mungell/mail-trends — explained in plain English
Analysis updated 2026-08-08 · repo last pushed 2011-11-10
Analyze a Gmail export to see who you email most often over the past decade.
Visualize your email sending patterns by time of day and day of week.
Track how your email volume has changed year over year.
Fork the code to adapt it for a specific email system or custom reporting needs.
| mungell/mail-trends | 0xallam/posthog | 0xustaz/streamgate | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | Python | Python | Python |
| Last pushed | 2011-11-10 | 2026-03-26 | — |
| Maintenance | Dormant | Maintained | — |
| Setup difficulty | hard | moderate | hard |
| Complexity | 3/5 | 3/5 | 4/5 |
| Audience | developer | pm founder | developer |
Figures from each repo's GitHub metadata at analysis time.
No installation steps, dependencies, or output format are documented, you must dig into the Python source code to figure out how to run it.
Mail-trends is a tool that analyzes your email archive and shows you trends and statistics about your mail. Think of it as a personal analytics dashboard for your inbox: it can tell you things like who you email most often, what times of day you send the most mail, or how your email volume has changed over time. The project works by importing your mail data, typically from files exported from your email client or server, and running it through a series of Python scripts that crunch the numbers and generate visual reports. You don't need to connect it to a live mail server, it processes mail you've already saved locally. The typical user is someone curious about their own email habits, or possibly a developer who wants to fork the code and adapt it for a specific mail system. For example, if you've used Gmail for ten years and exported your mail archive, you could use this to get a birds-eye view of your communication patterns over that whole period. The README doesn't go into detail on installation steps, dependencies, or output format, so you'd likely need to dig into the code itself to get it running. Given that this is a maintained fork with minimal documentation, it may appeal most to someone comfortable exploring a Python codebase and willing to figure things out hands-on rather than following a setup guide.
A Python tool that analyzes your locally saved email archive and generates visual reports showing trends like who you email most, peak sending times, and how your email volume changes over time.
Mainly Python. The stack also includes Python.
Dormant — no commits in 2+ years (last push 2011-11-10).
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.