# IMDB dataset link in kaggle :https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data

#import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#create the dataframe
df=pd.read_csv("IMDB-Movie-Data.csv")
df.head()

df.shape

Output:
(1000, 12)

#To get information about dataset
df.info()

#check missing values in the dataset
df.isnull().sum()

Output:

sns.heatmap(df.isnull())
plt.show()

#To get missing value percentage
percentage_missing=df.isnull().sum()*100/len(df)
print(percentage_missing)

#drop all missing values
df.dropna(axis=0)

Output:

#check for duplicate data
duplicate_data=df.duplicated().any()
print(duplicate_data)

False

#Tp get statistics about the dataframe
df.describe(include='all')

Output:

#display title of the movie having runtime >=180minutes
df.columns

Output:

Index(['Rank', 'Title', 'Genre', 'Description', 'Director', 'Actors', 'Year',
Index(['Rank', 'Title', 'Genre', 'Description', 'Director', 'Actors', 'Year', 'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)', 'Metascore'], dtype='object')

df[df['Runtime (Minutes)']>=180]['Title']

Output:

#In which year there was the highest average voting
df.groupby('Year')['Votes'].mean().sort_values(ascending=False)

Output:

Year
2012 285226.093750
2008 275505.384615
2006 269289.954545
2009 255780.647059
2010 252782.316667
2007 244331.037736
2011 240790.301587
2013 219049.648352
2014 203930.224490
2015 115726.220472
2016 48591.754209
Name: Votes, dtype: float64

sns.barplot(x='Year',y='Votes',data=df)
plt.title("Average votes by year")
plt.show()

#In which year there was the highest average revenue
df.columns

Output:

Index(['Rank', 'Title', 'Genre', 'Description', 'Director', 'Actors', 'Year', 'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)', 'Metascore'], dtype='object')

df.groupby('Director')['Rating'].mean().sort_values(ascending=False)

Output:

Director
Nitesh Tiwari        8.80
Christopher Nolan    8.68
Olivier Nakache      8.60
Makoto Shinkai       8.60
Aamir Khan           8.50
...
Micheal Bafaro       3.50
Jonathan Holbrook    3.20
Shawn Burkett        2.70
James Wong           2.70
Jason Friedberg      1.90
Name: Rating, Length: 644, dtype: float64

#To display top10 lengthy movies Title and Runtime
df.columns

Output:

Index(['Rank', 'Title', 'Genre', 'Description', 'Director', 'Actors', 'Year', 'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)', 'Metascore'], dtype='object')

top10=df.nlargest(10,'Runtime (Minutes)')[['Title','Runtime (Minutes)']]\
.set_index('Title')
print(top10)

Runtime (Minutes)
Title
Grindhouse                                    191
The Hateful Eight                             187
The Wolf of Wall Street                       180
La vie d'Adèle                                180
Inland Empire                                 180
Cloud Atlas                                   172
3 Idiots                                      170
Interstellar                                  169
Pirates of the Caribbean: At World's End      169
The Hobbit: An Unexpected Journey             169

sns.barplot(x='Runtime (Minutes)',y=top10.index,data=top10)
plt.show()

#To get no. of movies per year
df.columns

Output:

Index(['Rank', 'Title', 'Genre', 'Description', 'Director', 'Actors', 'Year', 'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)', 'Metascore'], dtype='object')

df['Year'].value_counts()

Output:

2016   297
2015   127
2014   98
2013   91
2012   64
2011   63
2010   60
2007   53
2008   52
2009   51
2006   44
Name: Year, dtype: int64

sns.countplot(x='Year',data=df)
plt.show()

#Find most popular movie title (Highest Revenue)
df.columns

Index(['Rank', 'Title', 'Genre', 'Description', 'Director', 'Actors', 'Year', 'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)', 'Metascore'], dtype='object')

df[df['Revenue (Millions)'].max()==df['Revenue (Millions)']]['Title']

Output:

50 Star Wars: Episode VII - The Force Awakens
Name: Title, dtype: object

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