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

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

This operation may clone data. If you want to ensure that in-place modifications of the output don’t affect the input, you may want to consider one of the following:

• Enable Copy-On-Write in pandas.
• Call DataFrame.clone() on the output of from_pandas.

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.

Note

If the input is a pandas DataFrame and has a nameless index which just enumerates the rows, then it will not be included in the result, regardless of this parameter. If you want to be sure to include it, please call .reset_index() prior to calling this function.

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 pandas.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
]