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Working with Dates and Times in Python
4.8(2,708)79k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
Time-based data introduces challenges like timezones, daylight saving time, and elapsed intervals. Working with real datasets on hurricanes and bike trips, this intermediate course covers parsing, counting, and plotting temporal data in Python using standard libraries, Pandas, and dateutil.
Who It's For
Data analysts and Python developers who handle temporal datasets and need to resolve timezone and date arithmetic challenges cleanly.
Key Takeaways
- Date Arithmetic & Elapsed Time: Calculate exact intervals between timestamps and manage day/month boundaries accurately.
- Python & Pandas Tooling: Work with dates in standard Python datetime, Pandas, and the official dateutil timezone library.
- Handling Timezones: Navigate daylight saving transitions and complex timezone conversions with confidence.
About this course
You'll probably never have a time machine, but how about a machine for analyzing time? As soon as time enters any analysis, things can get weird. It's easy to get tripped up on day and month boundaries, time zones, daylight saving time, and all sorts of other things that can confuse the unprepared. If you're going to do any kind of analysis involving time, you’ll want to use Python to sort it out. Working with data sets on hurricanes and bike trips, we’ll cover counting events, figuring out how much time has elapsed between events and plotting data over time. You'll work in both standard Python and in Pandas, and we'll touch on the dateutil library, the only timezone library endorsed by the official Python documentation. After this course, you'll confidently handle date and time data in any format like a champion.
Price
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