data_summaries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.058795Z",
     "start_time": "2024-01-12T18:49:36.057360Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "movies = pd.read_csv('movies.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.203980Z",
     "start_time": "2024-01-12T18:49:36.061016Z"
    }
   },
   "id": "c50fe81d2c960a19"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "                              movie  production_budget  domestic_gross  \\\n0                     Evan Almighty        175000000.0     100289690.0   \n1                        Waterworld        175000000.0      88246220.0   \n2  King Arthur: Legend of the Sword        175000000.0      39175066.0   \n3                          47 Ronin        175000000.0      38362475.0   \n4    Jurassic World: Fallen Kingdom        170000000.0     416769345.0   \n\n   worldwide_gross mpaa_rating      genre  \n0     1.741313e+08          PG     Comedy  \n1     2.642462e+08       PG-13     Action  \n2     1.399507e+08       PG-13  Adventure  \n3     1.517168e+08       PG-13     Action  \n4     1.304866e+09       PG-13     Action  ",
      "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>movie</th>\n      <th>production_budget</th>\n      <th>domestic_gross</th>\n      <th>worldwide_gross</th>\n      <th>mpaa_rating</th>\n      <th>genre</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Evan Almighty</td>\n      <td>175000000.0</td>\n      <td>100289690.0</td>\n      <td>1.741313e+08</td>\n      <td>PG</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Waterworld</td>\n      <td>175000000.0</td>\n      <td>88246220.0</td>\n      <td>2.642462e+08</td>\n      <td>PG-13</td>\n      <td>Action</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>King Arthur: Legend of the Sword</td>\n      <td>175000000.0</td>\n      <td>39175066.0</td>\n      <td>1.399507e+08</td>\n      <td>PG-13</td>\n      <td>Adventure</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47 Ronin</td>\n      <td>175000000.0</td>\n      <td>38362475.0</td>\n      <td>1.517168e+08</td>\n      <td>PG-13</td>\n      <td>Action</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Jurassic World: Fallen Kingdom</td>\n      <td>170000000.0</td>\n      <td>416769345.0</td>\n      <td>1.304866e+09</td>\n      <td>PG-13</td>\n      <td>Action</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect data\n",
    "movies.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.226867Z",
     "start_time": "2024-01-12T18:49:36.208882Z"
    }
   },
   "id": "dec60f14a793a395"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3401 entries, 0 to 3400\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   movie              3401 non-null   object \n",
      " 1   production_budget  3401 non-null   float64\n",
      " 2   domestic_gross     3401 non-null   float64\n",
      " 3   worldwide_gross    3401 non-null   float64\n",
      " 4   mpaa_rating        3264 non-null   object \n",
      " 5   genre              3401 non-null   object \n",
      "dtypes: float64(3), object(3)\n",
      "memory usage: 159.6+ KB\n"
     ]
    }
   ],
   "source": [
    "movies.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.256091Z",
     "start_time": "2024-01-12T18:49:36.229718Z"
    }
   },
   "id": "3c07f518824a30df"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "       production_budget  domestic_gross  worldwide_gross\ncount       3.401000e+03    3.401000e+03     3.401000e+03\nmean        3.328474e+07    4.542179e+07     9.411512e+07\nstd         3.489239e+07    5.882566e+07     1.409182e+08\nmin         2.500000e+05    0.000000e+00     0.000000e+00\n25%         9.000000e+06    6.118683e+06     1.061881e+07\n50%         2.000000e+07    2.553382e+07     4.015902e+07\n75%         4.500000e+07    6.032379e+07     1.176152e+08\nmax         1.750000e+08    4.745447e+08     1.304866e+09",
      "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>production_budget</th>\n      <th>domestic_gross</th>\n      <th>worldwide_gross</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3.401000e+03</td>\n      <td>3.401000e+03</td>\n      <td>3.401000e+03</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>3.328474e+07</td>\n      <td>4.542179e+07</td>\n      <td>9.411512e+07</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.489239e+07</td>\n      <td>5.882566e+07</td>\n      <td>1.409182e+08</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2.500000e+05</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>9.000000e+06</td>\n      <td>6.118683e+06</td>\n      <td>1.061881e+07</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2.000000e+07</td>\n      <td>2.553382e+07</td>\n      <td>4.015902e+07</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>4.500000e+07</td>\n      <td>6.032379e+07</td>\n      <td>1.176152e+08</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.750000e+08</td>\n      <td>4.745447e+08</td>\n      <td>1.304866e+09</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics\n",
    "movies.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.300633Z",
     "start_time": "2024-01-12T18:49:36.237202Z"
    }
   },
   "id": "f9decfbf1697602b"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "                 movie  production_budget  domestic_gross  worldwide_gross  \\\ncount             3401       3.401000e+03    3.401000e+03     3.401000e+03   \nunique            3400                NaN             NaN              NaN   \ntop     Tau ming chong                NaN             NaN              NaN   \nfreq                 2                NaN             NaN              NaN   \nmean               NaN       3.328474e+07    4.542179e+07     9.411512e+07   \nstd                NaN       3.489239e+07    5.882566e+07     1.409182e+08   \nmin                NaN       2.500000e+05    0.000000e+00     0.000000e+00   \n25%                NaN       9.000000e+06    6.118683e+06     1.061881e+07   \n50%                NaN       2.000000e+07    2.553382e+07     4.015902e+07   \n75%                NaN       4.500000e+07    6.032379e+07     1.176152e+08   \nmax                NaN       1.750000e+08    4.745447e+08     1.304866e+09   \n\n       mpaa_rating  genre  \ncount         3264   3401  \nunique           4      5  \ntop              R  Drama  \nfreq          1514   1236  \nmean           NaN    NaN  \nstd            NaN    NaN  \nmin            NaN    NaN  \n25%            NaN    NaN  \n50%            NaN    NaN  \n75%            NaN    NaN  \nmax            NaN    NaN  ",
      "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>movie</th>\n      <th>production_budget</th>\n      <th>domestic_gross</th>\n      <th>worldwide_gross</th>\n      <th>mpaa_rating</th>\n      <th>genre</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3401</td>\n      <td>3.401000e+03</td>\n      <td>3.401000e+03</td>\n      <td>3.401000e+03</td>\n      <td>3264</td>\n      <td>3401</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>3400</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>Tau ming chong</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>R</td>\n      <td>Drama</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1514</td>\n      <td>1236</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>NaN</td>\n      <td>3.328474e+07</td>\n      <td>4.542179e+07</td>\n      <td>9.411512e+07</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>NaN</td>\n      <td>3.489239e+07</td>\n      <td>5.882566e+07</td>\n      <td>1.409182e+08</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>NaN</td>\n      <td>2.500000e+05</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>NaN</td>\n      <td>9.000000e+06</td>\n      <td>6.118683e+06</td>\n      <td>1.061881e+07</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>NaN</td>\n      <td>2.000000e+07</td>\n      <td>2.553382e+07</td>\n      <td>4.015902e+07</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>NaN</td>\n      <td>4.500000e+07</td>\n      <td>6.032379e+07</td>\n      <td>1.176152e+08</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>NaN</td>\n      <td>1.750000e+08</td>\n      <td>4.745447e+08</td>\n      <td>1.304866e+09</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics for all the columns\n",
    "movies.describe(include='all')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.303197Z",
     "start_time": "2024-01-12T18:49:36.252991Z"
    }
   },
   "id": "59e173faebbbef4d"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33284743.23640106\n",
      "33284743.23640106\n"
     ]
    }
   ],
   "source": [
    "# find the mean production_budget for all movies and save it to a variable called mean_budget\n",
    "print(np.mean(movies.production_budget))    # applying np.mean()\n",
    "mean_budget = movies.production_budget.mean()\n",
    "print(mean_budget)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.354817Z",
     "start_time": "2024-01-12T18:49:36.276999Z"
    }
   },
   "id": "db28cc05fb20b8c2"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000000.0\n"
     ]
    }
   ],
   "source": [
    "# Save the median budget to a variable called med_budget\n",
    "med_budget = movies.production_budget.median()\n",
    "print(med_budget)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.355486Z",
     "start_time": "2024-01-12T18:49:36.284053Z"
    }
   },
   "id": "b27b1debc5a4efd1"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    20000000.0\n",
      "Name: production_budget, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Save the mode to a variable called mode_budget\n",
    "mode_budget = movies.production_budget.mode()\n",
    "print(mode_budget)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.355713Z",
     "start_time": "2024-01-12T18:49:36.293895Z"
    }
   },
   "id": "7a89697e72b7e402"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23949546.79078883\n"
     ]
    }
   ],
   "source": [
    "# Find the mean of the budget after removing 20% of the lowest and highest data points. Save the trimmed mean to a variable called trmean_budget\n",
    "from scipy.stats import trim_mean\n",
    "trmean_budget = trim_mean(movies.production_budget, proportiontocut=0.2)    # trim extreme 20%\n",
    "print(trmean_budget)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.357943Z",
     "start_time": "2024-01-12T18:49:36.301446Z"
    }
   },
   "id": "b34df7ad00cf3cb5"
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "248bd5b27789f16"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174750000.0\n"
     ]
    }
   ],
   "source": [
    "# find the range for production_budget and save it to a variable called range_budget\n",
    "range_budget = movies.production_budget.max() - movies.production_budget.min()\n",
    "print(range_budget)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.358591Z",
     "start_time": "2024-01-12T18:49:36.309115Z"
    }
   },
   "id": "a091a1e8e1c076cc"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36000000.0\n"
     ]
    }
   ],
   "source": [
    "# Save the interquartile range for budget to a variable called iqr_budget\n",
    "iqr_budget = movies.production_budget.quantile(0.75) - movies.production_budget.quantile(0.25)\n",
    "print(iqr_budget)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.419582Z",
     "start_time": "2024-01-12T18:49:36.317308Z"
    }
   },
   "id": "d0ac1cdd10d7e76f"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1217120944390785.2\n",
      "1217478921139135.5\n"
     ]
    }
   ],
   "source": [
    "# Save the variance to a variable called var_budget\n",
    "print(np.var(movies.production_budget))    # applying np.var()\n",
    "var_budget = movies.production_budget.var()\n",
    "print(var_budget)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.442122Z",
     "start_time": "2024-01-12T18:49:36.325197Z"
    }
   },
   "id": "1b2a144bdd29892b"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34887260.4884761\n",
      "34892390.59077402\n"
     ]
    }
   ],
   "source": [
    "# Save the standard deviation to a variable called std_budget\n",
    "print(np.std(movies.production_budget))    # applying np.std()\n",
    "std_budget = movies.production_budget.std()\n",
    "print(std_budget)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.442963Z",
     "start_time": "2024-01-12T18:49:36.328921Z"
    }
   },
   "id": "6ef10ec786018720"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "# Save the mean absolute deviation to a variable called mad_budget\n",
    "# mad_budget = movies.production_budget.mad()\n",
    "# print(mad_budget)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.443424Z",
     "start_time": "2024-01-12T18:49:36.336187Z"
    }
   },
   "id": "a50de0871ff7f9f2"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "22239033.277584027"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find median absolute deviation\n",
    "# from scipy import stats\n",
    "mad_budget = stats.median_abs_deviation(movies.production_budget, scale='normal')\n",
    "mad_budget"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.586690Z",
     "start_time": "2024-01-12T18:49:36.342670Z"
    }
   },
   "id": "6d1300f07a2abf2c"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the movies DataFrame, create a boxplot for production_budget using the boxplot() function from seaborn. Don’t forget to display the plot using plt.show() and close the plot using plt.close()\n",
    "# Create a boxplot for movie budget \n",
    "sns.boxplot(x='production_budget', data=movies)\n",
    "plt.show()\n",
    "plt.clf()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.734624Z",
     "start_time": "2024-01-12T18:49:36.417662Z"
    }
   },
   "id": "86cb85b6142ee8aa"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matth/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram for production_budget using the histplot() function from seaborn.\n",
    "# Create a histogram for movie budget\n",
    "sns.histplot(x='production_budget', data=movies)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "# From the plots, what do you notice about the distribution of movie budgets?"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.976191Z",
     "start_time": "2024-01-12T18:49:36.727309Z"
    }
   },
   "id": "cc8068da998d7ee0"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genre\n",
      "Drama        1236\n",
      "Comedy        813\n",
      "Action        573\n",
      "Adventure     481\n",
      "Horror        298\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Using the movies DataFrame, find the number of movies in each genre and save the counts to a variable called genre_counts\n",
    "genre_counts = movies.genre.value_counts()\n",
    "print(genre_counts)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:36.998906Z",
     "start_time": "2024-01-12T18:49:36.973950Z"
    }
   },
   "id": "8c7f69ee5df8b9a3"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genre\n",
      "Drama        0.363423\n",
      "Comedy       0.239047\n",
      "Action       0.168480\n",
      "Adventure    0.141429\n",
      "Horror       0.087621\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Using the movies DataFrame, find the proportion of movies in each genre and save them to a variable called genre_props\n",
    "genre_props = movies.genre.value_counts() / len(movies.genre)\n",
    "print(genre_props)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:37.000098Z",
     "start_time": "2024-01-12T18:49:36.979658Z"
    }
   },
   "id": "a98e822dd8614ecc"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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D8qzNTIcPH9Zrr70mSfrwww/15JNPOrWXlpYqLy+v3osDAACoT7UKPp06ddK+fftUVFQku92u7Oxsp/bmzZsrKSmpQQoEAACoL7UKPh4eHnriiSckSQsXLtSyZcsatCgAAICGUKvg82PLli1TZWWlioqKVF1d7dTWqVOneisMAACgvtU5+Lz99ttatGiRSktLHdMsy1KzZs301Vdf1WtxAAAA9anOwWfVqlVKTEzUqFGj5OlZ58UBAABcps7J5bvvvtN9991H6AEAAI1OnT/H54YbblB+fn5D1AIAANCg6nzYplevXpo4caJuvfVWXX311U5t9913X70VBgAAUN/qHHxyc3MVFBSkr7/+Wl9//bVjerNmzeq1MAAAgPpW5+Dz4osvNkQdAAAADa7Owef8V1f8lJEjR15GKQAAAA3rkm5n/7Hi4mKVl5crPDyc4AMAANxanYPPtm3bnP62LEtr1qzR6dOn66smAACABlHn29n/XbNmzXTPPffo9ddfr496AAAAGsxlBx9J+vbbb7mrCwAAuL06n+oaP368U8ipqqpSXl6eRowYUa+FAQAA1Lc6B5++ffs6/e3h4aGJEyfqlltuqbeiAAAAGkKdg8+PP525sLBQbdq04Xu7AABAo1Dna3yqqqq0fPlyhYWFadCgQQoPD9eiRYtUWVnZEPUBAADUmzoHn6eeekrZ2dlauXKlNm3apJUrV2rXrl1auXJlA5QHAABQf+p8jiozM1PPP/+8unTpIknq1q2bunXrpsTERM2dO7feCwQAAKgvdT7iU1xcrI4dOzpN69ixoyoqKuqtKAAAgIZQ5+ATEhKi9evXO01bv369goOD662o06dPa+7cuerbt6969+6te++9VwUFBZKkXbt2acyYMQoLC1N0dLTS09Odls3IyFBMTIx69uyp+Ph45ebm1ltdAACgcavzqa6ZM2fq7rvv1htvvKEuXbro4MGDys/P13PPPVdvRd1///1q06aN/va3v8nDw0MPPfSQFi1apEcffVRTpkzRAw88oNtuu005OTmaMWOGQkJC1KNHD2VnZ2vp0qVas2aNevToobS0NE2fPl3vvvuufH19660+AADQONU5+ERERGjBggXatWuXPD09FRUVpbFjx6pXr171UtAXX3yhXbt26aOPPlKrVq0kSUuXLtWJEye0ZcsW+fn5KTExUZLUv39/xcbGKi0tTT169FB6erqGDRum8PBwSdLEiRP1yiuvaPPmzRo9enS91AcAABqvS/p29oyMDD3//PO69tprtXXrVi1fvlzFxcWaNGnSZRf0+eefKzAwUH/961/1l7/8ReXl5brppps0b9487du3r8YptcDAQG3YsEGSlJ+fXyPgBAYGas+ePXWqwW63X3Qem81Wp3Wiptr0M4BLx37q8rCPajzqMlZ1Dj4bNmxQWlqa466uwYMHKygoSBMmTKiX4FNcXKy8vDz9+te/VkZGhioqKjR37lzNmzdPV199dY1TVj4+Pjpz5owkqays7ILttbV79+4Ltvv6+qp79+51WidqysvLU3l5uavLAJok9lOXj31U01Tn4FNaWvqTd3XVNVz8HG9vb0nSggUL1Lx5c7Vq1UozZ87U2LFjFR8fX+PusYqKCrVs2VLSuX/0n2r39/evUw2hoaG8U7oCQkJCXF0CAPws9lGNh91uv+hBi/PqHHxuuOEGPfvss7r33nsd09atW6df/epXdV3VTwoMDFR1dbWqqqrUvHlzSVJ1dbUk6frrr9fLL7/sNH9+fr6CgoIkSUFBQdq3b1+N9sjIyDrVYLPZCD5XAH0MwJ2xj2qa6nw7+/z58/XnP/9ZN998s26//XbdfPPNevHFF/XQQw/VS0EDBgxQly5d9Ic//EFlZWUqKirS448/rltuuUXDhw/XyZMnlZqaqqqqKmVlZSkzM9NxXU9CQoIyMzOVlZWlqqoqpaamqrCwUDExMfVSGwAAaNwu6YjPli1b9O6776qgoEAdO3bUzTffrKuuuqpeCvLy8tKLL76olJQUDR06VD/88IOio6O1YMECtW7dWuvWrVNycrJWrVqlgIAALVy4UP369ZN07i6vpKQkLV68WMePH1dgYKDWrFkjPz+/eqkNAAA0bpf0tept2rTRyJEj67mUf2nfvr0ef/zxn2wLDQ2t8QGKPxYXF6e4uLiGKg0AADRidT7VBQAA0FgRfAAAgDEIPmj0rGo+ZOxy0H8ATHJJ1/gA7qSZh00nN85X1clvXF1Ko+N19XW6Oj7F1WUAwBVD8EGTUHXyG1Ud+8rVZQAA3BynugAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMYg+ACoV/Zqu6tLaLToO6Dhebq6AABNi83DpiXvLNH+U/tdXUqjcq3/tUoamuTqMoAmj+ADoN7tP7Vfe0/sdXUZAFADp7oAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAY7ht8LHb7Ro/frzmz5/vmLZr1y6NGTNGYWFhio6OVnp6utMyGRkZiomJUc+ePRUfH6/c3NwrXTYAAHBjbht8nnzySe3cudPxd3FxsaZMmaKRI0cqJydHycnJeuSRR/T5559LkrKzs7V06VKlpKQoJydHI0aM0PTp01VeXu6qTQAAAG7GLYPPxx9/rC1btmjIkCGOaVu2bJGfn58SExPl6emp/v37KzY2VmlpaZKk9PR0DRs2TOHh4fLy8tLEiRPl7++vzZs3u2ozAACAm/F0dQH/rrCwUAsWLNBTTz2l1NRUx/R9+/YpODjYad7AwEBt2LBBkpSfn6/Ro0fXaN+zZ0+da7Db7Redx2az1Xm9cFabfq4NxuLy1ddYSIzH5WIs3Ed9jgUaVl3Gyq2CT3V1tX7/+9/rrrvu0q9+9SuntrKyMvn6+jpN8/Hx0ZkzZ2rVXhe7d+++YLuvr6+6d+9e5/XCWV5e3mWfimQs6kd9jIXEeNQHxsJ91NdYwL24VfB55pln5O3trfHjx9do8/X1VUlJidO0iooKtWzZ0tFeUVFRo93f37/OdYSGhvJO6QoICQlxdQn4f4yF+2As3Adj0XjY7faLHrQ4z62Cz+uvv66CggJFRERIkiPI/P3vf9fcuXO1Y8cOp/nz8/MVFBQkSQoKCtK+fftqtEdGRta5DpvNRvC5Auhj98FYuA/Gwn0wFk2TW13c/Pbbb+sf//iHdu7cqZ07d2r48OEaPny4du7cqZiYGJ08eVKpqamqqqpSVlaWMjMzHdf1JCQkKDMzU1lZWaqqqlJqaqoKCwsVExPj4q0CAADuwq2O+FyIv7+/1q1bp+TkZK1atUoBAQFauHCh+vXrJ0nq37+/kpKStHjxYh0/flyBgYFas2aN/Pz8XFs4AABwG24dfFJSUpz+Dg0N1fr16392/ri4OMXFxTV0WQAAoJFyq1NdAAAADYngAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAADay62nJ1CY1WffedZ72uDQAA1ODh0Uxb0v6hU8dLXV1Ko+LfvpWGJPaq13USfAAAuAJOHS/ViSPFri7DeJzqAgAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAy3DD579uzRXXfdpT59+mjgwIGaO3euioqKJEm7du3SmDFjFBYWpujoaKWnpzstm5GRoZiYGPXs2VPx8fHKzc11xSYAAAA35HbBp6KiQpMmTVJYWJg+/PBDbdq0SadPn9Yf/vAHFRcXa8qUKRo5cqRycnKUnJysRx55RJ9//rkkKTs7W0uXLlVKSopycnI0YsQITZ8+XeXl5S7eKgAA4A7cLvgcPXpUv/rVrzRjxgx5e3vL399ft912m3JycrRlyxb5+fkpMTFRnp6e6t+/v2JjY5WWliZJSk9P17BhwxQeHi4vLy9NnDhR/v7+2rx5s4u3CgAAuANPVxfw76677jqtXbvWado777yjG264Qfv27VNwcLBTW2BgoDZs2CBJys/P1+jRo2u079mzp0412O32i85js9nqtE7UVJt+rg3G4vLV11hIjMflYizcB2PhPi42FnUZK7cLPj9mWZZWrlypd999Vy+99JJeeOEF+fr6Os3j4+OjM2fOSJLKysou2F5bu3fvvmC7r6+vunfvXqd1oqa8vLzLPg3JWNSP+hgLifGoD4yF+2As3Ed9jYXkxsGntLRUDz30kP75z3/qpZdeUkhIiHx9fVVSUuI0X0VFhVq2bCnp3JOroqKiRru/v3+dHjs0NJR0fgWEhIS4ugT8P8bCfTAW7oOxcB8XGwu73X7RgxbnuWXwOXjwoCZPnqxOnTppw4YNCggIkCQFBwdrx44dTvPm5+crKChIkhQUFKR9+/bVaI+MjKzT49tsNoLPFUAfuw/Gwn0wFu6DsXAf9TkWbndxc3FxsSZMmKBevXrpueeec4QeSYqJidHJkyeVmpqqqqoqZWVlKTMz03FdT0JCgjIzM5WVlaWqqiqlpqaqsLBQMTExrtocAADgRtzuiM/GjRt19OhRvfXWW3r77bed2nJzc7Vu3TolJydr1apVCggI0MKFC9WvXz9JUv/+/ZWUlKTFixfr+PHjCgwM1Jo1a+Tn5+eCLQEAAO7G7YLPXXfdpbvuuutn20NDQ7V+/fqfbY+Li1NcXFxDlAYAABo5tzvVBQAA0FAIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBgEHwAAYAyCDwAAMAbBBwAAGIPgAwAAjEHwAQAAxiD4AAAAYxB8AACAMQg+AADAGAQfAABgDIIPAAAwBsEHAAAYg+ADAACMQfABAADGIPgAAABjEHwAAIAxCD4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMZocsGnsLBQ9957ryIiItS3b18lJyfr7Nmzri4LAAC4gSYXfGbOnKkWLVpo+/bt2rBhgz7++GOlpqa6uiwAAOAGmlTwOXDggD755BP9/ve/l6+vr7p06aJ7771XaWlpri4NAAC4AU9XF1Cf9u3bJz8/P7Vv394xrVu3bjp69Ki+//57tW7d+oLLW5YlSaqsrJTNZrvgvDabTUEd2sjb1uzyCzfMNe1ay263y26318v6bDabbO2CVe3hXS/rM4mt7bX1OhbSufEIDAiUl4dXva3TBNf4XdMgY+HbrZvkxVjUhW/Xrg0yFgEdW6rZhV9a8G/8f9GyVmNxvv386/iFNLNqM1cj8frrr+vxxx/Xe++955h28OBBxcTE6P3331eHDh0uuHxlZaV2797dwFUCAICGEBoaKm/vC78JblJHfFq0aKHy8nKnaef/btmy5UWX9/T0VGhoqDw8PNSsGUdyAABoDCzLUnV1tTw9Lx5rmlTwCQoK0unTp3Xy5EldffXVkqSvv/5aHTp00FVXXXXR5T08PC6aFAEAQOPVpC5uvvbaaxUeHq7ly5ertLRUhw4d0lNPPaWEhARXlwYAANxAk7rGR5JOnjyphx9+WNnZ2fLw8NDIkSM1Z86ci16sDAAAmr4mF3wAAAB+TpM61QUAAHAhBB8AAGAMgg8AADAGwQdoIHa7XYcOHXJ1GbgE+/fvd3UJABoIwceFvv32W82bN0+RkZEKCwvTLbfcoscee0xlZWWuLk0bN25UdHS0q8twG2lpaQoJCanTF97OmjVLr732miTp6NGjCgsL09GjRxumwEastn2bnZ2tkJCQBq9n27Ztuueeexr8cRqj6OhohYaGKiwsTGFhYerZs6fi4uKUnp7u6tKMFB0drY0bN9aYzv77wgg+LvKPf/xDo0aNUufOnfXaa68pNzdXa9as0a5du3T33XfX63fE4PKlpaXpjjvu0AsvvKCzZ8/WaplTp045fu/UqZNyc3PVqVOnhiqx0bqUvm1Ip0+frtX3/ZhqyZIlys3NVW5urj755BPNmDFDKSkpevbZZ11dGlArBB8X+e///m+NHDlSDzzwgAICAiRJv/zlL/X444+rbdu2OnTokI4cOaKZM2eqf//+GjhwoB588EEVFBRIOvfuNzo6WmvXrtXAgQMVHh6uFStWaOvWrRo6dKjCwsJ0//33q7KyUtK57yF74oknNHjwYPXp00eTJ0/WgQMHHPV8/fXXGj9+vMLCwhQbG6svv/zS0XbPPfdo0aJFTvVPnTpVTzzxREN3k1v4+OOPVVhYqPnz56u6ulrvvPOOo62oqEhz5sxR79691bdvX82aNUvFxcVasGCBdu7cqWeeeUbTpk3T4cOHFRISosOHD0tSrcZ29erVuummm9SnTx/df//9Ki0tdcn2N6QL9W1BQYGmTZumXr16afDgwdqxY4ejbe7cuXrwwQed1jVz5kwtWbJE0rnv6Js2bZr69u2rqKgoPf74447/hY0bN+qOO+7QsmXL1K9fP/Xv318LFixQVVWVsrOzlZSU5DhCd/z4cY0fP15/+tOfHI/z72MZEhKiZcuWqW/fvpo2bZok6aOPPlJCQoIiIiI0bNgwvfHGGw3TgS7m7e2tIUOGaN68eXryySdVWlpaoz8sy9Kzzz6r2NhYRUREqHfv3nrwwQdVUVEhSZo/f74efvhhTZ06VWFhYYqJidHHH3+spUuXqnfv3ho4cKDTEaVt27bp9ttvV//+/XXjjTdq3LhxnJq8gLy8PE2ePFl9+vRRZGSkFi9erJKSEknn/hfi4+N19913KyIiQpmZmRo/frzmz5+vqKgo3XzzzSotLa3zOtyehSvuwIEDVnBwsJWTk/Oz81RWVlpDhgyxZs+ebX3//fdWcXGxNXv2bGvUqFFWVVWVlZWVZQUHB1vLly+3Kisrrffee88KDg627rrrLuv06dPWwYMHrd69e1sZGRmWZVlWSkqKNXLkSOvgwYNWRUWF9ac//cmKjo62KioqrMrKSmvw4MHWkiVLrIqKCmvv3r3Wb37zGysqKsqyLMt68803rYiICOuHH36wLMuyTpw4Yd1www3WwYMHG7yv3MH06dOtxx57zLIsy3rmmWes+Ph4R9u4ceOsqVOnWkVFRVZJSYl19913W7NmzXK0rVq1yrIsyzp06JAVHBxsHTp0qNZjm5SUZJWXl1v79++3Bg4caD3zzDNXfuMb2IX69s4777RmzJhhlZSUWEePHrXi4uKs4OBgy7IsKycnx+rRo4dVUlJiWZZlFRcXW6GhodYXX3xhlZWVWVFRUdZjjz1mVVRUWEePHrUSEhIcj/Pqq69awcHB1lNPPWVVVlZau3btsnr27Glt2rTJ0X7+uW9ZzuNoWc5jaVmWFRwcbE2ePNk6c+aMVVxcbH311VdWjx49rHfeecc6e/as9emnn1p9+/a1PvjggwbsySsjKirKevXVV2tMP378uBUcHGy9//77NfrjzTfftAYOHGh9++23lmVZVn5+vtWnTx/rr3/9q2VZljVv3jyrR48eVk5OjmW3263Zs2db119/vfXCCy9YVVVV1p///Gfr17/+tfXDDz9Y3333nfXrX//a2rp1q2VZllVUVGTdeeed1pw5c65YH7iTqKgoq0ePHlZ4eLjTT48ePayoqCirqKjI6tOnj5WSkmKVl5dbBQUF1m9/+1tr2rRplmX9639h48aN1g8//GCVl5db48aNs2666Sbr2LFjVnFx8SWtw91xxMcFioqKJMnxfWI/ZefOnTp06JCWLFmiq666Sq1bt9aSJUu0Z88effHFF475pk6dKi8vLw0aNEiSdMcdd6hNmzbq0qWLgoKCdPjwYVmWpfXr12v27Nnq0qWLmjdvrhkzZqiqqkrvvfeecnNz9d1332nu3Llq3ry5goKCdNdddzke45ZbbpGHh4e2bdsmScrMzFRYWJi6dOnSEN3jVo4cOaLt27crMTFRkjR27Fjl5+frk08+0ZEjR/TJJ59o3rx58vf3V6tWrZSSkqLp06dfcJ21HdsZM2bIx8dH11xzjfr27atvv/22Qbf1SrtY3+7cuVNz5sxRq1at1LFjR913332OZSMiItSxY0e99dZbkqRNmzbpuuuu0w033KD33ntPlZWVmj17tpo3b66OHTvqd7/7ndLS0hzL+/j4aNq0afLy8lKPHj0UEhJyWf07fPhw+fr6qnXr1lq/fr0GDx6sIUOGyGazqVevXho7dqzT4zc1/v7+ks6dJpSc+yMyMlIbNmzQtddeq6KiIp06dUp+fn46fvy4Y/l+/fopIiJCHh4e6tevn1q0aKHx48fL09NTUVFRqqys1MmTJxUQEKA333xT0dHRKi0t1bFjx+Tv7++0LtMkJSVp586dTj9JSUmSpK1bt8rLy0tz5syRj4+P2rVrp0WLFmnbtm06ceKEJMnLy0txcXHy9vaWj4+PJCkyMlLt27dX69atL3kd7qxJfUlpY9GuXTtJ0okTJ3TttdfWaD958qQKCwsdL6bntWrVSn5+fjpy5IgjNJ3f4Zz/So7WrVs75vfw8JBlWSoqKtKZM2f0u9/9Th4e/8q6VVVVOnLkiCorK+Xv7+/0hO3atavjd29vbw0fPlyvv/66br31VmVkZOjuu++uh55wfy+//LLOnj2ruLg4x7SzZ89q3bp1jtManTt3drS1a9fOMb4/p7Zj++P1eHl5NbnrTi7Ut1OmTJEkp2uifvyclKQxY8bo9ddf15gxY5SRkaExY8ZIOheoioqK1Lt3b8e8lmWpqqpKhYWFkqS2bduqWbNmjvbL7d9f/OIXjt+PHDmirKwsRUREOKbZ7fYa9Tcl59/MtW3bVpJzf1iWpccff1zvvvuuAgICdP3116uqqsqpv/38/By/22w2p/3Y+XGqrq6Wl5eXNm3apPXr16tZs2YKDg5WaWlprb6R20SFhYXq1KmT01c2/cd//Iekc89T6dx+5sevC5Lz+F3qOtwZzxYX6Ny5s4KDg7V582annbN07kkWFRWl++67T6dOnVJpaanjBbKkpESnTp1Su3btHDuNH++8f46/v7+aN2+udevWqWfPno7p33zzjdq3b6+vvvpKRUVFKisrU8uWLSVJx44dc1rH6NGjNXbsWOXm5urw4cMaOnTo5XRBo/DDDz9ow4YNSk5O1oABAxzT9+7dqylTpmjy5MmSzt2xdT7A5ufna9OmTZo5c+bPrrdz5861Gtum7GJ9e/6uqkOHDqlbt26Saj4nR40apZUrV+qjjz5SXl6ehg8fLknq0KGDunbtqrffftsxb2lpqQoLCx3X09WFh4eHqqqqHH//+KL18378f9ihQweNGjVKDz/8sGNaQUFBkx7Xbdu2qUWLFrrxxhslOffHY489pqNHj2rbtm2O53tsbKzT8rXZj0nSW2+9pZdeekl/+ctfdM0110iSli5dqr1799bHZjQ5nTt31tGjR2W32x3B5eDBg5LOhZVvvvnmJ/v+x9MudR3urPFEtCZm0aJFevXVV/Xkk0/q1KlTsixLX331laZNm6YbbrhBd999twIDA5WUlKSSkhKVlJRo8eLF6tq1q3r16lWnx/Lw8FBCQoL+93//V8eOHVN1dbUyMjI0fPhwHThwQGFhYfrlL3+pZcuWqby8XAcOHNC6deuc1tG9e3cFBgbq4Ycf1n/913/J19e3PrvDLWVmZqpZs2aKjY1Vhw4dHD+RkZEKDg7Wa6+9poEDB+rRRx/V999/r9LSUv3P//yP47N7vL29HRcA/lhoaGi9jW1jdbG+feONNzRo0CA98sgjKi4u1okTJ/Tkk086rSMgIEBRUVFauHChhgwZojZt2kiSoqKiVFZWprVr16qyslLff/+95s2bp1mzZtVqB928eXOVl5c77jDr1q2btm/fru+//14lJSVas2bNBZdPSEjQpk2b9OGHH6q6ulr79+/XuHHjavxPNQWVlZXavHmzVqxYoVmzZjkdxTyvtLRUzZs3l81m0w8//KB169Zp7969TmGytkpKSuTh4SEfHx9ZlqUPPvhAr7322iWtywS/+c1vJJ0LnxUVFTpx4oSSk5PVr18/pyPVDb0Od0PwcZE+ffropZde0pdffqlhw4apV69eeuCBB9SvXz+tXbtWXl5eeuaZZ3T27FkNHTpUUVFRqqqq0vPPP39Jh3XnzZunG2+8UXfeeaciIiKUmpqqVatWqXv37rLZbHr22WdVUFCgAQMGaNKkSRo8eHCNdcTHx+vLL7/U6NGj66ML3N7LL7+s2NhYeXl51Wi77bbb9Prrr+vRRx9Vq1at9J//+Z8aPHiwAgICHHcWjRw5Uq+++qruvPNOp2U9PT3rdWwbo9r27VVXXaWoqCiNHj3a6cjQeWPHjtWRI0eUkJDgmNaqVSulpqYqOztbkZGRjmvUVq9eXavaevfurbZt26p3797Ky8vT1KlT1bZtWw0ePFhxcXEX/XyUG2+8UStWrNCKFSvUu3dvjRs3TtHR0TXuQmuskpKSHJ/jExkZqZdeeklLlizRb3/725+cf+bMmaqoqNCAAQMUHR2tzz77THFxcZd0lGbUqFEaMGCAhg0bpn79+mn16tWaMGGCvv32W8dde/iXq666Ss8//7z27t2r3/zmNxo+fLg6d+5cpzty62Md7oZvZ0etbd26VY899pjjglIAABobM95e4rKcOnVKx44d0+rVq3XHHXe4uhwAAC4Zp7pwUV988YVuv/12tWvXTrfffrurywEA4JJxqgsAABiDIz4AAMAYBB8AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMYg+AAAAGPwyc0AGo0vv/xSS5cu1Z49e9S1a1fdeuutSk9P17Zt2/TRRx9pxYoV2r9/v9q3b6+pU6dqxIgRkqT58+fL29tbBQUFys7OVkBAgCZMmOD4fqno6GgNGjRIW7duVbt27bRx40Z99dVXSklJ0Z49e+Tv768777xTEyZMaHTfRA3AGUd8ADQKpaWlmjRpkvr166fs7Gw9+uij+utf/ypJ2rNnj6ZPn64pU6YoOztbS5cu1fLly7V9+3bH8hs3btT48eOVk5OjyZMnKyUlRcePH3e0f/7553rrrbf0wgsv6MSJE5owYYJuvfVWffTRR3rqqaf08ssv65VXXrni2w2gfhF8ADQK27Ztk81m0/333y9vb2+FhIRo0qRJkqT169dr8ODBGjJkiGw2m3r16qWxY8cqLS3NsXzfvn01cOBAeXp6avTo0bLb7Tp48KCjfejQoWrdurVat26tN954Q926dVNiYqK8vLwUGBioe+65x2l9ABonTnUBaBSOHTumTp06ycPjX+/XunTpIkk6cuSIsrKyFBER4Wiz2+3q2rWr4+927do5fvfy8pIkVVdXO6b94he/cPx+5MgR/fOf/3RaX3V1tWw2Wz1uEQBXIPgAaBQ6deqko0ePyrIsx3U2R48elSR16NBBo0aN0sMPP+yYv6CgQHX5KsIfX7vToUMH9e3bV88995xj2qlTp1RWVna5mwHAxTjVBaBRiI6OlmVZevrpp1VZWalvvvnGEUwSEhK0adMmffjhh6qurtb+/fs1btw4rVu37pIeKzY2Vp999pneeOMNnT17VgUFBZo2bZpSUlLqc5MAuADBB0Cj0KJFCz311FPaunWr+vTpo9mzZ2vgwIHy8vLSjTfeqBUrVmjFihXq3bu3xo0bp+joaD344IOX9FidO3fW2rVr9corr2jAgAGKi4vTddddR/ABmoBmVl2OBQOAi5w6dUrffPONwsPDHdNefPFFvfnmm1q/fr0LKwPQmHDEB0CjYLfbNWHCBL3//vuSpMOHD+vll19WVFSUiysD0JhwxAdAo/H3v/9dTzzxhA4fPqzWrVtr1KhRuu++++TpyX0aAGqH4AMAAIzBqS4AAGAMgg8AADAGwQcAABiD4AMAAIxB8AEAAMYg+AAAAGMQfAAAgDEIPgAAwBj/B7NR1PwZ/+XQAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a bar chart for genre using the countplot() function from seaborn. Don’t forget to display the plot using plt.show() and close the plot using plt.close()\n",
    "sns.countplot(x='genre', data=movies)\n",
    "plt.show()\n",
    "plt.clf()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:37.123183Z",
     "start_time": "2024-01-12T18:49:36.988310Z"
    }
   },
   "id": "94aa060027e474f0"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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SupJSyprJcHwTz/SuR/cG1bQuRwjNLN99hgU7kmRghhNIaLkDmxWKL8KC4WAp1Lqa/7FZURbdDelH+XBUcxpEBmhdkRCamb5inwzMcAIJLXegKPDVPZCdrHUlVyvKRZk7EF1hJksfSiTU16R1RUJooshiY/zcX+X6loNJaLmD76bD8U1aV/H3clJQ5g7CW2/jm4kdkd1MhKc6lnZRrm85mLy9uDKbFfYvd8yagvZ27neURXcTHujNsodlDpfwXMt3n+HLX+T6lqNIaLkqmxVyzsCyf2hdSdkdWY+y5kluig7i3ZEtta5GCM1MW7GPExfk+pYjSGi5Kp0elj0ExXlaV1I+v3wM297mtpsiebxngtbVCKGJQrONcV+UXN9S5fqWXUlouSKbBX79DE5u0bqSivl2KhxcwyNd69K/eQ2tqxFCE8fSLvL88n0ocn3LriS0XI3NCvkZJRs6uitVRVnyAJz7ndcH30TL2sFaVySEJhbtTGbrkTTpJrQjCS1Xo9PDykdLFsR1Z+YClHlDUC6eZ+EDbage6K11RUJo4qmvf8dslW5Ce5HQciU2C+xdDIfWal2JfVxMQ/liAEa1iLUTO+ItY+GFBzqTXcjs1fulm9BO5F3EVdhsUJQHa5/SuhL7unAYZcFwgrwNrHlUhsILzzR/RxI/H0+XbkI7kNByFTodrHkCLl7QuhL7O7kFZcXD1KkWwH/HtNG6GiGcTlXhmSV/IB2ElSeh5QqsFjjyLfzxldaVOM6ehbDpJbrUq8b0fo20rkYIpztx4SJvbTgiSzxVkoSW1lQVrEWwcqLWlTjeppdR9yzknva1GZVYW+tqhHC6D344zqn0fKw26SasKAktrSkKrH8OclK0rsQplBWPQPJ2Zt3RkI51w7QuRwinKrbamLL4d/Q6eeutKHnmtGQ1Q9JP8OunWlfiPNZilAXDISuJ/97bkrgwX60rEsKpdpzIYNmuFBmUUUESWlrSG2HtlJIuQk9SmIUydwB6cx6rJrQnwNugdUVCONWc9YdkUEYFSWhpxWqGfcvg7B6tK9FG5kmUeYPxNSqsm9gJ6S0RnuR0ZgGfbTspK8FXgLxVaEWnh42ztK5CWym/oix+gBrBPnw1rr3W1QjhVO9sPEpBsVXrMtyOhJYWrBbYNQ/Sj2pdifYOrED59nlaxYQyZ0gzrasRwmmyC8y8vVGGwJeXhJYmVPjhX1oX4Tq2vY36y8cMalmDh7rU0boaIZzms20nScstkuAqBwktZ7NZSvacyk7WuhKXoqx9Co5u4KneCfRqFKl1OUI4RZHFxr/WHUQn6xKWmYSWs1ktsOXfWlfhemxWlK/uhbRDvDeiGY2qB2hdkRBOsWxXCofP58qgjDKS0HImmxV+egcupmldiWsqzkOZOwhdYQZLxicS7m/SuiIhHM6mwuxV+9HrpLVVFhJazmTOh21vaV2Fa8s9i/LFQLwUK+se7YTsZiI8weYjF9h+QlaBLwt5S3AW1VbSLejumzs6w/m9KItGEeZvYuUE2c5EeIb3vj+GQS9vyTciz5AzqCoUZML2D7SuxH0c3YCyejINawTx/qiWWlcjhMNtOpzGsdQ8ubZ1AxJaTqHCppdKugdF2f36Kfz4Jr0bR/FU7/paVyOEw733wzG5tnUDElrOUJgDv32udRXu6btpcGAFD3WJY2DLaK2rEcKhlu9O4UJuEarM2/pbElqOZrOUBJalSOtK3JOqoiwZC2d3M2dQY1rHhGhdkRAOY7aqfLTluMetoV0eElqOpjN41tYjjmApRJk3BCX3LPPvb010sLfWFQnhMAt2JFFkkVGEf0dCy5FsFjj2PWQc17oS95efjvLFAIy2QtY80hFfk7x0RdWUU2hh7vZTWGR342uSv3xH0hngl4+0rqLqSD+KsmAYgd4G1jxys9bVCOEwn2w9gYIMyLgWCS1HUVXIPQeH12ldSdVy6keUZeOJjfBn7v1tta5GCIc4m13Iyj1nZLLxNUhoOYpqg53/V7J0k7CvP76CjbPplBDBzP6Nta5GCIf44udTMtn4GuQZcSQZ5u44m19F3TWP0Ym1uKdDjNbVCGF3v57KJDkjX4a//4WEliNYzXBwdUn3oHAYZdVEOLWN6X0a0CUhXOtyhLC7L3cmIwtkXElCyxH0xpI9s4RjWc0oX46EzBP8390tqBPhp3VFQtjVkl9PI1ttXUlCy95UW8kQ9xM/aF2JZyjMRpk7EL05l5UPtyfQ26B1RULYzZnsQnacyMAqw99LSWg5wo4Pta7As2QlocwdjK8BvpnUCZ28qkUVsmhnMnp5UZeSZ8LebBbYvUDrKjzPmd9Qvr6PqCAfFo/voHU1QtjN2j/OUVAso5AvkdCyJ5sFjm2EwiytK/FMB1ejfPNPWtQO4bWhzbSuRgi7KDBbWfX7GcwyZwuQ0LIvRV8yalBo5+f3ULd/yIAWNZjQNV7raoSwi8W/ncYoc7YACS37UhRZAcMFKOumwJFvmdyzLrc1idK6HCEqbfuJDM5mFWhdhkuQ0LIX1QYpv0FeqtaVCNWG8vV9kHqQd+5qyk01ArWuSIhKUVVYu/ecdBEioWU/qgoHV2ldhbik+CLKvEHoCi7w9bi2RPibtK5IiErZeDBVugiR0LIfnR4OrdG6CnG53HMoXwzES7GwbmInTAZ5uQv3teNEBoVmGUUof8X2kn0aUg9oXYX4q9T9KF+OItTPxMoJHbWuRogKK7ba2Hw4zeP32ZLQsgerGfYv17oK8XeObURZNYn6UYF8dHcrrasRosI2HExF7+HrOklo2YPeCIfWal2FuJ7fPoctr9GjYSRP39ZA62qEqJDvD6aiSGiJSivKhaSftK5C3MjGmbBvKeNujmVI65paVyNEuaXmFnHgbI5Hb1cioVVZVjMcWleyGoZwbaqKsmw8pPzKvwY0om1ciNYVCVFu6/efx+rB+5VIaFWW3iijBt2JpQhlwV0oOWeYN6Y10SHeWlckRLl8fzDVo3c09tyf3F5sFjj6ndZViPLIz0D54k4M1gLWPtIRX5P8GQj3sed0Fln5xVqXoRn5a62sc3uhKEfrKkR5ZRxHmT+UAC896yberHU1QpSZqpYs6+Spe2xJaFWGtRhO/6J1FaKikn5GWTqO2mH+LHgwUetqhCiz305lAp45ilBCqzJ0RjizS+sqRGXsXQwbZtK+bjgvDLhJ62qEKJPdyVnodRJaorwUBc78pnUVorK2/Bv1t88Z0aYWYzrGal2NEDf0++lsbB46glBCqzLMhXDhsNZVCDtQVj0GJ7cw9fb6dK1fTetyhLiuArOVI6l5WpehCQmtilJVOPcH2GQByyrBZkH5chRkHOejUc2Ij/DTuiIhrmvnyQyP3KpEQquibGY4vUPrKoQ9FeWgzB2AvjiHFQ+3J8jHoHVFQvytXclZGDzwupaEVkXpTTIIoyrKPo0ydxA+epX1E29GJ38hwkXtSsr0yHUI5U+yMiS0qqazu1G+vpdqQd4sfUi2MxGu6fiFi+QVet7ycRJaFVV8ETKOaV2FcJRDa1HWTqFZrWDeGtZc62qEuIqqwq7kTGwetniuhFZFqGpJK8vDXiweZ8eH8PO79GtWnUe7x2tdjRBX2XcmB6vVs96HJLQqwmaGlF+1rkI4wzfPwuF1PNa9Lv2aVte6GiGucPLCRQx6z7quJaFVEXoTpMikYo+g2lC+vh/O7+PNoU1oEh2kdUVClDqZftHjBmNUKLROnDjBlClT6Ny5My1atKBHjx7MmTOHixcv2ru+cluyZAndunVz/InO/e74cwjXYM5HmTcYJT+Vr8e1JTLQS+uKhADgxAXt33Odrdyh9dtvvzFgwACio6NZtmwZu3bt4qOPPmLPnj2MGTMGq9UDJtvarJB1SusqhDPlpaJ8MRCTWszaRzvhZZBOCqG98zlFFFk84D33MuX+y3v++ee58847efTRRwkNDQUgLi6O119/nbCwMJKTk0lJSWHSpEm0b9+ejh07MnnyZFJTUwHYvn073bp14+OPP6Zjx460atWK1157jQ0bNtC7d29atGjBI488QnFxyX4xxcXFvPnmm3Tv3p22bdvy4IMPcurU/wLj2LFjjB49mhYtWtCvXz/2799fetv999/P1KlTr6h/3LhxvPnmm+V/pi53MVVWwvBEaQdRFo4gxNfAqkc6aV2NEAAkZ+RrXYJTlSu0kpKSOHLkCH379r3qtvDwcN59912io6MZM2YMer2e9evXs3btWgDGjx+PxVIypyAlJYW0tDQ2bdrEa6+9xgcffMC8efNYtGgRK1asYPv27axZU7Ib8Ouvv86mTZv47LPP2LJlC82aNWPMmDEUFRVhNpsZN24cCQkJ/Pzzz7z22mt8993/NmQcNGgQ69atKw3ACxcu8OOPPzJw4MCKPVuXZEory2Od+AFlxaMkRAbwyT2tta5GCI6k5mH1oMVzyxVaGRkZQElA/Z2dO3eSnJzMjBkzCAgIIDAwkBkzZnDw4EH27t1ber9x48ZhNBrp1KnkE+vw4cMJCgqiVq1aJCQkcPr0aVRVZeHChTz++OPUqlULLy8vHn74YcxmM5s2bWLXrl2cPXuWp556Ci8vLxISErjvvvtKz9GjRw90Oh0bN24EYOXKlbRo0YJatWqV58e+ktUMGccr/njh/nbPg82v0q1hJM/2aaB1NcLDnbxw0aPmapVrcbWIiAgA0tLSiI2Nver2CxcukJ6eTkhICP7+/qXf9/f3Jzg4mJSUlNLACwkJAUCv1wMQGBhYen+dToeqqmRkZJCfn8/EiRPRXbaejtlsJiUlheLiYkJCQvD29i69rXbt2qX/bzKZ6Nu3L8uXL+fWW29l6dKljBkzpjw/8jWokJ1cyWMIt7dxNmpwLA90HMix1Iss/EVeE0IbJy7ke9QahOUKrejoaOrVq8eaNWto06bNFbelp6fTtWtXJkyYQGZmJnl5eaXBlZubS2ZmJhEREah/fiIoyzDNkJAQvLy8+OSTT2jevHnp948fP05kZCQHDhwgIyODixcv4udXsir3uXPnrjjGoEGDGDp0KLt27eL06dP07t27PD/y1fQmyEqq3DFElaAs/wdqcC1evLMVJ9Mv8vPxDK1LEh7I04a9l3sgxtSpU1m8eDHvvPMOmZmZqKrKgQMHGD9+PI0bN2bMmDHEx8czbdo0cnNzyc3NZfr06dSuXZuWLVuWrzidjsGDB/Pvf/+bc+fOYbPZWLp0KX379uXUqVO0aNGCuLg4Zs+eTUFBAadOneKTTz654hiNGjUiPj6emTNncvvtt+Pj41PeH/lq2acrfwzh/qzFKAuGoWSn8MV9ragdaofXlhDllJQuAzGuq23btsydO5f9+/fTp08fWrZsyaOPPkq7du34+OOPMRqNfPDBB1gsFnr37k3Xrl0xm818+umnGAzl3+phypQpNGvWjBEjRtC6dWs+++wz3nrrLRo1aoRer+fDDz8kNTWVDh068MADD9C9e/erjjFw4ED279/PoEGDyn3+a8o9d+P7CM9QkIkydwAGy0VWT+iAn0m2MxHOlZlfrHUJTqWoquOu4D300EO89957V31/1KhRzJ0711GnvcqGDRuYM2dO6UjGSnslDvKlK0hcplZb1HtXczrbzM2vbNK6GuFhDs66FW+jXusynMLuHwtPnz7NsmXLANi6dSvvvPPOFbfn5eVx6NAhe5/2mjIzMzl37hzvvfcew4cPt89BbRYoyLTPsUTVkbwDZfGD1Br6X74c1467PvhZ64qEB8kttEhoVVSNGjU4cuQIGRkZWK1Wtm/ffsXtXl5eTJs2zd6nvaa9e/cyYcIEOnTowLBhw+xz0IJMWd1dXNv+ZfDtNBJ7zuBfA5swZckfWlckPERWfjERAZ6xvJhDuwefe+45Zs+e7ajDa+P8Pnivg9ZVCBem9nsTWt7NC2sO8vGWE1qXIzzAwrHtaFcnTOsynMKhV41nz55NcXExGRkZ2Gy2K26rUaOGI0/tGKoKOWe0rkK4OGX1ZNTg2jx7a2eOp11k48FUrUsSVVzGxWJsNhWdB8zXcmhorVu3jqlTp5KXl1f6PVVVURSFAwcOOPLUjmGzQn661lUIV2ezoCy6G/WB7/hoVHP6vP0TB8/nal2VqMKy8s1YVRUdElqV8tZbbzFy5EgGDBhQoeHurkctWcZJiBspykX5YiC6sZtY+lAiHV75gcx8ee0Ix8jKL/aYS+0O3V/h7NmzTJgwgZiYGKKjo6/4cluqrO4uyignBWXuILz1Nr6Z2AnZzUQ4SlaBGQ/oGQQcHFqNGzfm6NGjjjyFk6myJYkon3O/oyy6m4hAb5Y93FHrakQVlZlfjEHvGZ+KHNpn17JlS+69915uvfXWq1aGnzBhgiNP7TjS0hLldWQ9yponuanPv3lnRAsmzN+ldUWiiiky2258pyrCoaG1a9cuEhISOHbsGMeOHSv9vlsv7igtLVERv3wMIbH0af8wx3om8Pq3R7SuSFQhnrSflkND64svvnDk4Z1PRVpaouK+nQohcTza9TaOp+WxfPdZrSsSVYTVU0Zh4ODQurSc07Xceeedjjy140hLS1SUqqIseQD1vrW8PrgJpy7ks/t0ttZViSrA5kEtLYeuiNGtW7cr/p2dnU1BQQGtWrVyz1aYtRi2vQMbZmhdiXBnfhGoYzdj9avG76dzKLJ6zvUI4RghviYaVA+88R2rAIe2tC5tc3+Jqqp89NFHZGVlOfK0jiXdg6Kyolth9Q1BQUfL2FAACvI8Z56NsD+9B82ncGhL61qsViudO3fmxx9/dOZp7cNaDFteg00vaV2JcFf93sTaYjRJuUnE+tfGlp+Pzt+f9JQ8vp97kNSTsnKGKL+4ZuHc/lBTrctwCqfH84kTJ9x49KAiLS1RMb5hqI/8Bq3uZeXxldy16i7MliKyly3j7LPPERxqYPCU1nQZUQ8v36qweoxwJvd9Ty0/h/51jB49+oon02w2c+jQIe644w5HntaxbHL9QZRTvVuxDPkUq6Jn5tZnWXFsBQC51nxMtWpx/oUXyV61iuhXX6FRj57Et4xky6LDHN5xXuPChbtQPKd30LGhlZiYeMW/dTod9957Lz169HDkaR3LZtG6AuFO+r6BteXdJOcmMen7SRzPPl56U1rBBeJiY0v+UVxMysRJeDVsQM3/vEvPMY1p1KkGm+YdIut8vja1C7eheMoaTjg4tC5f9SI9PZ2goCD3XzhX5+b1C+fwCUW9/1uU8HhWHV3OC9tfoMBScMVdTuedpl6Nzld8r+jAQY5160bY2AeJmvAIw59vy2/rk/h1zUksHrTqgSgfvYcs4QQOvqZlNpt58cUXadGiBZ06daJVq1ZMnTqV4uJiR57WcVQVfEO1rkK4unq3Ynl8H8UhtXhu63M89+NzVwUWwNGso+hMJgwREVfdlv7hRxzt0IGCX3fSqncMI2a0I+Ymz9jkT5Sfl5/BY+ZqOTS03n33XbZv384bb7zBqlWreOONN9izZw9vvPGGI0/rODod+Mobh7iOvm9gHTaf0/mp3LXqLpYfW/63d92Xvg8AY82a17zdlpdH0t33kPTAA3grhfSd0Izbxt2Ef4hnbKsuys7b14gqoVV5K1eu5J133qFLly7UrVuXrl278s4777By5UpHntZxdAbwq6Z1FcIV+YSiTvgVWt/HquOrGLpqKMeyjl33IbvOlyyca6xV67r3y9+2jaPt25P++efENAlj5Ix2NO9RyyN2qRVl4+XnOZctHBpa2dnZVK9e/YrvVa9encLCQkee1rECIrWuQLiaMnYH/lV2cTZWczGmWtduaf1V6osvcbx7dyzHDtNhUDx3TW1LVB3PWAVBXJ+Xr9Fjhr07NLTq16/PwoULr/jewoULqVevniNP61jSPSguV47uwGsptBbesKV1Ocv585wYMJAzTz5FYKDCoKda03V0A4/6pC2u5u1nRKf3jNBy6Ct90qRJjBkzhhUrVlCrVi2SkpI4evQo//d//+fI0zqWT4jWFQhX4BOKev96lPCEvx0dWBaZ5hxCY2LL/bicVavIWbeOGv96mQa33kbdFhFs/eooB38+W7IbgfAoPgFGrUtwGocv4/Tll1+yZ88eDAYDUVFRREREMGTIEEee0vFerAHFF7WuQmgloReWof/FqtMz66dZ5W5dXe7T3p/S3BjHkY6dKnwMU0ICNd99F69aNTl7LJtN8w6ScUZen55k9Oz2BIb7aF2GUzi0e/Ctt97i/fffZ+zYscycOZP69evz/vvv8/HHHzvytI7nG37j+4iqqc/rWIcvrHB34F+dyjmFISwMxWSq8DGKjxzheM+enH/5X1Sr6cNdz7Wl/cC6GEyeM3fH03nS0l8OfVV//fXXfP7558T+Oeu/e/fufPrpp8ybN8+Rp3U8P7mu5XF8QlEn7IQ2Y1h9YnWZRgeWxaHMQwAYo6MrfayMzz7jaPv25P+0jeY9ajNqZnvimskHLE9g9JbQsou8vLxrjh7Mz3fzZWlkMIZnie/x5+jA2jy39Tme3fpsha5fXcvvab8DNx72Xla2/HyS73+ApHvuxmTN4/aHmtLn4aYEhHnb5fjC9Xj5Gjxq+oNDQ6tx48Z8+OGHV3zvk08+oUGDBo48reNJ96Dn6PNvrCMWkVKQxrBVwyrdHfhXB9IPoNpsZR72XlYFv+zkaMdOXPj4Y2o1CGbE9Ha07B3jMSPMPImnXMu6xKFtyqeffpoxY8awaNEioqKiOHfuHBaLxb2vadks4CehVeX5hKLe/w1KeD3WHFvJrJ9n2a11dTkbNizmIru1tP4qbc6/yfjvf6n13nu0u7MxDdpHsWneIc4cyXLI+YTzBUVIaNlN48aNWb9+Pd9//z2pqalUr16dW265hYCAAEee1rFUm7S0qrr4Hlju+gKrTs/sH6ey7Ogyh54u11ayRYmjWNMucHLwEAJ69STqxZcYMLklB38+y7bFRynINTvsvMI5gqr5YLPa0HnIorkOv3oXFBTEnXfe6ejTOI+ih5AYrasQjtLn31hb3UdK3mkmfT+Jo1lHHX7KC4XpxFzaosSBctd/S+53G6j+wgvU63cHcc0i+GnJUfZtPSNzu9xYkId1D3pGNNuTTg/Vm2ldhbA3n1DUCb9AmwdYc2INQ1YOcUpgAaTkpthl9GCZ2GycfeYZTvTrC+dPc8vIBgye0prwmv7OOb+wu+AoX49pZYGEVsUEx4BBVtquMv4cHWgOiWHqj1P559Z/OuT61d85nn0cnbc3+lDnbXtTfOIEx2+9jbMzZxEe5cXQf7ah45B4jF56p9Ug7COomq/WJTiVhFZF6PQQXl/rKoQ93D6ndHTgXauGOfz61bXcaIsSR8qaP5/D7dqRt2UzTbvWYtSs9tRtefX+XsI1GYw6fAMqPjHdHUloVVRkY60rEJXhHYz68C/Q9kHWnljr1O7Av9qduhvA7sPey6ywkNPjxnNq5AiMhdncOrYJ/R5t7nFDqd1RoIeNHAQJrYqxFktoubP4HlgeP4A5NJapP07lma3POLU78K/SCtKwmc0YazpuBGFZFO7azdHOnUl7912i4wMZMT2R1rfHojPI3C5X5WnD3UFCq2J0Boi8SesqREVc6g4sLJksrEV34LUUWQsdOuy9PC689TZHu3SmaO/vtO0bx4hpidSsL7sbuKLgSF9sHrJj8SUSWhWh6CCqidZViPLwDkZ9eMcV3YFHso5oXVWpTEsuxhjXmUphy8zk1LDhJE+YgJ+Xlf6PtaDn/Y3wDfSs6yeurlqMG895rSAJrYryCwdf5432EpVQ2h0Yx/M/Pq95d+C1nL94HlNMba3LuMrFjRs50rYtmV8uIr5FBKNmtaPJLdF4yCa5Li+qbrBHrTsIElqVU02ua7m821+9ojtw6dGlWld0TadyT2EIDweja27md27aNI7ddhvW5FN0HlafIf9sQ0Rtz/uU70p8Aoz4B3ve1BsJrYqyWSGykdZViL9T2h041iW7A//qcOZhFJ0OY40aWpfytyzJyZzo25czz00lJNzIkGdac/Oweph8PGdbDFdSLSZQ6xI0IaFVUapNWlquqm53l+8O/Ku9aXsBDYe9l0P2119zJDGR3O82cNPNNRg1qx0JbSK1LsvjRMYGYrPatC7D6SS0KkpvlOWcXNHtr2Id+RVnCi+4dHfgX+1L34dqs2k+7L3MiotJeeQRTg4Zgj4vg173N6b/Yy0IjvSs1Rm0FBkXiOKBFxcltCojokHJ6hhCe5d1B647uY7BKwe7dHfgXxXbirGaizFpsCpGZRTt38+xW7qS+vobVI/zZ9jzbWnbLw69Ud5aHC0yLhDFwwZhgIRW5Ri9Ibq11lWIut2xPL6/tDvw6S1Pu3x34LXk2QowukH34LWkf/ABRzt1ovC3X2l9WywjpydSu5GMrnWUwHBvvHxdc9COo0loVYbVAnW7aV2FZ7vtFawjv+JsYQbDVw93m+7Aa0kvysAUF6d1GRVmy8khafTdJI99EB99Mf0ebc6tY2/CL1jmdtmbpw7CAAmtytHpIKGX1lV4Ju9g1H9sh8RxfHPyGwavHMzhzMNaV1UpKXkpbtc9eC0Xt/7IkXbtyJg3j9imYYyc2Z5m3Wt5ZFeWo0TVDcJq8bxBGCChVTmKDqo3B+9grSvxLHW7lnQHhtVh2rZpTNkyhXxLvtZVVdrx7OPofH3RBQVpXYpdnJ81m+M9e2I9cZSOg+O569k2RMZ5bgvBnmIah6E3eObbt2f+1Pak00GdLlpX4TluewXryMWcLSrpDlxyZInWFdnNgfQDgHsMey8ry9mznOh/J2eefpqgED2Dp7TmlpH18fKVuV0V5Rfs5dGjNCW0Kstqhrrdta6i6vtrd+AK9+8O/Kvd53cDuM+w93LIWb6CI23bkrNmLQ07VGfUrPbUbxeldVluqVbDEFTVsxbJvZyEVmXpjXJdy9GqaHfgX53JP4PNYqlSLa0rWCykPP44JwcNQpeVSo97GzHgiZaEVvfTujK3UqthKGolVnbv1q0bS5Zc3UOxZMkSunVz/YFlElr2EFgdwuK1rqJqqsLdgddSbCnE6CJblDhK0cGDHOveg/OvziGyli93PdeWdnfWxWCSt6MbUqB24zB0es99rqRj2R5sNojvDuna7HxbJXkHoY75BqVaQ9afWMv0bdOrZOvqr7KseQTUdr3V3h0h4//+j6wFC6j5n3do2asd9RMj+WHBYU7+fkHr0lxWZEwg3n6On5916NAh5syZw549e/D29qZbt25MnjyZgIAAlixZwty5cwkODub3339n2rRpLFq0iOjoaLZv346qqqxatYqUlJRyHaNfv35lqs1z49quVKjbQ+siqo66XbE8fgBLWDzTt03nqc1PeURgAaTmp2JyoX21HM2Wn0/SfWM4de+9eNny6fOPptz+UBP8Qzxv9fKyiGkSZpf1BmfMmEHr1q2v+JoxYwYAmZmZ3H333cTHx7N582YWL17MiRMneOqpp0ofv2/fPvr168e2bdvo2bMnANu2bWPhwoWsWLECs9lcoWOUhYSWPej0ENcZ9DKJstJu/Vdpd+Cw1cNYfGSx1hU5VVJOEoZq1UDvWcuDFezYwdGOHUn/9FNqNwpl5Mx2tOhV2+P2irqRuGYRdpnvNm3aNHbu3HnF17Rp0wDYsGEDRqORJ554Am9vbyIiIpg6dSobN24kLS0NAKPRSP/+/TGZTHh7ewPQuXNnIiMjCQwMrPAxykJCy16M3lArUesq3JdXIOpDP0O78aw/tb5Kjg4siyOZR1D0eoxRnjmyLvVfr3C8ezfMhw/RfkBdhj3flurxVWPeWmX5BZsIr+nv8EVy09PTqVGjBvrLPjjV/HPSe0pKCgARERHodFfGR7Vq1Sp9jLKQ0LIXq7nkupYov7hbsEw+iCU8nhnbZnhUd+Bf/ZH+B0CVH4xxPZbUVE4OGkTK448T4A8Dn2hFt7sb4u3vmWvtXVKneTWnDHWPjo7mzJkzWK3W0u8lJSUBJUEDXDM4L/9eRY9RFhJa9qIzQJOhyD7k5dT7Zayjl3CuKJNhq4fx9ZGvta5IU3+k/oGqqpg8OLQuyV27jiNt25K1fDn1EyMZNbM9DTtWBw/9E2vQPgqcMD2rS5eSxRLmzJlDYWEhaWlpvPDCC7Rr147o6GinHePvSGjZi6JAUDTEdNK6EvdwqTuw/UOsP7WeQSsGeWR34F8V2gqxmovcdrV3u7PZODvlaU7c0Q/SztBtdEMGP9WKsGjPmtsVVM2HajHO2YokICCATz/9lMOHD9OlSxf69u1LdHQ0b775plOP8XcU1ZOnVtub1Qx7F8PScVpX4tribsEyfD6q3sSL21/0+NbVX/04dAt8/xMpjz+udSkuJ3jUKKo98QSKycTvG5LZseoE5iLrjR/o5tr0iaX17XHo9B7azLyMtLTsSW+ExgPAK0DrSlyXdAfeUHpxJqa4WK3LcElZc+dyuH178n/cStPutRg5sx11WkRoXZbDNWhfHUXerQEJLfu7FFziSl6BqA/9BO0f4ttT30p34HWczTuLsZL9/lVaQQHJD44ladRITMW53DauCX0faUZgeNmHTbuTyNhAAsN9HD5q0F1IaNmbqkLLe7SuwrXEXRodmMCMbTN4cvOTHjs6sCyOZx9HHxiIzt9f61JcWsFvuzh6882kvf8BNesFMWJ6O1rdFoPOULXe3Ou1jbTLhOKqQkLL3nR6qNkawupqXYlr6P0S1tGLOVeUyfDVw6U7sAwOZhwEwFgFNoR0hgtvvMHRzp0p2vcHif3qMOL5RKLrBWtdll3odAr1EqM8eq3Bv5JnwhFsFmg+UusqtFXaHfgPvju1gcErB3Mo85DWVbmF3am7AWTYeznYMjM5ddcwTj/yCH7eNu58vCU9xzTCJ8C953bVahTqlLUG3YmEliPoDNBiFB575TSuS2l34MyfZvLE5ie4aL6odVVu41TuKVSLRYa9V0Dehg0caZdI5tdfE9+yGqNmteemLtFuO32yfmKUdA3+hYe+qzqBfyTU7ap1Fc7X+8XS0YHDVw/nq8NfaV2RWyq2FGGqgptBOoXNxrnnpnL89tuwnUmiy/D6DH6mNRG13WtUr2+Qibotq0nX4F/Is+EoVjM0H611Fc7jFYj60DZo/7B0B9pBtvUiRg/ZosRRzElJnLi9D2enTScswoshT7fm5qEJmLzdYzHiJl2kpX0tElqOojdCw77gE6J1JY4X1wXL5ANYwutJd6CdpBakYor1nC1KHCnryy853L4deZu+56Yu0Yya1Z741tVu/EAN6Y06mtwSLZOJr0FCy5F0emgyROsqHKvXpe7ALOkOtKPTuadLVnqvwCrY4hoKCzn9j4c5NWwY+vwsej9wE/0nNSeomo/WlV1T/cQoTD6yR++1yF+EI6lAx0klAzOqmkvdgR2kO9ARjmYdRTEYMERGal1KlVL4xx8c69KF1LfepnqdAIZPS6Rt3zj0Btd6K2zes5ZTFsd1R671m6pqdLqSRXSbDtW6EvuK7SzdgQ6298JeAEwyV8sh0t99t2Ru157dtL49lhHTE6nVMFTrsgCo2TCEkEg/pyyO644ktBzNZoMuT5d0FVYFvV7AevdSzhdlM2LNCOkOdJA9qXtQVVWGvTuQLSuLUyNGkvzQQ/gYzdwxsTm9H2yMb5C2O5A371Fbhrlfh4SWo+l0EBIDjQdqXUnlmAJQx/8IHSbwXdIGBq0cVLpyg7C/PEseNnOxDHt3gos//MDRxEQyFiwgrlk4o2a2p2m3mpq0dIIjfYlpHCbD3K9DnhlnsFnhlqfdd7Jx7M1YnziIpVp9Zv08iyd+kO5AZ8i3FkpLy4nOz5jJ8d69sZ46TqchCQz9Z2siYwOdWkPTbjWllXUDbvou6mZ0egiLh0b9ta6k/HrNxnr3Ms4VZzNi9QgWHVqkdUUeI9OcjSk2VusyPIolJYUTd9zBmX/+k+BQA4OmtKLLiHp4+Tp+MJV/iBeNOtaQVtYNyLPjLKWtLTe5uGry/7M78BE2JG1k0ArpDnS2sxfPYpT1BzWRs3QZRxITyV2/nkYdazBqZnvqJUY59Jytb491m7cHLUloOYtODxENoP7tWldyY7E3Y33iUGl34OQfJkt3oAZOZJ/AEByM4uurdSmeyWwmZeIkTg4ehC4njZ73NeLOyS0IibL/7yMowoeG0soqE3mGnMlmgVue0bqK65PuQJdxKKNk3puppmwIqaWiAwc51q07qXP+TVRtf4ZNbUu7/nUwGO339tm2X1zJXnzihiS0nElngKgmkNBL60quJt2BLmdP2h4AjDKC0CWkf/wxRzt2oGDnL7ToHcOIGe2IuSms0scNi/YjoU2ktLLKSJ4lZ3PF1pZ0B7qkI1lHUK1W2QzShdjy8ki6516S778fbwroO6EZt41vgn+IV4WPmdi/DqpNWlllJaHlbDoDRLeEOi6ybUnPWaXdgSNXj5TuQBdjNhdhkmHvLif/p5842qED6Z/9l5jGoYyc0Y7mPWuhK+fcrsi4QOKaRkgrqxzkmdKCzQK9Zmu7SobJH3X8Vuj4KBuSNjJ4xWAOZBzQrh5xTTk22aLElaW+/DLHu3fDcuwwHQbGc9fUtkTVDSrz49vdWVfmZZWThJYWdAaIbAyt79fm/LGdsEw+hKVaA2b/PJvJP0wmz5ynTS3iutIKLshcLRdnSU3lxICBnHnySQIDFAY92Ypuoxvg7We87uNq1g+hZv0QaWWVkzxbWuoxvWSHY2fqORPr3ctJNecwcvVIvjz0pXPPL8rldO5pjNWru8/8Pg+Ws2o1R9olkr1yFfXbRTFqVjsadqgO1/jVKTqFm4fVk1ZWBUhoaUVRwOAFt77knPOZ/GHcFug4kY3J3zNoxSDpDnQDR7OPojOZMESEa12KKAuLhTNPPsmJ/v0h/Tzd7m7IoCdbEVrD74q7NekSTUiUr7SyKkCeMS3pDHDTIKhzi2PPE9MRy+RDmCMb8sLPL/D4pselO9BN7LuwD5Bh7+6m+OhRjvfsybmXXiKihg93PdeWDgPrYvTS4xNgpN2ddbQu0W0pqioz2jRls0JWEvynLViL7X/8njOxtn+Y8/mpPPb9Y+zP2G//cwiHCTIFsXX4Vs5MeZrs5cu1LkdUhI8Ptd5+C98OHSnIKSbzfD414oOklVVB8qxpTacv2bqk40T7Hvca3YESWO4nuzgbq7lYVnt3ZwUFJD/wIEmjR2OikOh6wRJYlSDPnCtQdNDlKQiJs8/xpDuwSim0FEr3YBVQ+PvvqFlZJRvDigqT0HIZCvSZU/nD9JyJ9Z4VpJpzGLV6FAsPLaz8MYWmMi05mGJjtC5DVFLYAw9grF0LRV9FdjHXiISWq9AbIb4HNLyjYo+/rDvw++RN0h1YhZy7eA6TbFHi1kxxsYT/4yEUnbzlVpY8g67EZitpbZn8y/e42h2wTD6IJbIRL25/kcc2PSbdgVXIyZyTGMLCULwqvr6d0JBeT41XX5W5dnYioeVKdDrwDS/ZLLKsekzHeu9K0sx5jFwzkgUHFziuPqGJS1uUGKNlixJ3FD5uHN6NGqEYHL/7sSeQ0HI1Oj20fxhiOlz/fia/ku7ATo/9rzswXboDq6LfL/wOIF2Ebsj7ppsIf/gf0i1oR/JMuiJVhcGfgk/ItW+v3aFk7cDLugNzzbnOrVE4zcH0g6g2mwx7dzOKtzfR/54jmzvamYSWK9LpwS8c+r9z9W3dp0l3oIexYcNiLpJh726m2uTJGGvWlG5BO5PQclU6AzToC63HlPzb6AvjNsPNj0t3oAfKteVjqi2h5S78OnQgdPQoGd7uAPIRwJWpKtz6MliLsd76MqrRh1e2vyitKw90oTCdGNmixC3oQ0Ko8cq/UK1WCS0HkNByZYoCih5rv7dIzU9l0rcPSOvKQ6XkphBfo73WZYgb0euJfuMN9MHBElgOIt2Drk5f8rli5/mdElge7FjWMXTe3uhDQ7UuRVxHxMRH8W3bRq5jOZCElhvQ6/T0q9uPQQmDtC5FaOTS6iZGGfbusvy7dSN87FgUmUTsUBJabkJVVZ5NfJaGoQ21LkVoYNf5XQCYZNi7SzLGxBA951VUWQzX4SS03ISiKCiKwpvd3iTE62/mb4kq60LhBWxmswx7d0GKjw+13v0Piskkk4idQJ5hN2LQGYjwieCd7u9g0pm0Lkc4WaG1UFpaLqj6zJmYYmPlOpaTSGi5GYPOQOPwxrzY6UUUpO/ck2RZcjHFyBYlriRs/HiC+vWVkYJOJKHlhvSKnt5xvZnQYoLWpQgnOn/xPEYJLZcR2K8f1SbZecdxcUMSWm5sbNOx3Bl/p9ZlCCc5lXOqZIsSo1HrUjyeb2Jbarz0IqqsK+h0ElpuTFVVprefTmJUotalCCc4nHUYRaeTLUo0ZoqPp+a778Kfg6OEc0loubFLfzBvdnuTOkF1NK5GONrvaSVblBhrSmhpxRARQe3/+xidl5dcx9KIhJab0+v0eOm9+KDnB4R5h2ldjnCgfen7SrYokWHvmlB8fan10YclXbQyUlAzElpVgEFnINwnnE9u/UTmcFVhFpsFi7lIhr1rQPHyotZ77+GVkCCBpTEJrSrCoDNQO6A2n9z6CUFeQVqXIxzkoq1QlnJyMsVkoua7/8G3dSvpEnQBElpViEFnIDYwlk96fUKgKVDrcoQDXChKxxQXp3UZnsNopObbb+HXrp0ElouQ0KpiDDoDdYLr8HGvjwkwBmhdjrCzM3lnMMnoQecwGKj5+mv43XyzBJYLkdCqggw6AwkhCXzY60P8jH5alyPs6HjWcXS+vuiDg7UupWrT66nx6iv4d+sm6wm6GPltVFEGnYGGoQ35sOeH+Bp8tS5H2MmBjAMAGGvKYAyH0emo8dJLBPbuLYHlguQ3UoXpdXoahzXm/Z7vS3BVEZe2KJHBGI6hGI1Ev/EGgX37SGC5KPmtVHF6nZ6m4U35723/lXlcVcDZ/LPYLGZMMsHY7nR+vtT6v48J6C5dgq5MfjMeQK/TEx8cz4I+C6gdUFvrckQlFVmKpKVlZ/rQUGLmzsW3lQxrd3USWh7CoDMQ4RvB/D7zuSn8Jq3LEZWQbc3DVFtWe7cXQ40axC5cUDJxWALL5UloeRCDzoC/0Z/Pen/GzdE3a12OqKDU/FRMsRJa9mCKjydu0ZcYa9SQlS7chISWh9Hr9Bj1Rt7u/jYDEwZqXY6ogKScJAwRESBvspXi27YNsQvmow8OlsByIxJaHkin6NChY0aHGYxvOl7rckQ5Hc48jKLXY4yK0roUtxU8bBi1P/0Una+vBJabkdDyUJe2NXm4xcO82OlFvPXeGlckymrvhb2ADHuvEIOBqOnTqD59GopeL9ew3JCEluD2uNuZ32c+NQNkwqo7+CPtD1RVldXey0kfHEzMp58SPHSo1qWISpDQEuh1euKC4viq71d0rtlZ63LEDRTaCrEWF8m+WuXgVS+BuKVL8GnRXOZguTn57QmgZGShr9GX/3T/Dw83fxidIi8NV5ZvK8QkSzmVSUDvXsR++SWGiAi5flUFyDuTKHUpqMY1Hcf7Pd6Xfblc2IXiDExxsVqX4dIULy+ipk+n5ptvonh5SWBVERJa4iqKotAmqg2L+y2mUVgjrcsR13A276wsmnsdpvh44pYuIXjIYADpEqxC5DcprsmgMxDmE8bc2+Zyd6O7pbvQxZzIPoE+IABdgOyZ9lfBdw0lbsliTLVry+jAKkjeicTfMugMGPVGnmj9BJ/d+pmMLnQhskXJ1XQBAUS/9SbVZ8xAMRqlO7CKktASN6QoCk3Cm7D0jqUMqTdE63IEsDt1N4AMe/+Tb2Jb6qxcQUC3bsD/5iGKqkdCS5SJQWfAS+/F8+2f56OeHxHpG6l1SR4tKTcJ1WLx+GHviq8vUdOeJ+a//5XRgR5CQkuU2aVPr62jWrPizhXcUfcOjSvybMWWIkwevCqGb/v21F29unSysFy/8gwSWqLcDDoD3gZvXuj0Am93e1taXRrJtuZhrO15+6Ppg4Op/q+Xifn0EwzVIiSsPIyElqiQS6MJO0V3YvWA1TzQ5AFMOpPGVXmW1II0j9uiJLBvX+p+s46gPn0AaV15IgktUSkGnQEvgxePtHiElQNWckutW7QuyWMk5yZjjIwED5iD5N24ETEL5hM951V0AQFy7cqDVf1Xu3AKnaIj0jeSt7u9zYc9PyQuME7rkqq8I5lHUAwGDJFVt3vWEBFB9ZdeJParr/Bp0gSQicKeTn77wm70upKumjZRbVjafymTW0/Gz+incVVV1770fUDVHPaumEyEjRtL3W/XE9SvH4pOJ60rAUhoCQcw6AzodXpGNxzN2oFrGZQwCIMibzj2tid1D6qqVrlh7wG9e1H3m3VETJyIzttbwkpcQUJLOIxepyfIK4jpHaazZtAa7oy/U8LLji5aLmIzF1eZYe/+XW8hbtlSar75JobISI/pBjx58qTWJbgVz3hVCM1cGmUY6RvJrI6zWDNQwsue8q2FGN28e9D/lluIW7qEWu+9h1dCAuC461bz5s2jfv36fPbZZ9e93/bt26lfv75Darjcxo0buf/++x1+nqpEQks4RWl4+ZWE1+qBqyW87CDDnIUpNlbrMiqkNKzefw+vevUAxw9hnzdvHsOHD+fzzz/HYrE49FxlkZWVhaqqWpfhViS0hFNdCq8ovygJLzs4m3fWvboHdToCevQgbslip4YVwE8//UR6ejpPP/00NpuNb775pvS21NRUxo8fT8uWLenevTs//vhj6W1PPfUUkydPvuJYkyZNYsaMGQAkJSUxfvx4EhMT6dq1K6+//jrFxcUALFmyhOHDhzN79mzatWtH+/btefbZZzGbzWzfvp1p06Zx5swZWrRowfnz5xk9ejRvv/126XlOnz5N/fr1OX36NAD169dn9uzZJCYmMn78eAC2bdvG4MGDad26NX369GHFihWOeQJdhISW0MRfw+vbId9y/033E+wVrG1hbuZkzkn0QUEovr5al3JduoAAQu+7l/gNG6j5ztt4/dn15szJwV988QVDhw7F29ubESNG8Mknn5Te9thjj2EwGNi8eTNz585l8+bNpbcNHTqU7777jry8PABycnLYuHEjgwcPJj8/n3vvvZeEhAQ2b97M/Pnz2bZt2xXB89tvvxEWFsaWLVv44IMPWLNmDevXrycxMZEZM2ZQo0YNdu3aRWQZpy4kJSWxadMmXnnlFQ4ePMhDDz3E2LFj2b59O7NmzeLFF19ky5YtdnrWXI+EltDUpfAK8w7j0ZaPsmHIBma0n0G9kHoaV+YeDmYcBMBUM1rjSq7NFBdL5NSpJGzZTLUnn8QQVfLG7OyVLFJSUtiyZQsjR44ESoLo6NGj7Nixg5SUFHbu3MkTTzyBv78/1atXZ8KECaWPbd26NdWrV2ft2rUArFq1ijp16tC4cWM2bdpEcXExjz/+OF5eXlSvXp2JEycyb9680sd7e3szfvx4jEYjTZs2pX79+pw4caLCP0vfvn3x8fEhMDCQhQsX0r17d3r16oVer6dly5YMHTr0ivNXNdInI1yCoigoKJj0Ju6Iv4OB9QayJ3UPCw4u4NtT31JsK9a6RJe0J3UPAMaatSg6fETjav6k0+HXsSOh99yNf6dOqBaL5sPW58+fj8VioX///qXfs1gsfPLJJ4wdOxaAGjVqlN5W+y9rOg4ZMoTly5czZMgQli5dypAhJVv0pKSkkJGRQZs2bUrvq6oqZrOZ9PR0AMLCwq7YKsVoNFbqOla1atVK/z8lJYWff/6Z1q1bl37ParVeVX9VIqElXI5BV/KyvCn8Jl7u/DL/LP4niw8vZunRpZzIrvgn1KroaPZRVKvVJa5rmerWJah/f4IHDsAQHo7650AHrQOrqKiIr7/+mhdeeIEOHTqUfv/w4cOMHTu2dPRecnIydevWBeDcuXNXHGPAgAG88cYbbNu2jUOHDtG3b18AoqKiqF27NuvWrSu9b15eHunp6YSGhpa7Vp1Oh9lsLv13ZmbmVfe5PACjoqIYMGAAM2fOLP1eampqlR7cId2DwmVdWmEj0BTI6EajWXHnCpb1X8b9N91PTX/3HuZtT2ZzkWbD3vXBwYSMGkncksXUXb2KsDH3YQgPB7QPq0tWrlyJoij069ePqKio0q/OnTtTr149VqxYQadOnXjppZfIzs4mLS2Nd95554pjhIaG0rVrV5577jl69epFUFAQAF27duXixYt8/PHHFBcXk5OTw5QpU3jsscfKtBGll5cXBQUFpSMZ69aty5YtW8jJySE3N5ePPvrouo8fPHgwq1atYuvWrdhsNk6ePMmoUaOuuF5X1UhoCbdwqfUVFxTHIy0eYe2gtXzZ90tGNxpNNd9qN3h01ZZju4jJid1BOj9fAnr3pua7/yHhx61E/vOfeDVoALhOUF1u/vz59OvXD6PReNVtd911F8uXL+eVV14hICCArl27MmjQoCtaZJcMHTqUlJQUBg8eXPo9f39/PvvsM7Zv307nzp3p0aMHOp2O9957r0y1tWnThrCwMNq0acOhQ4cYN24cYWFhdO/enf79+9Ptz52Y/06zZs147bXXeO2112jTpg2jRo2iW7duV412rEoUtSq3I0WVZlNtpf+/O3U3q0+sZsOpDaQXpmtYlfMt6ruIuhf9ONart8POYahWDf9u3Qjo0R2/xEQUo9ElrlUJzyOhJaqESwGmU3QcyTzCltNb+OnsT/x2/rcqP4jjtS6v0SP6Fg42bQZ2/HP2qpeAf7fuBPbqiXejRqg2G6iq7GElNCWhJaocVVWxqlYMOgPF1mJ+Pf8rW1O28vPZnzmceVjr8uzuoWYP8Y/m/+BI5y5YUlMrfBxTXCy+bdrg26Ytfh3aYwgLQ7VaQVE8Zh1A4fqkbS+qHEVRSlfYMOlNJFZPpG31tugVPZmFmWxN2crvab+zL30fhzMPU2Qt0rjiytmfvh8AY61a5QotU506JSGV2Ba/du0whIaWtKZsttJuP2lVCVcjLS1RZlarlTNnzlDLBYZXV4bFZkGn6NApOqw2KyeyT7AnbQ/7M/az78I+jmQecasuxSBTEFuHb+XM00+TvWz51XcwGPCqWxfvhg3wbtgI78aN8G7YEJ2f31UhJYSrk1eqh5o3bx4zZ87kmWee4d577y3TYx577DESEhJ45JFHOHPmDH369GH16tVXTMp0B5dGIkLJsPr4kHhigmIYoAwoDbJj2cc4kH6A5NxkknOTOZ17mtN5p8kozNCw8mvLLs7GWlyMqU6dkq+Y2phiYvCKT8C7SRO86sSh/DlyzmY2oxgMpcOxFZ0OpOtPuBFpaXmo22+/nbZt27J582bWr1+PoQyftEePHk3btm155JFHnFCh9iy2krkzl4dcgaWAM3lnOJVzqjTQ0gvSySnOKfkqKvlvnjnPbnX4GHwINAUS5BVEgCmAQFMg4T7hVPerTg3/GtQMqEmTkEboDP8b0q1arSUtqGsM8xbCnUlLywNdvtr1pk2b+Oabb+jTpw8AGRkZvPjii/zwww/odDo6dOjA9OnTeeWVV9i5cye7du1i3759PPfcc3Tv3p0NGzZQs2ZNUlJSePXVV9m+fTs6nY527doxZcoUqlWrxvbt23nmmWcYMmQI8+fPp6ioiMTERF566SX8/f01fjb+3uVhdYmPwYe6wXWJC4rDarOi1+lL10+8nNVmJd+ST25xLtlF2WQXZWNVraW3//Wzoo2S0Y9GnZEgr6CSgDIG4Gf0K51kfTlVVUu7Oa91u6LXg1yPElWQhJYHutZq15dCa+LEifj5+bF+/XqMRiMTJ05kxowZvPbaayQlJZW2tC5tlQBgNpsZM2YMN910E+vXr0dVVWbMmMH48eNZtGgRULJG2vnz5/n22285f/48I0eOZP78+aXrvrkbnaJDp//7bjW9Tk+AKYAAUwA1/O3ffaooCka9tKKE55HQ8jCXVrt+/vnngZJZ/v/5z3/YsWMH0dHR7Nixg3Xr1hESEgLAyy+/TFZW1nWPuXPnTpKTk1m8eHFpy2nGjBm0bduWvXv3lt7v4Ycfxtvbm5iYGBITEyu10rUQwjNJaHmY6612fWlTuejo/21zERERQURExHWPmZ6eTkhIyBVdff7+/gQHB5OSkkL4n2vRXX6cyq50LYTwTDJsyINcvtr18uXLS7/ee+89Nm3ahNVacs3lzJkzpY85evQob7zxxnWPGx0dTWZmZukmeQC5ublkZmbeMPCEEKI8JLQ8yI1Wu162bBkdO3bklVdeIScnh7y8PF599VWSk5MBMJlM5ObmXnXcJk2aEB8fz7Rp08jNzSU3N5fp06dTu3ZtWrZs6ewfUwhRhUloeZCyrnbt7+/PbbfdRvfu3QkNDWXGjBkA3HnnnSxevJgRI0Zc8ViDwcAHH3yAxWKhd+/edO3aFbPZzKefflqmofRCCFFWMk9LCCGE25CWlhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3IaElhBCCLchoSWEEMJtSGgJIYRwGxJaQggh3Mb/A52I1Zn5sjM9AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pie chart for genre using the .pie() method from pandas\n",
    "movies.genre.value_counts().plot.pie()\n",
    "plt.show()\n",
    "plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:37.209737Z",
     "start_time": "2024-01-12T18:49:37.119979Z"
    }
   },
   "id": "31b46cb199cc78ef"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-01-12T18:49:37.209944Z",
     "start_time": "2024-01-12T18:49:37.206280Z"
    }
   },
   "id": "cae59088210e7410"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}