8 datasets found
  1. A

    ‘1000 Netflix Shows’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘1000 Netflix Shows’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-1000-netflix-shows-774c/1a6199df/?iid=004-340&v=presentation
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    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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 28 January 2022.

    --- 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 ---

  2. 1000 Netflix Shows

    • kaggle.com
    zip
    Updated Jun 11, 2017
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    Chase Willden (2017). 1000 Netflix Shows [Dataset]. https://www.kaggle.com/chasewillden/netflix-shows
    Explore at:
    zip(10825 bytes)Available download formats
    Dataset updated
    Jun 11, 2017
    Authors
    Chase Willden
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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?

  3. Quarterly Netflix subscribers count worldwide 2013-2024

    • statista.com
    • ai-chatbox.pro
    Updated Jun 23, 2025
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    Statista (2025). Quarterly Netflix subscribers count worldwide 2013-2024 [Dataset]. https://www.statista.com/statistics/250934/quarterly-number-of-netflix-streaming-subscribers-worldwide/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    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.

  4. A

    ‘Netflix Shows’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 13, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Netflix Shows’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-netflix-shows-53e6/ea6268fc/?iid=004-315&v=presentation
    Explore at:
    Dataset updated
    Feb 13, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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 ---

  5. N

    Netflix Statistics

    • searchlogistics.com
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    Search Logistics, Netflix Statistics [Dataset]. https://www.searchlogistics.com/learn/statistics/netflix-statistics/
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    Dataset authored and provided by
    Search Logistics
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  6. Biggest Netflix libraries in the world 2024

    • statista.com
    • ai-chatbox.pro
    Updated Oct 21, 2024
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    Statista (2024). Biggest Netflix libraries in the world 2024 [Dataset]. https://www.statista.com/statistics/1013571/netflix-library-size-worldwide/
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    Dataset updated
    Oct 21, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jul 2024
    Area covered
    World
    Description

    Industry data revealed that Slovakia had the most extensive Netflix media library worldwide as of July 2024, with over 8,500 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 2024, Liechtenstein and Switzerland were the countries with the most expensive Netflix subscription rates. Viewers had to pay around 21.19 U.S. dollars per month for a standard subscription. Subscribers in these countries could choose from between around 6,500 and 6,900 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 2.90 to 4.65 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 mid-2024, "Red Notice" and "Don't Look Up" were the most popular English-language movies on Netflix, with over 230 million views in its 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.

  7. s

    Time Spent On Netflix

    • searchlogistics.com
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    Time Spent On Netflix [Dataset]. https://www.searchlogistics.com/learn/statistics/netflix-statistics/
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    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The average Netflix user spends 3.2 hours per day streaming content on Netflix.

  8. f

    Table_1_Challenges with using popular entertainment to address mental...

    • frontiersin.figshare.com
    • figshare.com
    xlsx
    Updated Aug 30, 2023
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    Hua Wang; Zhiying Yue; Divya S (2023). Table_1_Challenges with using popular entertainment to address mental health: a content analysis of Netflix series 13 Reasons Why controversy in mainstream news coverage.xlsx [Dataset]. http://doi.org/10.3389/fpsyt.2023.1214822.s001
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Aug 30, 2023
    Dataset provided by
    Frontiers
    Authors
    Hua Wang; Zhiying Yue; Divya S
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundMental health conditions and psychiatric disorders are among the leading causes of illness, disability, and death among young people around the globe. In the United States, teen suicide has increased by about 30% in the last decade. Raising awareness of warning signs and promoting access to mental health resources can help reduce suicide rates for at-risk youth. However, death by suicide remains a taboo topic for public discourse and societal intervention. An unconventional approach to address taboo topics in society is the use of popular media.MethodWe conducted a quantitative content analysis of mainstream news reporting on the controversial Netflix series 13 Reasons Why Season 1. Using a combination of top-down and bottom-up search strategies, our final sample consisted of 97 articles published between March 31 and May 31, 2017, from 16 media outlets in 3,150 sentences. We systematically examined the news framing in these articles in terms of content and valence, the salience of health/social issue related frames, and their compliance with the WHO guidelines.ResultsNearly a third of the content directly addressed issues of our interest: 61.6% was about suicide and 38.4% was about depression, bullying, sexual assault, and other related health/social issues; it was more negative (42.8%) than positive (17.4%). The criticism focused on the risk of suicide contagion, glamorizing teen suicide, and the portrayal of parents and educators as indifferent and incompetent. The praise was about the show raising awareness of real and difficult issues young people struggle with in their everyday life and serving as a conversation starter to spur meaningful discussions. Our evaluation of WHO guideline compliance for reporting on suicide yielded mixed results. Although we found recommended practices across all major categories, they were minimal and could be improved.ConclusionDespite their well intentions and best efforts, the 13 Reasons Why production team missed several critical opportunities to be better prepared and more effective in creating social impact entertainment and fostering difficult dialogs. There is an urgent need to train news reporters about established health communication guidelines and promote best practices in media reporting on sensitive topics such as suicide.

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    Learn how you can add new datasets to our index.

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Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘1000 Netflix Shows’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-1000-netflix-shows-774c/1a6199df/?iid=004-340&v=presentation

‘1000 Netflix Shows’ analyzed by Analyst-2

Explore at:
Dataset updated
Jan 28, 2022
Dataset authored and provided by
Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

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 28 January 2022.

--- 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 ---

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