElHQAAAACAAoIiHQAAAACAAoIiHQAAAACAAoIiHQAAAACAAoIiHQAAAACAAoIiHQAAAACAAoIiHQAAAACAAoLvSQcAIBciIyML9FxhYWFavHixJMnGxkalSpVSnTp11LdvX4WFhcnKit/LAwBQGPA3NgAARURwcLCSkpJ07tw5bdq0SUFBQXrxxRfVuXNnZWZm3nRMRkbGA84SAADcDkU6AABFhJ2dnTw8PFSxYkU1aNBAr7zyij777DNt2rRJ0dHRkiSDwaAFCxaoa9eucnJy0pQpU5SVlaXw8HD5+PjIwcFB1atX17vvvmsWOywsTN27d9fUqVNVvnx5ubu76/XXX1dmZqZefvlllSpVSpUqVVJUVJTZuDFjxsjPz0+Ojo6qUqWKxo8fzy8GAAC4Dba7AwBQhLVp00Z169bVmjVrNGjQIEn/bKefPn26Zs+eLRsbG2VnZ6tSpUr65JNPVLp0ae3fv1/PPPOMPD091atXL1OsHTt2qFKlStq9e7f27dun8PBw7d+/Xy1bttTXX3+tlStX6tlnn9Wjjz6qSpUqSZJcXFwUHR2tChUq6MSJExo8eLBcXFw0evTofHk/AAAo6CjSAQAo4vz9/XX8+HHT8VNPPaUBAwaY9Zk0aZLpZx8fHx04cECrVq0yK9JLlSqlOXPmyMrKStWrV9dbb72la9eu6ZVXXpEkjRs3TtOnT9fevXvVp08fSdJrr71mGu/t7a2IiAjFxMRQpAMAcAsU6QAAFHFGo1EGg8F03KhRoxx95s2bp48++kiJiYn6+++/df36ddWrV8+sT82aNc0eQFe+fHnVqlXLdGxtba3SpUvr0qVLpraVK1dqzpw5Onv2rFJTU5WZmSlXV1cLXh0AAEUL96QDAFDExcfHy8fHx3Ts5ORkdj4mJkYREREKDw/Xl19+qbi4OA0YMEDXr18362dra2t2bDAYbtqWnZ0tSTpw4IBCQkLUsWNHrV+/XkePHtWrr76aIy4AAPj/WEkHAKAI27Fjh06cOKGXXnrpln327dun5s2ba+jQoaa2s2fP3vPc+/fv10MPPaRXX33V1Hb+/Pl7jgsAQFFGkQ4AQBGRnp6uixcvKisrS7/++qs2b96sadOmqXPnzgoNDb3luGrVqmnJkiXasmWLfHx8tHTpUh06dMhs9f1uVKtWTYmJiYqJiVHjxo21YcMGrV279p5iAgBQ1FGkAwCQC5GRkfmdwh1t3rxZnp6esrGxUcmSJVW3bl3NmTNH/fv3N7uX/L+effZZHT16VL1795bBYFDfvn01dOhQbdq06Z7y6dq1q1566SUNHz5c6enp6tSpk8aPH18o3ksAAPKLwWg0GvM7iQcpJSVFbm5uSk5O5sE1AAAzaWlpSkhIkI+Pj+zt7fM7HRRCRfYzFOlm4XjJlo2HPIn3D7BovIBT8RaNBxRFealDeXAcAAAAAAAFBEU6AAAAAAAFBEU6AAAAAAAFBEU6AAAAAAAFBEU6AAAAAAAFBEU6AAAAAAAFBEU6AAAAAAAFBEU6AAAAAAAFBEU6AADIM29vb82ePdt0bDAYtG7dunzLBwCAooIiHQCAIiAsLEwGgyHH68yZM/dlvkOHDumZZ565L7EBACjObPI7AQAACoPtO6o+sLnatjl7V+OCg4MVFRVl1la2bFlLpJTD/YoLAEBxx0o6AABFhJ2dnTw8PMxe7777rmrXri0nJyd5eXlp6NChSk1NNY2Jjo6Wu7u71q9fr+rVq8vR0VFPPPGErl27psWLF8vb21slS5bUCy+8oKysLNO4/253/7c2bdpo+PDhZm2XL19WiRIltH379vty7QAAFBUU6QAAFGFWVlaaM2eOvvvuOy1evFg7duzQ6NGjzfpcu3ZNc+bMUUxMjDZv3qzY2Fg9/vjj2rhxozZu3KilS5fqf//7nz799NNczTlo0CCtWLFC6enpprZly5apYsWKatOmjUWvDwCAooYiHQCAImL9+vVydnY2vZ588kmNGDFCQUFB8vb2Vps2bTR58mStWrXKbFxGRoYWLFig+vXrq2XLlnriiSe0d+9eLVq0SDVq1FDnzp0VFBSknTt35iqPHj16SJI+++wzU1t0dLTpvnkAAHBr3JMOAEARERQUpAULFpiOnZyctG3bNk2bNk2nTp1SSkqKMjMzlZaWpmvXrsnR0VGS5OjoqKpV//899+XLl5e3t7ecnZ3N2i5dupSrPOzt7fX000/ro48+Uq9evfTNN9/o22+/1eeff26hKwUAoOjK15X03bt3q0uXLqpQoUKuvrolNjb2pk+uvXjx4oNJGACAAszJyUm+vr6mV3p6ujp37qw6depo9erVOnLkiObNmydJun79ummcra2tWRyDwXDTtuzs7FznMmjQIG3dulU///yzoqKi1KZNGz300EP3cHUAABQP+bqSfvXqVdWtW1cDBw40bY3LjdOnT8vV1dV0XK5cufuRHgAAhdqRI0eUnZ2tGTNmyMrqn9/L/3er+/1Su3ZtNWrUSB988IFWrFihuXPnPpB5AQDIjRm9O1s03qiV6y0WK1+L9A4dOqhDhw55HleuXDm5u7tbPiEAAIoQX19fZWRk6L333lOXLl20b98+LVy48IHNP2jQIA0fPlxOTk56/PHHH9i8AADciX3Jkfmdwi0VygfH1atXT56ennr00Ue1b9++2/ZNT09XSkqK2QsAgOKgbt26mjlzpt58803VqlVLy5cv17Rp0x7Y/H379pWNjY369u0re3v7BzYvAACFmcFoNBrzOwnpn3vd1q5dq+7du9+yz+nTpxUbG6tGjRopPT1dH374oZYuXaqvv/5aDRo0uOmYyMhITZo0KUd7cnKy2ZZ5AADS0tKUkJAgHx8fikoLOHfunKpWrapDhw7d8u/poqbIfoYi3SwcL9my8ZAn8f4BFo0XcCreovGAB2HekB0WjTds4e2/YjQlJUVubm65qkML1dPdq1evrurVq5uOmzdvrrNnz2rWrFlaunTpTceMGzdOI0f+/60MKSkp8vLyuu+5AgBQXGVkZOj333/Xa6+9pocffrjYFOgAAFhCoSrSb6ZJkybau3fvLc/b2dnJzs7uAWYEAEDxtm/fPgUFBcnPz0+ffvppfqcD4D96jbNsCXDCotEAFPoiPS4uTp6envmdBgAA+D+tW7dWAbmbDgCAQidfi/TU1FSdOXPGdJyQkKC4uDiVKlVKlStX1rhx43ThwgUtWbJEkjR79mz5+PioZs2aSktL04cffqgdO3boyy+/zK9LAAAAAAqVEwmJ+Z0CgNvI1yL98OHDCgoKMh3fuHe8f//+io6OVlJSkhIT////RK5fv65Ro0bpwoULcnR0VJ06dbRt2zazGAAAAAAAFFb5WqTfaTtcdHS02fHo0aM1evTo+5wVAAAAAAD5o1B+TzoAAAAAAEURRToAAAAAAAUERToAAAAAAAUERToAAAAAAAUERToAAEVAWFiYDAZDjte/v+r0XmJ379793pMEAAB3lK9PdwcAoLDw2Bn3wOa6GFTvrsYFBwcrKirKrK1s2bIWyAgAADworKQDAFBE2NnZycPDw+z17rvvqnbt2nJycpKXl5eGDh2q1NRU05jo6Gi5u7try5YtCggIkLOzs4KDg5WUlCRJioyM1OLFi/XZZ5+ZVudjY2MlSWPGjJGfn58cHR1VpUoVjR8/XhkZGabYx44dU1BQkFxcXOTq6qqGDRvq8OHDunr1qlxdXfXpp5+a5b9u3To5OTnpr7/+uv9vFgAABRRFOgAARZiVlZXmzJmj7777TosXL9aOHTs0evRosz7Xrl3TO++8o6VLl2r37t1KTExURESEJCkiIkK9evUyFe5JSUlq3ry5JMnFxUXR0dE6efKk3n33XX3wwQeaNWuWKW5ISIgqVaqkQ4cO6ciRIxo7dqxsbW3l5OSkPn365Fj1j4qK0hNPPCEXF5f7/K4AAFBwsd0dAIAiYv369XJ2djYdd+jQQZ988onp2NvbW5MnT9aQIUM0f/58U3tGRoYWLlyoqlWrSpKGDx+u119/XZLk7OwsBwcHpaeny8PDw2y+1157zSx2RESEYmJiTL8ESExM1Msvvyx/f39JUrVq1Uz9Bw0apObNmyspKUmenp66dOmSNm7cqG3btlnq7QAAoFCiSAcAoIgICgrSggULTMdOTk7atm2bpk2bplOnTiklJUWZmZlKS0vTtWvX5OjoKElydHQ0FeiSTEXznaxcuVJz5szR2bNnlZqaqszMTLm6uprOjxw5UoMGDdLSpUvVrl07Pfnkk6Z5mjRpopo1a2rx4sUaO3asli1bpoceekgtW7a01NsBAEChxHZ3AACKCCcnJ/n6+ppe6enp6ty5s+rUqaPVq1fryJEjmjdvniTp+vXrpnG2trZmcQwGg4xG423nOnDggEJCQtSxY0etX79eR48e1auvvmoWNzIyUt999506deqkHTt2qEaNGlq7dq3p/KBBgxQdHS3pn63uAwYMkMFguNe3AQCAQo0iHQCAIurIkSPKzs7WjBkz9PDDD8vPz0+//PJLnuOUKFFCWVlZZm379+/XQw89pFdffVWNGjVStWrVdP78+Rxj/fz89NJLL+nLL79Ujx49zO5D79evn86fP685c+bo5MmT6t+/f94vEgCAIoYiHQCAIsrX11cZGRl677339OOPP2rp0qVauHBhnuN4e3vr+PHjOn36tH777TdlZGSoWrVqSkxMVExMjM6ePas5c+aYrZL//fffGj58uGJjY3X+/Hnt27dPhw4dUkBAgKlPyZIl1aNHD7388st67LHHVKlSJYtcNwAAhRlFOgAARVTdunU1c+ZMvfnmm6pVq5aWL1+uadOm5TnO4MGDVb16dTVq1Ehly5bVvn371LVrV7300ksaPny46tWrp/3792v8+PGmMdbW1vr9998VGhoqPz8/9erVSx06dNCkSZPMYoeHh+v69esaOHDgPV8vAABFgcF4p5vOipiUlBS5ubkpOTnZ7OE2AACkpaUpISFBPj4+sre3z+90ioWlS5fqpZde0i+//KISJUrkdzr3rMh+hiLdLBwv2bLxkDf8eQKaN2SHReMNW9jmtufzUofydHcAAPDAXbt2TUlJSZo+fbqeffbZIlGgAwBgCWx3BwAAD9xbb70lf39/eXh4aNy4cfmdDgAABQZFOgAAeOAiIyOVkZGh7du3y9nZOb/TAQCgwKBIBwAAAACggKBIBwAAAACggKBIBwAAAACggKBIBwAAAACggOAr2AAAAAAAxcowj8ctHDHZYpFYSQcAAAAAoICgSAcAoAhr3bq1RowYYTr29vbW7Nmz8y2f3PhvzgAAFCcU6QAAFAFhYWEyGAw5Xm+99ZbeeOON+z5/ZGSkDAaDhgwZYtYeFxcng8Ggc+fO5TrWmjVrHkjOAAAURNyTDgBALniP3fDA5jo3vdNdjQsODlZUVJRZW9myZWVtbW2JtO7I3t5eixYt0qhRo1StWrW7jlOqVKnbnr9+/bpKlChx1/EBACjIWEkHAKCIsLOzk4eHh9mrbdu2t906bjAY9L///U+dO3eWo6OjAgICdODAAZ05c0atW7eWk5OTmjdvrrNnz95x/urVqysoKEivvvrqbfvt2rVLTZo0kZ2dnTw9PTV27FhlZmaazt9si/4bb7yh0NBQubq66plnnrljLgAAFFYU6QAAFHM3CuC4uDj5+/vrqaee0rPPPqtx48bp8OHDMhqNGj58eK5iTZ8+XatXr9bhw4dvev7ChQvq2LGjGjdurGPHjmnBggVatGiRJk+efNu477zzjurWraujR49q/Pjxeb5GAAAKC7a7AwBQRKxfv17Ozs6m4w4dOuRq3IABA9SrVy9J0pgxY9SsWTONHz9e7du3lyS9+OKLGjBgQK5iNWjQQL169dKYMWO0ffv2HOfnz58vLy8vzZ07VwaDQf7+/vrll180ZswYTZgwQVZWN18/aNOmjUaNGpWrHAAAKMxYSQcAoIgICgpSXFyc6TVnzpxcjatTp47p5/Lly0uSateubdaWlpamlJQUJSYmytnZ2fSaOnVqjniTJ0/Wnj179OWXX+Y4Fx8fr2bNmslgMJjaAgMDlZqaqp9//vmWOTZq1ChX1wIAQGHHSjoAAEWEk5OTfH198zzO1tbW9PON4vlmbdnZ2apQoYLi4uJM5272kLeqVatq8ODBGjt2rBYtWpTnfG7GycnJInEAACjoKNIBAECu2djY5OoXARMmTFDVqlUVExNj1h4QEKDVq1fLaDSaiv99+/bJxcVFlSpVui85AwBQmLDdHQAAWFz58uU1cuTIHFvuhw4dqp9++knPP/+8Tp06pc8++0wTJ07UyJEjb3k/OgAAxQl/GwIAgPsiIiLC7EF2klSxYkVt3LhRBw8eVN26dTVkyBCFh4frtddey6csAQAoWAxGo9GY30k8SCkpKXJzc1NycrJcXV3zOx0AQAGSlpamhIQE+fj4yN7ePr/TQSFUZD9DkW4Wjpds2XjIG/48gQf+30Fe6lBW0gEAAAAAKCAo0gEAAAAAKCAo0gEAAAAAKCAo0gEAAAAAKCAo0gEAAAAAKCAo0gEAAAAAKCAo0gEAAAAAKCAo0gEAAAAAKCBsctOpZMmSMhgMuQr4xx9/3FNCAAAAAAAUV7kq0mfPnm36+ffff9fkyZPVvn17NWvWTJJ04MABbdmyRePHj78vSQIAgOKpdevWqlevntm/RQAAKMpyVaT379/f9HPPnj31+uuva/jw4aa2F154QXPnztW2bdv00ksvWT5LAADyW6TbA5wr+a6GXbx4UdOmTdOGDRv0888/y83NTb6+vurXr5/69+8vR0dHCyd6b7KysvT2228rOjpa58+fl4ODg6pVq6bBgwdr0KBB+Z0eAAD5IldF+r9t2bJFb775Zo724OBgjR071iJJAQCAvPnxxx8VGBgod3d3TZ06VbVr15adnZ1OnDih999/XxUrVlTXrl3zO00zkyZN0v/+9z/NnTtXjRo1UkpKig4fPqw///wzv1MDABRx3mkrLBrvnAVj5fnBcaVLl9Znn32Wo/2zzz5T6dKlLZIUAADIm6FDh8rGxkaHDx9Wr169FBAQoCpVqqhbt27asGGDunTpIkmaOXOmateuLScnJ3l5eWno0KFKTU01xYmOjpa7u7vWr1+v6tWry9HRUU888YSuXbumxYsXy9vbWyVLltQLL7ygrKws07j09HRFRESoYsWKcnJyUtOmTRUbG3vbnD///HMNHTpUTz75pHx8fFS3bl2Fh4crIiLCrF92drZGjx6tUqVKycPDQ5GRkWbnExMT1a1bNzk7O8vV1VW9evXSr7/+KklKTk6WtbW1Dh8+bIpVqlQpPfzww6bxy5Ytk5eXV57fcwAA7oc8r6RPmjRJgwYNUmxsrJo2bSpJ+vrrr7V582Z98MEHFk8QAADc3u+//64vv/xSU6dOlZOT00373HgArJWVlebMmSMfHx/9+OOPGjp0qEaPHq358+eb+l67dk1z5sxRTEyM/vrrL/Xo0UOPP/643N3dtXHjRv3444/q2bOnAgMD1bt3b0nS8OHDdfLkScXExKhChQpau3atgoODdeLECVWrVu2mOXl4eGjHjh0aOnSoypYte8vrW7x4sUaOHKmvv/5aBw4cUFhYmAIDA/Xoo48qOzvbVKDv2rVLmZmZGjZsmHr37q3Y2Fi5ubmpXr16io2NVaNGjXTixAkZDAYdPXpUqamppnGtWrW627cfAACLyvNKelhYmPbt2ydXV1etWbNGa9askaurq/bu3auwsLD7kCIAALidM2fOyGg0qnr16mbtZcqUkbOzs5ydnTVmzBhJ0ogRIxQUFCRvb2+1adNGkydP1qpVq8zGZWRkaMGCBapfv75atmypJ554Qnv37tWiRYtUo0YNde7cWUFBQdq5c6ekf1ayo6Ki9Mknn6hFixaqWrWqIiIi9MgjjygqKuqWec+cOVOXL1+Wh4eH6tSpoyFDhmjTpk05+tWpU0cTJ05UtWrVFBoaqkaNGmn79u2SpO3bt+vEiRNasWKFGjZsqKZNm2rJkiXatWuXDh06JOmfh8/dWNWPjY3Vo48+qoCAAO3du9fURpEOACgo8rySLklNmzbV8uXLLZ0LAACwoIMHDyo7O1shISFKT0+XJG3btk3Tpk3TqVOnlJKSoszMTKWlpenatWumB8s5OjqqatWqpjjly5eXt7e3nJ2dzdouXbokSTpx4oSysrLk5+dnNn96errpVrh/j+3Xr58WLlyoGjVq6Ntvv9WRI0e0b98+7d69W126dFFYWJg+/PBDU/86deqYxfX09DTNHR8fLy8vL7Pt6jVq1JC7u7vi4+PVuHFjtWrVSosWLVJWVpZ27dqlxx57TB4eHoqNjVWdOnV05swZtW7d+q7fZwAALOmuivSzZ88qKipKP/74o2bPnq1y5cpp06ZNqly5smrWrGnpHAEAwG34+vrKYDDo9OnTZu1VqlSRJDk4OEiSzp07p86dO+u5557TlClTVKpUKe3du1fh4eG6fv26qUi3tbU1i2MwGG7alp2dLUlKTU2VtbW1jhw5Imtra7N+N4rzuLg4U5urq6vpZysrKzVu3FiNGzfWiBEjtGzZMj399NN69dVX5ePjc8t8bsydGy1bttRff/2lb775Rrt379bUqVPl4eGh6dOnq27duqpQocItt+QDAIqmtPYV8zuFW8pzkb5r1y516NBBgYGB2r17tyZPnqxy5crp2LFjWrRokT799NP7kScAALiF0qVL69FHH9XcuXP1/PPP3/K+9CNHjig7O1szZsyQldU/d7z9d6v73ahfv76ysrJ06dIltWjR4qZ9fH19cxWrRo0akqSrV6/mqn9AQIB++ukn/fTTT6bV9JMnT+rKlSumWO7u7qpTp47mzp0rW1tb+fv7q1y5curdu7fWr1/PVncAKIaWG3taOOJZi0XKc5E+duxYTZ48WSNHjpSLi4upvU2bNpo7d67FEgMAALk3f/58BQYGqlGjRoqMjFSdOnVkZWWlQ4cO6dSpU2rYsKF8fX2VkZGh9957T126dNG+ffu0cOHCe57bz89PISEhCg0N1YwZM1S/fn1dvnxZ27dvV506ddSpU6ebjnviiScUGBio5s2by8PDQwkJCRo3bpz8/Pzk7++fq7nbtWun2rVrKyQkRLNnz1ZmZqaGDh2qVq1aqVGjRqZ+rVu31nvvvacnnnhCklSqVCkFBARo5cqVmjdv3j2/BwCAwqX6l9GWDdjGcqHy/OC4EydO6PHHH8/RXq5cOf32228WSQoAAORN1apVdfToUbVr107jxo1T3bp11ahRI7333nuKiIjQG2+8obp162rmzJl68803VatWLS1fvlzTpk2zyPxRUVEKDQ3VqFGjVL16dXXv3l2HDh1S5cqVbzmmffv2+uKLL9SlSxf5+fmpf//+8vf315dffikbm9ytIxgMBn322WcqWbKkWrZsqXbt2qlKlSpauXKlWb9WrVopKyvL7N7z1q1b52gDACC/GYxGozEvAypVqqRVq1apefPmcnFx0bFjx1SlShWtXbtWEREROnvWcsv890NKSorc3NyUnJxsdk8cAABpaWlKSEiQj4+P7O3t8zsdFEJF9jMU6WbheMmWjYe84c8TULx/gEXjBZyKv+35vNSheV5J79Onj8aMGaOLFy+aHtyyb98+RUREKDQ0NK/hAAAAAADA/8lzkT516lT5+/vLy8tLqampqlGjhlq2bKnmzZvrtddeux85AgAAAABQLOT5wXElSpTQBx98oPHjx+vbb79Vamqq6tevz1eXAAAAAABwj/JcpP/444+qUqWKKleufNuHwQAAAAAAUBAFLfjYovEuWjBWnot0X19fVapUSa1atVLr1q3VqlWrXH/3KQAAAAAAuLU835P+008/adq0aXJwcNBbb70lPz8/VapUSSEhIfrwww/vR44AAAAAABQLeS7SK1asqJCQEL3//vs6ffq0Tp8+rXbt2mnVqlV69tln70eOAAAAAAAUC3ne7n7t2jXt3btXsbGxio2N1dGjR+Xv76/hw4erdevW9yFFAAAAAAAsZ8iudZYNGFTPYqHyXKS7u7urZMmSCgkJ0dixY9WiRQuVLFnSYgkBAAAAAFBc5Xm7e8eOHZWVlaWYmBjFxMTok08+0ffff38/cgMAABYUHR0td3f3/E4DAADcRp5X0tetWydJOn78uHbt2qUvv/xS48ePl42NjVq3bq3ly5dbOkcAAPJd7cW1H9hcJ/qfyPOYsLAwXblyxfT39A2xsbEKCgrSn3/+qd69e6tjx465ihcdHa0RI0boypUrec4FAADcvTwX6TfUrl1bmZmZun79utLS0rRlyxatXLmSIh0AgALKwcFBDg4OD3TOrKwsGQwGWVnlefMeAADFUp7/xpw5c6a6du2q0qVLq2nTpvr444/l5+en1atX6/Lly/cjRwAAYAH/3e5+7NgxBQUFycXFRa6urmrYsKEOHz6s2NhYDRgwQMnJyTIYDDIYDIqMjJQk/fnnnwoNDVXJkiXl6OioDh066Icffsgxx+eff64aNWrIzs5Oe/fula2trS5evGiWz4gRI9SiRYsHcekAABQaeV5J//jjj9WqVSs988wzatGihdzc3O5HXgAA4D4LCQlR/fr1tWDBAllbWysuLk62trZq3ry5Zs+erQkTJuj06dOSJGdnZ0n/bKv/4Ycf9Pnnn8vV1VVjxoxRx44ddfLkSdna2kr655tg3nzzTX344YcqXbq0vLy8VKVKFS1dulQvv/yyJCkjI0PLly/XW2+9lT8XDwBAAZXnIv3QoUP3Iw8AAHCP1q9fbyqmb8jKyrpl/8TERL388svy9/eXJFWrVs10zs3NTQaDQR4eHqa2G8X5vn371Lx5c0nS8uXL5eXlpXXr1unJJ5+U9E8BPn/+fNWtW9c0Njw8XFFRUaYi/YsvvlBaWpp69ep1j1cNAEDRclc3iO3Zs0f9+vVTs2bNdOHCBUnS0qVLtXfvXosmBwAAci8oKEhxcXFmrw8//PCW/UeOHKlBgwapXbt2mj59us6ePXvb+PHx8bKxsVHTpk1NbaVLl1b16tUVHx9vaitRooTq1KljNjYsLExnzpzRV199JemfbfG9evWSk5PT3VwqAABFVp6L9NWrV6t9+/ZycHDQ0aNHlZ6eLklKTk7W1KlTLZ4gAADIHScnJ/n6+pq9KlaseMv+kZGR+u6779SpUyft2LFDNWrU0Nq1a+85DwcHBxkMBrO2cuXKqUuXLoqKitKvv/6qTZs2aeDAgfc8FwAARU2ei/TJkydr4cKF+uCDD0z3nklSYGCgvvnmG4smBwAA7i8/Pz+99NJL+vLLL9WjRw9FRUVJ+mc1/L9b5QMCApSZmamvv/7a1Pb777/r9OnTqlGjxh3nGjRokFauXKn3339fVatWVWBgoGUvBgCAIiDPRfrp06fVsmXLHO1ubm58lyoAAIXE33//reHDhys2Nlbnz5/Xvn37dOjQIQUEBEiSvL29lZqaqu3bt+u3337TtWvXVK1aNXXr1k2DBw/W3r17dezYMfXr108VK1ZUt27d7jhn+/bt5erqqsmTJ2vAgAH3+xIBACiU8lyke3h46MyZMzna9+7dqypVqlgkKQAAcH9ZW1vr999/V2hoqPz8/NSrVy916NBBkyZNkiQ1b95cQ4YMUe/evVW2bFnTU9ijoqLUsGFDde7cWc2aNZPRaNTGjRvNdtfdipWVlcLCwpSVlaXQ0ND7en0AABRWBqPRaMzLgGnTpmnZsmX66KOP9Oijj2rjxo06f/68XnrpJY0fP17PP//8/crVIlJSUuTm5qbk5GS5urrmdzoAgAIkLS1NCQkJ8vHxkb29fX6nUySFh4fr8uXL+vzzz/M7lfuiyH6GIi38lbuRyZaNh7zhzxNQZGTkA42Xlzo0z1/BNnbsWGVnZ6tt27a6du2aWrZsKTs7O0VERBT4Ah0AAOSP5ORknThxQitWrCiyBToAAJaQ5yLdYDDo1Vdf1csvv6wzZ84oNTVVNWrUyPG9rAAAADd069ZNBw8e1JAhQ/Too4/mdzoAABRYeS7SbyhRokSunuQKAAAQGxub3ykAAFAo5KpI79Gjh6Kjo+Xq6qoePXrctu+aNWsskhgAAAAAAMVNrop0Nzc3GQwG088AAAAAAMDyclWkR0VF6Y8//jD9DAAAAAAALC/X35NeoUIF9enTR1u3br2f+QAAAAAAUGzlukj/4IMPdPnyZQUHB8vb21uRkZE6d+7cfUwNAAAAAIDiJddF+tNPP63t27frzJkz6t+/vxYvXixfX189+uijWrlypa5fv34/8wQAAAAAoMjLdZF+g4+PjyZNmqSEhARt3rxZ5cqV08CBA+Xp6akXXnjhfuQIAAAAAECxcNffky5J7dq1U7t27bR69Wo988wzmjdvnubMmWOp3AAAKDDi/QMe2FwBp+LzPCYsLExXrlzRunXrzNpjY2MVFBSkP//8U+7u7pZJEAAA3Dd5Xkm/4fz584qMjJSPj4969+6tBg0aaPny5ZbMDQAA5LOb3c6WlZWl7OzsPMe623EAABQneVpJT09P1+rVq/XRRx8pNjZWFStWVFhYmAYMGCBvb+/7lCIAALCU1atXa8KECTpz5ow8PT31/PPPa9SoUabz3t7eCg8P1w8//KB169apR48eat26tUaMGKElS5Zo7Nix+v7773XmzBm5ubnpxRdf1BdffKH09HS1atVKc+bMUbVq1SRJ0dHRNx3HvxmA/OWdtsKi8c5ZNBqAXK+kDx06VJ6enho4cKBKly6tjRs36ty5c5o0aRJ/2QIAUAgcOXJEvXr1Up8+fXTixAlFRkZq/Pjxio6ONuv3zjvvqG7dujp69KjGjx8vSbp27ZrefPNNffjhh/ruu+9Urlw5hYWF6fDhw/r888914MABGY1GdezYURkZGaZYNxsHAABuLdcr6Xv37tXEiRPVr18/lS5d+n7mBAAA7sL69evl7Oxs1paVlWX6eebMmWrbtq2p8Pbz89PJkyf19ttvKywszNSvTZs2Zqvre/bsUUZGhubPn6+6detKkn744Qd9/vnn2rdvn5o3by5JWr58uby8vLRu3To9+eSTkpRjHAAAuL1cF+nHjx+/n3kAAIB7FBQUpAULFpi1ff311+rXr58kKT4+Xt26dTM7HxgYqNmzZysrK0vW1taSpEaNGuWIXaJECdWpU8d0HB8fLxsbGzVt2tTUVrp0aVWvXl3x8fG3HAcAAG7vnp7uDgAACg4nJyf5+vqatf388893Fee/HBwcZDAY8hzrbscBAFBcUaQDAFBMBAQEaN++fWZt+/btk5+fn2kVPS+xMjMz9fXXX5u2u//+++86ffq0atSoYbGcAVheWvuK+Z0CgNu4669gAwAAhcuoUaO0fft2vfHGG/r++++1ePFizZ07VxEREXmOVa1aNXXr1k2DBw/W3r17dezYMfXr108VK1bMsaUeAADkHkU6AADFRIMGDbRq1SrFxMSoVq1amjBhgl5//XWzh8blRVRUlBo2bKjOnTurWbNmMhqN2rhxo2xtbS2bOAAAxYjBaDQa8zJg8+bNcnZ21iOPPCJJmjdvnj744APVqFFD8+bNU8mSJe9LopaSkpIiNzc3JScny9XVNb/TAQAUIGlpaUpISJCPj4/s7e3zOx0UQkX2MxTpZuF4yZaNhzzx2Bln0XgXg+pZNB7wIERGRj7QeHmpQ/O8kv7yyy8rJSVFknTixAmNGjVKHTt2VEJCgkaOHJnXcAAAAAAA4P/k+cFxCQkJpgfCrF69Wp07d9bUqVP1zTffqGPHjhZPEAAAAACA4iLPK+klSpTQtWvXJEnbtm3TY489JkkqVaqUaYUdAAAAAADkXZ5X0gMDAzVy5EgFBgbq4MGDWrlypSTp+++/V6VKlSyeIAAAAAAAxUWeV9LnzZsnW1tbffrpp1qwYIEqVvznexY3bdqk4OBgiycIAAAAAEBxkaeV9MzMTMXGxuqDDz6Qh4eH2blZs2ZZNDEAAAAAAIqbPK2k29jYaMiQIUpPT79f+QAAAAAAUGzlebt7kyZNdPTo0fuRCwAAAAAAxVqeHxw3dOhQjRo1Sj///LMaNmwoJycns/N16tSxWHIAAAAAABQneS7S+/TpI0l64YUXTG0Gg0FGo1EGg0FZWVmWyw4AABQ43t7eGjFihEaMGJHfqQAAUOTkuUhPSEi4H3kAAFCgzRuy44HNNWxhmzyPCQsL0+LFi/Xss89q4cKF5vGGDdP8+fPVv39/RUdH33N+hw4dyrGTDgAAWEaei/SHHnrofuQBAADukZeXl2JiYjRr1iw5ODhIktLS0rRixQpVrlzZYvOULVvWYrEAAIC5PD84bsmSJbd9AQCA/NGgQQN5eXlpzZo1prY1a9aocuXKql+/vqnN29tbs2fPNhtbr149RUZGSpKMRqMiIyNVuXJl2dnZqUKFCma3uf13/JUrV/Tss8+qfPnysre3V61atbR+/fr7co0AABR1eV5Jf/HFF82OMzIydO3aNZUoUUKOjo4KDQ21WHIAACBvBg4cqKioKIWEhEiSPvroIw0YMECxsbG5jrF69WrNmjVLMTExqlmzpi5evKhjx47dtG92drY6dOigv/76S8uWLVPVqlV18uRJWVtbW+JyAAAodvJcpP/555852n744Qc999xzevnlly2SFAAAuDv9+vXTuHHjdP78eUnSvn37FBMTk6ciPTExUR4eHmrXrp1sbW1VuXJlNWnS5KZ9t23bpoMHDyo+Pl5+fn6SpCpVqtzzdQAAcD8NSmub3yncUp63u99MtWrVNH369Byr7AAA4MEqW7asOnXqpOjoaEVFRalTp04qU6ZMnmI8+eST+vvvv1WlShUNHjxYa9euVWZm5k37xsXFqVKlSqYCHQAA3BuLFOmSZGNjo19++cVS4QAAwF0aOHCgoqOjtXjxYg0cODDHeSsrKxmNRrO2jIwM089eXl46ffq05s+fLwcHBw0dOlQtW7Y063PDjQfUAQAAy8jzdvfPP//c7NhoNCopKUlz585VYGCgxRIDAAB3Jzg4WNevX5fBYFD79u1znC9btqySkpJMxykpKTm+YtXBwUFdunRRly5dNGzYMPn7++vEiRNq0KCBWb86dero559/1vfff89qOgAAFpDnIr179+5mxwaDQWXLllWbNm00Y8YMS+UFAADukrW1teLj400//1ebNm0UHR2tLl26yN3dXRMmTDDrFx0draysLDVt2lSOjo5atmyZHBwcbvo1rK1atVLLli3Vs2dPzZw5U76+vjp16pQMBoOCg4Pv30UCAHAPVia8adF4o9TCYrHyXKRnZ2dbbHIAAHB/uLq63vLcuHHjlJCQoM6dO8vNzU1vvPGG2Uq6u7u7pk+frpEjRyorK0u1a9fWF198odKlS9803urVqxUREaG+ffvq6tWr8vX11fTp0y1+TQAAFAcG439vSsuDG0MNBoPFErrfUlJS5ObmpuTk5Nv+AwYAUPykpaUpISFBPj4+sre3z+90UAgV2c9QpJuF4yVbNh7yxGNnnEXjXQyqZ9F4wIMwo3dni8YbtXL9bc/npQ69qwfHLVmyRLVr15aDg4McHBxUp04dLV269G5CAQAAAACA/5Pn7e4zZ87U+PHjNXz4cNOD4vbu3ashQ4bot99+00svvWTxJAEAAAAAKA7yXKS/9957WrBggUJDQ01tXbt2Vc2aNRUZGUmRDgAAAADAXcrzdvekpCQ1b948R3vz5s3Nvs4lN3bv3q0uXbqoQoUKMhgMWrdu3R3HxMbGqkGDBrKzs5Ovr6+io6PzNCcAAAAAAAVVnot0X19frVq1Kkf7ypUrVa1atTzFunr1qurWrat58+blqn9CQoI6deqkoKAgxcXFacSIERo0aJC2bNmSp3kBAAAAACiI8rzdfdKkSerdu7d2795tuid937592r59+02L99vp0KGDOnTokOv+CxculI+Pj+n72AMCArR3717NmjVL7du3z9PcAAAAAAAUNHleSe/Zs6e+/vprlSlTRuvWrdO6detUpkwZHTx4UI8//vj9yNHkwIEDateunVlb+/btdeDAgfs6LwAAAAAAD0KeV9IlqWHDhlq2bJmlc7mjixcvqnz58mZt5cuXV0pKiv7++285ODjkGJOenq709HTTcUpKyn3PEwAAAACAu5HrIj23xe2dvpj9QZs2bZomTZqU32kAAAAAAHBHud7u7u7urpIlS97ydeP8/eTh4aFff/3VrO3XX3+Vq6vrTVfRJWncuHFKTk42vX766af7miMAAEWdt7e3Zs+ebbF4rVu31ogRIywWDwCAwizXK+k7d+40/Ww0GtWxY0d9+OGHqlix4n1J7GaaNWumjRs3mrVt3bpVzZo1u+UYOzs72dnZ3e/UAABF3IzenR/YXKNWrs/zmLCwMC1evFjPPvusFi5caHZu2LBhmj9/vvr372+Rry49dOiQnJyc7jkOAADIKddFeqtWrcyOra2t9fDDD6tKlSp3PXlqaqrOnDljOk5ISFBcXJxKlSqlypUra9y4cbpw4YKWLFkiSRoyZIjmzp2r0aNHa+DAgdqxY4dWrVqlDRs23HUOAAAUFV5eXoqJidGsWbNMO8zS0tK0YsUKVa5c2WLzlC1b1mKxAACAuTw/3d2SDh8+rPr166t+/fqSpJEjR6p+/fqaMGGCJCkpKUmJiYmm/j4+PtqwYYO2bt2qunXrasaMGfrwww/5+jUAACQ1aNBAXl5eWrNmjaltzZo1qly5sunvWunm29Xr1aunyMhISf/smIuMjFTlypVlZ2enChUq6IUXXrjl+CtXrujZZ59V+fLlZW9vr1q1amn9+n92A/z+++/q27evKlasKEdHR9WuXVsff/yx5S8eAIAi4q6e7m4prVu3ltFovOX5m23Ja926tY4ePXofswIAoPAaOHCgoqKiFBISIkn66KOPNGDAAMXGxuY6xurVqzVr1izFxMSoZs2aunjxoo4dO3bTvtnZ2erQoYP++usvLVu2TFWrVtXJkydlbW0t6Z+V/IYNG2rMmDFydXXVhg0b9PTTT6tq1apq0qTJPV8vgLxbbuxp4YhnLRwPKN7uqUg3GAyWygMAAFhAv379NG7cOJ0/f16StG/fPsXExOSpSE9MTJSHh4fatWsnW1tbVa5c+ZYF9bZt23Tw4EHFx8fLz89PksxuhatYsaIiIiJMx88//7y2bNmiVatWUaQDAHATuS7Se/ToYXaclpamIUOG5HhwzL+32AEAgAerbNmy6tSpk6Kjo2U0GtWpUyeVKVMmTzGefPJJzZ49W1WqVFFwcLA6duyoLl26yMYm5z8b4uLiVKlSJVOB/l9ZWVmaOnWqVq1apQsXLuj69etKT0+Xo6PjXV0fAABFXa6LdDc3N7Pjfv36WTwZAABw7wYOHKjhw4dLkubNm5fjvJWVVY7bzTIyMkw/e3l56fTp09q2bZu2bt2qoUOH6u2339auXbtka2trNu5WX4F6w9tvv613331Xs2fPVu3ateXk5KQRI0bo+vXrd3t5AAAUabku0qOiou5nHgAAwEKCg4N1/fp1GQyGmz5ctWzZskpKSjIdp6SkKCEhwayPg4ODunTpoi5dumjYsGHy9/fXiRMn1KBBA7N+derU0c8//6zvv//+pqvp+/btU7du3Uy/3M/Oztb333+vGjVqWOJSAQAocvL1wXEAAMDyrK2tFR8fb/r5v9q0aaPo6Gh16dJF7u7umjBhglm/6OhoZWVlqWnTpnJ0dNSyZcvk4OCghx56KEesVq1aqWXLlurZs6dmzpwpX19fnTp1SgaDQcHBwapWrZo+/fRT7d+/XyVLltTMmTP166+/UqQDAHAL+foVbAAA4P5wdXWVq6vrTc+NGzdOrVq1UufOndWpUyd1795dVatWNZ13d3fXBx98oMDAQNWpU0fbtm3TF198odKlS9803urVq9W4cWP17dtXNWrU0OjRo5WVlSVJeu2119SgQQO1b99erVu3loeHh7p3727x6wUAoKgwGG/3HWhFUEpKitzc3JScnHzLf7wAAIqntLQ0JSQkyMfHR/b29vmdDgqhIvsZinS7c588xUu2bDzkyfYdVe/cKQ/atuEr2FD4zOjd2aLxRq1cf9vzealDWUkHAAAAAKCAoEgHAAAAAKCA4MFxAAAAQDGyZ/fTFo3Xto1FwwHFHivpAAAAAAAUEBTpAAAAAAAUEBTpAAAAAAAUEBTpAAAAAAAUEBTpAAAAAAAUEBTpAAAAAAAUEHwFGwAAAFCMDEprm98pALgNinQAAHLh57F7Hthclaa3yPOYsLAwLV68WM8++6wWLlxodm7YsGGaP3+++vfvr+jo6HvKLTo6WiNGjNCVK1fuKQ6A/LMy4U2LxhulvP8/C8Ctsd0dAIAiwsvLSzExMfr7779NbWlpaVqxYoUqV66cj5nllJWVpezs7PxOAwCAAociHQCAIqJBgwby8vLSmjVrTG1r1qxR5cqVVb9+fUnSkiVLVLp0aaWnp5uN7d69u55++mlJ0rFjxxQUFCQXFxe5urqqYcOGOnz4sGJjYzVgwAAlJyfLYDDIYDAoMjJSkpSenq6IiAhVrFhRTk5Oatq0qWJjY03xo6Oj5e7urs8//1w1atSQnZ2d9u7dK1tbW128eNEslxEjRqhFC1bmgPvFvuRIi74AWBZFOgAARcjAgQMVFRVlOv7oo480YMAA0/GTTz6prKwsff7556a2S5cuacOGDRo4cKAkKSQkRJUqVdKhQ4d05MgRjR07Vra2tmrevLlmz54tV1dXJSUlKSkpSREREZKk4cOH68CBA4qJidHx48f15JNPKjg4WD/88INpnmvXrunNN9/Uhx9+qO+++06NGjVSlSpVtHTpUlOfjIwMLV++3JQLAADFDUU6AABFSL9+/bR3716dP39e58+f1759+9SvXz/TeQcHBz311FNmhfyyZctUuXJltW7dWpKUmJiodu3ayd/fX9WqVdOTTz6punXrqkSJEnJzc5PBYJCHh4c8PDzk7OysxMRERUVF6ZNPPlGLFi1UtWpVRURE6JFHHjGbJyMjQ/Pnz1fz5s1VvXp1OTo6Kjw83KzPF198obS0NPXq1ev+v1kAABRAFOkAABQhZcuWVadOnRQdHa2oqCh16tRJZcqUMeszePBgffnll7pw4YKkf7aih4WFyWAwSJJGjhypQYMGqV27dpo+fbrOnj172zlPnDihrKws+fn5ydnZ2fTatWuX2dgSJUqoTp06ZmPDwsJ05swZffXVV6ZcevXqJScnp3t+LwAAKIx4ujsAAEXMwIEDNXz4cEnSvHnzcpyvX7++6tatqyVLluixxx7Td999pw0bNpjOR0ZG6qmnntKGDRu0adMmTZw4UTExMXr88cdvOl9qaqqsra115MgRWVtbm51zdnY2/ezg4GD6RcAN5cqVU5cuXRQVFSUfHx9t2rTJ7F52AACKG4p0AACKmODgYF2/fl0Gg0Ht27e/aZ9BgwZp9uzZunDhgtq1aycvLy+z835+fvLz89NLL72kvn37KioqSo8//rhKlCihrKwss77169dXVlaWLl26dFcPfBs0aJD69u2rSpUqqWrVqgoMDMxzDAC51yZ2mIUjxls4HlC8sd0dAIAixtraWvHx8Tp58mSOle0bnnrqKf3888/64IMPzB7S9vfff2v48OGKjY013dN+6NAhBQQESJK8vb2Vmpqq7du367ffftO1a9fk5+enkJAQhYaGas2aNUpISNDBgwc1bdo0sxX6W2nfvr1cXV01efJks4fcAQBQHFGkAwBQBLm6usrV1fWW593c3NSzZ085Ozure/fupnZra2v9/vvvCg0NlZ+fn3r16qUOHTpo0qRJkqTmzZtryJAh6t27t8qWLau33npLkhQVFaXQ0FCNGjVK1atXV/fu3XXo0KFcfT+7lZWVwsLClJWVpdDQ0Hu7cAAACjmD0Wg05ncSD1JKSorc3NyUnJx823+8AACKn7S0NCUkJMjHx0f29vb5nc5917ZtW9WsWVNz5szJ71QUHh6uy5cvm301XGFUZD9DkW4Wjpds2XjIk3j/AIvGCzjFdncUPjN6d7ZovFEr19/2fF7qUO5JBwCgmPnzzz8VGxur2NhYzZ8/P19zSU5O1okTJ7RixYpCX6ADAGAJFOkAABQz9evX159//qk333xT1atXz9dcunXrpoMHD2rIkCF69NFH8zUXAAAKAop0AACKmXPnzuV3CiZ83RoAAOZ4cBwAAAAAAAUERToAAAAAAAUERToAAAAAAAUERToAAAAAAAUED44DAAAAABQr9iVH5ncKt8RKOgAAAAAABQRFOoD/x959hzV1/X8AfweQPcSJCgiIAxQU614MUXHvicpQq3XPOuoAraJWrbPFVgXUVq1WqXXUgaCCe4ALF6KodW9AhnB+f/gzXyOIaGPuBd+v58nzkJubk3dCIPncc+45REREREQkExzuTkRElA8BAQGyfixfX1+EhYUhKCgIEyZMUG4PDw9Hx44dIYRQY0IiIiL6XNiTTkREVEjo6+tjzpw5ePLkidRRiIiI6BOxSCciIiokPD09YWFhgaCgoPfuEx0djcaNG8PAwABWVlYYPnw4UlJSAABLly5FtWrVlPuGh4dDoVAgODhY5TEmT54MAIiLi4O7uztMTExgamqKr776CidOnPhMz46IiOjLwCKdiIiokNDW1sasWbOwZMkS3Lp1K8ftCQkJ8PLyQufOnXHmzBls2LAB0dHRGDp0KADA1dUVFy5cwIMHDwAA+/fvR4kSJRAVFQUAyMzMxOHDh+Hm5gYA8Pb2hqWlJY4fP46TJ09iwoQJKFKkiEaeKxERUWHFIp2IiKgQ6dixI2rUqIFp06bluC0oKAje3t4YOXIkKlasiAYNGmDx4sVYvXo10tLSUK1aNRQrVgz79+8HAERFRWHMmDHK68eOHUNmZiYaNGgAAEhKSoKnpyeqVKmCihUromvXrqhevbrmniwREVEhxCKdiIiokJkzZw7CwsIQHx+vsj0uLg6hoaEwNjZWXlq0aIHs7GwkJiZCoVCgSZMmiIqKwtOnT3HhwgUMHjwY6enpuHjxIvbv34/atWvD0NAQADB69Gj0798fnp6emD17NhISEqR4ukRERIUKi3QiIqJCpkmTJmjRogUmTpyosj05ORkDBw5EbGys8hIXF4crV66gQoUKAAA3NzdERUXh4MGDcHFxgampqbJw379/P1xdXZXtBQQE4Pz582jdujX27dsHR0dHbNmyRaPPlYiIqLDhEmxERESF0OzZs1GjRg1UrlxZua1mzZq4cOEC7O3t33s/V1dXjBw5Ehs3blSee+7m5oa9e/ciJiYGY8aMUdm/UqVKqFSpEkaNGoWePXsiJCQEHTt2/CzPiYiI6EvAIp2IiKgQcnJygre3NxYvXqzcNn78eNSrVw9Dhw5F//79YWRkhAsXLmDPnj1YunQpAMDZ2Rnm5ub4/fffsW3bNgCvi/SxY8dCoVCgYcOGAICXL19i3Lhx6NKlC2xtbXHr1i0cP34cnTt31vyTJaKP0m2iekuAs2ptjYg43J2IiKiQmj59OrKzs5XXnZ2dsX//fly+fBmNGzeGi4sLpk6dirJlyyr3USgUaNy4MRQKBRo1aqS8n6mpKWrVqgUjIyMAr2eSf/ToEfr27YtKlSqhW7duaNmyJQIDAzX7JImIiAoZ9qQTERHlQ0BAgNQR8hQaGppjm42NDdLT01W21a5dG7t3786zrfDwcJXrWlpaePz4sco2XV1drFu37pOyEpG0ziYmSR2BiPLAnnQiIiIiIiIimWBPOhERERHlySbtd7W2d12trdHHsnDdr9b27qq1NSJiTzoRERERERGRTLBIJyIiIiIiIpIJFulEREREREREMsEinYiIiIiIiEgmWKQTERERERERyQSLdCIiIiIiIiKZYJFOREREREREJBMs0omIiChffH190aFDB+V1Nzc3jBw5UrI8REREhZGO1AGIiIgKgoh9FTT2WE09Ej76Pr6+vggLCwMA6OjowNLSEl27dsX06dOhr6+v7ogAgM2bN6NIkSLK6zY2Nhg5cuQnF+4KhSLP29u0aYNt27bh8OHDqFevXo7bmzZtCjMzM2zevPmTHp+IiEgOWKQTEREVEl5eXggJCUFmZiZOnjwJHx8fKBQKzJkz57M8XrFixdTa3p07d5Q/b9iwAVOnTsWlS5eU24yNjdGoUSOsWrUqR5F+/fp1REZG4u+//1ZrJiIiIk3jcHciIqJCQk9PDxYWFrCyskKHDh3g6emJPXv2AACys7MRFBQEW1tbGBgYoHr16ti0aZPyvllZWejXr5/y9sqVK2PRokV5Pt7bw93d3Nxw48YNjBo1CgqFAgqFAikpKTA1NVV5HAAIDw+HkZERXrx4obLdwsJCeTEzM4NCoVDZZmxsjH79+mHDhg1ITU1VuW9oaCjKlCkDLy+vT335iIiIZIE96URERIXQuXPncOjQIZQvXx4AEBQUhLVr1yI4OBgVK1bEgQMH0Lt3b5QsWRKurq7Izs6GpaUlNm7ciOLFi+PQoUP4+uuvUaZMGXTr1u2Dj7d582ZUr14dX3/9NQYMGAAAMDIyQo8ePRASEoIuXboo931z3cTE5KOfl7e3N8aNG4dNmzahb9++AAAhBMLCwuDr6wttbe2PbpM+LK1FOakjEBF9MVikExERFRLbtm2DsbExXr16hfT0dGhpaWHp0qVIT0/HrFmzsHfvXtSvXx8AYGdnh+joaCxfvhyurq4oUqQIAgMDlW3Z2tri8OHD+OOPP/JVpBcrVgza2towMTGBhYWFcnv//v3RoEED3LlzB2XKlMH9+/exY8cO7N2795OeY7FixdCxY0esWrVKWaRHRkbi+vXr8PPz+6Q2iYiI5ITD3YmIiAoJd3d3xMbG4ujRo/Dx8YGfnx86d+6Mq1evIjU1Fc2aNYOxsbHysnr1aiQk/G+SumXLluGrr75CyZIlYWxsjF9++QVJSUn/KVOdOnVQtWpV5aR2a9euRfny5dGkSRMAUMkzaNCgfLXp7++PAwcOKLOvWrUKrq6usLe3/09ZiYiI5IA96URERIWEkZGRslBdtWoVqlevjpUrV6JatWoAgO3bt6NcOdVhy3p6egCA9evXY+zYsZg/fz7q168PExMT/PDDDzh69Oh/ztW/f38sW7YMEyZMQEhICPz8/JQzucfGxir3MzU1zVd7TZs2hbW1NUJDQzFu3Dhs3rwZy5cv/885iYiI5IBFOhERUSGkpaWFSZMmYfTo0bh8+TL09PSQlJQEV1fXXPePiYlBgwYNMHjwYOW2t3vZ80NXVxdZWVk5tvfu3RvffvstFi9ejAsXLsDHx0d526f0fmtpacHPzw8rV65EuXLloKurq3LOOxERUUHG4e5ERESFVNeuXaGtrY3ly5dj7NixGDVqFMLCwpCQkIBTp05hyZIlymHoFStWxIkTJ7Br1y5cvnwZU6ZMwfHjxz/q8WxsbHDgwAHcvn0bDx8+VG43NzdHp06dMG7cODRv3hyWlpb/+bn5+fnh9u3bmDRpEnr27AkDA4P/3CYREZEcsEgnIiIqpHR0dDB06FDMnTsXEydOxJQpUxAUFAQHBwd4eXlh+/btsLW1BQAMHDgQnTp1Qvfu3VG3bl08evRIpVc9P6ZPn47r16+jQoUKKFmypMpt/fr1Q0ZGBvz9/dXy3KytreHp6YknT56orU0iIiI5UAghhNQhNOn58+cwMzPDs2fP8n3uGxERfRnS0tKQmJgIW1tb6OvrSx2nUFmzZg1GjRqFf//9F7q6ulLH+WwK63vIIjJWre3dda+h1vbo40Tsq6DW9pp6fNypMURysGzQPrW2NyTYI8/bP6YO5TnpRERE9Nmkpqbizp07mD17NgYOHFioC3QiIiJ14HB3IiIi+mzmzp2LKlWqwMLCAhMnTpQ6DhERkeyxSCciIqLPJiAgAJmZmYiIiICxsbHUcYiIiGSPRToRERERERGRTLBIJyIiIiIiIpIJFulEREREREREMsEinYiIiIiIiEgmWKQTERERERERyQSLdCIiIiIiIiKZYJFOREREREREJBM6UgcgIiIqCCwiYzX2WHfda3z0fXx9fREWFgYA0NHRgaWlJbp27Yrp06dDX19fzQmJiIjoc2GRTkREVEh4eXkhJCQEmZmZOHnyJHx8fKBQKDBnzhypoxEREVE+cbg7ERFRIaGnpwcLCwtYWVmhQ4cO8PT0xJ49ewAA6enpGD58OEqVKgV9fX00atQIx48fV943KioKCoUCu3btgouLCwwMDODh4YH79+9j586dcHBwgKmpKXr16oXU1FTl/f755x80atQIRYsWRfHixdGmTRskJCQob79+/ToUCgU2b94Md3d3GBoaonr16jh8+LBK9piYGLi5ucHQ0BDm5uZo0aIFnjx5AgDIzs5GUFAQbG1tYWBggOrVq2PTpk2f86UkIiKSDIt0IiKiQujcuXM4dOgQdHV1AQDffvst/vzzT4SFheHUqVOwt7dHixYt8PjxY5X7BQQEYOnSpTh06BBu3ryJbt26YeHChfj999+xfft27N69G0uWLFHun5KSgtGjR+PEiROIiIiAlpYWOnbsiOzsbJV2v/vuO4wdOxaxsbGoVKkSevbsiVevXgEAYmNj0bRpUzg6OuLw4cOIjo5G27ZtkZWVBQAICgrC6tWrERwcjPPnz2PUqFHo3bs39u/f/zlfQiIiIklwuDsREVEhsW3bNhgbG+PVq1dIT0+HlpYWli5dipSUFPz8888IDQ1Fy5YtAQC//vor9uzZg5UrV2LcuHHKNr7//ns0bNgQANCvXz9MnDgRCQkJsLOzAwB06dIFkZGRGD9+PACgc+fOKhlWrVqFkiVL4sKFC6hWrZpy+9ixY9G6dWsAQGBgIKpWrYqrV6+iSpUqmDt3LmrVqoWffvpJuX/VqlUBvB4BMGvWLOzduxf169cHANjZ2SE6OhrLly+Hq6urWl9DIiIiqbEnnYiIqJBwd3dHbGwsjh49Ch8fH/j5+aFz585ISEhAZmamsvgGgCJFiqBOnTqIj49XacPZ2Vn5c+nSpWFoaKgs0N9su3//vvL6lStX0LNnT9jZ2cHU1BQ2NjYAgKSkpPe2W6ZMGQBQtvOmJz03V69eRWpqKpo1awZjY2PlZfXq1SrD6omIiAoL9qQTEREVEkZGRrC3twfwuke7evXqWLlyJWrXrp3vNooUKaL8WaFQqFx/s+3toext27ZF+fLl8euvv6Js2bLIzs5GtWrVkJGRkWe7AJTtGBgYvDdPcnIyAGD79u0oV66cym16enr5fl5EREQFBXvSiYiICiEtLS1MmjQJkydPRoUKFaCrq4uYmBjl7ZmZmTh+/DgcHR0/+TEePXqES5cuYfLkyWjatCkcHByUk719DGdnZ0REROR6m6OjI/T09JCUlAR7e3uVi5WV1SdnJyIikiv2pBMRERVSXbt2xbhx4/Dzzz/jm2++wbhx41CsWDFYW1tj7ty5SE1NRb9+/T65fXNzcxQvXhy//PILypQpg6SkJEyYMOGj25k4cSKcnJwwePBgDBo0CLq6uoiMjETXrl1RokQJjB07FqNGjUJ2djYaNWqEZ8+eISYmBqampvDx8fnk/ERE9OXyiBqi5hbjP7xLPrFIJyIiKqR0dHQwdOhQzJ07F4mJicjOzkafPn3w4sUL1KpVC7t27YK5ufknt6+lpYX169dj+PDhqFatGipXrozFixfDzc3to9qpVKkSdu/ejUmTJqFOnTowMDBA3bp10bNnTwDAjBkzULJkSQQFBeHatWsoWrQoatasiUmTJn1ydiIiIrlSCCGE1CE06fnz5zAzM8OzZ89gamoqdRwiIpKRtLQ0JCYmwtbWFvr6+lLHoQKosL6HLCJj1dreXfcaam2PPk7Evgpqba+pBydxpILHKcxJre2d9Tmb5+0fU4fynHQiIiIiIiIimWCRTkRERERERCQTLNKJiIiIiIiIZIJFOhEREREREZFMsEgnIiIiIiIikgkW6UREREREREQywSKdiIiIiIiISCZYpBMRERERERHJBIt0IiIiIiIiIpnQkToAERERSSs0NBQjR47E06dPP+n+vr6+ePr0KcLDw9Wai6igWzZon9raGhLsoba2iEjeWKQTERHlg82E7Rp7rOuzW3/0fR48eICpU6di+/btuHfvHszNzVG9enVMnToVDRs2zPO+3bt3R6tWrfLcJyAgAIGBgTm279mzB4sWLYIQ4qMzExERUU4s0omIiAqBzp07IyMjA2FhYbCzs8O9e/cQERGBR48effC+BgYGMDAw+OB+VatWxd69e1W2FStWDLq6up+cm4iIiFSxSCciIirgnj59ioMHDyIqKgqurq4AgPLly6NOnToq+4wfPx7h4eF49uwZ7O3tMXv2bLRp0ybfw911dHRgYWGRYzuHuxMVLAcP9FFre005Ep8KoLOJSVJHeC8W6URERAWcsbExjI2NER4ejnr16kFPT0/l9uzsbLRs2RIvXrzA2rVrUaFCBVy4cAHa2toSJSYiIqL3YZFORERUwOno6CA0NBQDBgxAcHAwatasCVdXV/To0QPOzs7Yu3cvjh07hvj4eFSqVAkAYGdn99GPc/bsWRgbGyuvOzo64tixY2p7HkSFzRCLjmps7Zka2yIiOeMSbERERIVA586d8e+//2Lr1q3w8vJCVFQUatasidDQUMTGxsLS0lJZoOclKSlJ2TNvbGyMWbNmKW+rXLkyYmNjlZc///zzcz4lIiKiLxJ70omIiAoJfX19NGvWDM2aNcOUKVPQv39/TJs2DWPHjs13G2XLlkVsbKzyerFixZQ/6+rqwt7eXp2RiYiI6B3sSSciIiqkHB0dkZKSAmdnZ9y6dQuXL1/+4H10dHRgb2+vvLxdpBMREdHnx550IiKiAu7Ro0fo2rUr/P394ezsDBMTE5w4cQJz585F+/bt4erqiiZNmqBz585YsGAB7O3tcfHiRSgUCnh5eUkdn4iIiN7CIp2IiKiAMzY2Rt26dfHjjz8iISEBmZmZsLKywoABAzBp0iQAwJ9//omxY8eiZ8+eSElJUS7BRkRERPKiEEIIqUNo0vPnz2FmZoZnz57B1NRU6jhERCQjaWlpSExMhK2tLfT19aWOQwVQYX0PWUTGqrW9u+411NqebAWYqbEt9c3uHhAQoLa2Pkd7RBqhzr9P4IN/ox9Th/KcdCIiIiIiIiKZYJFOREREREREJBM8J52IiIiIiKgAuzXhoNraspzdWG1t0adhkU5ERERERFSAWeq3UWNr6pv/gD4Ni3QiIiIiytNvorOaW0xQc3tERIUHi3QiIiIiylPl3aHqbdBDvc0Rfels0n5XW1vX1dYSfSpOHEdEREREREQkE+xJJyIiIiIiKsBWNh+uxtZaq7Et+hQs0omIiIiIiAowtZ6SwtNRJMcinYiI6AugUCiwZcsWdOjQQeooRESkZpzdvXBhkU5ERJQfAWYafKyP/4L04MEDTJ06Fdu3b8e9e/dgbm6O6tWrY+rUqWjYsOFnCPk/PABARESkPizSiYiICoHOnTsjIyMDYWFhsLOzw7179xAREYFHjx59tsfMyMiArq7uZ2ufiIjyx8J1v9rauqu2luhTyaJIX7ZsGX744QfcvXsX1atXx5IlS1CnTp1c9w0NDYWfn5/KNj09PaSlpWkiKhERkew8ffoUBw8eRFRUFFxdXQEA5cuXz/FZ+vDhQ3Ts2BG7du1CuXLlMH/+fLRr1055+/79+zFu3DjExcWhWLFi8PHxwffffw8dnddfF9zc3FCtWjXo6Ohg7dq1cHJyQmJiIgCgY8eOyse9fv26Bp41kfyxcCKiTyF5kb5hwwaMHj0awcHBqFu3LhYuXIgWLVrg0qVLKFWqVK73MTU1xaVLl5TXFQqFpuISERHJjrGxMYyNjREeHo569epBT08v1/0CAwMxd+5c/PDDD1iyZAm8vb1x48YNFCtWDLdv30arVq3g6+uL1atX4+LFixgwYAD09fUREBCgbCMsLAzffPMNYmJiAADFihVDqVKlEBISAi8vL2hra2viKRMVCL+JzmpsLUGNbRGRnEm+TvqCBQswYMAA+Pn5wdHREcHBwTA0NMSqVaveex+FQgELCwvlpXTp0hpMTEREJC86OjoIDQ1FWFgYihYtioYNG2LSpEk4c+aMyn6+vr7o2bMn7O3tMWvWLCQnJ+PYsWMAgJ9++glWVlZYunQpqlSpgg4dOiAwMBDz589Hdna2so2KFSti7ty5qFy5MipXroySJUsCAIoWLQoLCwvldSIiIvo0kvakZ2Rk4OTJk5g4caJym5aWFjw9PXH48OH33i85ORnly5dHdnY2atasiVmzZqFq1aq57pueno709HTl9efPn6vvCRAREclE586d0bp1axw8eBBHjhzBzp07MXfuXKxYsQK+vr4AAGdnZ+X+RkZGMDU1xf379wEA8fHxqF+/vsrotIYNGyI5ORm3bt2CtbU1AOCrr77S3JMiKuC4LBYRfQpJe9IfPnyIrKysHD3hpUuXxt27uZ95U7lyZaxatQp//fUX1q5di+zsbDRo0AC3bt3Kdf+goCCYmZkpL1ZWVmp/HkRERHKgr6+PZs2aYcqUKTh06BB8fX0xbdo05e1FihRR2V+hUKj0kueHkZGRWrISERFR7iQ/J/1j1a9fH/Xr11deb9CgARwcHLB8+XLMmDEjx/4TJ07E6NGjldefP3/OQp2IiL4Ijo6OCA8Pz9e+Dg4O+PPPPyGEUPamx8TEwMTEBJaWlnnet0iRIsjKyvqvcYmI6BNx/oPCRdKe9BIlSkBbWxv37t1T2X7v3j1YWFjkq40iRYrAxcUFV69ezfV2PT09mJqaqlyIiIgKk0ePHsHDwwNr167FmTNnkJiYiI0bN2Lu3Llo3759vtoYPHgwbt68iWHDhuHixYv466+/MG3aNIwePRpaWnl/XbCxsUFERATu3r2LJ0+eqOMpERERfbEkLdJ1dXXx1VdfISIiQrktOzsbERERKr3lecnKysLZs2dRpkyZzxWTiIhI1oyNjVG3bl38+OOPaNKkCapVq4YpU6ZgwIABWLp0ab7aKFeuHHbs2IFjx46hevXqGDRoEPr164fJkyd/8L7z58/Hnj17YGVlBRcXl//6dIiIiL5okg93Hz16NHx8fFCrVi3UqVMHCxcuREpKinIt9L59+6JcuXIICgoCAEyfPh316tWDvb09nj59ih9++AE3btxA//79pXwaRERU2AU8kzrBe+np6SEoKEj5WZkbIUSObU+fPlW57urqqpztPTdRUVG5bm/bti3atm2br6xEX5IX4V+rr7HZ8epri4hkTfIivXv37njw4AGmTp2Ku3fvokaNGvjnn3+Uk8klJSWpDLN78uQJBgwYgLt378Lc3BxfffUVDh06BEdHR6meAhEREREREZFaSF6kA8DQoUMxdOjQXG9796j9jz/+iB9//FEDqYiIiIiIiIg0S9Jz0omIiIiIiIjof1ikExEREREREckEi3QiIiIiIiIimZDFOelERERERIXNhh7d1dZWgNpaIiK5Y086ERERERERkUywJ52IiIiI8rQhcY5a2xuDxmptj4ioMGGRTkRERER50jcfLXUEIsrDwQN91NZWUw+1NUWfiMPdiYiIiIiIiGSCPelERET54BTmpLHHOutz9qPv8+DBA0ydOhXbt2/HvXv3YG5ujurVq2Pq1Klo2LDhZ0j5PzY2Nhg5ciRGjhz5WR+HiIjoS8AinYiIqBDo3LkzMjIyEBYWBjs7O9y7dw8RERF49OjRZ3vMjIwM6Orqfrb2iYiIvkQc7k5ERFTAPX36FAcPHsScOXPg7u6O8uXLo06dOpg4cSLatWsHAFAoFPj555/RsmVLGBgYwM7ODps2bVJp5+zZs/Dw8ICBgQGKFy+Or7/+GsnJycrbfX190aFDB8ycORNly5ZF5cqV4ebmhhs3bmDUqFFQKBRQKBQafe5ERESFDYt0IiKiAs7Y2BjGxsYIDw9Henr6e/ebMmUKOnfujLi4OHh7e6NHjx6Ij48HAKSkpKBFixYwNzfH8ePHsXHjRuzduxdDhw5VaSMiIgKXLl3Cnj17sG3bNmzevBmWlpaYPn067ty5gzt37nzW50pERFTYcbg7ERFRAaejo4PQ0FAMGDAAwcHBqFmzJlxdXdGjRw84Ozsr9+vatSv69+8PAJgxYwb27NmDJUuW4KeffsLvv/+OtLQ0rF69GkZGRgCApUuXom3btpgzZw5Kly4NADAyMsKKFStUhrlra2vDxMQEFhYWGnzWpEkeUUPU3GK8mtsjIio82JNORERUCHTu3Bn//vsvtm7dCi8vL0RFRaFmzZoIDQ1V7lO/fn2V+9SvX1/Zkx4fH4/q1asrC3QAaNiwIbKzs3Hp0iXlNicnJ56HTkRE9BmxSCciIiok9PX10axZM0yZMgWHDh2Cr68vpk2bptbHeLuIJyIiIvVjkU5ERFRIOTo6IiUlRXn9yJEjKrcfOXIEDg4OAAAHBwfExcWp7B8TEwMtLS1Urlw5z8fR1dVFVlaWGpMTERF9uVikExERFXCPHj2Ch4cH1q5dizNnziAxMREbN27E3Llz0b59e+V+GzduxKpVq3D58mVMmzYNx44dU04M5+3tDX19ffj4+ODcuXOIjIzEsGHD0KdPH+X56O9jY2ODAwcO4Pbt23j48OFnfa5ERESFHSeOIyIiKuCMjY1Rt25d/Pjjj0hISEBmZiasrKwwYMAATJo0SblfYGAg1q9fj8GDB6NMmTJYt24dHB0dAQCGhobYtWsXRowYgdq1a8PQ0BCdO3fGggULPvj406dPx8CBA1GhQgWkp6dDCPHZnisREVFhxyKdiIgoH876nJU6wnvp6ekhKCgIQUFBee5XtmxZ7N69+723Ozk5Yd++fe+9/e1J6N5Wr149xMXF5SsrERER5Y3D3YmIiIiIiIhkgkU6ERERERERkUxwuDsREdEXgOeJExERFQzsSSciIiIiIiKSCRbpRERERERERDLBIp2IiIiIiIhIJlikExEREREREckEi3QiIiIiIiIimWCRTkRERERERCQTLNKJiIi+YDY2Nli4cKHyukKhQHh4uFofIyoqCgqFAk+fPlVru0RERIUR10knIiLKh/gqDhp7LIeL8fneV6FQ5Hn7tGnTEBAQ8B8T5S0uLg5TpkzBkSNH8Pz5c1hYWKBu3bpYsmQJSpUq9Vkfm4iIqLBhkU5ERFSA3blzR/nzhg0bMHXqVFy6dEm5zdjY+LM+/oMHD9C0aVO0adMGu3btQtGiRXH9+nVs3boVKSkpn/WxiYg0KsBMjW09U19bVOiwSCciIirALCwslD+bmZlBoVAotyUkJGDgwIE4cuQIUlJS4ODggKCgIHh6eua7/WnTpuGXX37Brl274OzsnOP2mJgYPHv2DCtWrICOzuuvFba2tnB3d8+x78mTJzF+/HhcuHABNWrUQEhICCpXrqy8/eeff8a8efNw8+ZN2NraYvLkyejTpw8AYOzYsbh48SK2bdsGAFi4cCFGjRqFnTt3wsvLCwBgb2+PCRMmoH///vl+flTwze/eRm1tjdmwTW1tERF9KhbpREREebh/47nUET5ZcnIyWrVqhZkzZ0JPTw+rV69G27ZtcenSJVhbW+d5XyEEhg8fjm3btuHgwYOwt7fPdT8LCwu8evUKW7ZsQZcuXfIcfv/dd99h/vz5KFmyJAYNGgR/f3/ExMQAALZs2YIRI0Zg4cKF8PT0xLZt2+Dn5wdLS0u4u7vD1dUVK1asQFZWFrS1tbF//36UKFECUVFR8PLywu3bt5GQkAA3N7dPfr2oYGoVlyB1BCIiteLEcURERIVU9erVMXDgQFSrVg0VK1bEjBkzUKFCBWzdujXP+7169Qq9e/dGREQEoqOj31ugA0C9evUwadIk9OrVCyVKlEDLli3xww8/4N69ezn2nTlzJlxdXeHo6IgJEybg0KFDSEtLAwDMmzcPvr6+GDx4MCpVqoTRo0ejU6dOmDdvHgCgcePGePHiBU6fPg0hBA4cOIAxY8YgKioKwOvJ6cqVK5dnViIiooKARToREVEhlZycjLFjx8LBwQFFixaFsbEx4uPjkZSUlOf9Ro0ahaNHj+LAgQMoV66ccvusWbNgbGysvLxpZ+bMmbh79y6Cg4NRtWpVBAcHo0qVKjh79qxKu28Ply9TpgwA4P79+wCA+Ph4NGzYUGX/hg0bIj7+9SR6RYsWRfXq1REVFYWzZ89CV1cXX3/9NU6fPo3k5GTs378frq6un/hKERERyQeHuxMRERVSY8eOxZ49ezBv3jzY29vDwMAAXbp0QUZGRp73a9asGdatW4ddu3bB29tbuX3QoEHo1q2b8nrZsmWVPxcvXhxdu3ZF165dMWvWLLi4uGDevHkICwtT7lOkSBHlz2+GxWdnZ+f7+bi5uSEqKgp6enpwdXVFsWLF4ODggOjoaOzfvx9jxozJd1tUeHSbqL6vs2c/vAuRLPVPayp1BFIjFulERESFVExMDHx9fdGxY0cAr3vWr1+//sH7tWvXDm3btkWvXr2gra2NHj16AACKFSuGYsWKffD+urq6qFChwkfN7u7g4ICYmBj4+Pio5Hd0dFRed3V1xapVq6Cjo6OcLM7NzQ3r1q3D5cuXeT76Z6TOQhhgMUxElBcW6URERHkoVeT1pFSPJM7xKSpWrIjNmzejbdu2UCgUmDJlSr57rjt27Ig1a9agT58+0NHRQZcuXXLdb9u2bVi/fj169OiBSpUqQQiBv//+Gzt27EBISEi+s44bNw7dunWDi4sLPD098ffff2Pz5s3Yu3evcp8mTZrgxYsX2LZtG2bPng3gdZHepUsXlClTBpUqVcr34xEREckVi3QiIqJCasGCBfD390eDBg1QokQJjB8/Hs+f53+2+i5duiA7Oxt9+vSBlpYWOnXqlGMfR0dHGBoaYsyYMbh58yb09PRQsWJFrFixQrl8Wn506NABixYtwrx58zBixAjY2toiJCREpXfc3NwcTk5OuHfvHqpUqQLgdeGenZ3N89GJ6Iv219NMtbU1RG0t0adikU5ERJQPDvt+V09DZV3U004ufH194evrq7xuY2ODffv2qewzZIjq1693h78LIVSud+vWTeU89HfZ2dnhl19+yTOXm5tbjnZr1KiRY9s333yDb775Js+2YmNjVa4XK1bso85rp8LnbGLeEyESERU0nN2diIiIiIiISCZYpBMRERERERHJBIt0IiIiIiIiIplgkU5EREREREQkE5w4joiIiIiI6ANs0tQ0gSiA62priQojFulEREREREQfsLL5cDW21lqNbVFhw+HuRERERERERDLBIp2IiIiIiIhIJlikExEREREREckEi3QiIqJCLioqCgqFAk+fPv3o+7q5uWHkyJHK6zY2Nli4cKHyukKhQHh4+H/OSERERK9x4jgiIqJ8WDb9iZpa2vfBPYYEe+S7NYVCkeft06ZNg5ubW77be9fmzZtRpEiRT74/ERERfRwW6URERAXYnTt3lD9v2LABU6dOxaVLl5TbjI2NceLEiY9uNyMjA7q6uihWrJhacn7ocYiIiOg1DncnIiIqwCwsLJQXMzMzKBQKlW3GxsbKfU+ePIlatWrB0NAQDRo0UCnmAwICUKNGDaxYsQK2trbQ19cHkHO4+4fcvHkT3bp1Q9GiRVGsWDG0b98e169fV97u6+uLDh06YObMmShbtiwqV678n18DIiKiwoRFOhER0Rfiu+++w/z583HixAno6OjA399f5farV6/izz//xObNmxEbG/vR7WdmZqJFixYwMTHBwYMHERMTA2NjY3h5eSEjI0O5X0REBC5duoQ9e/Zg27Zt//VpERERFSoc7k5ERPSFmDlzJlxdXQEAEyZMQOvWrZGWlqbsNc/IyMDq1atRsmTJT2p/w4YNyM7OxooVK5TnyoeEhKBo0aKIiopC8+bNAQBGRkZYsWIFh7kTUYFSeXeo+hrL/9Qj9AVikU5ERPSFcHZ2Vv5cpkwZAMD9+/dhbW0NAChfvvwnF+gAEBcXh6tXr8LExERle1paGhISEpTXnZycWKATUYHzIvxr9TU2O159bVGhwyKdiIjoC/H2LO1verqzs7OV24yMjP5T+8nJyfjqq6/w22+/5bjt7eL/vz4Oad6L+NlSRyAi+mKwSCciIiK1qFmzJjZs2IBSpUrB1NRU6jikRmktykkdgUhy7j+vU1tbd9XWEhVGnDiOiIiI1MLb2xslSpRA+/btcfDgQSQmJiIqKgrDhw/HrVu3pI5HRERUILAnnYiIKB+GTDVXT0NlXdTTjgwZGhriwIEDGD9+PDp16oQXL16gXLlyaNq0KXvWiYiI8olFOhERUSHh6+sLX1/fHNvd3NwghFDZVqNGDZVtAQEBCAgIyHHfqKgoletvr3kOIEe7FhYWCAsLe2/G0NDQ995GRCRnv4nOamwt4cO70BeLw92JiIiIiIiIZIJFOhEREREREZFMsEgnIiIiIiIikgmek05ERERERFSAeUQNUWNr8Wpsiz4Fe9KJiIiIiIiIZIJFOhEREREREZFMcLg7ERERERFRAdZtovrKurNqa4k+FXvSiYiIiIiIiGSCRToRERERERGRTLBIJyIiIiIiIpIJnpNORESUD/NHTdHYY43ZsO2j9g8ODsa4cePw5MkT6Oi8/mhPTk6Gubk5GjZsiKioKOW+UVFRcHd3x9WrV1GhQgV1xiYiIiI1YE86ERFRAefu7o7k5GScOHFCue3gwYOwsLDA0aNHkZaWptweGRkJa2vrjy7QhRB49eqV2jITERFR7likExERFXCVK1dGmTJlcvSYt2/fHra2tjhy5IjKdnd3d6xZswa1atWCiYkJLCws0KtXL9y/f19lP4VCgZ07d+Krr76Cnp4eoqOjNfm0iIiIvkgs0omIiAoBd3d3REZGKq9HRkbCzc0Nrq6uyu0vX77E0aNH4e7ujszMTMyYMQNxcXEIDw/H9evX4evrm6PdCRMmYPbs2YiPj4ezs7Omng4REdEXi+ekExERFQLu7u4YOXIkXr16hZcvX+L06dNwdXVFZmYmgoODAQCHDx9Geno63N3dYW1trbyvnZ0dFi9ejNq1ayM5ORnGxsbK26ZPn45mzZpp/PkQERF9qdiTTkREVAi4ubkhJSUFx48fx8GDB1GpUiWULFkSrq6uyvPSo6KiYGdnB2tra5w8eRJt27aFtbU1TExM4OrqCgBISkpSabdWrVpSPB0iIqIvFnvSiYiICgF7e3tYWloiMjIST548URbdZcuWhZWVFQ4dOoTIyEh4eHggJSUFLVq0QIsWLfDbb7+hZMmSSEpKQosWLZCRkaHSrpGRkRRPh4iI6IvFIp2IiKiQcHd3R1RUFJ48eYJx48Yptzdp0gQ7d+7EsWPH8M033+DixYt49OgRZs+eDSsrKwBQmRmeiIhyOnigj9raauqhtqaoEOJwdyIiokLC3d0d0dHRiI2NVfakA4CrqyuWL1+OjIwM5fnourq6WLJkCa5du4atW7dixowZEiYnIiKiN9iTTmp1a8JBtbZnObuxWtsjIvpUY35UUxFb1kU97eTC3d0dL1++RJUqVVC6dGnldldXV7x48UK5VBsAhIaGYtKkSVi8eDFq1qyJefPmoV27dp8tGxEREeUPi3QiIqJCwsbGBkKIHNvLly+fY3vPnj3Rs2dPlW1v7+Pm5pZrW0RERPR5sUgntfrraaZa2xui1taIiIiIiIjkjeekExEREREREckEe9KJiIiIiIg+wCSeq2CQZrBIJyIiIiIi+gB989FSR6AvBIe7ExEREREREckEe9KJiIiIiEgeAszU3N4z9bZHpAEs0omIiIiowLJJ+11tbV1XW0tERJ+ORToRERERFVgrmw9XY2ut1dgWfYqIJiXU2l5TtbZGpBks0omIiIiISBYOHuij1vaaeqi1OSKNYJFORERUwAUHB2PcuHF48uQJdHRef7QnJyfD3NwcDRs2RFRUlHLfqKgouLu74+rVq6hQocInPd7169dha2uL06dPo0aNGmp4BkSkSf3T2L9MJGcs0omIiPLh1uJkNbV08IN7WM5u/FEturu7Izk5GSdOnEC9evVeP8rBg7CwsMDRo0eRlpYGfX19AEBkZCSsra0/uUBXt8zMTBQpUkTqGERERLLBIp2IiKiAq1y5MsqUKYOoqChlkR4VFYX27dtj3759OHLkCNzc3JTb3d3dkZ2djTlz5uCXX37B3bt3UalSJUyZMgVdunQBADx58gRDhw7F7t27kZycDEtLS0yaNAl+fn6wtbUFALi4uAAAXF1dlb31K1aswPz585GYmAgbGxsMHz4cgwcPBvC/Hvj169fjp59+wtGjRxEcHIyoqCg8ffoUjRo1wvz585GRkYEePXpg4cKFLODpg9Q5PJpDo4lIDlikExERFQLu7u6IjIzEhAkTALzuMf/222+RlZWFyMhIuLm54eXLlzh69Cj8/f0RFBSEtWvXIjg4GBUrVsSBAwfQu3dvlCxZEq6urpgyZQouXLiAnTt3okSJErh69SpevnwJADh27Bjq1KmDvXv3omrVqtDV1QUA/Pbbb5g6dSqWLl0KFxcXnD59GgMGDICRkRF8fHyUWSdMmID58+fDxcUF+vr6iIqKQmRkJMqUKYPIyEhcvXoV3bt3R40aNTBgwADNv5hEREQSYpFORERUCLi7u2PkyJF49eoVXr58idOnT8PV1RWZmZkIDg4GABw+fBjp6elwc3ODo6Mj9u7di/r16wMA7OzsEB0djeXLl8PV1RVJSUlwcXFBrVq1AAA2NjbKxypZsiQAoHjx4rCwsFBunzZtGubPn49OnToBAGxtbXHhwgUsX75cpUgfOXKkcp83zM3NsXTpUmhra6NKlSpo3bo1IiIiWKQTEdEXh0U6ERFRIeDm5oaUlBQcP34cT548QaVKlZS94n5+fkhLS0NUVBTs7OyQnJyM1NRUNGvWTKWNjIwM5RD2b775Bp07d8apU6fQvHlzdOjQAQ0aNHjv46ekpCAhIQH9+vVTKaxfvXoFMzMzlX3fFP5vq1q1KrS1tZXXy5Qpg7Nnz37Sa0FERFSQsUgnIiIqBOzt7WFpaYnIyEg8efIErq6uAICyZcvCysoKhw4dQmRkJDw8PJCc/HoSvO3bt6NcuXIq7ejp6QEAWrZsiRs3bmDHjh3Ys2cPmjZtiiFDhmDevHm5Pv6bNn/99VfUrVtX5ba3i28AMDIyynH/d889VygUyM7Ozu/Tp8/sN9FZzS0mqLk9IqLCg0U6ERFRIeHu7o6oqCg8efIE48aNU25v0qQJdu7ciWPHjuGbb76Bo6Mj9PT0kJSUpCzmc1OyZEn4+PjAx8cHjRs3xrhx4zBv3jzlOehZWVnKfUuXLo2yZcvi2rVr8Pb2/nxPkoiIqJBjkU5ERFRIuLu7Y8iQIcjMzFQpvl1dXTF06FBkZGTA3d0dJiYmGDt2LEaNGoXs7Gw0atQIz549Q0xMDExNTeHj44OpU6fiq6++QtWqVZGeno5t27bBwcEBAFCqVCkYGBjgn3/+gaWlJfT19WFmZobAwEAMHz4cZmZm8PLyQnp6Ok6cOIEnT55g9OjRUr0sREREBYqW1AGIiIhIPdzd3fHy5UvY29ujdOnSyu2urq548eKFcqk2AJgxYwamTJmCoKAgODg4wMvLC9u3b1cur6arq4uJEyfC2dkZTZo0gba2NtavXw8A0NHRweLFi7F8+XKULVsW7du3BwD0798fK1asQEhICJycnODq6orQ0FBlm0RERPRh7EmnL8ayQfvU2t6QYC6mSvQlsRxurJ6Gyrqop51c2NjYQAiRY3v58uVzbFcoFBgxYgRGjBiRa1uTJ0/G5MmT3/tY/fv3R//+/XNs79WrF3r16vVR+UJDQ3NsW7hw4Xsfm+htJe82kToCEZFasUgnIiJZiK/ioLa2HC7Gq60tIiIiIk1ikU5fDI+oIWpukUUA5Y6jNoiINEe9n+/8bCci6bFIpy+GQ49/pY5ARERERESUJ04cR0RERERERCQT7EknIlKzIRYd1dziMzW3R0RUeHSbqL6vs2fV1hIR0adjkU5ERLIgp1NScpuBnCg/+N6hgmBD4hy1tjcGjdXWVv+0pmpri6igYpFORET0/4oUKQIASE1NhYGBgcRp6LP597T62npnSb3U1FQA/3svERERfSwW6URERP9PW1sbRYsWxf379wEAhoaGULxSc89oWpp626OPp87f6f//PoUQSE1Nxf3791G0aFFoa2ur7zGIiOiLwiKdiIjoLRYWFgCgLNTx9IF6HyAlUb3t0cdT5+/0nd9n0aJFle8hIiKiT8EinYiI6C0KhQJlypRBqVKlkJmZCSztqt4HGHpCve3Rx1Pn7/St32eRIkXYg05ERP8Zi3QiIqJcaGtrvy64km+qt2F9ffW2Rx9Pnb9T/j6JiEjNuE46ERERERERkUywJ52IiCgPNmm/q7W962ptjYiIiAobFulERER58NU/ruYWW6u5PSosbk04qNb2LGerb+1qIiLSHBbpREREeeif1lTqCPSFaOkwWK3tncVZtbZHH4//P4joU7BIJyIiIpKBs4lJUkcgIiIZYJFOREREahexr4La2mrqkaC2tgD1zjNwXW0tEREVQgFmam7vmXrbkykW6URERPRFiYap1BFyZeG6X63t3VVra/K10CpV6gikRhsS56i1vTHg3AxS4v+1T8MinUgGlg3ap7a2hgR7qK0tAIiv4qC2thwuxqutLSKiwuY30VnNLap3BAIREWkGi3RSK4+oIWpu8cso6oZYdFRja1/GMCAiIiIikrdB+8PV26B7DfW2J1Ms0omIiAoqnutHRERU6LBIJyIiIpKBi3/8qtb2mqr37CfZOnigj9ra+lJeMyKSNy2pAxARERERERHRa+xJJ6ICSZ0T2gFf0KR26hwezaHRRERElAeT+BNSRyiQWKQTUZ66TVTfv4mzamtJvbkA9WajTxPRpITa2mqqtpaINEe9k4gCnEiUCiJ989FSRyA14u/z03xxRboQAgDw/PlziZP8N5e+qqW2tiqfVN8RruSsLLW1Baj595Qu1NcWAMg1m5rf2/eK/6y2ttT5+4y5eENtbQHqzVbt2Qq1tQUA576Q99qRpfZqa6t2LfVle5Geora2APW+1+xrbVdbWwBwVY3ZUlKy1daWuj+z7/zZT21tmU5S32fol/L7BNT7O01PT1dbW+p+r6nz/4c6s6VlZqqtLUC92V5myPd/rjqzqfu9lvVSfd/B5fqaAQW7NniT/U09mheFyM9ehcitW7dgZWUldQwiIiIiIiL6wty8eROWlpZ57vPFFenZ2dn4999/YWJiAoVC8Z/be/78OaysrHDz5k2YmpqqIaF6yDUXwGyfSq7Z5JoLYLZPJddscs0FMNunkms2ueYCmO1TyTWbXHMBzPap5JpNrrmALyebEAIvXrxA2bJloaWV9/ztX9xwdy0trQ8eufgUpqamsntTAfLNBTDbp5JrNrnmApjtU8k1m1xzAcz2qeSaTa65AGb7VHLNJtdcALN9Krlmk2su4MvIZmaWvwl8uQQbERERERERkUywSCciIiIiIiKSCRbp/5Genh6mTZsGPT09qaOokGsugNk+lVyzyTUXwGyfSq7Z5JoLYLZPJddscs0FMNunkms2ueYCmO1TyTWbXHMBzJabL27iOCIiIiIiIiK5Yk86ERERERERkUywSCciIiIiIiKSCRbpRERERERERDLBIp2IiIiIiIhIJlikf4TMzExUqFAB8fHxUkchIiIiIiKiQohF+kcoUqQI0tLSpI7xXtra2rh//36O7Y8ePYK2trYEif7Hzs4Ojx49yrH96dOnsLOzkyAREdF/I4RAUlKSrD8XiEieEhISMHnyZPTs2VP53W3nzp04f/68pLlcXV2xevVqvHz5UtIcpF4ZGRm4dOkSXr16JXUUAKwL8oNF+kcaMmQI5syZI5s3+dvet5peeno6dHV1NZxG1fXr15GVlZVje3p6Om7fvq3xPJ06dcLz58+VP+d1IVVdunTBP//88973mxycOnUKZ8+eVV7/66+/0KFDB0yaNAkZGRmS5eKXn0+zY8cO7Nq1K8f2Xbt2YefOnRIkek0IAXt7e9y8eVOyDO8j1/eatbU1+vbti5UrVyIhIUHqODmkpKRIHeG9IiMjpY5AarJ//344OTnh6NGj2Lx5M5KTkwEAcXFxmDZtmqTZXFxcMHbsWFhYWGDAgAE4cuSIpHneNm3aNNy4cUPqGDlkZWVh5cqV6NWrFzw9PeHh4aFykVJqair69esHQ0NDVK1aFUlJSQCAYcOGYfbs2ZLlkltd8K6nT59i9+7dWLt2LVavXq1y0RQdjT1SIXH8+HFERERg9+7dcHJygpGRkcrtmzdv1nimxYsXAwAUCgVWrFgBY2Nj5W1ZWVk4cOAAqlSpovFcALB161blz7t27YKZmZnyelZWFiIiImBjY6PxXGZmZlAoFMqf5ezgwYNYvnw5EhISsGnTJpQrVw5r1qyBra0tGjVqpPE8T548QevWrVG2bFn4+fnB19dXdkc9Bw4ciAkTJsDJyQnXrl1Djx490LFjR2zcuBGpqalYuHChJLnefPkZNmwYunXrhn79+qFevXqSZAGA0aNH53vfBQsWfMYkeZswYUKuXyaEEJgwYQJatmwpQSpAS0sLFStWxKNHj1CxYkVJMryP3N5rb8yaNQsHDhzAnDlzMGDAAJQrVw6urq5wdXWFm5ub5K9j6dKl0a1bN/j7+0vy/zUvXl5esLS0hJ+fH3x8fGBlZSV1JACvP8t//PFH/PHHH0hKSspxIPTx48cSJQNsbGzg7+8PX19fWFtbS5bjXRMmTMD333+P0aNHw8TERLndw8MDS5culTAZsHDhQsybNw9bt25FWFgYmjRpAnt7e/j7+6NPnz4oXbq0ZNn++usvzJw5E66urujXrx86d+4MPT09yfK8MWLECISGhqJ169aoVq2a8vulHEycOBFxcXGIioqCl5eXcrunpycCAgIwYcIEjeaRa13wtr///hve3t5ITk6Gqampyu9ToVCgb9++mgki6KP4+vrmeZGCjY2NsLGxEQqFQlhZWSmv29jYiEqVKonmzZuLI0eOSJJNoVC896KrqysqVaok/v77b0myRUREiMzMTEkeO782bdokDAwMRP/+/YWenp5ISEgQQgixZMkS0bJlS8lyXb9+XUybNk3Y2toKLS0t4e7uLn777TeRlpYmWaa3mZqaiqtXrwohhJg9e7Zo3ry5EEKI6OhoYWlpKWU0kZmZKf7880/Rrl07UaRIEeHg4CB++OEHcffuXY1ncXNzy9fF3d1d49nepq+vLxITE3NsT0xMFIaGhpoP9JatW7eKRo0aibNnz0qaIzdyeq/l5t9//xXr1q0T3t7eQkdHR2hpaUkdSWzZskW0b99eFClSRFSsWFEEBQWJ27dvSx1LCCHEgwcPxIIFC0T16tWFjo6OaN68udiwYYNIT0+XNNeUKVNEmTJlxLx584S+vr6YMWOG6NevnyhevLhYtGiRpNl+/PFHUb16daGtrS08PT3FunXrZPE5ZWRkJK5duyaEEMLY2Fj52Z6YmCj09PSkjJbDvXv3xIwZM4S+vr4oUqSIaN++vYiIiJAsz6lTp8SwYcNEiRIlRNGiRcWgQYPEsWPHJMsjhBDFixcX27dvlzTD+1hbW4vDhw8LIVTfa1euXBEmJiYaz/OmBtDS0pJdXfBGxYoVxYgRI0RKSoqkOVikFyJubm7iyZMnUsfIlY2NjXj48KHUMVRoaWmJe/fuKa/XrVtX3Lp1S8JEOdWoUUOEhYUJIVT/uZ46dUqULl1aymhKERERwtvbWxgaGgpzc3MxePBgceLECUkzmZiYiMuXLwshhPD09BQLFy4UQghx48YNoa+vL2U0FXL78iNXpUuXzvV12bNnjyhZsqQEif6naNGiQldXV2hpaQl9fX1hbm6ucpELOb3XUlJSxK5du8TEiRNFvXr1hJ6enqhRo4YYOXKkJHlyc//+fTF//nzh5OQkdHR0ROvWrcWff/4pmwO7J0+eFEOHDhXFixcXxYsXF8OGDROxsbGSZLGzsxPbtm0TQrz+nHpzgHTRokWiZ8+ekmR618mTJ5WFnbm5uRgyZIg4efKkZHnKlSsnYmJihBCqn+2bN28WdnZ2kuV619GjR8WgQYNE0aJFhbW1tZg6daro16+fMDAwEGPGjJE0W0ZGhvjzzz9FmzZtRJEiRYSTk5NYuHChePr0qcazlClTRly6dEnjj5sfBgYGyvfX2++12NhYYWpqKlkuGxsb8eDBA8kePy+GhobK10lKLNI/QWZmptizZ48IDg4Wz58/F0IIcfv2bfHixQvJMmVkZAg7Oztx4cIFyTK8T0ZGhvDw8FAWTXKhUChUivS3/3nJhYGBgbIH8e18CQkJsjva/vz5cxEcHCyKFSsmtLW1Jc3i7u4u+vbtK1avXi2KFCkirly5IoQQIioqSpQvX17SbG/I+cuP3Hz99dfCyclJ+eVfiNe9AM7OzqJfv34SJhMiNDQ0z4scyOm9Vr9+faGvry9cXFzEqFGjRHh4uHj8+LFGM3ysxYsXCz09PaFQKETJkiXFlClTJO9hEeL1945p06YJPT09YWRkJLS1tUWjRo3EuXPnNJrD0NBQ3LhxQwghhIWFhbL4TUhIkLQIyE1GRoZYuHCh0NPTE1paWqJ69epi5cqVIjs7W6M5xowZIxo1aiTu3LkjTExMxJUrV0R0dLSws7MTAQEBGs3yrnv37ol58+aJqlWrCl1dXdG5c2exc+dOldfo4MGDwsjISMKUQqSnp4v169eL5s2bCx0dHdGkSRNhb28vTExMxPr16zWaZd68eWLw4MEafx/lR+PGjcXixYuFEK+/R74ZwTF06FDRokULKaPJVseOHcWGDRukjiF4TvpHunHjBry8vJCUlIT09HQ0a9YMJiYmmDNnDtLT0xEcHCxJLjnPPF+kSBGcOXNG6hgFkoWFBa5evZrj/Jzo6GhZnQeemJiI0NBQhIaG4tmzZ/D09JQ0z8KFC9GrVy+Eh4fju+++g729PQBg06ZNaNCggWS57t+/jzVr1iAkJARXrlxB27ZtsW7dOrRo0UJ5zpOvry+8vLwwb948jec7ceLEe88rlWK+jTfmzp0LLy8vVKlSBZaWlgCAW7duoXHjxpK8Tm/z8fGR9PHfR67vtYsXL8LIyAhVqlRBlSpV4ODgAHNzc409fn7du3cPYWFhCA0NxY0bN9ClSxf069cPt27dwpw5c3DkyBHs3r1b47kyMzPx119/YdWqVdizZw9q1aqFpUuXomfPnnjw4AEmT56Mrl274sKFCxrLZGlpiTt37sDa2hoVKlTA7t27UbNmTRw/flwW5wsDr1+3LVu2ICQkBHv27EG9evWUv89JkyZh7969+P333zWWZ9asWRgyZAisrKyQlZUFR0dHZGVloVevXpg8ebLGcuTG0tISFSpUUJ7LX7JkyRz7ODs7o3bt2hKkA06ePImQkBCsW7cOenp66Nu3L5YtW6b8nF+yZAmGDx+O7t27ayxTdHQ0IiMjsXPnTlStWhVFihRRuV3Kz89Zs2ahZcuWuHDhAl69eoVFixbhwoULOHToEPbv36/RLG/m0MqP4cOHf8YkeWvdujXGjRuHCxcuwMnJKcfvs127dhrJoRBCxlM0y1CHDh1gYmKClStXonjx4oiLi4OdnR2ioqIwYMAAXLlyRbJss2bNwuXLl7FixQro6Mjr+MuoUaOgp6cn6UyS79LW1sbdu3eVH0CmpqaIi4uDra2txMn+JygoCGvXrsWqVavQrFkz7NixAzdu3MCoUaMwZcoUDBs2TLJsaWlp2LRpE1atWoUDBw7AysoKfn5+8PPzk82ERu9KS0uDtrZ2jn+4mqKrq/vBLz/Pnz9H+/btNT6T8/r169G3b1+0aNECu3fvRvPmzXH58mXcu3cPHTt2REhIiEbzvEsIgT179iAuLg4GBgZwdnZGkyZNJM30RlZWFsLDwxEfHw8AqFq1Ktq1ayfp0pdyfa8JIXD27FlERUVh//79OHDgAHR1deHq6gp3d3cMGDBAY1lys3nzZoSEhGDXrl1wdHRE//790bt3bxQtWlS5T0JCAhwcHDS+UsSwYcOwbt06CCHQp08f9O/fH9WqVVPZ5+7duyhbtiyys7M1lmvChAkwNTXFpEmTsGHDBvTu3Rs2NjZISkrCqFGjJP3cP3XqlLKg09LSQt++fdG/f3+VyXTPnTuH2rVrS7ISQlJSEs6dO4fk5GS4uLhIPnEi8Hqy2saNG0sdI1dOTk64ePEimjdvjgEDBqBt27Y5/s8+fPgQpUqV0ujfgJ+fX563S/35ee3aNQQFBSEuLg7JycmoWbMmxo8fDycnJ43myO/3a4VCgWvXrn3mNO+npfX+xc8UCkWus9J/DizSP1Lx4sVx6NAhVK5cGSYmJsoi/fr163B0dERqaqpk2Tp27IiIiAgYGxvLZub5N4YNG4bVq1ejYsWK+Oqrr3Jkk2LmaC0tLVSrVk15QOPMmTOoUqVKjuXqTp06pfFsbwghMGvWLAQFBSnfW3p6ehg7dixmzJghSaZjx45h1apV2LBhA9LS0tCxY0f4+/ujadOmspnRdOrUqXB3d0eDBg1k05MjhEB0dDRq1aoFAwMDqePk4OzsjIEDB2LIkCHK/222trYYOHAgypQpg8DAQKkjytLVq1fRqlUr3L59G5UrVwYAXLp0CVZWVti+fTsqVKig8Uxyf6+9IYTAyZMnsXTpUvz222/Izs7W2Jef9zEzM0OPHj3Qv3//9/YUvnz5EnPnztX4UllNmzZF//790alTp/f+X3v16hViYmLg6uqq0WxvO3z4MA4fPoyKFSuibdu2kuUAXh+Mb9asGfr164cOHTrkeoA2JSUFQ4cOlbyQog+bMWMG/P39Ua5cOamjFAiZmZkYOHAgpkyZIqsOKMofFukfydzcHDExMXB0dFQp0qOjo9G5c2fcu3dPsmxyPpLn7u7+3tsUCgX27dunwTSv5bfokHrNUgDIyMjA1atXkZycDEdHR5Vl9jRNS0sL1atXR79+/eDt7S3LoarNmjXD4cOH8erVK9SuXVu5vFPDhg0lK1qys7Ohr6+P8+fPy6K35F1GRkY4f/48bGxsULx4cURFRcHJyQnx8fHw8PDAnTt3NJpn8eLF+Prrr6Gvr//BIXJSDotr1aoVhBD47bffUKxYMQDAo0eP0Lt3b2hpaWH79u0azyTn99qpU6cQFRWFqKgoREdH48WLF3BycoKbmxtcXV3Rvn17SfOlpqbC0NBQ0gykHllZWVi7di3atWsnu88pIQQ2bdqEyMhI3L9/P0evr6Y7VVxcXPJ9kF3Kjgu5e/DgAS5dugQAqFy5cq4jmDTNzMwMsbGxLNILIBbpH6l79+4wMzPDL7/8AhMTE5w5cwYlS5ZE+/btYW1tzSOxpFbPnj1DVlaW8sv/G48fP4aOjg5MTU01nqldu3ZYv3697L/Ivnr1CkePHsWBAwewf/9+HDp0COnp6ahduzaio6MlyVS1alWsXLlSFutVv8vS0hI7d+6Ek5MTnJ2dMXHiRPTs2ROHDx+Gl5cXnj17ptE8tra2OHHiBIoXL57nlwuph8UZGRnhyJEjOYYNxsXFoWHDhkhOTpYkl1zfazo6OnBxcVGujd6kSROVdXKldurUKRQpUkT5+/zrr78QEhICR0dHBAQE5Bhp9bm9vabwh2jqPMnc/Pvvv4iOjs614JTyIJq+vj7i4+NlV6CMGDECy5cvh7u7O0qXLp2jQNb0d8m3Oy3S0tLw008/wdHREfXr1wcAHDlyBOfPn8fgwYMRFBSk0Wxvy8rKQmhoKCIiInJ9r0nR4QO8Ho3xZsTom0za2tro27cvlixZIun3JR8fH9SoUQOjRo2SLENu/P3987x91apVGkqSu/3792PevHnK09gcHR0xbtw4jZ4KIq8TlwuA+fPno0WLFnB0dERaWhp69eqFK1euoESJEli3bp3U8QDI80je227dugUAygmg5GT27NkYNGiQyvmHUurRowfatm2LwYMHq2z/448/sHXrVuzYsUPjmbZv347k5GTZF+k6Ojpo2LAhSpYsiWLFisHExATh4eG4ePGiZJlmz56NcePG4eeff85xLqnUmjRpgj179sDJyQldu3bFiBEjsG/fPuzZswdNmzbVeJ7ExMRcf5YbPT09vHjxIsf25ORkjRd0b5Pre+3x48eSHFzMr4EDB2LChAlwcnLCtWvX0KNHD3Ts2BEbN25EamoqFi5cqNE8HTp0yNd+mjxP8l2hoaEYOHAgdHV1Ubx4cZWCU6FQSFqkV6tWDdeuXZNdkb5mzRps3rwZrVq1kjoKANURg/3798fw4cNznFI3bdo03Lx5U9PRVIwYMQKhoaFo3bo1qlWrJptT7EaPHo39+/fj77//RsOGDQG8nkxu+PDhGDNmDH7++WfJslWsWBHTp09HTExMrqebSvX3+eTJE5XrmZmZOHfuHJ4+fQoPDw9JMr2xdu1a+Pn5oVOnTsrXJyYmBk2bNkVoaCh69eqlmSAan0++EMjMzBRr1qwR48aNE99884349ddfRWpqqtSxRHJysvDz8xPa2tpCoVAIhUIhdHR0hL+/v+TLxWRlZYnAwEBhamoqtLS0hJaWljAzMxPTp08XWVlZkmZ7m4mJiayWYTM3N891Wb34+HhRrFgxCRLlXLpOjpYvXy569uwpypYtK4oXLy46dOggFi5cKGJjYyVdIkXOa2o/evRI3L59Wwjx+u81KChItG3bVowePVp2S2S9evVKnD59Wha5+vTpI6pWrSqOHDkisrOzRXZ2tjh8+LCoVq2a8PHxkSyXnN9rQghx4sQJsWbNGrFmzRpJ16t+l6mpqXKpv9mzZ4vmzZsLIYSIjo4WlpaWUkaTLUtLS/H999/L6rP8jZ07d4oaNWqIv//+W/z777/i2bNnKhep2NjYiPj4eMkePy+mpqa5Lpl7+fJlyZfUK168uNi+fbukGXJTvHhxERkZmWP7vn37RIkSJTQf6C02Njbvvdja2kqa7V1ZWVni66+/FnPmzJE0R5UqVcSCBQtybJ8/f76oUqWKxnKwJ/0T6OjooHfv3lLHyEHOR/K+++47rFy5ErNnz1bJFhAQgLS0NMycOVOybG8TMjv7Iz09Ha9evcqxPTMzU5KZaN+Qy9Hr9xk0aBBKliyJMWPGYPDgwZKew/82TffCfYy3T6nQ0tLChAkTJEyjauTIkXByckK/fv2QlZWFJk2a4PDhwzA0NMS2bdvg5uYmWbbFixfDx8cH9evXV05K9erVK7Rr1w6LFi2SLJdc32v3799H9+7dsX//fuWIpadPn8Ld3R3r16+XfOSXEEI5XHXv3r1o06YNAMDKygoPHz6UMppspaamokePHnnOiCyVNz3V7dq1U/ncEkJIOvogICAAgYGBWLVqlewmdzQwMEBMTEyO+SxiYmKgr68vUarXdHV1lUutyUlqaipKly6dY3upUqUknVAakPdItHdpaWlh9OjRcHNzw7fffitZjmvXruU66WW7du0wadIkjeXgOen5UFDOCStRogQ2bdqU4wtrZGQkunXrhgcPHkgTDEDZsmURHByc4/X566+/MHjwYNy+fVuiZKrengxQDtzd3VGtWjUsWbJEZfuQIUNw5swZHDx4UOOZtLS0YGZm9sFC/fHjxxpKlFN4eDgOHDiAqKgoxMfHw8XFBW5ubnBzc0OjRo1kP1RfCjt27IC2tjZatGihsn337t3IyspCy5YtJUr2+tSY8PBw1KpVC+Hh4RgyZAgiIyOxZs0a7Nu3DzExMZLkEkLg5s2bKFmyJG7fvq08d83BwUGWXyTloHv37rh27RpWr14NBwcHAMCFCxfg4+MDe3t7yU8b8/DwgJWVFTw9PdGvXz9cuHAB9vb22L9/P3x8fHD9+nWN5ikIEyh+++23KFasmKwO7L3xoXWgpZoF/+XLl+jYsSNiYmJgY2OTY9Z5KSdnmz17NgIDAzFgwADUqVMHAHD06FGsWrUKU6ZMkfT3PH/+fFy7dg1Lly6VVWdB06ZNUbx4caxevVp5IOPly5fw8fHB48ePsXfvXokTFhw7duyAj4+PpDWLvb09xo0bh4EDB6psDw4Oxvz58zW23DaL9HzI79FhKY/KAoChoSFOnjyp/OLzxvnz51GnTh2kpKRIlOz15C1nzpxBpUqVVLZfunQJNWrUkLRX+G03b95E2bJlJV3f+G0xMTHw9PRE7dq1lecFR0RE4Pjx49i9e7cka5lqaWlh4cKFH5zsycfHR0OJ8vbs2TMcPHgQGzduVK6Vm5aWJlmehIQEhISEICEhAYsWLUKpUqWwc+dOWFtbo2rVqpLlcnZ2xuzZs3OcI/nPP/9g/PjxiIuLkyjZ6/8fV69ehaWlJb7++msYGhpi4cKFSExMRPXq1fH8+XNJcsl5FnVAnuu3m5mZYe/evTmWNzt27BiaN2+Op0+fShPs/505cwbe3t5ISkrC6NGjlefqDhs2DI8ePcLvv/+u0TwFYQLFrKwstGnTBi9fvoSTk1OOglOKJVblrlu3boiMjESXLl1ynThO6lVl/vjjDyxatEjlwOOIESPQrVs3jWfp1KmTyvV9+/ahWLFiqFq1ao73mlRLDZ87dw4tWrRAeno6qlevDuD15KH6+vrYtWuXpJ/tcp2gbfTo0SrXhRC4c+cOtm/fDh8fHyxdulSSXADw888/Y+TIkfD390eDBg0AvP4+HhoaikWLFuUo3j8XFumFiJyP5NWtWxd169bN0RMwbNgwHD9+HEeOHJEomfzFxsbihx9+QGxsLAwMDJQzb0tVFGhpaeHu3bsoVaqUJI+fX48ePcL+/fuVyz2dP38e5ubmaNy4MbZs2SJJpv3796Nly5Zo2LAhDhw4gPj4eNjZ2WH27Nk4ceIENm3aJEku4PUQx/j4eNjY2Khsv379OqpWrSrpQb7y5cvj119/RdOmTWFra4uff/4ZrVu3xvnz59GoUaMcE9BoklxnUZfj+u3A69FKBw8eRI0aNVS2nz59Gq6urpIdcPmQtLQ0aGtr57rO9pfu+++/x9SpU1G5cuUcBadUS6y+KzU1FUlJScjIyFDZ7uzsLEkeIyMj7Nq1C40aNZLk8QuSDy0v/DYpV1hKTU3Fb7/9ppyc1sHBAd7e3pKfztCxY0eV6+9O0CbVgY13l2bW0tJCyZIl4eHhAX9/f+joSHtG9pYtWzB//nyVA1Xjxo3T7DKhGjv7nT67s2fPKifK8vDwEB4eHqJ48eKiXLly4ty5c5Jmi4qKEkZGRsLBwUH4+/sLf39/4eDgIIyNjcWBAwc0nsfc3Fw8ePBACPF6gqV3J1WS0wRLcqOlpSX7ieOqVasmtLW1RYkSJUSnTp3E4sWLRVxcnNSxRL169cT8+fOFEEIYGxsrJyk8evSoKFeunJTRROnSpUVERESO7Xv27BElS5aUINH/TJs2TZiZmYkqVaoIa2trkZaWJoQQYuXKlaJevXqSZtu6dato1KiROHv2rKQ53tWyZUvh5eUlHj16pNz28OFD4eXlJVq1aiVZrnbt2okmTZooJykUQohbt24JV1dX0aFDB8ly0acrWrSoCAkJkTpGru7fvy9at26tnLD23YtUKleuLIvPpPd58uSJ+PXXX8XEiROV/0NOnjwpbt26JXEyUge5TNBGeWNPej586Dywt0m51Agg3yN5wOt1VJctW6aSbfDgwShbtqzGs4SFhaFHjx7Q09NDaGhonuc2ST1sOzs7G1evXs11TdAmTZpoPE9uPemDBw/G9OnTUaJECY3nyc2yZcvg6uoqq6WnAMDY2Bhnz56Fra2tyvwH169fR5UqVSQdhj9w4EAcPnwYW7ZsUfayXr16FZ07d0bt2rWxYsUKybIBwKZNm3Dz5k107dpVuXxjWFgYihYtqtkj2+8wNzdHamoqXr16BV1d3Rz/a6Wam0Gu67ffvHkT7dq1w/nz52FlZaXcVq1aNWzdulXypTmzsrLw448/4o8//si151XKuTaEENi0aRMiIyNz/TyQqkfMwsICBw8elOUpH97e3rhx4wYWLlwINzc3bNmyBffu3cP333+P+fPno3Xr1pLk2r59O5YsWYLg4OAco5ekdubMGXh6esLMzAzXr1/HpUuXYGdnh8mTJyMpKQmrV6+WOiLu37+vstSwFCP7tm7dipYtW6JIkSIfnLtKyvmq3ufSpUtwc3PDnTt3pI5C78EiPR/yu76mlOeEUeF05MgR9OrVCzdu3Mgx87zUcyC8zdTUFLGxsbKZcO+NjIwMJCYmokKFCpIPnQJeT4D2xx9/oEGDBipF+pYtWzB27FgkJCRIlu3Zs2fw8vLCiRMnlIXSrVu30LhxY2zevFk5EzepCgsLy/N2qQ7yFStWDNu2bVOeT/dGTEwM2rZtK3mxuXfvXpUDtp6enpLledvUqVOxYsUKjBkzBpMnT8Z3332H69evIzw8HFOnTpX0QPyIESOwfPlyuLu753oes1RDfYOCgnDnzp2P6tDQlDJlyuCvv/5CnTp1YGpqihMnTqBSpUrYunUr5s6di+joaElyvX1wz9DQMMdpFFL+fXp6eqJmzZqYO3euyufUoUOH0KtXL41Pnvi258+fY8iQIVi/fr3y+4+2tja6d++OZcuWfXCuHHV6u8Mir7mr5PRd7W1STNDm4uKS7wn/ND15YrFixXD58mWUKFEC5ubmeebU1N+n9N9aC4CCtHzBpUuXsGTJEpVzKIYOHYoqVapInOz1MjvHjh3LtQegb9++EqWS96zWgwYNQq1atbB9+3aUKVNGVrOZvk1ux/pevnyJoUOHKguoy5cvw87ODsOGDUO5cuUkm522R48eGD9+PDZu3AiFQoHs7GzExMRg7Nixkv4NAK8n9Dp06BD27NmDuLg45fwHUozWeNf06dPzvH3q1KkaSqIqMzMT+/fvx5QpU/J9MFdT2rRpg6+//horV65UmaF50KBBkvfqKBQKNGvWDM2aNZM0R25+++03/Prrr2jdujUCAgLQs2dPVKhQAc7Ozjhy5IikRfqaNWuwefPmHJM7Su3YsWPYt28ftm3bJqvJvAAgJSVF2ctqbm6OBw8eoFKlSnBycpJ0BnW5LpEIAMePH8fy5ctzbC9Xrhzu3r0rQaL/GTBgAE6fPo1t27ahfv36AIDDhw9jxIgRGDhwINavX6+xLG9/j333O62cfGiCNk3q0KGD8ue0tDT89NNPcHR0VP4ujxw5gvPnz2Pw4MEazQUAP/74I0xMTJQ/y+H7NnvSC5E///wTPXr0QK1atVTe8MePH8f69evRuXNnybL9/fff8Pb2RnJyMkxNTXNMLCPlUWM5z2ptZGSEuLg42S/nJLel60aMGIGYmBgsXLgQXl5eOHPmDOzs7PDXX38hICAAp0+fliRXRkYGhgwZgtDQUGRlZUFHRwdZWVno1asXQkNDZbOqgNy4uLioXM/MzERiYiJ0dHRQoUIFSb9sm5mZITY2VnZF+tOnT+Hj44O///47x/rtISEhko6MiIiIQERERK4HbKWaafgNIyMjxMfHw9raGmXKlMH27dtRs2ZNXLt2DS4uLnj27Jlk2WxtbbFz505ZHHR/24cm9pJyMq/atWvj+++/R4sWLdCuXTsULVoUQUFBWLx4MTZt2iTp6CW5KlWqFHbt2gUXFxeVz/Y9e/bA398fN2/elCzb+ybcO3jwILy8vCSb4HT16tXo3r079PT0VLZnZGRg/fr1kh6El+sEbf3790eZMmUwY8YMle3Tpk3DzZs3Jf8skAMW6fkwevRozJgxA0ZGRjmOSL1LyqVGKlSoAG9v7xy9TtOmTcPatWsl/TCqVKkSWrVqhVmzZslujWo5z2rt4eGBb7/9Fl5eXpJleJ+UlBQYGRlJHSNX5cuXx4YNG1CvXj2VLxlXr15FzZo1JZ9B+ubNmzh79iySk5Ph4uIi2bmcBWEN5vd5/vw5fH190bFjR/Tp00eyHD4+PqhRowZGjRolWYa8XL16VVbrtwcGBmL69OmoVatWrqODpFp54Y3KlStj9erVqFu3Lho1aoQ2bdpgwoQJ2LBhA4YNG4b79+9Lli0sLAz//PMPVq1aJYt5ZgqCtWvX4tWrV/D19cXJkyfh5eWFx48fQ1dXF6GhoejevbvGsjx//hympqbKn/PyZj8p9O/fH48ePcIff/yBYsWK4cyZM9DW1kaHDh3QpEkTSUcBWFtbY/v27Tnm2jhz5gxatWqFW7duSZJLW1sbd+7cyXFu/KNHj1CqVClZDneXmpmZGU6cOJHj+8+VK1dQq1YtSQ+Iyub3Kcl0dQWMm5ubePLkiRBCCFdXV+Hm5pbrxd3dXdKcBgYG4sqVKzm2X758WRgYGEiQ6H8MDQ2VM1nLjZxntd68ebNwdHQUISEh4sSJEyIuLk7lIiUjIyPh5+cnDh48KGmO3BgYGCjfb2/Poh4bGytMTU0lyxUYGChSUlJybE9NTRWBgYEaz2NjYyMePnyo/Pl9F1tbW41ny48zZ86I8uXLS5phxowZomjRoqJz585i1qxZYtGiRSoXqcjtvfaGhYWFWL16tWSP/yHjx48XM2fOFEIIsX79eqGjoyPs7e2Frq6uGD9+vKTZUlNTRYsWLYSxsbGoVq2acHFxUbnQh6WkpIiTJ08qV3fRpLdXRlEoFLnONv9mu5SePn0qPD09RdGiRYW2trawsrISRYoUEU2aNBHJycmSZlu+fLnw9PQUd+7cUW67c+eOaN68uQgODpYsl0KhEPfv38+xPTY2VvJVgtzd3ZU1zNuePXsmad1SunTpXFeFCAkJEaVKldJ8oLcoFIpcVzG6ffu20NfX11wOIdiTXli0atUKXbt2zTH0LCQkBOvXr8euXbskSgZ06tQJPXr0QLdu3STL8D5yntU6t8lIFAoFhBCST0YSHh6O0NBQ7NixAzY2NvD390ffvn0lma3/XU2aNEHXrl0xbNgwmJiY4MyZM7C1tcWwYcNw5coV/PPPP5Lkks3R2UIiOjoabdu2lXSd9LyGuUs5mahc32vFixfHsWPHJFun/WMdOXIEhw4dQsWKFdG2bVtJs3Tr1g2RkZHo0qVLrhPHTZs2TaJkr1dfeN+M+FKejjJ9+nSMHTs2xwi+ly9f4ocfftDofBb79+9Hw4YNoaOjg/379+e5r6urq4ZSvV90dDTOnDmD5ORk1KxZUxaTO7q4uODq1atIT0+HtbU1ACApKQl6eno5emQ18b57MxFaXFwcqlatqjJ0PCsrC4mJifDy8sIff/zx2bO8T24r8gCvZ8gvV64cMjMzJck1e/ZsBAYGYsCAASrzpqxcuRJTp06VZO6gNyMKR40ahRkzZsDY2Fh5W1ZWFg4cOIDr169r7JRJFukfITMzEwYGBoiNjZXd0k4AEBwcjKlTp6Jbt26oV68egNdfMDZu3IjAwECV4kkTEwe9vSTFgwcPMH36dPj5+cHJySnHxDJSTmSU26zWN2/eRJMmTSSf1frGjRt53l6+fHkNJXm/Bw8eYM2aNQgNDUV8fDxatGgBf39/tGvXTrJznaKjo9GyZUv07t0boaGhGDhwIC5cuIBDhw5h//79+OqrryTJpaWlhXv37qFkyZIq2/ft24fu3btrdJbVD8nKysLZs2dRvnx5mJubS5rl3aH44v8nvlmzZg1cXV3x+++/S5RMvuT6Xhs/fjyMjY0xZcoUSR7/Qw4cOIAGDRrk+N/16tUrHDp0SNKJFN93Pq7UFi9ejO+++w6+vr745Zdf4Ofnh4SEBBw/fhxDhgzBzJkzJcsmx4NVr169wqxZs+Dv7y/5koMFTWBgYL731cRBqzd5AgMDMWbMGJWiTldXFzY2NujcuTN0dXU/e5Z3nTlzBgBQo0YN7Nu3D8WKFVPelpWVhX/++QfLly+XdLb+P/74A4sWLVKekuXo6IgRI0bAwcFBkjrrzUH3GzduwNLSUmWeoDe/z+nTp6Nu3boaycMi/SO9WS6pevXqUkfJIa8lIN6mqR5YueXJixBCZVbr6tWro3HjxpJmKoiWLFmCcePGISMjAyVKlMCgQYMwYcIESeYhSEhIwOzZsxEXF6fsCRg/fnyOc9k04c1yHs+ePcsxcWJWVhaSk5MxaNAgLFu2TOPZ3hg5ciScnJzQr18/ZGVloUmTJjh8+DAMDQ2xbds2uLm5SZbt3d7qtye+mThxonJGVqm9+TiVclZYOb7X3p7LJTs7G2FhYXB2doazs3OOA7ZSzusCyLOoe6NKlSr4448/4OzsLFmG3FSpUgXTpk1Dz549VeYAmTp1Kh4/foylS5dKlk2uB6tMTExw9uxZ2a2R/sbx48cRGRmZ6+SOUv+NylFYWBi6d+8OfX19qaMoaWlpKf//51bqGRgYYMmSJfD399d0tFw9f/4c69atw8qVK3Hy5ElJ/9e6u7tj8+bNkndQsEj/SCtXrsTmzZuxZs0alaNSVPAcPnwYjx49Qps2bZTbwsLCMG3aNKSmpqJDhw5YsmRJjtk6pXDhwoVchxFKvZQSANy7dw9hYWEIDQ3FjRs30LFjR/Tr1w+3bt3CnDlzULZsWezevVvqmJIKCwuDEAL+/v5YuHChylqub47OvlmRQSqWlpYIDw9HrVq1EB4ejiFDhiAyMhJr1qzBvn37EBMTI2k+OVu9ejV++OEHXLlyBcDriTLHjRsnyYR2cnyvvTu78PsoFArs27fvM6fJ2/uKusuXL6NWrVqSTjq5fft2LFmyBMHBwbIq7gwNDREfH4/y5cujVKlS2LNnD6pXr44rV66gXr16ePTokcYzyfFg1dvat2+PTp06aXwJrPyYNWsWJk+ejMqVK+c4rUIOf6OUPzdu3IAQAnZ2djh27JjK/zRdXV2UKlVKFivKHDhwACtXrsSff/6JsmXLolOnTsrTTb90XCf9Iy1duhRXr15F2bJlUb58+RyzW0tx7lVuxebq1asxbdo0pKSkSFpsyjnb9OnT4ebmpsx29uxZDBgwAD4+PnBwcMAPP/yAsmXLIiAgQOPZ3rh27Ro6duyIs2fPKs9FB/7XUyflkcbNmzcjJCQEu3btgqOjIwYPHozevXurnB7QoEEDODg4aCzT20eO30ehUODVq1caSvTamy9itra2ynMS5ebhw4ewsLAAAOzYsQNdu3ZFpUqV4O/vj0WLFkmSKb9H+KVcqmXBggWYMmUKhg4dioYNGwJ4fbrFoEGD8PDhQ43P+i7H91pkZKTUET6oU6dOAF7/f/D19VX5TMrKysKZM2fQoEEDqeIBAHr37o3U1FRUqFABhoaGOUYhSLWUqYWFBR4/fozy5cvD2toaR44cQfXq1ZGYmJhrD54mLFy4UHmwKjAwUBYHq97WsmVLTJgwAWfPnsVXX32V47uklAfgFy1ahFWrVsHX11eyDO+TlZWFH3/88b3zH0j1NyDHXG9Oh5TjGu53795FaGgoVq5ciefPn6Nbt25IT09HeHg4HB0dpY6Hzp07o06dOhg/frzK9rlz5+L48ePYuHGjRnJI/+ldwHTo0EHqCDnkVmz269cPvr6+khebgYGBcHd3l2W22NhYlfUZ169fjzp16uDXX38FAFhZWWHatGmSFukjRoyAra0tIiIiYGtri2PHjuHRo0cYM2YM5s2bJ1ku4PXauD169EBMTMx7j3iWLVsW3333ncYy5bV80+HDh7F48WJJP7BMTEwQHx+vHHL/119/ISQkBI6OjggICJDkvLU3SpcujQsXLqBMmTL4559/8PPPPwMAUlNTJTvaHhoaivLly8PFxUWyL/sfsmTJEvz8888q6+C2a9cOVatWRUBAgGRLs8n5vfa2GzduICUlBVWqVMn3KVKfw5siTggBExMTlSXOdHV1Ua9ePQwYMECqeAAg6dJXefHw8MDWrVvh4uICPz8/jBo1Cps2bcKJEyeUBz807e2DVQ0aNMhxQENqgwcPBpD70HGpTwHU0tJSHnCUm8DAQKxYsQJjxozB5MmT8d133+H69esIDw/X6CSABSXXG1euXHnv6Quazte2bVscOHAArVu3xsKFC+Hl5QVtbW0EBwdrNEdeDhw4kOt3/5YtW2L+/PmaC6KxeeTps7GwsBDHjx9XXp80aZJo2LCh8voff/whHBwcpIgm62x6enoiKSlJeb1hw4bi+++/V15PTEwUxsbGUkRTKl68uHKpNVNTU3Hx4kUhhBARERGiRo0aUkbLdXknObp48aLo0KGD0NbWFn379hXXr1+XLEutWrXEpk2bhBBCJCQkCD09PdGzZ09hb28vRowYIVkuIYSYNm2aMDMzE1WqVBHW1tYiLS1NCCHEypUrRb169STJNHjwYGFubi5q1KghFi1aJB49eiRJjrzo6em9d+lLPT09CRK9Jrf32sqVK8X8+fNVtg0YMEC59JSDg4PK/2OpBAQESL7MVEGTlZUlMjMzldfXrVsnhg0bJhYvXizS09MlTPZaVlaWuHTpkjh48KDYv3+/yoVymjNnjuSfR+9jZ2cntm3bJoR4vbzq1atXhRBCLFq0SPTs2ZO5cvHLL78IbW1tUbp0aVG9enVRo0YN5UWKpRu1tbXFqFGjxOXLl1W26+joiPPnz2s8T2709fWV37ffFh8fr9El2Fikf6L09HRx8+ZNcePGDZWLFORcbMo5m7W1tfJDOj09XRgYGIi9e/cqbz9z5ozk61sWLVpUXLt2TQjx+kNg3759Qgghrl69KgwMDKSMprLm69sePnwo+TqvQrxez7J///6iSJEiok2bNuLs2bNSRxKmpqbKD+/Zs2eL5s2bCyGEiI6OFpaWllJGE0IIsXHjRrFgwQJx8+ZN5bbQ0FARHh4uWaa0tDTx+++/C09PT2FoaCi6du0q/vnnH5GdnS1ZprdVrVpVua7222bMmCGqVasmQaLX5PZeq1u3rli1apXy+s6dO4WOjo5Yu3atOHnypKhfv77o16+fxnMVVC9fvhTPnj1TuUghMzNTBAYGqvzPkJPDhw8LW1tb5Rrkb1+k+JyKiIgQDg4Ouf6+nj59KhwdHcWBAwc0nuttWVlZwsvLS9jZ2Yk2bdqIjh07qlykZGhoqPyubWFhIU6ePCmEeH0g0tTUlLlyYW1tLWbPni1phrcdPnxY9O/fX5iYmIg6deqIJUuWiAcPHsiqSK9du7YIDAzMsX3atGmiZs2aGsvB4e4f6fLly+jXrx8OHTqksl1IuG516dKlkZiYCCsrK2RkZODUqVMqy1S8ePFCsqFecs7WqlUrTJgwAXPmzEF4eDgMDQ1VZnQ/c+aM5Gv5VqtWDXFxcbC1tUXdunUxd+5c6Orq4pdffoGdnZ2k2cR7hh+np6dLOpT22bNnmDVrFpYsWYIaNWogIiJCNjP1CyGUQ8327t2rPA3EysoKDx8+lDIaAKBLly45tkk9sZGenh569uyJnj174saNGwgNDcXgwYPx6tUrnD9/XmXJGykEBgaie/fuOHDggHKIaExMDCIiIiRdG1du77UrV66gVq1ayut//fUX2rdvD29vbwCvJ6vy8/PTeK533bt3D2PHjkVERATu37+f4/+clMOQU1JSMH78ePzxxx+5TsYmRTYdHR3MnTtX5XQPORk0aBBq1aqF7du3o0yZMpKuvAC8PmVhwIABMDU1zXGbmZkZBg4ciAULFkj6mTV8+HBERkbC3d0dxYsXl/w1e5ulpSXu3LkDa2trVKhQAbt370bNmjVx/PhxSSf5lWsuAHjy5Am6du0qaYa31atXD/Xq1cPChQuxYcMGrFq1CqNHj0Z2djb27NkDKysryVdsmTJlCjp16oSEhAR4eHgAACIiIvD7779j06ZNGsvBIv0j+fn5QUdHB9u2bZPFP3xA3sWmnLPNmDEDnTp1gqurK4yNjREWFqZSXK5atQrNmzeXJNsbkydPRkpKCoDXcw+0adMGjRs3RvHixbFhwwZJMr1Zs1qhUGDFihUqRVJWVhYOHDiAKlWqSJJt7ty5mDNnDiwsLLBu3Tq0b99ekhzvU6tWLXz//ffw9PTE/v37led9JyYmonTp0pJmmz59ep63y+G8ujcTAwohJF+28Y3OnTvj6NGj+PHHHxEeHg4AcHBwwLFjx+Di4iJZLrm9116+fKlSmBw6dAj9+vVTXrezs8Pdu3c1nutdvr6+SEpKwpQpU2TzGf/Gt99+i8jISPz888/o06cPli1bhtu3b2P58uWYPXu2ZLmaNm2K/fv3y2rG+TeuXLmCTZs2wd7eXuooAIC4uDjMmTPnvbc3b95c8vlmwsLC8Oeff6J169aS5shNx44dERERgbp162LYsGHo3bs3Vq5ciaSkJMnm/5BzLgDo2rUrdu/ejUGDBkma411GRkbw9/eHv78/Ll26hJUrV2L27NmYMGECmjVrhq1bt0qWrW3btggPD8esWbOwadMm5dLM7643/7lxCbaPZGRkhJMnT0pWhOTm4cOH6NSpE6Kjo5XFZseOHZW3N23aFPXq1cPMmTOZLRfPnj2DsbFxjsmxHj9+DGNjY9lMsPTG48ePlcvLSOHNmtU3btyApaWlyuv2Ztbc6dOno27duhrPpqWlBQMDA3h6euY52dnmzZs1mOp/4uLi4O3tjZs3b2L06NGYNm0aAGDYsGF49OgRfv/9d0lyAchRUGZmZiIxMRE6OjqoUKGCJCtXAK9HZmzevBmrVq1CdHQ02rRpAz8/P3h5eUk60Vh+l+LKrcdME86cOQNvb28kJSXJ4r3m4OCAmTNnolOnTsqVBI4ePYqvvvoKAHDs2DG0a9dO8kLdxMQEBw8eRI0aNSTNkRtra2usXr0abm5uMDU1xalTp2Bvb481a9Zg3bp12LFjhyS5goODERgYCG9vb9nNVO7h4YFvv/0WXl5ekmV4m76+Ps6dO/fegwZXr16Fk5MTXr58qeFk/1O+fHns2rVLVt9z3+fIkSM4dOgQKlasiLZt20odR+nw4cM4fPiwLHIFBQVhwYIFaN26NZycnHKMXh0+fLhEyXLKysrC33//jVWrVklapL9LqjXcWaR/pNq1a+PHH39Eo0aNpI6Sg5yLTTlno0/j7u6OzZs3w9zcXOooSr6+vvk6eBESEqKBNPmXlpYGbW1t2c1A/Pz5c/j6+qJjx46SrPk9ePBgrF+/HlZWVvD394e3tzdKlCih8Ry5yc9yf4C0w6NzI9V7bfbs2Vi0aBEGDx6Mffv24cGDBzh37pzy9oULF2Lbtm3Yu3evRnO9y9HREb/99pukoyDex9jYGBcuXIC1tTUsLS2xefNm1KlTB4mJiXByckJycrIkufI6WCb1TOVbtmzB5MmTMW7cuFwLFGdnZ43mqVChAubPn//elYI2b96MsWPH4tq1axrN9baQkBD8888/CAkJgaGhoWQ53pWZmYmBAwdiypQpys4C+rC8XiuFQiHpe03upF7DnUV6PrzdY3LixAlMnjwZs2bNyvUfvlS9JlQ4paWlYcmSJe9dOkOq3k36ND4+PujXrx+aNGkidZR8O3v2LNq2bYvr169r/LG1tLRgbW0NFxeXPAtiKUZG7N+/X/mzEAKtWrXCihUrUK5cOZX9XF1dNR1NlrKzsxEQEIC///4bFhYWWLBgARwcHJS3d+3aFV5eXipD4KWwe/duzJ8/H8uXL5fd8G1nZ2csWbIErq6u8PT0RI0aNTBv3jwsXrwYc+fOxa1bt6SOKDu5HUB4c8qMFAcQhg0bhqioKBw/fhz6+voqt718+RJ16tSBu7u78rQyKbi4uCAhIQFCCNjY2OT4nivl9w4zMzPExsbKskj/999/ER0dnet3NTn1VlPeclvDPTg4GHFxcRpfw51Fej6822Py5p/726ScOI4KL29vb+zevRtdunRB6dKlc7zv3gxh1ZTRo0djxowZMDIywujRo/PcN7f1X790HTp0wI4dO1C+fHn4+fnBx8cnR1EnN9HR0Wjbti2ePHmi8ccuSCMjTExMEBcXJ+mEjsWKFcPly5dRokSJD54S8/jxYw0mKzjMzc2RmpqKV69ewdDQMEeBIsXrdu3aNdjY2GDRokXQ1tbG8OHDsXfvXrRt2xZCCGRmZmLBggUYMWKExrNlZ2cjNDQUmzdvxvXr16FQKGBnZ4fOnTujT58+kp/Tf+PGjTxvL1++vIaSvHbv3j3UrFkT2traGDp0KCpXrgwAuHjxIpYtW4asrCycOnVK0jlK3p7cNzea/t7xNh8fH9SoUUPy87zfFRoaioEDB0JXVzfHZHty6a3OyMhAYmIiKlSoAB0dTkmWm7fXcPf29lau4V6kSBEW6XL1do/Jh7DXhNTJzMwMO3bsUM4aLTV3d3ds2bIFRYsWhbu7+3v3UygU2LdvnwaTFRwPHjzAmjVrEBYWhgsXLsDT0xP+/v7o0KGDpMPd3+25EULgzp07WLNmDVxdXSU9X74gkEORHhYWhh49ekBPTw+hoaF5FkhSz9oPvD6dYfr06bI5hQF4/RrmRYrXTVtbG3fu3EGpUqUAAN27d8fixYuRlpaGkydPwt7eXuPDtoHX/yPatm2LHTt2oHr16qhSpQqEEIiPj8fZs2fRrl075WSK9D83btzAN998g127dilXD1AoFGjRogWWLVsmy15iufj+++8xf/58NG3aNNf5D6TqsbayssKgQYMwceJESedKyU1qaiqGDRum/N92+fJl2NnZYdiwYShXrhwmTJggcUL50NHRwfDhw/HNN9+gYsWKyu0s0mVu+vTpGDt2rKzOz6HCz9HREevXr5fkCxh9fqdOnUJISIhylvzevXtj8ODBKh8OmvLuF0MtLS2ULFkSHh4emDhxouRLosidHIp0QP4T2r2bITY2VvLXTO60tLRw9+5dZZEul/daSEgIRowYgb/++ivHQdt9+/ahQ4cOWLp0qeTLs61ZswbBwcFITEzE4cOHUb58eSxcuBC2traSrgDy5MkTXL16FUIIVKxYUVbzuwDAyZMnER8fDwCoWrWqLOZpkOv51cWLF8exY8ckX7Y3NyNGjEBMTAwWLlwILy8vnDlzBnZ2dvjrr78QEBCA06dPSx1RNo4cOYKVK1diw4YNcHBwQJ8+fdCjRw+UKVNGkiIdGlmNvRDQ0tIS9+7dkzoGfWF27NghvLy8xPXr16WOQmr277//itmzZ4vKlSsLIyMj0bdvX9G0aVOho6MjFixYIHU8+kjGxsbi2rVrUscQCoVCaGlpffAiB8bGxiIhIUHqGO/18uVL8ezZM5WLFBQKhcr3D7m8bs2aNRNBQUHvvX3mzJmiefPmGkyU008//SRKlCghvv/+e2FgYKB83UJCQoSbm5uk2eTq3r17wt3dXSgUCmFubi7Mzc2FQqEQHh4e4v79+1LHk6Vx48bl+bcgJWtra3H48GEhhOr/jitXrggTExMpo8lWcnKyWLlypWjYsKEoUqSI0NLSEgsXLhTPnz/XaA72pOfTu0eyiTThwYMH6NatGw4cOCCL8yM7deqU732lWuZMzjIzM7F161aEhIRg9+7dcHZ2Rv/+/dGrVy9lz+aWLVvg7++vsXPA/f3987XfqlWrPnOSguXdv4W///4bHh4eOYZfavrvoCBNaCeXHuG3paSkYPz48fjjjz/w6NGjHLdLMe+MtrY27t69i5IlSwJ4/bqdOXNG8mHRFhYW+Oeff967XN3p06fRsmVLSZfVc3R0xKxZs9ChQweV99u5c+fg5uaGhw8fSpZNrrp3745r165h9erVyskdL1y4AB8fH9jb22PdunUSJ5SfrKwstGnTBi9fvsx1Umkp5+gxNDTEuXPnYGdnp/I3EBcXhyZNmuDZs2eSZSsI3qzhvmbNGjx9+lSja7hz5oCPIPUEKPTl6dmzJ27fvo1Zs2blOnGcppmZmUn6+AVdmTJlkJWVhV69euHYsWO5frl1d3dH0aJFNZYpNDQU5cuXh4uLC3jMNv/e/Vvo3bu3RElUvVt8a2tro169erIqhN948eKF1BFy+PbbbxEZGYmff/4Zffr0wbJly3D79m0sX74cs2fPliSTEAK+vr7Q09MD8HrVj0GDBkl+QOjx48d5TnBWunRpSSacfFtiYmKuw7T19PSQkpIiQSL5++eff7B3716V1RccHR2xbNkyNG/eXMJkeO+EtQqFAvr6+rC3t0f79u1RrFgxjeYKCgrCrl27lBMBvjtxnJRq1aqF7du3Y9iwYSp5VqxYgfr160sZrUCoXLky5s6di6CgIOUa7prCIv0jVKpU6YN/bJwxl9Tp0KFDOHz4MKpXry51FADymEW7IPvxxx/RtWvXHEvvvK1o0aJITEzUWKZvvvkG69atQ2JiIvz8/NC7d2+Nf8EpiPi38OkSEhIQEhKCa9euYeHChShVqhR27twJa2trVK1aVdJsf//9N1avXg03Nzf4+fmhcePGsLe3R/ny5fHbb7/B29tb45nenaxOLgeEsrKy8pwlWltbG69evdJgopxsbW0RGxubYxb3f/75R6UIpf/Jzs7OdRLTIkWK5FhaTNNOnz6NU6dOISsrS1kQX758Gdra2qhSpQp++uknjBkzBtHR0Ro9f3j+/PlYtWoVfH19NfaY+TVr1iy0bNkSFy5cwKtXr7Bo0SJcuHABhw4d+qiJsb902tra6NChAzp06KCxx2SR/hECAwPZk0gaVaVKFbx8+VLqGPQfvT2kPDIy8r37STGkfNmyZViwYAE2b96MVatWYeLEiWjdujX69euH5s2bS94LQIXL/v370bJlSzRs2BAHDhzA999/j1KlSiEuLg4rV67Epk2bJM33+PFj5agDU1NT5YH3Ro0a4ZtvvpEkk1wPCL3bw/+u9PR0DSfKafTo0RgyZAjS0tIghMCxY8ewbt06BAUFYcWKFVLHkyUPDw+MGDEC69atQ9myZQEAt2/fxqhRo9C0aVNJs73pJQ8JCVGeIvbs2TP0798fjRo1woABA9CrVy+MGjUKu3bt0lguPT092azC865GjRohNjYWs2fPhpOTE3bv3o2aNWvi8OHDcHJykjoe5YHnpOcTz0knKezevRuBgYGYOXNmruc5ST1D86ZNm/DHH38gKSkJGRkZKredOnVKolTyo6Wlla8h5Vu2bNFgqtzduHEDoaGhWL16NV69eoXz58/D2NhY6lj0ieRy/vIb9evXR9euXTF69GiV8yOPHTuGTp064datW5Lmc3Z2xpIlS+Dq6gpPT0/UqFED8+bNw+LFizF37lzJ88mJn59fvvaT+iDDb7/9hoCAACQkJAAAypYti8DAQPTr10/SXHJ18+ZNtGvXDufPn4eVlZVyW7Vq1bB161ZYWlpKlq1cuXLYs2dPjl7y8+fPo3nz5rh9+zZOnTqF5s2ba3S+gaCgINy5cyfHUqZE/wV70vOJvUkkBS8vLwDIcfRaCAGFQiHJJEZvLF68GN999x18fX3x119/wc/PDwkJCTh+/DiGDBkiWS45KkhDyrW0tKBQKCCEkPT9RZ/m3Qnt5HL+8htnz57F77//nmN7qVKlJJ3E69q1a7CxsYGfnx/i4uLg6uqKCRMmoG3btli6dCkyMzMlnfxJjqQuvvPL29sb3t7eSE1NRXJyMjtbPsDKygqnTp3C3r17cfHiRQCAg4MDPD09JU72utf8/v37OYr0Bw8eKJefLFq0aI5Og8/t2LFj2LdvH7Zt24aqVavm6FCRciJdT09P9O7dG506dZK8Y4c+Dov0fOKAA5JCXkOjpfbTTz/hl19+Qc+ePREaGopvv/0WdnZ2mDp1KudmeIfch5Snp6crs0VHR6NNmzZYunQpvLy8oKWlJWk2+jhyndDujaJFi+LOnTs5evZPnz6dYwZ6TapYsSLu3LmDUaNGAXg9w/XixYtx8eJFnDx5Evb29nB2dpYsH/13hoaGMDQ0lDqGbO3btw9Dhw7FkSNHYGpqimbNmqFZs2YAXhfHVatWRXBwMBo3bixZxvbt28Pf3x/z589H7dq1AQDHjx/H2LFjlecKHzt2DJUqVdJorqJFi37U6jeaVLVqVUycOBGDBw9G69at0bt3b7Rq1SrXeQdIXjjcnYg+iaGhIeLj41G+fHmUKlUKe/bsQfXq1XHlyhXUq1cv1+WL6DU5DSkfPHgw1q9fDysrK/j7+8Pb2xslSpSQJAsVfmPHjsXRo0exceNGVKpUCadOncK9e/fQt29f9O3bF9OmTZMk17untMlxeTj6eI8ePcLUqVMRGRmJ+/fv55j4jAeU/6ddu3Zwd3dXHqh61+LFixEZGSnpaVnJyckYNWqU8rMTAHR0dODj44Mff/wRRkZGiI2NBYD3Lg34JcrOzsbevXvx+++/Y8uWLdDW1kaXLl3g7e0ti+U4KXcs0olk7unTp1i5ciXi4+MBvD4q6u/vL/kkhnZ2dvjzzz/h4uKCWrVqYcCAARg4cCB2796NHj168MtPHm7evImQkBCEhoYiIyMDFy9elKxI19LSgrW1NVxcXPLs0ee696QOGRkZGDJkCEJDQ5Wzg79ZljA0NBTa2tqS5GKRXji1atUKV69eRb9+/XJdxvTdmfO/ZOXLl89z1vuLFy+iefPmSEpK0nCynJKTk3Ht2jUAr7+LcN6U/EtLS8Pff/+NmTNn4uzZszytTcY43J1Ixk6cOIEWLVrAwMAAderUAQAsWLAAM2fOVM7QKRUPDw9s3boVLi4u8PPzw6hRo7Bp0yacOHFCtsO+pCTXIeV9+/aVfLg9fTl0dXXx66+/YsqUKTh37hySk5Ph4uKCihUrSppLoVDk+Dvg30XBd/DgQURHR8tmGVM5u3fvXp5DoHV0dPDgwQMNJno/Y2Nj5bwuUhXoNWvWREREBMzNzT94kFsuE+nevXsX69evx9q1a3HmzBnl90qSJxbpRDI2atQotGvXDr/++qtyPdpXr16hf//+GDlyJA4cOCBZtl9++UU5dHDIkCEoXrw4Dh06hHbt2mHgwIGS5ZKjd4eUr1u3TjZDykNDQ6WOQF8ga2trWFtbSx1D6d3lxOQ24R59Gi5jmn/lypXDuXPnYG9vn+vtZ86cQZkyZTScSlV2dja+//57zJ8/H8nJyQBej3oZM2YMvvvuO40e8G7fvr3y/4Um187+WM+fP8eff/6J33//HVFRUbCzs4O3tzc2bNiAChUqSB2P8sDh7kQyZmBggNOnT6NKlSoq2y9cuIBatWohNTVVomT0MTiknOg1IQQ2bdr03nOEpfobKCjLidHHOX78OCZMmICpU6eiWrVqslvGVE6GDRuGqKgoHD9+HPr6+iq3vXz5EnXq1IG7u7uky4xNnDgRxQguaAAAEuhJREFUK1euRGBgoHJd8ujoaAQEBGDAgAGYOXOmZNnkysDAAObm5ujevTu8vb1Rq1YtqSNRPrEnnUjGTE1NkZSUlKNIv3nzJkxMTCRK9T8HDx7E8uXLkZCQgE2bNqFcuXJYs2YNbG1t0ahRI6njyQaHlBO9NnLkSCxfvhzu7u65niMsFRbfhVPRokXx/PlzeHh4qGyXwzKmcjN58mRs3rwZlSpVwtChQ1G5cmUAr89FX7ZsGbKysvDdd99JmjEsLAwrVqxAu3btlNucnZ1Rrlw5DB48WPIiPSMjI9eDj1KOGtq6dSuaNm3KlVoKIBbpRDLWvXt39OvXD/PmzUODBg0AADExMRg7dix69OghabY///wTffr0gbe3N06fPo309HQAr5dqmTVrFnbs2CFpPjnhkHKi19asWYPNmzejVatWUkehL4C3tzeKFCmC33//XVYHheSodOnSOHToEL755htMnDhRufSwQqFAixYtsGzZMpQuXVrSjI8fP87RaQG8Pq1ByslqL1++jH79+uHQoUMq2+VwMOjNMnpU8HC4O5GMZWRkYNy4cQgODsarV68ghICurq7yiLGBgYFk2VxcXDBq1Cj07dtXZSbk06dPo2XLlrh7965k2YhInmxtbbFz585cv2gTqZuhoSFOnz6t7BWm/Hny5AmuXr0KIQQqVqwIc3NzqSMBAOrWrYu6devmGHI/bNgwHD9+HEeOHJEkV8OGDaGjo4MJEyagTJkyOQ4GaXriwg+dWvc2uUxqRzmxJ51IxnR1dbFo0SIEBQUhISEBAFChQgX8/PPPsLW1lbQQvnTpEpo0aZJju5mZGZ4+far5QEQkewEBAQgMDMSqVaskPchIX4ZatWrh5s2bLNI/krm5OWrXri11jBzmzp2L1q1bY+/evahfvz4A4PDhw7h586ako/diY2Nx8uRJ2Rx8fHsiu7S0NPz0009wdHRUvmZHjhzB+fPnMXjwYIkSUn6wSCeSofT0dAQEBGDPnj3Q09PDuHHj0KFDB4SEhMDLywva2toYNWqUpBktLCxw9epV2NjYqGyPjo7m2sJElKtu3bph3bp1KFWqFGxsbHJM5MVeHVKnYcOGYcSIERg3bhycnJxyvN+cnZ0lSkafwtXVFZcvX8ayZctw8eJFAECnTp0wePBglC1bVrJcjo6OePjwoWSP/65p06Ypf+7fvz+GDx+OGTNm5Njn5s2bmo5GH4HD3YlkaPz48Vi+fDk8PT1x6NAhPHjwAH5+fjhy5AgmTZqErl27QltbW9KMQUFBWLt2LVatWoVmzZphx44duHHjBkaOHImpU6di2LBhkuYjIvnp1q0bIiMj0aVLl1zPEX77yyXRf5XbZFkKhUIW5wpT4bFv3z5MnjwZs2bNyvVgkJSrCJiZmeHEiROoWLGiyvYrV66gVq1aePbsmUTJ6EPYk04kQxs3bsTq1avRrl07nDt3Ds7Oznj16hXi4uJkM/HNhAkTkJ2djaZNmyI1NRVNmjRR9vr3799f6nhEJEPbt2/Hrl27uPoDaURiYqLUEeg/OnPmDKpVqwYtLS2cOXMmz32lGhnh6ekJAGjatKnKdjkcDDIwMEBMTEyOIj0mJibHUnskLyzSiWTo1q1b+OqrrwAA1apVg56eHkaNGiWbAh143Rvx3XffYdy4cbh69SqSk5Ph6OiI5cuXS36+PBHJk5WVFdemJo0pX7681BHoP6pRowbu3r2LUqVKoUaNGsqREO+SshiOjIx8721nz57VYJKcRo4ciW+++QanTp1CnTp1AABHjx7FypUrMXXqVEmzUd5YpBPJUFZWFnR1dZXXdXR0YGxsLGGi/8nrfPmOHTvK4nx5IpKn+fPn49tvv8X/tXfnQVXVfRzHP1e2UHBJmStuIaGGBommY+O4ES5kFx2dHBvFpWma3Mspl3HaZooxx1wKlzIMhRm01BjFNXEbl4YlRTTFJG3UCTHxBiigcO/zh8/DdMO1R+453t6vvzi/czz3cxn+8PtbV65cWWc/C+BRW7t27T3vjxs3zk1J8E+dO3dOQUFBtT+bUb9+/Vyuy8rKlJaWpq+//lq5ubmaOnWqQcluz3oMDQ3V0qVLlZqaKun2Gvo1a9YoPDzcsFy4P9akAybUoEEDxcbGys/PT5K0ZcsWRUdHq1GjRi7Pbdq0ye3ZHof18gDMqVmzZrpx44aqq6vVsGHDOms3jTzrGJ7n70eH3bp1Szdu3JCvr68aNmzI39tj5urVq2revLkk6cKFC1q1apUqKioUFxenPn36GJxOOnDggJKSkrRx40a1atVKI0aM0MiRI021U35paanS0tKUlJSk3Nxc9mUwMUbSARMaP368y/XYsWMNSlLX47BeHoA5LVmyxOgI+Be5du1anbZffvlFkyZN0rvvvmtAIvwT+fn5stlsunDhgjp06KB169ZpyJAhun79uho0aKDFixdrw4YNLkePuUtRUZGSk5OVlJSk0tJSjRo1SlVVVUpPT1fnzp3dnudu7tSBsGzZMqNj4R4YSQfwUHx9fXXu3Dm1bt1a0u1NSbKyshQREWFwMgAA7i8nJ0djx46tPcYL5hYbGytvb2/NmTNHKSkpysjI0ODBg7Vq1SpJt4/ay83N1Y8//ujWXDabTQcOHNDQoUM1ZsyY2iNyfXx8lJeXZ3iRfqcOhJUrV5oiG+6PIh3AQ/Hy8lJRUVHtGrHAwEAdP35c7du3NzgZgMdJZWWlbt686dLGpnJwh2PHjqlv374qLS01OgoeQIsWLbRnzx5FRkaqvLxcjRs3VnZ2du0Gu6dPn1avXr1kt9vdmsvb21vTp0/XpEmTXHZPN0ORbvYOBNwf090BPBSn06kJEybUrpevrKzUm2++aYr18gDM7fr165o9e7a+/fZbXb16tc591kfiUdq8ebPLtdPp1O+//67ExET17t3boFR4WCUlJWrZsqUkKSAgQI0aNXLZb6BZs2YqKytze66DBw8qKSlJ3bt3V3h4uOLj4zV69Gi357iT7du337EDAY8PinQAD8XM6+UBmNusWbO0d+9erVixQvHx8Vq2bJkuXbqkL7/8UvPnzzc6HjzM39coWywWBQUFKTo6Wp999pkxofCP/H3PGzPsgdOrVy/16tVLS5Ys0fr167V69WrNnDlTDodDP/zwg9q2bavAwEBDspm5AwEPhunuAADALdq1a6e1a9eqf//+aty4sX766SeFhYUpJSVFaWlp2rZtm9ERAZjM/U68qaqq0o4dO0wxE6egoEBJSUlKSUmR3W7XwIED68zocKfr16/XdiBkZWWppqZGixYt0muvvWZYBwIeDEU6AABwi4CAAP38889q166d2rRpo02bNqlnz546d+6cIiIiVF5ebnREACYzceLEB3rum2++qeckD66mpkZbtmzR6tWrDS3S/8psHQi4N4p0AADgFpGRkfriiy/Ur18/xcTEqGvXrlq4cKE+//xzLViwQBcvXjQ6IjzIyJEj1bNnT82ePdulfcGCBcrOztZ3331nUDLAOGbsQEBdFOkAAMAtFi9eLC8vL02fPl27d++WzWaT0+nUrVu3tGjRIs2YMcPoiPAgQUFB2rNnT50jQvPz8xUTE6PLly8blAwA7o0iHQAAGOK3335Tbm6uwsLCFBkZaXQceBh/f38dO3ZMnTp1cmk/ffq0oqKiVFFRYVAyALi3BkYHAAAAnq2iokIZGRm113PnztXMmTO1dOlSHTx4UKmpqaqsrDQwITxRRESE1q9fX6d93bp1nBMNwNQ4gg0AANSrNWvWaOvWrXr55ZclSYmJierSpYv8/f0l3R7ZDA4O1ttvv21kTHiY9957TyNGjFBhYaGio6MlSZmZmUpLS2M9OgBTY7o7AACoV3369NGsWbNks9kkSYGBgcrLy1NoaKgkKTU1VcuWLdORI0eMjAkPtHXrViUkJOjYsWPy9/dXZGSkPvjgA/Xr18/oaABwVxTpAACgXgUHB+vIkSMKCQmRdHtDr+zs7NrrM2fOqEePHvrzzz+NCwkAgEkw3R0AANQru92uqqqq2usrV6643Hc4HC73gUcpNzdXp06dkiR16dJFUVFRBicCgHujSAcAAPWqTZs2OnHiRJ1dtv/n+PHjatOmjZtTwdMVFxdr9OjR2rdvn5o2bSrpdofRgAEDtG7dOgUFBRkbEADugt3dAQBAvXrppZf0/vvv33EH94qKCn300UcaOnSoAcngyaZNm6aysjKdPHlSJSUlKikp0YkTJ1RaWqrp06cbHQ8A7oo16QAAoF5dvnxZXbt2la+vr6ZOnaqOHTtKkgoKCpSYmKjq6modPXpUVqvV4KTwJE2aNNHu3bvVo0cPl/asrCwNGjRIdrvdmGAAcB9MdwcAAPXKarXq8OHDmjRpkubMmaP/jQ9YLBYNHDhQy5cvp0DHI+dwOOTj41On3cfHRw6Hw4BEAPBgGEkHAABuU1JSorNnz0qSwsLC9OSTTxqcCJ5q2LBhstvtSktLU6tWrSRJly5d0pgxY9SsWTN9//33BicEgDujSAcAAIDHuXDhguLi4nTy5Em1bdu2tu3ZZ5/V5s2b2awQgGlRpAMAAMAjOZ1OZWZm1h7BFh4erpiYGINTAcC9sSYdAAAAHsXhcCg5OVmbNm3S+fPnZbFY1L59ezVp0kROp1MWi8XoiABwV4ykAwAAwGM4nU7ZbDZt27ZNzz33nJ555hk5nU6dOnVK+fn5iouLU3p6utExAeCuGEkHAACAx0hOTtaBAweUmZmpAQMGuNzbs2ePhg8frrVr12rcuHEGJQSAe2MkHQAAAB5j0KBBio6O1pw5c+54PyEhQfv379fOnTvdnAwAHkwDowMAAAAAj8rx48c1ZMiQu96PjY1VXl6eGxMBwMOhSAcAAIDHKCkpkdVqvet9q9Wqa9euuTERADwcinQAAAB4jJqaGnl7333bJS8vL1VXV7sxEQA8HDaOAwAAgMdwOp2aMGGC/Pz87ni/qqrKzYkA4OFQpAMAAMBjjB8//r7PsLM7ADNjd3cAAAAAAEyCNekAAAAAAJgERToAAAAAACZBkQ4AAAAAgElQpAMAAAAAYBIU6QAA/AsUFRVpxowZCgsL0xNPPCGr1arevXtrxYoVunHjhtHxAADAf3EEGwAAHu7XX39V79691bRpUyUkJCgiIkJ+fn7Kz8/XV199pdatWysuLq5ePvvmzZvy9fWtl3cDAOCJGEkHAMDDTZ48Wd7e3srJydGoUaMUHh6u0NBQDRs2TFu3bpXNZpMk2e12vf766woKClLjxo0VHR2tvLy82vd8+OGH6tq1q1JSUhQSEqImTZpo9OjRKisrq32mf//+mjp1qt566y21aNFCgwcPliSdOHFCsbGxCggIkNVqVXx8vP744w/3/iIAAHgMUKQDAODBrl69ql27dmnKlClq1KjRHZ+xWCySpFdeeUXFxcXavn27cnNz1a1bN7344osqKSmpfbawsFDp6enKyMhQRkaG9u/fr/nz57u8b82aNfL19dWhQ4e0cuVK2e12RUdHKyoqSjk5OdqxY4cuX76sUaNG1d8XBwDgMcV0dwAAPNjZs2fldDrVqVMnl/YWLVqosrJSkjRlyhTZbDZlZWWpuLhYfn5+kqSFCxcqPT1dGzZs0BtvvCFJcjgcSk5OVmBgoCQpPj5emZmZ+uSTT2rf3aFDBy1YsKD2+uOPP1ZUVJQSEhJq21avXq22bdvqzJkz6tixY/18eQAAHkMU6QAA/AtlZWXJ4XBozJgxqqqqUl5ensrLy9W8eXOX5yoqKlRYWFh7HRISUlugS1JwcLCKi4td/k337t1drvPy8rR3714FBATUyVFYWEiRDgDAX1CkAwDgwcLCwmSxWFRQUODSHhoaKkny9/eXJJWXlys4OFj79u2r846mTZvW/uzj4+Nyz2KxyOFwuLT9fVp9eXm5bDabPv300zrvDg4OfuDvAgDAvwFFOgAAHqx58+YaOHCgEhMTNW3atLuuS+/WrZuKiork7e2tkJCQR5qhW7du2rhxo0JCQuTtzX89AAC4FzaOAwDAwy1fvlzV1dV6/vnntX79ep06dUoFBQVKTU3V6dOn5eXlpZiYGL3wwgsaPny4du3apfPnz+vw4cOaN2+ecnJy/q/PnzJlikpKSvTqq68qOztbhYWF2rlzpyZOnKiamppH9C0BAPAMdGcDAODhnn76aR09elQJCQmaO3euLl68KD8/P3Xu3FnvvPOOJk+eLIvFom3btmnevHmaOHGirly5opYtW6pv376yWq3/1+e3atVKhw4d0uzZszVo0CBVVVXpqaee0pAhQ9SgAeMFAAD8lcXpdDqNDgEAAAAAAJjuDgAAAACAaVCkAwAAAABgEhTpAAAAAACYBEU6AAAAAAAmQZEOAAAAAIBJUKQDAAAAAGASFOkAAAAAAJgERToAAAAAACZBkQ4AAAAAgElQpAMAAAAAYBIU6QAAAAAAmARFOgAAAAAAJvEfC5z+zNz7IdEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a visualization of the Hours Viewed by 'genre_0' and 'genre_1'. Use a stacked bar chart\n",
    "# Group the data by 'genre_0' and 'genre_1' and sum the 'Hours Viewed' column\n",
    "# Use the unstack() method to convert the resulting Series into a DataFrame\n",
    "# Sort the columns in descending order\n",
    "# Plot the stacked bar chart\n",
    "# Add labels and title\n",
    "# Set the figsize=(12, 8))\n",
    "# Show the plot\n",
    "\n",
    "netflix_imdb.groupby(['genre_0', 'genre_1'])['Hours Viewed'].sum().unstack().sort_values(by='genre_0', ascending=False).plot(kind='bar', stacked=True, figsize=(12, 8))\n",
    "plt.xlabel('Genre')\n",
    "plt.ylabel('Hours Viewed')\n",
    "plt.title('Hours Viewed by Genre')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Title', 'Available Globally?', 'Release Date', 'Hours Viewed',\n",
       "       'tconst', 'titleType', 'primaryTitle', 'originalTitle', 'isAdult',\n",
       "       'startYear', 'endYear', 'runtimeMinutes', 'genres', 'genre_0',\n",
       "       'genre_1', 'genre_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_imdb.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "titleType\n",
       "tvEpisode       14329\n",
       "movie            5868\n",
       "short            5784\n",
       "video             695\n",
       "tvSeries          627\n",
       "tvMovie           392\n",
       "tvSpecial         291\n",
       "videoGame         215\n",
       "tvMiniSeries      147\n",
       "tvShort            28\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_imdb['titleType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Genre 1=Short<br>Genre 0=%{x}<br>Hours Viewed=%{y}<extra></extra>",
         "legendgroup": "Short",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Short",
         "offsetgroup": "Short",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Drama",
          "Comedy",
          "Horror",
          "Documentary",
          "Action",
          "Sci-Fi",
          "Mystery",
          "Crime",
          "Music",
          "Animation",
          "Fantasy",
          "Romance",
          "History",
          "Musical",
          "Biography",
          "Adventure",
          "Family",
          "News",
          "Adult",
          "Reality-TV"
         ],
         "xaxis": "x",
         "y": [
          10743100000,
          3469900000,
          2847800000,
          1839200000,
          921600000,
          916700000,
          782500000,
          729300000,
          720800000,
          531900000,
          353900000,
          265900000,
          188100000,
          94400000,
          85000000,
          82800000,
          52700000,
          46800000,
          32400000,
          32100000
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "Genre 1=Talk-Show<br>Genre 0=%{x}<br>Hours Viewed=%{y}<extra></extra>",
         "legendgroup": "Talk-Show",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Talk-Show",
         "offsetgroup": "Talk-Show",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Comedy",
          "Short",
          "Reality-TV",
          "Music",
          "Documentary",
          "News",
          "Sport",
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        },
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   "source": [
    "# Use 'plotly' to create a visualization of the Hours Viewed by 'genre_0' and 'genre_1'. Use a stacked bar chart\n",
    "# Think of the steps required to create the visualization\n",
    "# Begin by grouping the data by 'genre_0' and 'genre_1' and sum the 'Hours Viewed' column. \n",
    "# Sort the resulting DataFrame by Hours Viewed for 'genre_0' and 'genre_1' in descending order\n",
    "# Plot the stacked bar chart\n",
    "# Use 'genre_0' for the x-axis and 'Hours Viewed' for the y-axis\n",
    "# Use 'genre_1' for the color\n",
    "# Add labels and title\n",
    "# Set the figsize=(12, 8))\n",
    "# Show the plot\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "# Assuming 'df' is your DataFrame and it has columns 'genre_0', 'genre_1', and 'Hours Viewed'\n",
    "grouped_df = netflix_imdb.groupby(['genre_0', 'genre_1'])['Hours Viewed'].sum().reset_index()\n",
    "\n",
    "# Sort the DataFrame\n",
    "sorted_df = grouped_df.sort_values(by='Hours Viewed', ascending=False)\n",
    "\n",
    "# Create the plot\n",
    "fig = px.bar(sorted_df, x='genre_0', y='Hours Viewed', color='genre_1', title='Hours Viewed by Genre',\n",
    "             labels={'genre_0':'Genre 0', 'Hours Viewed':'Hours Viewed', 'genre_1':'Genre 1'}, \n",
    "             height=400, width=800)\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
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        },
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   "source": [
    "# Use 'plotly' to create a visualization of the Hours Viewed by 'genre_0' and 'genre_1'. Use a stacked bar chart\n",
    "# Think of the steps required to create the visualization\n",
    "# Begin by grouping the data by 'genre_0' and 'genre_1' and sum the 'Hours Viewed' column. \n",
    "# Sort the resulting DataFrame by Hours Viewed for 'genre_0' and 'genre_1' in descending order\n",
    "# Plot the stacked bar chart\n",
    "# Use 'genre_0' for the x-axis and 'Hours Viewed' for the y-axis\n",
    "# Use 'genre_1' for the color\n",
    "# Add labels and title\n",
    "# Set the figsize=(12, 8))\n",
    "# Show the plot\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "# Assuming 'df' is your DataFrame and it has columns 'genre_0', 'genre_1', and 'Hours Viewed'\n",
    "grouped_df = netflix_imdb[netflix_imdb['titleType'] == 'movie'].groupby(['genre_0', 'genre_1'])['Hours Viewed'].sum().reset_index()\n",
    "\n",
    "# Sort the DataFrame\n",
    "sorted_df = grouped_df.sort_values(by='Hours Viewed', ascending=False)\n",
    "\n",
    "# Create the plot\n",
    "fig = px.bar(sorted_df, x='genre_0', y='Hours Viewed', color='genre_1', title='Hours Viewed by Genre',\n",
    "             labels={'genre_0':'Genre 0', 'Hours Viewed':'Hours Viewed', 'genre_1':'Genre 1'}, \n",
    "             height=400, width=800)\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Assuming 'df' is your DataFrame and it has columns 'genre_0', 'genre_1', and 'Hours Viewed'\n",
    "grouped_df = netflix_imdb.loc[\n",
    "    (netflix_imdb['titleType'] == 'movie') &\n",
    "    (netflix_imdb['Available Globally?'] == 'No')\n",
    "    ].groupby(['genre_0', 'genre_1'])['Hours Viewed'].sum().reset_index()\n",
    "\n",
    "# Sort the DataFrame\n",
    "sorted_df = grouped_df.sort_values(by='Hours Viewed', ascending=False)\n",
    "\n",
    "# Create the plot\n",
    "fig = px.bar(sorted_df, x='genre_0', y='Hours Viewed', color='genre_1', title='Movie Non-Global Hours Viewed by Genre',\n",
    "             labels={'genre_0':'Genre 0', 'Hours Viewed':'Hours Viewed', 'genre_1':'Genre 1'}, \n",
    "             height=400, width=800)\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Title</th>\n",
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       "      <th>500</th>\n",
       "      <td>A Man Called Otto</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>movie</td>\n",
       "      <td>A Man Called Otto</td>\n",
       "      <td>A Man Called Otto</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>\\N</td>\n",
       "      <td>126</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>I See You</td>\n",
       "      <td>No</td>\n",
       "      <td>2020-07-09</td>\n",
       "      <td>30000000</td>\n",
       "      <td>tt0857381</td>\n",
       "      <td>movie</td>\n",
       "      <td>I See You</td>\n",
       "      <td>I See You</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>\\N</td>\n",
       "      <td>122</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2555</th>\n",
       "      <td>This Is Where I Leave You</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>18600000</td>\n",
       "      <td>tt1371150</td>\n",
       "      <td>movie</td>\n",
       "      <td>This Is Where I Leave You</td>\n",
       "      <td>This Is Where I Leave You</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>\\N</td>\n",
       "      <td>103</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>Instant Family</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>16500000</td>\n",
       "      <td>tt7401588</td>\n",
       "      <td>movie</td>\n",
       "      <td>Instant Family</td>\n",
       "      <td>Instant Family</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>118</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103</th>\n",
       "      <td>Polar</td>\n",
       "      <td>No</td>\n",
       "      <td>2019-01-25</td>\n",
       "      <td>15700000</td>\n",
       "      <td>tt6032076</td>\n",
       "      <td>movie</td>\n",
       "      <td>Polar</td>\n",
       "      <td>Polar</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>\\N</td>\n",
       "      <td>81</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3240</th>\n",
       "      <td>Devotion</td>\n",
       "      <td>No</td>\n",
       "      <td>2023-01-20</td>\n",
       "      <td>15100000</td>\n",
       "      <td>tt0021798</td>\n",
       "      <td>movie</td>\n",
       "      <td>Devotion</td>\n",
       "      <td>Devotion</td>\n",
       "      <td>0</td>\n",
       "      <td>1931</td>\n",
       "      <td>\\N</td>\n",
       "      <td>81</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3637</th>\n",
       "      <td>Maid in Manhattan</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>14100000</td>\n",
       "      <td>tt0252076</td>\n",
       "      <td>movie</td>\n",
       "      <td>Maid in Manhattan</td>\n",
       "      <td>Maid in Manhattan</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>\\N</td>\n",
       "      <td>105</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3740</th>\n",
       "      <td>The Dilemma</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>13700000</td>\n",
       "      <td>tt1578275</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Dilemma</td>\n",
       "      <td>The Dilemma</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>\\N</td>\n",
       "      <td>111</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4179</th>\n",
       "      <td>This Is 40</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>12500000</td>\n",
       "      <td>tt1758830</td>\n",
       "      <td>movie</td>\n",
       "      <td>This Is 40</td>\n",
       "      <td>This Is 40</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>\\N</td>\n",
       "      <td>134</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4201</th>\n",
       "      <td>Easy A</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>12400000</td>\n",
       "      <td>tt1282140</td>\n",
       "      <td>movie</td>\n",
       "      <td>Easy A</td>\n",
       "      <td>Easy A</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>\\N</td>\n",
       "      <td>92</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4659</th>\n",
       "      <td>Diary of a Mad Black Woman</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>11500000</td>\n",
       "      <td>tt0422093</td>\n",
       "      <td>movie</td>\n",
       "      <td>Diary of a Mad Black Woman</td>\n",
       "      <td>Diary of a Mad Black Woman</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>\\N</td>\n",
       "      <td>116</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848</th>\n",
       "      <td>Gatta Kusthi</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>11200000</td>\n",
       "      <td>tt21617996</td>\n",
       "      <td>movie</td>\n",
       "      <td>Gatta Kusthi</td>\n",
       "      <td>Gatta Kusthi</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>\\N</td>\n",
       "      <td>144</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5056</th>\n",
       "      <td>Forever My Girl</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>10700000</td>\n",
       "      <td>tt4103724</td>\n",
       "      <td>movie</td>\n",
       "      <td>Forever My Girl</td>\n",
       "      <td>Forever My Girl</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>108</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5119</th>\n",
       "      <td>Stepmom</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>10600000</td>\n",
       "      <td>tt0120686</td>\n",
       "      <td>movie</td>\n",
       "      <td>Stepmom</td>\n",
       "      <td>Stepmom</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>\\N</td>\n",
       "      <td>125</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237</th>\n",
       "      <td>Magic Mike XXL</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>10400000</td>\n",
       "      <td>tt2268016</td>\n",
       "      <td>movie</td>\n",
       "      <td>Magic Mike XXL</td>\n",
       "      <td>Magic Mike XXL</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>\\N</td>\n",
       "      <td>115</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5915</th>\n",
       "      <td>Love &amp; Other Drugs</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>9600000</td>\n",
       "      <td>tt0758752</td>\n",
       "      <td>movie</td>\n",
       "      <td>Love &amp; Other Drugs</td>\n",
       "      <td>Love &amp; Other Drugs</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>\\N</td>\n",
       "      <td>112</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5940</th>\n",
       "      <td>Dumplin'</td>\n",
       "      <td>No</td>\n",
       "      <td>2018-12-07</td>\n",
       "      <td>9500000</td>\n",
       "      <td>tt4878482</td>\n",
       "      <td>movie</td>\n",
       "      <td>Dumplin'</td>\n",
       "      <td>Dumplin'</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>\\N</td>\n",
       "      <td>110</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6115</th>\n",
       "      <td>Burnt</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>9100000</td>\n",
       "      <td>tt2503944</td>\n",
       "      <td>movie</td>\n",
       "      <td>Burnt</td>\n",
       "      <td>Burnt</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>\\N</td>\n",
       "      <td>101</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6121</th>\n",
       "      <td>Burnt</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>9100000</td>\n",
       "      <td>tt4607118</td>\n",
       "      <td>movie</td>\n",
       "      <td>Burnt</td>\n",
       "      <td>Burnt</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6220</th>\n",
       "      <td>Barbershop 2: Back in Business</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8900000</td>\n",
       "      <td>tt0337579</td>\n",
       "      <td>movie</td>\n",
       "      <td>Barbershop 2: Back in Business</td>\n",
       "      <td>Barbershop 2: Back in Business</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>\\N</td>\n",
       "      <td>106</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6707</th>\n",
       "      <td>Just Like Heaven</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8500000</td>\n",
       "      <td>tt0425123</td>\n",
       "      <td>movie</td>\n",
       "      <td>Just Like Heaven</td>\n",
       "      <td>Just Like Heaven</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>\\N</td>\n",
       "      <td>95</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6912</th>\n",
       "      <td>Notting Hill</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8100000</td>\n",
       "      <td>tt0125439</td>\n",
       "      <td>movie</td>\n",
       "      <td>Notting Hill</td>\n",
       "      <td>Notting Hill</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>\\N</td>\n",
       "      <td>124</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7190</th>\n",
       "      <td>Jerry Maguire</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7700000</td>\n",
       "      <td>tt0116695</td>\n",
       "      <td>movie</td>\n",
       "      <td>Jerry Maguire</td>\n",
       "      <td>Jerry Maguire</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>\\N</td>\n",
       "      <td>139</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7510</th>\n",
       "      <td>P.S. I Love You</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7300000</td>\n",
       "      <td>tt0431308</td>\n",
       "      <td>movie</td>\n",
       "      <td>P.S. I Love You</td>\n",
       "      <td>P.S. I Love You</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>\\N</td>\n",
       "      <td>126</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7687</th>\n",
       "      <td>Spanglish</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>7100000</td>\n",
       "      <td>tt0371246</td>\n",
       "      <td>movie</td>\n",
       "      <td>Spanglish</td>\n",
       "      <td>Spanglish</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>\\N</td>\n",
       "      <td>131</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8716</th>\n",
       "      <td>How Do You Know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6000000</td>\n",
       "      <td>tt1341188</td>\n",
       "      <td>movie</td>\n",
       "      <td>How Do You Know</td>\n",
       "      <td>How Do You Know</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>\\N</td>\n",
       "      <td>121</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9116</th>\n",
       "      <td>The Best Man Holiday</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5700000</td>\n",
       "      <td>tt2083355</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Best Man Holiday</td>\n",
       "      <td>The Best Man Holiday</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>\\N</td>\n",
       "      <td>123</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9378</th>\n",
       "      <td>The Breakfast Club</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5500000</td>\n",
       "      <td>tt0088847</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Breakfast Club</td>\n",
       "      <td>The Breakfast Club</td>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>\\N</td>\n",
       "      <td>97</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9714</th>\n",
       "      <td>Sleepless in Seattle</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5200000</td>\n",
       "      <td>tt0108160</td>\n",
       "      <td>movie</td>\n",
       "      <td>Sleepless in Seattle</td>\n",
       "      <td>Sleepless in Seattle</td>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>\\N</td>\n",
       "      <td>105</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10173</th>\n",
       "      <td>Crazy, Stupid, Love.</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4800000</td>\n",
       "      <td>tt1570728</td>\n",
       "      <td>movie</td>\n",
       "      <td>Crazy, Stupid, Love.</td>\n",
       "      <td>Crazy, Stupid, Love.</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>\\N</td>\n",
       "      <td>118</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10245</th>\n",
       "      <td>Home Again</td>\n",
       "      <td>No</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>4800000</td>\n",
       "      <td>tt5719700</td>\n",
       "      <td>movie</td>\n",
       "      <td>Home Again</td>\n",
       "      <td>Home Again</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>\\N</td>\n",
       "      <td>97</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10356</th>\n",
       "      <td>Inseparables</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4700000</td>\n",
       "      <td>tt5680678</td>\n",
       "      <td>movie</td>\n",
       "      <td>Inseparables</td>\n",
       "      <td>Inseparables</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>\\N</td>\n",
       "      <td>108</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10603</th>\n",
       "      <td>Bhool Bhulaiyaa 2</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4500000</td>\n",
       "      <td>tt6455162</td>\n",
       "      <td>movie</td>\n",
       "      <td>Bhool Bhulaiyaa 2</td>\n",
       "      <td>Bhool Bhulaiyaa 2</td>\n",
       "      <td>0</td>\n",
       "      <td>2022</td>\n",
       "      <td>\\N</td>\n",
       "      <td>143</td>\n",
       "      <td>Comedy,Drama,Horror</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10670</th>\n",
       "      <td>Labor Day</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4500000</td>\n",
       "      <td>tt0464077</td>\n",
       "      <td>movie</td>\n",
       "      <td>Labor Day</td>\n",
       "      <td>Labor Day</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>\\N</td>\n",
       "      <td>58</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10744</th>\n",
       "      <td>28 Days</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4400000</td>\n",
       "      <td>tt0191754</td>\n",
       "      <td>movie</td>\n",
       "      <td>28 Days</td>\n",
       "      <td>28 Days</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>\\N</td>\n",
       "      <td>103</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11453</th>\n",
       "      <td>The Five-Year Engagement</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4100000</td>\n",
       "      <td>tt1195478</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Five-Year Engagement</td>\n",
       "      <td>The Five-Year Engagement</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>\\N</td>\n",
       "      <td>124</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11590</th>\n",
       "      <td>The Edge of Seventeen</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4000000</td>\n",
       "      <td>tt1878870</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Edge of Seventeen</td>\n",
       "      <td>The Edge of Seventeen</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>\\N</td>\n",
       "      <td>104</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11739</th>\n",
       "      <td>It's Complicated</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3900000</td>\n",
       "      <td>tt1230414</td>\n",
       "      <td>movie</td>\n",
       "      <td>It's Complicated</td>\n",
       "      <td>It's Complicated</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>\\N</td>\n",
       "      <td>121</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11793</th>\n",
       "      <td>It's Complicated</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3900000</td>\n",
       "      <td>tt6510258</td>\n",
       "      <td>movie</td>\n",
       "      <td>It's Complicated</td>\n",
       "      <td>It's Complicated</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>\\N</td>\n",
       "      <td>88</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12379</th>\n",
       "      <td>Divergent</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3800000</td>\n",
       "      <td>tt16760422</td>\n",
       "      <td>movie</td>\n",
       "      <td>Divergent</td>\n",
       "      <td>Divergent</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12525</th>\n",
       "      <td>The Cable Guy</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3800000</td>\n",
       "      <td>tt0115798</td>\n",
       "      <td>movie</td>\n",
       "      <td>The Cable Guy</td>\n",
       "      <td>The Cable Guy</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>\\N</td>\n",
       "      <td>96</td>\n",
       "      <td>Comedy,Drama,Thriller</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12574</th>\n",
       "      <td>6 Days</td>\n",
       "      <td>No</td>\n",
       "      <td>2017-11-03</td>\n",
       "      <td>3700000</td>\n",
       "      <td>tt1377259</td>\n",
       "      <td>movie</td>\n",
       "      <td>6 Days</td>\n",
       "      <td>6 Days</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>\\N</td>\n",
       "      <td>87</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12589</th>\n",
       "      <td>Bad Words</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3700000</td>\n",
       "      <td>tt2170299</td>\n",
       "      <td>movie</td>\n",
       "      <td>Bad Words</td>\n",
       "      <td>Bad Words</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>\\N</td>\n",
       "      <td>89</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12782</th>\n",
       "      <td>You Will Meet a Tall Dark Stranger</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3700000</td>\n",
       "      <td>tt1182350</td>\n",
       "      <td>movie</td>\n",
       "      <td>You Will Meet a Tall Dark Stranger</td>\n",
       "      <td>You Will Meet a Tall Dark Stranger</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>\\N</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12827</th>\n",
       "      <td>Half Brothers</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3600000</td>\n",
       "      <td>tt10515926</td>\n",
       "      <td>movie</td>\n",
       "      <td>Half Brothers</td>\n",
       "      <td>Half Brothers</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>\\N</td>\n",
       "      <td>96</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12848</th>\n",
       "      <td>He's Just Not That Into You</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3600000</td>\n",
       "      <td>tt1001508</td>\n",
       "      <td>movie</td>\n",
       "      <td>He's Just Not That Into You</td>\n",
       "      <td>He's Just Not That Into You</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>\\N</td>\n",
       "      <td>129</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12872</th>\n",
       "      <td>Problem Child</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3600000</td>\n",
       "      <td>tt0757924</td>\n",
       "      <td>movie</td>\n",
       "      <td>Problem Child</td>\n",
       "      <td>Problem Child</td>\n",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>\\N</td>\n",
       "      <td>91</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13126</th>\n",
       "      <td>El Jeremías</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3500000</td>\n",
       "      <td>tt4457020</td>\n",
       "      <td>movie</td>\n",
       "      <td>El Jeremías</td>\n",
       "      <td>El Jeremías</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>\\N</td>\n",
       "      <td>95</td>\n",
       "      <td>Comedy,Drama,Family</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13625</th>\n",
       "      <td>Silver Linings Playbook</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3200000</td>\n",
       "      <td>tt1045658</td>\n",
       "      <td>movie</td>\n",
       "      <td>Silver Linings Playbook</td>\n",
       "      <td>Silver Linings Playbook</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>\\N</td>\n",
       "      <td>122</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14034</th>\n",
       "      <td>17 Again</td>\n",
       "      <td>No</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3000000</td>\n",
       "      <td>tt0974661</td>\n",
       "      <td>movie</td>\n",
       "      <td>17 Again</td>\n",
       "      <td>17 Again</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>\\N</td>\n",
       "      <td>102</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Title Available Globally? Release Date  \\\n",
       "500                     A Man Called Otto                  No          NaT   \n",
       "1268                            I See You                  No   2020-07-09   \n",
       "2555            This Is Where I Leave You                  No          NaT   \n",
       "2933                       Instant Family                  No          NaT   \n",
       "3103                                Polar                  No   2019-01-25   \n",
       "3240                             Devotion                  No   2023-01-20   \n",
       "3637                    Maid in Manhattan                  No          NaT   \n",
       "3740                          The Dilemma                  No          NaT   \n",
       "4179                           This Is 40                  No          NaT   \n",
       "4201                               Easy A                  No          NaT   \n",
       "4659           Diary of a Mad Black Woman                  No          NaT   \n",
       "4848                         Gatta Kusthi                  No          NaT   \n",
       "5056                      Forever My Girl                  No          NaT   \n",
       "5119                              Stepmom                  No          NaT   \n",
       "5237                       Magic Mike XXL                  No          NaT   \n",
       "5915                   Love & Other Drugs                  No          NaT   \n",
       "5940                             Dumplin'                  No   2018-12-07   \n",
       "6115                                Burnt                  No          NaT   \n",
       "6121                                Burnt                  No          NaT   \n",
       "6220       Barbershop 2: Back in Business                  No          NaT   \n",
       "6707                     Just Like Heaven                  No          NaT   \n",
       "6912                         Notting Hill                  No          NaT   \n",
       "7190                        Jerry Maguire                  No          NaT   \n",
       "7510                      P.S. I Love You                  No          NaT   \n",
       "7687                            Spanglish                  No          NaT   \n",
       "8716                      How Do You Know                  No          NaT   \n",
       "9116                 The Best Man Holiday                  No          NaT   \n",
       "9378                   The Breakfast Club                  No          NaT   \n",
       "9714                 Sleepless in Seattle                  No          NaT   \n",
       "10173                Crazy, Stupid, Love.                  No          NaT   \n",
       "10245                          Home Again                  No   2018-01-12   \n",
       "10356                        Inseparables                  No          NaT   \n",
       "10603                   Bhool Bhulaiyaa 2                  No          NaT   \n",
       "10670                           Labor Day                  No          NaT   \n",
       "10744                             28 Days                  No          NaT   \n",
       "11453            The Five-Year Engagement                  No          NaT   \n",
       "11590               The Edge of Seventeen                  No          NaT   \n",
       "11739                    It's Complicated                  No          NaT   \n",
       "11793                    It's Complicated                  No          NaT   \n",
       "12379                           Divergent                  No          NaT   \n",
       "12525                       The Cable Guy                  No          NaT   \n",
       "12574                              6 Days                  No   2017-11-03   \n",
       "12589                           Bad Words                  No          NaT   \n",
       "12782  You Will Meet a Tall Dark Stranger                  No          NaT   \n",
       "12827                       Half Brothers                  No          NaT   \n",
       "12848         He's Just Not That Into You                  No          NaT   \n",
       "12872                       Problem Child                  No          NaT   \n",
       "13126                         El Jeremías                  No          NaT   \n",
       "13625             Silver Linings Playbook                  No          NaT   \n",
       "14034                            17 Again                  No          NaT   \n",
       "\n",
       "       Hours Viewed      tconst titleType                        primaryTitle  \\\n",
       "500        61300000   tt7405458     movie                   A Man Called Otto   \n",
       "1268       30000000   tt0857381     movie                           I See You   \n",
       "2555       18600000   tt1371150     movie           This Is Where I Leave You   \n",
       "2933       16500000   tt7401588     movie                      Instant Family   \n",
       "3103       15700000   tt6032076     movie                               Polar   \n",
       "3240       15100000   tt0021798     movie                            Devotion   \n",
       "3637       14100000   tt0252076     movie                   Maid in Manhattan   \n",
       "3740       13700000   tt1578275     movie                         The Dilemma   \n",
       "4179       12500000   tt1758830     movie                          This Is 40   \n",
       "4201       12400000   tt1282140     movie                              Easy A   \n",
       "4659       11500000   tt0422093     movie          Diary of a Mad Black Woman   \n",
       "4848       11200000  tt21617996     movie                        Gatta Kusthi   \n",
       "5056       10700000   tt4103724     movie                     Forever My Girl   \n",
       "5119       10600000   tt0120686     movie                             Stepmom   \n",
       "5237       10400000   tt2268016     movie                      Magic Mike XXL   \n",
       "5915        9600000   tt0758752     movie                  Love & Other Drugs   \n",
       "5940        9500000   tt4878482     movie                            Dumplin'   \n",
       "6115        9100000   tt2503944     movie                               Burnt   \n",
       "6121        9100000   tt4607118     movie                               Burnt   \n",
       "6220        8900000   tt0337579     movie      Barbershop 2: Back in Business   \n",
       "6707        8500000   tt0425123     movie                    Just Like Heaven   \n",
       "6912        8100000   tt0125439     movie                        Notting Hill   \n",
       "7190        7700000   tt0116695     movie                       Jerry Maguire   \n",
       "7510        7300000   tt0431308     movie                     P.S. I Love You   \n",
       "7687        7100000   tt0371246     movie                           Spanglish   \n",
       "8716        6000000   tt1341188     movie                     How Do You Know   \n",
       "9116        5700000   tt2083355     movie                The Best Man Holiday   \n",
       "9378        5500000   tt0088847     movie                  The Breakfast Club   \n",
       "9714        5200000   tt0108160     movie                Sleepless in Seattle   \n",
       "10173       4800000   tt1570728     movie                Crazy, Stupid, Love.   \n",
       "10245       4800000   tt5719700     movie                          Home Again   \n",
       "10356       4700000   tt5680678     movie                        Inseparables   \n",
       "10603       4500000   tt6455162     movie                   Bhool Bhulaiyaa 2   \n",
       "10670       4500000   tt0464077     movie                           Labor Day   \n",
       "10744       4400000   tt0191754     movie                             28 Days   \n",
       "11453       4100000   tt1195478     movie            The Five-Year Engagement   \n",
       "11590       4000000   tt1878870     movie               The Edge of Seventeen   \n",
       "11739       3900000   tt1230414     movie                    It's Complicated   \n",
       "11793       3900000   tt6510258     movie                    It's Complicated   \n",
       "12379       3800000  tt16760422     movie                           Divergent   \n",
       "12525       3800000   tt0115798     movie                       The Cable Guy   \n",
       "12574       3700000   tt1377259     movie                              6 Days   \n",
       "12589       3700000   tt2170299     movie                           Bad Words   \n",
       "12782       3700000   tt1182350     movie  You Will Meet a Tall Dark Stranger   \n",
       "12827       3600000  tt10515926     movie                       Half Brothers   \n",
       "12848       3600000   tt1001508     movie         He's Just Not That Into You   \n",
       "12872       3600000   tt0757924     movie                       Problem Child   \n",
       "13126       3500000   tt4457020     movie                         El Jeremías   \n",
       "13625       3200000   tt1045658     movie             Silver Linings Playbook   \n",
       "14034       3000000   tt0974661     movie                            17 Again   \n",
       "\n",
       "                            originalTitle isAdult startYear endYear  \\\n",
       "500                     A Man Called Otto       0      2022      \\N   \n",
       "1268                            I See You       0      2006      \\N   \n",
       "2555            This Is Where I Leave You       0      2014      \\N   \n",
       "2933                       Instant Family       0      2018      \\N   \n",
       "3103                                Polar       0      2019      \\N   \n",
       "3240                             Devotion       0      1931      \\N   \n",
       "3637                    Maid in Manhattan       0      2002      \\N   \n",
       "3740                          The Dilemma       0      2011      \\N   \n",
       "4179                           This Is 40       0      2012      \\N   \n",
       "4201                               Easy A       0      2010      \\N   \n",
       "4659           Diary of a Mad Black Woman       0      2005      \\N   \n",
       "4848                         Gatta Kusthi       0      2022      \\N   \n",
       "5056                      Forever My Girl       0      2018      \\N   \n",
       "5119                              Stepmom       0      1998      \\N   \n",
       "5237                       Magic Mike XXL       0      2015      \\N   \n",
       "5915                   Love & Other Drugs       0      2010      \\N   \n",
       "5940                             Dumplin'       0      2018      \\N   \n",
       "6115                                Burnt       0      2015      \\N   \n",
       "6121                                Burnt       0      2014      \\N   \n",
       "6220       Barbershop 2: Back in Business       0      2004      \\N   \n",
       "6707                     Just Like Heaven       0      2005      \\N   \n",
       "6912                         Notting Hill       0      1999      \\N   \n",
       "7190                        Jerry Maguire       0      1996      \\N   \n",
       "7510                      P.S. I Love You       0      2007      \\N   \n",
       "7687                            Spanglish       0      2004      \\N   \n",
       "8716                      How Do You Know       0      2010      \\N   \n",
       "9116                 The Best Man Holiday       0      2013      \\N   \n",
       "9378                   The Breakfast Club       0      1985      \\N   \n",
       "9714                 Sleepless in Seattle       0      1993      \\N   \n",
       "10173                Crazy, Stupid, Love.       0      2011      \\N   \n",
       "10245                          Home Again       0      2017      \\N   \n",
       "10356                        Inseparables       0      2016      \\N   \n",
       "10603                   Bhool Bhulaiyaa 2       0      2022      \\N   \n",
       "10670                           Labor Day       0      2009      \\N   \n",
       "10744                             28 Days       0      2000      \\N   \n",
       "11453            The Five-Year Engagement       0      2012      \\N   \n",
       "11590               The Edge of Seventeen       0      2016      \\N   \n",
       "11739                    It's Complicated       0      2009      \\N   \n",
       "11793                    It's Complicated       0      2017      \\N   \n",
       "12379                           Divergent       0      2021      \\N   \n",
       "12525                       The Cable Guy       0      1996      \\N   \n",
       "12574                              6 Days       0      2009      \\N   \n",
       "12589                           Bad Words       0      2013      \\N   \n",
       "12782  You Will Meet a Tall Dark Stranger       0      2010      \\N   \n",
       "12827                       Half Brothers       0      2020      \\N   \n",
       "12848         He's Just Not That Into You       0      2009      \\N   \n",
       "12872                       Problem Child       0      1980      \\N   \n",
       "13126                         El Jeremías       0      2015      \\N   \n",
       "13625             Silver Linings Playbook       0      2012      \\N   \n",
       "14034                            17 Again       0      2009      \\N   \n",
       "\n",
       "      runtimeMinutes                 genres genre_0 genre_1   genre_2  \n",
       "500              126           Comedy,Drama  Comedy   Drama      None  \n",
       "1268             122   Comedy,Drama,Fantasy  Comedy   Drama   Fantasy  \n",
       "2555             103           Comedy,Drama  Comedy   Drama      None  \n",
       "2933             118           Comedy,Drama  Comedy   Drama      None  \n",
       "3103              81           Comedy,Drama  Comedy   Drama      None  \n",
       "3240              81   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "3637             105   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "3740             111           Comedy,Drama  Comedy   Drama      None  \n",
       "4179             134   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "4201              92   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "4659             116   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "4848             144   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "5056             108     Comedy,Drama,Music  Comedy   Drama     Music  \n",
       "5119             125           Comedy,Drama  Comedy   Drama      None  \n",
       "5237             115     Comedy,Drama,Music  Comedy   Drama     Music  \n",
       "5915             112   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "5940             110           Comedy,Drama  Comedy   Drama      None  \n",
       "6115             101           Comedy,Drama  Comedy   Drama      None  \n",
       "6121              \\N           Comedy,Drama  Comedy   Drama      None  \n",
       "6220             106           Comedy,Drama  Comedy   Drama      None  \n",
       "6707              95   Comedy,Drama,Fantasy  Comedy   Drama   Fantasy  \n",
       "6912             124   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "7190             139   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "7510             126   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "7687             131   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "8716             121   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "9116             123           Comedy,Drama  Comedy   Drama      None  \n",
       "9378              97           Comedy,Drama  Comedy   Drama      None  \n",
       "9714             105   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "10173            118   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "10245             97   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "10356            108           Comedy,Drama  Comedy   Drama      None  \n",
       "10603            143    Comedy,Drama,Horror  Comedy   Drama    Horror  \n",
       "10670             58   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "10744            103           Comedy,Drama  Comedy   Drama      None  \n",
       "11453            124   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "11590            104           Comedy,Drama  Comedy   Drama      None  \n",
       "11739            121   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "11793             88           Comedy,Drama  Comedy   Drama      None  \n",
       "12379             \\N           Comedy,Drama  Comedy   Drama      None  \n",
       "12525             96  Comedy,Drama,Thriller  Comedy   Drama  Thriller  \n",
       "12574             87           Comedy,Drama  Comedy   Drama      None  \n",
       "12589             89           Comedy,Drama  Comedy   Drama      None  \n",
       "12782             98   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "12827             96           Comedy,Drama  Comedy   Drama      None  \n",
       "12848            129   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "12872             91           Comedy,Drama  Comedy   Drama      None  \n",
       "13126             95    Comedy,Drama,Family  Comedy   Drama    Family  \n",
       "13625            122   Comedy,Drama,Romance  Comedy   Drama   Romance  \n",
       "14034            102   Comedy,Drama,Fantasy  Comedy   Drama   Fantasy  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_imdb.loc[\n",
    "    (netflix_imdb['titleType'] == 'movie') &\n",
    "    (netflix_imdb['Available Globally?'] == 'No') &\n",
    "    (netflix_imdb['genre_0'] == 'Comedy') &\n",
    "    (netflix_imdb['genre_1'] == 'Drama')\n",
    "    ].head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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