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# replacing all the zeros from revenue and budget cols.

cols = ['budget', 'revenue']
movies_df[cols] = movies_df[cols].replace(0, np.nan)

# dropping all the rows with na in the columns mentioned above in the list.
movies_df.dropna(subset=cols, inplace=True)
movies_df.shape

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# replacing all the zeros from revenue and budget cols.

cols = ['budget', 'revenue']
movies_df[cols] = movies_df[cols].replace(0, np.nan)

# dropping all the rows with na in the columns mentioned above in the list.
movies_df.dropna(subset=cols, inplace=True)
movies_df.shape

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#  dropping the duplicates from the dataset.
print(movies_df.shape)
movies_df = movies_df.drop_duplicates(keep='first')
print(movies_df.shape)

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# First step is to clean the data and see which are the redundant or unnecessary cols

del_col_list = ['keywords', 'homepage', 'status', 'tagline', 'original_language', 'homepage', 
                'overview', 'production_companies', 'original_title', 'title_y']

movies_df = movies_df.drop(del_col_list, axis=1)
movies_df.head()

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# Let's load the dataset and create their dataframes

credits_df = pd.read_csv('./tmdb-5000-movie-dataset/tmdb_5000_credits.csv')
movies_df = pd.read_csv('./tmdb-5000-movie-dataset/tmdb_5000_movies.csv')
movies_df.head()

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# Importing the required packages here

import numpy as np
import pandas as pd
import seaborn as sns
import ast, json

from datetime import datetime
import matplotlib.pyplot as plt
% matplotlib inline