By Jeferson Peter • Nov 13, 2025 — Polars & Pandas
Pandas and Polars overlap in functionality, but each shines in different contexts. Let’s compare when to use one or the other.
By Jeferson Peter • Nov 10, 2025 — Python
Using `__slots__` in a class prevents the creation of `__dict__` for each instance, saving memory when you create many objects.
By Jeferson Peter • Nov 6, 2025 — Polars & Pandas
Pandas executes operations immediately. Polars, however, can use lazy evaluation — building a plan and running it only when needed.
By Jeferson Peter • Nov 3, 2025 — Python
`map()` and `filter()` let you apply functions and filter collections without explicit loops. Let’s see how they compare to list comprehensions.
By Jeferson Peter • Oct 27, 2025 — Python
`lambda` lets you create small, throwaway functions in a single line. Perfect for quick tasks without defining a full function.
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 • Oct 20, 2025 — Python
The built-in `range()` function doesn’t create a list of numbers. It creates a lazy sequence that generates numbers on demand.
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 13, 2025 — Python
The unpacking operators `*` and `**` let you expand lists and dictionaries in flexible ways. Let’s see practical 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.
