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.

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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

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.

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.

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'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.

In 2024, Netflix generated an average monthly revenue per streaming customer of 11.7 U.S. dollars, up by one percent compared with the previous year. The company had approximately 241 million paying memberships on average during the previous year, and 260 million paying subscribers at the end of 2023.

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.

In 2024, Netflix spent approximately $ 2.92 billion on marketing activities. This figure increased by around 10 percent compared to the previous year. Netflix in numbers Netflix is one of the most influential SVOD streaming services and entertainment companies worldwide. Initially started as a DVD-by-mail rental service in 1997, the California-based business has since become an undisputed champion in the world of video streaming. In 2024, Netflix's annual revenue amounted to approximately 39 billion U.S. dollars, having grown from 5.5 billion dollars a decade ago. Marketing the Netflix way Netflix is not only a global trendsetter when it comes to online video streaming, but the company also continues to set new bars for marketing. One of the most vital contributors to Netflix’s multimedia marketing success is its use of social media. The streaming giant can be found across all major social platforms, and in addition to promoting its content with a blend of humor and relatable pop culture references, Netflix also boosts audience engagement by posting polls and challenges. On a more personalized level, Netflix successfully employs user data and browsing behavior for e-mail marketing purposes. For instance, the company only notifies its subscribers of upcoming releases that are relevant to them without spamming their inboxes daily.

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 ---

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.

Other Platform's Datasets (Click on the logos to view)

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• Amazon Prime Video Movies and TV Shows
• Disney+ Movies and TV Shows
• Netflix Prime Video Movies and TV Shows
• Hulu Movies and TV Shows

Netflix Movies and TV Shows

About this Dataset: Netflix is one of the most popular media and video streaming platforms. They have over 8000 movies or tv shows available on their platform, as of mid-2021, they have over 200M 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.

Featured Notebooks: Click Here to View Featured Notebooks Milestone: Oct 18th, 2021: Most Upvoted Dataset on Kaggle by an Individual Contributor

Interesting Task Ideas

1. Understanding what content is available in different countries
2. Identifying similar content by matching text-based features
3. Network analysis of Actors / Directors and find interesting insights
4. Does Netflix has more focus on TV Shows than movies in recent years.

Check my Other Datasets

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 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.

TV Shows and Movies listed on Netflix

This dataset consists of tv shows and movies available on Netflix as of 2019. The dataset is collected from Flixable which is a third-party Netflix search engine.

In 2018, they released an interesting report which shows that the number of TV shows on Netflix has nearly tripled since 2010. The streaming service’s number of movies has decreased by more than 2,000 titles since 2010, while its number of TV shows has nearly tripled. It will be interesting to explore what all other insights can be obtained from the same dataset.

Integrating this dataset with other external datasets such as IMDB ratings, rotten tomatoes can also provide many interesting findings.

Inspiration

Some of the interesting questions (tasks) which can be performed on this dataset -

1. Understanding what content is available in different countries
2. Identifying similar content by matching text-based features
3. Network analysis of Actors / Directors and find interesting insights
4. Is Netflix has increasingly focusing on TV rather than movies in recent years.

Welcome to the Netflix Stock Prices and Performance Data dataset! This dataset is your go-to resource for analyzing the financial performance of Netflix, Inc. over time. Whether you’re a seasoned data scientist, a finance enthusiast, or a beginner looking to practice your time series analysis skills, this dataset provides all the key metrics you need.

Date: The trading day (YYYY-MM-DD format) Open: Opening price of the stock on that day High: Highest price reached during the trading day Low: Lowest price reached during the trading day Close: Closing price of the stock on that day Volume: Number of shares traded Potential Uses:

Trend Analysis: Examine how Netflix’s stock price has evolved over time and identify significant trends or events that impacted its performance.

Technical Analysis: Apply various technical indicators to forecast future stock movements.

Investment Strategy Development: Create and backtest trading strategies based on historical data.

Correlation Studies: Compare Netflix’s stock performance with other stocks or indices to uncover correlations.

Market Sentiment Analysis: Integrate with news or social media sentiment data to see how external factors influence stock prices.

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.

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.

Analyzing streaming service prices allows consumers to make informed decisions based on their budget, ensuring they get the best value for their entertainment preferences. This dataset contains price history since 2011 for major streaming services: Netflix, Amazon Prime Video, Hulu, Disney+, HBO Max, Apple TV+, Peacock, Paramount+, Shudder, Crunchyroll.

All prices are for ad-free, lowest-cost monthly subscriptions.

For use case and analysis reference, please take a look at the Streaming Service Prices Study notebook.

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To standardize, all prices follow the below condition. - U.S. price. - Lowest cost. - No ads. - No bundle.

This dataset is a cleaned and preprocessed version of the original Netflix Movies and TV Shows dataset available on Kaggle. All cleaning was done using Microsoft Excel — no programming involved.

🎯 What’s Included: - Cleaned Excel file (standardized columns, proper date format, removed duplicates/missing values) - A separate "formulas_used.txt" file listing all Excel formulas used during cleaning (e.g., TRIM, CLEAN, DATE, SUBSTITUTE, TEXTJOIN, etc.) - Columns like 'date_added' have been properly formatted into DMY structure - Multi-valued columns like 'listed_in' are split for better analysis - Null values replaced with “Unknown” for clarity - Duration field broken into numeric + unit components

🔍 Dataset Purpose: Ideal for beginners and analysts who want to: - Practice data cleaning in Excel - Explore Netflix content trends - Analyze content by type, country, genre, or date added

📁 Original Dataset Credit: The base version was originally published by Shivam Bansal on Kaggle: https://www.kaggle.com/shivamb/netflix-shows

📌 Bonus: You can find a step-by-step cleaning guide and the same dataset on GitHub as well — along with screenshots and formulas documentation.