How to deal with boolean arrays in numpy

The following is a detailed explanation of the processing method of Boolean arrays in numpy. It has a good reference value and I hope it will be helpful to everyone. Let's take a look together

There are two main ways to operate Boolean arrays. any is used to check whether there is a True value in the array, and all is used to check whether the array is all True.

If used for calculation, the Boolean quantity will be converted to 1 and 0, True will be converted to 1, and False will be converted to 0. This method can count the number of True in a Boolean array.

If ordinary arrays are used for Boolean operations, there will be similar data type conversions. Among them, non-zero values ​​are converted to True, and 0 is converted to False.

In [30]: arr = randn(100)

##In [31]: arr

Out[31]:

array([ 1.38474589, -1.51489066,-0.81053544, 1.47875437, -0.53638642,

0.09856211, 1.39931492,-0.04226221, -0.6 6064836, 0.31829036,

-0.33759781, -0.35793518, 0.66974626, 1.5989403, 0.98361013,

0.0209635, -0.56165749, 0.59473585, -0.06956145, -0.5038 4339,

-0.51207066, -0.41794862, 2.12230002, 0.55457739 . 29060339, -0.18960502,

-0.91537419 . 72333408, -0.9656567, -0.04391422, -0.53504402, -0.3695063,

-0.57323435, -0.09923021, -0.8819845, -0.31904228, -0.34805511,

-1.39372713, -0.32243494, 1.18074562, -0.77189808, 0.1 4011272,

-0.12029721, 0.91164114 0.3052017 29870036,-0.71204709, 0.46825521, -0.76507537,

-0.67755756, 1.38798882, 0.44536155, 0.41104869, -0.24990925,

##-0.38003931, 1.13801121, 0.19761371, 0.84638972, 1.0581644 6,

-0.03591458, 2.35862529, 1.69183501, 0.77490116, -1.47556029,

-0.54755786, -0.93202001, 0.69240349, -0.02720469, 0.49363318,

##0.55501151, -1.67184849, -1.61725652, -0.95964244, 0.12177 363])

In [32]: arr > 0

Out[32]:

array([ True, False, False, True, False, True, True, False, False ,

True, False, False, True, True, True, True, False, True,

False, False, False, False, True, True, False, False, False,

False, True, False, True, True, False, True, False, False,

False, True, True, True, False, True, False, False,False,

True, False, False, False, False, False, False, False, False,

False, False, False, True,False, True, False, True, True,

False, True, True, True, True, True, False, False, True,

False, True, False, False, True, True, True, False, False,

True , True, True, True, False, True, True, True, False,

False, False, True, False, True, True, False, False, False, True],dtype=bool)

In [33]: (arr > 0).sum()

Out[33]: 46

In [34]: arr.any()

Out[34]: True

In [35]: arr.all ()

Out[35]: True

In [36]: (arr > 0).all()

Out[36]: False

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