Netflix TV Shows and Movies: Comprehensive Data Analysis & Insights
Welcome to this deep-dive data analysis of Netflix's catalog of TV shows and movies. Using Python, Pandas, Matplotlib, Seaborn, and Prophet inside Google Colab, we explored content distribution, top creators, geographical trends, genres, and forecast future additions up to 2026.
1. Content Overview: Movies vs. TV Shows
To understand the high-level composition of Netflix's library, we analyzed the split between movies and TV series. The dataset contains a total of 8,807 entries, heavily weighted toward films.
| Content Type | Count |
|---|---|
| Movie | 6,131 |
| TV Show | 2,676 |
2. Global Content Distribution: Top 5 Countries
Geographically, Netflix sources its content worldwide, but production is heavily concentrated in a few powerhouse nations. The United States leads by a wide margin, followed closely by India and the United Kingdom.
| Country | Number of Shows |
|---|---|
| United States | 2,818 |
| India | 972 |
| United Kingdom | 419 |
| Japan | 245 |
| South Korea | 199 |
3. Top Directors on the Platform
When looking at individual contributors, Indian filmmaker Rajiv Chilaka tops the chart with 19 credited titles.
4. Top Genres and Themes
An extraction and tokenization of the listed_in category column reveal that international focus and narrative depth drive the catalog:
- International Movies: 2,752 titles
- Dramas: 2,427 titles
- Comedies: 1,674 titles
- International TV Shows: 1,351 titles
- Documentaries: 869 titles
5. Visualizing Text Themes: Word Cloud of Descriptions
By aggregating the summary descriptions of all titles, we generated a word cloud to capture the core vocabulary used in Netflix synopses. Prominent keywords include life, family, young, love, world, story, friend, and man.
6. Age Ratings Over Time (TV-MA vs. TV-14)
Analyzing the evolution of ratings over the last decade (2011–2021) shows a sharp rise in mature content. Both TV-MA and TV-14 titles saw significant upward trajectories peaking toward the late 2010s.
7. Time Series Forecasting to 2026
Using Facebook's Prophet time-series forecasting library, we modeled historical release trends (filtering out early historical anomalies prior to 2000) to predict content trajectories through 2026.
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