Visualization of Netflix Data using Python

In this tutorial, you will learn how to visualize the Netflix data using python.

Visualization of Netflix Data in Python

Steps involved:

• Loading packages
• Understanding the data
• Data preprocessing
• Data visualization

Loading packages:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

Dataset used: netflix_dataset.csv

Reading the data:

df = pd.read_csv('PATH OF THE DATASET')
df.head()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8807 entries, 0 to 8806 
Data columns (total 12 columns):
# Column Non-Null Count Dtype
-----------------------------
0 show_id 8807 non-null object
1 type 8807 non-null object
2 title 8807 non-null object
3 director 6173 non-null object 
4 cast 7982 non-null object 
5 country 7976 non-null object 
6 date_added 8797 non-null object 
7 release_year 8807 non-null int64 
8 rating 8803 non-null object 
9 duration 8804 non-null object 
10 listed_in 8807 non-null object 
11 description 8807 non-null object 
dtypes: int64(1), object(11) 
memory usage: 825.8+ KB

Data Preprocessing:

Finding the percentage of missing values in each column:

for i in df.columns:
    missing_per = 100 * df[i].isna().sum()/len(df) 
    print("{} missing value percentage: {}%".format(i,round(missing_per,4)))

show_id missing value percentage: 0.0%
type missing value percentage: 0.0%
title missing value percentage: 0.0%
director missing value percentage: 29.908%
cast missing value percentage: 9.3675%
country missing value percentage: 9.4357%
date_added missing value percentage: 0.1135%
release_year missing value percentage: 0.0%
rating missing value percentage: 0.0454%
duration missing value percentage: 0.0341%
listed_in missing value percentage: 0.0%
description missing value percentage: 0.0%

Replacing the missing data with a suitable value:

df['director'].replace(np.nan,'No data is available',inplace=True)
df['country'] = df['country'].fillna(df['country'].mode()[0])
df['cast'].replace(np.nan,'No data is available',inplace=True)
df.dropna(inplace=True)
df.drop_duplicates(inplace=True)

Finding the percentage of Movies and TV shows in the overall dataset:

a = df.groupby(['type'])['type'].count()
b = len(df)
val = ((a/b)).round(2)
per = pd.DataFrame(val).T
print(per)

type Movie TV Show 
type  0.7    0.3

Dropping irrelevant columns:

df = df.drop(columns =["show_id","director","cast","duration","description"])
df_net_new = df.drop(columns =["date_added","release_year","title","listed_in"])
df_net_new = df_net_new.dropna().reset_index()
df_net_new.head()

Consider only one first country:

for i in range(len(df_net_new["country"])):
    c_string = df_net_new["country"][i]
    split_string = c_string.split(",", 1)
    df_net_new["country"][i] = split_string[0]
df_net_country = df_net_new.groupby(by = ["country","type"])["rating"].count().reset_index()

      country     type     rating
1    Argentina    Movie      51
3    Australia    Movie      52
14   Canada       Movie      162
17   China        Movie      56
25   Egypt        Movie      95

Data Visualization:

Aggregate countries with ratings less than 40 to “other countries” and delete the countries with ratings less than 40.

Movie =0
TV =0
for i in range(len(df_net_country['rating'])):
    if ((df_net_country['rating'][i] < 40) & (df_net_country['type'][i] == "Movie")):
        Movie = Movie + df_net_country['rating'][i]
    if ((df_net_country['rating'][i] < 40) & (df_net_country['type'][i] == "TV Show")):
        TV = TV + df_net_country['rating'][i]
#add a new row to the dataframe        
new_row = {'country':'Other Countries', 'type':'Movie', 'rating':Movie}
df_net_country = df_net_country.append(new_row, ignore_index=True)
new_row = {'country':'Other Countries', 'type':'TV Show', 'rating':TV}
df_net_country = df_net_country.append(new_row, ignore_index=True)
df_net_country = df_net_country[df_net_country.rating >= 40]
df_net_country.head(5)

The percentage of ratings of all countries:

import matplotlib.pyplot as plt
colors = ( "orange", "yellow", "green",
          "blue", "pink", "red")

def func(pct, allvalues):
    absolute = int(pct / 100.*np.sum(allvalues))
    return "{:.1f}%)".format(pct, absolute)
plt.pie(df_net_country["rating"], autopct = lambda pct: func(pct, df_net_country["rating"]), labels = df_net_country["country"],radius=3,textprops = dict(color ="black"), colors = colors,shadow = True)
plt.show()