6 posts · page 1 of 1
Practical pandas speedups: vectorization, dtype tuning, categorical columns, eval/query, and chunked I/O patterns that turn slow scripts into responsive pipelines.
A practical guide to Pandas MultiIndex: when to use it, how it really works, and the slicing, stacking, and groupby patterns that make hierarchical data manageable.
Learn how to use pandas pivot_table to summarize, reshape, and aggregate data with multiple indexes, columns, and custom aggregation functions in real workflows.
A practical guide to combining and summarising DataFrames — groupby with named aggregations, multi-column aggregates, the four merge styles, and stacking with concat.
A practical guide to the daily DataFrame moves — read_csv and read_json, head and info, column selection, loc vs iloc, boolean filtering, sorting, and value_counts.
A clear introduction to pandas — what DataFrames and Series are, why analysts and ML engineers live in it, how to install it, and a tiny first end-to-end example.