This dataset comprises information about over 4,000 Netflix movies, including titles, genres, release years, availability, and other metadata. The data was collected to provide insights into Netflix's movie catalog and can be used for exploratory analysis, recommendation systems, or other data-driven projects.

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.

Netflix is a streaming service and production company. Crawl feeds team extracted more than 100 records from netflix for quality analysis purposes. Get in touch with crawl feeds team for complete dataset. Last extracted on 5 mar 2022

description of each column in the provided Netflix dataset:

1. title: The title of the movie or TV show.
2. rating: The rating assigned to the movie or TV show, indicating the appropriate audience age group or content warnings (e.g., PG-13, R, TV-14).
3. ratingLevel: Additional information about the content rating, providing details on the reasons for the rating (e.g., "crude and sexual humor, language and some drug use").
4. ratingDescription: A numerical value representing the rating description, providing a quantitative measure of the content rating.
5. release year: The year when the movie or TV show was released.
6. user rating score: The user rating score for the movie or TV show, if available.
7. user rating size: The size of the user rating dataset, indicating the number of users who have rated the movie or TV show.

nuilbg/netflix-movie-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Industry data revealed that Iceland had the most extensive Netflix media library worldwide as of February 2025, with over 9,700 titles available on the platform. Interestingly, the top 10 ranking was spearheaded by European countries. Where do you get the most bang for your Netflix buck? In February 2025, Liechtenstein and Switzerland were the countries with the most expensive Netflix subscription rates. Viewers had to pay around 22.89 U.S. dollars per month for a standard subscription. Subscribers in these countries could choose from between around 7,900 and 8,500 titles. On the other end of the spectrum, Pakistan, Egypt, and Nigeria are some of the countries with the cheapest Netflix subscription costs, at around 2.87 to 3.66 U.S. dollars per month. Popular content on Netflix While viewing preferences can differ across countries and regions, some titles have proven particularly popular with international audiences. As of September 2025, "KPop Demon Hunters" and "Red Notice" were the most popular English-language movies on Netflix, with over 200 million views in their first 91 days available on the platform. Meanwhile, "Troll" ranks first among the top non-English language Netflix movies of all time. The monster film has amassed 103 million views on Netflix, making it the most successful Norwegian-language film on the platform to date.

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.

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.

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.

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.

The latest addition to the ranking of Netflix's most popular English-language movies of all time is also the most popular movie on the platform - “KPop Demon Hunters” reached about 325.1 million views as of September 2025. The action comedy film “Red Notice” came second, with nearly 230.9 million views in its first 91 days on Netflix.

This dataset was constructed to support participants in the Netflix Prize. See [Web Link] for details about the prize.

There are over 480,000 customers in the dataset, each identified by a unique integer id.

The title and release year for each movie is also provided. There are over 17,000 movies in the dataset, each identified by a unique integer id.

The dataset contains over 100 million ratings. The ratings were collected between October 1998 and December 2005 and reflect the distribution of all ratings received during this period. Each rating has a customer id, a movie id, the date of the rating, and the value of the rating.

As part of the original Netflix Prize a set of ratings was identified whose rating values were not provided in the original dataset. The object of the Prize was to accurately predict the ratings from this 'qualifying' set. These missing ratings are now available in the grand_prize.tar.gz dataset file.

Explore on FlixGem.com, powered by Polymer Search.

Context

Netflix has a quantity-over-quality problem. This is part of an effort to help solve this. I was trying to figure out a way to find hidden gems in their catalog but found it exceedingly hard to get the latest dataset that has ratings and many other attributes to help make sense of it. To help me and others dig deep into the latest Netflix content, I created this dataset. This is the same dataset that powers FlixGem.com, the aforementioned side project.

Content

This dataset combines data sources from Netflix, Rotten Tomatoes, IMBD, posters, box office information, trailers on YouTube, and more using a variety of APIs. Note that there is no official Netflix API.

I also added a unique metric called "Hidden Gem Score", which I calculated using low review count and high rating. Lower the review count and higher the user rating, higher the hidden gem score.

Freshness

Recent Netflix data is incredibly hard to come up. This dataset is updated every month. This was lasted updated in early April 2021.

Inspiration

• Find correlations between ratings, actors, directors, box office, and more.
• Find patterns around the quality of movies and their various characteristics like language, genre, actors, etc.
• Discover hidden gems in different regions.

Interactively browsing the dataset with Flixgem.com

Explore this dataset using Polymer Search: FlixGem.com.

Polymer Search uses data algorithms and some AI to auto-create a fully interactive search & knowledge discovery interface for any structured data set.

Dataset Card for Dataset: NetFlix Shows

  Dataset Summary

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.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/netflix-shows.

"Discover the Netflix Movie Dataset, a comprehensive compilation of streaming content information that provides intriguing insights into viewer preferences and the world of digital entertainment. This dataset presents a diverse array of features, offering valuable information about each movie This dataset encompasses a wide range of movie content available on the Netflix platform, reflecting the diversity of genres, languages, and themes that cater to the streaming audience. By exploring this dataset, researchers, enthusiasts, and analysts can gain deeper insights into viewer preferences, content trends, and the impact of different variables on movie popularity. Whether you're interested in analyzing release patterns, actor popularity, or exploring the influence of directors, this dataset offers a rich source for data-driven exploration and examination of the digital entertainment landscape."

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

The Netflix Prize was a competition devised by Netflix to improve the accuracy of its recommendation system. To facilitate this Netflix released real ratings about movies from the users (voters) of the system. Any set of movies can be transformed into an election via a process outlined by Mattei, Forshee, and Goldsmith.This data set includes all 4 candidate elections with at least 350 voters generated by this process from 500 randomly chosen movies. Extending beyond prior work by Mattei et al. we allow for weak preferences, i.e., a voter is indifferent between a set of movies if he assigns the same rating to each of them. Thus, there are 75 possibilities to rank a given set of four movies.The archive is gzip compressed and includes 958,857 elections in PrefLib.org's TOC file format (Orders with Ties - Complete List).

The global movie production market, valued at $92.56 billion in 2025, is projected to experience robust growth, driven by a compound annual growth rate (CAGR) of 14.6% from 2025 to 2033. This expansion is fueled by several key factors. The increasing popularity of streaming services like Netflix, Disney+, and Amazon Prime Video has significantly broadened the audience reach for films, stimulating demand for diverse content across various genres and languages. Technological advancements, particularly in visual effects (VFX) and digital filmmaking, have lowered production costs and enabled greater creative flexibility, further boosting market growth. Furthermore, the rising disposable incomes in emerging economies, especially in APAC, are driving increased cinema attendance and home entertainment consumption, contributing significantly to market expansion. Geographic diversification in production locations is also a significant trend, with countries beyond traditional Hollywood hubs becoming increasingly attractive due to government incentives and lower production costs. However, challenges remain. The market faces constraints from fluctuating box office revenues due to economic uncertainty and the ongoing impact of the COVID-19 pandemic. Competition among studios and the increasing costs associated with securing talent and distribution rights also present obstacles to market growth. Nevertheless, the long-term outlook for the movie production market remains positive, driven by consistent innovation in storytelling and distribution, as well as the continued global appetite for diverse and engaging cinematic experiences. The segmentation by language (English, French, Spanish, Mandarin, Others) and genre (Drama, Action, Comedy, Others) reflects the multifaceted nature of the market, highlighting the diverse content preferences of global audiences. The presence of major players like Disney, Warner Bros., and Sony Pictures underscores the industry's consolidation and the need for continuous adaptation to shifting viewer preferences.

Netflix Movies and TV Shows Analysis Dashboard

Overview

This Power BI dashboard provides a comprehensive analysis of Netflix's content library from 1925 to 2021. The dashboard visualizes key metrics such as the total number of shows, ratings distribution, popular genres, and the breakdown of movies vs. TV shows.

Key Highlights

• Analyzed over 8,000 shows and movies
• Uncovered trends in ratings, genres, and content distribution
• Visualized insights on content evolution over the years

![Dashboard Screenshot]https://storage.googleapis.com/kagglesdsdata/datasets/6747672/10862207/nitflix%20board.png?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20250226%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20250226T160052Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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

Methodology

1. Data Collection: Gathered data from Netflix's public dataset
2. Data Cleaning: Cleaned and processed the data using Power BI
3. Visualization: Created interactive visuals to highlight key insights

Insights

• Popular Genres: Dramas, International Movies, Documentaries, and Stand-Up Comedy are among the most popular genres.
• Ratings Distribution: TV-MA, TV-14, and TV-PG are the most common ratings.
• Content Evolution: A steady increase in the number of shows and movies over the years.

Belarus Google Search Trends: Online Movie: Netflix data was reported at 22.000 Score in 26 Nov 2025. This records an increase from the previous number of 16.000 Score for 25 Nov 2025. Belarus Google Search Trends: Online Movie: Netflix data is updated daily, averaging 22.000 Score from Dec 2021 (Median) to 26 Nov 2025, with 1457 observations. The data reached an all-time high of 100.000 Score in 16 Nov 2024 and a record low of 0.000 Score in 24 Oct 2025. Belarus Google Search Trends: Online Movie: Netflix data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Belarus – Table BY.Google.GT: Google Search Trends: by Categories.