xarray.ufuncs.floor_divide

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xarray.ufuncs.floor_divide#

xarray.ufuncs.floor_divide = <xarray.ufuncs._binary_ufunc object>#

xarray specific variant of numpy.floor_divide(). Handles xarray objects by dispatching to the appropriate function for the underlying array type.

Documentation from numpy:

Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python // operator and pairs with the Python % (remainder), function so that a = a % b + b * (a // b) up to roundoff.

Parameters

  • x1 (array_like) – Numerator.

  • x2 (array_like) – Denominator. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

  • out (ndarray, 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.

  • where (array_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

y (ndarray) – y = floor(x1/x2) This is a scalar if both x1 and x2 are scalars.

See also

remainder

Remainder complementary to floor_divide.

divmod

Simultaneous floor division and remainder.

divide

Standard division.

floor

Round a number to the nearest integer toward minus infinity.

ceil

Round a number to the nearest integer toward infinity.

Examples

>>> np.floor_divide(7,3)
2
>>> np.floor_divide([1., 2., 3., 4.], 2.5)
array([ 0.,  0.,  1.,  1.])

The // operator can be used as a shorthand for np.floor_divide on ndarrays.

>>> x1 = np.array([1., 2., 3., 4.])
>>> x1 // 2.5
array([0., 0., 1., 1.])
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