polars.from_dataframe#

polars.from_dataframe( df: SupportsInterchange | ArrowArrayExportable | ArrowStreamExportable, *, allow_copy: bool | None = None, rechunk: bool = True, ) → DataFrame[source]#

Build a Polars DataFrame from any dataframe supporting the PyCapsule Interface.

Changed in version 1.23.0: from_dataframe uses the PyCapsule Interface instead of the Dataframe Interchange Protocol for conversion, only using the latter as a fallback.

Parameters:

df: Object supporting the dataframe PyCapsule Interface.
allow_copy: Allow memory to be copied to perform the conversion. If set to False, may cause conversions that are not zero-copy to fail.
rechunkbool, default True: Make sure that all data is in contiguous memory.

Notes

Details on the PyCapsule Interface: https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html.
Details on the Python dataframe interchange protocol: https://data-apis.org/dataframe-protocol/latest/index.html. Using a dedicated function like from_pandas() or from_arrow() is a more efficient method of conversion.

Examples

Convert a pandas dataframe to Polars.

>>> import pandas as pd
>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["x", "y"]})
>>> pl.from_dataframe(df_pd)
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 3.0 ┆ x   │
│ 2   ┆ 4.0 ┆ y   │
└─────┴─────┴─────┘