Changing the chain order of the methods in NumPy is giving None and different output-CodePudding

I ran the following code related to Numpy arrays.

import numpy as np

a = np.array([[1,4],[3,1]])

np_array = np.sort(a.flatten())
print(np_array)

np_array2 = a.flatten().sort()
print(np_array2)

I got the following output.

[1 1 3 4]
None

I expected this output.

[1 1 3 4]
[1 1 3 4]

Why is this difference in the output? What concept of programming is this?

CodePudding user response：

You get none because .sort() operates differently from sorted() or np.sort(). The *.sort() is in place (ie. the original list) whereas the other methods both create a new list.

So this is an example that works (the way you want it):

import numpy as np

a = np.array([[1,4],[3,1]])

np_array = np.sort(a.flatten())
print('nparraay', np_array)

np_array2 = a.flatten()
print('nparraay2 (part 1):', np_array2)

np_array2 = np.sort(np_array2)
print('nparraay2 (part 2):', np_array2)

result:

nparraay [1 1 3 4]
nparraay2 (part 1): [1 4 3 1]
nparraay2 (part 2): [1 1 3 4]