Netflix stands as a leading force in the realm of media and video streaming. With a staggering array of over 8,000 movies and TV shows accessible on their platform, as of mid-2021, their global subscriber count exceeds 200 million. This tabulated dataset comprehensively catalogues all offerings on Netflix, including vital details such as cast, directors, ratings, release year, duration, and more.

Dataset Overview:

The Netflix Titles dataset is a comprehensive compilation of movies and TV shows available on Netflix, covering various aspects such as the title type, director, cast, country of production, release year, rating, duration, genres (listed in), and a brief description. This dataset is instrumental for analyzing trends in Netflix content, understanding genre popularity, and examining the distribution of content across different regions and time periods.

Key Details:

• Total Entries: The dataset contains 8,809 entries, each representing a unique movie or TV show.
• Columns: There are 12 columns in the dataset:
  1. show_id: A unique identifier for each title.
  2. type: The category of the title, which is either 'Movie' or 'TV Show'.
  3. title: The name of the movie or TV show.
  4. director: The director(s) of the movie or TV show. (Contains null values for some entries, especially TV shows where this information might not be applicable.)
  5. cast: The list of main actors/actresses in the title. (Some entries might not have this information.)
  6. country: The country or countries where the movie or TV show was produced.
  7. date_added: The date the title was added to Netflix.
  8. release_year: The year the movie or TV show was originally released.
  9. rating: The age rating of the title.
  10. duration: The duration of the title, in minutes for movies and seasons for TV shows.
  11. listed_in: The genres the title falls under.
  12. description: A brief summary of the title.

Potential Use Cases:

• Content Analysis: This dataset can be used to perform detailed content analysis, such as genre popularity over time, distribution of content production across different countries, and trends in movie versus TV show production.
• Recommendation Systems: For developers and data scientists working on recommendation systems, this dataset provides a rich source of metadata for content similarity and user preference modeling.
• Market Analysis: Market researchers can utilize this dataset to analyze Netflix's content strategy, including their focus on international markets, genre diversification, and investment in original content.

Whether you are a data enthusiast, a content creator, or a market analyst, the Netflix Titles dataset offers valuable insights into the evolving landscape of digital content. Explore this dataset to uncover trends, patterns, and opportunities in the world of streaming entertainment.

If you find the dataset intriguing, please consider upvoting. Thank you.

The raw data is Web Scrapped through Selenium. It contains Unlabelled text data of around 9000 Netflix Shows and Movies along with Full details like Cast, Release Year, Rating, Description, etc.

Overview: This dataset is a synthetic collection of 3000 rows simulating information about Netflix movies and TV shows. It is designed to capture details such as content type, genre, release year, ratings, duration, and production country.

Dataset Columns: Title:

Unique title of the movie or TV show. Example: Title 1, Title 2. Type:

Specifies whether the content is a Movie or a TV Show. Example: Movie, TV Show. Genre:

Describes the genre of the content. Example: Drama, Comedy, Action, Romance, Sci-Fi. Release Year:

The year when the movie or TV show was released. Range: 1950 to 2023. Example: 2001, 2015. Rating:

Age ratings assigned to the content. Categories: G, PG, PG-13, R, TV-MA, TV-14, TV-PG. Example: TV-MA, PG. Duration:

For movies: Runtime in minutes. For TV shows: Number of seasons. Example: 120 min (for movies), 2 Seasons (for TV shows). Country:

The country where the movie or TV show was produced. Example: United States, India, Japan, United Kingdom.

Example Data (Preview): Title Type Genre Release Year Rating Duration Country Title 1 Movie Drama 2015 PG-13 120 min United States Title 2 TV Show Comedy 2020 TV-MA 2 Seasons India Title 3 Movie Action 1998 R 90 min United Kingdom

Potential Use Cases: Exploratory Data Analysis (EDA):

Analyze the distribution of genres, ratings, or release years. Recommendation Systems:

Build content recommendation models based on genre, type, or user preferences. Visualization:

Visualize trends in Netflix content over the years. Compare the production of movies vs. TV shows. Machine Learning:

Classify content type based on attributes like genre and release year.

Analysis of ‘1000 Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/chasewillden/netflix-shows on 21 November 2021.

--- Dataset description provided by original source is as follows ---

Context

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Content

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Acknowledgements

The data set and the research article can be found at The Concept Center

Inspiration

I was watching Netflix with my wife and we asked ourselves, why are there so many R and TV-MA rating shows?

--- Original source retains full ownership of the source dataset ---

Don't forget to hit the upvote🙏🙏

About this Dataset: Netflix is one of the most popular media and video streaming platforms. They have over 8k+ movies or tv shows available on their platform, as of mid-2024, they have over 282 million Subscribers globally. This tabular dataset consists of listings of all the movies and tv shows available on Netflix, along with details such as - cast, directors, ratings, release year, duration more.

Columns and Descriptions:

- show_id: Unique identifier for each show (s1, s2).

- type: Specifies whether the title is a "Movie" or "TV Show".

- title: The name of the Netflix title.

- director: The director of the title

- cast: The main actors involved in the title.

- country: The country where the title was produced.

- date_added: The date when the title was added to Netflix.

- release_year: The year the title was originally released.

- rating: The content rating ("PG-13", "TV-MA").

- duration: Duration of the movie (in minutes) or the number of seasons for TV shows.

- listed_in: Categories or genres the title falls under ("Documentaries", "TV Dramas").

- description: The summary description

Dive into the Netflix Movies and TV Shows Dataset, a detailed collection of web-scraped data featuring popular streaming titles. Discover trending movies, binge-worthy TV series, genres, ratings, release years, and audience preferences. Gain insights into Netflix originals, global streaming trends, and viewer favorites to inform market analysis and entertainment research.

Perfect for exploring content diversity, production trends, and streaming platform dynamics.

DataSet at Github

Background

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase in show variety. However, do people understand the distribution of ratings on Netflix shows?

Netflix Suggestion Engine

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include : G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

1) Data Introduction • The Netflix Movies and TV Shows Dataset contains various metadata on movies and TV shows available on Netflix. • Key features include the title, director, cast, country, date added, release year, rating, genre, and total duration (in minutes or number of seasons) of the content.

2) Data Utilization (1) Characteristics of the Netflix Movies and TV Shows Dataset • This dataset helps in understanding content trends and markets, as well as analyzing global preferences and changing consumer tastes. • It is useful for analyzing the characteristics of content available in different countries, including genre, cast, director, and more.

(2) Applications of the Netflix Movies and TV Shows Dataset • Content Analysis: Analyze how Netflix's content is distributed, and understand preferences based on genre or country. • Recommendation System Development: Develop algorithms that recommend similar content based on user viewing patterns. • Market Analysis: Identify which content is popular in different countries and analyze if Netflix focuses more on specific countries or genres.

Content

Regarding this dataset, Netflix is among the most popular websites for streaming movies and videos. They have more than 200 million members globally as of the middle of 2021, and their platform offers over 8,000 movies and TV shows. This tabular dataset contains listings of all the movies and TV shows available on Netflix, together with details about the actors, directors, ratings, length, year of release, and other details.

Interesting Ideas to do Tasks for the people from different backgrounds

For Analysts of Data

Content Trends Over Time - Examine the annual changes in Netflix's movie and TV show counts. 2. Genre Popularity - Discover the most popular genres and how their popularity changes by location or year. 3. Country Insights - Find out which nations produce the most shows and what kinds of content they contribute. 4. Ratings Distribution - Show how the mature ratings (G, PG, R, TV-MA) are distributed throughout Netflix material. 5. Best Directors & Actors - Find the actors or directors who show up on Netflix the most.

For Data Scientists

Create a content-based recommender by utilizing genres and title descriptions in the Recommendation System Prototype. 2. Text Analysis on Descriptions - Apply natural language processing (NLP) to identify trends in the way Netflix characterizes its material using terms like "crime," "adventure," and "love." 3. Classification Models - Use metadata to determine if a title is a movie or a TV show. Using genres, lengths, and descriptions, group films and television series into clusters. 5. Trend Forecasting - Forecast future growth in the Netflix library using time-series analysis.

For Students (Study Assignments)

1. Data Cleaning & Preprocessing - Standardize formats and deal with missing variables (such as directors/countries).
2. Exploratory Data Analysis (EDA): Make notebooks or dashboards with a ton of graphics that illustrate Netflix trends.
3. Data Visualization Practice - Create imaginative graphics such as word clouds or heatmaps using Matplotlib, Seaborn, or Plotly. Storytelling with Data: Compose a data tale on how Netflix changed from renting out DVDs to becoming a major worldwide streaming service.
4. Beginner Machine Learning – Start small: use genre or description to forecast maturity rating.

Approach to the Netflix Dataset

1. Understand the Data (Initial Exploration)

   • Load the dataset and check its size, columns, and data types.
   • Get a sense of the key fields: title, type, country, release_year, rating, etc.
   • Look for unique values (e.g., how many genres, countries, ratings).

2. Data Cleaning & Preprocessing

   • Handle missing values (some entries don’t have directors or countries).
   • Standardize inconsistent formats (e.g., dates in date_added).
   • Split multi-valued columns (like genres or cast) if needed.
   • Convert durations into numeric values (minutes or seasons).

3. Exploratory Data Analysis (EDA)

   • Compare Movies vs. TV Shows count.
   • Analyze content growth trend by release year or date added.
   • Study genre popularity across different countries.
   • Explore rating distribution (family-friendly vs. mature content).
   • Identify most frequent directors, actors, and countries.

4. Visualization & Storytelling

   • Create bar charts, pie charts, heatmaps, and timelines.
   • Use word clouds for descriptions and genres.
   • Highlight interesting trends (e.g., rise of international TV shows).

5. Advanced Analysis / Data Science Tasks

   • Build a recommendation system (based on genres & descriptions).
   • Perform sentiment/keyword analysis on descriptions.
   • Apply clustering to group similar shows/movies.
   • Predict whether a title is a movie or TV show from metadata.

6. Insights & Reporting

   • Summarize key findings (e.g., “TV shows are growing faster than movies,” “US and India dominate Netflix content”).
   • Create dashboards (Tableau, Power BI, or Python libraries like Plotly).
   • Share a story rather than just numbers—make it human and relatable.

Explore the Netflix Titles dataset, featuring detailed insights on over 8,800 movies and TV shows. Ideal for content analysis, recommendation systems, and market research, covering genre trends, directors, cast, production countries, release years, and ratings.

Content

In this dataset, you will find 1000 Netflix Tv shows and their ratings.

Acknowledgements

This dataset comes from https://data.world/chasewillden/netflix-shows

Netflix's lower-cost ad-supported plan reached 70 million monthly active users globally in November 2024, marking an increase of 57 million monthly active users compared to the beginning of the year. Netflix introduced an ad-supported tier in November 2022 in response to subscriber losses during the first half of 2022.

This dataset pertains to Netflix, a highly popular media and video streaming service. As of mid-2021, Netflix boasts more than 8,000 movies and TV shows in its library and has amassed a global subscriber base exceeding 200 million. The dataset in tabular form includes information on all the movies and TV shows accessible on Netflix, encompassing details like cast, directors, ratings, release year, duration, and more.

A total of *** million viewers are reported to have watched the South Korean series 'Squid Game' in Netflix, as of October 2021. Ranking in second came the movie Extraction with ** million viewers, followed by Bird Box with ** million viewers. Netflix viewership measurement The method of Netflix's measurement of viewership has changed, and now counts viewers who expressed the intention to watch the show and continued watching for as little as two minutes. This is similar to how YouTube video views are measured and further away from traditional calculations of TV ratings and viewers. Netflix has justified the change in its methodology as a way to accommodate all types of content, regardless of length. Citing other media companies such as BBC iPlayer and The New York Times as having similar ranking models, Netflix discloses the viewer counts are approximately ** percent higher using the new method.

Methodology Every Tuesday, we publish four global Top 10 lists for films and TV: Film (English), TV (English), Film (Non-English), and TV (Non-English). These lists rank titles based on ‘views’ for each title from Monday to Sunday of the previous week. We define views for a title as the total hours viewed divided by the total runtime. Values are rounded to 100,000.

We consider each season of a series and each film on their own, so you might see both Stranger Things seasons 2 and 3 in the Top 10. Because titles sometimes move in and out of the Top 10, we also show the total number of weeks that a season of a series or film has spent on the list.

To give you a sense of what people are watching around the world, we also publish Top 10 lists for nearly 100 countries and territories (the same locations where there are Top 10 rows on Netflix). Country lists are also ranked by views.

Finally, we provide a list of the Top 10 most popular Netflix films and TV overall (branded Netflix in any country) in each of the four categories based on the views of each title in its first 91 days.

Some TV shows have multiple premiere dates, whether weekly or in parts, and therefore the runtime increases over time. For the weekly lists, we show the views based on the total hours viewed during the week divided by the total runtime available at the end of the week. On the Most Popular List, we wait until all episodes have premiered, so you see the views of the entire season. For titles that are Netflix branded in some countries but not others, we still include all of the hours viewed.

Information on the site starts from June 28, 2021 and any lists published before June 20, 2023 are ranked by hours viewed.

During the first half of 2024, the top-rated anime season on Netflix was the first season of the original "Delicious in Dungeon," with 8.8 million of views. It was followed by "Demon Slayer: Kimetsu no Yaiba: Hashira Training Arc" and "Demon Slayer: Kimetsu no Yaiba: Entertainment District Arc," with 7.8 and 7.6 million of views respectively.

Introduction

This datasets about Netflix Movies & TV Shows. Datasets have 12 columns with some null values. To analysis of dataset are used Pandas, plotly.express and Datetime libraries. Analysis process I divided into several parts for step wise analysis and to find out trending questions on social media for Bollywood actors and actress.

Data Manipulation

Missing Data

There are many representations of missing data. They are Null values, missing values. I used some of methods used in data analysis process to clean missing values.

Data Munging

String Method

There I used some string method on column such as 'cast', 'Lested_in' to extract data

Datetime data type

Converting an object type into datatype objects with the to_datetime function then we have a datatime object, can extract various part of data such as year, month and day

EDA

Here, I find out several eye catching question. the following questions are like as- - Show the all Movies & TV Shows released by month - Count the all types of unique rating & which rating are with most number - Salman, Shah Rukh and Akshay Kumar all movie - Find out the Movies & Series have Maximum time length - Year on Year show added on Netflix by its type - Akshay Kumar all comedies movies, Shah Rukh movies with Kajol and Salman-Akshay Movies - Who Director has made the most TV Shows - Actors and Actress who have given most Number of Movies - Find out which types of genre has most movies and TV Shows

During the second half of 2024, the daily time spent streaming Netflix around the world amounted to one hour and 46 minutes. While the streaming giant's subscriber number increased in the past year, viewing time dropped by 10 minutes compared to the first half of 2024.

As of September 2025, the most popular English-language Netflix TV show of all time was the first season of the comedy horror TV show “Wednesday,” with over 252.1 million views in the show's first 91 days on the platform. The second most watched and latest addition to the top 10 of the streaming platform was the British TV series “Adolescence,” which amassed over 142.6 million views. The fourth season of the American science-fiction series “Stranger Things” follows closely, counting around 140.7 million views. Is high content spending a guarantee for success? The fourth season of “Stranger Things” belongs not only to the most popular Netflix titles but was also by far the most expensive series for the company as of April 2025, with production costs of 30 million U.S. dollars per episode. In order to stay competitive in the crowded streaming environment, Netflix reported ever rising budgets to produce their own content. With amassing the most viewing minutes in the top 10 of the most-watched original streaming series in 2024, it seems the video-on-demand provider has been successful with this strategy. TV series are being canceled quickly For years, the number of original scripted TV shows released in the U.S. has steadily been growing, peaking in 2022 at 600 broadcast, cable, and streaming series. However, in times of originals continuing to soar, streaming services have canceled several TV shows after just the first or second season. Even alleged popular Netflix series such as “1899” or “Warrior Nun” had to go with the story left incomplete, as they fell short of the company’s expectations.

This dataset contains information about movies and TV shows available on Netflix as of 2021. The data includes various details about each title, such as:

• Title: The name of the movie or TV show.
• Genre: The genre(s) associated with the title.
• Director: The director(s) of the movie or show.
• Cast: Main actors and actresses featured.
• Country: The country where the movie or show was produced.
• Date Added: The date when the title was added to Netflix.
• Release Year: The year the movie or show was released.
• Rating: The maturity rating (e.g., PG, TV-MA).
• Duration: The length of the movie or number of seasons for TV shows.
• Description: A brief summary of the movie or show.

Data Collection and Sources The dataset has been compiled from publicly available sources and includes titles available on Netflix globally. It provides a comprehensive view of Netflix’s content library up to the year 2021, making it an excellent resource for analyzing trends in streaming content, examining genre popularity, and exploring the evolution of Netflix’s offerings over time.

This dataset is provided for educational and research purposes. All data is based on publicly available information and should be used responsibly, respecting the original content creators' rights.