Function mode

pandas/core/algorithms.py:996–1039

Returns the mode(s) of an array. Parameters ---------- values : array-like Array over which to check for duplicate values. dropna : bool, default True Don't consider counts of NaN/NaT. Returns ------- Union[Tuple[np.ndarray, npt.NDArray[np.bool_]],

(
    values: ArrayLike, dropna: bool = True, mask: npt.NDArray[np.bool_] | None = None
)

def mode(
    values: ArrayLike, dropna: bool = True, mask: npt.NDArray[np.bool_] | None = None
) -> tuple[np.ndarray, npt.NDArray[np.bool_]] | ExtensionArray:
    """
    Returns the mode(s) of an array.

    Parameters
    ----------
    values : array-like
        Array over which to check for duplicate values.
    dropna : bool, default True
        Don't consider counts of NaN/NaT.

    Returns
    -------
    Union[Tuple[np.ndarray, npt.NDArray[np.bool_]], ExtensionArray]
    """
    values = _ensure_arraylike(values, func_name="mode")
    original = values

    if needs_i8_conversion(values.dtype):
        # Got here with ndarray; dispatch to DatetimeArray/TimedeltaArray.
        values = ensure_wrapped_if_datetimelike(values)
        values = cast("ExtensionArray", values)
        return values._mode(dropna=dropna)

    values = _ensure_data(values)

    npresult, res_mask = htable.mode(values, dropna=dropna, mask=mask)
    if res_mask is None:
        res_mask = np.zeros(npresult.shape, dtype=np.bool_)
    else:
        return npresult, res_mask

    try:
        npresult = safe_sort(npresult)
    except TypeError as err:
        warnings.warn(
            f"Unable to sort modes: {err}",
            stacklevel=find_stack_level(),
        )

    result = _reconstruct_data(npresult, original.dtype, original)
    return result, res_mask


def rank(