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numpy.empty_like(a, dtype=None, order='K', subok=True) -
Return a new array with the same shape and type as a given array.
Parameters: a : array_like
The shape and data-type of
adefine these same attributes of the returned array.dtype : data-type, optional
Overrides the data type of the result.
New in version 1.6.0.
order : {?C?, ?F?, ?A?, or ?K?}, optional
Overrides the memory layout of the result. ?C? means C-order, ?F? means F-order, ?A? means ?F? if
ais Fortran contiguous, ?C? otherwise. ?K? means match the layout ofaas closely as possible.New in version 1.6.0.
subok : bool, optional.
If True, then the newly created array will use the sub-class type of ?a?, otherwise it will be a base-class array. Defaults to True.
Returns: out : ndarray
Array of uninitialized (arbitrary) data with the same shape and type as
a.See also
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ones_like - Return an array of ones with shape and type of input.
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zeros_like - Return an array of zeros with shape and type of input.
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empty - Return a new uninitialized array.
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ones - Return a new array setting values to one.
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zeros - Return a new array setting values to zero.
Notes
This function does not initialize the returned array; to do that use
zeros_likeorones_likeinstead. It may be marginally faster than the functions that do set the array values.Examples
>>> a = ([1,2,3], [4,5,6]) # a is array-like >>> np.empty_like(a) array([[-1073741821, -1073741821, 3], #random [ 0, 0, -1073741821]]) >>> a = np.array([[1., 2., 3.],[4.,5.,6.]]) >>> np.empty_like(a) array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000],#random [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]]) -
numpy.empty_like()
2017-01-10 18:13:46
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