numpy.divide()

numpy.divide(x1, x2[, out]) =

Divide arguments element-wise.

Parameters:

Parameters:	x1 : array_like Dividend array. x2 : array_like Divisor array. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs.
Returns:	y : ndarray or scalar The quotient `x1/x2`, element-wise. Returns a scalar if both `x1` and `x2` are scalars.

x1 : array_like

Dividend array.

x2 : array_like

Divisor array.

out : ndarray, optional

Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. See doc.ufuncs.

Returns:

y : ndarray or scalar

The quotient x1/x2, element-wise. Returns a scalar if both x1 and x2 are scalars.

Notes

Equivalent to x1 / x2 in terms of array-broadcasting.

Behavior on division by zero can be changed using seterr.

In Python 2, when both x1 and x2 are of an integer type, divide will behave like floor_divide. In Python 3, it behaves like true_divide.

Examples

>>> np.divide(2.0, 4.0)
0.5
>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> np.divide(x1, x2)
array([[ NaN,  1. ,  1. ],
       [ Inf,  4. ,  2.5],
       [ Inf,  7. ,  4. ]])

Note the behavior with integer types (Python 2 only):

>>> np.divide(2, 4)
0
>>> np.divide(2, 4.)
0.5

Division by zero always yields zero in integer arithmetic (again, Python 2 only), and does not raise an exception or a warning:

>>> np.divide(np.array([0, 1], dtype=int), np.array([0, 0], dtype=int))
array([0, 0])

Division by zero can, however, be caught using seterr:

>>> old_err_state = np.seterr(divide='raise')
>>> np.divide(1, 0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
FloatingPointError: divide by zero encountered in divide

>>> ignored_states = np.seterr(**old_err_state)
>>> np.divide(1, 0)
0

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.divide.html

doc_NumPy

2025-01-10 15:47:30

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