Function arrays_to_mgr

pandas/core/internals/construction.py:96–152

Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases.

(
    arrays,
    columns: Index,
    index,
    *,
    dtype: DtypeObj | None = None,
    verify_integrity: bool = True,
    consolidate: bool = True,
)

def arrays_to_mgr(
    arrays,
    columns: Index,
    index,
    *,
    dtype: DtypeObj | None = None,
    verify_integrity: bool = True,
    consolidate: bool = True,
) -> Manager:
    """
    Segregate Series based on type and coerce into matrices.

    Needs to handle a lot of exceptional cases.
    """
    if verify_integrity:
        # figure out the index, if necessary
        if index is None:
            index = _extract_index(arrays)
        else:
            index = ensure_index(index)

        # don't force copy because getting jammed in an ndarray anyway
        arrays, refs = _homogenize(arrays, index, dtype)
        # _homogenize ensures
        #  - all(len(x) == len(index) for x in arrays)
        #  - all(x.ndim == 1 for x in arrays)
        #  - all(isinstance(x, (np.ndarray, ExtensionArray)) for x in arrays)
        #  - all(type(x) is not NumpyExtensionArray for x in arrays)

    else:
        index = ensure_index(index)
        arrays = [extract_array(x, extract_numpy=True) for x in arrays]
        # with _from_arrays, the passed arrays should never be Series objects
        refs = [None] * len(arrays)

        # Reached via DataFrame._from_arrays; we do minimal validation here
        for arr in arrays:
            if (
                not isinstance(arr, (np.ndarray, ExtensionArray))
                or arr.ndim != 1
                or len(arr) != len(index)
            ):
                raise ValueError(
                    "Arrays must be 1-dimensional np.ndarray or ExtensionArray "
                    "with length matching len(index)"
                )

    columns = ensure_index(columns)
    if len(columns) != len(arrays):
        raise ValueError("len(arrays) must match len(columns)")

    # from BlockManager perspective
    axes = [columns, index]

    return create_block_manager_from_column_arrays(
        arrays, axes, consolidate=consolidate, refs=refs
    )