The landscape of file reading and writing functions in Python can be a bit confusing for a newcomer, so I will focus mainly on the read_csv and read_table functions in pandas. It will at times be useful to load
It comes with zero required dependencies, and this shows in the import times: polars: 70ms numpy: 104ms pandas: 520ms Handles larger-than-RAM data If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a ...
COMPAT: prepare for pandas 3.0 string dtype (#493) Apr 30, 2025 .gitattributes Add CI using Github Actions and Windows-related fixes to setup.py and… Mar 16, 2021 .gitignore MNT: Switch from flake8 to ruff for linting (#342)
unicode_ U Fixed-length Unicode type (number of bytes platform specific); same specification semantics as string_ (e.g., 'U10') You can explicitly convert or cast an array from one dtype to another using ndarray’s astype method: In [37]: arr = np.array([1, 2, 3, 4, 5]) In ...
pandas: 520ms Handles larger-than-RAM data If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a streaming fashion. This drastically reduces memory requirements, so you might be able to process your 250GB dataset...
pandas: 520ms Handles larger-than-RAM data If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a streaming fashion. This drastically reduces memory requirements, so you might be able to process your 250GB dataset...
It comes with zero required dependencies, and this shows in the import times: polars: 70ms numpy: 104ms pandas: 520ms Handles larger-than-RAM data If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a ...
It comes with zero required dependencies, and this shows in the import times: polars: 70ms numpy: 104ms pandas: 520ms Handles larger-than-RAM data If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a ...
It comes with zero required dependencies, and this shows in the import times: polars: 70ms numpy: 104ms pandas: 520ms Handles larger-than-RAM data If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a ...
It comes with zero required dependencies, and this shows in the import times: polars: 70ms numpy: 104ms pandas: 520ms Handles larger-than-RAM data If you have data that does not fit into memory, Polars' query engine is able to process your query (or parts of your query) in a ...