🐼 Pandas 2.1 Paves A Way For Predictable Data Analysis

Pandas 2.1 improves on PyArrow, mapping, stacking, mutability and date handling to offer predictable data analysis

On March, 1, 2023 I was reporting on Pandas 2.0. Now, after seven months we can update to the newly released Panda 2.1.

Let’s see what’s new!

Better PyArrow Support

PyArrow as a backend became available in Panda 2.0 offering a performance and resource improvement over NumPy.

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With Pandas 3 PyArrow will become a required dependency meaning it’s here to stay.

Pandas 2.1 enhanches the PyArrow support. For example you’ll be able to preserve PyArrow types during DataFrame reductions.

You Can Ignore NaN Like Values While Mapping All Array Types

So far calling map on an array type other than nullable numerics triggered an error when there was a nan like value.

Now, thanks to a review of all methods, na_action=”ignore” will ignore nan values in all types of arrays, as we’ve expected it.