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%ls
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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
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pixar_movies = pd.read_csv('PixarMovies.csv')
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pixar_movies.shape
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pixar_movies
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pixar_movies.dtypes
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pixar_movies.dropna().describe()
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Because Domestic % and International % columns data end with %, and its data type are objects, it is necessary to transfer its data type to float.
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pixar_movies['Domestic %'] = pixar_movies['Domestic %'].str.rstrip('%').astype('float')
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pixar_movies['International %'] = pixar_movies['International %'].str.rstrip('%').astype('float')
for the score columns, RT Score and Metacritic Score are 100 point scale, but IMDB Score is 10 point scale. IMDB Score could be changed to 100 point scale.
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pixar_movies['IMDB Score'] = pixar_movies['IMDB Score'] * 10
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filtered_pixar = pixar_movies.dropna()
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pixar_movies.set_index('Movie', inplace=True)
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filtered_pixar.set_index('Movie', inplace=True)
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pixar_movies
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critics_reviews = pixar_movies[['RT Score', 'IMDB Score', 'Metacritic Score']]
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critics_reviews.plot(figsize=(10,6))
plt.show()
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critics_reviews.plot(kind='box', figsize=(9,5))
plt.show()
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revenue_proportions = filtered_pixar[['Domestic %', 'International %']]
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revenue_proportions.plot(kind='bar', stacked=True, figsize=(12,6))
#sns.plt.show()
plt.show()
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filtered_pixar[['Oscars Nominated', 'Oscars Won']].plot(kind='bar', figsize=(12,6))
plt.show()
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