Function _get_values

pandas/core/nanops.py:256–324

Utility to get the values view, mask, dtype, dtype_max, and fill_value. If both mask and fill_value/fill_value_typ are not None and skipna is True, the values array will be copied. For input arrays of boolean or integer dtypes, copies will only occur if a precomputed mask, a f

(
    values: np.ndarray,
    skipna: bool,
    fill_value: Any = None,
    fill_value_typ: str | None = None,
    mask: npt.NDArray[np.bool_] | None = None,
)

def _get_values(
    values: np.ndarray,
    skipna: bool,
    fill_value: Any = None,
    fill_value_typ: str | None = None,
    mask: npt.NDArray[np.bool_] | None = None,
) -> tuple[np.ndarray, npt.NDArray[np.bool_] | None]:
    """
    Utility to get the values view, mask, dtype, dtype_max, and fill_value.

    If both mask and fill_value/fill_value_typ are not None and skipna is True,
    the values array will be copied.

    For input arrays of boolean or integer dtypes, copies will only occur if a
    precomputed mask, a fill_value/fill_value_typ, and skipna=True are
    provided.

    Parameters
    ----------
    values : ndarray
        input array to potentially compute mask for
    skipna : bool
        boolean for whether NaNs should be skipped
    fill_value : Any
        value to fill NaNs with
    fill_value_typ : str
        Set to '+inf' or '-inf' to handle dtype-specific infinities
    mask : Optional[np.ndarray[bool]]
        nan-mask if known

    Returns
    -------
    values : ndarray
        Potential copy of input value array
    mask : Optional[ndarray[bool]]
        Mask for values, if deemed necessary to compute
    """
    # In _get_values is only called from within nanops, and in all cases
    #  with scalar fill_value.  This guarantee is important for the
    #  np.where call below

    mask = _maybe_get_mask(values, skipna, mask)

    dtype = values.dtype

    datetimelike = False
    if values.dtype.kind in "mM":
        # changing timedelta64/datetime64 to int64 needs to happen after
        #  finding `mask` above
        values = np.asarray(values.view("i8"))
        datetimelike = True

    if skipna and (mask is not None):
        # get our fill value (in case we need to provide an alternative
        # dtype for it)
        fill_value = _get_fill_value(
            dtype, fill_value=fill_value, fill_value_typ=fill_value_typ
        )