Function median

dask/array/reductions.py:1484–1517

This works by automatically chunking the reduced axes to a single chunk if necessary and then calling ``numpy.median`` function across the remaining dimensions

(a, axis=None, keepdims=False, out=None)

@derived_from(np)
def median(a, axis=None, keepdims=False, out=None):
    """
    This works by automatically chunking the reduced axes to a single chunk if necessary
    and then calling ``numpy.median`` function across the remaining dimensions
    """
    if axis is None:
        raise NotImplementedError(
            "The da.median function only works along an axis.  "
            "The full algorithm is difficult to do in parallel"
        )

    if not isinstance(axis, Iterable):
        axis = (axis,)

    axis = [ax + a.ndim if ax < 0 else ax for ax in axis]

    # rechunk if reduced axes are not contained in a single chunk
    if builtins.any(a.numblocks[ax] > 1 for ax in axis):
        a = a.rechunk({ax: -1 if ax in axis else "auto" for ax in range(a.ndim)})

    result = a.map_blocks(
        np.median,
        axis=axis,
        keepdims=keepdims,
        drop_axis=axis if not keepdims else None,
        chunks=(
            [1 if ax in axis else c for ax, c in enumerate(a.chunks)]
            if keepdims
            else None
        ),
    )

    result = handle_out(out, result)
    return result


@derived_from(np)