{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# =======================================================\n", "# PART 3: NEXT STEPS\n", "# =======================================================" ] }, { "cell_type": "code", "execution_count": null, "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", "\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", "# 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(span=40,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 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\"]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1971" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies)\n", "#We did not have any movies with a 0 budget " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#We are going to drop any movies with a DomesticBoxOffice of 0 \n", "movies = movies[movies[\"DomesticBoxOffice\"] != \"$0\"]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1952" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['CreativeType', 'DomesticBoxOffice', 'Genre', 'Infl.Adj.Dom.BoxOffice',\n", " 'InternationalBoxOffice', 'MaximumTheaters', 'OpeningWeekendRevenue',\n", " 'OpeningWeekendTheaters', 'ProductionBudget', 'ProductionMethod',\n", " 'Released', 'ReleasedWorldwide', 'ReleasedWorldwide_2', 'Released_2',\n", " 'Source', 'TheatricalDistributor', 'TheatricalEngagements', 'Title',\n", " 'WorldwideBoxOffice'],\n", " dtype='object')" ] }, "execution_count": 13, "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": 14, "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": 15, "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": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.head()" ] }, { "cell_type": "code", "execution_count": 16, "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": 16, "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": 17, "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": 17, "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": 18, "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": 19, "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": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Checking data types again \n", "movies.dtypes" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CategoricalDtype(categories=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], ordered=True)" ] }, "execution_count": 20, "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": 23, "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": 24, "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": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Looking to see if that helped\n", "movies.head()" ] }, { "cell_type": "code", "execution_count": 25, "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": 26, "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": 26, "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": 26, "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": 27, "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": 28, "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", "movies = left_merge_2_conditions(movies, dom_ct_ma, \"creative_type\", \"released\")\n", "movies = left_merge_2_conditions(movies, int_ct_ma, \"creative_type\", \"released\")\n", "movies = left_merge_2_conditions(movies, ww_ct_ma, \"creative_type\", \"released\")\n", "movies = left_merge_2_conditions(movies, dom_genre_ma, \"genre\", \"released\")\n", "movies = left_merge_2_conditions(movies, int_genre_ma, \"genre\", \"released\")\n", "movies = left_merge_2_conditions(movies, ww_genre_ma, \"genre\", \"released\")\n", "movies = left_merge_2_conditions(movies, dom_pm_ma, \"production_method\", \"released\")\n", "movies = left_merge_2_conditions(movies, int_pm_ma, \"production_method\", \"released\")\n", "movies = left_merge_2_conditions(movies, ww_pm_ma, \"production_method\", \"released\")\n", "movies = left_merge_2_conditions(movies, dom_source_ma, \"source\", \"released\")\n", "movies = left_merge_2_conditions(movies, int_source_ma, \"source\", \"released\")\n", "movies = left_merge_2_conditions(movies, ww_source_ma, \"source\", \"released\")\n", "movies = left_merge_2_conditions(movies, dom_distributor_ma, \"distributor\", \"released\")\n", "movies = left_merge_2_conditions(movies, int_distributor_ma, \"distributor\", \"released\")\n", "movies = left_merge_2_conditions(movies, ww_distributor_ma, \"distributor\", \"released\")\n", "movies = left_merge_2_conditions(movies, dom_month_ma, \"month\", \"released\")\n", "movies = left_merge_2_conditions(movies, int_month_ma, \"month\", \"released\")\n", "movies = left_merge_2_conditions(movies, ww_month_ma, \"month\", \"released\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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creative_typedomestic_box_officegenreinflated_adj_dom_box_officeint_box_officemax_theatersopen_wkend_revopen_wkend_theatersbudgetproduction_method...int_cumulative_mean_distributorint_exp_mean_distributorww_cumulative_mean_distributorww_exp_mean_distributordom_cumulative_mean_monthdom_exp_mean_monthint_cumulative_mean_monthint_exp_mean_monthww_cumulative_mean_monthww_exp_mean_month
0Fantasy317871467Adventure5104372086571761393672902946213672125000000Animation/Live Action...27.42843621.539307192.197044229.52443581.08653365.61508213.63731855.181602194.723851220.796684
1Fantasy315544750Adventure5029993945716662353381472114903359109000000Animation/Live Action...17.549794-53.381194304.874991477.270669137.721726269.516736108.456341225.039103346.178067594.555839
2Kids Fiction289423425Adventure4496354282703342943649625770673237115000000Digital Animation...1.336186-24.745212150.01767682.74846058.88979441.88202611.03776443.924968169.927558185.806994
3Kids Fiction267655011Adventure430801615224157783371542347760358750000000Digital Animation...105.017328-34.731348314.68562259.55792779.38676584.66764193.108241141.140128272.495007325.807769
4Kids Fiction260044825Adventure438980791850965783256558203303127123000000Live Action...83.354325147.055898384.951864410.78715739.22838339.22838349.36727249.367272188.595656188.595656
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5 rows × 65 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 ... int_cumulative_mean_distributor \\\n", "0 125000000 Animation/Live Action ... 27.428436 \n", "1 109000000 Animation/Live Action ... 17.549794 \n", "2 115000000 Digital Animation ... 1.336186 \n", "3 50000000 Digital Animation ... 105.017328 \n", "4 123000000 Live Action ... 83.354325 \n", "\n", " int_exp_mean_distributor ww_cumulative_mean_distributor \\\n", "0 21.539307 192.197044 \n", "1 -53.381194 304.874991 \n", "2 -24.745212 150.017676 \n", "3 -34.731348 314.685622 \n", "4 147.055898 384.951864 \n", "\n", " ww_exp_mean_distributor dom_cumulative_mean_month dom_exp_mean_month \\\n", "0 229.524435 81.086533 65.615082 \n", "1 477.270669 137.721726 269.516736 \n", "2 82.748460 58.889794 41.882026 \n", "3 59.557927 79.386765 84.667641 \n", "4 410.787157 39.228383 39.228383 \n", "\n", " int_cumulative_mean_month int_exp_mean_month ww_cumulative_mean_month \\\n", "0 13.637318 55.181602 194.723851 \n", "1 108.456341 225.039103 346.178067 \n", "2 11.037764 43.924968 169.927558 \n", "3 93.108241 141.140128 272.495007 \n", "4 49.367272 49.367272 188.595656 \n", "\n", " ww_exp_mean_month \n", "0 220.796684 \n", "1 594.555839 \n", "2 185.806994 \n", "3 325.807769 \n", "4 188.595656 \n", "\n", "[5 rows x 65 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1952, 65)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.shape" ] }, { "cell_type": "code", "execution_count": 31, "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", " 'int_cumulative_mean_creative_type', 'int_exp_mean_creative_type',\n", " 'ww_cumulative_mean_creative_type', 'ww_exp_mean_creative_type',\n", " 'dom_cumulative_mean_genre', 'dom_exp_mean_genre',\n", " 'int_cumulative_mean_genre', 'int_exp_mean_genre',\n", " 'ww_cumulative_mean_genre', 'ww_exp_mean_genre',\n", " 'dom_cumulative_mean_production_method',\n", " 'dom_exp_mean_production_method',\n", " 'int_cumulative_mean_production_method',\n", " 'int_exp_mean_production_method',\n", " 'ww_cumulative_mean_production_method', 'ww_exp_mean_production_method',\n", " 'dom_cumulative_mean_source', 'dom_exp_mean_source',\n", " 'int_cumulative_mean_source', 'int_exp_mean_source',\n", " 'ww_cumulative_mean_source', 'ww_exp_mean_source',\n", " 'dom_cumulative_mean_distributor', 'dom_exp_mean_distributor',\n", " 'int_cumulative_mean_distributor', 'int_exp_mean_distributor',\n", " 'ww_cumulative_mean_distributor', 'ww_exp_mean_distributor',\n", " 'dom_cumulative_mean_month', 'dom_exp_mean_month',\n", " 'int_cumulative_mean_month', 'int_exp_mean_month',\n", " 'ww_cumulative_mean_month', 'ww_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies.columns" ] }, { "cell_type": "code", "execution_count": 32, "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 = movies[movies[\"dom_cumulative_mean_creative_type\"] != 0]\n", "movies = movies[movies[\"dom_cumulative_mean_genre\"] != 0]\n", "movies = movies[movies[\"dom_cumulative_mean_production_method\"] != 0]\n", "movies = movies[movies[\"dom_cumulative_mean_source\"] != 0]\n", "movies = movies[movies[\"dom_cumulative_mean_distributor\"] != 0]\n", "movies = movies[movies[\"dom_cumulative_mean_month\"] != 0]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1859" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(movies) #We still have 1859 movies in our df " ] }, { "cell_type": "code", "execution_count": 34, "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[\"creative_type\"] = cat_fun(movies, \"creative_type\")\n", "movies[\"genre\"] = cat_fun(movies, \"genre\")\n", "movies[\"production_method\"] = cat_fun(movies, \"production_method\")\n", "movies[\"source\"] = cat_fun(movies, \"source\")\n", "movies[\"distributor\"] = cat_fun(movies, \"distributor\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countgenre
0377Action
1538Adventure
29Black Comedy
3315Comedy
42Concert/Performance
52Documentary
6249Drama
791Horror
830Musical
976Romantic Comedy
10158Thriller/Suspense
1112Western
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" ], "text/plain": [ " count genre\n", "0 377 Action\n", "1 538 Adventure\n", "2 9 Black Comedy\n", "3 315 Comedy\n", "4 2 Concert/Performance\n", "5 2 Documentary\n", "6 249 Drama\n", "7 91 Horror\n", "8 30 Musical\n", "9 76 Romantic Comedy\n", "10 158 Thriller/Suspense\n", "11 12 Western" ] }, "execution_count": 35, "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_genre = groupby_count(movies, \"genre\", \"genre\")\n", "movies_genre" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'genre')" ] }, "execution_count": 36, "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_genre.sort_values(['count'], ascending=[False], inplace = True)\n", "bar_graph_count(movies_genre, \"genre\", \"count\", \"Visualization of the Number of Movies per Genre\")" ] }, { "cell_type": "code", "execution_count": 37, "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": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Creating a data frame of the movies creative_type count \n", "movies_ct = groupby_count(movies, \"creative_type\", \"creative_type\") \n", "movies_ct[\"creative_type\"]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'creative_type')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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19742019
\n", "
" ], "text/plain": [ " count year\n", "0 26 2000\n", "1 90 2001\n", "2 91 2002\n", "3 100 2003\n", "4 101 2004\n", "5 83 2005\n", "6 107 2006\n", "7 86 2007\n", "8 98 2008\n", "9 98 2009\n", "10 94 2010\n", "11 102 2011\n", "12 95 2012\n", "13 101 2013\n", "14 100 2014\n", "15 96 2015\n", "16 103 2016\n", "17 96 2017\n", "18 118 2018\n", "19 74 2019" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_year = groupby_count(movies, \"year\", \"genre\")\n", "movies_year" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'year')" ] }, "execution_count": 40, "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": [ "bar_graph_count(movies_year, \"year\", \"count\", \"Visualization of the Number of Movies per Year\")\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmonth
0711
11212
21583
3944
41905
52256
62157
71378
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910810
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\n", "
" ], "text/plain": [ " count month\n", "0 71 1\n", "1 121 2\n", "2 158 3\n", "3 94 4\n", "4 190 5\n", "5 225 6\n", "6 215 7\n", "7 137 8\n", "8 85 9\n", "9 108 10\n", "10 215 11\n", "11 240 12" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_month = groupby_count(movies, \"month\", \"genre\")\n", "movies_month" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'month')" ] }, "execution_count": 42, "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": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countsource
0176Based on Comic/Graphic Novel
172Based on Factual Book/Article
2387Based on Fiction Book/Short Story
324Based on Folk Tale/Legend/Fairytale
415Based on Game
53Based on Movie
64Based on Musical or Opera
710Based on Play
877Based on Real Life Events
93Based on Religious Text
106Based on Short Film
11107Based on TV
1210Based on Theme Park Ride
1314Based on Toy
14803Original Screenplay
15118Remake
1630Spin-Off
\n", "
" ], "text/plain": [ " count source\n", "0 176 Based on Comic/Graphic Novel\n", "1 72 Based on Factual Book/Article\n", "2 387 Based on Fiction Book/Short Story\n", "3 24 Based on Folk Tale/Legend/Fairytale\n", "4 15 Based on Game\n", "5 3 Based on Movie\n", "6 4 Based on Musical or Opera\n", "7 10 Based on Play\n", "8 77 Based on Real Life Events\n", "9 3 Based on Religious Text\n", "10 6 Based on Short Film\n", "11 107 Based on TV\n", "12 10 Based on Theme Park Ride\n", "13 14 Based on Toy\n", "14 803 Original Screenplay\n", "15 118 Remake\n", "16 30 Spin-Off" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_source = groupby_count(movies, \"source\", \"genre\")\n", "movies_source" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'source')" ] }, "execution_count": 44, "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_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": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countdistributor
024920th Century Fox
136Dreamworks SKG
22FilmDistrict
39Focus Features
412Fox Searchlight
569Lionsgate
617MGM
720Miramax
814Miramax/Dimension
939New Line
10244Paramount Pictures
116Relativity
127STX Entertainment
13292Sony Pictures
1410Summit Entertainment
151United Artists
16260Universal
17259Walt Disney
18301Warner Bros.
1912Weinstein Co.
\n", "
" ], "text/plain": [ " count distributor\n", "0 249 20th Century Fox\n", "1 36 Dreamworks SKG\n", "2 2 FilmDistrict\n", "3 9 Focus Features\n", "4 12 Fox Searchlight\n", "5 69 Lionsgate\n", "6 17 MGM\n", "7 20 Miramax\n", "8 14 Miramax/Dimension\n", "9 39 New Line\n", "10 244 Paramount Pictures\n", "11 6 Relativity\n", "12 7 STX Entertainment\n", "13 292 Sony Pictures\n", "14 10 Summit Entertainment\n", "15 1 United Artists\n", "16 260 Universal\n", "17 259 Walt Disney\n", "18 301 Warner Bros.\n", "19 12 Weinstein Co." ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_distributor = groupby_count(movies, \"distributor\", \"genre\")\n", "movies_distributor" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countdistributor
024920th Century Fox
136Dreamworks SKG
22FilmDistrict
39Focus Features
412Fox Searchlight
569Lionsgate
617MGM
720Miramax
814Miramax/Dimension
939New Line
10244Paramount Pictures
116Relativity
127STX Entertainment
13292Sony Pictures
1410Summit Entertainment
151United Artists
16260Universal
17259Walt Disney
18301Warner Bros.
1912Weinstein Co.
\n", "
" ], "text/plain": [ " count distributor\n", "0 249 20th Century Fox\n", "1 36 Dreamworks SKG\n", "2 2 FilmDistrict\n", "3 9 Focus Features\n", "4 12 Fox Searchlight\n", "5 69 Lionsgate\n", "6 17 MGM\n", "7 20 Miramax\n", "8 14 Miramax/Dimension\n", "9 39 New Line\n", "10 244 Paramount Pictures\n", "11 6 Relativity\n", "12 7 STX Entertainment\n", "13 292 Sony Pictures\n", "14 10 Summit Entertainment\n", "15 1 United Artists\n", "16 260 Universal\n", "17 259 Walt Disney\n", "18 301 Warner Bros.\n", "19 12 Weinstein Co." ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_distributor = movies_distributor[movies_distributor[\"count\"] > 0]\n", "movies_distributor" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'distributor')" ] }, "execution_count": 47, "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": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countproduction_method
0176Animation/Live Action
1216Digital Animation
214Hand Animation
31451Live Action
42Stop-Motion Animation
\n", "
" ], "text/plain": [ " count production_method\n", "0 176 Animation/Live Action\n", "1 216 Digital Animation\n", "2 14 Hand Animation\n", "3 1451 Live Action\n", "4 2 Stop-Motion Animation" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_production_method = groupby_count(movies, \"production_method\", \"genre\")\n", "movies_production_method" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'production_method')" ] }, "execution_count": 49, "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": 50, "metadata": {}, "outputs": [], "source": [ "#Discretizing the df \n", "movies_discretized = movies.copy()" ] }, { "cell_type": "code", "execution_count": 51, "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", " 'int_cumulative_mean_creative_type', 'int_exp_mean_creative_type',\n", " 'ww_cumulative_mean_creative_type', 'ww_exp_mean_creative_type',\n", " 'dom_cumulative_mean_genre', 'dom_exp_mean_genre',\n", " 'int_cumulative_mean_genre', 'int_exp_mean_genre',\n", " 'ww_cumulative_mean_genre', 'ww_exp_mean_genre',\n", " 'dom_cumulative_mean_production_method',\n", " 'dom_exp_mean_production_method',\n", " 'int_cumulative_mean_production_method',\n", " 'int_exp_mean_production_method',\n", " 'ww_cumulative_mean_production_method', 'ww_exp_mean_production_method',\n", " 'dom_cumulative_mean_source', 'dom_exp_mean_source',\n", " 'int_cumulative_mean_source', 'int_exp_mean_source',\n", " 'ww_cumulative_mean_source', 'ww_exp_mean_source',\n", " 'dom_cumulative_mean_distributor', 'dom_exp_mean_distributor',\n", " 'int_cumulative_mean_distributor', 'int_exp_mean_distributor',\n", " 'ww_cumulative_mean_distributor', 'ww_exp_mean_distributor',\n", " 'dom_cumulative_mean_month', 'dom_exp_mean_month',\n", " 'int_cumulative_mean_month', 'int_exp_mean_month',\n", " 'ww_cumulative_mean_month', 'ww_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Getting a list of all of our columns \n", "movies_discretized.columns" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# We are going to descritize our data based on the quartiles. The categories are: \n", "# extremely_low, low, high, extremely_high\n", "# We are using our quartile_discretize function that takes the following arguments: \n", "#(df, column, category)\n", "categories = [\"extremely_low\", \"low\", \"high\", \"extremely_high\"]\n", "movies_discretized[\"domestic_box_office\"] = quartile_discretize(movies_discretized, \"domestic_box_office\", categories)\n", "movies_discretized[\"inflated_adj_dom_box_office\"] = quartile_discretize(movies_discretized, \"inflated_adj_dom_box_office\", categories)\n", "movies_discretized[\"int_box_office\"] = quartile_discretize(movies_discretized, \"int_box_office\", categories)\n", "movies_discretized[\"max_theaters\"] = quartile_discretize(movies_discretized, \"max_theaters\", categories)\n", "movies_discretized[\"open_wkend_rev\"] = quartile_discretize(movies_discretized, \"open_wkend_rev\", categories)\n", "movies_discretized[\"open_wkend_theaters\"] = quartile_discretize(movies_discretized, \"open_wkend_theaters\", categories)\n", "movies_discretized[\"budget\"] = quartile_discretize(movies_discretized, \"budget\", categories)\n", "movies_discretized[\"engagements\"] = quartile_discretize(movies_discretized, \"engagements\", categories)\n", "movies_discretized[\"world_wide_box_office\"] = quartile_discretize(movies_discretized, \"world_wide_box_office\", categories)" ] }, { "cell_type": "code", "execution_count": 53, "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", " 'int_cumulative_mean_creative_type', 'int_exp_mean_creative_type',\n", " 'ww_cumulative_mean_creative_type', 'ww_exp_mean_creative_type',\n", " 'dom_cumulative_mean_genre', 'dom_exp_mean_genre',\n", " 'int_cumulative_mean_genre', 'int_exp_mean_genre',\n", " 'ww_cumulative_mean_genre', 'ww_exp_mean_genre',\n", " 'dom_cumulative_mean_production_method',\n", " 'dom_exp_mean_production_method',\n", " 'int_cumulative_mean_production_method',\n", " 'int_exp_mean_production_method',\n", " 'ww_cumulative_mean_production_method', 'ww_exp_mean_production_method',\n", " 'dom_cumulative_mean_source', 'dom_exp_mean_source',\n", " 'int_cumulative_mean_source', 'int_exp_mean_source',\n", " 'ww_cumulative_mean_source', 'ww_exp_mean_source',\n", " 'dom_cumulative_mean_distributor', 'dom_exp_mean_distributor',\n", " 'int_cumulative_mean_distributor', 'int_exp_mean_distributor',\n", " 'ww_cumulative_mean_distributor', 'ww_exp_mean_distributor',\n", " 'dom_cumulative_mean_month', 'dom_exp_mean_month',\n", " 'int_cumulative_mean_month', 'int_exp_mean_month',\n", " 'ww_cumulative_mean_month', 'ww_exp_mean_month'],\n", " dtype='object')" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized.columns" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "#The other columns that are going to be discretized contain information regarding percent profit... We have decided to \n", "# use the following categories for percent profit \n", "#We are creating new label categories ; Discretized Percent Profit \n", "#We cannot use our function on this, because we are not discretizing by quartiles\n", "categories = [\"negative\", \"low\", \"high\", \"extremely_high\"]\n", "\n", "movies_discretized[\"dom_percent_profit\"] = pd.cut(movies_discretized[\"dom_percent_profit\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_percent_profit\"] = pd.cut(movies_discretized[\"int_percent_profit\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_percent_profit\"] = pd.cut(movies_discretized[\"ww_percent_profit\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_cumulative_mean_creative_type\"] = pd.cut(movies_discretized[\"dom_cumulative_mean_creative_type\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_exp_mean_creative_type\"] = pd.cut(movies_discretized[\"dom_exp_mean_creative_type\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_cumulative_mean_creative_type\"] = pd.cut(movies_discretized[\"int_cumulative_mean_creative_type\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_exp_mean_creative_type\"] = pd.cut(movies_discretized[\"int_exp_mean_creative_type\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_cumulative_mean_creative_type\"] = pd.cut(movies_discretized[\"ww_cumulative_mean_creative_type\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_exp_mean_creative_type\"] = pd.cut(movies_discretized[\"ww_exp_mean_creative_type\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_cumulative_mean_genre\"] = pd.cut(movies_discretized[\"dom_cumulative_mean_genre\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_exp_mean_genre\"] = pd.cut(movies_discretized[\"dom_exp_mean_genre\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_cumulative_mean_genre\"] = pd.cut(movies_discretized[\"int_cumulative_mean_genre\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_exp_mean_genre\"] = pd.cut(movies_discretized[\"int_exp_mean_genre\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_cumulative_mean_genre\"] = pd.cut(movies_discretized[\"ww_cumulative_mean_genre\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_exp_mean_genre\"] = pd.cut(movies_discretized[\"ww_exp_mean_genre\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_cumulative_mean_production_method\"] = pd.cut(movies_discretized[\"dom_cumulative_mean_production_method\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_exp_mean_production_method\"] = pd.cut(movies_discretized[\"dom_exp_mean_production_method\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_cumulative_mean_production_method\"] = pd.cut(movies_discretized[\"int_cumulative_mean_production_method\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_exp_mean_production_method\"] = pd.cut(movies_discretized[\"int_exp_mean_production_method\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_cumulative_mean_production_method\"] = pd.cut(movies_discretized[\"ww_cumulative_mean_production_method\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_exp_mean_production_method\"] = pd.cut(movies_discretized[\"ww_exp_mean_production_method\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_cumulative_mean_source\"] = pd.cut(movies_discretized[\"dom_cumulative_mean_source\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_exp_mean_source\"] = pd.cut(movies_discretized[\"dom_exp_mean_source\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_cumulative_mean_source\"] = pd.cut(movies_discretized[\"int_cumulative_mean_source\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_exp_mean_source\"] = pd.cut(movies_discretized[\"int_exp_mean_source\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_cumulative_mean_source\"] = pd.cut(movies_discretized[\"ww_cumulative_mean_source\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_exp_mean_source\"] = pd.cut(movies_discretized[\"ww_exp_mean_source\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_cumulative_mean_distributor\"] = pd.cut(movies_discretized[\"dom_cumulative_mean_distributor\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_exp_mean_distributor\"] = pd.cut(movies_discretized[\"dom_exp_mean_distributor\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_cumulative_mean_distributor\"] = pd.cut(movies_discretized[\"int_cumulative_mean_distributor\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_exp_mean_distributor\"] = pd.cut(movies_discretized[\"int_exp_mean_distributor\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_cumulative_mean_distributor\"] = pd.cut(movies_discretized[\"ww_cumulative_mean_distributor\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_exp_mean_distributor\"] = pd.cut(movies_discretized[\"ww_exp_mean_distributor\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_cumulative_mean_month\"] = pd.cut(movies_discretized[\"dom_cumulative_mean_month\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"dom_exp_mean_month\"] = pd.cut(movies_discretized[\"dom_exp_mean_month\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_cumulative_mean_month\"] = pd.cut(movies_discretized[\"int_cumulative_mean_month\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"int_exp_mean_month\"] = pd.cut(movies_discretized[\"int_exp_mean_month\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_cumulative_mean_month\"] = pd.cut(movies_discretized[\"ww_cumulative_mean_month\"], [-100, 0, 50, 150, 999999], labels = categories)\n", "movies_discretized[\"ww_exp_mean_month\"] = pd.cut(movies_discretized[\"ww_exp_mean_month\"], [-100, 0, 50, 150, 999999], labels = categories)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 1.859000e+03\n", "mean 5.330808e+07\n", "std 8.312150e+07\n", "min -2.841540e+08\n", "25% 9.498052e+06\n", "50% 4.270688e+07\n", "75% 7.955942e+07\n", "max 6.306622e+08\n", "Name: dom_profit, dtype: float64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized.dom_profit.describe()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "#negative = -999999999 - 0 \n", "#low = 0 - 40000000\n", "# high = 40000000 - 79559420\n", "# extremely_high = 79559420 - 9999999999\n", "movies_discretized[\"dom_profit\"] = pd.cut(movies_discretized[\"dom_profit\"], [-999999999, 0, 40000000, 79559420, 999999999], labels = categories)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 1.859000e+03\n", "mean 1.074237e+08\n", "std 1.714774e+08\n", "min -1.694891e+08\n", "25% 3.747336e+06\n", "50% 4.991367e+07\n", "75% 1.488067e+08\n", "max 1.604198e+09\n", "Name: int_profit, dtype: float64" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized.int_profit.describe()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "#negative = -999999999 - 0 \n", "#low = 0 - 3747336\n", "# high = 3747336 - 49913670\n", "# extremely_high = 49913670 - 9999999999\n", "movies_discretized[\"int_profit\"] = pd.cut(movies_discretized[\"int_profit\"], [-999999999, 0, 3747336, 49913670, 9999999999], labels = categories)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 1.859000e+03\n", "mean 2.477746e+08\n", "std 2.614642e+08\n", "min -1.036431e+08\n", "25% 8.632919e+07\n", "50% 1.561279e+08\n", "75% 3.031389e+08\n", "max 2.395446e+09\n", "Name: ww_profit, dtype: float64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized.ww_profit.describe()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "#negative = -999999999 - 0 \n", "#low = 0 - 10000000\n", "# high = 10000000 - 303138900\n", "# extremely_high = 303138900 - 9999999999\n", "movies_discretized[\"ww_profit\"] = pd.cut(movies_discretized[\"ww_profit\"], [-999999999, 0, 10000000, 303138900, 9999999999], labels = categories)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "#We are setting new categories for the day column by creating a new column for week \n", "# week_1 is the first 7 days of the month, week_2 is days 8 - 14, week_3 is days 15 - 21, and week_4 are the \n", "# rest of the days\n", "categories = [\"week_1\", \"week_2\", \"week_3\", \"week_4\"]\n", "\n", "movies_discretized[\"week\"] = pd.cut(movies_discretized[\"day\"], [0, 8, 15, 22, 32], labels = categories)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countgenredom_percent_profit
0125Actionnegative
1106Actionlow
2110Actionhigh
336Actionextremely_high
4160Adventurenegative
5151Adventurelow
6130Adventurehigh
797Adventureextremely_high
82Black Comedynegative
92Black Comedylow
101Black Comedyhigh
114Black Comedyextremely_high
1220Comedynegative
1335Comedylow
1497Comedyhigh
15163Comedyextremely_high
162Concert/Performanceextremely_high
172Documentarylow
1836Dramanegative
1945Dramalow
2063Dramahigh
21105Dramaextremely_high
224Horrornegative
237Horrorlow
2414Horrorhigh
2566Horrorextremely_high
262Musicalnegative
278Musicallow
2810Musicalhigh
2910Musicalextremely_high
303Romantic Comedynegative
3111Romantic Comedylow
3238Romantic Comedyhigh
3324Romantic Comedyextremely_high
3430Thriller/Suspensenegative
3533Thriller/Suspenselow
3647Thriller/Suspensehigh
3748Thriller/Suspenseextremely_high
384Westernnegative
393Westernlow
403Westernhigh
412Westernextremely_high
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" ], "text/plain": [ " count genre dom_percent_profit\n", "0 125 Action negative\n", "1 106 Action low\n", "2 110 Action high\n", "3 36 Action extremely_high\n", "4 160 Adventure negative\n", "5 151 Adventure low\n", "6 130 Adventure high\n", "7 97 Adventure extremely_high\n", "8 2 Black Comedy negative\n", "9 2 Black Comedy low\n", "10 1 Black Comedy high\n", "11 4 Black Comedy extremely_high\n", "12 20 Comedy negative\n", "13 35 Comedy low\n", "14 97 Comedy high\n", "15 163 Comedy extremely_high\n", "16 2 Concert/Performance extremely_high\n", "17 2 Documentary low\n", "18 36 Drama negative\n", "19 45 Drama low\n", "20 63 Drama high\n", "21 105 Drama extremely_high\n", "22 4 Horror negative\n", "23 7 Horror low\n", "24 14 Horror high\n", "25 66 Horror extremely_high\n", "26 2 Musical negative\n", "27 8 Musical low\n", "28 10 Musical high\n", "29 10 Musical extremely_high\n", "30 3 Romantic Comedy negative\n", "31 11 Romantic Comedy low\n", "32 38 Romantic Comedy high\n", "33 24 Romantic Comedy extremely_high\n", "34 30 Thriller/Suspense negative\n", "35 33 Thriller/Suspense low\n", "36 47 Thriller/Suspense high\n", "37 48 Thriller/Suspense extremely_high\n", "38 4 Western negative\n", "39 3 Western low\n", "40 3 Western high\n", "41 2 Western extremely_high" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Looking at the relationship between genre and percent profit \n", "movies_discretized_genre_pp = groupby_2_count(movies_discretized, \"genre\", \"dom_percent_profit\", \"genre\")\n", "movies_discretized_genre_pp" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "genre\n", "Action 377\n", "Adventure 538\n", "Black Comedy 9\n", "Comedy 315\n", "Concert/Performance 2\n", "Documentary 2\n", "Drama 249\n", "Horror 91\n", "Musical 30\n", "Romantic Comedy 76\n", "Thriller/Suspense 158\n", "Western 12\n", "Name: count, dtype: int64" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Now we are getting the sum of each genre category... We do not have a function for sum... we could go back and rework \n", "#our function.\n", "movies_discretized_genre_pp.groupby(\"genre\")[\"count\"].sum()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre\"]" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# We ultimately want a column that contains the total counts for each genre group. We are probably doing this in \n", "# a roundabout way, but as I am extremely new to python this is the best way I can think of doing it. We are going to create \n", "# a new column that replicates the genre column called genre_count and then we will use the replace function to \n", "# replace the genre names with their total count \n", "#First, replicating the income level column in a column named budget_category_count\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre\"] \n", "#Now replacing the income level with the total count for each income level \n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Action\"], 377 )\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Adventure\"], 538)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Black Comedy\"], 9)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Comedy\"], 315)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Concert/Performance\"], 2)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Documentary\"], 2)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Drama\"], 249)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Horror\"], 91)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Musical\"], 30)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Romantic Comedy\"], 76)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Thriller/Suspense\"], 158)\n", "movies_discretized_genre_pp[\"genre_count\"] = movies_discretized_genre_pp[\"genre_count\"].replace([\"Western\"], 12)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countgenredom_percent_profitgenre_count
0125Actionnegative377
1106Actionlow377
2110Actionhigh377
336Actionextremely_high377
4160Adventurenegative538
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" ], "text/plain": [ " count genre dom_percent_profit genre_count\n", "0 125 Action negative 377\n", "1 106 Action low 377\n", "2 110 Action high 377\n", "3 36 Action extremely_high 377\n", "4 160 Adventure negative 538" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized_genre_pp.head()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "movies_discretized_genre_pp[\"genre_count\"] = pd.to_numeric(movies_discretized_genre_pp[\"genre_count\"])" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "#Okay, we are one step closer... Now, we need to create a column that takes the counts/genre_counts * 100 \n", "movies_discretized_genre_pp[\"percent\"] = movies_discretized_genre_pp[\"count\"]/movies_discretized_genre_pp[\"genre_count\"] *100" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countgenredom_percent_profitgenre_countpercent
0125Actionnegative37733.156499
1106Actionlow37728.116711
2110Actionhigh37729.177719
336Actionextremely_high3779.549072
4160Adventurenegative53829.739777
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" ], "text/plain": [ " count genre dom_percent_profit genre_count percent\n", "0 125 Action negative 377 33.156499\n", "1 106 Action low 377 28.116711\n", "2 110 Action high 377 29.177719\n", "3 36 Action extremely_high 377 9.549072\n", "4 160 Adventure negative 538 29.739777" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies_discretized_genre_pp.head()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 70, "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": [ "'''Attempting to graph this data using a grouped bar chart: \n", "formula: df.pivot(columns, group, values).plot(kind = \"type of graph\", color = [\"color to use, can be a list of colors\"], \n", "title = \"you can set the title of your graph here\")'''\n", "graph = movies_discretized_genre_pp.pivot(\"genre\", \"dom_percent_profit\", \n", " \"percent\").plot(kind=\"bar\", color = [\"crimson\", \"salmon\", \"palegreen\", \"darkgreen\"],\n", " title = \"Percent of Percent Profit to Genre Category\")\n", "#Changing the y label of our graph to Percent\n", "plt.ylabel(\"Percent\")\n", "#Changing the x axis label of our graph to Budget Category \n", "plt.xlabel(\"Genre\")\n", "#How to change the tick labels (we ended up not needing this, but want to keep for future reference)\n", "#plt.Axes.set_xticklabels(graph, labels = ['extremely low', 'low', 'high', 'extremely high'])\n", "#moving the legend position to underneath the graph, also setting it to have 4 columns so the legend is in a \n", "#straight single line and adding a legend title\n", "plt.legend( loc = \"lower center\", bbox_to_anchor = (.5, -.6), ncol = 4, title = \"Percent Makeup of Genre Category\")\n" ] }, { "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 }