{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Taking what we have learned from analyzing the kaggle dataset, we decided that we needed more information to try to \n", "# predict profit. We are applying what we learned from our Kaggle analysis to our the numbers dataset. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Ali and Kendra Final Project \n", "\n", "#importing pandas, csv, \n", "import csv \n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import statistics\n", "#To create testing and training dfs and labels \n", "from sklearn.model_selection import train_test_split \n", "# To model the Gaussian Navie Bayes classifier\n", "from sklearn.naive_bayes import GaussianNB\n", "# To calculate the accuracy score of the model\n", "from sklearn.metrics import accuracy_score\n", "#confusion matrix \n", "from sklearn.metrics import confusion_matrix, classification_report\n", "#To get a count or tally of a category in our df \n", "from collections import Counter\n", "#for pre-processing to fit all numeric data on the standard scale \n", "from sklearn.preprocessing import StandardScaler\n", "#for applying PCA function on training and testing sets \n", "from sklearn.decomposition import PCA\n", "#logistic regression \n", "from sklearn.linear_model import LogisticRegression\n", "#SVMs \n", "from sklearn.svm import SVC\n", "#For association rule mining \n", "from apyori import apriori\n", "#This will allow us to silence the warnings\n", "import warnings \n", "warnings.simplefilter(\"ignore\")\n", "#For the confusion matrix \n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Functions that we are going to use in our file: \n", "\n", "#Creating a function that will change a column data type to category \n", "def cat_fun(df, column): \n", " df[column] = df[column].astype(\"category\") \n", " return(df[column])\n", "\n", "#Creating a function that will remove anything in our df and replace it with nothing \n", "def remove(df, column, object_to_remove): \n", " df[column] = df[column].str.replace(object_to_remove, \"\")\n", " return(df[column])\n", "\n", "#Creating a function that will discretize our columns based on quartiles \n", "def quartile_discretize(df, column, categories): \n", " df[column] = pd.qcut(df[column], 4, labels = categories)\n", " return(df[column])\n", "\n", "#Creating a function that will merge our dfs with a left join\n", "def left_merge_2_conditions(df1, df2, column1, column2):\n", " df = pd.merge(df1, df2, how = \"left\", on=[column1, column2])\n", " return(df)\n", "\n", "#Creating a function that groups by, counts, creates a new column from the index, drops the index and changes the column names\n", "def groupby_count(df, groupby_column, count_column): \n", " new_df = pd.DataFrame(df.groupby(groupby_column)[count_column].count())\n", " new_df.columns = [\"count\"]\n", " new_df[groupby_column] = new_df.index.get_level_values(0)\n", " new_df.reset_index(drop = True, inplace = True)\n", " return(new_df)\n", "\n", "#Creating a function that groups by, counts, creates a new column from the index, drops the index and changes the column names\n", "def groupby_2_count(df, groupby_column1, groupby_column2, count_column): \n", " new_df = pd.DataFrame(df.groupby([groupby_column1, groupby_column2 ])[count_column].count())\n", " new_df.columns = [\"count\"]\n", " new_df[groupby_column1] = new_df.index.get_level_values(0)\n", " new_df[groupby_column2] = new_df.index.get_level_values(1)\n", " new_df.reset_index(drop = True, inplace = True)\n", " return(new_df)\n", "\n", "#Creating a function that groups by, counts, creates a new column from the index, drops the index and changes the column names\n", "def groupby_3_count(df, groupby_column1, groupby_column2, groupby_column3, count_column): \n", " new_df = pd.DataFrame(df.groupby([groupby_column1, groupby_column2, groupby_column3 ])[count_column].count())\n", " new_df.columns = [\"count\"]\n", " new_df[groupby_column1] = new_df.index.get_level_values(0)\n", " new_df[groupby_column2] = new_df.index.get_level_values(1)\n", " new_df[groupby_column3] = new_df.index.get_level_values(2)\n", " new_df.reset_index(drop = True, inplace = True)\n", " return(new_df)\n", "\n", "# Going to use matplotlib for plotting... \n", "# To create a plot we followed the following formula: \n", "# df.plot(x-axis, y-axis, kind = type of plot, color = [(we specified colors to use here)], legend = False (we did not \n", "# want a legend displayed), title = \"Title\") then we added a ylabel with plt.ylabel(\"Type label here\") and an x label \n", "# with plt.xlabel(\"type label here\"). Finally, we wanted to change the direction of the xtick names from a 90 degree angle \n", "# to no angle with plt.xticks(rotation = rotation angle desired)\n", "def bar_graph_count(df, x_column, y_column, title):\n", " g = df.plot(x_column, y_column, kind = \"bar\", legend = False, title = title)\n", " g = plt.ylabel(y_column)\n", " g = plt.xlabel(x_column)\n", " return(g)\n", "\n", "\n", "#This will calculate the exponential moving average of the columns we want\n", "#exponential moving averages give more weight to the most recent data and less weight to older data \n", "def exp_moving_avg(d, column_to_be_meaned):\n", " d[\"exp_moving_avg\"] = d[column_to_be_meaned].ewm(com = 0.5,adjust=False).mean()\n", " exp_moving_avg = list(d[\"exp_moving_avg\"])\n", " #Adding a 0 to the first entry to exp_moving_avg\n", " exp_moving_avg = [0] + exp_moving_avg\n", " #Removing the last entry in the list \n", " exp_moving_avg.pop()\n", " #Creating a column named exp_moving_avg with the results \n", " d[\"exp_moving_avg\"] = exp_moving_avg\n", " return(exp_moving_avg)\n", "\n", "#This will calculate the cumulative moving average\n", "def cumulative_moving_avg(d): \n", " d[\"moving_avg\"] = d.expanding(min_periods = 1).mean()\n", " moving_avg = list(d[\"moving_avg\"])\n", " #Adding a 0 to the first entry to moving avg\n", " cumulative_moving_avg = [0] + moving_avg\n", " #Removing the last entry in the list \n", " cumulative_moving_avg.pop()\n", " return(cumulative_moving_avg)\n", "\n", "#This will get the list of all of the entries in the column that we are interested in for calculating the averages\n", "def getting_list_of_entries(df, column_interested_in, column_to_be_meaned): \n", " avg_people = pd.DataFrame(df.groupby([column_interested_in, \"released\"])[column_to_be_meaned].mean())\n", " avg_column_scores = pd.DataFrame()\n", " column_interested = list(df[column_interested_in].unique())\n", " return([avg_people, column_interested])\n", "\n", "#This will make a df for our moving averages that we are calculating \n", "def making_df(people_df, column_interested_in, released, person, cumulative_avg, exp_avg): \n", " df_2 = pd.DataFrame({column_interested_in: person, \"released\": released, \"cumulative_mean\": cumulative_avg, \"exp_mean\": exp_avg})\n", " return(df_2)\n", "\n", "#This includes the functions above, and will calculate the exponential and cumulative moving averages for which ever \n", "#column we specify and return a df will the column interested in, released, cumulative_mean, exp_mean\n", "def calculating_moving_avg(df, column_interested_in, column_to_be_meaned, ty):\n", " people_df = pd.DataFrame()\n", " people = getting_list_of_entries(df, column_interested_in, column_to_be_meaned)\n", " cumulative_avg = []\n", " avg_people = people[0]\n", " avg_people\n", " for person in people[1]: \n", " d = avg_people.groupby(column_interested_in).get_group(person)\n", " cumulative_avg = cumulative_moving_avg(d)\n", " exp_avg = exp_moving_avg(d, column_to_be_meaned)\n", " d.reset_index(inplace = True)\n", " released = d[\"released\"]\n", " df = pd.DataFrame({column_interested_in: person, \"released\": released, ty+\"_cumulative_mean_\"+column_interested_in : cumulative_avg, ty+\"_exp_mean_\"+column_interested_in: exp_avg})\n", " people_df = people_df.append(df)\n", " return(people_df)\n", "\n", "#Confusion Matrix Graph Function \n", "def confusion_matrix_graph (cm, accuracy_label, type_of_df): \n", " g = plt.figure(figsize=(2,2))\n", " g = sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', cbar = False);\n", " g = plt.ylabel('Actual');\n", " g = plt.xlabel('Predicted');\n", " g = all_sample_title = type_of_df +' Accuracy Score: {0}'.format(round(accuracy_label, 4))\n", " g = plt.title(all_sample_title, size = 12);\n", " return(g)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#reading in the V2_TN_reports.csv that we scraped \n", "movies = pd.read_csv(\"V2_TN_reports_dates.csv\", encoding = \"ISO-8859-1\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0CreativeTypeDomesticBoxOfficeGenreInfl.Adj.Dom.BoxOfficeInternationalBoxOfficeMaximumTheatersOpeningWeekendRevenueOpeningWeekendTheatersProductionBudgetProductionMethodReleasedReleasedWorldwideReleasedWorldwide_2Released_2SourceTheatricalDistributorTheatricalEngagementsTitleWorldwideBoxOffice
00Fantasy$317,871,467Adventure$510,437,208$657,176,1393,672$90,294,6213,672$125,000,000Animation/Live ActionNov 16, 2001Nov 15, 200120012001Based on Fiction Book/Short StoryWarner Bros.36,120Harry Potter and the Sorcerer’s Stone$975,047,606
11Fantasy$315,544,750Adventure$502,999,394$571,666,2353,381$47,211,4903,359$109,000,000Animation/Live ActionDec 19, 2001Dec 19, 200120012001Based on Fiction Book/Short StoryNew Line39,234The Lord of the Rings: The Fellowship…$887,210,985
22Kids Fiction$289,423,425Adventure$449,635,428$270,334,2943,649$62,577,0673,237$115,000,000Digital AnimationNov 2, 2001Nov 2, 200120012001Original ScreenplayWalt Disney45,411Monsters, Inc.$559,757,719
33Kids Fiction$267,655,011Adventure$430,801,615$224,157,7833,715$42,347,7603,587$50,000,000Digital AnimationMay 18, 2001May 18, 200120012001Based on Fiction Book/Short StoryDreamworks SKG40,234Shrek$491,812,794
44Kids Fiction$260,044,825Adventure$438,980,791$85,096,5783,256$55,820,3303,127$123,000,000Live ActionNov 17, 2000Nov 17, 200020002000Based on Fiction Book/Short StoryUniversal27,579How the Grinch Stole Christmas$345,141,403
\n", "
" ], "text/plain": [ " Unnamed: 0 CreativeType DomesticBoxOffice Genre \\\n", "0 0 Fantasy $317,871,467 Adventure \n", "1 1 Fantasy $315,544,750 Adventure \n", "2 2 Kids Fiction $289,423,425 Adventure \n", "3 3 Kids Fiction $267,655,011 Adventure \n", "4 4 Kids Fiction $260,044,825 Adventure \n", "\n", " Infl.Adj.Dom.BoxOffice InternationalBoxOffice MaximumTheaters \\\n", "0 $510,437,208 $657,176,139 3,672 \n", "1 $502,999,394 $571,666,235 3,381 \n", "2 $449,635,428 $270,334,294 3,649 \n", "3 $430,801,615 $224,157,783 3,715 \n", "4 $438,980,791 $85,096,578 3,256 \n", "\n", " OpeningWeekendRevenue OpeningWeekendTheaters ProductionBudget \\\n", "0 $90,294,621 3,672 $125,000,000 \n", "1 $47,211,490 3,359 $109,000,000 \n", "2 $62,577,067 3,237 $115,000,000 \n", "3 $42,347,760 3,587 $50,000,000 \n", "4 $55,820,330 3,127 $123,000,000 \n", "\n", " ProductionMethod Released ReleasedWorldwide ReleasedWorldwide_2 \\\n", "0 Animation/Live Action Nov 16, 2001 Nov 15, 2001 2001 \n", "1 Animation/Live Action Dec 19, 2001 Dec 19, 2001 2001 \n", "2 Digital Animation Nov 2, 2001 Nov 2, 2001 2001 \n", "3 Digital Animation May 18, 2001 May 18, 2001 2001 \n", "4 Live Action Nov 17, 2000 Nov 17, 2000 2000 \n", "\n", " Released_2 Source TheatricalDistributor \\\n", "0 2001 Based on Fiction Book/Short Story Warner Bros. \n", "1 2001 Based on Fiction Book/Short Story New Line \n", "2 2001 Original Screenplay Walt Disney \n", "3 2001 Based on Fiction Book/Short Story Dreamworks SKG \n", "4 2000 Based on Fiction Book/Short Story Universal \n", "\n", " TheatricalEngagements Title \\\n", "0 36,120 Harry Potter and the Sorcerer’s Stone \n", "1 39,234 The Lord of the Rings: The Fellowship… \n", "2 45,411 Monsters, Inc. \n", "3 40,234 Shrek \n", "4 27,579 How the Grinch Stole Christmas \n", "\n", " WorldwideBoxOffice \n", "0 $975,047,606 \n", "1 $887,210,985 \n", "2 $559,757,719 \n", "3 $491,812,794 \n", "4 $345,141,403 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#We are dropping the first column named Unnamed:0 \n", "movies.drop(\"Unnamed: 0\", axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1987, 19)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#We have 1987 movies and 19 columns in our current df \n", "#We are going to drop any rows if they have nas or missing values for budget\n", "movies.dropna(inplace = True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1971" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#We are going to check to see if we have any duplicates \n", "movies.drop_duplicates(subset =\"Title\", \n", " keep = \"first\", inplace = True) " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1060" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies.Title.unique())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#We had 16 movies with missing values... \n", "#Now we are going to drop any movies with 0s in budget \n", "movies = movies[movies[\"ProductionBudget\"] != \"$0\"]\n", "movies = movies.reset_index()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1060" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies)\n", "#We did not have any movies with a 0 budget " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#We are going to drop any movies with a DomesticBoxOffice of 0 \n", "movies = movies[movies[\"DomesticBoxOffice\"] != \"$0\"]\n", "movies = movies.reset_index()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1041" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['level_0', 'index', 'CreativeType', 'DomesticBoxOffice', 'Genre',\n", " 'Infl.Adj.Dom.BoxOffice', 'InternationalBoxOffice', 'MaximumTheaters',\n", " 'OpeningWeekendRevenue', 'OpeningWeekendTheaters', 'ProductionBudget',\n", " 'ProductionMethod', 'Released', 'ReleasedWorldwide',\n", " 'ReleasedWorldwide_2', 'Released_2', 'Source', 'TheatricalDistributor',\n", " 'TheatricalEngagements', 'Title', 'WorldwideBoxOffice'],\n", " dtype='object')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We had 19 movies with missing domestic box office info \n", "\n", "#We are going to change column names to something a little more user friendly. First, we will look at the column names \n", "movies.columns" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "movies.drop(['level_0', 'index'], axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "column_names = [\"creative_type\", \"domestic_box_office\", \"genre\", \"inflated_adj_dom_box_office\", \"int_box_office\", \"max_theaters\", \n", " \"open_wkend_rev\", \"open_wkend_theaters\", \"budget\", \"production_method\", \"released\", \"released_ww\", \"year\",\n", " \"year_ww\", \"source\", \"distributor\", \"engagements\", \"title\", \"world_wide_box_office\"]\n", "movies.columns = column_names" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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creative_typedomestic_box_officegenreinflated_adj_dom_box_officeint_box_officemax_theatersopen_wkend_revopen_wkend_theatersbudgetproduction_methodreleasedreleased_wwyearyear_wwsourcedistributorengagementstitleworld_wide_box_office
0Fantasy$317,871,467Adventure$510,437,208$657,176,1393,672$90,294,6213,672$125,000,000Animation/Live ActionNov 16, 2001Nov 15, 200120012001Based on Fiction Book/Short StoryWarner Bros.36,120Harry Potter and the Sorcerer’s Stone$975,047,606
1Fantasy$315,544,750Adventure$502,999,394$571,666,2353,381$47,211,4903,359$109,000,000Animation/Live ActionDec 19, 2001Dec 19, 200120012001Based on Fiction Book/Short StoryNew Line39,234The Lord of the Rings: The Fellowship…$887,210,985
2Kids Fiction$289,423,425Adventure$449,635,428$270,334,2943,649$62,577,0673,237$115,000,000Digital AnimationNov 2, 2001Nov 2, 200120012001Original ScreenplayWalt Disney45,411Monsters, Inc.$559,757,719
3Kids Fiction$267,655,011Adventure$430,801,615$224,157,7833,715$42,347,7603,587$50,000,000Digital AnimationMay 18, 2001May 18, 200120012001Based on Fiction Book/Short StoryDreamworks SKG40,234Shrek$491,812,794
4Kids Fiction$260,044,825Adventure$438,980,791$85,096,5783,256$55,820,3303,127$123,000,000Live ActionNov 17, 2000Nov 17, 200020002000Based on Fiction Book/Short StoryUniversal27,579How the Grinch Stole Christmas$345,141,403
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" ], "text/plain": [ " creative_type domestic_box_office genre inflated_adj_dom_box_office \\\n", "0 Fantasy $317,871,467 Adventure $510,437,208 \n", "1 Fantasy $315,544,750 Adventure $502,999,394 \n", "2 Kids Fiction $289,423,425 Adventure $449,635,428 \n", "3 Kids Fiction $267,655,011 Adventure $430,801,615 \n", "4 Kids Fiction $260,044,825 Adventure $438,980,791 \n", "\n", " int_box_office max_theaters open_wkend_rev open_wkend_theaters \\\n", "0 $657,176,139 3,672 $90,294,621 3,672 \n", "1 $571,666,235 3,381 $47,211,490 3,359 \n", "2 $270,334,294 3,649 $62,577,067 3,237 \n", "3 $224,157,783 3,715 $42,347,760 3,587 \n", "4 $85,096,578 3,256 $55,820,330 3,127 \n", "\n", " budget production_method released released_ww year \\\n", "0 $125,000,000 Animation/Live Action Nov 16, 2001 Nov 15, 2001 2001 \n", "1 $109,000,000 Animation/Live Action Dec 19, 2001 Dec 19, 2001 2001 \n", "2 $115,000,000 Digital Animation Nov 2, 2001 Nov 2, 2001 2001 \n", "3 $50,000,000 Digital Animation May 18, 2001 May 18, 2001 2001 \n", "4 $123,000,000 Live Action Nov 17, 2000 Nov 17, 2000 2000 \n", "\n", " year_ww source distributor engagements \\\n", "0 2001 Based on Fiction Book/Short Story Warner Bros. 36,120 \n", "1 2001 Based on Fiction Book/Short Story New Line 39,234 \n", "2 2001 Original Screenplay Walt Disney 45,411 \n", "3 2001 Based on Fiction Book/Short Story Dreamworks SKG 40,234 \n", "4 2000 Based on Fiction Book/Short Story Universal 27,579 \n", "\n", " title world_wide_box_office \n", "0 Harry Potter and the Sorcerer’s Stone $975,047,606 \n", "1 The Lord of the Rings: The Fellowship… $887,210,985 \n", "2 Monsters, Inc. $559,757,719 \n", "3 Shrek $491,812,794 \n", "4 How the Grinch Stole Christmas $345,141,403 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.head()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "creative_type object\n", "domestic_box_office object\n", "genre object\n", "inflated_adj_dom_box_office object\n", "int_box_office object\n", "max_theaters object\n", "open_wkend_rev object\n", "open_wkend_theaters object\n", "budget object\n", "production_method object\n", "released object\n", "released_ww object\n", "year int64\n", "year_ww int64\n", "source object\n", "distributor object\n", "engagements object\n", "title object\n", "world_wide_box_office object\n", "dtype: object" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Looking at the data type for each column in our df \n", "movies.dtypes" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 1041\n", "unique 8\n", "top Contemporary Fiction\n", "freq 441\n", "Name: creative_type, dtype: object" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.creative_type.describe()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Eventually, we need to change the following to numeric: \n", " # domestic_box_office \n", " # inflated_adj_dom_box_office \n", " # int_box_office\n", " # max_theathers \n", " # open_wkend_rev \n", " # open_wkend_theaters \n", " # budget \n", " # engagements \n", " # world_wide_box_office \n", "# We need to change the following to category: \n", " # creative_type \n", " # genre \n", " # production_method \n", " # source \n", " # distributor \n", "# We need to change the following to date: \n", " # released \n", " # released ww\n", "#Once we are done cleaning the data we are going to change the data types of the above questions. \n", "#If we change them now, when we clean the df and removed rows, the old categories \n", "#remain, and still show as possible categories. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "#First we need to replace the $ and ',' in the columns to be changed to numeric \n", "#First, creating a list of columns that we want to change to numeric\n", "numeric_columns = [\"domestic_box_office\", \"inflated_adj_dom_box_office\", \"int_box_office\", \n", " \"max_theaters\", \"open_wkend_rev\", \"open_wkend_theaters\", \"budget\", \"engagements\", \n", " \"world_wide_box_office\"]\n", "#We are using our remove function which takes the following arguments: df, column, item to remove\n", "movies[\"domestic_box_office\"] = remove(movies, \"domestic_box_office\", \"$\")\n", "movies[\"domestic_box_office\"] = remove(movies, \"domestic_box_office\", \",\")\n", "movies[\"inflated_adj_dom_box_office\"] = remove(movies, \"inflated_adj_dom_box_office\", \"$\")\n", "movies[\"inflated_adj_dom_box_office\"] = remove(movies, \"inflated_adj_dom_box_office\", \",\")\n", "movies[\"int_box_office\"] = remove(movies, \"int_box_office\", \"$\")\n", "movies[\"int_box_office\"] = remove(movies, \"int_box_office\", \",\")\n", "movies[\"max_theaters\"] = remove(movies, \"max_theaters\", \",\")\n", "movies[\"open_wkend_theaters\"] = remove(movies, \"open_wkend_theaters\", \",\")\n", "movies[\"open_wkend_rev\"] = remove(movies, \"open_wkend_rev\", \"$\")\n", "movies[\"open_wkend_rev\"] = remove(movies, \"open_wkend_rev\", \",\")\n", "movies[\"budget\"] = remove(movies, \"budget\", \"$\")\n", "movies[\"budget\"] = remove(movies, \"budget\", \",\")\n", "movies[\"engagements\"] = remove(movies, \"engagements\", \",\")\n", "movies[\"world_wide_box_office\"] = remove(movies, \"world_wide_box_office\", \"$\")\n", "movies[\"world_wide_box_office\"] = remove(movies, \"world_wide_box_office\", \",\")\n", "#Changing all of the columns in numeric_columns to numeric \n", "movies[numeric_columns] = movies[numeric_columns].apply(pd.to_numeric)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# We need to change the following to date: released, released ww\n", "movies[\"released\"] = pd.to_datetime(movies[\"released\"])\n", "movies[\"released_ww\"] = pd.to_datetime(movies[\"released_ww\"])\n", "#Separating the month, day and year into their own columns in case we would like to analyze based on month, day or year\n", "movies[\"month\"], movies[\"day\"] = movies[\"released\"].dt.month, movies[\"released\"].dt.day\n", "movies[\"month_ww\"], movies[\"day_ww\"] = movies[\"released_ww\"].dt.month, movies[\"released_ww\"].dt.day" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "creative_type object\n", "domestic_box_office int64\n", "genre object\n", "inflated_adj_dom_box_office int64\n", "int_box_office int64\n", "max_theaters int64\n", "open_wkend_rev int64\n", "open_wkend_theaters int64\n", "budget int64\n", "production_method object\n", "released datetime64[ns]\n", "released_ww datetime64[ns]\n", "year int64\n", "year_ww int64\n", "source object\n", "distributor object\n", "engagements int64\n", "title object\n", "world_wide_box_office int64\n", "month int64\n", "day int64\n", "month_ww int64\n", "day_ww int64\n", "dtype: object" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Checking data types again \n", "movies.dtypes" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CategoricalDtype(categories=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], ordered=True)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Changing the month to an ordered category \n", "cat = list(range(1,13))\n", "#Changing the month data type from int to ordered category \n", "movies[\"month\"] = pd.Categorical(movies[\"month\"], ordered = True, categories = cat)\n", "movies[\"month_ww\"] = pd.Categorical(movies[\"month_ww\"], ordered = True, categories = cat)\n", "#Checking to see if it worked \n", "movies.month.dtype" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "#Creating columns named domestic_profit, int_profit, ww_profit \n", "#We want to be able to look at the profit for each movie... Therefore we are creating a \n", "#profit column which is gross - budget \n", "movies[\"dom_profit\"] = movies[\"domestic_box_office\"] - movies[\"budget\"]\n", "movies[\"int_profit\"] = movies[\"int_box_office\"] - movies[\"budget\"]\n", "movies[\"ww_profit\"] = movies[\"world_wide_box_office\"] - movies[\"budget\"]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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creative_typedomestic_box_officegenreinflated_adj_dom_box_officeint_box_officemax_theatersopen_wkend_revopen_wkend_theatersbudgetproduction_method...engagementstitleworld_wide_box_officemonthdaymonth_wwday_wwdom_profitint_profitww_profit
0Fantasy317871467Adventure5104372086571761393672902946213672125000000Animation/Live Action...36120Harry Potter and the Sorcerer’s Stone97504760611161115192871467532176139850047606
1Fantasy315544750Adventure5029993945716662353381472114903359109000000Animation/Live Action...39234The Lord of the Rings: The Fellowship…88721098512191219206544750462666235778210985
2Kids Fiction289423425Adventure4496354282703342943649625770673237115000000Digital Animation...45411Monsters, Inc.559757719112112174423425155334294444757719
3Kids Fiction267655011Adventure430801615224157783371542347760358750000000Digital Animation...40234Shrek491812794518518217655011174157783441812794
4Kids Fiction260044825Adventure438980791850965783256558203303127123000000Live Action...27579How the Grinch Stole Christmas34514140311171117137044825-37903422222141403
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5 rows × 26 columns

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" ], "text/plain": [ " creative_type domestic_box_office genre inflated_adj_dom_box_office \\\n", "0 Fantasy 317871467 Adventure 510437208 \n", "1 Fantasy 315544750 Adventure 502999394 \n", "2 Kids Fiction 289423425 Adventure 449635428 \n", "3 Kids Fiction 267655011 Adventure 430801615 \n", "4 Kids Fiction 260044825 Adventure 438980791 \n", "\n", " int_box_office max_theaters open_wkend_rev open_wkend_theaters \\\n", "0 657176139 3672 90294621 3672 \n", "1 571666235 3381 47211490 3359 \n", "2 270334294 3649 62577067 3237 \n", "3 224157783 3715 42347760 3587 \n", "4 85096578 3256 55820330 3127 \n", "\n", " budget production_method ... engagements \\\n", "0 125000000 Animation/Live Action ... 36120 \n", "1 109000000 Animation/Live Action ... 39234 \n", "2 115000000 Digital Animation ... 45411 \n", "3 50000000 Digital Animation ... 40234 \n", "4 123000000 Live Action ... 27579 \n", "\n", " title world_wide_box_office month \\\n", "0 Harry Potter and the Sorcerer’s Stone 975047606 11 \n", "1 The Lord of the Rings: The Fellowship… 887210985 12 \n", "2 Monsters, Inc. 559757719 11 \n", "3 Shrek 491812794 5 \n", "4 How the Grinch Stole Christmas 345141403 11 \n", "\n", " day month_ww day_ww dom_profit int_profit ww_profit \n", "0 16 11 15 192871467 532176139 850047606 \n", "1 19 12 19 206544750 462666235 778210985 \n", "2 2 11 2 174423425 155334294 444757719 \n", "3 18 5 18 217655011 174157783 441812794 \n", "4 17 11 17 137044825 -37903422 222141403 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Looking to see if that helped\n", "movies.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "#Creating a percent profit column to have a normalized way to compare profits. \n", "#percent_profit = profit/budget*100 \n", "movies[\"dom_percent_profit\"] = movies[\"dom_profit\"]/movies[\"budget\"]*100\n", "movies[\"int_percent_profit\"] = movies[\"int_profit\"]/movies[\"budget\"]*100\n", "movies[\"ww_percent_profit\"] = movies[\"ww_profit\"]/movies[\"budget\"]*100" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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creative_typedomestic_box_officegenreinflated_adj_dom_box_officeint_box_officemax_theatersopen_wkend_revopen_wkend_theatersbudgetproduction_method...monthdaymonth_wwday_wwdom_profitint_profitww_profitdom_percent_profitint_percent_profitww_percent_profit
0Fantasy317871467Adventure5104372086571761393672902946213672125000000Animation/Live Action...11161115192871467532176139850047606154.297174425.740911680.038085
1Fantasy315544750Adventure5029993945716662353381472114903359109000000Animation/Live Action...12191219206544750462666235778210985189.490596424.464436713.955032
2Kids Fiction289423425Adventure4496354282703342943649625770673237115000000Digital Animation...112112174423425155334294444757719151.672543135.073299386.745843
3Kids Fiction267655011Adventure430801615224157783371542347760358750000000Digital Animation...518518217655011174157783441812794435.310022348.315566883.625588
4Kids Fiction260044825Adventure438980791850965783256558203303127123000000Live Action...11171117137044825-37903422222141403111.418557-30.815790180.602767
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5 rows × 29 columns

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" ], "text/plain": [ " creative_type domestic_box_office genre inflated_adj_dom_box_office \\\n", "0 Fantasy 317871467 Adventure 510437208 \n", "1 Fantasy 315544750 Adventure 502999394 \n", "2 Kids Fiction 289423425 Adventure 449635428 \n", "3 Kids Fiction 267655011 Adventure 430801615 \n", "4 Kids Fiction 260044825 Adventure 438980791 \n", "\n", " int_box_office max_theaters open_wkend_rev open_wkend_theaters \\\n", "0 657176139 3672 90294621 3672 \n", "1 571666235 3381 47211490 3359 \n", "2 270334294 3649 62577067 3237 \n", "3 224157783 3715 42347760 3587 \n", "4 85096578 3256 55820330 3127 \n", "\n", " budget production_method ... month day month_ww day_ww \\\n", "0 125000000 Animation/Live Action ... 11 16 11 15 \n", "1 109000000 Animation/Live Action ... 12 19 12 19 \n", "2 115000000 Digital Animation ... 11 2 11 2 \n", "3 50000000 Digital Animation ... 5 18 5 18 \n", "4 123000000 Live Action ... 11 17 11 17 \n", "\n", " dom_profit int_profit ww_profit dom_percent_profit int_percent_profit \\\n", "0 192871467 532176139 850047606 154.297174 425.740911 \n", "1 206544750 462666235 778210985 189.490596 424.464436 \n", "2 174423425 155334294 444757719 151.672543 135.073299 \n", "3 217655011 174157783 441812794 435.310022 348.315566 \n", "4 137044825 -37903422 222141403 111.418557 -30.815790 \n", "\n", " ww_percent_profit \n", "0 680.038085 \n", "1 713.955032 \n", "2 386.745843 \n", "3 883.625588 \n", "4 180.602767 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#checking to see that worked \n", "movies.head()\n", "#Writing the clean version of the df to a csv file \n", "#movies.to_csv(\"clean.csv\", index = False)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# #For some reason the functions do not work without rereading in the csv file... \n", "# movies = pd.read_csv(\"clean.csv\", encoding = \"ISO-8859-1\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1041" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies.domestic_box_office.unique())" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": false }, "outputs": [], "source": [ "#Aggregating a moving average column and calculating the mean average pp for each creative type;\n", "#by calculating the mean pp for all creative types but for only the movies prior to the \n", "#movie we are calculting the mean for. \n", "dom_ct_ma = calculating_moving_avg(movies, \"creative_type\", \"dom_percent_profit\", \"dom\")\n", "int_ct_ma = calculating_moving_avg(movies, \"creative_type\", \"int_percent_profit\", \"int\")\n", "ww_ct_ma = calculating_moving_avg(movies, \"creative_type\", \"ww_percent_profit\", \"ww\")\n", "# #Genres: \n", "dom_genre_ma = calculating_moving_avg(movies, \"genre\", \"dom_percent_profit\", \"dom\")\n", "int_genre_ma = calculating_moving_avg(movies, \"genre\", \"int_percent_profit\", \"int\")\n", "ww_genre_ma = calculating_moving_avg(movies, \"genre\", \"ww_percent_profit\", \"ww\")\n", "# production_method: \n", "dom_pm_ma = calculating_moving_avg(movies, \"production_method\", \"dom_percent_profit\", \"dom\")\n", "int_pm_ma = calculating_moving_avg(movies, \"production_method\", \"int_percent_profit\", \"int\")\n", "ww_pm_ma = calculating_moving_avg(movies, \"production_method\", \"ww_percent_profit\", \"ww\")\n", "# source\n", "dom_source_ma = calculating_moving_avg(movies, \"source\", \"dom_percent_profit\", \"dom\")\n", "int_source_ma = calculating_moving_avg(movies, \"source\", \"int_percent_profit\", \"int\")\n", "ww_source_ma = calculating_moving_avg(movies, \"source\", \"ww_percent_profit\", \"ww\")\n", "# distributor: \n", "dom_distributor_ma = calculating_moving_avg(movies, \"distributor\", \"dom_percent_profit\", \"dom\")\n", "int_distributor_ma = calculating_moving_avg(movies, \"distributor\", \"int_percent_profit\", \"int\")\n", "ww_distributor_ma = calculating_moving_avg(movies, \"distributor\", \"ww_percent_profit\", \"ww\")\n", "#Month \n", "dom_month_ma = calculating_moving_avg(movies, \"month\", \"dom_percent_profit\", \"dom\")\n", "int_month_ma = calculating_moving_avg(movies, \"month\", \"int_percent_profit\", \"int\")\n", "ww_month_ma = calculating_moving_avg(movies, \"month\", \"ww_percent_profit\", \"ww\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#We are going to use our left_merge_2_conditions function: \n", "#Inputs: df1, df2, column to merge on 1 and column to merge on 2 \n", "\n", "#Creating a movies domestic df \n", "movies_dom = left_merge_2_conditions(movies, dom_ct_ma, \"creative_type\", \"released\")\n", "movies_dom = left_merge_2_conditions(movies_dom, dom_genre_ma, \"genre\", \"released\")\n", "movies_dom = left_merge_2_conditions(movies_dom, dom_pm_ma, \"production_method\", \"released\")\n", "movies_dom = left_merge_2_conditions(movies_dom, dom_source_ma, \"source\", \"released\")\n", "movies_dom = left_merge_2_conditions(movies_dom, dom_distributor_ma, \"distributor\", \"released\")\n", "movies_dom = left_merge_2_conditions(movies_dom, dom_month_ma, \"month\", \"released\")\n", "\n", "#Creating a movies_int df \n", "movies_int = left_merge_2_conditions(movies, int_ct_ma, \"creative_type\", \"released\")\n", "movies_int = left_merge_2_conditions(movies_int, int_genre_ma, \"genre\", \"released\")\n", "movies_int = left_merge_2_conditions(movies_int, int_pm_ma, \"production_method\", \"released\")\n", "movies_int = left_merge_2_conditions(movies_int, int_source_ma, \"source\", \"released\")\n", "movies_int = left_merge_2_conditions(movies_int, int_distributor_ma, \"distributor\", \"released\")\n", "movies_int = left_merge_2_conditions(movies_int, int_month_ma, \"month\", \"released\")\n", "\n", "#Creating a movies_ww df \n", "movies_ww = left_merge_2_conditions(movies, ww_ct_ma, \"creative_type\", \"released\")\n", "movies_ww = left_merge_2_conditions(movies_ww, ww_genre_ma, \"genre\", \"released\")\n", "movies_ww = left_merge_2_conditions(movies_ww, ww_pm_ma, \"production_method\", \"released\")\n", "movies_ww = left_merge_2_conditions(movies_ww, ww_source_ma, \"source\", \"released\")\n", "movies_ww = left_merge_2_conditions(movies_ww, ww_distributor_ma, \"distributor\", \"released\")\n", "movies_ww = left_merge_2_conditions(movies_ww, ww_month_ma, \"month\", \"released\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "1041" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Looking at the movies_dom df head \n", "len(movies_dom)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['creative_type', 'domestic_box_office', 'genre',\n", " 'inflated_adj_dom_box_office', 'int_box_office', 'max_theaters',\n", " 'open_wkend_rev', 'open_wkend_theaters', 'budget', 'production_method',\n", " 'released', 'released_ww', 'year', 'year_ww', 'source', 'distributor',\n", " 'engagements', 'title', 'world_wide_box_office', 'month', 'day',\n", " 'month_ww', 'day_ww', 'dom_profit', 'int_profit', 'ww_profit',\n", " 'dom_percent_profit', 'int_percent_profit', 'ww_percent_profit',\n", " 'dom_cumulative_mean_creative_type', 'dom_exp_mean_creative_type',\n", " 'dom_cumulative_mean_genre', 'dom_exp_mean_genre',\n", " 'dom_cumulative_mean_production_method',\n", " 'dom_exp_mean_production_method', 'dom_cumulative_mean_source',\n", " 'dom_exp_mean_source', 'dom_cumulative_mean_distributor',\n", " 'dom_exp_mean_distributor', 'dom_cumulative_mean_month',\n", " 'dom_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Getting the column names for movies_dom\n", "movies_dom.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['creative_type', 'genre', 'open_wkend_rev', 'open_wkend_theaters',\n", " 'budget', 'production_method', 'released', 'year', 'source',\n", " 'distributor', 'title', 'month', 'day', 'dom_percent_profit',\n", " 'dom_cumulative_mean_creative_type', 'dom_exp_mean_creative_type',\n", " 'dom_cumulative_mean_genre', 'dom_exp_mean_genre',\n", " 'dom_cumulative_mean_production_method',\n", " 'dom_exp_mean_production_method', 'dom_cumulative_mean_source',\n", " 'dom_exp_mean_source', 'dom_cumulative_mean_distributor',\n", " 'dom_exp_mean_distributor', 'dom_cumulative_mean_month',\n", " 'dom_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#removing released_ww, year_ww, world_wide_box_office, month_ww, day_ww, int_profit, ww_profit\n", "columns_to_remove = [\"released_ww\", \"year_ww\", \"world_wide_box_office\", \"month_ww\", \"day_ww\", \"int_profit\", \"ww_profit\", \"int_percent_profit\", \"ww_percent_profit\", \"int_box_office\", \n", " \"domestic_box_office\", \"inflated_adj_dom_box_office\", \"max_theaters\", \"engagements\", \"dom_profit\"]\n", "movies_dom.drop(columns_to_remove, axis = 1, inplace = True)\n", "movies_dom.columns" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "#Creating an aggregated column to see if the open_wken_rev/open_wkend_theaters shows if a movie is in more demand \n", "movies_dom[\"open_wkend_rev/open_wkend_theaters\"] = movies_dom[\"open_wkend_rev\"]/movies_dom[\"open_wkend_theaters\"]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "#We are removing any rows that have 0s for the newly calculated columns \n", "#Looking to see what happens if we remove all the movies with a 0 for exp_mean_director and exp_mean_star\n", "movies_dom = movies_dom[movies_dom[\"dom_cumulative_mean_creative_type\"] != 0]\n", "movies_dom = movies_dom[movies_dom[\"dom_cumulative_mean_genre\"] != 0]\n", "movies_dom = movies_dom[movies_dom[\"dom_cumulative_mean_production_method\"] != 0]\n", "movies_dom = movies_dom[movies_dom[\"dom_cumulative_mean_source\"] != 0]\n", "movies_dom = movies_dom[movies_dom[\"dom_cumulative_mean_distributor\"] != 0]\n", "movies_dom = movies_dom[movies_dom[\"dom_cumulative_mean_month\"] != 0]\n", "\n", "movies_dom.dropna(inplace = True)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "968" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies_dom) #We still have 1859 movies in our df " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "#Changing creative_type, genre, production_method, source, distributor to category \n", "#We are using our cat_fun which takes the following inputs: df, column to change\n", "movies_dom[\"creative_type\"] = cat_fun(movies_dom, \"creative_type\")\n", "movies_dom[\"genre\"] = cat_fun(movies_dom, \"genre\")\n", "movies_dom[\"production_method\"] = cat_fun(movies_dom, \"production_method\")\n", "movies_dom[\"source\"] = cat_fun(movies_dom, \"source\")\n", "movies_dom[\"distributor\"] = cat_fun(movies_dom, \"distributor\")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['creative_type', 'domestic_box_office', 'genre',\n", " 'inflated_adj_dom_box_office', 'int_box_office', 'max_theaters',\n", " 'open_wkend_rev', 'open_wkend_theaters', 'budget', 'production_method',\n", " 'released', 'released_ww', 'year', 'year_ww', 'source', 'distributor',\n", " 'engagements', 'title', 'world_wide_box_office', 'month', 'day',\n", " 'month_ww', 'day_ww', 'dom_profit', 'int_profit', 'ww_profit',\n", " 'dom_percent_profit', 'int_percent_profit', 'ww_percent_profit',\n", " 'int_cumulative_mean_creative_type', 'int_exp_mean_creative_type',\n", " 'int_cumulative_mean_genre', 'int_exp_mean_genre',\n", " 'int_cumulative_mean_production_method',\n", " 'int_exp_mean_production_method', 'int_cumulative_mean_source',\n", " 'int_exp_mean_source', 'int_cumulative_mean_distributor',\n", " 'int_exp_mean_distributor', 'int_cumulative_mean_month',\n", " 'int_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Repeating the above process for movies_int \n", "#Looking at the movies_int columns \n", "movies_int.columns " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['creative_type', 'genre', 'open_wkend_rev', 'open_wkend_theaters',\n", " 'budget', 'production_method', 'released_ww', 'year_ww', 'source',\n", " 'distributor', 'title', 'month_ww', 'day_ww', 'int_percent_profit',\n", " 'int_cumulative_mean_creative_type', 'int_exp_mean_creative_type',\n", " 'int_cumulative_mean_genre', 'int_exp_mean_genre',\n", " 'int_cumulative_mean_production_method',\n", " 'int_exp_mean_production_method', 'int_cumulative_mean_source',\n", " 'int_exp_mean_source', 'int_cumulative_mean_distributor',\n", " 'int_exp_mean_distributor', 'int_cumulative_mean_month',\n", " 'int_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Removing columns that might unduly influence our prediction method for movies_int \n", "columns_to_remove = [\"domestic_box_office\", \"inflated_adj_dom_box_office\", \"int_box_office\", \"max_theaters\", \"world_wide_box_office\", \n", " \"engagements\", \"dom_profit\", \"int_profit\", \"ww_profit\", \"dom_percent_profit\", \"ww_percent_profit\", \n", " \"released\", \"year\", \"month\", \"day\"]\n", "movies_int.drop(columns_to_remove, axis = 1, inplace = True)\n", "movies_int.columns" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "#Aggregating a new column for open_wken_rev_by_theater; we think that it might show if a movie is more in demand or not. \n", "#If a movie made $1 million, but was only shown in 100 theaters and another movie made $1 million but was shown, in 300 \n", "#theaters, it might show that movie 1 was more in demand than movie 2... \n", "movies_int[\"open_wkend_rev/open_wkend_theaters\"] = movies_int[\"open_wkend_rev\"]/movies_int[\"open_wkend_theaters\"]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "#We are removing any rows that have 0s for the newly calculated columns \n", "#Looking to see what happens if we remove all the movies with a 0 for exp_mean_director and exp_mean_star\n", "movies_int = movies_int[movies_int[\"int_cumulative_mean_creative_type\"] != 0]\n", "movies_int = movies_int[movies_int[\"int_cumulative_mean_genre\"] != 0]\n", "movies_int = movies_int[movies_int[\"int_cumulative_mean_production_method\"] != 0]\n", "movies_int = movies_int[movies_int[\"int_cumulative_mean_source\"] != 0]\n", "movies_int = movies_int[movies_int[\"int_cumulative_mean_distributor\"] != 0]\n", "movies_int = movies_int[movies_int[\"int_cumulative_mean_month\"] != 0]\n", "\n", "movies_int.dropna(inplace = True)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "968" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#We still have 1859 movies after removing any of the aggregated columns with a 0 in them \n", "len(movies_int)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "#Changing creative_type, genre, production_method, source, distributor to category \n", "#We are using our cat_fun which takes the following inputs: df, column to change\n", "movies_int[\"creative_type\"] = cat_fun(movies_int, \"creative_type\")\n", "movies_int[\"genre\"] = cat_fun(movies_int, \"genre\")\n", "movies_int[\"production_method\"] = cat_fun(movies_int, \"production_method\")\n", "movies_int[\"source\"] = cat_fun(movies_int, \"source\")\n", "movies_int[\"distributor\"] = cat_fun(movies_int, \"distributor\")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['creative_type', 'domestic_box_office', 'genre',\n", " 'inflated_adj_dom_box_office', 'int_box_office', 'max_theaters',\n", " 'open_wkend_rev', 'open_wkend_theaters', 'budget', 'production_method',\n", " 'released', 'released_ww', 'year', 'year_ww', 'source', 'distributor',\n", " 'engagements', 'title', 'world_wide_box_office', 'month', 'day',\n", " 'month_ww', 'day_ww', 'dom_profit', 'int_profit', 'ww_profit',\n", " 'dom_percent_profit', 'int_percent_profit', 'ww_percent_profit',\n", " 'ww_cumulative_mean_creative_type', 'ww_exp_mean_creative_type',\n", " 'ww_cumulative_mean_genre', 'ww_exp_mean_genre',\n", " 'ww_cumulative_mean_production_method', 'ww_exp_mean_production_method',\n", " 'ww_cumulative_mean_source', 'ww_exp_mean_source',\n", " 'ww_cumulative_mean_distributor', 'ww_exp_mean_distributor',\n", " 'ww_cumulative_mean_month', 'ww_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#repeating the process for ww \n", "movies_ww.columns" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['creative_type', 'genre', 'open_wkend_rev', 'open_wkend_theaters',\n", " 'budget', 'production_method', 'released', 'released_ww', 'year',\n", " 'year_ww', 'source', 'distributor', 'title', 'month', 'day', 'month_ww',\n", " 'day_ww', 'ww_percent_profit', 'ww_cumulative_mean_creative_type',\n", " 'ww_exp_mean_creative_type', 'ww_cumulative_mean_genre',\n", " 'ww_exp_mean_genre', 'ww_cumulative_mean_production_method',\n", " 'ww_exp_mean_production_method', 'ww_cumulative_mean_source',\n", " 'ww_exp_mean_source', 'ww_cumulative_mean_distributor',\n", " 'ww_exp_mean_distributor', 'ww_cumulative_mean_month',\n", " 'ww_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Removing columns that would be unfair in our prediction methods \n", "columns_to_remove = [\"domestic_box_office\", \"inflated_adj_dom_box_office\", \"int_box_office\", \"max_theaters\", \n", " \"engagements\", \"world_wide_box_office\", \"dom_profit\", \"int_profit\", \"ww_profit\", \"dom_percent_profit\", \n", " \"int_percent_profit\"]\n", "movies_ww.drop(columns_to_remove, axis = 1, inplace = True)\n", "movies_ww.columns" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "#Aggregating a new column for open_wken_rev_by_theater; we think that it might show if a movie is more in demand or not. \n", "#If a movie made $1 million, but was only shown in 100 theaters and another movie made $1 million but was shown, in 300 \n", "#theaters, it might show that movie 1 was more in demand than movie 2... \n", "movies_ww[\"open_wkend_rev/open_wkend_theaters\"] = movies_ww[\"open_wkend_rev\"]/movies_ww[\"open_wkend_theaters\"]" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "#We are removing any rows that have 0s for the newly calculated columns \n", "#Looking to see what happens if we remove all the movies with a 0 for exp_mean_director and exp_mean_star\n", "movies_ww = movies_ww[movies_ww[\"ww_cumulative_mean_creative_type\"] != 0]\n", "movies_ww = movies_ww[movies_ww[\"ww_cumulative_mean_genre\"] != 0]\n", "movies_ww = movies_ww[movies_ww[\"ww_cumulative_mean_production_method\"] != 0]\n", "movies_ww = movies_ww[movies_ww[\"ww_cumulative_mean_source\"] != 0]\n", "movies_ww = movies_ww[movies_ww[\"ww_cumulative_mean_distributor\"] != 0]\n", "movies_ww = movies_ww[movies_ww[\"ww_cumulative_mean_month\"] != 0]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "977" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies_ww) #We still have 1859 movies in our df " ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "#Changing creative_type, genre, production_method, source, distributor to category \n", "#We are using our cat_fun which takes the following inputs: df, column to change\n", "movies_ww[\"creative_type\"] = cat_fun(movies_ww, \"creative_type\")\n", "movies_ww[\"genre\"] = cat_fun(movies_ww, \"genre\")\n", "movies_ww[\"production_method\"] = cat_fun(movies_ww, \"production_method\")\n", "movies_ww[\"source\"] = cat_fun(movies_ww, \"source\")\n", "movies_ww[\"distributor\"] = cat_fun(movies_ww, \"distributor\")" ] }, { "cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [], "source": [ "# movies_dom.to_csv(\"movies_dom.csv\")\n", "# movies_int.to_csv(\"movies_int.csv\")\n", "# movies_ww.to_csv(\"movies_ww.csv\")" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "#We have the same movies with the same discrete columns in our 3 dfs... Therefore, we are only going to perform \n", "#exploratory data analysis on one, but it will mimic the other 2 dfs " ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countgenre
0192Action
1273Adventure
25Black Comedy
3165Comedy
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6132Drama
747Horror
815Musical
942Romantic Comedy
1088Thriller/Suspense
117Western
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" ], "text/plain": [ " count genre\n", "0 192 Action\n", "1 273 Adventure\n", "2 5 Black Comedy\n", "3 165 Comedy\n", "4 1 Concert/Performance\n", "5 1 Documentary\n", "6 132 Drama\n", "7 47 Horror\n", "8 15 Musical\n", "9 42 Romantic Comedy\n", "10 88 Thriller/Suspense\n", "11 7 Western" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#What is the breakdown of genre in our df?\n", "\n", "#Getting the count of movies for each genre in our df and saving it as a pandas df. \n", "#We are grouping by genre and then getting the count of the genre column in each group by \n", "#we could have used any column to get the count of... \n", "#We are using the groupby_count function that takes the following arguments (df, groupby_column, count_column)\n", "movies_dom_genre = groupby_count(movies_dom, \"genre\", \"genre\")\n", "movies_dom_genre" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'genre')" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Using our bar_graph_count function to visualize the movies_genre group \n", "#It takes the following inputs: df, x_column, y_column, title\n", "movies_dom_genre.sort_values(['count'], ascending=[False], inplace = True)\n", "bar_graph_count(movies_dom_genre, \"genre\", \"count\", \"Visualization of the Number of Movies per Genre\")" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Contemporary Fiction\n", "1 Dramatization\n", "2 Factual\n", "3 Fantasy\n", "4 Historical Fiction\n", "5 Kids Fiction\n", "6 Science Fiction\n", "7 Super Hero\n", "Name: creative_type, dtype: category\n", "Categories (8, object): [Contemporary Fiction, Dramatization, Factual, Fantasy, Historical Fiction, Kids Fiction, Science Fiction, Super Hero]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Creating a data frame of the movies creative_type count \n", "movies_ct = groupby_count(movies_dom, \"creative_type\", \"creative_type\") \n", "movies_ct[\"creative_type\"]" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'creative_type')" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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19452019
\n", "
" ], "text/plain": [ " count year\n", "0 17 2000\n", "1 45 2001\n", "2 47 2002\n", "3 52 2003\n", "4 53 2004\n", "5 42 2005\n", "6 55 2006\n", "7 44 2007\n", "8 53 2008\n", "9 49 2009\n", "10 47 2010\n", "11 50 2011\n", "12 48 2012\n", "13 51 2013\n", "14 51 2014\n", "15 48 2015\n", "16 53 2016\n", "17 50 2017\n", "18 68 2018\n", "19 45 2019" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_year = groupby_count(movies_dom, \"year\", \"genre\")\n", "movies_year" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'year')" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bar_graph_count(movies_year, \"year\", \"count\", \"Visualization of the Number of Movies per Year\")\n" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmonth
0381
1642
2823
3504
41015
51166
61087
7718
8479
95510
1011011
1112612
\n", "
" ], "text/plain": [ " count month\n", "0 38 1\n", "1 64 2\n", "2 82 3\n", "3 50 4\n", "4 101 5\n", "5 116 6\n", "6 108 7\n", "7 71 8\n", "8 47 9\n", "9 55 10\n", "10 110 11\n", "11 126 12" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_month = groupby_count(movies_dom, \"month\", \"genre\")\n", "movies_month" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'month')" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bar_graph_count(movies_month, \"month\", \"count\", \"Visualization of the Number of Movies per Month\")" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsource
088Based on Comic/Graphic Novel
139Based on Factual Book/Article
2201Based on Fiction Book/Short Story
312Based on Folk Tale/Legend/Fairytale
48Based on Game
52Based on Movie
62Based on Musical or Opera
75Based on Play
839Based on Real Life Events
92Based on Religious Text
103Based on Short Film
1155Based on TV
125Based on Theme Park Ride
138Based on Toy
14421Original Screenplay
1563Remake
1615Spin-Off
\n", "
" ], "text/plain": [ " count source\n", "0 88 Based on Comic/Graphic Novel\n", "1 39 Based on Factual Book/Article\n", "2 201 Based on Fiction Book/Short Story\n", "3 12 Based on Folk Tale/Legend/Fairytale\n", "4 8 Based on Game\n", "5 2 Based on Movie\n", "6 2 Based on Musical or Opera\n", "7 5 Based on Play\n", "8 39 Based on Real Life Events\n", "9 2 Based on Religious Text\n", "10 3 Based on Short Film\n", "11 55 Based on TV\n", "12 5 Based on Theme Park Ride\n", "13 8 Based on Toy\n", "14 421 Original Screenplay\n", "15 63 Remake\n", "16 15 Spin-Off" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_source = groupby_count(movies_dom, \"source\", \"genre\")\n", "movies_source" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'source')" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "movies_source.sort_values(['count'], ascending=[False], inplace = True)\n", "bar_graph_count(movies_source, \"source\", \"count\", \"Visualization of the Number of Movies per Source\")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countdistributor
013120th Century Fox
119Dreamworks SKG
21FilmDistrict
37Focus Features
46Fox Searchlight
540Lionsgate
69MGM
711Miramax
87Miramax/Dimension
921New Line
10126Paramount Pictures
113Relativity
126STX Entertainment
13151Sony Pictures
145Summit Entertainment
151United Artists
16135Universal
17131Walt Disney
18152Warner Bros.
196Weinstein Co.
\n", "
" ], "text/plain": [ " count distributor\n", "0 131 20th Century Fox\n", "1 19 Dreamworks SKG\n", "2 1 FilmDistrict\n", "3 7 Focus Features\n", "4 6 Fox Searchlight\n", "5 40 Lionsgate\n", "6 9 MGM\n", "7 11 Miramax\n", "8 7 Miramax/Dimension\n", "9 21 New Line\n", "10 126 Paramount Pictures\n", "11 3 Relativity\n", "12 6 STX Entertainment\n", "13 151 Sony Pictures\n", "14 5 Summit Entertainment\n", "15 1 United Artists\n", "16 135 Universal\n", "17 131 Walt Disney\n", "18 152 Warner Bros.\n", "19 6 Weinstein Co." ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_distributor = groupby_count(movies_dom, \"distributor\", \"genre\")\n", "movies_distributor" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countdistributor
013120th Century Fox
119Dreamworks SKG
21FilmDistrict
37Focus Features
46Fox Searchlight
540Lionsgate
69MGM
711Miramax
87Miramax/Dimension
921New Line
10126Paramount Pictures
113Relativity
126STX Entertainment
13151Sony Pictures
145Summit Entertainment
151United Artists
16135Universal
17131Walt Disney
18152Warner Bros.
196Weinstein Co.
\n", "
" ], "text/plain": [ " count distributor\n", "0 131 20th Century Fox\n", "1 19 Dreamworks SKG\n", "2 1 FilmDistrict\n", "3 7 Focus Features\n", "4 6 Fox Searchlight\n", "5 40 Lionsgate\n", "6 9 MGM\n", "7 11 Miramax\n", "8 7 Miramax/Dimension\n", "9 21 New Line\n", "10 126 Paramount Pictures\n", "11 3 Relativity\n", "12 6 STX Entertainment\n", "13 151 Sony Pictures\n", "14 5 Summit Entertainment\n", "15 1 United Artists\n", "16 135 Universal\n", "17 131 Walt Disney\n", "18 152 Warner Bros.\n", "19 6 Weinstein Co." ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_distributor = movies_distributor[movies_distributor[\"count\"] > 0]\n", "movies_distributor" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'distributor')" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "movies_distributor.sort_values(['count'], ascending=[False], inplace = True)\n", "bar_graph_count(movies_distributor, \"distributor\", \"count\", \"Visualization of the Number of Movies per Distributor\")" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countproduction_method
087Animation/Live Action
1110Digital Animation
28Hand Animation
3762Live Action
41Stop-Motion Animation
\n", "
" ], "text/plain": [ " count production_method\n", "0 87 Animation/Live Action\n", "1 110 Digital Animation\n", "2 8 Hand Animation\n", "3 762 Live Action\n", "4 1 Stop-Motion Animation" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_production_method = groupby_count(movies_dom, \"production_method\", \"genre\")\n", "movies_production_method" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'production_method')" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "movies_production_method.sort_values(['count'], ascending=[False], inplace = True)\n", "bar_graph_count(movies_production_method, \"production_method\", \"count\", \"Visualization of the Number of Movies per Production Method\")" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "######################################################################################################################\n", "#\n", "# We are ready to create our testing and training dfs, we are going to see if we can predict percent_profit \n", "# For the domestic movies we have a company called \"Flops are Us\" and we want to see if we can predict if a \n", "# movie will be a flop or not after the first weekend. \n", "#\n", "# We will also try to predict percent profit without the open_wkend_rev and open_wken_rev/open_wkend_theaters\n", "#\n", "######################################################################################################################" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "#################################################################\n", "#Naive Bayes \n", "#**All Numerica Data *** \n", "#################################################################" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [], "source": [ "#Creating a test_train df for each of our 3 dfs \n", "test_train_movies_dom = movies_dom.copy() \n", "test_train_movies_int = movies_int.copy() \n", "test_train_movies_ww = movies_ww.copy() " ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [], "source": [ "test_train_movies_dom.columns\n", "#Need to remove \"creative_type\", \"genre\", \"production_method\", \"released\", \"source\", \"distributor\", \"title\"\n", "columns_to_remove = [\"creative_type\", \"genre\", \"production_method\", \"released\", \"source\", \"distributor\", \"title\", \"open_wkend_rev/open_wkend_theaters\", \"open_wkend_rev\", \"budget\", \"year\", \"month\", \"day\", \"open_wkend_theaters\"]\n", "columns_to_remove_ex = [\"dom_cumulative_mean_creative_type\", \"dom_cumulative_mean_genre\", \"dom_cumulative_mean_production_method\", \n", " \"dom_cumulative_mean_source\", \"dom_cumulative_mean_distributor\", \"dom_cumulative_mean_month\"]\n", "columns_to_remove_cumulative = [\"dom_exp_mean_creative_type\", \"dom_exp_mean_genre\", \"dom_exp_mean_production_method\", \n", " \"dom_exp_mean_source\", \"dom_exp_mean_distributor\", \"dom_exp_mean_month\"]\n", "test_train_movies_dom.drop(columns_to_remove, axis = 1, inplace = True)\n", "test_train_movies_dom_ex = test_train_movies_dom.copy() \n", "test_train_movies_dom_cumulative = test_train_movies_dom.copy() \n", "test_train_movies_dom_ex.drop(columns_to_remove_ex, axis = 1, inplace = True)\n", "test_train_movies_dom_cumulative.drop(columns_to_remove_cumulative, axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['dom_percent_profit', 'dom_cumulative_mean_creative_type',\n", " 'dom_exp_mean_creative_type', 'dom_cumulative_mean_genre',\n", " 'dom_exp_mean_genre', 'dom_cumulative_mean_production_method',\n", " 'dom_exp_mean_production_method', 'dom_cumulative_mean_source',\n", " 'dom_exp_mean_source', 'dom_cumulative_mean_distributor',\n", " 'dom_exp_mean_distributor', 'dom_cumulative_mean_month',\n", " 'dom_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 208, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_train_movies_dom.columns" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [], "source": [ "test_train_movies_int.columns\n", "#Need to remove \"creative_type\", \"genre\", \"production_method\", \"released_ww\", \"source\", \"distributor\", \"title\"\n", "columns_to_remove = [\"creative_type\", \"genre\", \"production_method\", \"released_ww\", \"source\", \"distributor\", \"title\"]\n", "test_train_movies_int.drop(columns_to_remove, axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [], "source": [ "test_train_movies_ww.columns \n", "#Need to remove \"creative_type\", \"genre\", \"production_method\", \"released_ww\", \"released\", \"source\", \"distributor\", \"title\"\n", "columns_to_remove = [\"creative_type\", \"genre\", \"production_method\", \"released_ww\", \"released\", \"source\", \"distributor\", \"title\"]\n", "test_train_movies_ww.drop(columns_to_remove, axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [], "source": [ "#We have to descritze percent profit... We are interested if we will have a negative percent profit or a positive percent profit \n", "categories = [\"negative\", \"positive\"]\n", "#Negative anything less than or equal to .0001 \n", "#positive anything greater than .0001 \n", "test_train_movies_dom[\"percent_profit\"] = pd.cut(test_train_movies_dom[\"dom_percent_profit\"], [-101, 0.0001, 999999], labels = categories)\n", "test_train_movies_dom_ex[\"percent_profit\"] = pd.cut(test_train_movies_dom[\"dom_percent_profit\"], [-101, 0.0001, 999999], labels = categories)\n", "test_train_movies_dom_cumulative[\"percent_profit\"] = pd.cut(test_train_movies_dom[\"dom_percent_profit\"], [-101, 0.0001, 999999], labels = categories)\n", "\n", "# test_train_movies_int[\"percent_profit\"] = pd.cut(test_train_movies_int[\"int_percent_profit\"], [-101, 0.0001, 999999], labels = categories)\n", "# test_train_movies_ww[\"percent_profit\"] = pd.cut(test_train_movies_ww[\"ww_percent_profit\"], [-101, 0.0001, 999999], labels = categories)" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "percent_profit\n", "negative 204\n", "positive 764\n", "Name: percent_profit, dtype: int64" ] }, "execution_count": 212, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Getting the count of each category in our test_train_movies_dom df \n", "test_train_movies_dom_count = test_train_movies_dom.groupby(\"percent_profit\")[\"percent_profit\"].count()\n", "test_train_movies_dom_count" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "764\n", "204\n" ] } ], "source": [ "# We are going to create a testing and training df that contains 386 negative, 386 positive percent_profits \n", "#First we are going to subset the positive percent profits and the negative per+cent_profits \n", "positive = test_train_movies_dom[test_train_movies_dom[\"percent_profit\"] == \"positive\"]\n", "test_train_movies_dom = test_train_movies_dom[test_train_movies_dom[\"percent_profit\"] == \"negative\"]\n", "\n", "positive_ex = test_train_movies_dom_ex[test_train_movies_dom_ex[\"percent_profit\"] == \"positive\"]\n", "test_train_movies_dom_ex = test_train_movies_dom_ex[test_train_movies_dom_ex[\"percent_profit\"] == \"negative\"]\n", "\n", "positive_cumulative = test_train_movies_dom_cumulative[test_train_movies_dom_cumulative[\"percent_profit\"] == \"positive\"]\n", "test_train_movies_dom_cumulative = test_train_movies_dom_cumulative[test_train_movies_dom_cumulative[\"percent_profit\"] == \"negative\"]\n", "#Getting the length to make sure that we have 204 negative, 771 postive in our df \n", "print(len(positive))\n", "print(len(test_train_movies_dom))" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "204\n" ] } ], "source": [ "#Now getting a random sample of 198 entries in the positive df and setting the seed to 123\n", "#to reproduce the results \n", "positive = positive.sample(n = 204, random_state = 1201)\n", "positive_ex = positive_ex.sample(n = 204, random_state = 1201)\n", "positive_cumulative = positive_cumulative.sample(n = 204, random_state = 1201)\n", "#Getting the length to make sure that it worked \n", "print(len(positive))" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "408" ] }, "execution_count": 215, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Adding the positive movies back to the test_train_movies_pp df \n", "test_train_movies_dom = pd.concat([test_train_movies_dom, positive])\n", "test_train_movies_dom_ex = pd.concat([test_train_movies_dom_ex, positive_ex])\n", "test_train_movies_dom_cumulative = pd.concat([test_train_movies_dom_cumulative, positive_cumulative])\n", "#Getting the length to make sure that the 2 df were combined correctly and if it did we would have 408 movies in our df\n", "len(test_train_movies_dom)" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [], "source": [ "# #Repeating the process for test_train_movies_int\n", "# #Getting the count of each category in our test_train_movies_int df \n", "# test_train_movies_int_count = test_train_movies_int.groupby(\"percent_profit\")[\"percent_profit\"].count()\n", "# test_train_movies_int_count" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [], "source": [ "# # We are going to create a testing and training df that contains 420 negative, 420 positive percent_profits \n", "# #First we are going to subset the positive percent profits and the negative per+cent_profits \n", "# positive = test_train_movies_int[test_train_movies_int[\"percent_profit\"] == \"positive\"]\n", "# test_train_movies_int = test_train_movies_int[test_train_movies_int[\"percent_profit\"] == \"negative\"]\n", "# #Getting the length to make sure that we have 229 negative, 739 postive in our df \n", "# print(len(positive))\n", "# print(len(test_train_movies_int))" ] }, { "cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [], "source": [ "# #Now getting a random sample of 420 entries in the positive df and setting the seed to 123\n", "# #to reproduce the results \n", "# positive = positive.sample(n = 229, random_state = 1201)\n", "# #Getting the length to make sure that it worked \n", "# print(len(positive))" ] }, { "cell_type": "code", "execution_count": 219, "metadata": {}, "outputs": [], "source": [ "# #Adding the positive movies back to the test_train_movies_pp df \n", "# test_train_movies_int = pd.concat([test_train_movies_int, positive])\n", "# #Getting the length to make sure that the 2 df were combined correctly and if it did we would have 458 movies in our df\n", "# len(test_train_movies_int)" ] }, { "cell_type": "code", "execution_count": 220, "metadata": {}, "outputs": [], "source": [ "# #Repeating the process for test_train_movies_ww \n", "# #Getting the count of each category in our test_train_movies_ww df \n", "# test_train_movies_ww_count = test_train_movies_ww.groupby(\"percent_profit\")[\"percent_profit\"].count()\n", "# test_train_movies_ww_count" ] }, { "cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [], "source": [ "#We do not have sufficient information to predict a negative or positive percent profit for world wide movies \n", "# We need more movies with a negative world wide percent profit... Although this is extremely interesting and \n", "# suggests that movies that have a negative domestic profit should release the movie internationally to recover " ] }, { "cell_type": "code", "execution_count": 222, "metadata": {}, "outputs": [], "source": [ "# #Changing the data type of month day and year to numeric\n", "# columns = [\"month\", \"day\"]\n", "# columns_ww = [\"month_ww\", \"day_ww\"]\n", "# test_train_movies_dom[columns] = test_train_movies_dom[columns].apply(pd.to_numeric)\n", "# test_train_movies_dom_ex[columns] = test_train_movies_dom_ex[columns].apply(pd.to_numeric)\n", "# test_train_movies_dom_cumulative[columns] = test_train_movies_dom_cumulative[columns].apply(pd.to_numeric)\n", "\n", "# # test_train_movies_ww[columns_ww] = test_train_movies_ww[columns_ww].apply(pd.to_numeric)" ] }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [], "source": [ "# test_train_movies_dom.reset_index(inplace = True)\n", "# test_train_movies_int.reset_index(inplace = True)\n", "\n", "# test_train_movies_dom.drop(\"level_0\", axis = 1, inplace = True)\n", "# test_train_movies_int.drop(\"level_0\", axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [], "source": [ "# #removing the label from the test_train_movies_dom and int df and saving it in a label df\n", "test_train_movies_dom_label = test_train_movies_dom[\"percent_profit\"]\n", "test_train_movies_dom.drop(\"percent_profit\", axis = 1, inplace = True)\n", "test_train_movies_dom_ex_label = test_train_movies_dom_ex[\"percent_profit\"]\n", "test_train_movies_dom_ex.drop(\"percent_profit\", axis = 1, inplace = True)\n", "test_train_movies_dom_cumulative_label = test_train_movies_dom_cumulative[\"percent_profit\"]\n", "test_train_movies_dom_cumulative.drop(\"percent_profit\", axis = 1, inplace = True)\n", "#repeating the process for int\n", "# test_train_movies_int_label = test_train_movies_int[\"percent_profit\"]\n", "# test_train_movies_int.drop(\"percent_profit\", axis = 1, inplace = True)\n" ] }, { "cell_type": "code", "execution_count": 225, "metadata": {}, "outputs": [], "source": [ "#Creating 4 df: 1: the training df with label removed, 2: the testing df with label removed, 3: the training label, 4: testing label\n", "from sklearn.model_selection import train_test_split \n", "dom_train, dom_test, dom_train_label, dom_test_label = train_test_split(test_train_movies_dom, test_train_movies_dom_label, test_size = .3, random_state = 116)\n", "dom_ex_train, dom_ex_test, dom_ex_train_label, dom_ex_test_label = train_test_split(test_train_movies_dom_ex, test_train_movies_dom_ex_label, test_size = .3, random_state = 116)\n", "dom_cum_train, dom_cum_test, dom_cum_train_label, dom_cum_test_label = train_test_split(test_train_movies_dom_cumulative, test_train_movies_dom_cumulative_label, test_size = .3, random_state = 116)\n", "# int_train, int_test, int_train_label, int_test_label = train_test_split(test_train_movies_int, test_train_movies_int_label, test_size = .3, random_state = 123)" ] }, { "cell_type": "code", "execution_count": 226, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counter({'negative': 62, 'positive': 61})\n" ] } ], "source": [ "#Getting a count of percent_profit in our test label scores in out test label\n", "#We want to have roughly the same number of positive and negative movies in our test df\n", "print(Counter(dom_test_label)) \n", "# print(Counter(int_test_label))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 227, "metadata": {}, "outputs": [], "source": [ "#Using the standard scale to help preprocess and normalize the data \n", "# performing preprocessing part \n", "sc = StandardScaler()\n", "dom_train = sc.fit_transform(dom_train)\n", "dom_test = sc.transform(dom_test)\n", "\n", "dom_ex_train = sc.fit_transform(dom_ex_train)\n", "dom_ex_test = sc.transform(dom_ex_test)\n", "\n", "dom_cum_train = sc.fit_transform(dom_cum_train)\n", "dom_cum_test = sc.transform(dom_cum_test)\n", "# int_train = sc.fit_transform(int_train)\n", "# int_test = sc.transform(int_test)" ] }, { "cell_type": "code", "execution_count": 228, "metadata": {}, "outputs": [], "source": [ "#Attempt 1: all variables\n", "clf = GaussianNB()\n", "clf.fit(dom_train, dom_train_label)\n", "test_predicted_dom_nb = clf.predict(dom_test)\n", "\n", "clf.fit(dom_ex_train, dom_ex_train_label)\n", "test_predicted_dom_ex_nb = clf.predict(dom_ex_test)\n", "\n", "clf.fit(dom_cum_train, dom_cum_train_label)\n", "test_predicted_dom_cum_nb = clf.predict(dom_cum_test)\n", "\n", "# clf.fit(int_train, int_train_label)\n", "# test_predicted_int_nb = clf.predict(int_test)" ] }, { "cell_type": "code", "execution_count": 229, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1, 'Dom Accuracy Score: 0.9024')" ] }, "execution_count": 229, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Accuracy for dom \n", "dom_accuracy_nb = accuracy_score(dom_test_label, test_predicted_dom_nb, normalize = True)\n", "cm = confusion_matrix(dom_test_label, test_predicted_dom_nb)\n", "confusion_matrix_graph(cm, dom_accuracy_nb, \"Dom\")" ] }, { "cell_type": "code", "execution_count": 230, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1, 'Dom Accuracy Score: 0.935')" ] }, "execution_count": 230, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dom_ex_accuracy_nb = accuracy_score(dom_ex_test_label, test_predicted_dom_ex_nb, normalize = True)\n", "cm = confusion_matrix(dom_ex_test_label, test_predicted_dom_ex_nb)\n", "confusion_matrix_graph(cm, dom_ex_accuracy_nb, \"Dom\")" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1, 'Dom Accuracy Score: 0.9024')" ] }, "execution_count": 231, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dom_cum_accuracy_nb = accuracy_score(dom_cum_test_label, test_predicted_dom_cum_nb, normalize = True)\n", "cm = confusion_matrix(dom_cum_test_label, test_predicted_dom_cum_nb)\n", "confusion_matrix_graph(cm, dom_cum_accuracy_nb, \"Dom\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }