Netflix Exploratory Data Analysis 📊

An in-depth exploratory data analysis of Netflix's content catalog, investigating trends in movie durations, genre distributions, and content characteristics over time.

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🎯 Project Overview

This project analyzes Netflix's content catalog to uncover insights about:

• Movie duration trends over the past decades
• Genre distributions and their impact on content length
• Content characteristics across different categories
• Platform evolution and content strategy

Key Question

Are movies getting shorter over time?

Through data analysis and visualization, we explore this hypothesis and discover the factors influencing average movie durations on Netflix.

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📈 Key Findings

1. Movie Duration Trends (2011-2020)

• Average movie duration shows a declining trend from 103 minutes (2011) to 90 minutes (2020)
• The trend becomes more pronounced in recent years
• However, the decline isn't uniform across all genres

2. Genre Impact

The analysis reveals that shorter content is primarily driven by specific genres:

Genre	Typical Duration	Impact
Children	20-45 minutes	High concentration of short content
Documentaries	30-90 minutes	Variable, often shorter than features
Stand-Up	45-75 minutes	Typically under 90 minutes
Feature Films	90-150 minutes	Traditional movie lengths

3. Content Diversity

• Netflix hosts a wide variety of content types beyond traditional feature films
• Short-form content (< 60 minutes) represents a significant portion of the catalog
• Platform strategy includes children's programming, documentaries, and stand-up specials

4. Conclusion

Movies aren't necessarily getting shorter - the apparent trend is largely explained by:

• Increased diversity in content types
• Growing catalog of non-feature content
• Platform expansion into children's programming and documentaries

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🛠️ Tech Stack

Libraries Used:

• pandas - Data manipulation and analysis
• numpy - Numerical computing
• matplotlib - Static visualizations
• seaborn - Statistical data visualization
• plotly - Interactive charts and graphs

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📦 Installation

Prerequisites

• Python 3.11 or higher
• pip package manager

Setup

1. Clone the repository

   git clone https://github.com/utsavpaudel/Netflix-EDA.git
   cd Netflix-EDA

2. Create a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Launch Jupyter Notebook

   jupyter notebook

5. Open the analysis notebook Navigate to notebooks/netflix_analysis.ipynb in the Jupyter interface

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📁 Project Structure

Netflix-EDA/
├── data/                          # Dataset directory
│   ├── netflix_data.csv          # Main Netflix catalog data
│   ├── color_data.csv            # Color mapping data
│   └── README.md                 # Data documentation
├── notebooks/                     # Jupyter notebooks
│   └── netflix_analysis.ipynb    # Main analysis notebook
├── outputs/                       # Generated outputs
│   └── figures/                  # Saved visualizations
├── workspace/                     # Original workspace (legacy)
├── requirements.txt               # Python dependencies
├── .gitignore                    # Git ignore rules
├── LICENSE                       # MIT License
└── README.md                     # This file

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🚀 Usage

Running the Analysis

1. Start Jupyter Notebook

   jupyter notebook

2. Open the notebook Navigate to notebooks/netflix_analysis.ipynb

3. Run all cells

   • Use Cell > Run All to execute the complete analysis
   • Or run cells individually to explore step by step

Exploring the Data

import pandas as pd

# Load the Netflix data
netflix_df = pd.read_csv('data/netflix_data.csv')

# View basic statistics
print(netflix_df.describe())

# Check data types
print(netflix_df.info())

# Explore unique values
print(netflix_df['type'].value_counts())
print(netflix_df['genre'].value_counts())

Creating Visualizations

The notebook includes various visualizations:

• Line plots for temporal trends
• Scatter plots for duration analysis
• Color-coded visualizations to distinguish genres
• Distribution plots for statistical insights

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📊 Analysis Workflow

1. Data Loading

• Import Netflix catalog data
• Inspect data structure and quality
• Check for missing values

2. Data Cleaning

• Filter for movies vs. TV shows
• Select relevant columns
• Handle missing or inconsistent data

3. Exploratory Analysis

• Calculate summary statistics
• Identify trends in movie durations
• Analyze genre distributions

4. Visualization

• Create temporal trend visualizations
• Build scatter plots with genre coloring
• Generate comparative charts

5. Insights & Conclusions

• Interpret findings
• Answer the research question
• Identify areas for future analysis

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📝 Key Insights from Visualizations

Duration Trends

The scatter plot of movie durations over time reveals:

• Black dots: Feature films (90-150 minutes)
• Red dots: Children's content (typically < 60 minutes)
• Blue dots: Documentaries (wide range)
• Green dots: Stand-up specials (45-75 minutes)

Genre Distribution

Short movies (< 60 minutes) are predominantly:

• Children's programming
• Documentary shorts
• Stand-up comedy specials
• Special features

This explains why the overall average duration appears to decline - it's a composition effect rather than traditional movies becoming shorter.

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🔮 Future Enhancements

• Add analysis of TV show trends and season counts
• Explore content additions by country and region
• Analyze director and cast data for patterns
• Create interactive dashboards with Plotly/Dash
• Include sentiment analysis of content descriptions
• Compare Netflix data with other streaming platforms
• Time series forecasting for content trends
• Network analysis of cast and director collaborations

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingAnalysis)
3. Commit your changes (git commit -m 'Add new analysis')
4. Push to the branch (git push origin feature/AmazingAnalysis)
5. Open a Pull Request

Ideas for Contributions

• Additional visualizations
• Statistical tests and modeling
• New data sources integration
• Interactive dashboard development
• Documentation improvements

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📚 Learning Resources

This project demonstrates key data analysis skills:

• Data manipulation with pandas
• Data visualization with matplotlib
• Exploratory analysis techniques
• Statistical thinking and hypothesis testing
• Jupyter notebook best practices

Perfect for:

• Data science portfolio projects
• Learning Python data analysis
• Practicing visualization techniques
• Understanding real-world datasets

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👨‍💻 Author

Utsav Paudel

• GitHub: @utsavpaudel
• Email: utsavpaudel111@gmail.com
• LinkedIn: utsavpaudel

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🙏 Acknowledgments

• Netflix data sourced from publicly available datasets
• Inspired by DataCamp's data analysis projects
• Built with the Python data science ecosystem

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📧 Contact

For questions, suggestions, or collaboration opportunities, feel free to reach out!

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⭐ If you find this analysis interesting, please consider starring the repository!