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When I shrink a numpy array using the resize method (i.e. the array gets smaller due to the resize), is it guaranteed that no copy is made?
Example:
a = np.arange(10) # array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
a.resize(5, refcheck=False) # array([0, 1, 2, 3, 4])
From my understanding this should always be possible without making a copy. My question: Does the implementation indeed guarantee that this is always the case? Unfortunately the documentation of resize says nothing about it.
767
You can use numpy. squeeze() to remove all dimensions of size 1 from the NumPy array ndarray . squeeze() is also provided as a method of ndarray .
The shape of the array can also be changed using the resize() method. If the specified dimension is larger than the actual array, The extra spaces in the new array will be filled with repeated copies of the original array.
Use numpy. abs(x) to convert the elements of array x to their absolute values. Use numpy. amax(a) with this result to calculate the maximum value in a . Divide a number x by the maximum value and multiply by the original array to scale the elements of the original array to be between -x and x .
Resize() resize() function is used to create a new array of different sizes and dimensions. resize() can create an array of larger size than the original array.
A numpy array is a fixed size array in the background, any type of resizing will always copy the array.
Having that said, you could create a slice of the array effectively only using a subset of the array without having to resize/copy.
>>> import numpy
>>> a = numpy.arange(10)
>>> b = a[:5]
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> b
array([0, 1, 2, 3, 4])
>>>
>>> a += 10
>>> a
array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
>>> b
array([10, 11, 12, 13, 14])
>>>
>>> b += 10
>>> a
array([20, 21, 22, 23, 24, 15, 16, 17, 18, 19])
>>> b
array([20, 21, 22, 23, 24])
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