{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A bit of a review\n",
    "\n",
    "Start: import our usual things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = pd.read_csv(\"https://raw.githubusercontent.com/UIUC-iSchool-DataViz/spring2020/master/week01/data/GDP.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>DATE</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1947-01-01</td>\n",
       "      <td>243.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1947-04-01</td>\n",
       "      <td>245.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1947-07-01</td>\n",
       "      <td>249.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1947-10-01</td>\n",
       "      <td>259.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>265.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>20749.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>20897.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>21098.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>21340.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>21542.540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE        GDP\n",
       "0    1947-01-01    243.164\n",
       "1    1947-04-01    245.968\n",
       "2    1947-07-01    249.585\n",
       "3    1947-10-01    259.745\n",
       "4    1948-01-01    265.742\n",
       "..          ...        ...\n",
       "286  2018-07-01  20749.752\n",
       "287  2018-10-01  20897.804\n",
       "288  2019-01-01  21098.827\n",
       "289  2019-04-01  21340.267\n",
       "290  2019-07-01  21542.540\n",
       "\n",
       "[291 rows x 2 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>DATE</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1947-01-01</td>\n",
       "      <td>243.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1947-04-01</td>\n",
       "      <td>245.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1947-07-01</td>\n",
       "      <td>249.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1947-10-01</td>\n",
       "      <td>259.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>265.742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE      GDP\n",
       "0  1947-01-01  243.164\n",
       "1  1947-04-01  245.968\n",
       "2  1947-07-01  249.585\n",
       "3  1947-10-01  259.745\n",
       "4  1948-01-01  265.742"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>291.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6143.539148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6239.154340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>243.164000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>723.990500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3578.848000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10597.058000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21542.540000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GDP\n",
       "count    291.000000\n",
       "mean    6143.539148\n",
       "std     6239.154340\n",
       "min      243.164000\n",
       "25%      723.990500\n",
       "50%     3578.848000\n",
       "75%    10597.058000\n",
       "max    21542.540000"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.describe() # some statistics about our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE    1948-01-01\n",
       "GDP        265.742\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.iloc[4] # grabs a particular row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DATE', 'GDP'], dtype='object')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.columns # grabbed a \"list\" of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1947-01-01\n",
       "1      1947-04-01\n",
       "2      1947-07-01\n",
       "3      1947-10-01\n",
       "4      1948-01-01\n",
       "          ...    \n",
       "286    2018-07-01\n",
       "287    2018-10-01\n",
       "288    2019-01-01\n",
       "289    2019-04-01\n",
       "290    2019-07-01\n",
       "Name: DATE, Length: 291, dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp['DATE'] # indexing by column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(gdp['DATE'], gdp['GDP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# did our first round of data formatting\n",
    "gdp['DATE'] = pd.to_datetime(gdp['DATE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(gdp['DATE'], gdp['GDP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp2 = pd.read_csv('GDP.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>DATE</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1947-01-01</td>\n",
       "      <td>243.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1947-04-01</td>\n",
       "      <td>245.968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1947-07-01</td>\n",
       "      <td>249.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1947-10-01</td>\n",
       "      <td>259.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>265.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>20749.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>20897.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>21098.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>21340.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>21542.540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE        GDP\n",
       "0    1947-01-01    243.164\n",
       "1    1947-04-01    245.968\n",
       "2    1947-07-01    249.585\n",
       "3    1947-10-01    259.745\n",
       "4    1948-01-01    265.742\n",
       "..          ...        ...\n",
       "286  2018-07-01  20749.752\n",
       "287  2018-10-01  20897.804\n",
       "288  2019-01-01  21098.827\n",
       "289  2019-04-01  21340.267\n",
       "290  2019-07-01  21542.540\n",
       "\n",
       "[291 rows x 2 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('datasets_674388_1186156_tv_shows2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here is reading from another folder\n",
    "#movies2 = pd.read_csv('/Users/jillnaiman/Downloads/datasets_674388_1186156_tv_shows_downloadsFolder.csv')\n",
    "#movies2 = pd.read_csv('/Users/jillnaiman/Downloads/datasets_674388_1186156_tv_shows_downloadsFolder.csv')\n",
    "#movies2 = pd.read_csv('C:/Users/USERNAME/Downloads') # something like this in windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Unnamed: 0</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Breaking Bad</td>\n",
       "      <td>2008</td>\n",
       "      <td>18+</td>\n",
       "      <td>9.5</td>\n",
       "      <td>96%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Stranger Things</td>\n",
       "      <td>2016</td>\n",
       "      <td>16+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>93%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Money Heist</td>\n",
       "      <td>2017</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.4</td>\n",
       "      <td>91%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sherlock</td>\n",
       "      <td>2010</td>\n",
       "      <td>16+</td>\n",
       "      <td>9.1</td>\n",
       "      <td>78%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Better Call Saul</td>\n",
       "      <td>2015</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.7</td>\n",
       "      <td>97%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5606</th>\n",
       "      <td>5606</td>\n",
       "      <td>Tut's Treasures: Hidden Secrets</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5607</th>\n",
       "      <td>5607</td>\n",
       "      <td>Paradise Islands</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5608</th>\n",
       "      <td>5608</td>\n",
       "      <td>Wild Russia</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5609</th>\n",
       "      <td>5609</td>\n",
       "      <td>Love &amp; Vets</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5610</th>\n",
       "      <td>5610</td>\n",
       "      <td>United States of Animals</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5611 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                            Title  Year  Age  IMDb  \\\n",
       "0              0                     Breaking Bad  2008  18+   9.5   \n",
       "1              1                  Stranger Things  2016  16+   8.8   \n",
       "2              2                      Money Heist  2017  18+   8.4   \n",
       "3              3                         Sherlock  2010  16+   9.1   \n",
       "4              4                 Better Call Saul  2015  18+   8.7   \n",
       "...          ...                              ...   ...  ...   ...   \n",
       "5606        5606  Tut's Treasures: Hidden Secrets  2018  NaN   NaN   \n",
       "5607        5607                 Paradise Islands  2017  NaN   NaN   \n",
       "5608        5608                      Wild Russia  2018  NaN   NaN   \n",
       "5609        5609                      Love & Vets  2017  NaN   NaN   \n",
       "5610        5610         United States of Animals  2016  NaN   NaN   \n",
       "\n",
       "     Rotten Tomatoes  Netflix  Hulu  Prime Video  Disney+  type  \n",
       "0                96%        1     0            0        0     1  \n",
       "1                93%        1     0            0        0     1  \n",
       "2                91%        1     0            0        0     1  \n",
       "3                78%        1     0            0        0     1  \n",
       "4                97%        1     0            0        0     1  \n",
       "...              ...      ...   ...          ...      ...   ...  \n",
       "5606             NaN        0     0            0        1     1  \n",
       "5607             NaN        0     0            0        1     1  \n",
       "5608             NaN        0     0            0        1     1  \n",
       "5609             NaN        0     0            0        1     1  \n",
       "5610             NaN        0     0            0        1     1  \n",
       "\n",
       "[5611 rows x 11 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2008\n",
       "1       2016\n",
       "2       2017\n",
       "3       2010\n",
       "4       2015\n",
       "        ... \n",
       "5606    2018\n",
       "5607    2017\n",
       "5608    2018\n",
       "5609    2017\n",
       "5610    2016\n",
       "Name: Year, Length: 5611, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's make a plot of IMDb rating as a function of year using matplotlib -- plt\n",
    "# let's first look at the Year column\n",
    "movies['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       9.5\n",
       "1       8.8\n",
       "2       8.4\n",
       "3       9.1\n",
       "4       8.7\n",
       "       ... \n",
       "5606    NaN\n",
       "5607    NaN\n",
       "5608    NaN\n",
       "5609    NaN\n",
       "5610    NaN\n",
       "Name: IMDb, Length: 5611, dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at IMDb rating column\n",
    "movies['IMDb']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's make a plot of IMDb rating as a function of year using matplotlib -- plt\n",
    "plt.plot(movies['Year'],movies['IMDb'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's re-try this weird line plot using the .plot\n",
    "movies.plot(x='Year', y='IMDb') # we note: the function is actually the whole \"movies.plot\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAD4CAYAAAAtrdtxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAWsUlEQVR4nO3df7DddX3n8eerAQmIjEIuNObC3tDJKAnWEWLEutu1UjVbraHrIGHWGizbuBaruDqaWGdhRjODYxV1LG7xR0XRhIhWsloVGmudnUFjECu/11QiXIkkYlt+VMKv9/5xvmEOl5tw8uWce84lz8fMnfP9vr+f7/m+cya5r3x/nO83VYUkSW38xrAbkCTNXoaIJKk1Q0SS1JohIklqzRCRJLV20LAbGJR58+bVxMTEsNuQpFnlmmuu+WVVjfU6/ikbIhMTE2zdunXYbUjSrJLkZ/sz3sNZkqTWDBFJUmuGiCSptafsORFJ6tWDDz7I5OQk999//7BbmTFz585lfHycgw8++Em9jyEi6YA3OTnJM57xDCYmJkgy7HYGrqq46667mJycZOHChU/qvTycJemAd//993PUUUcdEAECkISjjjqqL3tehogkwQETIHv0689riEiSWvOciCRNMbHm6319v+0XvOoJxxx++OHce++9bN++nYULF/Le976X973vfQD88pe/ZP78+bzpTW/i4x//OOeffz6f/OQnGRsb47777uN5z3se73//+1m8eHGn/+bL1vPmzevrn2M6hoikgev3L+Ve9fLLexQdf/zxfO1rX3s0RL70pS+xZMmSx4x5+9vfzjvf+U4ALrvsMl72spdx3XXXMTbW8x1L+sLDWZI0Yg499FBOOOGER2/ddNlll/G6171ur+PPOOMMXvGKV/DFL37x0doHP/hBli1bxrJly9i2bdvAejVEJGkErVy5kg0bNjA5OcmcOXN49rOfvc/xJ510EjfffPOj80cccQRbtmzhLW95C+eee+7A+jREJGkELV++nKuuuor169dzxhlnPOH4qnrM/Jlnnvno69VXXz2QHmGAIZLkM0l2Jrl+mmXvTFJJ5nXV1ibZluSWJK/sqp+c5Lpm2cdyoF2HJ+mA9LSnPY2TTz6ZD33oQ7z2ta99wvHXXnstJ5xwwqPz3b8qB/lrc5B7Ip8Flk8tJjkWeDlwW1dtMbASWNKsc1GSOc3iTwCrgUXNz+PeU5Keit7xjnfwgQ98gKOOOmqf47785S9z5ZVXPrr3AZ3zKHteX/ziFw+sx4FdnVVV300yMc2iC4F3AVd01VYAG6pqN3Brkm3AsiTbgSOq6mqAJJ8DTgO+Mai+JWlUrupasmTJ467K2uPCCy/k0ksv5b777uPEE0/k29/+9mOuzNq9ezcvetGLeOSRR1i/fv3AepzRS3yTvAb4eVX905TdqwXA97rmJ5vag8301LokPaXce++9QOc7Htdf/7izAJx11lmcddZZAJx//vmcf/75e32v7du3A3Deeef1u83HmbEQSXIY8BfAK6ZbPE2t9lHf2zZW0zn0xXHHHdeiS0nS/pjJq7N+C1gI/FNzmGoc+GGS36Szh3Fs19hx4I6mPj5NfVpVdXFVLa2qpTP9hRtJOhDNWIhU1XVVdXRVTVTVBJ2AOKmqfgFsAlYmOSTJQjon0LdU1Q7gniSnNFdlvYHHnkuRpL6YeonsU12//ryDvMR3PXA18Jwkk0nO3tvYqroB2AjcCHwTOKeqHm4Wvxn4FLAN+Gc8qS6pz+bOnctdd911wATJnueJzJ0790m/1yCvzjrzCZZPTJlfB6ybZtxW4MS+NidJXcbHx5mcnGTXrl3DbmXG7Hmy4ZPlDRglHfAOPvjgJ/2EvwOVtz2RJLVmiEiSWjNEJEmtGSKSpNYMEUlSa4aIJKk1Q0SS1JohIklqzRCRJLVmiEiSWjNEJEmtGSKSpNYMEUlSa4aIJKk1Q0SS1JohIklqzRCRJLVmiEiSWjNEJEmtDSxEknwmyc4k13fVPpjk5iQ/TvK3SZ7ZtWxtkm1Jbknyyq76yUmua5Z9LEkG1bMkaf8Mck/ks8DyKbWrgBOr6reB/wesBUiyGFgJLGnWuSjJnGadTwCrgUXNz9T3lCQNycBCpKq+C/xqSu3Kqnqomf0eMN5MrwA2VNXuqroV2AYsSzIfOKKqrq6qAj4HnDaoniVJ+2eY50T+BPhGM70AuL1r2WRTW9BMT61PK8nqJFuTbN21a1ef25UkTTWUEEnyF8BDwBf2lKYZVvuoT6uqLq6qpVW1dGxs7Mk3Kknap4NmeoNJVgGvBk5tDlFBZw/j2K5h48AdTX18mrokaQTM6J5IkuXAu4HXVNW/dy3aBKxMckiShXROoG+pqh3APUlOaa7KegNwxUz2LEnau4HtiSRZD7wUmJdkEjiPztVYhwBXNVfqfq+q/kdV3ZBkI3AjncNc51TVw81bvZnOlV6H0jmH8g0kSSNhYCFSVWdOU/70PsavA9ZNU98KnNjH1iRJfeI31iVJrRkikqTWDBFJUmuGiCSpNUNEktSaISJJam3Gv7EuSTNlYs3Xh7bt7Re8amjbnknuiUiSWjNEJEmtGSKSpNYMEUlSa4aIJKk1Q0SS1JohIklqzRCRJLXmlw2lA8gwv3ynpyb3RCRJrRkikqTWDBFJUmsDC5Ekn0myM8n1XbUjk1yV5CfN67O6lq1Nsi3JLUle2VU/Ocl1zbKPJcmgepYk7Z9B7ol8Flg+pbYG2FxVi4DNzTxJFgMrgSXNOhclmdOs8wlgNbCo+Zn6npKkIRlYiFTVd4FfTSmvAC5ppi8BTuuqb6iq3VV1K7ANWJZkPnBEVV1dVQV8rmsdSdKQzfQ5kWOqagdA83p0U18A3N41brKpLWimp9anlWR1kq1Jtu7atauvjUuSHm9UTqxPd56j9lGfVlVdXFVLq2rp2NhY35qTJE1vpkPkzuYQFc3rzqY+CRzbNW4cuKOpj09TlySNgJkOkU3AqmZ6FXBFV31lkkOSLKRzAn1Lc8jrniSnNFdlvaFrHUnSkA3stidJ1gMvBeYlmQTOAy4ANiY5G7gNOB2gqm5IshG4EXgIOKeqHm7e6s10rvQ6FPhG8yNJGgEDC5GqOnMvi07dy/h1wLpp6luBE/vYmiSpT0blxLokaRYyRCRJrRkikqTWDBFJUmuGiCSptZ5CJIlXR0mSHqfXPZH/nWRLkj9L8syBdiRJmjV6CpGq+o/Af6Nza5KtSb6Y5OUD7UySNPJ6PidSVT8B3gu8G/jPwMeS3Jzkvw6qOUnSaOv1nMhvJ7kQuAl4GfCHVXVCM33hAPuTJI2wXm978nHgk8B7qurXe4pVdUeS9w6kM0nSyOs1RP4A+PWemyIm+Q1gblX9e1V9fmDdSZJGWq/nRP6ezl109zisqUmSDmC9hsjcqrp3z0wzfdhgWpIkzRa9hsh9SU7aM5PkZODX+xgvSToA9HpO5FzgS0n2PJp2PnDGYFqSJM0WPYVIVf0gyXOB5wABbq6qBwfamSRp5O3Pkw1fCEw067wgCVX1uYF0JUmaFXoKkSSfB34L+BGw59nnBRgiknQA63VPZCmwuKpqkM1IkmaXXq/Ouh74zX5tNMnbk9yQ5Pok65PMTXJkkquS/KR5fVbX+LVJtiW5Jckr+9WHJOnJ6TVE5gE3JvlWkk17ftpsMMkC4K3A0qo6EZgDrATWAJurahGwuZknyeJm+RJgOXBRkjltti1J6q9eD2edP4DtHprkQTpfWrwDWAu8tFl+CfAdOncMXgFsqKrdwK1JtgHLgKv73JMkaT/1+jyRfwS2Awc30z8Afthmg1X1c+AvgduAHcC/VdWVwDFVtaMZswM4ulllAXB711tMNrXHSbI6ydYkW3ft2tWmPUnSfuj1VvB/ClwO/HVTWgB8tc0Gm3MdK4CFwLOBpyd5/b5WmaY27Qn+qrq4qpZW1dKxsbE27UmS9kOv50TOAV4C3A2PPqDq6H2usXe/D9xaVbuaLyx+Bfgd4M4k8wGa153N+Ek6T1TcY5zO4S9J0pD1GiK7q+qBPTNJDmIvewM9uA04JclhSQKcSudhV5uAVc2YVcAVzfQmYGWSQ5IsBBYBW1puW5LUR72eWP/HJO+hczL85cCfAf+nzQar6vtJLqdzTuUh4FrgYuBwYGOSs+kEzenN+BuSbARubMafs+e5JpKk4eo1RNYAZwPXAW8C/g74VNuNVtV5wHlTyrvp7JVMN34dsK7t9iRJg9HrDRgfofN43E8Oth1J0mzS672zbmWacyBVdXzfO5IkzRr7c++sPebSOV9xZP/bkSTNJr1+2fCurp+fV9VHgJcNuDdJ0ojr9XDWSV2zv0Fnz+QZA+lIkjRr9Ho460Nd0w/RuQXK6/rejSRpVun16qzfG3QjkqTZp9fDWf9zX8ur6sP9aUeSNJvsz9VZL6RzCxKAPwS+y2PvritJOsD0GiLzgJOq6h6AJOcDX6qq/z6oxiRJo6/XGzAeBzzQNf8AMNH3biRJs0qveyKfB7Yk+Vs631z/I+BzA+tKkjQr9Hp11rok3wD+U1N6Y1VdO7i2JEmzQa+Hs6DzLPS7q+qjwGTzbA9J0gGs18fjnge8G1jblA4GLh1UU5Kk2aHXPZE/Al4D3AdQVXfgbU8k6YDXa4g8UFVFczv4JE8fXEuSpNmi1xDZmOSvgWcm+VPg7/EBVZJ0wHvCq7OSBLgMeC5wN/Ac4H9V1VUD7k2SNOKeMESqqpJ8tapOBvoSHEmeSecZ7SfSOUT2J8AtdMJqguYuwVX1L834tXSe8f4w8Naq+lY/+pAkPTm9Hs76XpIX9nG7HwW+WVXPBZ4P3ASsATZX1SJgczNPksXASmAJsBy4KMmcPvYiSWqp1xD5PTpB8s9JfpzkuiQ/brPBJEcAvwt8GqCqHqiqfwVWAJc0wy4BTmumVwAbqmp3Vd0KbAOWtdm2JKm/9nk4K8lxVXUb8F/6uM3jgV3A3yR5PnAN8DbgmKraAVBVO5Ic3YxfAHyva/3JpjZdv6uB1QDHHXdcH1uWJE3nifZEvgpQVT8DPlxVP+v+abnNg4CTgE9U1QvofPdkzT7GZ5paTTewqi6uqqVVtXRsbKxle5KkXj1RiHT/Aj++T9ucBCar6vvN/OV0QuXOJPMBmtedXeOP7Vp/HLijT71Ikp6EJwqR2st0a1X1C+D2JM9pSqcCN9J54NWqprYKuKKZ3gSsTHJIc7+uRcCWfvQiSXpynugS3+cnuZvOHsmhzTTNfFXVES23++fAF5I8Dfgp8EY6gbYxydnAbcDpdDZyQ5KNdILmIeCcqnq45XYlSX20zxCpqoFcSltVP6LzyN2pTt3L+HXAukH0Iklqb39uBS9J0mMYIpKk1gwRSVJrhogkqTVDRJLUmiEiSWrNEJEktWaISJJaM0QkSa0ZIpKk1gwRSVJrhogkqTVDRJLUmiEiSWrNEJEktWaISJJaM0QkSa0ZIpKk1gwRSVJrhogkqbWDhrXhJHOArcDPq+rVSY4ELgMmgO3A66rqX5qxa4GzgYeBt1bVt4bStNQHE2u+PuwWpL4Z5p7I24CbuubXAJurahGwuZknyWJgJbAEWA5c1ASQJGnIhhIiScaBVwGf6iqvAC5ppi8BTuuqb6iq3VV1K7ANWDZTvUqS9m5YeyIfAd4FPNJVO6aqdgA0r0c39QXA7V3jJpva4yRZnWRrkq27du3qf9eSpMeY8RBJ8mpgZ1Vd0+sq09RquoFVdXFVLa2qpWNjY617lCT1Zhgn1l8CvCbJHwBzgSOSXArcmWR+Ve1IMh/Y2YyfBI7tWn8cuGNGO5YkTWvG90Sqam1VjVfVBJ0T5t+uqtcDm4BVzbBVwBXN9CZgZZJDkiwEFgFbZrhtSdI0hnaJ7zQuADYmORu4DTgdoKpuSLIRuBF4CDinqh4eXpuSpD2GGiJV9R3gO830XcCpexm3Dlg3Y41JknriN9YlSa0ZIpKk1gwRSVJrhogkqTVDRJLUmiEiSWrNEJEktWaISJJaM0QkSa0ZIpKk1gwRSVJrhogkqTVDRJLUmiEiSWrNEJEktWaISJJaM0QkSa0ZIpKk1gwRSVJrMx4iSY5N8g9JbkpyQ5K3NfUjk1yV5CfN67O61lmbZFuSW5K8cqZ7liRNbxh7Ig8B76iqE4BTgHOSLAbWAJurahGwuZmnWbYSWAIsBy5KMmcIfUuSppjxEKmqHVX1w2b6HuAmYAGwArikGXYJcFozvQLYUFW7q+pWYBuwbGa7liRNZ6jnRJJMAC8Avg8cU1U7oBM0wNHNsAXA7V2rTTa16d5vdZKtSbbu2rVrUG1LkhpDC5EkhwNfBs6tqrv3NXSaWk03sKourqqlVbV0bGysH21KkvZhKCGS5GA6AfKFqvpKU74zyfxm+XxgZ1OfBI7tWn0cuGOmepUk7d0wrs4K8Gngpqr6cNeiTcCqZnoVcEVXfWWSQ5IsBBYBW2aqX0nS3h00hG2+BPhj4LokP2pq7wEuADYmORu4DTgdoKpuSLIRuJHOlV3nVNXDM9+2JGmqGQ+Rqvq/TH+eA+DUvayzDlg3sKYkSa0MY09EGgkTa74+7BakWc/bnkiSWjNEJEmtGSKSpNYMEUlSa4aIJKk1Q0SS1JohIklqzRCRJLXmlw0laQCG9WXW7Re8aka3556IJKk190Q0VN56RJrd3BORJLVmiEiSWjNEJEmtGSKSpNYMEUlSa16dJcCrpCS1456IJKk1Q0SS1NqsCZEky5PckmRbkjXD7keSNEvOiSSZA/wV8HJgEvhBkk1VdeNwO+svz0tImm1mRYgAy4BtVfVTgCQbgBXAQELEX+aS1JvZEiILgNu75ieBF00dlGQ1sLqZvTfJLTPQ26DNA3457CZGlJ/N3vnZ7N1T+rPJB1qvuudz+Q/7s9JsCZFMU6vHFaouBi4efDszJ8nWqlo67D5GkZ/N3vnZ7J2fzfTafi6z5cT6JHBs1/w4cMeQepEkNWZLiPwAWJRkYZKnASuBTUPuSZIOeLPicFZVPZTkLcC3gDnAZ6rqhiG3NVOeUofn+szPZu/8bPbOz2Z6rT6XVD3u1IIkST2ZLYezJEkjyBCRJLVmiIyoJMcm+YckNyW5Icnbht3TKEkyJ8m1Sb427F5GSZJnJrk8yc3N350XD7unUZHk7c2/peuTrE8yd9g9DUuSzyTZmeT6rtqRSa5K8pPm9Vm9vJchMroeAt5RVScApwDnJFk85J5GyduAm4bdxAj6KPDNqnou8Hz8jABIsgB4K7C0qk6kc4HOyuF2NVSfBZZPqa0BNlfVImBzM/+EDJERVVU7quqHzfQ9dH4ZLBhuV6MhyTjwKuBTw+5llCQ5Avhd4NMAVfVAVf3rcLsaKQcBhyY5CDiMA/i7ZlX1XeBXU8orgEua6UuA03p5L0NkFkgyAbwA+P5wOxkZHwHeBTwy7EZGzPHALuBvmkN9n0ry9GE3NQqq6ufAXwK3ATuAf6uqK4fb1cg5pqp2QOc/scDRvaxkiIy4JIcDXwbOraq7h93PsCV5NbCzqq4Zdi8j6CDgJOATVfUC4D56PCTxVNcc318BLASeDTw9yeuH29VTgyEywpIcTCdAvlBVXxl2PyPiJcBrkmwHNgAvS3LpcFsaGZPAZFXt2WO9nE6oCH4fuLWqdlXVg8BXgN8Zck+j5s4k8wGa1529rGSIjKgkoXNs+6aq+vCw+xkVVbW2qsaraoLOidFvV5X/owSq6hfA7Ume05ROZUCPS5iFbgNOSXJY82/rVLzoYKpNwKpmehVwRS8rzYrbnhygXgL8MXBdkh81tfdU1d8NsSeNvj8HvtDcY+6nwBuH3M9IqKrvJ7kc+CGdKx+v5QC+/UmS9cBLgXlJJoHzgAuAjUnOphO6p/f0Xt72RJLUloezJEmtGSKSpNYMEUlSa4aIJKk1Q0SS1JohIklqzRCRJLX2/wEKHU0f1tbHfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.plot(y='IMDb', kind='hist') # histogram plot, summarizing the IMDb ratings\n",
    "# we say \"y\" = IMDb and it's really only plotting 1 variable -- IMDb\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.plot(x='Year', y='IMDb', marker='o') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD8CAYAAABuHP8oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO29e3gc1Z3n/T3V3epuqa2LLcm6+YKxbMeWbfkyNhvEAnYSwJjYmIQMBobZOJNkeJN4M5M3mDG7SZ44N54J7+J43qzZyewkESaZHfBCYnNJDMtABmwMiJsJNmCQr7GEbV1sXbvP/lF9Sqeqq6qrqqvVF/0+z+PHUlfVqXNOSUe/Ouf7+x7GOQdBEARReCi5rgBBEAThDRrACYIgChQawAmCIAoUGsAJgiAKFBrACYIgChQawAmCIAqUtAM4Y+yfGGNnGGNvSp9NZoz9jjF2JPl/VXarSRAEQRhxEoH/M4BrDZ9tAbCPc94MYF/ye4IgCGIcYU4SeRhjMwH8lnPekvz+HQBXcc5PMcbqAfwfzvncbFaUIAiC0BP0eN1UzvkpAEgO4rVOLqquruYzZ870eEuCIIiJycsvv9zNOa8xfu51AHcMY+yLAL4IANOnT8fBgwezfUuCIIiigjH2odnnXlUof0pOnSD5/xmrEznnD3DOl3POl9fUpPwBIQiCIDzidQB/DMAdya/vAPCoP9UhCIIgnOJERvgQgBcAzGWMHWeMbQLwQwCfZIwdAfDJ5PcEQRDEOJJ2DpxzfovFodU+14UgCIJwAWViEgRBFCg0gBMEQSQ50zuIm3e+gDN9g7muiiNoACcIgkiyfd8RvPTBWWz//ZFcV8URWdeBEwRB5Dtz73kcQ6MJ7fv2/Z1o39+JcFDBO9uuy2HN7KEInCDyjEJ7jc8VfvbTc9+8Gp9ubUA4yAAA4SDDutYGPHfX1RmXnU1oACeIPKPQXuNzhZ/9VFsewaRwEEOjqjfU0CjHpHAQtZMiGZedTRyZWfnF8uXLOaXSExOFM72D+MpDr2LHxiWOBgLja7wg31/jzXDbdjdko5/yve8ZYy9zzpcbP6cInCCyhNsIUbzGR0Lqr2UkpBTEa7wZ2XyLyEY/FWrf0yImQfiM1wWxsdf4BBgDhkYTBfEaL2PV9pKAgtbplb5E5HI/hYOKo35K90bgpUw7svkGIkMROEH4TCbRXHf/EJprYuAcaK6Joat/KNvV9RWrtt+wqN7XiLy7fwi3rpyB3XdejltXzkjbT07eCNyWmen9/IDmwAkiC2zd/QZ2HVAjz+F4AreumI5tNy60vWY852H9iBCtypDbbtYewLpNfkWuopyOY+cxnKZPnd7TeJ78fXffED6380UMjoxixKTJmT5DmgMniHHESzQ3nvOwfkSIVmXIbb9paSPqKiKO2+RX5CrKWbuwPm2fOr2n8Tz5+82/6kDf0CiaJpeN61w6ReAEkUd4idzd4EeU77YMJ23y6+3DqhxRlnx/p/e0K9MO4/0ygSJwgigA/JyHNcOPKN9tGU7a5Ee9zvQO4mP15bhmwVSEg2o54aCC+ooIblrWmHJ/p/ccS/IZK7OxMoqSADOtRySkoG32FAQUhjUt9VldxyAVCkHkETtvHwuytq1v8b18P9QWbstw0iY/6rV93xG8dvw8ZtfEtIh5aDSB1fNqtQhYvr/Te8rniTJLSwIYjpvPXkyrKsWfeodwcTiOw3/qw+/+5krHbXALReAEAErfdkox9JMfUb7bMpz0m1mZdteJY3O27sXMLXvQvr8TnANHzvTrzmvf34nZd+/BpXfvwfPvduHQyR4s/NaTOHSqB939Q9iwpBHNtTFsWNqI4+cu6u53pncQl2zZgwf3d+rKNN4DAJIBOo6c6deOHznTj5lb9mDmlj1Z+bmhOXACAHDP7jfw4IFO3+dciw3qJ2947Te768SxDUsaMZLgeOqt0xgcSSDAABEcBxiwdnEDnn77DPqGRlEeCWJqeQRHzvSjuTaG3/3Nlbp7ANDdTxybMbkUp3sHMTiiRuvVsTC6+wcxNMoRCSm4ZkEdtl7/MXT3DeGvfvEyTpwf0OrZVBnF4mkV2Pvmac8/N1Zz4DSAT3DyPYU4X3DST+OVvOE3buqdbXsAJ/I/AJ4WFf2kuTaGd7v6tYXZaxfU4fkj3fj1ly/D5oc6TCN0Gbe/X7SISZhSqCnE442TfipUEyo39fZqDxBQ1AW/gGLv8udE/mf2LOorIrhpaRPaN61EaSgAsbxovsw4RjQUQElA/VqRTmYAGiujunPF4VCAoWdgRDfd82+Hu9A3NIrND3WgZ2BEG1gZ1ME6W79ftIg5wfE7hbhYseunQvWSdlNvr2284t5ndNfFExyPdpzEE2+etpXqPfLqCe1rs59L47OQFypvXNqIXQc6EbZJJBIMjMS1rxPSZAQHdNMg4jMAGIlznOkbQvuLH6L9xQ915xgjb46xt4Vs/H5RBE5kXbpmhpPFqXxbKLTqp/F8i5H7JtN+sqr37js/nlKu2bnXLpiKj9WX297/uW9ejbqKCITiLsCA+oqIjVRvzI/bTP4nFiCPdl9IeRbi2PvdF3BtSx0CCsOKmZW2faAwoG32ZDDoI/B0REMBtH9hBfZ+rS0lUje9R/MUbaHUz98visCJrEvXzJBfxY2LOnbHcolVP43nW4xxCiOTfrKq9679nSnlmp37XtcFvNvVb3v/2vIIVs+rVSPiZGLL6nm1NlK9MT9uM/nfJ+97Fn1Do+jqG8Kuv7rM9Fh33xC6+1QZ37mL6sJl7+Coaf02rpiOx147qUbXhuVAlvqRRijA0Da7BgBQKuZgLK6LhYOYObkMD77bidZplfjx7a0WpbqHFjGJccVuUQswX5zyOhUxnouKX/rlQdRMimDjiunYdaATXX2DugE/U5xkA3rpJ7nea3/ynG4aQWbv5jbc//sjqJkUwa9f6sSIiQa6JMDQOr0KOzYuATi0vv8v//tNlIWDOHy6D3PryvFR/xAujsS153LoZA/WbH/e9L6izANHz7pqV77j1yImDeDEuHKmdxDb9r6tSb5kCRY4LI95GYCLSfIn+m3P66cQT3AwAIyp87aZ9pPxHnL/BxWG/qG4JrnT1eW1k4jzMaleUGF45NUTtnI8s2OfvO9ZHDnTj1g4gP6hsTnpda1jZV7XUofXjvWkyPMeuGMZwJEi3XNCSUBBPJGART5OWiJBhsHR9BcHks+JQ13EXbuo3vWzKugBvFDlWYQ5Rm+MG1sbcfz8AHZsXIL7f38ED+7v1F5Db1vpfvDNlTTy0MkefG7ni/j1ly/D/PoKz+WY/bzbReCMIe0fKbvfIfnY/b8/gl0H1IQYK8QUCuGdCSUjLFR5FmGOcTHwpQ/Oas+3u38IzbUxAKrW1suCT66kkcKRbvNDHRmVY/bzblwMBJILaZtWZuyHLR8Tz+YntyxJmdttqoxi7+a2ZF3CumORkKJNgykMWj2Fb4hYnDTah0RD+nvIGGV9VouM0ydHXS1A5hqzRVyv5HUETkkmxY1Tlzcvzzvbrn4yM7fssTz2wQ+vd1xOup93vz3GAfs1BzG1IRDTKHbtlaNzsWg5u0ZNehnHoaYgcPOzUZAROCWZuCNf5XdWGJ9vOMiS0Zrz523V5vGSRp7pHcSChvKUiLSpMor2TSssJX9mcsDdd3485ef9qjnVCAaY5tsht8no22GGnVRwfn05PmVw7pNlhOcuDmPO1Bh23LIEc6bG0DMworVXXCNQGLB6Xg2aa2NoqIygoSKiyeZ6BkZw45JGXFpTlt8DzjgRCSnYu7nNl7LyWkZISSbuyFf5nRXG5zscFy5vzp+3VZvHSxq5fd8RHDrVi0lh/a9StCSAJ948bSn5M5MD7trfmfLz/krneVwYimPzQx06V7tt61u0hcF0Mj4rqWCHiXOfLCOUI/y1ixsAqAvDor1y9H5pTQz1FVE8/U6XbqGydVolDmz9BO7Z/Qbe776ASQZJn51Uzyj/CwWYqfol3zG2cVpVaUZrJLqy83kKBci+PCubeF18zbbfRD7xpV8eRCwcxDun+zC3fhKeO9yNTy2oS/u8c91mq/sHFSDT9b0AY4jb/F5aLSLKMj6xxdevv3yZJv/buGI6bvjJ87ZlWxFSYLpVmFfsBu6Jgh9TKHk/gBcy2XBgM8NOmlcIbyte+slKyjZebXYjhwwwdbBKcKQ42SkM4CYSMytXuwfuWIbqsrDpvWUZ3/6jZ3WOe1b11teNoToWwemeAbVPFYZoSEH/UByXVJdhYVOF1t9GoqGALi1dprEyqitzWlUUH3x0Eavm1eCF985aXmd2j6GROApd/yKeo5so3GoAz+splELFq2+E1+u8TDU5lZX5NRg6kca58RBJ8djgMPXYyBbp+lwcY5K1KWOQponUD+XEGeET8vgbp9A6vSplx5doSUD7pZfvPTiSwKMdJ7Xz2iXvauFHDagRn6j34EgCDFLdoGY/yn8w4gmu6bKPdl/A0e4Llv1hNwgby/zgo4sAgKf/2GV5jdt7FBLHzw9gzf3Pu4rAraA1hSzgdfE1k0Vbt4t2TmVlfmEljfPaXiFlk13u/JRnOcGuz7v7h9BcEwPnQFlJALGSADgHmmtimpNd+6YViIbGfgWFT8gNixvw0gdncbp3UBvES5IOeGb33rC0EXUVYa0fjQj5n65uSalmWUkAZUm54CXVZQgHlRSpn6CQpHr5zo6N/qTT0xRKlvAqY8tE/uYkcvY7lV2+pzzvKiLFbEjjBOJaQb5kXLpJe5fb7zQ5ZsUlk1Oe8dbdb6TsGmN2PzfSTUrWyS5FLyMsZLzK2DKRvzmJnO2iXs27WXKOSxcRy/c0S2RJF2Vn0l45ym2u8Zb0kw2MbQ6wsejVrv1OIunrF9aZPmM5qjYSDira/dLVTfhq777zckRDirZNWFlJ+qHCqMQhrPFLRkgReBHgVpFhFfW6KcdpJPfBD6/PSlJNrlUo6TCLrIVU0q794jqnv5bpdqgxS7N3Ujenz5fwDkXgBAD3c8l2vtZO55WN9zQiz7umi7LtklyM3xuTXmT/aLs2ZyvJyS6RSGyW21ARxrSqqKPNerv7h3BtSx2i0q4yAoWNDdgi6cZshxqFAdOqopg7dZLmP23cyPe6pF/21EkluqQbUTeR5COy6UNK+jnwIE2SO2Zda50v5WQ0gDPGvs4Ye4sx9iZj7CHGWP5r1ooQtyqUnbcvx7b1LZjfUI5t61s0nbXq3TwVCc4RDipIcG7q3Wx2TyOyYsLqfgLj1I/d9+LrsaSXMf9ouzZny0/Hqtydty9HNBTAW6d6sWreVDx31ypd++2ue/dP/RgYiWNSJAiWHLQZU5NljEk3tZMiKc+CA4iEAjh8pg/RYAA7b1+um97aeftyHPlTPy4Ox1EeLcGqebV461Svdu72fUfQcfw8jnZdwHBS+DGSUO9vx6iFF63V1A6gJudMRO7/82W+lON5CoUx1gjgeQDzOecDjLF/AbCXc/7PVtfQFEr2cJvwZLXg6aYc+dybfvoHKIzhRzctwvanj+D8xREc2PoJ2zpn4zXdOIXiZarFiaug18Vgq2PpUBhMvbpFO8SzsPLqJvKPnCbyJAfwFwEsBtAL4H8D2M45f8rqGhrA84d88Mo2JuMoDKiviKK7fygZTaqJJeJ7hUHTLhsTYqySl7wkOQkTJ2MSjGndk/7cchKOSOQxSzIy8zwPKgr6h0YxrSqKBIer5J107S0JAAFFn2QTkLTpMgqA+sqx/jdidR2gJtkMjsRTsiuFi6DZdemyMe2SgwqZaCiA/3HHMm1HHyf4nsjDOT/BGPt7AJ0ABgA8ZTd4E97xK7HmTO8gVn5/n+6Xxix5ZjwsAMzqIg9eVt4oQGpCjN20kZvpJaPLnjEJRsZus15AH2Ubk4xkH5HBkQSQzC08di51Q4Lj5wdw4z/8u3adSMAZHElth+j/psqodt5wHEBcPwhaDcIJpG7k6+Q6wDrJhttcly50LMbBG1Dbdds/HshtIg9jrArAOgCXAGgAUMYYu83kvC8yxg4yxg52dbnLvCJU/Jq/3b7vCMCAmVNKbRc8vd7PzXWiLtESRbdoGg4qmozt1pUztKSX3XdejmlVUZSZJMSkkyA6lSqabVBrTIIR2G3Wa+aXLS8GC/kjAJgsH+iQJYDpvNJF/7/0wVntvEuqy0wXH0MWC47hoIJZU8rsK+UTE3P2W+X7Ny7wpZxMplA+C+Bazvmm5Pd/AeAyzvmdVtfQFIo7/JLKOd3Nxev9/JIf2u0sM16yQSsPbDO8yDEBZ3PgTmV9mcyrE7kl1zLCTgCXMcZKGWMMwGoAb2dQXtHjVsrmVh5oVb61zCyGDUuaNJlZMMBw1Zxq3ywAhK+0XB+zusycUpZ2Z5nx8obvGRjBrOpSzK4pw6zqUl36uhFjZP9eVz8WfutJ/Owvl1vKMe3kl2KfyzUL61RZX/LZyO2XZZPXLJiK+Q3lKZJKBWOSv3BQcZUCrzAgKJ1fXxFBNBTA6nk1alnOi0ppmyAcVBAJKlhxSZXH0gofvxJ5PA/gnPP9AP4VwCsA3kiW9YAvtSpS3E5NuJUHWpVvLTPrRzSkaDKzC0NxvNJ53rX/up3ntLE+ZnVpmz0Fbc3VphJDr33hlQNbP4GPX1qN97ov4OOXVtsqaYzyyO7+YfQNjeI7jx2ylGNayS/DQQVIvoFMLi1RZX3JZ6Nv/5hs8v2uC+g4dj5FUpnAmPPhcDyBmCFD0k66FwsHIe/Te2FoFAMjcXSeHcBnljWlOAEKiaNdmaGAuvoszg0HFQyOJnDuwgjKIxMve7O5NjZx/MCLgUxe/9PJ+s70DmLlD/aZZu7J5XuRmd122Qx09Q3iu+ta0i5OyvW08pw2St7cerxn2xve6jkxBuz/u9WonRQxlRjabTHW0lCOuXXl6B8a0er6pV8eRFk4iMOn+3D2wjAAYHJZCQ6d6rWUCgLup0nkneuzRSjAkEjAk8f4RIf8wAuEbPp137P7DbTv78TMKaU43TuYtvxUmRlDQFF0K/5Gv+KJ4k9uVu+68gg++Ogibluptt1MYnjoZI+pd/eiaRV4/M3Tpv0m9ymg7l5zY2sjRjn34DGul1vK9d6wRF+m0fPbSDqpYDyRwHCcm9bNyit8XWsDnn77DPqGRk3LtFKbGOtiV7dC4+7r5uBLVzY7Pp/8wHNINl7/jdGi8FgGxiRm4MDNO1/QRc5GP+jhOE+RmQnpGqCP+tL5dcsyQmN7gwrDV3b5I4X026tcID8nQO1H0a+i7QKjxNC4g/vx8wM4nhzQ5X4DUvtU8MirJ7SvRb8F2Fi/2fl4l5YETOttVmamUkHGoHumHcfOY9jm7UD2Krcq00ldimXwBoAfPH7Y1QBuBXmhjBN+b7JrthgYC6v+GUJiZjUnLsvRLplSmrLIJaRrbhcO5fsZ23vg6FnfpJDZSIkXyBK/WHhsUFagRosyssSwZ2BE2wB4VnWpulBn4/ho6wa4rFHrNyELFH1q5uPdXKtKKptrY2DJeluVedPSRlvPb8Ba2qgwYFZ1mSbjFM907cL6FNkkYc/nL5/uSzk0hVLA+O1cZ+aW58RJ0G+PcTPGQ0boNrXfi8TQeCzbboBO3QiJ8SfXMkIix4got33TSsyUIulwkKGxMqoNoLKs72P15bhmwVSdHM0Y9Rl3lpEj6ePnBtJKA80iTqOLniwxdOJGKNzxsikjNJP4iUA1oDAwBkyfHMWlNWWYVV3mSmJo7NNrNTfAVKdCs7qEg0rymSalgkwvFWysjGq795hJM41uhKJu0ZCCSJAhElIwtTxsKzk0SgzFuSGF0W49Lrn7ujm+lENz4AWMrMC4fHY1PjzQaZmCLmR9rx0/j9k1MZ0cbfW8Wi1C27a+xfIe29a3aItv239/RLvG6f6Q6v3MJYbi6203LtRNk4jvO7R6Z09GaNYODn10DMDRgq6x34zHPnnfs7g4HEdjZVSL4uXzjHUxsw4QDI2m7rMppJltzdUA1AQl4UYovzW8/d3rtIXZaVUhlEdCukQmmVg4iN7BsYVIoW4ZSXCUR/THZEIB5slgy+t1hYAf898ATaHkJcaFOicbEJeWBNBUVapJ7J566zQ+taAOG1dMx9qfPJdWSpZptqWVxC8bjoOhAMPn/my6Z8dFO+R2fOmX6s/qztuXW/ZhSUBB6/RKx/ewkxzu3dym0wd/6ZcHEQsH8c7pPsytn4TnDnfjUwvqHEtBaVu0/IZkhEWKUbZnJ+NzIvEzuv4BY5IspxI/UcZjkqJgXWuD4+uMMjYhebOTwzl1HHTaj5lgJY0MMoZHOk44voeZ5DCoMIwmuOmculkbrPpULlM8m+6+IVOJo6IAnWcHEFCAuDTGR0MBJDjXno38d8JO8hcNBTA0Gjf9IxcNBVBVFsLJ84OIhQMYGEkgnuDJDSqK03EwHV9dNQt/+6mPOT6fBvA8xy4hx0i6xUgRyYkItKPznPZ6bcTOf0TGLnIUm+yCwzTiFRvuCvvQ5toY3u3qN13Em10zdszseyd1zdaCp7yJstVzSiextHsWxnLSbQZtt4lxunKI3EOLmEXE9n1HwLneKdBqMdJK4idkb2JTYTGXfMPiBp03BwBNRuZ0M+C9X2tL8e+IhBTdJrtOZItC8iY7DMqLeEaHQaeOgzLZ8k2RN1GeVV2W4kboRGJ5w+KxBV0zhDRR22Ba8lMxboYs0tBLTTYcVhjQ/oUVeO6bV9vez+w6r5sT00Kmc65Z4NwL3A6KwHNMOnc+uwjUrYzQDifRqdGpLx12i1DZ3nw4k42UzebOnc7ly2n36d6qzKJjMY1i9xbhpB6TwkF8rKE8bZINkTsoAi8CrBJygPSe10IO9pNblqRkAkZDAU1WpiVzLG1C+6YVOsmhm+i0Z2AEFcmob1I4oEtWEbvlAGPRYlPSWzsWDjiOVP0ik8QpszcJLSJOtkNhSHkjKQ2pXuXyXp7Gtyr5WQgZnwiQKyJBTZpo5zdu5lsOqP285dq5qIgGcWF4VEqyiejqbQVLczwd4Qm6v6UX2r+wwpdyKALPA9JF0l6i4/JIEH1Do2kTSTKdV/ZKtiNwL4yH57Yb/3W//cbTRe8sOX1ilAPSPHp2oAi8gJGTVawSctxGxyKVe87UGAZHE7hxSWOKrzSQPjo18xU3S8gRCUDtm1YgKkWjsh+1ETmSdIpTH3U3fut2bZSjZdlzW15HEIk0Zu00rl0oyV2Q2r+wQutvOTlJTqoyznObPSezee1wUMHuOz9umvBUXxHBmkWqx/iahfUpqfSRoIJ5dZOwYUkTBkcT2s9RWUkAkaCC5toYWpvKTfsxHDD9mEjDV1fN8qUcSuTJEfJrulVCjptkFdm3eu3iBgCqBO2tU71onVaJH988dg+7JBNj3cySdQA1ypMTgDYsbdJFi2aRHKDK0oQ3tlPM6pPJeU7aKPr//a4LeLerH7v2d2L1vKnYJSVLza6JpawJmCVSDccTaJtdjbbZNdpGtvfsfkNKThpLqpKft9Vzqi2P4DPLmnQqlM8ua8Ku/Z2mCU+r59UCAB4/dRqt0yrxmWVNunZMm1yKd/7Uh+UzqnBYeitau7hBJ2N87Xhvyj6WwwngtpXTsf/oWcv1EaE+cnusmHEjIbSDplDGGa8JMU7QpGoWC1dek3VKkouRZj8pJQGG1ulVKA0paJpcZusH7qYudvUxXmtX79bpVY4WI+X+LwsHsfuVE6btVRjw269eYZnYI5KM5EQqt4lNdhtMHzrZgzXbn7e9XsCS/8zupjB1rcJskbkkoGAkkfBlcZywhhJ5CpBse4On85V2k3SjJasoDI+8egIzJqd6jotjdokmYoHTTeKQm74yJioFmBo92tVtz+unEE9wBBSGtYvqdWXKHuvHzg2Ynuf1ORqvk5NlGIBPG5KjjIk8Yq0jFg5iNJFIJvKkJjzVlUfw4VnVD3wkwVP6xsxjXE5OevjVEzqP+ZCiJluZiYqM95fbpAAIWyQABRhQVxG1tbctVsgPvEAZD29wMw9oL1ujDY4kdF7ORs9x+ZjRK9zYRmNdZK9yqwQgp311xb3P6Nof57CsG6Bf5IsnOB7tOInHOk4iFGC6JBu5veK8J948jXe2Xaerm/DHtutjM690UVcBh1rvRztOpkwtGP3I+6XNEcSUjZiKkf3AH37lBGRE34h2OH3eIzYvDcb7y21KwNrzO84xIQdvgPzAC5pse4Ob+Uo7vYdctw1LG1FXETbdgFgcs0s0sUrWMXqV23l8O+krVXIn1SUp8ROLg8YEqLoK/SAbDan7Ud6wuMFyw2GzxVc5sSddQpSZV/qOW1pTfLkjQTU5CkwvPywJpPqRl5UEsGNjq5bwJJKlYuGgzpkyElI0iSeDvh1y/960tBF1FRHd8xZrpTGL1Urj/Rlg6zUuM5FFhyQjJHRkkrjipVyv6epu54C91tMqAcqpHNL45mDs00zn58V5bpOjzMoB3EscrfrYb495whqSERIabqN6p5K77v4hbBByxKWNOimbVaKJHWaJS6KMTFLeje03JkAdP3cRN+98ISkHjJhGicYkG7M3B6t2mNX9TO+g5L+u90MX58nyz0uqyxAOjr05MAANFRFEQwpWz6sFk5KlGNRFWiFxNPZpU1UU8+om4fqFdQgHFe0XXYH6nH52x3LNH9woab16bg0YgEiaUNq4c5NZyr6VnHRe3SSsmudPOnkhsq61zpdyaA68SEgnDTTiVHK38/blejni7a0A1Pnp1fNqdXI0J/JAs3ltwN1cvVU9BdvWt+j6QPYxV+WAY/U23l+WRj531ypdGenaYaz79n1HJP91vR+6OM8o/5RNqjjUue6BkQQ6z17UFC3h5FvFjCll6Dh2Hrv2d6Z4h0dDAU0aKEsOE1BlnN/5zSHNH3zlJZN1ktZF334SHMCQYcXS6Pn92WVNpp7vAlGX5lq93LK5NqbVzXhMxrgOYGfNUGhSxfv/fJkv5dAUygTDzdRHppJHKz9uK89tUcZ317X4tnGxVRsCjOE3X20zvb+TNhjbsetAJ46fvYCLIwlHjoNu+tuI1UCmQB2oxnOwsptCKeYNGTLF7TQhyQgJAO5kjJlKHr36cWfDx9tONuhXXcS5RumejJlU0KquRpoqo3jgjmWoLgtbyj0ffkUv/47XqIIAACAASURBVBNe4ad7BhDnsPXgtpcK6r3a5Z8FKzmilR+53BcNlVGc6hmw9BEXHuPpJIdG7/J8xomPvhGSERIA3MkYvUoejZGkUWLo93V2pEgMDXJAM8ySZazqcqZ3ECu/v08X9RqlezKyVNC4iGWsq5FoSUDbscep/G9olOsGvQS3lvWNcnVzibhhJGTJclSpoFq/wZEEggrDV3aNSSMHRxJgyWMBph6zexvhsJcRyvVMJzkslMEbsH7+XqBFzAmImwVPL5JHr37c2fDx9rLYuvlXqp96LBxMW5ft+46kSP6EdM9qDTASVLB3c1vauor6fu/GFsyZGtNtomwm95T93oX3yk1LG3XeJ3bLks01MYwmuOmgIHzchRtlRSSIA0fP6qSRsuf7Sx+cdeR/PhERXu1+QBH4BMTNgme6c83miL1G7tlIcnKz2GrcdUhOljHWxfi2IEe9w3GO2TVleLfLfHFucDSBNfc/nxKBmdX1lhXTcevKGbh15QwAwPNHunDHPx3Azzet0HxV9rx+KiVy/+Cji9j0zwfxzrbrEAkFUhZtzTicXEw0BrMcSFlo7BkcRU9yQbPdsDOQfK7d28hEJcGB2/7xAEXgRO6x24XHS7KS30lObso089mWk2XsZIRykpMsY9xxS2vKfRQG0wjcSV3vfPAVxDlwZ/sruroYk5OsknU2LG20lPaZJQtpdYb1MQBorIzqypW94ce86Fea3nsi2ojv2Jj6c+EFWsQkPJGtfSdzjTGxxmyjYYHT5Ck3ZVphtyepHcYoL90OUFYukplCnuKpUCIPkTOyte9krjH6qsvzzkacRvY9AyOYVV2K2TVlmFVdmlKmk6Qq1XM9ddel9i+sUPcrNfEHb2ks18oU9zD6mjOoOwmJN4fB0QQaKiOak6EgkPQ+b6yMaDs9GTH7VKTu777z41jQUJ4yH64w4PpFdfje+gWWbdedu7Aef/kfZqQ9N9+5+7o5vpRDc+CEJ7IxX50PmPmqW+F0LeHA1k/YyhGdJFW1NdcgFGCQx/5QgGnz4NMml+qi/HBQwVsne7UyxT3MfM03LG1EW3M12pqrsW19i5bIEwowjCa47g1j/9GzGI4Ppmi8jUk+Ag41cWjX/k4cOtWbkvBzaU0M/7BRTWr50RPv6MowJuBcWhPDP9y6FADwzy98aNnfhYAfRlYATaEQGZCJd/lEwcv2Z1bTUHPueRzRkIK/vvJS/PTZ9zAwHNf8ztdufx6VpSG833UBo2aiagMKgPkN5ZhbPwn9g6PYeftyz9M0XggqwKyaGM5fHMFvv9qGrzz0Kl7tPKf+URlNWEoPQwrDiIP2FQLkB04QeY5dMpRdAozXJCPj/YRXt6yhXtdq7pX+/JEu/NUvXtbpr0UUPK0qioSFFlthQH1FVEvyMRINBRBPxDEcN/eDl9shdva5ZEoZFk6rSPExf/1YD45+dAGxcFCnEmqsjOLU+QHTzSvyjQAD7r+lFWsXNTq+hhJ5CCIHpJtqMjvW3TeE1X//LH795cu0xB2BXZKR2IGoqTKqK9M46Dr1SgfGpjCOnbNOorEa2AXiD4Kx/WaJW4KjH13A0Y8uaN8bPd7lwRsoLF/xOAe+sqvD1QBuBS1iEkSWsVvsNDu2+VcdmtGUEbskoxsWN2gJNKLM6xbWpUj31HT61MXn5755taXEUFw3Y0oU31vfkpJsFA4qmDWlzPLa6xfWm27O/OlW63UGedNou42yCxG/FjFpCoUg8gS/56DTeXWb+Zyn8wMX8/OyhDKdPNBKNum0vaKe2ZI45gqSERJEEWGWSNRUGcXezW22SUbGnZOMUbWZdM9qtybxRmCWdCP8v+XzzHbyAdQdfMwsAIAxSWP7phWmux8ByQ2ZGdA2ewpG4gmsmleDwdEEGisLW+UkyAsZIWOsEsA/AmiBOl32ec75C35UjCAmGvMbKlBaYtB6SwZWxmMNlVGsXdSItYsatYjYbJ79M8uadAlHG6WI2yh/lFVEt6yYrvmIA3r/b/m8H9/cqrv/cDyB9a2NOgsAGSFpfOLN05hWVWrqB84B3LZiOh577SQSHHjp6Dkc3nYdFn37SQCpVrV21rX5aGvrl4ww0wj8fgBPcM7nAVgM4O3Mq0QQ3rBLiHG6A1GusUsksjtm3Dnp+LkB3S47xnn2dP0x957HdYM3oC4yztm61/Q6J0lNc+95HDO37EH7fnWKpn1/p+12cu37O7Upk97BUczcskf73jgg2w3Q+TZ4A/5Nl3meA2eMlQN4DcAs7rAQmgMnsoldsoyfHuP5itxGALbtTdcfVvJHM/mhU1IljqrHuJUfuBuP70LyAweAz18+Hf/1Buf957sOnDHWCuABAIegRt8vA9jMOb9gdQ0N4EQ28DNZphBxspOPkBha+XMzBuz/u9U6/bmThUqrfjS6VB462YPP7XwRVzRX4/G3Tjta/Cxm/NqRJ5MplCCApQB+yjlfAuACgC0mN/4iY+wgY+xgV1dXBrcjCHPsfFmK1bNFxtjGgCS/M0oMb1jckNIfM6eUgnPYOkraLZSaYXSpFNLIZw936coMBxVbj3Kx/mpjhGh7fb7i189fJouYxwEc55zvT37/rzAZwDnnD0CN1LF8+fICeskhCgUvyTKF7tkiY7dRtHG3HqM/9+BIQvMyN+46JC9U3mdYqLTqR6tdlQQXhuNof/FDtL/4oSOHQnHYYhMhAPm3YbETVnxvX279wDnnpwEcY4zNTX60Gup0CkGMO26TZewolAVPGbmN06qiaKiIoLk2hjWL6lIi5/qKCNYsrMe8ukloqoqmROtW0WF3/xDWtNQhwBjWLKwz7Ufj20A4qKS4KNZXRNDSWJ50RoykDEINFRGUlShgTN2nE1D/r6+IoG325BSnRK/UxEp8KMU9kZD5jkxeyCiRJzkP/o8ASgC8D+A/cc7PWZ1Pc+BEIVAMC55yGziQ4lsOqIucs2tieLerP62nuUB4m7vxSTcm4JRHgugbGtXqJqtdmmvV+pgdu22lKis0S+bxIhUMBRhuXj4tRW2TSZlO8OIHT2ZWBJEGq8VABmD/1tWup13MtpvL5nWAdRsUBvz2q1fghp88j7jJ73wowPC5P5tu6ShpJ3szTgUYXSr/5eAxzJxSive7+jGB1y1ToExMgvARswXPmVNKAZa6wOcEq+3msnUdYL2g++Lfrcb8hnK8cPcqfLq1QdvQIaAwrGttwB+2rMK29S2WdsB2WaJGdt6+HNvWt2B+Qzm2rW/B4W3X4amvX4l/37Jad28G623aQorzrdYCLLmlm83xttlTnBU2DoiNOPyAInCiaMgkchWk8wIpCShonV5pew+v2835tU2d3VZvbu8h+tRKfuhURmh374lGeSSI1799jatrKAInip5MIleB7AUyc0ppqhxvUX3ae3iVLvolebRbtBUbIIvoNsD0GyAbEX16w+KGFE8V2RvF6jq5n4z3FqUZt25r37QSG5a6s1pVWOHICQd9/CNGEXgW8SMizAWFVu9sbbDsJJFFJMgY+8rphsdW9xRkYyHVSd3somXhd3JjayOOnx9wFGXLLoYP7u/UNooQi5aiLhuWNOLYuQF0HDuP4SKP1mkOPM/xIyLMBYVW72wl69g57hkTZOySYJxIF+Xrmmti4Bxorok5vs5ru6zqZtan9RUR3LS0SbtOeI8bo2y7Z9HdP4Tm2hgAdfDuGRjR1eXAUbXMtQvrUVeR/8GDV8gPPI/JVkSYbQq13oD3iNfrPdymljsh3/rfqk+dRNlurwOsfcuLFYrA85RCTd8u1HoD3iNer/dwm1ruhFz1v1XiklWfOomy3Vz3szuWIxhguGpOtTaYlwSYrzvwREMKmqrGIvpLa8py6i3ulwqF9sTMAulSu/OVQq03oPenNnpcZ+MeTlPL3ZCr/penzOS3Fqs+TVdPt9d95zeHcGEojlc6z2tR+HCcozySmgBktSNPumOfXtyABw904raVeqdGsYmylzK97g5UHgmibXaNp2uN0BRKljAmM1glSOQbhVrvXJCNvjKWefzsRVwciWsLhX4uMHuZshH3Ly0JoKmqVNf2765rSVs3uX1rtj+XUf0LGb/cCGkAJ4g8xpjW72eav5Xn99brP2Y5APvpuX7oZA/+6hcvm+4oXxJgCCiKtqM9oCbAxBMJDMc5AkxVsSQ4NF9xuRyhcgkwoK4iqnmOi63aElxNFkrw8fURDzBg7eIG2z42gwbwIqXQJH+EM5wmvfgplRQLjl9b3ew4CSdTz3XhrWK8zq7tjEFLtHLiaJiP5IMfOJEHFJrkj3CGmatfY2UU4aC6spcNqaRYcLRKwsmG57q8TVxZSQDRkKItEhsXMRUGzEp6l5eVBDCtKordd16ONQvrbBc8rY4FczT62SVOuYUWMQsUK9/lQpD8EekxLvgNxxMoLQlgOM59XeCU5+z/18Fjtj9T2fBcP7D1E9rXaxc3aF/fd3MroqGATraZ4MD7Se/yC8NxXBgewI3//x8AwHRLNoHVsVwF7qd6BnPvB07klkKW/BHOMEbHxqQXv6WSXuWBZseOn7voyVNdljQ6SaQSbwDpdvbJJ/z0A6cIvEApZMkf4QyjHE+W5GVDKulVHmh2TCxoGqWJ6ZCnb+Qyf5xGtvmZZU2Wvt52iMVOM7z6gRuvM34/raoU8+srXJdrBg3gBYyIUGQpF1FcjPcidaY/U1ZTe6EAQyQYwK+/fJnp4GV5ncIQCanXWdXNzqs8HXbDs9fNHIzXie933LIE258+gvMXRzyVawapUAgijym03YGspImvHzuPox9dtNyNxvq6Hhz96ILtLjZ2ckSFAfWSjNBINBTQSRX9IMD00sSmyigeuGNZRlE3yQgJooCwku1ZuR/mE048Y4BULxDZidFuWFpxyWTs2LgEh0/34Y5/OoCfb1qB7zx2yDKjMh/I9LmRjJAgCgirBUUr98N8Ql58vH5hHSIGvZ7VTj6yE+O0qqjWdvm66xfWae2/88FXEOfAne2voGdgBBVRdUZYXsxkUKNwP31V7DC7TTioZO25UQROEHlKtt0Pxwtjso7ZdMhE262HEnkIAtZOesVAtt0PveClv+VknTlTYzh3cTilDLM3jnBQwazqMuy4ZQkuqS5DOKigxHwLTQSYmhpvhEEdLFfPq0l5E8iUxsoILLb0TK1f8v9w0N/nRgM4UdAUcyaqvDnwfTe3YvW8qTmXjXrp7wNbP4Gnvn4l1i5uwFNfvxLXLqhLKcNMwvjZZU14+htXYe3iBjzzjavwmWVNGLZYb1QUhlE+ltov4AA+u6wJnWcHMDiaQMiwU3J5JFWIFw4qYMz+2G0rp6O0JIiRePqt3BgAUW2/nxtNoRAFSb5tfjAeZMsp0olU0Y/+TleGXfvspIKTwgH0D8fVvUxf/NBRXdJx09Im/Ob1k5g5pRRfW9WMux5+HfFEArNrJ+HNk72+3MOPDR1oACcKEi9OeoQ5TqSKfvR3JmWYSQXLSgK4OBzHrSvH6m12nlHWJ2M8ZlamQPTTdS11eO1Yj7lsEUB9ZRTd/UPa24TCmE6q6EVWaDWAUyIPUZC4zUQl18ZU3Pjp+JH566QM43OSvy81TIBfSM6niHqXJDMejWO1XT6O8ZixTDO3xb1vnLYsLwFoA7vwsJkUDmJAyt2JlgR8y8SkOXCiYHGzjVoxz5V7xa2fjh/b1qUrw/ic5O/FYuj31rdgUngs9gwoTJNYwkIyOCmsDv4MatS945YlmFVdpn1mJMCQ4rYYSBYcSCNLDAcV3LSsUWvj4GhCt4jbM0CZmAThiIk4V+6G8dgM2glOZYQhBRjxQW0ob8Bs56Hi1Q8FGEs48mMnJZIREhMScm20Zzw2g3aClf+5zLrWBjRNVqPmAFP/AeogFgkpkAUm4ssAE5JEcx/17v4hNNfGtHPl6+srImhK1kG+n5OcoLZLp1i+SfgJzYETRQ25NtozHptBO8HsORkXCR/tOKl9LQfFCQCDhrCcS8emVZXi3a7+lOdvjPrlMjlU326zY2BAedh+U+Pn3/sIwNhcusBv336KwImiJ1dRZjEkGY1nG7r7h7BhSSOaa2NYPa8mbfp7WUkAOza2YsPSRoSD+gi8tCSA9i+s0HzUr5pbg5F4Aqvm1mrPX0T9Ru04MJaCLxMNKYgEFaxZWKfNa3/l6ktN6yaSisSbhFh/DQeZr2+AFIETRU+uokz5tbkQnATNGM827Lx9Oe7Z/QbeOtWL1mnTcWlNTJeCb5yPbqiMYu2iRqxd1Kjt3iOUHxuWNKJtdg3aZtdg2/oWLPr2k0hw4KUPzuL1b18DQB/1G+FQdxmSo+zRBMdInOPw6X4clqLnX7zwoe68UIBhNMENOympx4ZGOSXyEEQ+U+gLp2d6B7Hy+/tMvbKz1Qa7RcwdtyzBVx961dK7e8fGVvzmtVO6JKDjZy/i4kgcB46etbxnSVDBcIb+Kx/88HrMuedxREMK/vrKS/HTZ99D7+Co5lt+w0+eR9xkjPXLC4UGcILwmUJPMhIJKzMml+J07+C4tCFdn5kdHxpJgEONeI98b41pG66aU4MX3z+rS6QRyTsbljRilHPsef0U4gkOBnXH+0QyJb86FkZ3/yCGRlPHSKfJOKLee147iThX7712cYPrfqREHoIYJ3K1cJqpVM0YBX+Q3EAY8N/Dw0i6PjNOd8iLliNxrqXai2sFz7zTlXIvMQvzyKsndJ9zjPmQD42qUx9mgzcAHD8/gDX3P6+lwxv7/tDJHnxu54sYHI3rpn3iXF2MfeLN07SISRD5Si4WTjOVqhmlfAoDZk4pQ/umlePShnR9Jo7fde3clGsDTJ1KSZUjWq+EhoNKikNhgKkLmM21asJNc23MVDaoMOg8zY19v/lXHegbGkVTZSnqKsJjSUAKQ31FxLdFTJpCIYgCx88593xJ7ElH89a9KRsHi2kUpzsCZXT/pKe5Gx9zsajppU8pkYcgihQ/k5XSRcGyrNCNxNCrHNHqutEER0gBppaHEVLU78W5J85d1Npw9bwa03K/v74F1y2ss5QqhoMsKf8bGyInRYL4/voWXTr8mBRRLSikqPtsykRCClbMrFJljPNqfH2TyXgAZ4wFGGOvMsZ+60eFCIJwh59z7rIH+bb1LSl2tfJUgZspG6/TO1bXHf3B9fjcn03Hmb4hfO7PpuPoD67Xzm2qKtXa8D//ckWKr3d5JIiNl83AT29dhktrYin3VCNlnpT/jUXX6xY3YONlM/DU16/Ega2fACD3vfo2MJJAiuf4tKpS/PF0nypjPHrOFwtgQcZTKIyxvwGwHEA553yt3bk0hUIQ2SFbXuECJ1MFZhv3ep3esbsOgKNpC7tzGYD9W1dj7fbnUVkawtdWNWPLI69jJJ5AcxrPb7Fw+fyRLtz2swOW511aU4b3ui6kLccJWZERMsaaAPwcwPcA/A0N4ARRnBhlfEpysS/OoUn+ggrDI6+e0M3xepVU2l0HDt0xIfk71TOABFcXCtcuqjc9NxJSUFcewYdnL5rORdt5fhulg4u+/SR6B0cRVNTEHcG61gYEGcMjHSdw9dwavPCeXsYYDQXwP+5YhrbZ5tM7ZmRLRvjfAHwTwKQMyyEIIo8xm6YB1Ch3cCSh8ykx+n249QC3up98nZ1vSjzBdVI9o/xQyCPden4fPz+AG//h31MiennwBvSeLU//MVXGGAowV4O3HZ7nwBljawGc4Zy/nOa8LzLGDjLGDnZ1pTaGIIjCQF7gnFYVxbSqqKMNl916gJvdz3idfOwmgxdKgEEn1RPntm9aiZlTSrWFS7meZm6I8gKn2Bz5ubuuRvumFSkLlTIKg+Z9Egmp5QhvlJIAw6CPyhjPUyiMsR8AuB3AKIAIgHIAj3DOb7O6hqZQCKI48SI/zIX80e4847HZNTG829Wfcm669YDySBB9Q6O2Mka/Uuk9R+Cc87s5502c85kA/hzA03aDN0EQ48d4OyG6SVwSddt958dT5I/XLJiK+Q3lruvd3T+EG5NOhhuWNFneX3Y83LC00TKqFy6G8vfvdfVj4beexM/uWG4qP5wxOYpZ1WUYHE3o3g7qKiLaNE046K8fvS+JPIyxqwB8gxYxCSI/cLJRca6Q68YB06jXS72dttlr33zyvmdx5Ew/mmtjWHHJZN1OPs211vU27vpzm8mGyekgMyuCmADksxOiVd0CjEFRYLp1mZN6O22z174RPituEGX69TwoE5MgJgD5vIWcVd1e+LtV+MNdqzzX22mbvfbN3q+1pWzvFrDI4JQ3Q5bvKS+w+vk8aAAniCIil1vIpZt3t6tbJvUW1wqHwsER82ud3MOsDfMbKlBaoledWO1zLNwGr/jRMwCAK+59Bo91nNTONx7PFBrACaLIyNUWck7S5Z1KA93Wu7t/CCXJMLckwGwXMb1IGnsGRrTyQ2n2epMljM9982pyIyQIIn/J5ry7E49zuzlqp+nqVm0IKQwjCfdjpGwrcP/vj2Ts8Ehz4ARBZIVszrs7ierNEmuiIXVTY6doc9VSpLyutQFNVaXJ7/Xn2wXh9RUR3LC4Qat3Nt+IaEcegiAyIhvz7saI2JieL9PWXINQgCHp8ArAfbr6Ffc+o7ufSMcf+15/vl1QfqpnEA+/ckKrN6C+jWxb3+L7ptoUgRNEnjHeSTh+4CWRx659bqP6weQfjaaq5IKmy3R1da46oqlFGKyjbPGx1XE1lZ7p6r37zo9n5ZnSAE4QeUamW6PlgnQ+4jJO2uc2qj+87Tqsa23AifODWNfagMMu595ryyNYPa8WCSRtaBkQC6dOUIhjzbUxcIxZ1srEwkGMJLiu3rv2d2blmdIiJkHkCfmchOMHbtvn1OPcr34z3u9fDh7DzCmlmlf4aILjkb++HLsOdOKpt07jiuZqvHO6D++e6YeSVJdwrroW3rx8GjaumI61P3nOdLrFr0QeGsAJIk/w6p1dKGSrfaLcPa+peusAA9Yubsh6v8kp+QBM0/P9ajOpUAgiz8llEk62EXLAoMJ8b5/bZBnjHPyhkz1Y+K0ncehUT8oxs/n6ufc8jplb9qB9fyc4H1tgFV/P3LIHc+95HED2nykN4ASRR+QqCSfbiHnvl46e9b19bpNljHPwm3/Vgb6hUWx+qCPlmNl8vXGBVexOJO5tXGzN5jOlKRSCILLGeM3rO/EDd7KvZzpEvcX9rIZPv9tHUygEQYw72Uzykac3jFHu8XMDKVMfxrqUBGC5s044qKCxMopwUC8HTN3lZwWiobFhVEnuBvSzO5Zr0zLZhAZwgiCyRjbngOXpDaOMsakqmjL1YazLSEJN+DESDqpRfGlJAMNxblpvcb+25hpsWNqkXZvgwOp5tfjObw5p0zLZhKZQCILIKk7lgE6xm5YBYHpMeJOUhhRMjoVx+HQf5taV4zevn9Skgnc9/DoSnONhSSr4qQV1lvU+0zuIld/fB6cjqFNfFjNIRkgQRFFgJxsEh+mxoMLwyKsnbCV/bhEywhmTS3G6dxCDIwmUBICAEsDASFw7r6kyigfuWIb59RWe70UDOEEQOcOJq6BTvETgdrhdcPSyGJrpoiYtYhIEkTP8tAcQskEZIRs0SgoVpi5AmnmceF1QNZMRzpxShvZNK7FhaSOYYVo9HFSytiMSDeAEQWQNs6QXOdHFC1fc+wxO9+i11Kd6BnHFj55JeppMRYKri48Jru7QI5J8OMacBL0uqBoXQzmAttlT0NZcjT2vn0qRFg6NJnzbgccIDeAEQWQNv2WEZ3oH8bH6ctROsk7ckSWFNy1tRDio6PawjIYUtH9hhZZU48X9sbt/CDcuaURzbQwbljRpyTlGV8MAg6878BihAZwgiKzht4xw+74jeO34eVREQ1KUzbF6Xm2KxG9+Qzl+fHMrPrOsSXMZZAy4aWkT2mbXaK6JXqZ3dt6+HKWhAN461YtoSNHUKUZXwwSgq5vf0IYOBEFkFRERy3I8txgXDo+c6QcAJDhXI2mbMq3u72bTCLu6GK/zo71OIRUKQRB5TzacDL2WmQvXSFKhEAThmnzZHchsKiaoMHxl16ue6+Z1eiefXCNpACcIwpJ82h3I6Hdy4OjZjOvm1SkwX1wjaQqFIIgU8nl3oGzWzS7hyM9kJLfQFApBEI7JpotgpmSzbnZvHPn0NiIgFQpBECnk0zzveNTNTlkCwJNaZTygCJwgCFNyMc/rdNHUz7qd6R3E/PpyfGrBVNOoPp/fRigCJwjCFNk6ddv6lnG5pzxNYecU6Gfdtu87go7j5zG7JmYZ1efr2wgN4ARB5ByvSTV+3tMuOSjT5JxsLYCSCoUgiJxj5/GdrUh3PBNyhHe4Vw9yKxUKReAEQeScK+59RhcNxznwaMdJPPHm6axF4OOxUJvtNwtaxCQIIucYfbyNDoPZItsLtdleAKUInCCInCN8vHcd6NQ2Fc6mi58g2wu12Y7yKQInCCIvyJf0dBk/vGCy2S7Pi5iMsWkAfgGgDkACwAOc8/vtrqFFTIIgColMFx/9IhuLmKMA/pZz/gpjbBKAlxljv+OcH8qgTIIgiKziRNKXC1mjFzxPoXDOT3HOX0l+3QfgbQCNflWMIAgiGzjxNMnn7EsZXxYxGWMzASwBsN+P8giCIPzGTVSdz14wMhkvYjLGYgAeBvCfOee9Jse/yBg7yBg72NXVlentCIIgPOE2qs7HRVUjGUXgjLEQ1MH7Qc75I2bncM4fAPAAoC5iZnI/giAIr7iNqnPhBeMWzxE4Y4wB+BmAtznn9/lXJYIgJiqZyPYOnezBwm89iUOneizPyVZUnaut5zKZQrkcwO0AVjHGOpL/1vhUL4IgJiCZbJqw+Vcd6BsaxeaHOizP2Xn7cmxb34L5DeXYtr5FF2VnQq42eyAzK4Igck4m26TN3LLH8tgHP7w+47rZYVXvkoCC1umVvrkP0pZqBEHkLZnI9vZ+rQ2NlVHdZ02VUezd3JaVuspY1fuGRfXjEpGTFwpBEDknE9ne9rg+MgAABh1JREFU/IYKlJYEdJ9FSwKYX1+RrepqGOs9OJLAox0ntePZTgCiCJwgiLwgkwXGnoERzJkaw45blmDO1Bh6BkayWFM9cr1vWtqIuorIuCUA0Rw4QRCEj2zd/QZ2HehESUB1VfTDR4U2dCAIghgHMt1+zQ0UgRMEQeQ5pEIhCIIoMmgAJwiCKFBoACcIgihQaAAnCIIoUGgAJwiCKFBoACcIgihQaAAnCIIoUGgAJwiCKFBoACcIgihQaAAnCIKwIFc77TiFBnCCIAgLcrXTjlPIzIogCMKAcaedbPt6e4UicIIgCAOZ7BA0ntAAThAEYSCTHYLGE5pCIQiCMGE8fb29Qn7gBEEQeQ75gRMEQRQZNIATBEEUKDSAEwRBFCg0gBMEQRQoNIATBEEUKDSAEwRBFCjjKiNkjHUB+HDcbpieagDdua5EnkJ9Yw31jTXUN9Zk0jczOOc1xg/HdQDPNxhjB820lQT1jR3UN9ZQ31iTjb6hKRSCIIgChQZwgiCIAmWiD+AP5LoCeQz1jTXUN9ZQ31jje99M6DlwgiCIQmaiR+AEQRAFS9EN4Iyxf2KMnWGMvSl9tpgx9gJj7A3G2G8YY+WGa6YzxvoZY9+QPluWPP9dxth2xhgbz3ZkA7d9wxhblDz2VvJ4JPl5UfWNm35hjIUYYz9Pfv42Y+xu6Zqi6hcAYIxNY4w9k2zrW4yxzcnPJzPGfscYO5L8v0q65u5kH7zDGLtG+ryo+sdt3zDGPskYeznZBy8zxlZJZXnrG855Uf0D8B8BLAXwpvTZSwCuTH79eQDfNVzzMID/BeAb0mcHAPwHAAzA4wCuy3XbxrNvoHrFvw5gcfL7KQACxdg3LvtlI4BfJb8uBfABgJnF2C/JNtUDWJr8ehKAwwDmA7gXwJbk51sA/Cj59XwArwEIA7gEwHtF/HPjtm+WAGhIft0C4IRUlqe+KboInHP+bwDOGj6eC+Dfkl//DsBN4gBjbD2A9wG8JX1WD6Ccc/4CV3v3FwDWZ7Pe44HLvvkUgNc5568lr/2Icx4vxr5x2S8cQBljLAggCmAYQG8x9gsAcM5Pcc5fSX7dB+BtAI0A1gH4efK0n2Osreug/oEb4pwfBfAugBXF2D9u+4Zz/irn/GTy87cARBhj4Uz6pugGcAveBPDp5NefBTANABhjZQDuAvAdw/mNAI5L3x9PflaMmPYNgDkAOGPsScbYK4yxbyY/nyh9Y9Uv/wrgAoBTADoB/D3n/CwmQL8wxmZCjSL3A5jKOT8FqAMZgNrkaY0AjkmXiX4o6v5x2DcyNwF4lXM+hAz6ZqIM4J8H8P8wxl6G+qoznPz8OwD+P855v+F8s/mnYpXrWPVNEEAbgFuT/9/IGFuNidM3Vv2yAkAcQAPUKYK/ZYzNQpH3C2MsBnWq8T9zznvtTjX5jNt8XvC46Btx/gIAPwLwJfGRyWmO+mZC7InJOf8j1CkBMMbmALg+eWglgM8wxu4FUAkgwRgbhPowmqQimgCcRBFi0zfHATzLOe9OHtsLdZ64HROgb2z6ZSOAJzjnIwDOMMb+AGA5gOdQpP3CGAtB/Z14kHP+SPLjPzHG6jnnp5JTAGeSnx/H2NsKMNYPx1GE/eOyb8AYawKwG8BfcM7fS37suW8mRATOGKtN/q8AuAfAfwcAzvkVnPOZnPOZAP4bgO9zznckX3v6GGOXJVeD/wLAo7mpfXax6hsATwJYxBgrTc73Xgng0ETpG5t+6QSwiqmUAbgMwB+LtV+SbfkZgLc55/dJhx4DcEfy6zsw1tbHAPx5cm73EgDNAA4UY/+47RvGWCWAPQDu5pz/QZycUd/keiXX738AHoI6PzkC9S/bJgCboa4QHwbwQyQTmAzXfRt6FcpyqPOg7wHYYXZNof1z2zcAboO62PImgHuLtW/c9AuAGFTF0lsADgH4f4u1X5JtaoP6Ov86gI7kvzVQVUn7ABxJ/j9ZumZrsg/egaSmKLb+cds3UAOBC9K5HQBqM+kbysQkCIIoUCbEFApBEEQxQgM4QRBEgUIDOEEQRIFCAzhBEESBQgM4QRBEgUIDOEEQRIFCAzhBEESBQgM4QRBEgfJ/AVVKZfdvfk8lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot(gdp['DATE'], gdp['GDP'])\n",
    "#plt.show()\n",
    "\n",
    "plt.plot(movies['Year'], movies['IMDb'], '*')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarize\n",
    " * we've done matplotlib plots using `plt`\n",
    " * we've done data driven plots using `.plot` function with Pandas data\n",
    " \n",
    "Let's focus a bit on doing \"engine - driven\" plots, fancy of saying let's hone our matplotlib skills more!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use plt to make this histogram plot we made before\n",
    "plt.hist(movies['IMDb'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAWzklEQVR4nO3df7DddX3n8efLgCBYFplcaExgL7ZZEJha4MqyxXZRSqEFCd0Obdy1Zizb7Fq2IKurie0uznQyQ2dbUcaFbYrUoCyYopV0rT9o1LKdQfCCKITIkgLChUhSbQUtGwy+94/zve3hcm44+XLPPeeS52PmzPl+P9/P93zfcwbyup/v93s+31QVkiS18bJhFyBJWrgMEUlSa4aIJKk1Q0SS1JohIklqbb9hFzAoixcvrvHx8WGXIUkLyp133vm3VTXWb/+XbIiMj48zOTk57DIkaUFJ8q296e/pLElSa4aIJKk1Q0SS1JohIklqzRCRJLVmiEiSWjNEJEmtGSKSpNYMEUlSay/ZX6xLGh3jaz4zlOM+fPk5QznuvsSRiCSpNUNEktSaISJJam1gIZLk2iQ7ktzbY9u7k1SSxV1ta5NsS3J/krO62k9Ock+z7cokGVTNkqS9M8iRyEeBs2c2JjkSOBN4pKvtOGAlcHyzz1VJFjWbrwZWA8ub1/M+U5I0HAMLkaq6Ffhuj01XAO8BqqttBXBjVe2qqoeAbcApSZYAh1TVbVVVwHXA+YOqWZK0d+b1mkiS84DHqurrMzYtBR7tWp9q2pY2yzPbJUkjYN5+J5LkIOB3gF/otblHW+2hfbZjrKZz6oujjjqqRZWSpL0xnyORnwCOBr6e5GFgGXBXkh+nM8I4sqvvMuDxpn1Zj/aeqmp9VU1U1cTYWN+PCJYktTRvIVJV91TV4VU1XlXjdALipKr6NrAJWJnkgCRH07mAfkdVbQeeSnJqc1fW24Cb56tmSdKeDfIW3xuA24BjkkwluXC2vlW1BdgI3Ad8Drioqp5tNr8DuIbOxfa/AT47qJolSXtnYNdEquotL7B9fMb6OmBdj36TwAlzWpwkaU74i3VJUmuGiCSpNUNEktSaISJJas0QkSS1ZohIklozRCRJrRkikqTWDBFJUmuGiCSpNUNEktSaISJJas0QkSS1ZohIklozRCRJrRkikqTWDBFJUmuGiCSpNUNEktSaISJJam1gIZLk2iQ7ktzb1fbfk3wzyTeS/FmSQ7u2rU2yLcn9Sc7qaj85yT3NtiuTZFA1S5L2ziBHIh8Fzp7RdgtwQlX9FPB/gbUASY4DVgLHN/tclWRRs8/VwGpgefOa+ZmSpCEZWIhU1a3Ad2e0faGqdjerXwGWNcsrgBuraldVPQRsA05JsgQ4pKpuq6oCrgPOH1TNkqS9M8xrIr8BfLZZXgo82rVtqmlb2izPbO8pyeokk0kmd+7cOcflSpJmGkqIJPkdYDdw/XRTj261h/aeqmp9VU1U1cTY2NiLL1SStEf7zfcBk6wCzgXOaE5RQWeEcWRXt2XA4037sh7tkqQRMK8jkSRnA+8Fzquqf+jatAlYmeSAJEfTuYB+R1VtB55KcmpzV9bbgJvns2ZJ0uwGNhJJcgNwOrA4yRRwGZ27sQ4Abmnu1P1KVf3HqtqSZCNwH53TXBdV1bPNR72Dzp1er6BzDeWzSJJGwsBCpKre0qP5I3vovw5Y16N9EjhhDkuTJM0Rf7EuSWrNEJEktWaISJJaM0QkSa0ZIpKk1gwRSVJr8/6LdUmaL+NrPjO0Yz98+TlDO/Z8ciQiSWrNEJEktWaISJJaM0QkSa0ZIpKk1gwRSVJrhogkqTVDRJLUmj82lPYhw/zxnV6aHIlIklozRCRJrRkikqTWBhYiSa5NsiPJvV1thyW5JckDzfururatTbItyf1JzupqPznJPc22K5NkUDVLkvbOIEciHwXOntG2BthcVcuBzc06SY4DVgLHN/tclWRRs8/VwGpgefOa+ZmSpCEZWIhU1a3Ad2c0rwA2NMsbgPO72m+sql1V9RCwDTglyRLgkKq6raoKuK5rH0nSkM33NZEjqmo7QPN+eNO+FHi0q99U07a0WZ7Z3lOS1Ukmk0zu3LlzTguXJD3fqFxY73Wdo/bQ3lNVra+qiaqaGBsbm7PiJEm9zXeIPNGcoqJ539G0TwFHdvVbBjzetC/r0S5JGgHzHSKbgFXN8irg5q72lUkOSHI0nQvodzSnvJ5KcmpzV9bbuvaRJA3ZwKY9SXIDcDqwOMkUcBlwObAxyYXAI8AFAFW1JclG4D5gN3BRVT3bfNQ76Nzp9Qrgs81LkjQCBhYiVfWWWTadMUv/dcC6Hu2TwAlzWJokaY6MyoV1SdICZIhIklozRCRJrRkikqTWDBFJUmt9hUgS746SJD1PvyOR/5nkjiS/leTQgVYkSVow+gqRqnoD8O/oTE0ymeR/JTlzoJVJkkZe39dEquoB4HeB9wL/GrgyyTeT/JtBFSdJGm39XhP5qSRXAFuBNwFvrqrXNstXDLA+SdII63fakw8Dfwy8r6qenm6sqseT/O5AKpMkjbx+Q+SXgKenJ0VM8jLgwKr6h6r62MCqkySNtH6vifwlnVl0px3UtEmS9mH9hsiBVfX96ZVm+aDBlCRJWij6DZEfJDlpeiXJycDTe+gvSdoH9HtN5J3AnyaZfjTtEuDXBlOSJGmh6CtEquqrSY4FjgECfLOqfjjQyiRJI29vnmz4emC82efEJFTVdQOpSpK0IPQVIkk+BvwEcDcw/ezzAgwRSdqH9TsSmQCOq6oaZDGSpIWl37uz7gV+fK4OmuTSJFuS3JvkhiQHJjksyS1JHmjeX9XVf22SbUnuT3LWXNUhSXpx+g2RxcB9ST6fZNP0q80BkywFLgYmquoEYBGwElgDbK6q5cDmZp0kxzXbjwfOBq5KsqjNsSVJc6vf01nvH8BxX5Hkh3R+tPg4sBY4vdm+AfgynRmDVwA3VtUu4KEk24BTgNvmuCZJ0l7q93kifwU8DOzfLH8VuKvNAavqMeAPgEeA7cD3quoLwBFVtb3psx04vNllKfBo10dMNW3Pk2R1kskkkzt37mxTniRpL/Q7FfxvAjcBf9Q0LQU+3eaAzbWOFcDRwKuBg5O8dU+79GjreYG/qtZX1URVTYyNjbUpT5K0F/q9JnIRcBrwJPzjA6oO3+Mes/t54KGq2tn8YPFTwM8ATyRZAtC872j6T9F5ouK0ZXROf0mShqzfENlVVc9MryTZj1lGA314BDg1yUFJApxB52FXm4BVTZ9VwM3N8iZgZZIDkhwNLAfuaHlsSdIc6vfC+l8leR+di+FnAr8F/HmbA1bV7UluonNNZTfwNWA98EpgY5IL6QTNBU3/LUk2Avc1/S+afq6JJGm4+g2RNcCFwD3AfwD+Arim7UGr6jLgshnNu+iMSnr1Xwesa3s8SdJg9DsB44/oPB73jwdbjiRpIel37qyH6HENpKpeM+cVSZIWjL2ZO2vagXSuVxw29+VIkhaSfn9s+J2u12NV9UHgTQOuTZI04vo9nXVS1+rL6IxMfmwgFUmSFox+T2f9YdfybjpToPzqnFcjSVpQ+r07642DLkSStPD0ezrrP+9pe1V9YG7KkSQtJHtzd9br6UxBAvBm4FaeO7uuJGkf02+ILAZOqqqnAJK8H/jTqvr3gypMkjT6+p2A8Sjgma71Z4DxOa9GkrSg9DsS+RhwR5I/o/PL9V8GrhtYVZKkBaHfu7PWJfks8LNN09ur6muDK0uStBD0ezoLOs9Cf7KqPgRMNc/2kCTtw/p9PO5lwHuBtU3T/sDHB1WUJGlh6Hck8svAecAPAKrqcZz2RJL2ef2GyDNVVTTTwSc5eHAlSZIWin5DZGOSPwIOTfKbwF/iA6okaZ/3gndnJQnwCeBY4EngGOC/VdUtA65NkjTiXjBEqqqSfLqqTgbmJDiSHErnGe0n0DlF9hvA/XTCapxmluCq+rum/1o6z3h/Fri4qj4/F3VIkl6cfk9nfSXJ6+fwuB8CPldVxwKvA7YCa4DNVbUc2Nysk+Q4YCVwPHA2cFWSRXNYiySppX5D5I10guRvknwjyT1JvtHmgEkOAX4O+AhAVT1TVX8PrAA2NN02AOc3yyuAG6tqV1U9BGwDTmlzbEnS3Nrj6awkR1XVI8AvzuExXwPsBP4kyeuAO4FLgCOqajtAVW1PcnjTfynwla79p5q2XvWuBlYDHHXUUXNYsiSplxcaiXwaoKq+BXygqr7V/Wp5zP2Ak4Crq+pEOr89WbOH/unRVr06VtX6qpqoqomxsbGW5UmS+vVCIdL9D/hr5uiYU8BUVd3erN9EJ1SeSLIEoHnf0dX/yK79lwGPz1EtkqQX4YVCpGZZbq2qvg08muSYpukM4D46D7xa1bStAm5uljcBK5Mc0MzXtRy4Yy5qkSS9OC90i+/rkjxJZ0TyimaZZr2q6pCWx/1t4PokLwceBN5OJ9A2JrkQeAS4gM5BtiTZSCdodgMXVdWzLY8rSZpDewyRqhrIrbRVdTedR+7OdMYs/dcB6wZRiySpvb2ZCl6SpOcwRCRJrRkikqTWDBFJUmuGiCSpNUNEktSaISJJas0QkSS1ZohIklozRCRJrRkikqTWDBFJUmuGiCSpNUNEktSaISJJas0QkSS1ZohIklozRCRJrRkikqTWDBFJUmv7DevASRYBk8BjVXVuksOATwDjwMPAr1bV3zV91wIXAs8CF1fV54dStDQHxtd8ZtglSHNmmCORS4CtXetrgM1VtRzY3KyT5DhgJXA8cDZwVRNAkqQhG0qIJFkGnANc09W8AtjQLG8Azu9qv7GqdlXVQ8A24JT5qlWSNLthjUQ+CLwH+FFX2xFVtR2geT+8aV8KPNrVb6ppe54kq5NMJpncuXPn3FctSXqOeQ+RJOcCO6rqzn536dFWvTpW1fqqmqiqibGxsdY1SpL6M4wL66cB5yX5JeBA4JAkHweeSLKkqrYnWQLsaPpPAUd27b8MeHxeK5Yk9TTvI5GqWltVy6pqnM4F8y9W1VuBTcCqptsq4OZmeROwMskBSY4GlgN3zHPZkqQehnaLbw+XAxuTXAg8AlwAUFVbkmwE7gN2AxdV1bPDK1OSNG2oIVJVXwa+3Cx/Bzhjln7rgHXzVpgkqS/+Yl2S1JohIklqzRCRJLVmiEiSWjNEJEmtGSKSpNYMEUlSa4aIJKk1Q0SS1JohIklqzRCRJLVmiEiSWjNEJEmtGSKSpNYMEUlSa4aIJKk1Q0SS1JohIklqzRCRJLU27yGS5MgkX0qyNcmWJJc07YcluSXJA837q7r2WZtkW5L7k5w13zVLknobxkhkN/CuqnotcCpwUZLjgDXA5qpaDmxu1mm2rQSOB84GrkqyaAh1S5JmmPcQqartVXVXs/wUsBVYCqwANjTdNgDnN8srgBuraldVPQRsA06Z36olSb0M9ZpIknHgROB24Iiq2g6doAEOb7otBR7t2m2qaev1eauTTCaZ3Llz56DKliQ1hhYiSV4JfBJ4Z1U9uaeuPdqqV8eqWl9VE1U1MTY2NhdlSpL2YCghkmR/OgFyfVV9qml+IsmSZvsSYEfTPgUc2bX7MuDx+apVkjS7YdydFeAjwNaq+kDXpk3AqmZ5FXBzV/vKJAckORpYDtwxX/VKkma33xCOeRrw68A9Se5u2t4HXA5sTHIh8AhwAUBVbUmyEbiPzp1dF1XVs/NftiRppnkPkar6a3pf5wA4Y5Z91gHrBlaUJKmVYYxEpJEwvuYzwy5BWvCc9kSS1JohIklqzRCRJLVmiEiSWjNEJEmtGSKSpNYMEUlSa4aIJKk1f2woSQMwrB+zPnz5OfN6PEcikqTWHIloqJx6RFrYHIlIklozRCRJrRkikqTWDBFJUmuGiCSpNe/OEuBdUpLacSQiSWrNEJEktbZgQiTJ2UnuT7ItyZph1yNJWiDXRJIsAv4HcCYwBXw1yaaqum+4lc0tr0tIWmgWRIgApwDbqupBgCQ3AiuAgYSI/5hLUn8WSogsBR7tWp8C/uXMTklWA6ub1e8nuX8eahu0xcDfDruIEeV3Mzu/m9m9pL+b/H7rXae/l3++NzstlBBJj7Z6XkPVemD94MuZP0kmq2pi2HWMIr+b2fndzM7vpre238tCubA+BRzZtb4MeHxItUiSGgslRL4KLE9ydJKXAyuBTUOuSZL2eQvidFZV7U7yn4DPA4uAa6tqy5DLmi8vqdNzc8zvZnZ+N7Pzu+mt1feSquddWpAkqS8L5XSWJGkEGSKSpNYMkRGV5MgkX0qyNcmWJJcMu6ZRkmRRkq8l+d/DrmWUJDk0yU1Jvtn8t/Ovhl3TqEhyafP/0r1Jbkhy4LBrGpYk1ybZkeTerrbDktyS5IHm/VX9fJYhMrp2A++qqtcCpwIXJTluyDWNkkuArcMuYgR9CPhcVR0LvA6/IwCSLAUuBiaq6gQ6N+isHG5VQ/VR4OwZbWuAzVW1HNjcrL8gQ2REVdX2qrqrWX6Kzj8GS4db1WhIsgw4B7hm2LWMkiSHAD8HfASgqp6pqr8fblUjZT/gFUn2Aw5iH/6tWVXdCnx3RvMKYEOzvAE4v5/PMkQWgCTjwInA7cOtZGR8EHgP8KNhFzJiXgPsBP6kOdV3TZKDh13UKKiqx4A/AB4BtgPfq6ovDLeqkXNEVW2Hzh+xwOH97GSIjLgkrwQ+Cbyzqp4cdj3DluRcYEdV3TnsWkbQfsBJwNVVdSLwA/o8JfFS15zfXwEcDbwaODjJW4db1UuDITLCkuxPJ0Cur6pPDbueEXEacF6Sh4EbgTcl+fhwSxoZU8BUVU2PWG+iEyqCnwceqqqdVfVD4FPAzwy5plHzRJIlAM37jn52MkRGVJLQObe9tao+MOx6RkVVra2qZVU1TufC6Beryr8ogar6NvBokmOapjMY0OMSFqBHgFOTHNT8v3UG3nQw0yZgVbO8Cri5n50WxLQn+6jTgF8H7klyd9P2vqr6iyHWpNH328D1zRxzDwJvH3I9I6Gqbk9yE3AXnTsfv8Y+PP1JkhuA04HFSaaAy4DLgY1JLqQTuhf09VlOeyJJasvTWZKk1gwRSVJrhogkqTVDRJLUmiEiSWrNENE+L8n3m/fxJJXk97q2LU7ywyQfbtbfn+SxJHc3s51+qntizCQPJ1k8R3WNJ/m3XesTSa6ci8+W5oohIj3Xg8C5XesXADMfxXxFVf10M9vpJ4AvJhlrc7BmMsDZjAP/GCJVNVlVF7c5jjQohoj0XE8DW5NMNOu/BmycrXNVfQL4Al3/2AP/JckdzesnZ+7TjGbWJ/kCcF0z4vg/Se5qXtPTcVwO/Gwz6rk0yenTz09pPuPaJF9O8mCSi7s+/782zxO5pXluxrtf1Dci7YG/WJee70ZgZZJvA8/SmTL81XvofxdwbNf6k1V1SpK30Zlx+Nwe+5wMvKGqnk5yEHBmVf2/JMuBG4AJOpMnvruqzgVIcvqMzzgWeCPwY8D9Sa6m8wyRX6Ez6/N+TW1OVqmBMUSk5/sc8HvAE3ROV72QzFi/oev9iln22VRVTzfL+wMfTvLTdELrX/RZ52eqahewK8kO4AjgDcDN05+d5M/7/CypFU9nSTNU1TN0/np/F51ZlF/IiTx3Mr+aZbnbD7qWL6UTWK+jMwJ5eZ+l7upafpbOH4UzA00aKENE6u0PgfdW1Xf21CnJrwC/wD+NPqBzHWX6/bY+jvXPgO1V9SM6k24uatqfonOqam/8NfDmJAc2z6I5Zy/3l/aKp7OkHqpqC8+/K2vapc0DjQ4G7gXeVFU7u7YfkOR2On+kvaWPw10FfDLJBcCX+KdRyjeA3Um+TueZ2F/ro+6vJtkEfB34FjAJfK+PGqRWnMVXeolJ8sqq+n5zwf5WYHVV3TXsuvTS5EhEeulZ3/wA8kBggwGiQXIkIklqzQvrkqTWDBFJUmuGiCSpNUNEktSaISJJau3/A6ZnRrrA6K6nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's put some labels on our data\n",
    "plt.hist(movies['IMDb']) # this defaults to making a histogram of the column you give it\n",
    "plt.xlabel('IMDb rating')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Unnamed: 0</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Breaking Bad</td>\n",
       "      <td>2008</td>\n",
       "      <td>18+</td>\n",
       "      <td>9.5</td>\n",
       "      <td>96%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Stranger Things</td>\n",
       "      <td>2016</td>\n",
       "      <td>16+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>93%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Money Heist</td>\n",
       "      <td>2017</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.4</td>\n",
       "      <td>91%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sherlock</td>\n",
       "      <td>2010</td>\n",
       "      <td>16+</td>\n",
       "      <td>9.1</td>\n",
       "      <td>78%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Better Call Saul</td>\n",
       "      <td>2015</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.7</td>\n",
       "      <td>97%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0             Title  Year  Age  IMDb Rotten Tomatoes  Netflix  \\\n",
       "0           0      Breaking Bad  2008  18+   9.5             96%        1   \n",
       "1           1   Stranger Things  2016  16+   8.8             93%        1   \n",
       "2           2       Money Heist  2017  18+   8.4             91%        1   \n",
       "3           3          Sherlock  2010  16+   9.1             78%        1   \n",
       "4           4  Better Call Saul  2015  18+   8.7             97%        1   \n",
       "\n",
       "   Hulu  Prime Video  Disney+  type  \n",
       "0     0            0        0     1  \n",
       "1     0            0        0     1  \n",
       "2     0            0        0     1  \n",
       "3     0            0        0     1  \n",
       "4     0            0        0     1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2008\n",
       "1       2016\n",
       "2       2017\n",
       "3       2010\n",
       "4       2015\n",
       "        ... \n",
       "5606    2018\n",
       "5607    2017\n",
       "5608    2018\n",
       "5609    2017\n",
       "5610    2016\n",
       "Name: Year, Length: 5611, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's put some labels on our data\n",
    "plt.hist(movies['Year']) # this defaults to making a histogram of the column you give it\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2) # 1 row of plots and 2 columns of plots\n",
    "# fig is an object that contains both plots\n",
    "# ax contains our individual plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig # this is sort of like the canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fa5303209d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fa5104904d0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplots?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(4, 2)) # 1 row of plots and 2 columns of plots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# before side by side, let's start with making 1 plot\n",
    "#. Let's redo our IMDb plot with this new calling sequence\n",
    "\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 1, figsize=(4, 2)) \n",
    "\n",
    "ax.hist(movies['IMDb']) # now we have to be contious about layout\n",
    "ax.set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax.set_ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'hist'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-68-dfdf683c97d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mncols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmovies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'IMDb'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# now we have to be contious about layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'IMDb rating'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# note this is called with a \"set_\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Frequency'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'hist'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(4, 2)) \n",
    "\n",
    "ax.hist(movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax.set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax.set_ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fa5088677d0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fa530c0bed0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax # my array of axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5088677d0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[0] # so this element I can actually plot with because its a matplotlib axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(6, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(6, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "# on my SECOND set of axis I want to do a distribution of \"Year\" column\n",
    "#ax[0].hist(movies['Years']) # This doesn't work because \"Years\" is NOT \"Year\"\n",
    "ax[0].hist(movies['Year']) # I've updated the column that is being histogrammed\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()\n",
    "# here we didn't update what axes we are plotting on so we overplotted stuff on \n",
    "#.  our first axes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(6, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "# on my SECOND set of axis I want to do a distribution of \"Year\" column\n",
    "#ax[0].hist(movies['Years']) # This doesn't work because \"Years\" is NOT \"Year\"\n",
    "ax[1].hist(movies['Year']) # I've updated the column that is being histogrammed\n",
    "ax[1].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()\n",
    "# layout is a little smooshed AND my x-axis label on the 2nd set of axis is wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(6, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "# on my SECOND set of axis I want to do a distribution of \"Year\" column\n",
    "#ax[0].hist(movies['Years']) # This doesn't work because \"Years\" is NOT \"Year\"\n",
    "ax[1].hist(movies['Year']) # I've updated the column that is being histogrammed\n",
    "ax[1].set_xlabel('Year') # note this is called with a \"set_\"\n",
    "ax[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()\n",
    "# layout still a little funny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(10, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "# on my SECOND set of axis I want to do a distribution of \"Year\" column\n",
    "#ax[0].hist(movies['Years']) # This doesn't work because \"Years\" is NOT \"Year\"\n",
    "ax[1].hist(movies['Year']) # I've updated the column that is being histogrammed\n",
    "ax[1].set_xlabel('Year') # note this is called with a \"set_\"\n",
    "ax[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.352273x0.755)\n",
      "AxesSubplot(0.547727,0.125;0.352273x0.755)\n"
     ]
    }
   ],
   "source": [
    "for myAxes in ax:\n",
    "    print(myAxes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If you are interested here is some more fancy Pandas stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(movies) # DataFrame object, popular for stats and also machine learning stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Unnamed: 0</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Breaking Bad</td>\n",
       "      <td>2008</td>\n",
       "      <td>18+</td>\n",
       "      <td>9.5</td>\n",
       "      <td>96%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Stranger Things</td>\n",
       "      <td>2016</td>\n",
       "      <td>16+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>93%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Money Heist</td>\n",
       "      <td>2017</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.4</td>\n",
       "      <td>91%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sherlock</td>\n",
       "      <td>2010</td>\n",
       "      <td>16+</td>\n",
       "      <td>9.1</td>\n",
       "      <td>78%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Better Call Saul</td>\n",
       "      <td>2015</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.7</td>\n",
       "      <td>97%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0             Title  Year  Age  IMDb Rotten Tomatoes  Netflix  \\\n",
       "0           0      Breaking Bad  2008  18+   9.5             96%        1   \n",
       "1           1   Stranger Things  2016  16+   8.8             93%        1   \n",
       "2           2       Money Heist  2017  18+   8.4             91%        1   \n",
       "3           3          Sherlock  2010  16+   9.1             78%        1   \n",
       "4           4  Better Call Saul  2015  18+   8.7             97%        1   \n",
       "\n",
       "   Hulu  Prime Video  Disney+  type  \n",
       "0     0            0        0     1  \n",
       "1     0            0        0     1  \n",
       "2     0            0        0     1  \n",
       "3     0            0        0     1  \n",
       "4     0            0        0     1  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>Unnamed: 0</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>The Office</td>\n",
       "      <td>2005</td>\n",
       "      <td>16+</td>\n",
       "      <td>8.9</td>\n",
       "      <td>81%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Black Mirror</td>\n",
       "      <td>2011</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>83%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         Title  Year  Age  IMDb Rotten Tomatoes  Netflix  Hulu  \\\n",
       "5           5    The Office  2005  16+   8.9             81%        1     0   \n",
       "6           6  Black Mirror  2011  18+   8.8             83%        1     0   \n",
       "\n",
       "   Prime Video  Disney+  type  \n",
       "5            0        0     1  \n",
       "6            0        0     1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.iloc[5:7,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Unnamed: 0</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>The Office</td>\n",
       "      <td>2005</td>\n",
       "      <td>16+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Black Mirror</td>\n",
       "      <td>2011</td>\n",
       "      <td>18+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         Title  Year  Age\n",
       "5           5    The Office  2005  16+\n",
       "6           6  Black Mirror  2011  18+"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.iloc[5:7,:4] # 7, 6 rows AND only the first 3 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breaking Bad</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stranger Things</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Money Heist</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sherlock</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Better Call Saul</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5606</th>\n",
       "      <td>Tut's Treasures: Hidden Secrets</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5607</th>\n",
       "      <td>Paradise Islands</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5608</th>\n",
       "      <td>Wild Russia</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5609</th>\n",
       "      <td>Love &amp; Vets</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5610</th>\n",
       "      <td>United States of Animals</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5611 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Title  Year\n",
       "0                        Breaking Bad  2008\n",
       "1                     Stranger Things  2016\n",
       "2                         Money Heist  2017\n",
       "3                            Sherlock  2010\n",
       "4                    Better Call Saul  2015\n",
       "...                               ...   ...\n",
       "5606  Tut's Treasures: Hidden Secrets  2018\n",
       "5607                 Paradise Islands  2017\n",
       "5608                      Wild Russia  2018\n",
       "5609                      Love & Vets  2017\n",
       "5610         United States of Animals  2016\n",
       "\n",
       "[5611 rows x 2 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# also use .loc instead to subset by name\n",
    "movies.loc[:, ['Title','Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "5606    False\n",
       "5607    False\n",
       "5608    False\n",
       "5609    False\n",
       "5610    False\n",
       "Name: Year, Length: 5611, dtype: bool"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['Year'] < 1970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Unnamed: 0</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>The Twilight Zone</td>\n",
       "      <td>1959</td>\n",
       "      <td>7+</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82%</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>Star Trek</td>\n",
       "      <td>1966</td>\n",
       "      <td>7+</td>\n",
       "      <td>8.3</td>\n",
       "      <td>80%</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>112</td>\n",
       "      <td>Monty Python's Flying Circus</td>\n",
       "      <td>1969</td>\n",
       "      <td>16+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>100%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>360</td>\n",
       "      <td>The Andy Griffith Show</td>\n",
       "      <td>1960</td>\n",
       "      <td>all</td>\n",
       "      <td>8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>558</td>\n",
       "      <td>Dad's Army</td>\n",
       "      <td>1968</td>\n",
       "      <td>7+</td>\n",
       "      <td>8.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4965</th>\n",
       "      <td>4965</td>\n",
       "      <td>I've Got a Secret</td>\n",
       "      <td>1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4985</th>\n",
       "      <td>4985</td>\n",
       "      <td>Classic Popeye</td>\n",
       "      <td>1960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>5039</td>\n",
       "      <td>Television Playhouse</td>\n",
       "      <td>1947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5442</th>\n",
       "      <td>5442</td>\n",
       "      <td>The Wackiest Works of Tex Avery</td>\n",
       "      <td>1945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5578</th>\n",
       "      <td>5578</td>\n",
       "      <td>Spin and Marty</td>\n",
       "      <td>1955</td>\n",
       "      <td>all</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                            Title  Year  Age  IMDb  \\\n",
       "59            59                The Twilight Zone  1959   7+   9.0   \n",
       "99            99                        Star Trek  1966   7+   8.3   \n",
       "112          112     Monty Python's Flying Circus  1969  16+   8.8   \n",
       "360          360           The Andy Griffith Show  1960  all   8.3   \n",
       "558          558                       Dad's Army  1968   7+   8.1   \n",
       "...          ...                              ...   ...  ...   ...   \n",
       "4965        4965                I've Got a Secret  1961  NaN   NaN   \n",
       "4985        4985                   Classic Popeye  1960  NaN   NaN   \n",
       "5039        5039             Television Playhouse  1947  NaN   NaN   \n",
       "5442        5442  The Wackiest Works of Tex Avery  1945  NaN   NaN   \n",
       "5578        5578                   Spin and Marty  1955  all   8.2   \n",
       "\n",
       "     Rotten Tomatoes  Netflix  Hulu  Prime Video  Disney+  type  \n",
       "59               82%        1     1            0        0     1  \n",
       "99               80%        1     1            1        0     1  \n",
       "112             100%        1     0            0        0     1  \n",
       "360              NaN        1     0            1        0     1  \n",
       "558              NaN        1     0            0        0     1  \n",
       "...              ...      ...   ...          ...      ...   ...  \n",
       "4965             NaN        0     0            1        0     1  \n",
       "4985             NaN        0     0            1        0     1  \n",
       "5039             NaN        0     0            1        0     1  \n",
       "5442             NaN        0     0            1        0     1  \n",
       "5578             NaN        0     0            0        1     1  \n",
       "\n",
       "[105 rows x 11 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.loc[movies['Year'] < 1970] # .loc NOT .iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_movies = movies.loc[movies['Year'] < 1970]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Unnamed: 0</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>The Twilight Zone</td>\n",
       "      <td>1959</td>\n",
       "      <td>7+</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82%</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>Star Trek</td>\n",
       "      <td>1966</td>\n",
       "      <td>7+</td>\n",
       "      <td>8.3</td>\n",
       "      <td>80%</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>112</td>\n",
       "      <td>Monty Python's Flying Circus</td>\n",
       "      <td>1969</td>\n",
       "      <td>16+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>100%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>360</td>\n",
       "      <td>The Andy Griffith Show</td>\n",
       "      <td>1960</td>\n",
       "      <td>all</td>\n",
       "      <td>8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>558</td>\n",
       "      <td>Dad's Army</td>\n",
       "      <td>1968</td>\n",
       "      <td>7+</td>\n",
       "      <td>8.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4965</th>\n",
       "      <td>4965</td>\n",
       "      <td>I've Got a Secret</td>\n",
       "      <td>1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4985</th>\n",
       "      <td>4985</td>\n",
       "      <td>Classic Popeye</td>\n",
       "      <td>1960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>5039</td>\n",
       "      <td>Television Playhouse</td>\n",
       "      <td>1947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5442</th>\n",
       "      <td>5442</td>\n",
       "      <td>The Wackiest Works of Tex Avery</td>\n",
       "      <td>1945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5578</th>\n",
       "      <td>5578</td>\n",
       "      <td>Spin and Marty</td>\n",
       "      <td>1955</td>\n",
       "      <td>all</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                            Title  Year  Age  IMDb  \\\n",
       "59            59                The Twilight Zone  1959   7+   9.0   \n",
       "99            99                        Star Trek  1966   7+   8.3   \n",
       "112          112     Monty Python's Flying Circus  1969  16+   8.8   \n",
       "360          360           The Andy Griffith Show  1960  all   8.3   \n",
       "558          558                       Dad's Army  1968   7+   8.1   \n",
       "...          ...                              ...   ...  ...   ...   \n",
       "4965        4965                I've Got a Secret  1961  NaN   NaN   \n",
       "4985        4985                   Classic Popeye  1960  NaN   NaN   \n",
       "5039        5039             Television Playhouse  1947  NaN   NaN   \n",
       "5442        5442  The Wackiest Works of Tex Avery  1945  NaN   NaN   \n",
       "5578        5578                   Spin and Marty  1955  all   8.2   \n",
       "\n",
       "     Rotten Tomatoes  Netflix  Hulu  Prime Video  Disney+  type  \n",
       "59               82%        1     1            0        0     1  \n",
       "99               80%        1     1            1        0     1  \n",
       "112             100%        1     0            0        0     1  \n",
       "360              NaN        1     0            1        0     1  \n",
       "558              NaN        1     0            0        0     1  \n",
       "...              ...      ...   ...          ...      ...   ...  \n",
       "4965             NaN        0     0            1        0     1  \n",
       "4985             NaN        0     0            1        0     1  \n",
       "5039             NaN        0     0            1        0     1  \n",
       "5442             NaN        0     0            1        0     1  \n",
       "5578             NaN        0     0            0        1     1  \n",
       "\n",
       "[105 rows x 11 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(10, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(early_movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "# on my SECOND set of axis I want to do a distribution of \"Year\" column\n",
    "#ax[0].hist(movies['Years']) # This doesn't work because \"Years\" is NOT \"Year\"\n",
    "ax[1].hist(early_movies['Year']) # I've updated the column that is being histogrammed\n",
    "ax[1].set_xlabel('Year') # note this is called with a \"set_\"\n",
    "ax[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# another subset of LATE movies\n",
    "late_movies = movies.loc[movies['Year'] >= 1970]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Unnamed: 0</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>IMDb</th>\n",
       "      <th>Rotten Tomatoes</th>\n",
       "      <th>Netflix</th>\n",
       "      <th>Hulu</th>\n",
       "      <th>Prime Video</th>\n",
       "      <th>Disney+</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Breaking Bad</td>\n",
       "      <td>2008</td>\n",
       "      <td>18+</td>\n",
       "      <td>9.5</td>\n",
       "      <td>96%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Stranger Things</td>\n",
       "      <td>2016</td>\n",
       "      <td>16+</td>\n",
       "      <td>8.8</td>\n",
       "      <td>93%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Money Heist</td>\n",
       "      <td>2017</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.4</td>\n",
       "      <td>91%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sherlock</td>\n",
       "      <td>2010</td>\n",
       "      <td>16+</td>\n",
       "      <td>9.1</td>\n",
       "      <td>78%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Better Call Saul</td>\n",
       "      <td>2015</td>\n",
       "      <td>18+</td>\n",
       "      <td>8.7</td>\n",
       "      <td>97%</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5606</th>\n",
       "      <td>5606</td>\n",
       "      <td>Tut's Treasures: Hidden Secrets</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5607</th>\n",
       "      <td>5607</td>\n",
       "      <td>Paradise Islands</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5608</th>\n",
       "      <td>5608</td>\n",
       "      <td>Wild Russia</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5609</th>\n",
       "      <td>5609</td>\n",
       "      <td>Love &amp; Vets</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5610</th>\n",
       "      <td>5610</td>\n",
       "      <td>United States of Animals</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5506 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                            Title  Year  Age  IMDb  \\\n",
       "0              0                     Breaking Bad  2008  18+   9.5   \n",
       "1              1                  Stranger Things  2016  16+   8.8   \n",
       "2              2                      Money Heist  2017  18+   8.4   \n",
       "3              3                         Sherlock  2010  16+   9.1   \n",
       "4              4                 Better Call Saul  2015  18+   8.7   \n",
       "...          ...                              ...   ...  ...   ...   \n",
       "5606        5606  Tut's Treasures: Hidden Secrets  2018  NaN   NaN   \n",
       "5607        5607                 Paradise Islands  2017  NaN   NaN   \n",
       "5608        5608                      Wild Russia  2018  NaN   NaN   \n",
       "5609        5609                      Love & Vets  2017  NaN   NaN   \n",
       "5610        5610         United States of Animals  2016  NaN   NaN   \n",
       "\n",
       "     Rotten Tomatoes  Netflix  Hulu  Prime Video  Disney+  type  \n",
       "0                96%        1     0            0        0     1  \n",
       "1                93%        1     0            0        0     1  \n",
       "2                91%        1     0            0        0     1  \n",
       "3                78%        1     0            0        0     1  \n",
       "4                97%        1     0            0        0     1  \n",
       "...              ...      ...   ...          ...      ...   ...  \n",
       "5606             NaN        0     0            0        1     1  \n",
       "5607             NaN        0     0            0        1     1  \n",
       "5608             NaN        0     0            0        1     1  \n",
       "5609             NaN        0     0            0        1     1  \n",
       "5610             NaN        0     0            0        1     1  \n",
       "\n",
       "[5506 rows x 11 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "late_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAACaCAYAAAAUwRQIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAVc0lEQVR4nO3de5SkdX3n8feHi1xlkQy4yMXG3YkuuuE2ErKaBHHxElTMclB0E8hqQsyyGyUmy2DclRwP5+CeRBMOGxNUFDSCeEdRIxg3rFl0mOEi94Aw4ghxjJ6EyyIX+e4fz9OxMnT3VHVX9fNUz/t1Tp1+6ldVT32eme5vfeu5pqqQJElSf23XdQBJkiQtzIZNkiSp52zYJEmSes6GTZIkqeds2CRJknrOhk2SJKnndug6wKSsWrWqZmZmuo4haZls2LDh76tq765zjIP1S9r2bK2GrdiGbWZmhvXr13cdQ9IySfLtrjOMi/VL2vZsrYa5SVSSJKnnbNgkSZJ6zoZNkiSp51bsPmzStJlZe/nE5r3xnOMmNm9JmlbTVHddwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPWfDJkmS1HM2bJIkST1nwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPWfDJkmS1HM2bJIkST1nwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPWfDJkmS1HM2bJIkST1nwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPbdD1wEkTd7M2ssnNu+N5xw3sXlLkhquYZMkSeq5oRq2JM+bdBBJmhRrmKRpN+watj9Lsi7Jf06y5zAvSHJBks1JbhoY2yvJFUnuaH8+beCxM5PcmeT2JC8dGD8iyY3tY+cmydBLJ0mNkWpYkgOSfDXJrUluTvLmdtwaJqkTQ+3DVlUvTLIaeAOwPsk64INVdcUCL/sQcB5w0cDYWuArVXVOkrXt/TOSHAycBDwXeAZwZZKfrqofA+8FTgW+DnwBeBnwxRGWURqbSe4LpslZRA17HHhrVV2b5KnAhiRXAL+GNUxSB4beh62q7gDeDpwB/CJwbpLbkvyHeZ5/FfDDLYaPBy5spy8EXj0wfklVPVJVdwN3Akcm2RfYo6qurqqiaf5ejSSNaJQaVlX3VdW17fQDwK3AfljDJHVk2H3YfibJe2iK1jHAK6vq37TT7xnh/Z5eVfdBUxCBfdrx/YDvDDxvUzu2Xzu95bgkDW0pNSzJDHAY8A2sYZI6MuxpPc4D3ge8raoenh2sqnuTvH0MOebap6MWGJ97JsmpNJseOPDAA8cQS9IKsagalmR34JPAW6rq/gV2P1tyDbN+SVrIsJtEfwn46GyhS7Jdkl0BqurDI7zf99pNBLQ/N7fjm4ADBp63P3BvO77/HONzqqrzq2pNVa3Ze++9R4glaYUbuYYl2ZGmWfuLqvpUOzyxGmb9krSQYRu2K4FdBu7v2o6N6jLglHb6FOCzA+MnJdkpyUHAamBdu8nhgSRHtUdWnTzwGkka1kg1rK03HwBurap3DzxkDZPUiWE3ie5cVQ/O3qmqB2e/nc4nycXA0cCqJJuAdwDnAJcmeSNwD3BiO7+bk1wK3EJzdNZp7dFVAL9Fc8TpLjRHVnl0laRRjVrDXgD8KnBjkuvbsbdhDZPUkWEbtoeSHD571FSSI4CHF3pBVb1unodePM/zzwbOnmN8PeBJLyUtxUg1rKq+xtz7n4E1TFIHhm3Y3gJ8PMnsvhf7Aq+dTCRJGjtrmKSpNuyJc69J8hzg2TTfOm+rqscmmkySxsQaJmnaDbuGDeD5wEz7msOSUFUXLfwSSeoNa5ikqTVUw5bkw8C/Aq4HZneknT1rtyT1mjVM0rQbdg3bGuDg9tIqkjRtrGGSptqw52G7CfiXkwwiSRNkDZM01YZdw7YKuCXJOuCR2cGqetVEUknSeFnDJE21YRu2syYZQpIm7KyuA0jSUgx7Wo+/TvJMYHVVXdmeIXz7yUaTpPGwhkmadkPtw5bkN4BPAH/eDu0HfGZSoSRpnKxhkqbdsAcdnEZzbb37AarqDmCfSYWSpDGzhkmaasM2bI9U1aOzd5LsQHMOI0maBtYwSVNt2Ibtr5O8DdglybHAx4HPTS6WJI2VNUzSVBu2YVsLfB+4EfhN4AvA2ycVSpLGzBomaaoNe5ToE8D72pskTRVrmKRpN+y1RO9mjv09qupZY08kSWNmDZM07Ua5luisnYETgb3GH0eSJsIaJk2pmbWXdx2hF4bah62qfjBw+25V/TFwzISzSdJYWMMkTbthN4kePnB3O5pvq0+dSCJJGjNrmKRpN+wm0T8amH4c2Ai8ZuxpJGkyrGGSptqwR4m+aNJBJGlSrGGSpt2wm0R/Z6HHq+rd44kjSeNnDZM07UY5SvT5wGXt/VcCVwHfmUQoSRoza5ikqTZsw7YKOLyqHgBIchbw8ar69UkFk6QxsoZJmmrDXprqQODRgfuPAjNjTyNJk2ENkzTVhl3D9mFgXZJP05wt/JeBiyaWSpLGyxomaaoNe5To2Um+CPx8O/Sfquq6ycWSpPGxhkmadsNuEgXYFbi/qv4E2JTkoAllkqRJsIZJmlpDNWxJ3gGcAZzZDu0IfGSxb5pkY5Ibk1yfZH07tleSK5Lc0f582sDzz0xyZ5Lbk7x0se8radu0mBqW5IIkm5PcNDA2cp1KckRb7+5Mcm6SjHfpJG0Lhl3D9svAq4CHAKrqXpZ+WZcXVdWhVTV7Uea1wFeqajXwlfY+SQ4GTgKeC7wM+NMk2y/xvSVtWxZTwz5EU3MGLaZOvRc4FVjd3racpyRt1bAN26NVVTQ765JktwlkOR64sJ2+EHj1wPglVfVIVd0N3AkcOYH3l7RyjVzDquoq4IdbDI9Up5LsC+xRVVe373/RwGskaWjDNmyXJvlzYM8kvwFcCbxvCe9bwJeTbEhyajv29Kq6D6D9uU87vh///OSWm9oxSRrWuGrYqHVqv3Z6y3FJGslWjxJt97f4GPAc4H7g2cD/qKorlvC+L6iqe5PsA1yR5LaFIswxVvNkPZVm0wMHHnjgEuJJWikmVMOe9DZzjNUC40+egfVL0gK22rBVVSX5TFUdAYylwLX7j1BVm9vzIh0JfC/JvlV1X7sZYXP79E3AAQMv3x+4d575ng+cD7BmzZo5i6KkbcuYa9iodWpTO73l+Fw5rV+S5jXsJtGvJ3n+ON4wyW5Jnjo7DbwEuInmGn+ntE87BfhsO30ZcFKSndrD8FcD68aRRdI2Y1w1bKQ61W42fSDJUe2avpMHXiNJQxv2SgcvAt6UZCPNUVah+eL6M4t4z6cDn26PbN8B+GhVfSnJNTT7mbwRuAc4keZNbk5yKXAL8DhwWlX9eBHvK2nbNXINS3IxcDSwKskm4B3AOYxep36L5ojTXYAvtjdJGsmCDVuSA6vqHuDl43rDqroLOGSO8R8AL57nNWcDZ48rg6Rtw1JqWFW9bp6HRqpTVbUeeN6o7y9Jg7a2hu0zwOFV9e0kn6yqE5YjlCSNiTVM0oqwtYZt8AinZ00yiDQuM2sv7zqC+sMaJmlF2NpBBzXPtCRNA2uYpBVha2vYDklyP8231F3aafjJDrt7TDSdJC2NNUzSirBgw1ZVXrNT0tSyhklaKYY9D5skSZI6YsMmSZLUczZskiRJPWfDJkmS1HM2bJIkST1nwyZJktRzw178XZIkaU5eYWbyXMMmSZLUczZskiRJPWfDJkmS1HM2bJIkST1nwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPeeJc7XsPMGiJEmjcQ2bJElSz7mGTZKkbYBbN6aba9gkSZJ6zoZNkiSp59wkqjm56lzDmuTvysZzjpvYvKU+svZqPq5hkyRJ6jkbNkmSpJ6bmk2iSV4G/AmwPfD+qjqn40iSNBTr18riZkt1YSoatiTbA/8LOBbYBFyT5LKquqXbZN2yaEj9Z/3qhvVRK81UNGzAkcCdVXUXQJJLgOMBC56kvrN+zcOmShretDRs+wHfGbi/CfjZcc3coiFpgiZav8AaJm0LpqVhyxxj9aQnJacCp7Z3H0xy+0RTLc4q4O+7DjEBK3G5VuIywRQtV9410tOfPaEYS7Uc9Wtq/k+3YO7lZe5llHeNnPuZCz04LQ3bJuCAgfv7A/du+aSqOh84f7lCLUaS9VW1pusc47YSl2slLhOs7OXqOsM8Jl6/pvX/1NzLy9zLa9y5p+W0HtcAq5MclOQpwEnAZR1nkqRhWL8kLdlUrGGrqseT/BfgL2kOi7+gqm7uOJYkbZX1S9I4TEXDBlBVXwC+0HWOMej1JtslWInLtRKXCVyuZbcM9au3y74V5l5e5l5eY82dqift+ypJkqQemZZ92CRJkrZZNmzLJMkBSb6a5NYkNyd5c9eZxiXJ9kmuS/L5rrOMS5I9k3wiyW3t/9nPdZ1pqZKc3v7u3ZTk4iQ7d51pMZJckGRzkpsGxvZKckWSO9qfT+sy41LNs4yHJLk6yY1JPpdkj3b8Pya5fuD2RJJD28eOaJ9/Z5Jzk8x1ipGucu+Y5MJ2/NYkZw68ps+5n5Lkg+34DUmO7jD3nJ8rC/09JDmzzXd7kpd2kX3U3El+qn3+g0nO22Jefc59bJINbb4NSY5ZUu6q8rYMN2Bf4PB2+qnA3wIHd51rTMv2O8BHgc93nWWMy3Qh8Ovt9FOAPbvOtMTl2Q+4G9ilvX8p8Gtd51rksvwCcDhw08DY/wTWttNrgXd1nXMCy3gN8Ivt9BuAd87xun8L3DVwfx3wczTngvsi8PK+5AZeD1zSTu8KbARmpiD3acAH2+l9gA3Adh3lnvNzZb6/h/axG4CdgIOAbwHbL3f2ReTeDXgh8CbgvC3m1efchwHPaKefB3x3Kbldw7ZMquq+qrq2nX4AuJXmQ3SqJdkfOA54f9dZxqX9Jv0LwAcAqurRqvqHblONxQ7ALkl2oPmAfNK5wKZBVV0F/HCL4eNpmmzan69e1lBjNs8yPhu4qp2+Ajhhjpe+DrgYIMm+wB5VdXU1nxAXMeF/lxFzF7Bb+/u4C/AocP8U5D4Y+Er7us3APwBrOso93+fKfH8Px9M0yY9U1d3AncCRy5191NxV9VBVfQ340eB8piD3dVU1W2dvBnZOstNic9uwdSDJDE3n/Y1uk4zFHwP/DXii6yBj9Czg+8AH02zqfX+S3boOtRRV9V3gD4F7gPuAf6yqL3ebaqyeXlX3QVNUadZ8rDQ3Aa9qp0/kn5+Md9ZraRs2mg+STQOPbaKbL4nz5f4E8BDN7+M9wB9W1Q/pf+4bgOOT7JDkIOCI9rFOc2/xuTLf38Ncl0nbjw6zD5l7PtOU+wTguqp6hEXmtmFbZkl2Bz4JvKWq7u86z1IkeQWwuao2dJ1lzHag2Tzy3qo6jOZDZW23kZam3afieJrNIM+gWbPxK92m0ojeAJyWZAPN5phHBx9M8rPA/6uq2f2whrok1jKYL/eRwI9pfh8PAt6a5Fn0P/cFNB+w62m+sP5f4HE6zD3C58p8GTvJPobPw6nIneS5wLuA35wdmuNpW81tw7aMkuxI85/8F1X1qa7zjMELgFcl2QhcAhyT5CPdRhqLTcCmqppdA/oJmgZumv174O6q+n5VPQZ8Cvh3HWcap++1mxlmN5Ns7jjP2FXVbVX1kqo6gmYt2re2eMpJ/GTtGjS/x/sP3J/zkliTtkDu1wNfqqrH2k2LfwOsoee5q+rxqjq9qg6tquOBPYE7uso9z+fKfH8P810mbdmzj5h7Pr3P3e429Gng5Kqa/d1fVG4btmXSHgHyAeDWqnp313nGoarOrKr9q2qG5sPir6pq6tfaVNXfAd9JMnsx8RcDt3QYaRzuAY5Ksmv7u/himv0vVorLgFPa6VOAz3aYZSKS7NP+3A54O/BnA49tR7PZ7pLZsXbTzANJjmr/z0+mg3+XBXLfQ/MlL+0uB0cBt/U9d/s3tFs7fSzweFXd0kXuBT5X5vt7uAw4qd2P6iBgNbBuubMvIvec+p47yZ7A5cCZVfU3S849yhES3pZ0dMkLaVZ5fhO4vr39Ute5xrh8R7OyjhI9lGaTxzeBzwBP6zrTGJbpD4DbaPbN+TCwU9eZFrkcF9Ps9/QYzTfVNwI/RbMj+B3tz726zjmBZXwzzVFpfwucQ3vi8/b5RwNfn2M+a9r/728B5w2+puvcwO7Ax2l2xr4F+L0pyT0D3E7zhedK4Jkd5p7zc2Whvwfg99t8tzNwZOJyZl9k7o00B4Y82P4fHdz33DSN/kMDz70e2Gexub3SgSRJUs+5SVSSJKnnbNgkSZJ6zoZNkiSp52zYJEmSes6GTZIkqeds2DRRSR5sf84kqSTvHHhsVZLHkpzX3j8ryXeTXJ/kjiSfSnLwwPM3Jlk1plwzSV4/cH9NknPHMW9JGtSea+5rSV4+MPaaJF/qMpemiw2bltNdwCsG7p9Icw6mQe+p5gziq4GPAX+VZO/FvFl7Uen5zNCcaR2AqlpfVb+9mPeRpIVUc/6sNwHvTrJze+Lds4HTFjO/JNuPM5+mgw2bltPDwK1J1rT3XwtcOt+Tq+pjwJcZaKyA30uyrr396y1f066lOz/Jl4GL2jVp/yfJte1t9nJM5wA/367NOz3J0Uk+PzCPC5L87yR3Jfntgfn/9yS3JbkiycVJfndJ/yKStgnVXOP1c8AZwDuAjwC/n+SaJNclOR7+ae3/k2pWW6O+muSjwI1dLYe6s9AaCGkSLqG5NMrf0Vz4+V6aiz/P51rgOQP376+qI5OcTHPh5VfM8ZojgBdW1cNJdgWOraofJVlNc0bzNTQXc//dqnoFNMVwi3k8B3gRzUWfb0/yXuAQ4ATgMJq/nWuBlXbhe0mT8wc0deNR4PM0l/N7Q3sJo3VJrqS5DuVcNQvgSOB5VXV3B9nVMRs2LbcvAe8EvkezyXNrssX9iwd+vmee11xWVQ+30zsC5yU5lKZB/Okhc15eVY8AjyTZDDyd5rIkn52dd5LPDTkvSaKqHkryMZrLK70GeOXAWvqdgQNpvsTOV7PW2axtu2zYtKyq6tEkG4C3As8FXrmVlxxGc03Pf5rFPNODHhqYPp2mOTyEZheAHw0Z9ZGB6R/T/K1s2TxK0qieaG8BTqiq2wcfTHIW89eswdqmbYz7sKkLfwScUVU/WOhJSU4AXsJP1qpBs9/b7M+rh3ivfwHcV1VPAL8KzO6s+wDN5s5RfI3mG/HOSXYHjhvx9ZI06y+B/5okAEkOa8fnq1naxrmGTcuuqm7myUeHzjo9ya8AuwE3AcdU1fcHHt8pyTdovmy8boi3+1Pgk0lOBL7KT76hfhN4PMkNwIeA64bIfU2Sy4AbgG/TrPn7xyEySNKW3kmzH+4326ZtI80+ufPVLG3j0hxtLGkYSXavqgfbgxmuAk6tqmu7ziVJWtlcwyaN5vz2ZL47AxfarEmSloNr2CRJknrOgw4kSZJ6zoZNkiSp52zYJEmSes6GTZIkqeds2CRJknrOhk2SJKnn/j/yqyg2Gs91OgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(10, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(late_movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "\n",
    "# on my SECOND set of axis I want to do a distribution of \"Year\" column\n",
    "#ax[0].hist(movies['Years']) # This doesn't work because \"Years\" is NOT \"Year\"\n",
    "ax[1].hist(late_movies['Year']) # I've updated the column that is being histogrammed\n",
    "ax[1].set_xlabel('Year') # note this is called with a \"set_\"\n",
    "ax[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(10, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(early_movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "ax[0].set_xlim(1,10)\n",
    "\n",
    "# on my SECOND set of axis I want to do a distribution of \"Year\" column\n",
    "#ax[0].hist(movies['Years']) # This doesn't work because \"Years\" is NOT \"Year\"\n",
    "ax[1].hist(early_movies['Year']) # I've updated the column that is being histogrammed\n",
    "ax[1].set_xlabel('Year') # note this is called with a \"set_\"\n",
    "ax[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAACaCAYAAAAUwRQIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAVg0lEQVR4nO3de7SldX3f8feHi1y1SAYocnGwnWjRhttISDUJYvESUExZKNoEUk2IKW2UmJTB2EqWi7WgK9GERWOCioJGEO8oagRjQ03RYYaL3APCiAPEMboilyIX+faP5zlxO5xzZu9z9j7Ps8+8X2vtdZ7925fn88yc893f/VxTVUiSJKm/tuk6gCRJkuZnwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPWfDJkmS1HPbdR1gUlasWFErV67sOoakJbJ+/fp/rKo9us4xDtYvaeuzpRq2bBu2lStXsm7duq5jSFoiSb7ddYZxsX5JW58t1TA3iUqSJPWcDZskSVLP2bBJkiT13LLdh03qu5VrLp/4PDacfczE5yFJ02qSdXjc9dc1bJIkST1nwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPWfDJkmS1HM2bJIkST1nwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPWfDJkmS1HM2bJIkST1nwyZJktRzNmySJEk9Z8MmSZLUczZskiRJPWfDJkmS1HM2bJIkST1nwyZJktRzNmySJEk9Z8MmSZLUc9t1HUDS5Kxcc/lE33/D2cdM9P0lSQ3XsEmSJPXcUA1bkhdMOogkTYo1TNK0G3YN218kWZvkPyfZbZgXJLkgyaYkNw2M7Z7kiiR3tD+fOfDYGUnuTHJ7kpcPjB+W5Mb2sXOTZOilk6TGSDUsyX5Jvprk1iQ3J3lLO24Nk9SJofZhq6oXJ1kFvBFYl2Qt8MGqumKel30IOA+4aGBsDfCVqjo7yZr2/ulJDgROBJ4PPAu4MsnPVtWPgfcCpwBfB74AvAL44gjLKC3IpPf/0tJZQA17AnhbVV2b5OnA+iRXAL+BNUxSB4beh62q7gDeAZwO/DJwbpLbkvyHOZ5/FfCDzYaPAy5spy8EXjMwfklVPVpVdwN3Aocn2Rt4RlVdXVVF0/y9Bkka0Sg1rKrur6pr2+kHgVuBfbCGSerIsPuw/VyS99AUraOAV1XVv2mn3zPC/PaqqvuhKYjAnu34PsB3Bp63sR3bp53efFyShraYGpZkJXAI8A2sYZI6MuxpPc4D3ge8vaoemRmsqvuSvGMMOWbbp6PmGZ/9TZJTaDY9sP/++48hlqRlYkE1LMmuwCeBt1bVA/PsfrboGmb9kjSfYTeJ/grw0ZlCl2SbJDsDVNWHR5jfd9tNBLQ/N7XjG4H9Bp63L3BfO77vLOOzqqrzq2p1Va3eY489RoglaZkbuYYl2Z6mWfurqvpUOzyxGmb9kjSfYRu2K4GdBu7v3I6N6jLg5Hb6ZOCzA+MnJtkhyQHAKmBtu8nhwSRHtEdWnTTwGkka1kg1rK03HwBurap3DzxkDZPUiWE3ie5YVQ/N3Kmqh2a+nc4lycXAkcCKJBuBdwJnA5cmeRNwD3BC+343J7kUuIXm6KxT26OrAH6H5ojTnWiOrPLoKkmjGrWGvQj4deDGJNe3Y2/HGiapI8M2bA8nOXTmqKkkhwGPzPeCqnr9HA+9dI7nnwWcNcv4OsCTXkpajJFqWFV9jdn3PwNrmKQODNuwvRX4eJKZfS/2Bl43mUiSNHbWMElTbdgT516T5HnAc2m+dd5WVY9PNJkkjYk1TNK0G3YNG8ALgZXtaw5JQlVdNP9LJKk3rGGSptZQDVuSDwP/CrgemNmRduas3ZLUa9YwSdNu2DVsq4ED20urSNK0sYZJmmrDnoftJuBfTjKIJE2QNUzSVBt2DdsK4JYka4FHZwar6tUTSSVJ42UNkzTVhm3YzpxkCEmasDO7DiBJizHsaT3+NsmzgVVVdWV7hvBtJxtNksbDGiZp2g21D1uS3wI+AfxlO7QP8JlJhZKkcbKGSZp2wx50cCrNtfUeAKiqO4A9JxVKksbMGiZpqg3bsD1aVY/N3EmyHc05jCRpGljDJE21YRu2v03ydmCnJEcDHwc+N7lYkjRW1jBJU23Yhm0N8D3gRuC3gS8A75hUKEkaM2uYpKk27FGiTwLva2+SNFWsYZKm3bDXEr2bWfb3qKrnjD2RJI2ZNUzStBvlWqIzdgROAHYffxxJmghrmDSlVq65vOsIvTDUPmxV9f2B271V9afAURPOJkljYQ2TNO2G3SR66MDdbWi+rT59IokkacysYZKm3bCbRP9kYPoJYAPw2rGnkaTJsIZJmmrDHiX6kkkHkaRJsYZJmnbDbhL9vfker6p3jyeOJI2fNUzStBvlKNEXApe1918FXAV8ZxKhJGnMrGGSptqwDdsK4NCqehAgyZnAx6vqNycVTJLGyBomaaoNe2mq/YHHBu4/BqwcexpJmgxrmKSpNuwatg8Da5N8muZs4b8KXDSxVJI0XtYwSVNt2KNEz0ryReAX26H/VFXXTS6WJI2PNUzStBt2kyjAzsADVfVnwMYkB0wokyRNgjVM0tQaqmFL8k7gdOCMdmh74CMLnWmSDUluTHJ9knXt2O5JrkhyR/vzmQPPPyPJnUluT/Lyhc5X0tZpITUsyQVJNiW5aWBs5DqV5LC23t2Z5NwkGe/SSdoaDLuG7VeBVwMPA1TVfSz+si4vqaqDq2rmosxrgK9U1SrgK+19khwInAg8H3gF8OdJtl3kvCVtXRZSwz5EU3MGLaROvRc4BVjV3jZ/T0naomEbtseqqmh21iXJLhPIchxwYTt9IfCagfFLqurRqrobuBM4fALzl7R8jVzDquoq4AebDY9Up5LsDTyjqq5u53/RwGskaWjDNmyXJvlLYLckvwVcCbxvEfMt4MtJ1ic5pR3bq6ruB2h/7tmO78NPn9xyYzsmScMaVw0btU7t005vPi5JI9niUaLt/hYfA54HPAA8F/gfVXXFIub7oqq6L8mewBVJbpsvwixjNUfWU2g2PbD//vsvIp6k5WJCNewps5llrOYZf+obWL8kzWOLDVtVVZLPVNVhwFgKXLv/CFW1qT0v0uHAd5PsXVX3t5sRNrVP3wjsN/DyfYH75njf84HzAVavXj1rUZS0dRlzDRu1Tm1spzcfny2n9UvSnIbdJPr1JC8cxwyT7JLk6TPTwMuAm2iu8Xdy+7STgc+205cBJybZoT0MfxWwdhxZJG01xlXDRqpT7WbTB5Mc0a7pO2ngNZI0tGGvdPAS4M1JNtAcZRWaL64/t4B57gV8uj2yfTvgo1X1pSTX0Oxn8ibgHuAEmpncnORS4BbgCeDUqvrxAuYraes1cg1LcjFwJLAiyUbgncDZjF6nfofmiNOdgC+2N0kaybwNW5L9q+oe4JXjmmFV3QUcNMv494GXzvGas4CzxpVB0tZhMTWsql4/x0Mj1amqWge8YNT5S9KgLa1h+wxwaFV9O8knq+r4pQglSWNiDZO0LGypYRs8wuk5kwwijWrlmsu7jqD+s4ZJWha2dNBBzTEtSdPAGiZpWdjSGraDkjxA8y11p3YafrLD7jMmmk6SFscaJmlZmLdhqyqv2SlpalnDJC0Xw56HTZIkSR2xYZMkSeo5GzZJkqSes2GTJEnqORs2SZKknrNhkyRJ6rlhL/4uSZI0K688M3muYZMkSeo5GzZJkqSes2GTJEnqORs2SZKknrNhkyRJ6jkbNkmSpJ6zYZMkSeo5GzZJkqSe88S5mhhPpChJ0ni4hk2SJKnnXMMmSdJWwK0e0801bJIkST1nwyZJktRzbhLdSrlqXOMw6d+jDWcfM9H3l/rG2qy5uIZNkiSp52zYJEmSem5qNokmeQXwZ8C2wPur6uyOI0nSUKxfy4ubLdWFqWjYkmwL/C/gaGAjcE2Sy6rqlm6TTY4FQVoetsb61QfWUC03U9GwAYcDd1bVXQBJLgGOAyx4kvrO+jUHmyppeNPSsO0DfGfg/kbg5+d7wY33/tBiIKkPRq5fo7LWScvftDRsmWWsnvKk5BTglPbuQ98+59jbJ5rqp60A/nEJ57fUlvvywfJfxqlbvpwz0tOfO6EYi7Wg+pVklPo1df+3LXMvLXMvoZwzcu5nz/fgtDRsG4H9Bu7vC9y3+ZOq6nzg/KUKNSjJuqpa3cW8l8JyXz5Y/su4NSxf1xnmMPH6Na3/t+ZeWuZeWuPOPS2n9bgGWJXkgCRPA04ELus4kyQNw/oladGmYg1bVT2R5L8Af01zWPwFVXVzx7EkaYusX5LGYSoaNoCq+gLwha5zzKOTTbFLaLkvHyz/ZXT5OrIE9au3y74F5l5a5l5aY82dqqfs+ypJkqQemZZ92CRJkrZaNmyLkGS/JF9NcmuSm5O8petMk5Bk2yTXJfl811kmIcluST6R5Lb2//IXus40TklOa38/b0pycZIdu860WEkuSLIpyU0DY7snuSLJHe3PZ3aZcTHmWL6Dklyd5MYkn0vyjHb8Pya5fuD2ZJKD28cOa59/Z5Jzk8x2ipGucm+f5MJ2/NYkZwy8ps+5n5bkg+34DUmO7DD3rJ9B8/0tJDmjzXd7kpd3kX3U3El+pn3+Q0nO2+y9+pz76CTr23zrkxy1qNxV5W2BN2Bv4NB2+unA3wMHdp1rAsv5e8BHgc93nWVCy3ch8Jvt9NOA3brONMZl2we4G9ipvX8p8Btd5xrDcv0ScChw08DY/wTWtNNrgHO6zjnm5bsG+OV2+o3Au2Z53b8F7hq4vxb4BZpzwX0ReGVfcgNvAC5pp3cGNgArpyD3qcAH2+k9gfXANh3lnvUzaK6/hfaxG4AdgAOAbwHbLnX2BeTeBXgx8GbgvM3eq8+5DwGe1U6/ALh3Mbldw7YIVXV/VV3bTj8I3ErzAblsJNkXOAZ4f9dZJqH91vxLwAcAquqxqvqnblON3XbATkm2o/lgfMo5wKZNVV0F/GCz4eNomm/an69Z0lBjNMfyPRe4qp2+Ajh+lpe+HrgYIMnewDOq6upqPiEuYsL/JiPmLmCX9vdyJ+Ax4IEpyH0g8JX2dZuAfwJWd5R7rs+guf4WjqNpkh+tqruBO4HDlzr7qLmr6uGq+hrwo8H3mYLc11XVTL29GdgxyQ4LzW3DNiZJVtJ009/oNsnY/Snw34Anuw4yIc8Bvgd8MM1m3/cn2aXrUONSVfcCfwzcA9wP/LCqvtxtqonZq6ruh6aw0qz9WE5uAl7dTp/AT5+Md8braBs2mg+SjQOPbaSbL5Rz5f4E8DDN7+U9wB9X1Q/of+4bgOOSbJfkAOCw9rFOc2/2GTTX38Jsl0nbhw6zD5l7LtOU+3jguqp6lAXmtmEbgyS7Ap8E3lpVD3SdZ1ySHAtsqqr1XWeZoO1oNoW8t6oOofkAWdNtpPFp96U4jmbzx7No1mj8WreptEBvBE5Nsp5mc8xjgw8m+Xng/1XVzH5YQ10SawnMlftw4Mc0v5cHAG9L8hz6n/sCmg/YdTRfaP8v8AQd5h7hM2iujJ1kH8Nn51TkTvJ84Bzgt2eGZnnaFnPbsC1Sku1p/uP+qqo+1XWeMXsR8OokG4BLgKOSfKTbSGO3EdhYVTNrRj9B08AtF/8euLuqvldVjwOfAv5dx5km5bvtpoaZTSWbOs4zVlV1W1W9rKoOo1mL9q3NnnIiP1m7Bs3v9r4D92e9JNakzZP7DcCXqurxdtPi3wGr6Xnuqnqiqk6rqoOr6jhgN+COrnLP8Rk019/CXJdJW/LsI+aeS+9zt7sVfRo4qapmfvcXlNuGbRHaozo+ANxaVe/uOs+4VdUZVbVvVa2k+TD4m6paVmtnquofgO8kmblw+EuBWzqMNG73AEck2bn9fX0pzX4Xy9FlwMnt9MnAZzvMMnZJ9mx/bgO8A/iLgce2odlsd8nMWLtp5sEkR7T/9yfRwb/JPLnvofkSmHY3hCOA2/qeu/1b2qWdPhp4oqpu6SL3PJ9Bc/0tXAac2O5HdQCwCli71NkXkHtWfc+dZDfgcuCMqvq7Rece5QgJb085YuTFNKsxvwlc395+petcE1rWI1m+R4keTLN545vAZ4Bndp1pzMv3R8BtNPvkfBjYoetMY1imi2n2fXqc5tvqm4CfodkZ/I725+5d5xzz8r2F5qi0vwfOpj3xefv8I4Gvz/I+q9v/928B5w2+puvcwK7Ax2l2xr4F+IMpyb0SuJ3mi8+VwLM7zD3rZ9B8fwvAH7b5bmfgyMSlzL7A3BtoDgx5qP0/OrDvuWka/YcHnns9sOdCc3ulA0mSpJ5zk6gkSVLP2bBJkiT1nA2bJElSz9mwSZIk9ZwNmyRJUs/ZsGmikjzU/lyZpJK8a+CxFUkeT3Jee//MJPcmuT7JHUk+leTAgedvSLJiTLlWJnnDwP3VSc4dx3tL0qD2XHNfS/LKgbHXJvlSl7k0XWzYtJTuAo4duH8CzTmYBr2nmjOIrwI+BvxNkj0WMrP2otJzWUlzpnUAqmpdVf3uQuYjSfOp5vxZbwbenWTH9sS7ZwGnLuT9kmw7znyaDjZsWkqPALcmWd3efx1w6VxPrqqPAV9moLEC/iDJ2vb2rzd/TbuW7vwkXwYuatek/Z8k17a3mcsynQ38Yrs277QkRyb5/MB7XJDkfye5K8nvDrz/f09yW5Irklyc5PcX9S8iaatQzTVePwecDrwT+Ajwh0muSXJdkuPgn9f+P6VmtTXqq0k+CtzY1XKoO/OtgZAm4RKaS6P8A82Fn++jufjzXK4Fnjdw/4GqOjzJSTQXXj52ltccBry4qh5JsjNwdFX9KMkqmjOar6a5wPvvV9Wx0BTDzd7jecBLaC76fHuS9wIHAccDh9D87VwLrB96ySVt7f6Ipm48Bnye5nJ/b2wvYbQ2yZU016GcrWYBHA68oKru7iC7OmbDpqX2JeBdwHdpNnluSTa7f/HAz/fM8ZrLquqRdnp74LwkB9M0iD87ZM7Lq+pR4NEkm4C9aC5L8tmZ907yuSHfS5KoqoeTfIzm8kqvBV41sJZ+R2B/mi+xc9WstTZrWy8bNi2pqnosyXrgbcDzgVdt4SWH0Fzn85/fYo7pQQ8PTJ9G0xweRLMLwI+GjProwPSPaf5WNm8eJWlUT7a3AMdX1e2DDyY5k7lr1mBt01bGfdjUhT8BTq+q78/3pCTHAy/jJ2vVoNnvbebn1UPM618A91fVk8CvAzM76z5Is7lzFF+j+Ua8Y5JdgWNGfL0kzfhr4L8mCUCSQ9rxuWqWtnKuYdOSq6qbeerRoTNOS/JrwC7ATcBRVfW9gcd3SPINmi8brx9idn8OfDLJCcBX+ck31G8CTyS5AfgQcN0Qua9JchlwA/BtmjV/PxwigyRt7l00++F+s23aNtDskztXzdJWLs3RxpKGkWTXqnqoPZjhKuCUqrq261ySpOXNNWzSaM5vT+a7I3ChzZokaSm4hk2SJKnnPOhAkiSp52zYJEmSes6GTZIkqeds2CRJknrOhk2SJKnnbNgkSZJ67v8DrDQsknSLxnIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, finally!  let's do some side-by-side plots\n",
    "fig, ax = plt.subplots(nrows = 1,ncols = 2, figsize=(10, 2)) # figsize(width, height)\n",
    "\n",
    "# plotting on my FIRST set of axes by indexing my \"ax\" ARRAY with its first index\n",
    "ax[0].hist(late_movies['IMDb']) # ax object is NOT something I can plot with\n",
    "ax[0].set_xlabel('IMDb rating') # note this is called with a \"set_\"\n",
    "ax[0].set_ylabel('Frequency')\n",
    "ax[0].set_xlim(1,10)\n",
    "\n",
    "# on my SECOND set of axis I want to do a distribution of \"Year\" column\n",
    "#ax[0].hist(movies['Years']) # This doesn't work because \"Years\" is NOT \"Year\"\n",
    "ax[1].hist(late_movies['Year']) # I've updated the column that is being histogrammed\n",
    "ax[1].set_xlabel('Year') # note this is called with a \"set_\"\n",
    "ax[1].set_ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.113258426966292"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can actually do stats calcualtions by hand\n",
    "movies['IMDb'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.54659090909091"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "early_movies['IMDb'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.113258426966292"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['IMDb'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.641418641953306"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['IMDb'].sum()/len(movies['IMDb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(list(movies['IMDb']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}