polars.from_pandas#

polars.from_pandas(
data: pd.DataFrame | pd.Series[Any] | pd.Index[Any] | pd.DatetimeIndex,
*,
schema_overrides: SchemaDict | None = None,
rechunk: bool = True,
nan_to_null: bool = True,
include_index: bool = False,
) DataFrame | Series[source]#

Construct a Polars DataFrame or Series from a pandas DataFrame or Series.

This operation clones data.

This requires that pandas and pyarrow are installed.

Parameters:
datapandas.DataFrame or pandas.Series or pandas.Index

Data represented as a pandas DataFrame, Series, or Index.

schema_overridesdict, default None

Support override of inferred types for one or more columns.

rechunkbool, default True

Make sure that all data is in contiguous memory.

nan_to_nullbool, default True

If data contains NaN values PyArrow will convert the NaN to None

include_indexbool, default False

Load any non-default pandas indexes as columns.

Returns:
DataFrame

Examples

Constructing a DataFrame from a pandas.DataFrame:

>>> import pandas as pd
>>> pd_df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "b", "c"])
>>> df = pl.from_pandas(pd_df)
>>> df
    shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 2   ┆ 3   │
│ 4   ┆ 5   ┆ 6   │
└─────┴─────┴─────┘

Constructing a Series from a pd.Series:

>>> import pandas as pd
>>> pd_series = pd.Series([1, 2, 3], name="pd")
>>> df = pl.from_pandas(pd_series)
>>> df
shape: (3,)
Series: 'pd' [i64]
[
    1
    2
    3
]