Final_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "import statistics\n",
    "import scipy\n",
    "import scipy.stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from pandasql import sqldf\n",
    "pysqldf = lambda q: sqldf(q, globals())\n",
    "\n",
    "import data_cleaning_greg  as dc\n",
    "import hypothesis_tests as ht\n",
    "import visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in the data and begin cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below dataset is based on the last five years' worth of films released in the United States. It is a combination of data scraped from the International Movie Database (IMDb) website, csv files found on IMDb and the OMDb API. \n",
    "The scraped data was used for the gross profit of each film; by using budget and revenue we can estimate the profit .\n",
    "Full_OMDB_Table.csv contains a list of movies (country USA, language English) from November 2014 to October 2019 taken from IMDB. Budget and revenue info are from IMDB as well.  Ratings (IMDB, Rotten Tomatoes and Metascore), released date, actors and production info are from OMDB, unoffical IMDB API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2015 entries, 0 to 2014\n",
      "Data columns (total 22 columns):\n",
      "tconst                      2015 non-null object\n",
      "titleType                   2015 non-null object\n",
      "primaryTitle                2015 non-null object\n",
      "originalTitle               2015 non-null object\n",
      "isAdult                     2015 non-null int64\n",
      "startYear                   2015 non-null int64\n",
      "endYear                     2015 non-null object\n",
      "runtimeMinutes              2015 non-null int64\n",
      "genres                      2015 non-null object\n",
      "title                       2015 non-null object\n",
      "check                       2015 non-null int64\n",
      "averageRating               2015 non-null float64\n",
      "numVotes                    2015 non-null int64\n",
      "budget_LCY                  1004 non-null object\n",
      "gross_USA_USD               1085 non-null float64\n",
      "cumm_worldwide_gross_USD    1386 non-null float64\n",
      "Released_Date               2005 non-null object\n",
      "Released_Month              2005 non-null float64\n",
      "Rotten_Tomatoes             1522 non-null float64\n",
      "Metascore                   1461 non-null float64\n",
      "Actors                      2015 non-null object\n",
      "Production                  1678 non-null object\n",
      "dtypes: float64(6), int64(5), object(11)\n",
      "memory usage: 346.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Read in data as pandas dataframe\n",
    "\n",
    "df=pd.read_csv(\"2016_2019_USmovies_API.csv\")\n",
    "df.head()\n",
    "\n",
    "# Check what our column titles are so we can rename/remove them\n",
    "df.columns\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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_ID</th>\n",
       "      <th>Release_Year</th>\n",
       "      <th>Runtime(Minutes)</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Title</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Number_of_Votes</th>\n",
       "      <th>Budget(LCY)</th>\n",
       "      <th>USA_Gross($)</th>\n",
       "      <th>Worldwide_Gross($)</th>\n",
       "      <th>Released_Date</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Rotten_Tomatoes</th>\n",
       "      <th>Metascore</th>\n",
       "      <th>Actors</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>2018</td>\n",
       "      <td>122</td>\n",
       "      <td>Drama</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>02 Nov 2018</td>\n",
       "      <td>11.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>John Huston, Oja Kodar, Peter Bogdanovich, Sus...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0339736</td>\n",
       "      <td>2017</td>\n",
       "      <td>98</td>\n",
       "      <td>Horror</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2572</td>\n",
       "      <td>$6000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30 Aug 2017</td>\n",
       "      <td>8.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sean Patrick Flanery, Dina Meyer, Frederick Ko...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0360556</td>\n",
       "      <td>2018</td>\n",
       "      <td>100</td>\n",
       "      <td>Drama,Sci-Fi,Thriller</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>4.9</td>\n",
       "      <td>15295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20 May 2018</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michael B. Jordan, Aaron Davis, Cindy Katz, Mi...</td>\n",
       "      <td>HBO Films</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0365545</td>\n",
       "      <td>2018</td>\n",
       "      <td>98</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21 Sep 2018</td>\n",
       "      <td>9.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Sanaa Lathan, Ricky Whittle, Lyriq Bent, Lynn ...</td>\n",
       "      <td>Netflix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0369610</td>\n",
       "      <td>2015</td>\n",
       "      <td>124</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>7.0</td>\n",
       "      <td>549806</td>\n",
       "      <td>$150000000</td>\n",
       "      <td>652.27</td>\n",
       "      <td>1670.4</td>\n",
       "      <td>12 Jun 2015</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...</td>\n",
       "      <td>Universal Pictures</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title_ID  Release_Year  Runtime(Minutes)                    Genre  \\\n",
       "0  tt0069049          2018               122                    Drama   \n",
       "1  tt0339736          2017                98                   Horror   \n",
       "2  tt0360556          2018               100    Drama,Sci-Fi,Thriller   \n",
       "3  tt0365545          2018                98     Comedy,Drama,Romance   \n",
       "4  tt0369610          2015               124  Action,Adventure,Sci-Fi   \n",
       "\n",
       "                        Title  Rating  Number_of_Votes Budget(LCY)  \\\n",
       "0  The Other Side of the Wind     6.9             4989         NaN   \n",
       "1             The Evil Within     5.5             2572    $6000000   \n",
       "2              Fahrenheit 451     4.9            15295         NaN   \n",
       "3          Nappily Ever After     6.4             6718         NaN   \n",
       "4              Jurassic World     7.0           549806  $150000000   \n",
       "\n",
       "   USA_Gross($)  Worldwide_Gross($) Released_Date  Released_Month  \\\n",
       "0           NaN                 NaN   02 Nov 2018            11.0   \n",
       "1           NaN                 NaN   30 Aug 2017             8.0   \n",
       "2           NaN                 NaN   20 May 2018             5.0   \n",
       "3           NaN                 NaN   21 Sep 2018             9.0   \n",
       "4        652.27              1670.4   12 Jun 2015             6.0   \n",
       "\n",
       "   Rotten_Tomatoes  Metascore  \\\n",
       "0             83.0       78.0   \n",
       "1            100.0        NaN   \n",
       "2             32.0        NaN   \n",
       "3             71.0       63.0   \n",
       "4             72.0       59.0   \n",
       "\n",
       "                                              Actors          Production  \n",
       "0  John Huston, Oja Kodar, Peter Bogdanovich, Sus...             Netflix  \n",
       "1  Sean Patrick Flanery, Dina Meyer, Frederick Ko...                 NaN  \n",
       "2  Michael B. Jordan, Aaron Davis, Cindy Katz, Mi...           HBO Films  \n",
       "3  Sanaa Lathan, Ricky Whittle, Lyriq Bent, Lynn ...             Netflix  \n",
       "4  Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...  Universal Pictures  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's clean the data and rename columns so we know what we're looking \n",
    "\n",
    "columns=[\"tconst\",\"title\",\"isAdult\",\"startYear\",\"runtimeMinutes\",\"check\",\"averageRating\",\"genres\",\"numVotes\",\n",
    "         \"budget_LCY\",\"gross_USA_USD\",\"cumm_worldwide_gross_USD\",]\n",
    "names=[\"Title_ID\",\"Title\",\"Adult\",\"Release_Year\",\"Runtime(Minutes)\",\"Check\",\"Rating\",\"Genre\",\"Number_of_Votes\",\n",
    "      \"Budget(LCY)\",\"USA_Gross($)\",\"Worldwide_Gross($)\"]\n",
    "columns_to_drop=[\"titleType\",\"primaryTitle\",\"originalTitle\",\"endYear\",\"Adult\",\"Check\"]\n",
    "\n",
    "dc.full_clean(df, columns, names, columns_to_drop)\n",
    "\n",
    "# Save as csv\n",
    "# df.to_csv(\"Full_OMDB_Table.csv\",index=False)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Title_ID', 'Release_Year', 'Runtime(Minutes)', 'Genre', 'Title',\n",
       "       'Rating', 'Number_of_Votes', 'Budget(LCY)', 'USA_Gross($)',\n",
       "       'Worldwide_Gross($)', 'Released_Date', 'Released_Month',\n",
       "       'Rotten_Tomatoes', 'Metascore', 'Actors', 'Production'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Release_Year  Runtime(Minutes)       Rating  Number_of_Votes  \\\n",
      "count   2015.000000       2015.000000  2015.000000      2015.000000   \n",
      "mean    2016.715633        101.181141     5.920447     42005.702233   \n",
      "std        1.355422         15.783114     1.051272     91376.330598   \n",
      "min     2014.000000         46.000000     1.400000       984.000000   \n",
      "25%     2016.000000         90.000000     5.300000      2377.500000   \n",
      "50%     2017.000000         98.000000     6.000000      7601.000000   \n",
      "75%     2018.000000        109.000000     6.600000     36685.500000   \n",
      "max     2019.000000        209.000000    10.000000    844981.000000   \n",
      "\n",
      "       USA_Gross($)  Worldwide_Gross($)  Released_Month  Rotten_Tomatoes  \\\n",
      "count   1085.000000         1386.000000     2005.000000      1522.000000   \n",
      "mean      46.304341           91.579856        6.684788        55.818660   \n",
      "std       91.560188          225.040751        3.378692        28.335085   \n",
      "min        0.000000            0.000000        1.000000         0.000000   \n",
      "25%        0.650000            0.260000        4.000000        31.000000   \n",
      "50%       11.960000            7.230000        7.000000        59.000000   \n",
      "75%       49.950000           72.265000       10.000000        82.000000   \n",
      "max      936.660000         2797.800000       12.000000       100.000000   \n",
      "\n",
      "         Metascore  \n",
      "count  1461.000000  \n",
      "mean     53.517454  \n",
      "std      17.309092  \n",
      "min       3.000000  \n",
      "25%      41.000000  \n",
      "50%      54.000000  \n",
      "75%      66.000000  \n",
      "max      99.000000  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2015 entries, 0 to 2014\n",
      "Data columns (total 16 columns):\n",
      "Title_ID              2015 non-null object\n",
      "Release_Year          2015 non-null int64\n",
      "Runtime(Minutes)      2015 non-null int64\n",
      "Genre                 2015 non-null object\n",
      "Title                 2015 non-null object\n",
      "Rating                2015 non-null float64\n",
      "Number_of_Votes       2015 non-null int64\n",
      "Budget(LCY)           1004 non-null object\n",
      "USA_Gross($)          1085 non-null float64\n",
      "Worldwide_Gross($)    1386 non-null float64\n",
      "Released_Date         2005 non-null object\n",
      "Released_Month        2005 non-null float64\n",
      "Rotten_Tomatoes       1522 non-null float64\n",
      "Metascore             1461 non-null float64\n",
      "Actors                2015 non-null object\n",
      "Production            1678 non-null object\n",
      "dtypes: float64(6), int64(3), object(7)\n",
      "memory usage: 252.0+ KB\n",
      "None\n",
      "(2015, 16)\n"
     ]
    }
   ],
   "source": [
    "# Now to check our data makes sense and we have no nulls\n",
    "print(df.describe())\n",
    "print(df.info())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subset our data to what we're interested in.\n",
    "Since we will be looking at genres we want to see what different genres we have and split these into different categories. We will also be interested in the production company -> IE Netflix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Title_ID', 'Release_Year', 'Runtime(Minutes)', 'Genre', 'Title',\n",
       "       'Rating', 'Number_of_Votes', 'Budget(LCY)', 'USA_Gross($)',\n",
       "       'Worldwide_Gross($)', 'Released_Date', 'Released_Month',\n",
       "       'Rotten_Tomatoes', 'Metascore', 'Actors', 'Production'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns which contain nulls as they aren't relevant to ratings and also average our 3 ratings\n",
    "# We will drop rotten tomatoes scores as the user base is far smaller than imdb and will skew our results.\n",
    "# Metascore is also not comparable as these are not user based scores they are weighted critic reviews\n",
    "# We will compare the scores for each of these platforms in another section\n",
    "\n",
    "df_ratings=df.drop(columns=[\"Released_Month\",\"Released_Date\",\"Rotten_Tomatoes\",\"Metascore\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are  112 Drama movies in this dataset\n",
      "There are  101 Comedy movies in this dataset\n",
      "There are  64  Horror movies in this dataset\n",
      "There are  7   Action movies in this dataset\n",
      "There are  4   Sci-Fi movies in this dataset\n",
      "There are  3   Romance movies in this dataset\n",
      "There are  0   Adventure movies in this dataset\n"
     ]
    }
   ],
   "source": [
    "# Now split into separate databases based on genre\n",
    "\n",
    "df_comedy=df_ratings.loc[df_ratings[\"Genre\"]==\"Comedy\",:]\n",
    "df_drama=df_ratings.loc[df_ratings[\"Genre\"]==\"Drama\",:]\n",
    "df_horror=df_ratings.loc[df_ratings[\"Genre\"]==\"Horror\",:]\n",
    "df_scifi=df_ratings.loc[df_ratings[\"Genre\"]==\"Sci-Fi\",:]\n",
    "df_action=df_ratings.loc[df_ratings[\"Genre\"]==\"Action\",:]\n",
    "df_romance=df_ratings.loc[df_ratings[\"Genre\"]==\"Romance\",:]\n",
    "df_adventure=df_ratings.loc[df_ratings[\"Genre\"]==\"Adventure\",:]\n",
    "\n",
    "\n",
    "# Print shape of dataframe so we know the sample sizes\n",
    "\n",
    "print(\"There are \",len(df_drama),\"Drama movies in this dataset\")\n",
    "print(\"There are \",len(df_comedy),\"Comedy movies in this dataset\")\n",
    "print(\"There are \",len(df_horror),\" Horror movies in this dataset\")\n",
    "print(\"There are \",len(df_action),\"  Action movies in this dataset\")\n",
    "print(\"There are \",len(df_scifi),\"  Sci-Fi movies in this dataset\")\n",
    "print(\"There are \",len(df_romance),\"  Romance movies in this dataset\")\n",
    "print(\"There are \",len(df_adventure),\"  Adventure movies in this dataset\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we need to plot our data to check for normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a243bad30>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Remove our sample set from the population so it doesn't skew our mean and plot against each other.\n",
    "\n",
    "df_no_drama=df_ratings.loc[df_ratings[\"Genre\"]!=\"Drama\",:]\n",
    "\n",
    "sns.distplot(df_no_drama.Rating)\n",
    "sns.distplot(df_drama.Rating)\n",
    "\n",
    "# sns.distplot(df_comedy.Rating)\n",
    "# sns.distplot(df_horror.Rating)\n",
    "# sns.distplot(df_scifi.Rating)\n",
    "# sns.distplot(df_action.Rating)\n",
    "# sns.distplot(df_romance.Rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the population the standard deviation is: 1.055  with a median value of: 6.0  and a mean value of: 5.92\n",
      "For Drama the standard deviation is: 0.917  with a median value of: 6.4  and a mean value of: 6.292\n"
     ]
    }
   ],
   "source": [
    "print(\"For the population the standard deviation is:\",round(df_no_drama.Rating.std(),3),\" with a median value of:\",round(df_no_drama.Rating.median(),3),\" and a mean value of:\",round(df.Rating.mean(),3))\n",
    "print(\"For Drama the standard deviation is:\",round(df_drama.Rating.std(),3),\" with a median value of:\",round(df_drama.Rating.median(),3),\" and a mean value of:\",round(df_drama.Rating.mean(),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The drama movies sample set is not normal given:  NormaltestResult(statistic=15.285855141424646, pvalue=0.00047942285126625054) \n",
      " It has  112  values.\n",
      "The rest of the data is not normal given:  NormaltestResult(statistic=58.066922240332374, pvalue=2.459959997803095e-13) \n",
      " It has  1903  values.\n"
     ]
    }
   ],
   "source": [
    "print(\"The drama movies sample set is not normal given: \",st.normaltest(df_drama.Rating),\"\\n It has \",len(df_drama),\" values.\")\n",
    "print(\"The rest of the data is not normal given: \",st.normaltest(df_no_drama.Rating),\"\\n It has \",len(df_no_drama),\" values.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improve our data set such that it is normal and we can perform our tests!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can we perform an ANOVA on our data for 3 main title types to see if statistically different?\n",
    "# sample sizes vary between all genres so choosing the top 3 and will take n=50 random sample\n",
    "\n",
    "comedy=df_comedy.Rating.sample(n=50)\n",
    "drama=df_drama.Rating.sample(n=50)\n",
    "horror=df_horror.Rating.sample(n=50)\n",
    "\n",
    "# Concat tables together to plot boxplots on same graph\n",
    "df_cdh=df_ratings.loc[((df_ratings[\"Genre\"]==\"Comedy\")|(df_ratings[\"Genre\"]==\"Drama\")|(df_ratings[\"Genre\"]==\"Horror\")),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting our normalised distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a245030f0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(comedy)   # Blue\n",
    "sns.distplot(drama)    # Green\n",
    "sns.distplot(horror)   # Red"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for normality in our concatenated table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=6.05835527266188, pvalue=0.04835538749348774)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_cdh.Rating)\n",
    "print(st.normaltest(df_cdh.Rating))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not normal so will normalise and re-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ht' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-41601413eb87>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcdh_normal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mht\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_sample_distribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_cdh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRating\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcdh_normal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormaltest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcdh_normal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ht' is not defined"
     ]
    }
   ],
   "source": [
    "cdh_normal=ht.create_sample_distribution(df_cdh.Rating,300,n=15)\n",
    "sns.distplot(cdh_normal)\n",
    "print(st.normaltest(cdh_normal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=0.43102232230149196, pvalue=0.8061292728775716)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "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": [
    "drama_normal=ht.create_sample_distribution(df_drama.Rating,300,n=15)\n",
    "sns.distplot(drama_normal)\n",
    "print(st.normaltest(drama_normal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=1.02446147076803, pvalue=0.599157525053464)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "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": [
    "no_drama_normal=ht.create_sample_distribution(df_no_drama.Rating,300,n=15)\n",
    "sns.distplot(no_drama_normal)\n",
    "print(st.normaltest(no_drama_normal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the population the standard deviation is: 0.261  with a median value of: 5.927  and a mean value of: 5.909\n",
      "For Drama the standard deviation is: 0.235  with a median value of: 6.3  and a mean value of: 6.3\n"
     ]
    }
   ],
   "source": [
    "print(\"For the population the standard deviation is:\",round(statistics.pstdev(no_drama_normal),3),\" with a median value of:\",round(statistics.median(no_drama_normal),3),\" and a mean value of:\",round(statistics.mean(no_drama_normal),3))\n",
    "\n",
    "print(\"For Drama the standard deviation is:\",round(statistics.stdev(drama_normal),3),\" with a median value of:\",round(statistics.median(drama_normal),3),\" and a mean value of:\",round(statistics.mean(drama_normal),3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does the rating of each genre differ from each other?\n",
    "### What is the best genre to choose to maximise rating?\n",
    "\n",
    "In this section we will perform a simple one-way ANOVA just to see if our main 3 genres difference in rankings is statistically significant. From this and the accompanying visualization, we will then see if Drama (our highest ranking genre) garners a higher rating than the average movie (IE compared to the population of all genres other than Drama). The reason for this approach is so we can see which genre would be best to make a movie for.\n",
    "\n",
    "Our hypothesis is that there will be a signifcant difference between genres and that drama will be the best performing, such that the rating of drama films is higher than the average rating for the population.\n",
    "Our Null is that there is no difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing a simple one-way ANOVA for our 3 main genres...\n",
    "The aim of this is to see whether the difference in ratings between genres is signifcantly different.\n",
    "If so, we will we be interested in to see the how drama performs against the population. We will not perform ad-hoc comparisons between genres as we are not interested in which specific combinations are signifcant but rather just interested in seeing if any observed difference is real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24856208>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the different sample sizes to observe the variance and mean values\n",
    "\n",
    "sns.boxplot(data=df_cdh,y=\"Rating\",x=\"Genre\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'We reject the null as our p-value (1.2067323535983743e-08) is less than our alpha (0.05) so we can say this result is signifcant.'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now perform a on-way ANOVA test for these random samples\n",
    "\n",
    "anova=scipy.stats.f_oneway(comedy,drama,horror)     # Very small P-value and large F -> signifcant difference between genres\n",
    "\n",
    "ht.significance(anova[1],alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the above genres are statistically different from each other, so genres definitely affect the rating. As drama has the highest rating, we now want to know if it statistically different from the population, thereby saying drama films do garner a better rating, and can advise which is the genre most likely to be a success."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One sample z-test for Drama movies against the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "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": [
    "a = sns.distplot(no_drama_normal,label=\"Population\").set_title(\"Drama Movies vs. Population\").get_figure().savefig(\"Drama_vs_Popluation.png\")\n",
    "b = sns.distplot(drama_normal,label=\"Drama\")\n",
    "plt.xlabel(\"Film Rating\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The z-statistic is 25.968, which corresponds to a p-value of 0.0.\n",
      "The effect size, cohen's d = 1.575.\n",
      "From this we Reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "alpha =  0.05                                   # The alpha value - likelihood to avoid a Type 1 error\n",
    "\n",
    "ht.z_test(drama_normal, no_drama_normal, alpha)               # Using the z-test from our library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore the difference in rating between Drama and the population is significant with a large effect size!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the difference\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "plt.fill_between(x=np.arange(-5,3.92,0.01),\n",
    "                 y1= st.norm.pdf(np.arange(-5,3.92,0.01)),\n",
    "                 facecolor='green',\n",
    "                 alpha=0.35,\n",
    "                 label= 'Area below z-statistic'\n",
    "                 )\n",
    "\n",
    "plt.fill_between(x=np.arange(3.92,5,0.01), \n",
    "                 y1= st.norm.pdf(np.arange(3.92,5,0.01)),\n",
    "                 facecolor='blue',\n",
    "                 alpha=0.35, \n",
    "                 label= 'Area above z-statistic')\n",
    "plt.legend()\n",
    "plt.title ('z-statistic = 3.92');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do Netflix films compare to the population?\n",
    "In this section we will use a Welch's T-Test as our variance in the Netflix subset and population do not have equal variance (as are not uniform) so this test will be more appropriate without having to normalise our dataset as we did previously. \n",
    "\n",
    "Our hypothesis is that Netflix films will perform better or worse than the population - IE they will differ in rating.\n",
    "Our null is that there is no difference between Netflix films and the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Netflix has  122  films in total.\n",
      "Netflix has  10   Drama films.\n"
     ]
    }
   ],
   "source": [
    "# How many films does Netflix have here and how many are drama?\n",
    "print(\"Netflix has \",len(df_ratings.loc[df_ratings[\"Production\"]==\"Netflix\",:]),\" films in total.\")\n",
    "print(\"Netflix has \",len(df_ratings.loc[(df_ratings[\"Production\"]==\"Netflix\")&(df_ratings[\"Genre\"]==\"Drama\"),:]),\"  Drama films.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2015 entries, 0 to 2014\n",
      "Data columns (total 12 columns):\n",
      "Title_ID              2015 non-null object\n",
      "Release_Year          2015 non-null int64\n",
      "Runtime(Minutes)      2015 non-null int64\n",
      "Genre                 2015 non-null object\n",
      "Title                 2015 non-null object\n",
      "Rating                2015 non-null float64\n",
      "Number_of_Votes       2015 non-null int64\n",
      "Budget(LCY)           1004 non-null object\n",
      "USA_Gross($)          1085 non-null float64\n",
      "Worldwide_Gross($)    1386 non-null float64\n",
      "Actors                2015 non-null object\n",
      "Production            1678 non-null object\n",
      "dtypes: float64(3), int64(3), object(6)\n",
      "memory usage: 189.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Title_ID', 'Release_Year', 'Runtime(Minutes)', 'Genre', 'Title',\n",
       "       'Rating', 'Number_of_Votes', 'Budget(LCY)', 'USA_Gross($)',\n",
       "       'Worldwide_Gross($)', 'Actors', 'Production'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.info()\n",
    "df_ratings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_netflix = df_ratings.loc[df_ratings[\"Production\"]==\"Netflix\",:]\n",
    "df_rest    = df_ratings.loc[df_ratings[\"Production\"]!=\"Netflix\",:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a237616d8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_netflix.Rating)\n",
    "sns.distplot(df_rest.Rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0629724003438397 5.911251980982567\n",
      "0.8400315637194714 6.06311475409836\n"
     ]
    }
   ],
   "source": [
    "print(df_rest.Rating.std(),df_rest.Rating.mean())\n",
    "print(df_netflix.Rating.std(),df_netflix.Rating.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=0.19956478582927786, pvalue=0.9050343384940904)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "netflix_normal=ht.create_sample_distribution(df_netflix.Rating,100,n=10)\n",
    "sns.distplot(netflix_normal)\n",
    "print(st.normaltest(netflix_normal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NormaltestResult(statistic=0.03035306754413554, pvalue=0.9849380494256516)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rest_normal=ht.create_sample_distribution(df_rest.Rating,100,n=10)\n",
    "sns.distplot(rest_normal)\n",
    "print(st.normaltest(rest_normal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/flatironschool/anaconda3/envs/learn-env/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=sns.distplot(rest_normal,label=\"Population\").set_title(\"Netflix Movies vs. Population\").get_figure().savefig(\"Netflix_vs_Popluation.png\")\n",
    "b=sns.distplot(netflix_normal,label=\"Netflix\")\n",
    "plt.xlabel(\"Rating\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(rest_normal))\n",
    "len(netflix_normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform simple z-test\n",
    "As we have normalized our sample sets here, and they both have the same sample size, we can perform a z-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The z-statistic is 5.315, which corresponds to a p-value of 5.3367447905294796e-08.\n",
      "The effect size, cohen's d = 0.569.\n",
      "From this we Reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "z_netflix = ht.z_test(netflix_normal,rest_normal,alpha)       \n",
    "z_netflix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform Welch's T-Test\n",
    "If we do not want to use our normlized data, perhaps for better accuracy as this method uses the true distribution, we can use a Welch's T-test which accounts for non-uniformity and different sample sizes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This gives us a t-statistic of 1.901, and a corresponding p-value of 0.059\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Fail to reject the null as our p-value (0.059) is greater than our alpha (0.05) so the result is not signifcant.'"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can perform a welchs t-test in one line using scipy.stats\n",
    "\n",
    "t_stat, p_value = st.ttest_ind(df_netflix.Rating, df_rest.Rating, equal_var=False)\n",
    "\n",
    "print(f\"This gives us a t-statistic of {round(t_stat,3)}, and a corresponding p-value of {round(p_value,3)}\")\n",
    "\n",
    "ht.significance(p_value,alpha)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is it more profitable to release movies during the winter months in the US?\n",
    "\n",
    "The dataset is base on the last five years' worth movies released in the US scraped from IMDB for the ratings and gross profit.  Profitability will be measured using the budget (proxy for overall production cost converted into USD) and gross US revenue (not worldwide revenue as focus is revenue during the winter) scraped from each of the movie IMDB webpages. t-test will be performed on the mean profits of winter (Dec, Jan, Feb) and summer (Jun, Jul, Aug) movies.\n",
    "\n",
    "#### Perform EDA on  the data taken from IMDB.  Check for null values and outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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_ID</th>\n",
       "      <th>Release_Year</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Production</th>\n",
       "      <th>Budget(LCY)</th>\n",
       "      <th>USA_Gross($)</th>\n",
       "      <th>Worldwide_Gross($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0069049</td>\n",
       "      <td>2018</td>\n",
       "      <td>11.0</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Netflix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0339736</td>\n",
       "      <td>2017</td>\n",
       "      <td>8.0</td>\n",
       "      <td>The Evil Within</td>\n",
       "      <td>Horror</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$6000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt0360556</td>\n",
       "      <td>2018</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Fahrenheit 451</td>\n",
       "      <td>Drama,Sci-Fi,Thriller</td>\n",
       "      <td>HBO Films</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt0365545</td>\n",
       "      <td>2018</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Nappily Ever After</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Netflix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>$150000000</td>\n",
       "      <td>652.27</td>\n",
       "      <td>1670.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title_ID  Release_Year  Released_Month                       Title  \\\n",
       "0  tt0069049          2018            11.0  The Other Side of the Wind   \n",
       "1  tt0339736          2017             8.0             The Evil Within   \n",
       "2  tt0360556          2018             5.0              Fahrenheit 451   \n",
       "3  tt0365545          2018             9.0          Nappily Ever After   \n",
       "4  tt0369610          2015             6.0              Jurassic World   \n",
       "\n",
       "                     Genre          Production Budget(LCY)  USA_Gross($)  \\\n",
       "0                    Drama             Netflix         NaN           NaN   \n",
       "1                   Horror                 NaN    $6000000           NaN   \n",
       "2    Drama,Sci-Fi,Thriller           HBO Films         NaN           NaN   \n",
       "3     Comedy,Drama,Romance             Netflix         NaN           NaN   \n",
       "4  Action,Adventure,Sci-Fi  Universal Pictures  $150000000        652.27   \n",
       "\n",
       "   Worldwide_Gross($)  \n",
       "0                 NaN  \n",
       "1                 NaN  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4              1670.4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the columns needed for the test:  movies released during the winter months more profitable\n",
    "df_movie_revenue = df[[\"Title_ID\",\"Release_Year\",\"Released_Month\",\"Title\",\"Genre\",\"Production\",\"Budget(LCY)\",\"USA_Gross($)\",\"Worldwide_Gross($)\"]]\n",
    "df_movie_revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2015 entries, 0 to 2014\n",
      "Data columns (total 9 columns):\n",
      "Title_ID              2015 non-null object\n",
      "Release_Year          2015 non-null int64\n",
      "Released_Month        2005 non-null float64\n",
      "Title                 2015 non-null object\n",
      "Genre                 2015 non-null object\n",
      "Production            1678 non-null object\n",
      "Budget(LCY)           1004 non-null object\n",
      "USA_Gross($)          1085 non-null float64\n",
      "Worldwide_Gross($)    1386 non-null float64\n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 141.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check dtype and for null values\n",
    "display(df_movie_revenue.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 771 entries, 4 to 2004\n",
      "Data columns (total 9 columns):\n",
      "Title_ID              771 non-null object\n",
      "Release_Year          771 non-null int64\n",
      "Released_Month        771 non-null float64\n",
      "Title                 771 non-null object\n",
      "Genre                 771 non-null object\n",
      "Production            738 non-null object\n",
      "Budget(LCY)           771 non-null object\n",
      "USA_Gross($)          771 non-null float64\n",
      "Worldwide_Gross($)    771 non-null float64\n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 60.2+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Drop null values under the money columns\n",
    "df_movie_revenue.dropna(subset = [\"Budget(LCY)\", \"USA_Gross($)\", \"Worldwide_Gross($)\"], inplace = True)\n",
    "df_movie_revenue.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/pandas/core/indexing.py:205: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "#Convert budget column to USD, use current exchange rate\n",
    "df_movie_revenue[\"Budget($)\"] = pd.Series([])\n",
    "\n",
    "for i in range(len(df_movie_revenue)):\n",
    "    if str(df_movie_revenue[\"Budget(LCY)\"].iloc[i])[0] == \"$\":\n",
    "        df_movie_revenue[\"Budget($)\"].iloc[i] = int(str(df_movie_revenue[\"Budget(LCY)\"].iloc[i]).replace(\"$\",\"\"))/1000000\n",
    "    elif str(df_movie_revenue[\"Budget(LCY)\"].iloc[i])[0:3] == \"EUR\":\n",
    "        df_movie_revenue[\"Budget($)\"].iloc[i] = int(str(df_movie_revenue[\"Budget(LCY)\"].iloc[i]).replace(\"EUR\",\"\"))/1000000 * 1.1\n",
    "    elif str(df_movie_revenue[\"Budget(LCY)\"].iloc[i])[0:3] == \"GBP\":\n",
    "        df_movie_revenue[\"Budget($)\"].iloc[i] = int(str(df_movie_revenue[\"Budget(LCY)\"].iloc[i]).replace(\"GBP\",\"\"))/1000000 * 1.28\n",
    "    elif str(df_movie_revenue[\"Budget(LCY)\"].iloc[i])[0:3] == \"CAD\":\n",
    "        df_movie_revenue[\"Budget($)\"].iloc[i] = int(str(df_movie_revenue[\"Budget(LCY)\"].iloc[i]).replace(\"CAD\",\"\"))/1000000 * 0.75\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "df_movie_revenue.drop(columns = [\"Budget(LCY)\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Applications/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "#Calculate US profit\n",
    "df_movie_revenue[\"Profit_US(%)\"] = round((df_movie_revenue[\"USA_Gross($)\"] / df_movie_revenue[\"Budget($)\"]) - 1, 2)\n",
    "\n",
    "#Calculate Worldwide profit\n",
    "df_movie_revenue[\"Profit_W(%)\"] = round((df_movie_revenue[\"Worldwide_Gross($)\"] / df_movie_revenue[\"Budget($)\"]) - 1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 771 entries, 4 to 2004\n",
      "Data columns (total 11 columns):\n",
      "Title_ID              771 non-null object\n",
      "Release_Year          771 non-null int64\n",
      "Released_Month        771 non-null float64\n",
      "Title                 771 non-null object\n",
      "Genre                 771 non-null object\n",
      "Production            738 non-null object\n",
      "USA_Gross($)          771 non-null float64\n",
      "Worldwide_Gross($)    771 non-null float64\n",
      "Budget($)             771 non-null float64\n",
      "Profit_US(%)          771 non-null float64\n",
      "Profit_W(%)           771 non-null float64\n",
      "dtypes: float64(6), int64(1), object(4)\n",
      "memory usage: 72.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_movie_revenue.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,5])\n",
    "sns.scatterplot(data = df_movie_revenue, x = \"Budget($)\", y = \"Worldwide_Gross($)\", label = \"Worldwide Gross Revenue\")\n",
    "sns.scatterplot(data = df_movie_revenue, x = \"Budget($)\", y = \"USA_Gross($)\", label = \"US Gross Revenue\")\n",
    "plt.xlabel(\"Budget US$ mm\")\n",
    "plt.ylabel(\"Revenue US$ mm\")\n",
    "plt.legend()\n",
    "plt.savefig(\"BudgetvsGrossRevenues\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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_ID</th>\n",
       "      <th>Release_Year</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Production</th>\n",
       "      <th>USA_Gross($)</th>\n",
       "      <th>Worldwide_Gross($)</th>\n",
       "      <th>Budget($)</th>\n",
       "      <th>Profit_US(%)</th>\n",
       "      <th>Profit_W(%)</th>\n",
       "      <th>Season</th>\n",
       "      <th>Season_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>652.27</td>\n",
       "      <td>1670.40</td>\n",
       "      <td>150.00</td>\n",
       "      <td>3.35</td>\n",
       "      <td>10.14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0451279</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Wonder Woman</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>Warner Bros. Pictures</td>\n",
       "      <td>412.56</td>\n",
       "      <td>821.85</td>\n",
       "      <td>149.00</td>\n",
       "      <td>1.77</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt1124037</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Free State of Jones</td>\n",
       "      <td>Action,Biography,Drama</td>\n",
       "      <td>STX Entertainment</td>\n",
       "      <td>20.81</td>\n",
       "      <td>25.04</td>\n",
       "      <td>50.00</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt1489889</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Central Intelligence</td>\n",
       "      <td>Action,Comedy,Crime</td>\n",
       "      <td>Warner Bros. Pictures</td>\n",
       "      <td>127.44</td>\n",
       "      <td>216.97</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1.55</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt1628841</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Independence Day: Resurgence</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>103.14</td>\n",
       "      <td>389.68</td>\n",
       "      <td>165.00</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>1.36</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>766</td>\n",
       "      <td>tt7547410</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Dora and the Lost City of Gold</td>\n",
       "      <td>Adventure,Family</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>60.48</td>\n",
       "      <td>116.55</td>\n",
       "      <td>49.00</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.38</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>767</td>\n",
       "      <td>tt7668870</td>\n",
       "      <td>2018</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Searching</td>\n",
       "      <td>Drama,Mystery,Thriller</td>\n",
       "      <td>Screen Gems</td>\n",
       "      <td>26.02</td>\n",
       "      <td>75.46</td>\n",
       "      <td>0.88</td>\n",
       "      <td>28.57</td>\n",
       "      <td>84.75</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>768</td>\n",
       "      <td>tt7798634</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ready or Not</td>\n",
       "      <td>Comedy,Horror,Mystery</td>\n",
       "      <td>Fox Searchlight Pictures</td>\n",
       "      <td>28.71</td>\n",
       "      <td>57.15</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.78</td>\n",
       "      <td>8.52</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>769</td>\n",
       "      <td>tt8186318</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Overcomer</td>\n",
       "      <td>Drama,Sport</td>\n",
       "      <td>Affirm Films</td>\n",
       "      <td>34.70</td>\n",
       "      <td>35.71</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>770</td>\n",
       "      <td>tt8266310</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Blinded by the Light</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>New Line Cinema</td>\n",
       "      <td>11.90</td>\n",
       "      <td>18.02</td>\n",
       "      <td>15.00</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>771 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Title_ID  Release_Year  Released_Month                           Title  \\\n",
       "0    tt0369610          2015             6.0                  Jurassic World   \n",
       "1    tt0451279          2017             6.0                    Wonder Woman   \n",
       "2    tt1124037          2016             6.0             Free State of Jones   \n",
       "3    tt1489889          2016             6.0            Central Intelligence   \n",
       "4    tt1628841          2016             6.0    Independence Day: Resurgence   \n",
       "..         ...           ...             ...                             ...   \n",
       "766  tt7547410          2019             8.0  Dora and the Lost City of Gold   \n",
       "767  tt7668870          2018             8.0                       Searching   \n",
       "768  tt7798634          2019             8.0                    Ready or Not   \n",
       "769  tt8186318          2019             8.0                       Overcomer   \n",
       "770  tt8266310          2019             8.0            Blinded by the Light   \n",
       "\n",
       "                        Genre                Production  USA_Gross($)  \\\n",
       "0     Action,Adventure,Sci-Fi        Universal Pictures        652.27   \n",
       "1    Action,Adventure,Fantasy     Warner Bros. Pictures        412.56   \n",
       "2      Action,Biography,Drama         STX Entertainment         20.81   \n",
       "3         Action,Comedy,Crime     Warner Bros. Pictures        127.44   \n",
       "4     Action,Adventure,Sci-Fi          20th Century Fox        103.14   \n",
       "..                        ...                       ...           ...   \n",
       "766          Adventure,Family        Paramount Pictures         60.48   \n",
       "767    Drama,Mystery,Thriller               Screen Gems         26.02   \n",
       "768     Comedy,Horror,Mystery  Fox Searchlight Pictures         28.71   \n",
       "769               Drama,Sport              Affirm Films         34.70   \n",
       "770        Comedy,Drama,Music           New Line Cinema         11.90   \n",
       "\n",
       "     Worldwide_Gross($)  Budget($)  Profit_US(%)  Profit_W(%)  Season  Season_  \n",
       "0               1670.40     150.00          3.35        10.14       3        2  \n",
       "1                821.85     149.00          1.77         4.52       3        2  \n",
       "2                 25.04      50.00         -0.58        -0.50       3        2  \n",
       "3                216.97      50.00          1.55         3.34       3        2  \n",
       "4                389.68     165.00         -0.37         1.36       3        2  \n",
       "..                  ...        ...           ...          ...     ...      ...  \n",
       "766              116.55      49.00          0.23         1.38       3        2  \n",
       "767               75.46       0.88         28.57        84.75       3        2  \n",
       "768               57.15       6.00          3.78         8.52       3        2  \n",
       "769               35.71       5.00          5.94         6.14       3        2  \n",
       "770               18.02      15.00         -0.21         0.20       3        2  \n",
       "\n",
       "[771 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add categorial columns for the seasons\n",
    "df_months = pd.DataFrame({\"Released_Month\" : list(range(1,13)), \"Season\" : [1,1,2,2,2,3,3,3,4,4,4,1], \"Season_\" : [1,1,2,2,2,2,2,2,1,1,1,1]})\n",
    "df_season_revenue = pd.merge(df_movie_revenue, df_months, on = \"Released_Month\")\n",
    "df_season_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a236f5048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the distribution of the profit column\n",
    "plt.figure(figsize = [20,5])\n",
    "sns.boxplot(df_season_revenue[\"Profit_US(%)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    771.000000\n",
       "mean       1.206615\n",
       "std        8.641546\n",
       "min       -1.000000\n",
       "25%       -0.550000\n",
       "50%        0.060000\n",
       "75%        1.285000\n",
       "max      226.600000\n",
       "Name: Profit_US(%), dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_season_revenue[\"Profit_US(%)\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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_ID</th>\n",
       "      <th>Release_Year</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Production</th>\n",
       "      <th>USA_Gross($)</th>\n",
       "      <th>Worldwide_Gross($)</th>\n",
       "      <th>Budget($)</th>\n",
       "      <th>Profit_US(%)</th>\n",
       "      <th>Profit_W(%)</th>\n",
       "      <th>Season</th>\n",
       "      <th>Season_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>tt5052448</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Get Out</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>176.04</td>\n",
       "      <td>255.41</td>\n",
       "      <td>4.50</td>\n",
       "      <td>38.12</td>\n",
       "      <td>55.76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>260</td>\n",
       "      <td>tt2309260</td>\n",
       "      <td>2015</td>\n",
       "      <td>7.0</td>\n",
       "      <td>The Gallows</td>\n",
       "      <td>Horror,Mystery,Thriller</td>\n",
       "      <td>New Line Cinema</td>\n",
       "      <td>22.76</td>\n",
       "      <td>42.96</td>\n",
       "      <td>0.10</td>\n",
       "      <td>226.60</td>\n",
       "      <td>428.60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>732</td>\n",
       "      <td>tt3832914</td>\n",
       "      <td>2015</td>\n",
       "      <td>8.0</td>\n",
       "      <td>War Room</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Sony Pictures</td>\n",
       "      <td>67.79</td>\n",
       "      <td>73.26</td>\n",
       "      <td>3.00</td>\n",
       "      <td>21.60</td>\n",
       "      <td>23.42</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>767</td>\n",
       "      <td>tt7668870</td>\n",
       "      <td>2018</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Searching</td>\n",
       "      <td>Drama,Mystery,Thriller</td>\n",
       "      <td>Screen Gems</td>\n",
       "      <td>26.02</td>\n",
       "      <td>75.46</td>\n",
       "      <td>0.88</td>\n",
       "      <td>28.57</td>\n",
       "      <td>84.75</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Title_ID  Release_Year  Released_Month        Title  \\\n",
       "171  tt5052448          2017             2.0      Get Out   \n",
       "260  tt2309260          2015             7.0  The Gallows   \n",
       "732  tt3832914          2015             8.0     War Room   \n",
       "767  tt7668870          2018             8.0    Searching   \n",
       "\n",
       "                       Genre          Production  USA_Gross($)  \\\n",
       "171  Horror,Mystery,Thriller  Universal Pictures        176.04   \n",
       "260  Horror,Mystery,Thriller     New Line Cinema         22.76   \n",
       "732                    Drama       Sony Pictures         67.79   \n",
       "767   Drama,Mystery,Thriller         Screen Gems         26.02   \n",
       "\n",
       "     Worldwide_Gross($)  Budget($)  Profit_US(%)  Profit_W(%)  Season  Season_  \n",
       "171              255.41       4.50         38.12        55.76       1        1  \n",
       "260               42.96       0.10        226.60       428.60       3        2  \n",
       "732               73.26       3.00         21.60        23.42       3        2  \n",
       "767               75.46       0.88         28.57        84.75       3        2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The above boxplot and table are showing 4 outliers above 15/20%\n",
    "#Remove the outliers profit > 15%\n",
    "df_season_outliers = df_season_revenue[df_season_revenue[\"Profit_US(%)\"] > 15]\n",
    "df_season_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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_ID</th>\n",
       "      <th>Release_Year</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Production</th>\n",
       "      <th>USA_Gross($)</th>\n",
       "      <th>Worldwide_Gross($)</th>\n",
       "      <th>Budget($)</th>\n",
       "      <th>Profit_US(%)</th>\n",
       "      <th>Profit_W(%)</th>\n",
       "      <th>Season</th>\n",
       "      <th>Season_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>652.27</td>\n",
       "      <td>1670.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>10.14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>tt0451279</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Wonder Woman</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>Warner Bros. Pictures</td>\n",
       "      <td>412.56</td>\n",
       "      <td>821.85</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tt1124037</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Free State of Jones</td>\n",
       "      <td>Action,Biography,Drama</td>\n",
       "      <td>STX Entertainment</td>\n",
       "      <td>20.81</td>\n",
       "      <td>25.04</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>tt1489889</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Central Intelligence</td>\n",
       "      <td>Action,Comedy,Crime</td>\n",
       "      <td>Warner Bros. Pictures</td>\n",
       "      <td>127.44</td>\n",
       "      <td>216.97</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt1628841</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Independence Day: Resurgence</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>103.14</td>\n",
       "      <td>389.68</td>\n",
       "      <td>165.0</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>1.36</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>765</td>\n",
       "      <td>tt7365604</td>\n",
       "      <td>2018</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Dog Days</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>LD Entertainment</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.82</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>766</td>\n",
       "      <td>tt7547410</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Dora and the Lost City of Gold</td>\n",
       "      <td>Adventure,Family</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>60.48</td>\n",
       "      <td>116.55</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.38</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>768</td>\n",
       "      <td>tt7798634</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ready or Not</td>\n",
       "      <td>Comedy,Horror,Mystery</td>\n",
       "      <td>Fox Searchlight Pictures</td>\n",
       "      <td>28.71</td>\n",
       "      <td>57.15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>8.52</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>769</td>\n",
       "      <td>tt8186318</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Overcomer</td>\n",
       "      <td>Drama,Sport</td>\n",
       "      <td>Affirm Films</td>\n",
       "      <td>34.70</td>\n",
       "      <td>35.71</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>770</td>\n",
       "      <td>tt8266310</td>\n",
       "      <td>2019</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Blinded by the Light</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>New Line Cinema</td>\n",
       "      <td>11.90</td>\n",
       "      <td>18.02</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>767 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Title_ID  Release_Year  Released_Month                           Title  \\\n",
       "0    tt0369610          2015             6.0                  Jurassic World   \n",
       "1    tt0451279          2017             6.0                    Wonder Woman   \n",
       "2    tt1124037          2016             6.0             Free State of Jones   \n",
       "3    tt1489889          2016             6.0            Central Intelligence   \n",
       "4    tt1628841          2016             6.0    Independence Day: Resurgence   \n",
       "..         ...           ...             ...                             ...   \n",
       "765  tt7365604          2018             8.0                        Dog Days   \n",
       "766  tt7547410          2019             8.0  Dora and the Lost City of Gold   \n",
       "768  tt7798634          2019             8.0                    Ready or Not   \n",
       "769  tt8186318          2019             8.0                       Overcomer   \n",
       "770  tt8266310          2019             8.0            Blinded by the Light   \n",
       "\n",
       "                        Genre                Production  USA_Gross($)  \\\n",
       "0     Action,Adventure,Sci-Fi        Universal Pictures        652.27   \n",
       "1    Action,Adventure,Fantasy     Warner Bros. Pictures        412.56   \n",
       "2      Action,Biography,Drama         STX Entertainment         20.81   \n",
       "3         Action,Comedy,Crime     Warner Bros. Pictures        127.44   \n",
       "4     Action,Adventure,Sci-Fi          20th Century Fox        103.14   \n",
       "..                        ...                       ...           ...   \n",
       "765      Comedy,Drama,Romance          LD Entertainment          6.81   \n",
       "766          Adventure,Family        Paramount Pictures         60.48   \n",
       "768     Comedy,Horror,Mystery  Fox Searchlight Pictures         28.71   \n",
       "769               Drama,Sport              Affirm Films         34.70   \n",
       "770        Comedy,Drama,Music           New Line Cinema         11.90   \n",
       "\n",
       "     Worldwide_Gross($)  Budget($)  Profit_US(%)  Profit_W(%)  Season  Season_  \n",
       "0               1670.40      150.0          3.35        10.14       3        2  \n",
       "1                821.85      149.0          1.77         4.52       3        2  \n",
       "2                 25.04       50.0         -0.58        -0.50       3        2  \n",
       "3                216.97       50.0          1.55         3.34       3        2  \n",
       "4                389.68      165.0         -0.37         1.36       3        2  \n",
       "..                  ...        ...           ...          ...     ...      ...  \n",
       "765                6.82       10.0         -0.32        -0.32       3        2  \n",
       "766              116.55       49.0          0.23         1.38       3        2  \n",
       "768               57.15        6.0          3.78         8.52       3        2  \n",
       "769               35.71        5.0          5.94         6.14       3        2  \n",
       "770               18.02       15.0         -0.21         0.20       3        2  \n",
       "\n",
       "[767 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_season_revenue = df_season_revenue[df_season_revenue[\"Profit_US(%)\"] <= 15]\n",
    "df_season_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23b943c8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [20,5])\n",
    "sns.boxplot(df_season_revenue[\"Profit_US(%)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24081080>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(data = df_season_revenue, x = \"Budget($)\", y = \"USA_Gross($)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    193.000000\n",
       "mean       0.697306\n",
       "std        1.944261\n",
       "min       -1.000000\n",
       "25%       -0.560000\n",
       "50%        0.130000\n",
       "75%        1.290000\n",
       "max       14.370000\n",
       "Name: Profit_US(%), dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a subset for winter movies\n",
    "df_winter = df_season_revenue[(df_season_revenue[\"Season\"] == 1)]\n",
    "df_winter[\"Profit_US(%)\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    188.000000\n",
       "mean       1.043511\n",
       "std        2.392632\n",
       "min       -1.000000\n",
       "25%       -0.427500\n",
       "50%        0.310000\n",
       "75%        1.687500\n",
       "max       15.000000\n",
       "Name: Profit_US(%), dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a subset for summer movies which will be tested against winter movies\n",
    "df_summer = df_season_revenue[(df_season_revenue[\"Season\"] == 3)]\n",
    "df_summer[\"Profit_US(%)\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a242405c0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Check distribution\n",
    "sns.distplot(df_winter[\"Profit_US(%)\"])\n",
    "sns.distplot(df_summer[\"Profit_US(%)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For the above subsets, perform random sampling then compare sample variances and check for normality.\n",
    "\n",
    "H0: sample comes from a normal distribution\n",
    "\n",
    "HA: sample does not from a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Below functions perform random sampling and calculate for sample variance\n",
    "def get_sample_mean(sample):\n",
    "    return sum(sample) / len(sample)\n",
    "def get_sample(data, n):\n",
    "    sample = []\n",
    "    while len(sample) != n:\n",
    "        x = np.random.choice(data)\n",
    "        sample.append(x)  \n",
    "    return sample\n",
    "def create_sample_distribution(data, dist_size=100, n=30):\n",
    "    sample_dist = []\n",
    "    while len(sample_dist) != dist_size:\n",
    "        sample = get_sample(data, n)\n",
    "        sample_mean = get_sample_mean(sample)\n",
    "        sample_dist.append(sample_mean)\n",
    "    return sample_dist\n",
    "def sample_variance(sample):\n",
    "    sample_mean = np.mean(sample)\n",
    "    return np.sum((sample - sample_mean) **2)/ (len(sample) -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=1.6050401827039047, pvalue=0.4481980396955242)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Perfom random sampling and normality test on winter movies\n",
    "winter_sample = create_sample_distribution(df_winter[\"Profit_US(%)\"], dist_size=100, n=30)\n",
    "stats.normaltest(winter_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=8.051918556194892, pvalue=0.017846296378241568)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Perfom random sampling and normality test on summer movies\n",
    "summer_sample = create_sample_distribution(df_summer[\"Profit_US(%)\"], dist_size=100, n=30)\n",
    "stats.normaltest(summer_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": [
    "sns.distplot(winter_sample, label = \"Winter Movies Ave Profit (%)\")\n",
    "sns.distplot(summer_sample, label = \"Summer Movies Ave Profit (%)\")\n",
    "plt.legend()\n",
    "plt.savefig(\"WinterSummer\", bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "winter sample mean is 0.6654733333333333\n",
      "winter sample variance is 0.12232010141414143\n"
     ]
    }
   ],
   "source": [
    "winter_variance = sample_variance(winter_sample)\n",
    "winter_mean = np.mean(winter_sample)\n",
    "print(\"winter sample mean is\", winter_mean)\n",
    "print(\"winter sample variance is\", winter_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "summer sample mean is 1.04269\n",
      "summer sample variance is 0.21416900618406287\n"
     ]
    }
   ],
   "source": [
    "summer_variance = sample_variance(summer_sample)\n",
    "summer_mean = np.mean(summer_sample)\n",
    "print(\"summer sample mean is\", summer_mean)\n",
    "print(\"summer sample variance is\", summer_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.37721666666666664"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winter_mean - summer_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - p-values for the above normality tests are greater than the alpha = 0.05, H0 cannot be rejected then\n",
    "\n",
    " - winter and summer movie samples have similar variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform one-tailed and two-tailed t-tests (alpha = 0.05)\n",
    "\n",
    "H0: The mean profit difference between winter and summer movies is equal to zero (winter - summer = 0)\n",
    "\n",
    "HA1: The mean profit difference between winter and summer movies is not equal to zero (winter - summer != 0)\n",
    "\n",
    "HA2: The mean profit difference between winter and summer movies is greater than zero (winter - summer > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.05\n",
    "df = len(winter_sample) + len(summer_sample) - 2 #degrees of freedom\n",
    "t_stat, p_value = stats.ttest_ind(winter_sample, summer_sample)\n",
    "#t_crit = stats.t.ppf(1 - alpha, df) #same for all null and alternative hypotheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-statistic is  -6.502874429516654\n",
      "p-value is  6.283067644557132e-10\n",
      "\n",
      "\n",
      "p-value < alpha/2: we reject the null hypothesis at 5% significance level in support of the alternative\n"
     ]
    }
   ],
   "source": [
    "#HA1: The mean profit difference between winter and summer movies is not equal to zero (winter - summer != 0)\n",
    "\n",
    "print(\"t-statistic is \", t_stat)\n",
    "print(\"p-value is \", p_value)\n",
    "print(\"\\n\")\n",
    "if p_value < alpha/2:\n",
    "    print(\"p-value < alpha/2: we reject the null hypothesis at 5% significance level in support of the alternative\")\n",
    "else:\n",
    "    print(\"p-value > alpha/2: we do not the null hypothesis at 5% significance level in support of the alternative\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-statistic is  -6.502874429516654\n",
      "p-value is  6.283067644557132e-10\n",
      "\n",
      "\n",
      "p-value < alpha: we reject the null hypothesis at 5% significance level in support of the alternative\n"
     ]
    }
   ],
   "source": [
    "#HA2: The mean profit difference between winter and summer movies is greater than zero (winter - summer > 0)\n",
    "\n",
    "print(\"t-statistic is \", t_stat)\n",
    "print(\"p-value is \", p_value)\n",
    "print(\"\\n\")\n",
    "if p_value < alpha:\n",
    "    print(\"p-value < alpha: we reject the null hypothesis at 5% significance level in support of the alternative\")\n",
    "else:\n",
    "    print(\"p-value > alpha: we do not the null hypothesis at 5% significance level in support of the alternative\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are comedy movies more profitable than dramas?\n",
    "\n",
    "Test is focused on last 5 years English movies released in the US taken from IMDB.  Profitability will be measured using the budget (proxy for overall production cost converted into USD) and gross worldwide revenue (both in millions) scraped from each of the movie IMDB webpages. t-test will be performed on the mean profits of comedy and drama movies.\n",
    "\n",
    "#### Perform EDA on  the data taken from IMDB.\n",
    "\n",
    "Cleaned data *df_movie_revenue* will be used as a starting point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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_ID</th>\n",
       "      <th>Release_Year</th>\n",
       "      <th>Released_Month</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Production</th>\n",
       "      <th>USA_Gross($)</th>\n",
       "      <th>Worldwide_Gross($)</th>\n",
       "      <th>Budget($)</th>\n",
       "      <th>Profit_US(%)</th>\n",
       "      <th>Profit_W(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>652.27</td>\n",
       "      <td>1670.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3.35</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>tt0385887</td>\n",
       "      <td>2019</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Motherless Brooklyn</td>\n",
       "      <td>Crime,Drama</td>\n",
       "      <td>Warner Bros. Pictures</td>\n",
       "      <td>7.33</td>\n",
       "      <td>7.48</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>tt0437086</td>\n",
       "      <td>2019</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Alita: Battle Angel</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>85.71</td>\n",
       "      <td>404.85</td>\n",
       "      <td>170.0</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>tt0448115</td>\n",
       "      <td>2019</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Shazam!</td>\n",
       "      <td>Action,Adventure,Comedy</td>\n",
       "      <td>New Line Cinema</td>\n",
       "      <td>140.37</td>\n",
       "      <td>364.57</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>tt0451279</td>\n",
       "      <td>2017</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Wonder Woman</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>Warner Bros. Pictures</td>\n",
       "      <td>412.56</td>\n",
       "      <td>821.85</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1.77</td>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1968</td>\n",
       "      <td>tt8385474</td>\n",
       "      <td>2019</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A Dog's Journey</td>\n",
       "      <td>Comedy,Drama,Family</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>22.55</td>\n",
       "      <td>71.75</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1979</td>\n",
       "      <td>tt8663516</td>\n",
       "      <td>2019</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Child's Play</td>\n",
       "      <td>Horror</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.21</td>\n",
       "      <td>44.90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1988</td>\n",
       "      <td>tt8772262</td>\n",
       "      <td>2019</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Midsommar</td>\n",
       "      <td>Drama,Horror,Mystery</td>\n",
       "      <td>A24</td>\n",
       "      <td>27.43</td>\n",
       "      <td>41.12</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997</td>\n",
       "      <td>tt9024106</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unplanned</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>Pure Flix</td>\n",
       "      <td>19.01</td>\n",
       "      <td>19.32</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2004</td>\n",
       "      <td>tt9354944</td>\n",
       "      <td>2019</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Jexi</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Lionsgate</td>\n",
       "      <td>6.55</td>\n",
       "      <td>7.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>771 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Title_ID  Release_Year  Released_Month                Title  \\\n",
       "4     tt0369610          2015             6.0       Jurassic World   \n",
       "6     tt0385887          2019            11.0  Motherless Brooklyn   \n",
       "8     tt0437086          2019             2.0  Alita: Battle Angel   \n",
       "9     tt0448115          2019             4.0              Shazam!   \n",
       "10    tt0451279          2017             6.0         Wonder Woman   \n",
       "...         ...           ...             ...                  ...   \n",
       "1968  tt8385474          2019             5.0      A Dog's Journey   \n",
       "1979  tt8663516          2019             6.0         Child's Play   \n",
       "1988  tt8772262          2019             7.0            Midsommar   \n",
       "1997  tt9024106          2019             3.0            Unplanned   \n",
       "2004  tt9354944          2019            10.0                 Jexi   \n",
       "\n",
       "                         Genre             Production  USA_Gross($)  \\\n",
       "4      Action,Adventure,Sci-Fi     Universal Pictures        652.27   \n",
       "6                  Crime,Drama  Warner Bros. Pictures          7.33   \n",
       "8      Action,Adventure,Sci-Fi       20th Century Fox         85.71   \n",
       "9      Action,Adventure,Comedy        New Line Cinema        140.37   \n",
       "10    Action,Adventure,Fantasy  Warner Bros. Pictures        412.56   \n",
       "...                        ...                    ...           ...   \n",
       "1968       Comedy,Drama,Family     Universal Pictures         22.55   \n",
       "1979                    Horror                    NaN         29.21   \n",
       "1988      Drama,Horror,Mystery                    A24         27.43   \n",
       "1997           Biography,Drama              Pure Flix         19.01   \n",
       "2004                    Comedy              Lionsgate          6.55   \n",
       "\n",
       "      Worldwide_Gross($)  Budget($)  Profit_US(%)  Profit_W(%)  \n",
       "4                1670.40      150.0          3.35        10.14  \n",
       "6                   7.48       26.0         -0.72        -0.71  \n",
       "8                 404.85      170.0         -0.50         1.38  \n",
       "9                 364.57      100.0          0.40         2.65  \n",
       "10                821.85      149.0          1.77         4.52  \n",
       "...                  ...        ...           ...          ...  \n",
       "1968               71.75       16.0          0.41         3.48  \n",
       "1979               44.90       10.0          1.92         3.49  \n",
       "1988               41.12       10.0          1.74         3.11  \n",
       "1997               19.32        6.0          2.17         2.22  \n",
       "2004                7.26        5.0          0.31         0.45  \n",
       "\n",
       "[771 rows x 11 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_genre_revenue = df_movie_revenue.copy()\n",
    "df_genre_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,5])\n",
    "sns.scatterplot(data = df_genre_revenue, x = \"Budget($)\", y = \"Worldwide_Gross($)\")\n",
    "plt.savefig(\"BudgetvsWGross\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54 entries, 0 to 53\n",
      "Data columns (total 11 columns):\n",
      "Title_ID              54 non-null object\n",
      "Release_Year          54 non-null int64\n",
      "Released_Month        54 non-null float64\n",
      "Title                 54 non-null object\n",
      "Genre                 54 non-null object\n",
      "Production            52 non-null object\n",
      "USA_Gross($)          54 non-null float64\n",
      "Worldwide_Gross($)    54 non-null float64\n",
      "Budget($)             54 non-null float64\n",
      "Profit_US(%)          54 non-null float64\n",
      "Profit_W(%)           54 non-null float64\n",
      "dtypes: float64(6), int64(1), object(4)\n",
      "memory usage: 4.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Profit_W(%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Horror</td>\n",
       "      <td>5.954000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Drama</td>\n",
       "      <td>2.924545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.906538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Musical</td>\n",
       "      <td>0.270000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Profit_W(%)\n",
       "Genre               \n",
       "Horror      5.954000\n",
       "Drama       2.924545\n",
       "Comedy      1.906538\n",
       "Musical     0.270000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check top genres excluding those movies under multiple genres\n",
    "q = \"\"\"SELECT * FROM df_genre_revenue WHERE Genre NOT LIKE \"%,%\";\"\"\"\n",
    "df_genre_ = pysqldf(q)\n",
    "display(df_genre_.info())\n",
    "df_genre_[[\"Genre\", \"Profit_W(%)\"]].groupby(\"Genre\").mean().sort_values(\"Profit_W(%)\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24698cc0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,5])\n",
    "sns.boxplot(data = df_genre_, x = df_genre_[\"Genre\"], y = df_genre_[\"Profit_W(%)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5.00000\n",
       "mean      5.95400\n",
       "std       7.56309\n",
       "min      -0.24000\n",
       "25%       2.30000\n",
       "50%       3.49000\n",
       "75%       5.21000\n",
       "max      19.01000\n",
       "Name: Profit_W(%), dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check drama stats\n",
    "df_genre_.query(\"Genre == 'Horror'\")[\"Profit_W(%)\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    26.000000\n",
       "mean      1.906538\n",
       "std       1.890460\n",
       "min      -0.930000\n",
       "25%       0.697500\n",
       "50%       1.950000\n",
       "75%       2.755000\n",
       "max       8.200000\n",
       "Name: Profit_W(%), dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check comedy stats\n",
    "df_genre_.query(\"Genre == 'Comedy'\")[\"Profit_W(%)\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22.000000\n",
       "mean      2.924545\n",
       "std       6.007583\n",
       "min      -0.950000\n",
       "25%      -0.772500\n",
       "50%       0.345000\n",
       "75%       4.342500\n",
       "max      23.420000\n",
       "Name: Profit_W(%), dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check horror stats\n",
    "df_genre_.query(\"Genre == 'Drama'\")[\"Profit_W(%)\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create subsets for horror movies.  All movies under genre horror only.\n",
    "df_genre_comedy = df_genre_.query(\"Genre == 'Comedy'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create subsets for comedy movies.  All movies under genre comedy only.\n",
    "df_genre_drama = df_genre_.query(\"Genre == 'Drama'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24766c88>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Check distribution\n",
    "sns.distplot(df_genre_comedy[\"Profit_W(%)\"])\n",
    "sns.distplot(df_genre_drama[\"Profit_W(%)\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For the above subsets, perform random sampling then compare sample variances and check for normality.\n",
    "\n",
    "H0: sample comes from a normal distribution\n",
    "\n",
    "HA: sample does not from a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=3.2341355086702035, pvalue=0.19847983829116334)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Perfom random sampling and normality test on comedy movies\n",
    "comedy_sample = create_sample_distribution(df_genre_comedy[\"Profit_W(%)\"], dist_size=100, n=30)\n",
    "stats.normaltest(comedy_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=0.3186970495131137, pvalue=0.852699120421283)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Perfom random sampling and normality test on comedy movies\n",
    "drama_sample = create_sample_distribution(df_genre_drama[\"Profit_W(%)\"], dist_size=100, n=30)\n",
    "stats.normaltest(drama_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "sns.distplot(comedy_sample, label = \"Comedy Average Profit(%)\")\n",
    "sns.distplot(drama_sample, label = \"Drama Average Profit(%)\")\n",
    "plt.legend()\n",
    "plt.savefig(\"ComedyDrama\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comedy sample mean is 1.91006\n",
      "comedy sample variance is 0.10002939703703709\n"
     ]
    }
   ],
   "source": [
    "comedy_variance = sample_variance(comedy_sample)\n",
    "comedy_mean = np.mean(comedy_sample)\n",
    "print(\"comedy sample mean is\", comedy_mean)\n",
    "print(\"comedy sample variance is\", comedy_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drama sample mean is 2.8150100000000005\n",
      "drama sample variance is 1.0138231951851857\n"
     ]
    }
   ],
   "source": [
    "drama_variance = sample_variance(drama_sample)\n",
    "drama_mean = np.mean(drama_sample)\n",
    "print(\"drama sample mean is\", drama_mean)\n",
    "print(\"drama sample variance is\", drama_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9049500000000004"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comedy_mean - drama_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - p-values for the above normality tests are greater than the alpha = 0.05, H0 cannot be rejected then\n",
    "\n",
    " - comedy and drama movie sample variances are not very similar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform one-tailed and two-tailed Welch's t-tests (alpha = 0.05)\n",
    "\n",
    "H0: The mean profit difference between drama and comedy movies is equal to zero (comedy - drama = 0)\n",
    "\n",
    "HA1: The mean profit difference between drama and comedy movies is not equal to zero (comedy - drama != 0)\n",
    "\n",
    "HA2: The mean profit difference between drama and comedy movies is greater than zero (comedy - drama > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.05\n",
    "df = len(drama_sample) + len(comedy_sample) - 2 #degrees of freedom\n",
    "t_stat, p_value = stats.ttest_ind(comedy_sample , drama_sample, equal_var=False) #two-tailed p-value\n",
    "#t_crit = stats.t.ppf(1 - alpha, df) #same for all null and alternative hypotheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-statistic is  -8.574537902443938\n",
      "p-value is  4.4626117564021676e-14\n",
      "\n",
      "\n",
      "p-value < alpha/2: we reject the null hypothesis at 5% significance level in support of the alternative\n"
     ]
    }
   ],
   "source": [
    "#HA1: The mean profit difference between drama and comedy movies is not equal to zero (drama - comedy != 0)\n",
    "\n",
    "print(\"t-statistic is \", t_stat)\n",
    "print(\"p-value is \", p_value)\n",
    "print(\"\\n\")\n",
    "if p_value < alpha/2:\n",
    "    print(\"p-value < alpha/2: we reject the null hypothesis at 5% significance level in support of the alternative\")\n",
    "else:\n",
    "    print(\"p-value > alpha/2: we do not the null hypothesis at 5% significance level in support of the alternative\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-statistic is  -8.574537902443938\n",
      "p-value is  4.4626117564021676e-14\n",
      "\n",
      "\n",
      "p-value < alpha: we reject the null hypothesis at 5% significance level in support of the alternative\n"
     ]
    }
   ],
   "source": [
    "#HA2: The mean profit difference between drama and comedy movies is greater than zero (drama - comedy > 0)\n",
    "\n",
    "print(\"t-statistic is \", t_stat)\n",
    "print(\"p-value is \", p_value)\n",
    "print(\"\\n\")\n",
    "if p_value < alpha:\n",
    "    print(\"p-value < alpha: we reject the null hypothesis at 5% significance level in support of the alternative\")\n",
    "else:\n",
    "    print(\"p-value > alpha: we do not the null hypothesis at 5% significance level in support of the alternative\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:learn-env] *",
   "language": "python",
   "name": "conda-env-learn-env-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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