Practical, code-first guides to the pandas DataFrame library — the everyday workhorse of Python data analysis.
Connect with usPandas is the default DataFrame library for Python, and probably the single most useful tool a data scientist learns. It's not the fastest option on big data anymore (that's where Polars shines), but for the 95% of analytics work that fits in memory, pandas is still the cleanest, best-documented, most-googlable choice.
These tutorials are written in the same Minimal Viable Analytics style I use with clients: each one tackles a single core operation — filtering, sorting, grouping, merging, creating columns, plotting — with working Python you can paste straight into a notebook. New to pandas? Start with filtering. Already fluent? Jump to whatever you need this afternoon.
The Learn Pandas page runs the same six skills as runnable browser examples — great if you want to play with the code before reading the deep dives.
