Dive into Python, clean code practices, and real-world development tips.
By Jeferson Peter • Dec 18, 2025 — Polars & Pandas
A personal look at the moment my data workflows outgrew Pandas — and how moving to Polars made my pipelines faster, clearer, and more predictable.
By Jeferson Peter • Sep 11, 2025 — Polars & Pandas
Creating a DataFrame is the first step in any data analysis. Let’s see how to do it in Pandas and Polars, side by side, from dicts, records, and lists.
By Jeferson Peter • Sep 18, 2025 — Polars & Pandas
See how to select data in Pandas (`loc`/`iloc`) and Polars (`select`/`filter`) with simple examples.
By Jeferson Peter • Oct 9, 2025 — Polars & Pandas
Merging and joining tables is fundamental in data analysis. Let’s compare how Pandas and Polars perform joins side by side.
By Jeferson Peter • Oct 16, 2025 — Polars & Pandas
Null values are common in datasets. Let’s see how Pandas and Polars deal with missing data using `fill` and `drop` methods.
By Jeferson Peter • Oct 23, 2025 — Polars & Pandas
Sorting and ranking data is common in analysis. Let’s see how Pandas and Polars handle it with simple examples.
By Jeferson Peter • Sep 30, 2025 — Polars & Pandas
Dates and times are everywhere in datasets. Let’s see how Pandas and Polars parse and handle them.
By Jeferson Peter • Sep 25, 2025 — Polars & Pandas
Reading CSV files is one of the most common tasks in data workflows. This article compares Pandas and Polars when it comes to CSV reading performance, focusing on real-world implications rather than synthetic benchmarks.
By Jeferson Peter • Oct 2, 2025 — Polars & Pandas
GroupBy operations are at the core of many data pipelines. This article compares how Pandas and Polars handle grouping and aggregation, focusing on readability, performance, and real-world usage.
By Jeferson Peter • Nov 6, 2025 — Polars & Pandas
Pandas executes each operation immediately. Polars, on the other hand, can defer execution using lazy evaluation — building an optimized query plan and running it only when needed.
