Netflix's global subscriber base has reached an impressive milestone, surpassing *** million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly ** million subscribers compared to the previous quarter, solidifying Netflix's position as a dominant force in the streaming industry. Adapting to customer losses Netflix's growth has not always been consistent. During the first half of 2022, the streaming giant lost over *** million customers. In response to these losses, Netflix introduced an ad-supported tier in November of that same year. This strategic move has paid off, with the lower-cost plan attracting ** million monthly active users globally by November 2024, demonstrating Netflix's ability to adapt to changing market conditions and consumer preferences. Global expansion Netflix continues to focus on international markets, with a forecast suggesting that the Asia Pacific region is expected to see the most substantial growth in the upcoming years, potentially reaching around **** million subscribers by 2029. To correspond to the needs of the non-American target group, the company has heavily invested in international content in recent years, with Korean, Spanish, and Japanese being the most watched non-English content languages on the platform.

Here is the full breakdown of Netflix global subscribers by year since 2013.

This dataset offers a comprehensive historical record of Netflix’s stock price movements, capturing the company’s financial journey from its early days to its position as a global streaming giant.

From its IPO in May 2002, Netflix (Ticker: NFLX) has transformed from a DVD rental service to a powerhouse in on-demand digital content. With its disruptive innovation, strategic shifts, and global expansion, Netflix has seen dramatic shifts in stock prices, reflecting not just market trends but also cultural impact. This dataset provides a window into that evolution.

What’s Included?

Each row in this dataset represents daily trading activity on the stock market and includes the following columns:

• Date – The trading day (from 2002 onward)
• Open – Stock price when the market opened
• High – Highest trading price of the day
• Low – Lowest trading price of the day
• Close – Final price at market close
• Adj Close – Closing price adjusted for splits and dividends
• Volume – Number of shares traded that day

The data is structured in CSV format and is clean, easy to use, and ready for immediate analysis.

Why Use This Dataset?

Whether you're learning data science, building a financial model, or exploring machine learning in the real world, this dataset is a goldmine of insights. Netflix's market history includes:

• Periods of explosive growth during digital transformation
• Volatility during market crashes and global events (e.g., 2008, COVID-19)
• Strategic pivots such as the shift to original content
• Market reactions to earnings, acquisitions, and subscriber milestones

This makes the dataset ideal for:

• Time-series forecasting (ARIMA, Prophet, LSTM)
• Technical and trend analysis (moving averages, RSI, Bollinger Bands)
• Predictive modeling with machine learning
• Investment simulation projects
• Stock market visualization and storytelling
• Financial dashboards (Tableau, Power BI, Streamlit, etc.)

Who Can Use It?

This dataset is designed for:

• Aspiring data scientists practicing EDA and modeling
• Financial analysts and traders exploring trends
• AI researchers working on time-series models
• Students building ML projects
• Developers creating stock visualization tools
• Kaggle competitors seeking real-world datasets

Data Source & Credits

The dataset is derived from publicly available historical stock price data, such as Yahoo Finance, and has been cleaned and organized for educational and research purposes. It is continuously maintained to ensure accuracy.

Start Exploring

Netflix’s rise is more than just a business story — it’s a data-driven journey. With this dataset, you can analyze the company’s stock behavior, train models to predict future trends, or simply visualize how tech reshapes the market.

Analysis of ‘Netflix "Top 10" TV Shows and Films’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/dhruvildave/netflix-top-10-tv-shows-and-films on 28 January 2022.

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

Every Tuesday, Netflix publishes 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 weekly hours viewed: the total number of hours that members around the world watched each title from Monday to Sunday of the previous week.

Each season of a series and each film is considered 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, there is also the total number of weeks that a season of a series or film has spent on the list.

Netflix also publishes 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 based on hours viewed but don’t show country-level viewing directly.

Finally, Netflix provides a list of the Top 10 most popular Netflix films and TV (branded Netflix in any country) in each of the four categories based on the hours that each title was viewed during its first 28 days.

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

Analysis of ‘Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/netflix-showse on 13 February 2022.

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

About this dataset

Background

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?

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 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.).

Source

Access to the study can be found at The Concept Center

This dataset was created by Chase Willden and contains around 1000 samples along with User Rating Score, Rating Description, technical information and other features such as: - Release Year - Title - and more.

How to use this dataset

• Analyze User Rating Size in relation to Rating
• Study the influence of Rating Level on User Rating Score
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

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

In this post, you'll see how the Netflix platform is evolving, how many users Netflix has and how they perform against the growing competition.

In the fourth quarter of 2024, Netflix generated total revenue of over **** billion U.S. dollars, up from about *** billion dollars in the corresponding quarter of 2023. The company's annual revenue in 2024 amounted to around ** billion U.S. dollars, continuing the impressive year-on-year growth Netflix has enjoyed over the last decade. Netflix’s global position Netflix’s revenue has been heavily impacted by its ever-growing global subscriber base. The leading Netflix market is Europe, Middle East, and Africa, surpassing the U.S. and Canada in terms of subscriber count. Netflix has also significantly increased its licensed and produced content assets since 2016. Despite concerns among investors that the company’s content spend was negatively affecting cash flow, Netflix’s plans to amortize its content assets long-term along with generating revenue from other sources such as licensing and merchandise should ensure the company’s future profitability. Netflix’s original content Netflix is also fortunate in that many of its original shows have been a hit with consumers across the globe. Shows such as “Orange is the New Black,” “Black Mirror,” and “House of Cards” won the hearts of subscribers long ago, but newer content such as English-language shows “Bridgerton,” “Wednesday,” and “Stranger Things,” as well as local TV shows such as “Squid Game” have also been favorably reviewed and proved popular among users.

This dataset provides a detailed list and metadata for approximately 7,000 TV shows and movies available on Netflix as of June 2021. Sourced from the IMDB website, it offers insights into content characteristics, popularity, and categorisation, making it suitable for various analytical and machine learning applications.

Columns

• imdb_id: A unique identifier for each show or movie.
• title: The title of the television programme or film.
• popular_rank: The ranking assigned by IMDB based on popularity.
• certificate: Age certifications received by the content; it is noted that many values may be null.
• startYear: The year the show was first broadcast or the film was released.
• endYear: The year a show concluded, if applicable.
• episodes: The total number of episodes in a series; for films, this value is 1.
• runtime: The running time of the content.
• type: Specifies whether the content is a 'Movie' or 'Series'.
• orign_country: The country of origin for the show or movie.
• language: The primary language of the content.
• plot: A synopsis of the show or movie.
• summary: A concise summary of the story.
• rating: The average user rating for the content.
• numVotes: The total number of votes received for the content's rating.
• genres: The genre(s) to which the show or movie belongs.
• isAdult: A binary indicator (1 for adult content, 0 otherwise).
• cast: The main cast members listed in a suitable format.
• image_url: A link to the poster image for the content.

Distribution

The dataset is typically provided as a CSV file, specifically named netflix_list.csv. It contains approximately 7,000 records, with 7,008 unique identifiers for shows and movies. This dataset is listed as version 1.0 and was added to the platform on 11 June 2025.

Usage

This dataset is ideally suited for developing recommender systems, performing natural language processing (NLP) tasks on plot summaries, and conducting market analysis of entertainment content. It can be used to explore trends in movie and TV show production, analyse viewer preferences, and facilitate content categorisation efforts.

Coverage

The dataset offers global coverage, with information on content originating from various countries. The startYear of content spans from 1932 to 2022, with the majority of content released between 2004 and 2022. The endYear ranges from 1969 to 2022, with most data concentrated from 2011 to 2022. It includes age certification information and an indicator for adult content, allowing for demographic considerations related to content suitability.

License

CCO

Who Can Use It

This dataset is valuable for data scientists and machine learning engineers working on content recommendation engines or text analysis projects. It is also beneficial for researchers studying media consumption patterns and entertainment industry analysts interested in exploring the Netflix content catalogue programmatically.

Dataset Name Suggestions

• Netflix Content Metadata (June 2021)
• Global Netflix Catalogue
• Netflix IMDB Dataset
• Streaming Content Insights (Netflix)
• Netflix Movie and TV Show Archive

Attributes

Original Data Source:Netflix Movie and TV Shows (June 2021)

Overview

Streaming Services Statistics: Streaming services have transformed the entertainment landscape, revolutionizing how people consume content.

The advent of high-speed internet and the proliferation of smart devices have fueled the growth of these platforms, offering a wide array of movies, TV shows, music, and more, at the viewers' convenience.

This introduction provides an overview of key statistics that shed light on the impact, trends, and challenges within the streaming industry.

Analysis of ‘FAANG- Complete Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aayushmishra1512/faang-complete-stock-data on 14 February 2022.

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

Context

There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

Content

The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.

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

Here is the full breakdown of Netflix subscribers by region.

Context

According to Investopedia:

FAANG is an acronym referring to the stocks of the five most popular and best-performing American technology companies: Facebook, Amazon, Apple, Netflix and Alphabet (formerly known as Google). In addition to being widely known among consumers, the five FAANG stocks are among the largest companies in the world, with a combined market capitalization of over $4.1 trillion as of January 2020. Some have raised concerns that the FAANG stocks may be in the midst of a bubble, whereas others argue that their growth is justified by the stellar financial and operational performance they have shown in recent years.

Regardless of the myriad of accolades, comments, and even controversies surrounding the FAANG stocks, they are nevertheless a data science/mining treasure and the bellwether of the NASDAQ index, if not the entire US technology sector.

This Kaggle dataset contains over 20 years of daily historical data for the five FAANG constituents, as retrieved from this free stock API. It is a public-domain dataset that gives the data science practitioners (a.k.a., you!) the full flexibility to derive second-order insights and investment heuristics from it.

Content

Over 20 years of daily historical data (2000-01-01 to 2020-10-01) for the five FAANG stocks: Facebook, Amazon, Apple, Netflix, and Alphabet/Google. For completeness, both raw and adjusted prices are included, along with historical split events and dividend payouts (check out here for how stock market API providers perform price adjustments).

Acknowledgements

Data source: https://www.alphavantage.co/