We may never have a time machine, but we do have Python—a powerful tool for analyzing time!️
Time-based data can get tricky with day/month boundaries, time zones, daylight saving time, and more. But with the right techniques, we can organize, analyze, and visualize time data like pros.
In my latest learning journey, I worked with hurricane and bike trip datasets to:
- Count events over time.
- Calculate time differences between events.
- Plot time series data effectively.
- Work with Python, Pandas, and dateutil (the only timezone library endorsed by Python’s official docs).
Now, I can confidently handle date and time data in any format—no confusion, just insights!
Check here for details.