Numpy 'logical and' producing different type list-CodePudding

I am doing a logical operation on a data frame column data using NumPy. I am checking if a row value is less than two compared to the preceding and successful row. I am getting output but different than what I expected

ad = pd.DataFrame({'a':[1,3,4,6,7,5,9,10]})
op=np.array(np.logical_and.reduce([ad['a'].\
                       diff(x).abs().le(2) for x in [1,-1]]))
print(op)

Present output:

[False  True  True  True  True False False False]

Expected output:

[False,  True,  True,  True,  True, False, False, False]

CodePudding user response：

You can use numpy.ndarray.tolist to convert the array to a list.

`>>> op.tolist()`

[False, True, True, True, True, False, False, False]

CodePudding user response：

This is just how the output for printing Numpy Arrays look. If you truly want the comma separated print output then you can just cast the original array as a list. otherwise these two outputs you've presented are the same

'''

ad = pd.DataFrame({'a':[1,3,4,6,7,5,9,10]})
op=np.array(np.logical_and.reduce([ad['a'].\
                   diff(x).abs().le(2) for x in [1,-1]]))
print(op)  
op = list(op)
print(op)

''''

output:

before list

[False True True True True False False False]

after list

[False, True, True, True, True, False, False, False]