polars.from_dataframe#

polars.from_dataframe(df: SupportsInterchange, *, allow_copy: bool = True) → DataFrame[source]#

Build a Polars DataFrame from any dataframe supporting the interchange protocol.

Parameters:

df: Object supporting the dataframe interchange protocol, i.e. must have implemented the __dataframe__ method.
allow_copy: Allow memory to be copied to perform the conversion. If set to False, causes conversions that are not zero-copy to fail.

Notes

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.

Polars currently relies on pyarrow’s implementation of the dataframe interchange protocol. Therefore, pyarrow>=11.0.0 is required for this function to work.

Because Polars can not currently guarantee zero-copy conversion from Arrow for categorical columns, allow_copy=False will not work if the dataframe contains categorical data.

Examples

Convert a pandas dataframe to Polars through the interchange protocol.

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