NetworkTraceAnalyzer

This project provides tools and visualizations to analyze network trace data captured from tools like Proxyman and Charles. The analysis is performed using a Jupyter Notebook (NetworkTraceAnalyzer.ipynb) and leverages Python libraries such as pandas, matplotlib, and seaborn.

Features

• Load and inspect network trace CSV files (Proxyman, Charles)
• Bucket and count network requests by URL, host, or other attributes
• Visualize network activity over time
• Identify most impactful network calls by frequency and data usage
• Generate heatmaps and scatter plots for deeper insights

Snapshots & Visualizations

Below are example outputs generated by the notebook:

1. Most Impactful Calls by Frequency

2. Most Impactful Calls by Network Usage

3. Scatter Plot of Requests Over Time

4. Heatmap of Requests

Usage

1. Open NetworkTraceAnalyzer.ipynb in VS Code or Jupyter.
2. Install required Python packages:

   pip install pandas matplotlib seaborn

3. Run the notebook cells to load your network trace CSV and generate visualizations.
4. Modify or extend the notebook to suit your analysis needs.

Data Files

• NetworkTrace-Proxyman.csv — Example network trace from Proxyman
• NetworkTrace-Charles.csv — Example network trace from Charles

Requirements

• Python 3.7+
• Jupyter Notebook (or VS Code with Jupyter extension)
• pandas, matplotlib, seaborn

License

MIT License