78 lines
3.4 KiB
Python
78 lines
3.4 KiB
Python
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#!/usr/bin/env python3.7
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import pandas as pd
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import seaborn as sns
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import datetime
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import matplotlib.pyplot as plt
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netflix=pd.read_csv('netflix_titles_enriched.csv')
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netflix_cleaned = netflix[netflix.rottentomatoes_audience_score > 0].sort_values(by = 'rottentomatoes_audience_score')
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netflix_cleaned.rottentomatoes_audience_score /= 100
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netflix_cleaned.drop(['rottentomatoes_audience_#reviews',
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'rottentomatoes_audience_review', 'rottentomatoes_tomatometer_score',
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'rottentomatoes_critics_#reviews', 'rottentomatoes_critic_review'], axis = 1)
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netflix_cleaned.date_added = netflix_cleaned.date_added.dropna().apply(lambda x: datetime.datetime.strptime(x[1:] if x[0] == ' ' else x, '%B %d, %Y'))
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netflix_cleaned.update(netflix_cleaned.select_dtypes(include = 'object').apply(lambda col: col.str.lower()))
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movies = netflix_cleaned[netflix_cleaned.type == 'movie']
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series = netflix_cleaned[netflix_cleaned.type == 'tv show']
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movies.duration = movies.duration.str.extract(r'(\d*)( min)')[0].astype('int32')
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from sklearn.preprocessing import MultiLabelBinarizer
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mlb = MultiLabelBinarizer()
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movies = movies.join(pd.DataFrame(mlb.fit_transform(movies.pop('listed_in').str.split(', ')),
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columns=mlb.classes_,
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index=movies.index))
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movies.drop(['movies'], axis = 1)
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import sklearn
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from sklearn.model_selection import train_test_split
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movies_train, movies_test = sklearn.model_selection.train_test_split(movies,test_size=0.20, random_state=42)
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movies_test, movies_val = sklearn.model_selection.train_test_split(movies_test,test_size=0.50, random_state=42)
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movies_subsets = [movies_train, movies_test, movies_val]
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for subset in movies_subsets:
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print(subset.shape[0])
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print(subset.describe(include='all'))
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movies_subsets = pd.concat(movies_subsets, keys = ['Train','Test','Validation'])
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movies_subsets = movies_subsets.reset_index()
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ax = sns.boxplot(data = movies_subsets, x = 'level_0', y = 'rottentomatoes_audience_score')
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ax.set(title = 'Audience score distribution between subsets', ylabel = 'Audience score on Rotten Tomatoes', xlabel = 'SUBSET')
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plt.show(ax)
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series.duration = series.duration.str.extract(r'(\d*)( seasons?)')[0].astype('int32')
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series = series.rename(columns = {'Unnamed: 0': 'Season'})
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series['Id'] = series.Season.str.extract(r'(s\d+)(|\',\ )(\d+)')[0]
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series.Season = series.Season.str.extract(r'(s\d+)(|\',\ )(\d+)')[2].astype('int32')
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series = series[series.Season > 0]
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mlb = MultiLabelBinarizer()
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series = series.join(pd.DataFrame(mlb.fit_transform(series.pop('listed_in').str.split(', ')),
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columns=mlb.classes_,
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index=series.index))
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series_train, series_test = sklearn.model_selection.train_test_split(series,test_size=0.20, random_state=42)
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series_test, series_val = sklearn.model_selection.train_test_split(series_test,test_size=0.50, random_state=42)
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series_subsets = [series_train, series_test, series_val]
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for subset in series_subsets:
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print(subset.shape[0])
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print(subset.describe(include='all'))
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series_subsets = pd.concat(series_subsets, keys = ['Train','Test','Validation'])
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series_subsets = series_subsets.reset_index()
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ax = sns.boxplot(data = series_subsets, x = 'level_0', y = 'rottentomatoes_audience_score')
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ax.set(title = 'Audience score distribution between subsets', ylabel = 'Audience score on Rotten Tomatoes', xlabel = 'SUBSET')
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plt.show(ax)
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