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cast

yohou.utils.polars.cast(df, schema)

Cast columns according to schema with integer rounding.

Casts columns to specified dtypes, with special handling for integer types. Integer target dtypes trigger rounding before casting to avoid truncation. Columns not present in the schema are preserved as-is.

Parameters

Name Type Description Default
df DataFrame

DataFrame to cast.

 required
schema dict of str to pl.DataType

Mapping from column names to target dtypes. Only columns present in this dict will be cast.

 required

Returns

Type Description
DataFrame

DataFrame with specified columns cast to target dtypes.

Examples

>>> import polars as pl
>>> df = pl.DataFrame({"a": [1.7, 2.3, 3.9], "b": [10.0, 20.0, 30.0], "c": ["x", "y", "z"]})
>>> schema = {"a": pl.Int32, "b": pl.Float32}
>>> result = cast(df, schema)
>>> dict(result.schema)
{'a': Int32, 'b': Float32, 'c': String}
>>> result["a"].to_list()
[2, 2, 4]

Notes

Integer casting behavior: - Values are rounded to nearest integer before casting - Prevents data loss from truncation (1.9 → 2, not 1) - Follows standard statistical rounding rules

Columns in df but not in schema are left unchanged, allowing preservation of extra columns from model predictions.

See Also

Source Code

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def cast(
    df: pl.DataFrame,
    schema: dict[str, pl.DataType],
) -> pl.DataFrame:
    """Cast columns according to schema with integer rounding.

    Casts columns to specified dtypes, with special handling for integer types.
    Integer target dtypes trigger rounding before casting to avoid truncation.
    Columns not present in the schema are preserved as-is.

    Parameters
    ----------
    df : pl.DataFrame
        DataFrame to cast.
    schema : dict of str to pl.DataType
        Mapping from column names to target dtypes. Only columns present
        in this dict will be cast.

    Returns
    -------
    pl.DataFrame
        DataFrame with specified columns cast to target dtypes.

    Examples
    --------
    >>> import polars as pl
    >>> df = pl.DataFrame({"a": [1.7, 2.3, 3.9], "b": [10.0, 20.0, 30.0], "c": ["x", "y", "z"]})
    >>> schema = {"a": pl.Int32, "b": pl.Float32}
    >>> result = cast(df, schema)
    >>> dict(result.schema)
    {'a': Int32, 'b': Float32, 'c': String}
    >>> result["a"].to_list()
    [2, 2, 4]

    Notes
    -----
    Integer casting behavior:
    - Values are rounded to nearest integer before casting
    - Prevents data loss from truncation (1.9 → 2, not 1)
    - Follows standard statistical rounding rules

    Columns in df but not in schema are left unchanged, allowing
    preservation of extra columns from model predictions.

    See Also
    --------
    - [`get_numeric_columns`][yohou.utils.polars.get_numeric_columns] : List numeric column names from a DataFrame.

    """
    exprs = []

    for col, target_dtype in schema.items():
        if col not in df.columns:
            continue

        if target_dtype.is_integer():
            exprs.append(pl.col(col).round().cast(target_dtype).alias(col))
        elif not target_dtype.is_numeric():
            # Non-numeric types (Categorical, Enum, String, Boolean, etc.)
            # are cast directly without rounding.
            exprs.append(pl.col(col).cast(target_dtype).alias(col))
        else:
            exprs.append(pl.col(col).cast(target_dtype).alias(col))

    return df.with_columns(exprs)