Netflix Dataset Details¶In this work, exploratory data analysis has been carried out on Netflix movies and TV shows Dataset.
Feature Details:
Type - The category of a show, can be either a Movie or a TV Show
Title - Name of the show
Director - Name of the director(s) of the show
Cast - Name of the actors
Country - Name of countries the show is available to watch on Netflix
Date Added - Date when the show was added on Netflix
Rating - Show rating on Netflix
Release Year - The release year of the show
Duration - Time duration of the show
Listed In - Genre of the show
Insights:
Importing Libraries and Loading Dataset¶import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("netflix_dataset.csv")
df.head()
| show_id | type | title | director | cast | country | date_added | release_year | rating | duration | listed_in | description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | s1 | TV Show | 3% | NaN | João Miguel, Bianca Comparato, Michel Gomes, R... | Brazil | August 14, 2020 | 2020 | TV-MA | 4 Seasons | International TV Shows, TV Dramas, TV Sci-Fi &... | In a future where the elite inhabit an island ... |
| 1 | s2 | Movie | 7:19 | Jorge Michel Grau | Demián Bichir, Héctor Bonilla, Oscar Serrano, ... | Mexico | December 23, 2016 | 2016 | TV-MA | 93 min | Dramas, International Movies | After a devastating earthquake hits Mexico Cit... |
| 2 | s3 | Movie | 23:59 | Gilbert Chan | Tedd Chan, Stella Chung, Henley Hii, Lawrence ... | Singapore | December 20, 2018 | 2011 | R | 78 min | Horror Movies, International Movies | When an army recruit is found dead, his fellow... |
| 3 | s4 | Movie | 9 | Shane Acker | Elijah Wood, John C. Reilly, Jennifer Connelly... | United States | November 16, 2017 | 2009 | PG-13 | 80 min | Action & Adventure, Independent Movies, Sci-Fi... | In a postapocalyptic world, rag-doll robots hi... |
| 4 | s5 | Movie | 21 | Robert Luketic | Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar... | United States | January 1, 2020 | 2008 | PG-13 | 123 min | Dramas | A brilliant group of students become card-coun... |
print("The shape of the Netflix Dataset is", df.shape)
The shape of the Netflix Dataset is (7787, 12)
print(df.describe())
release_year count 7787.000000 mean 2013.932580 std 8.757395 min 1925.000000 25% 2013.000000 50% 2017.000000 75% 2018.000000 max 2021.000000
This shows that there is only 1 numeric column in our Dataset.
print(df.dtypes)
show_id object type object title object director object cast object country object date_added object release_year int64 rating object duration object listed_in object description object dtype: object
print(df.isnull().sum())
show_id 0 type 0 title 0 director 2389 cast 718 country 507 date_added 10 release_year 0 rating 7 duration 0 listed_in 0 description 0 dtype: int64
Data Cleaning and Formatting¶We saw that there are some columns for which there are some null values present. Now, we will remove the null values and replace them with some meaningful data.
For cast and director columns, we will replace null values with "No Data". As these have large number of null values present.
Next, we will replace country, date_added and rating with the mode of the column.
Again printing the number of null values in each column. This time each column should have zero null values present.
df["director"].fillna("No Data", inplace = True)
df["cast"].fillna("No Data", inplace = True)
df["country"].fillna(df["country"].mode()[0], inplace = True)
df["date_added"].fillna(df["date_added"].mode()[0], inplace = True)
df["rating"].fillna(df["rating"].mode()[0], inplace = True)
print(df.isnull().sum())
show_id 0 type 0 title 0 director 0 cast 0 country 0 date_added 0 release_year 0 rating 0 duration 0 listed_in 0 description 0 dtype: int64
df["date_added"] = pd.to_datetime(df["date_added"])
df["year_added"] = df['date_added'].dt.year
Data Analysis and Visualization¶type_show = df["type"].value_counts()
show = type_show.index
count = type_show.values
explode = (0, 0.05)
color = ["#221f1f", "#E50914"]
plt.pie(count, labels = show, autopct = "%.1f%%", colors = color, explode = explode, startangle = 90)
plt.axis("equal")
plt.title("Distribution of Content Type")
plt.show()
top_10 = df["country"].value_counts().head(10)
countries = top_10.index
show_count = top_10.values
colors = ["#221f1f" if i % 2 == 0 else "#b20710" for i in range(len(countries))]
plt.bar(countries, show_count, color = colors)
plt.xticks(rotation = 90)
plt.title("Top 10 Countries with the Most Content")
plt.xlabel("Countries")
plt.ylabel("Count")
for i, v in enumerate(show_count):
plt.text(i, v, str(v), ha = "center", va = "bottom")
plt.show()
most_cont = df["year_added"].value_counts()
year = most_cont.index
value = most_cont.values
colors = ["#221f1f" if i % 2 == 0 else "#b20710" for i in range(len(year))]
plt.bar(year, value, color = colors)
plt.title("Content added over Years")
plt.xlabel("Year")
plt.ylabel("Count")
plt.show()
india = df[df["country"] == "India"]
cont_type = india["type"].value_counts()
cont_name = cont_type.index
cont_value = cont_type.values
explode = (0, 0.05)
color = ["#221f1f", "#E50914"]
plt.pie(cont_value, labels = cont_name, autopct = "%.2f%%", colors = color, explode = explode, startangle = 90)
plt.axis("equal")
plt.title("Distribution of Content Type Popular in India")
plt.show()
rat_type = df["rating"].value_counts()
rat_name = rat_type.index
rat_value = rat_type.values
colors = ["#221f1f" if i % 2 == 0 else "#b20710" for i in range(len(rat_name))]
plt.bar(rat_name, rat_value, color = colors)
plt.xticks(rotation = 45)
plt.title("Most Popular Ratings in India")
plt.xlabel("Ratings")
plt.ylabel("Count")
for i, v in enumerate(rat_value):
plt.text(i, v, str(v), ha = "center", va = "bottom")
plt.show()
df_movie = df[df['type'] == 'Movie'].copy()
df_movie['duration_value'] = df_movie['duration'].str.split().str[0].astype(int)
df_movie = df_movie[df_movie['duration_value'] == df_movie['duration_value'].max()]
movie_name = df_movie['title'].iloc[0]
year = df_movie['release_year'].iloc[0]
print(movie_name, "is the movie with the longest duration, released in the year", year)
Black Mirror: Bandersnatch is the movie with the longest duration, released in the year 2018