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, Series, or Index.

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.

Note

If the input is a pandas Series or 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
]