Exploratory Data Analysis of a Movie Dataset

Exploratory Analysis is a crucial step in Data Analysis process. It involves summarizing, investigating and visualising data to understand its characteristics and identify tends and patterns.

The dataset was downloaded from Kaggle.

#importing the required libraries
import pandas as pd
import numpy as np
import matplotlib as plt

#loading and viewing movie data
df = pd.read_csv("archive/movie.csv")
# df.head()
# df.shape[0]
df[df.columns[0]].count()
df

	movieId	title	genres
0	1	Toy Story (1995)	Adventure|Animation|Children|Comedy|Fantasy
1	2	Jumanji (1995)	Adventure|Children|Fantasy
2	3	Grumpier Old Men (1995)	Comedy|Romance
3	4	Waiting to Exhale (1995)	Comedy|Drama|Romance
4	5	Father of the Bride Part II (1995)	Comedy
...	...	...	...
27273	131254	Kein Bund für's Leben (2007)	Comedy
27274	131256	Feuer, Eis & Dosenbier (2002)	Comedy
27275	131258	The Pirates (2014)	Adventure
27276	131260	Rentun Ruusu (2001)	(no genres listed)
27277	131262	Innocence (2014)	Adventure|Fantasy|Horror

27278 rows × 3 columns

#splitting the title column into title and year column
split_title = df['title'].str.rsplit(' ', n=1, expand=True).fillna("")

df['title'] = split_title[0]
df['year'] = split_title[1]

df

	movieId	title	genres	year
0	1	Toy Story	Adventure|Animation|Children|Comedy|Fantasy	(1995)
1	2	Jumanji	Adventure|Children|Fantasy	(1995)
2	3	Grumpier Old Men	Comedy|Romance	(1995)
3	4	Waiting to Exhale	Comedy|Drama|Romance	(1995)
4	5	Father of the Bride Part II	Comedy	(1995)
...	...	...	...	...
27273	131254	Kein Bund für's Leben	Comedy	(2007)
27274	131256	Feuer, Eis & Dosenbier	Comedy	(2002)
27275	131258	The Pirates	Adventure	(2014)
27276	131260	Rentun Ruusu	(no genres listed)	(2001)
27277	131262	Innocence	Adventure|Fantasy|Horror	(2014)

27278 rows × 4 columns

#Removing the parenthesis from the year column
import re
pattern = r"[()]"
df['year'] = df['year'].str.replace(pattern,"",regex=True)
df

	movieId	title	genres	year
0	1	Toy Story	Adventure|Animation|Children|Comedy|Fantasy	1995
1	2	Jumanji	Adventure|Children|Fantasy	1995
2	3	Grumpier Old Men	Comedy|Romance	1995
3	4	Waiting to Exhale	Comedy|Drama|Romance	1995
4	5	Father of the Bride Part II	Comedy	1995
...	...	...	...	...
27273	131254	Kein Bund für's Leben	Comedy	2007
27274	131256	Feuer, Eis & Dosenbier	Comedy	2002
27275	131258	The Pirates	Adventure	2014
27276	131260	Rentun Ruusu	(no genres listed)	2001
27277	131262	Innocence	Adventure|Fantasy|Horror	2014

27278 rows × 4 columns

#extracting all the unique genres from the genres col.
#since some of them have multiple genres in a single cell

genres_list = df["genres"].str.split("|").sum()
unique_genres = list(set(genres_list))

print(unique_genres)

['Sci-Fi', '(no genres listed)', 'Romance', 'Mystery', 'Musical', 'Horror', 'Children', 'IMAX', 'Documentary', 'Comedy', 'Animation', 'Fantasy', 'Film-Noir', 'Adventure', 'Western', 'Thriller', 'Crime', 'War', 'Action', 'Drama']

#using the unique genres extracted earlier as the definition
#to count the number of occurance of each genres

df_genres = df["genres"].str.split("|").explode() #splits each genres into separate items
genre_counts = df_genres[df_genres.isin(unique_genres)].value_counts()
print(genre_counts)

genres
Drama                 13344
Comedy                 8374
Thriller               4178
Romance                4127
Action                 3520
Crime                  2939
Horror                 2611
Documentary            2471
Adventure              2329
Sci-Fi                 1743
Mystery                1514
Fantasy                1412
War                    1194
Children               1139
Musical                1036
Animation              1027
Western                 676
Film-Noir               330
(no genres listed)      246
IMAX                    196
Name: count, dtype: int64

#Plotting the count of each genres
genre_counts.plot(kind="bar", color="skyblue")

<Axes: xlabel='genres'>

Count of Each Genres