numpy.divmod

numpy.divmod(x1, x2, [out1, out2, ]/, [out=(None, None), ]*, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'divmod'>

Return element-wise quotient and remainder simultaneously.

New in version 1.13.0.

np.divmod(x, y) is equivalent to (x // y, x % y), but faster because it avoids redundant work. It is used to implement the Python built-in function divmod on NumPy arrays.

Parameters

x1array_like

Dividend array.

x2array_like

Divisor array. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

wherearray_like, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

**kwargs

For other keyword-only arguments, see the ufunc docs.

Returns

out1ndarray

Element-wise quotient resulting from floor division. This is a scalar if both x1 and x2 are scalars.

out2ndarray

Element-wise remainder from floor division. This is a scalar if both x1 and x2 are scalars.

See also

floor_divide

Equivalent to Python’s // operator.

remainder

Equivalent to Python’s % operator.

modf

Equivalent to divmod(x, 1) for positive x with the return values switched.

Examples

>>> np.divmod(np.arange(5), 3)
(array([0, 0, 0, 1, 1]), array([0, 1, 2, 0, 1]))

The divmod function can be used as a shorthand for np.divmod on ndarrays.

>>> x = np.arange(5)
>>> divmod(x, 3)
(array([0, 0, 0, 1, 1]), array([0, 1, 2, 0, 1]))

numpy.remainder numpy.angle