pandas is used to manipulate table data in Python, commonly used in data pipelines, machine learning pipelines etc.
polars is a better pandas
Reasons why I say this on a high level
Speed — polars is faster than pandas as it is built in rust whereas pandas is written in Python/Cython
Memory efficiency — polars uses the Apache Arrow columnar format, which means that it handles larger-than-RAM data pretty well where pandas struggles.
Memory efficiency again — polars allows for lazy evaluation (data only materializes when needed) while pandas uses eager execution (data is materialized immediately with any operation)
2) pandera VS pydantic
Both are data validation libraries in Python, but they serve different purposes.
pydantic validates row-level data — it's great for validating individual objects like API request bodies, config files, or single records.
