Overview This dataset contains 25,000 fictional Netflix user records generated for analysis, visualization, and machine learning practice. It includes demographic details, subscription type, watch time, and login history for each user.

Columns User_ID – Unique identifier for each user Name – Randomly generated name Age – Age of the user (13 to 80) Country – User’s country (randomly chosen from 10 options) Subscription_Type – Type of Netflix plan (Basic, Standard, Premium) Watch_Time_Hours – Total hours watched in the last month Favorite_Genre – User’s preferred genre Last_Login – Last recorded login date within the past year

Use Cases Data visualization and analytics Customer segmentation and trend analysis Machine learning model testing (e.g., churn prediction, recommendation systems) This dataset is synthetic and does not contain real user data. Feel free to use it for experiments and projects! 🚀

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.

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.

Netflix is a popular streaming service that offers a vast catalog of movies, TV shows, and original contents. This dataset is a cleaned version of the original version which can be found here. The data consist of contents added to Netflix from 2008 to 2021. The oldest content is as old as 1925 and the newest as 2021. This dataset will be cleaned with PostgreSQL and visualized with Tableau. The purpose of this dataset is to test my data cleaning and visualization skills. The cleaned data can be found below and the Tableau dashboard can be found here .

Data Cleaning

We are going to: 1. Treat the Nulls 2. Treat the duplicates 3. Populate missing rows 4. Drop unneeded columns 5. Split columns Extra steps and more explanation on the process will be explained through the code comments

--View dataset

SELECT * 
FROM netflix;

--The show_id column is the unique id for the dataset, therefore we are going to check for duplicates
                                  
SELECT show_id, COUNT(*)                                                                                      
FROM netflix 
GROUP BY show_id                                                                                              
ORDER BY show_id DESC;

--No duplicates

--Check null values across columns

SELECT COUNT(*) FILTER (WHERE show_id IS NULL) AS showid_nulls,
    COUNT(*) FILTER (WHERE type IS NULL) AS type_nulls,
    COUNT(*) FILTER (WHERE title IS NULL) AS title_nulls,
    COUNT(*) FILTER (WHERE director IS NULL) AS director_nulls,
    COUNT(*) FILTER (WHERE movie_cast IS NULL) AS movie_cast_nulls,
    COUNT(*) FILTER (WHERE country IS NULL) AS country_nulls,
    COUNT(*) FILTER (WHERE date_added IS NULL) AS date_addes_nulls,
    COUNT(*) FILTER (WHERE release_year IS NULL) AS release_year_nulls,
    COUNT(*) FILTER (WHERE rating IS NULL) AS rating_nulls,
    COUNT(*) FILTER (WHERE duration IS NULL) AS duration_nulls,
    COUNT(*) FILTER (WHERE listed_in IS NULL) AS listed_in_nulls,
    COUNT(*) FILTER (WHERE description IS NULL) AS description_nulls
FROM netflix;

We can see that there are NULLS. 
director_nulls = 2634
movie_cast_nulls = 825
country_nulls = 831
date_added_nulls = 10
rating_nulls = 4
duration_nulls = 3 

The director column nulls is about 30% of the whole column, therefore I will not delete them. I will rather find another column to populate it. To populate the director column, we want to find out if there is relationship between movie_cast column and director column

-- Below, we find out if some directors are likely to work with particular cast

WITH cte AS
(
SELECT title, CONCAT(director, '---', movie_cast) AS director_cast 
FROM netflix
)

SELECT director_cast, COUNT(*) AS count
FROM cte
GROUP BY director_cast
HAVING COUNT(*) > 1
ORDER BY COUNT(*) DESC;

With this, we can now populate NULL rows in directors 
using their record with movie_cast 

UPDATE netflix 
SET director = 'Alastair Fothergill'
WHERE movie_cast = 'David Attenborough'
AND director IS NULL ;

--Repeat this step to populate the rest of the director nulls
--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET director = 'Not Given'
WHERE director IS NULL;

--When I was doing this, I found a less complex and faster way to populate a column which I will use next

Just like the director column, I will not delete the nulls in country. Since the country column is related to director and movie, we are going to populate the country column with the director column

--Populate the country using the director column

SELECT COALESCE(nt.country,nt2.country) 
FROM netflix AS nt
JOIN netflix AS nt2 
ON nt.director = nt2.director 
AND nt.show_id <> nt2.show_id
WHERE nt.country IS NULL;
UPDATE netflix
SET country = nt2.country
FROM netflix AS nt2
WHERE netflix.director = nt2.director and netflix.show_id <> nt2.show_id 
AND netflix.country IS NULL;


--To confirm if there are still directors linked to country that refuse to update

SELECT director, country, date_added
FROM netflix
WHERE country IS NULL;

--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET country = 'Not Given'
WHERE country IS NULL;

The date_added rows nulls is just 10 out of over 8000 rows, deleting them cannot affect our analysis or visualization

--Show date_added nulls

SELECT show_id, date_added
FROM netflix_clean
WHERE date_added IS NULL;

--DELETE nulls

DELETE F...

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.

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.

Complete database of Netflix's mergers and acquisitions

Traffic analytics, rankings, and competitive metrics for netflix.com as of September 2025

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.

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

1) Data Introduction • The Netflix Users Dataset World Wide is a user-analyzed dataset that summarizes various attributes such as subscription types, countries, subscription dates, viewing patterns, and device information of Netflix users around the world.

2) Data Utilization (1) Netflix Users Dataset World Wide has characteristics that: • Each row contains a variety of user and behavior data, including User ID, Subscription Type (Basic/Standard/Premium), Country, Subscription Date, Latest Payment Date, Account Status (Active/Disactive), Key View Devices, Monthly View Time, Preferred Genre, Average Session Length, and Monthly Subscription Sales. • Data is designed to enable various analyses such as regional trends, usage behaviors, churn rates, and viewing preferences. (2) Netflix Users Dataset World Wide can be used to: • User Segmentation and Marketing Strategy: Data such as subscription type, country, viewing pattern, etc. can be used to define customer groups and to establish customized marketing and recommendation strategies. • Service improvement and departure prediction: Based on behavioral data such as device, viewing time, and account status, it can be applied to service improvement, departure risk prediction, and development of new features.

About this Dataset

This dataset shows the number of paid subscribers to the Netflix streaming service at the end of each quarter going back to 3/31/2016. The data is also broken down by geographical region.

Category

Business Information & Financials

Keywords

Netflix,streaming,subscriber data

Row Count

30

Price

$99.00

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12038776%2Fdbabda1e8f2d39e88b030173303b2724%2FNetflix.jpg?generation=1713257307281984&alt=media" alt="">

Dataset Overview

Netflix is one of the most popular media and video streaming platforms. They have over 10000 movies or tv shows available on their platform, as of mid-2021, they have over 222M 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, etc.

Key details about the dataset:

1. Number of Entries: The dataset contains 8807 entries.
2. Columns: There are 12 columns in total.
3. Column Details:
   • show_id: Unique identifier for each show.
   • type: Indicates whether the entry is a movie or a TV show.
   • title: The title of the movie or TV show.
   • director: The director(s) of the movie or TV show.
   • cast: The cast members of the movie or TV show.
   • country: The country or countries where the movie or TV show was produced.
   • date_added: The date when the movie or TV show was added to Netflix.
   • release_year: The year when the movie or TV show was released.
   • rating: The rating assigned to the movie or TV show.
   • duration: The duration of the movie or TV show.
   • listed_in: The categories or genres that the movie or TV show belongs to.

Usage

This dataset can be used for various analytical purposes such as exploring trends in Netflix content, analyzing user preferences, building recommendation systems, and more.

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.

Netflix reported $1.58B in Trade Debtors for its fiscal quarter ending in June of 2025. Data for Netflix | NFLX - Trade Debtors including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Traffic analytics, rankings, and competitive metrics for whats-on-netflix.com as of September 2025

Netflix Streaming Services Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Netflix reported $11.51B in Sales Revenues for its fiscal quarter ending in September of 2025. Data for Netflix | NFLX - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Netflix reported $3.42B in EBITDA for its fiscal quarter ending in September of 2025. Data for Netflix | NFLX - Ebitda including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The Measurable AI Netflix and Other Streaming Services Email Receipt Datasets details data from subscription and cancellation email such as premium members, family plans, most popular shows, cancellation emails etc.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the Careem Now food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.