numpy array check whether not all elements are the same where elements are arrays of different lengt-CodePudding

I need to check if not all elements are the same in numpy array.

import numpy as np
from datetime import date

arr1 = np.array(
[np.array([date(2022,12,14)]),
np.array([date(2022,12,15)])]
)

arr2 = np.array(
[np.array([date(2022,12,14)]),
np.array([date(2022,12,19), date(2022, 12, 20), date(2022, 12, 21)])]
)

Now, arr1 != arr1[0] returns array([[False],[ True]]), and taking this into np.all() correctly returns False. However, arr2 != arr2[0] just returns True instead of expected array([[False],[ True]]). Why does it happen? Is there a quick workaround?

@edit I'm thinking of using

[np.array_equiv(arr2[0], dates) for dates in arr2]

Then I'm correctly getting [False, True] upon which I can apply np.all(), but is it the best solution for that case?

CodePudding user response：

Either you are using an old numpy version, or have deliberately omitted/ignored the warnings:

In [2]: from datetime import date
   ...: 
   ...: arr1 = np.array(
   ...: [np.array([date(2022,12,14)]),
   ...: np.array([date(2022,12,15)])]
   ...: )
   ...: 
   ...: arr2 = np.array(
   ...: [np.array([date(2022,12,14)]),
   ...: np.array([date(2022,12,19), date(2022, 12, 20), date(2022, 12, 21)])]
   ...: )
C:\Users\paul\AppData\Local\Temp\ipykernel_1604\1472092167.py:8: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  arr2 = np.array(

Now look at the resulting arrays - they are quite different:

In [3]: arr1
Out[3]: 
array([[datetime.date(2022, 12, 14)],
       [datetime.date(2022, 12, 15)]], dtype=object)

In [4]: arr2
Out[4]: 
array([array([datetime.date(2022, 12, 14)], dtype=object),
       array([datetime.date(2022, 12, 19), datetime.date(2022, 12, 20),
              datetime.date(2022, 12, 21)], dtype=object)              ],
      dtype=object)

And the test:

In [5]: arr1 == arr1[0]
Out[5]: 
array([[ True],
       [False]])

In [6]: arr2 == arr2[0]
C:\Users\paul\AppData\Local\Temp\ipykernel_1604\3122922660.py:1: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
  arr2 == arr2[0]
Out[6]: False

Again a warning that you are doing something wrong.

Look at the list comprehension:

In [7]: [np.array_equiv(arr2[0], dates) for dates in arr2]
Out[7]: [True, False]

Or with the simple equality that worked for arr1:

In [8]: [dates==arr2[0] for dates in arr2]
Out[8]: [array([ True]), array([False, False, False])]

Notice that the 2nd comparison has a value for each element of the inner array.

Object dtype arrays are a lot like lists, and arrays containing arrays which themselves are object dtype are especially complicated.