Logging data is a useful tool for projects of some complexity. It’s worth the time to develop utility routines to help with recording the data regardless of the storage media.

It is a handy tool for keeping a record of data explorations, creating charts, styling text and sharing the results of that work. For data analysis, the cornerstone package in Python is “Pandas”.

Identifying causal relationships from observational data is not easy. Still, researchers are often interested in examining the effects of policy changes or other decisions. In those analyses, ...

Use Python lists to store data in one-dimensional rows, access them by indexes, and sort them any which way you like.

Discover how Python in Excel transforms data analysis with advanced features. Is it worth the hype? Find out if it’s right for your workflow.