Netflix Data Analysis with Python

Netflix is one of the largest providers of online streaming services. It collects a huge amount of data because it has a very large subscriber base. In this article, I’m going to introduce you to a data science project on Netflix data analysis with Python.

Netflix Data Analysis

We can analyze a lot of data and models from Netflix because this platform has consistently focused on changing business needs by shifting its business model from on-demand DVD movie rental and now focusing a lot about the production of their original shows.

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In this article, I’ll take a look at some very important models of Netflix data to understand what’s best for their business. Some of the most important tasks that we can analyze from Netflix data are:

  1. understand what content is available
  2. understand the similarities between the content
  3. understand the network between actors and directors
  4. what exactly Netflix is focusing on
  5. and sentiment analysis of content available on Netflix.

Netflix Data Analysis with Python

The dataset I use for the Netflix data analytics task consists of TV shows and movies streamed on Netflix as of 2019. The dataset is provided by Flixable which is an engine of third-party research available on Netflix.

I’ll start this Netflix data analysis task with Python by importing the dataset and all the Python libraries needed for this task:

(6234, 12)

So the data consists of 6234 rows and 12 columns, now let’s look at the column names:

Index(['show_id', 'type', 'title', 'director', 'cast', 'country', 'date_added',
       'release_year', 'rating', 'duration', 'listed_in', 'description'],

Distribution of Content:

To begin the task of analyzing Netflix data, I’ll start by looking at the distribution of content ratings on Netflix:

z = dff.groupby(['rating']).size().reset_index(name='counts')
pieChart = px.pie(z, values='counts', names='rating', 
                  title='Distribution of Content Ratings on Netflix',
distribution of content on netflix

The graph above shows that the majority of content on Netflix is categorized as “TV-MA”, which means that most of the content available on Netflix is intended for viewing by mature and adult audiences.

Top 5 Actors and Directors:

Now let’s see the top 5 successful directors on this platform:

top 5 directors: Netflix data analysis

From the above graph it is derived that the top 5 directors on this platform are:

  1. Raul Campos
  2. Jan Suter
  3. Jay Karas
  4. Marcus Raboy
  5. Jay Chapman

Now let’s have a look at the top 5 successful actors on this platform:

top 5 actors: Netflix data analysis

From the above plot, it is derived that the top 5 actors on Netflix are:

  1. Anupam Kher
  2. Om Puri
  3. Shah Rukh Khan
  4. Takahira Sakurai
  5. Boman Irani

Analyzing Content on Netflix:

The next thing to analyze from this data is the trend of production over the years on Netflix:

Netflix content

The above line graph shows that there has been a decline in the production of the content for both movies and other shows since 2018.

At last, to conclude our analysis, I will analyze the sentiment of content on Netflix:

Netflix content sentiments

So the above graph shows that the overall positive content is always greater than the neutral and negative content combined.

I hope you liked this article on a data science project on Netflix Data Analysis with Python programming language. Feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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