polars.from_pandas#
- polars.from_pandas(data: DataFrame, *, schema_overrides: SchemaDict | None = None, rechunk: bool = True, nan_to_null: bool = True, include_index: bool = False) DataFrame [source]#
- polars.from_pandas(data: Series | DatetimeIndex, *, schema_overrides: SchemaDict | None = None, rechunk: bool = True, nan_to_null: bool = True, include_index: bool = False) Series
Construct a Polars DataFrame or Series from a pandas DataFrame or Series.
This operation clones data.
This requires that
pandas
andpyarrow
are installed.- Parameters:
- data: :class:`pandas.DataFrame`, :class:`pandas.Series`, :class:`pandas.DatetimeIndex`
Data represented as a pandas DataFrame, Series, or DatetimeIndex.
- 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
toNone
- include_indexbool, default False
Load any non-default pandas indexes as columns.
- Returns:
DataFrame
Examples
Constructing a
DataFrame
from apandas.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 ]