polars.from_pandas¶
- polars.from_pandas(df: pd.DataFrame, rechunk: bool = True, nan_to_none: bool = True) polars.internals.frame.DataFrame¶
- polars.from_pandas(df: Union['pd.Series', 'pd.DatetimeIndex'], rechunk: bool = True, nan_to_none: bool = True) polars.internals.series.Series
Construct a Polars DataFrame or Series from a pandas DataFrame or Series.
This requires that pandas and pyarrow are installed.
- Parameters
- dfpandas DataFrame, Series, or DatetimeIndex
Data represented as a pandas DataFrame, Series, or DatetimeIndex.
- rechunkbool, default True
Make sure that all data is contiguous.
- nan_to_nonebool, default True
If data contains NaN values PyArrow will convert the NaN to None
- 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 ]