Python detailed analysis of np.where() code application

This article brings you relevant knowledge about Python3. There is a very magical function in numpy called np.where() function. The following article mainly introduces you to Python. The detailed explanation of np.where() and related information on code application are introduced in detail through example code. I hope it will be helpful to everyone.

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np.where has two usages:

A kind of np.where(condition, x, y), that is, condition is the condition. When the condition is met, the output is x, if the condition is not met, the output is y. Directly enter the code:

a = np.arange(10)
//array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
print(np.where(a > 5, 1, -1))
//array([-1, -1, -1, -1, -1, -1,  1,  1,  1,  1])

Above It is quite easy to understand, but the example on the official website is not easy to understand, as shown below:

np.where([[True,False], [True,True]],   
			 [[1,2], [3,4]],
             [[9,8], [7,6]])
// 输出 array([[1, 8], [3, 4]])

It can be understood in this way. The bool value in the first line represents the condition, which means whether to take the value. First, look at [ True, False], that is, the first True value means that the first row takes the value 1 in [1, 2] in the first row, instead of taking the 9 below, False means not taking the value in [1, 2] in the first row 2, and take the 8 in the second row [9, 8]. The following is the same as [3, 4].
For the convenience of understanding, let’s give another example:

a = 10
>>> np.where([[a > 5,a < 5], [a == 10,a == 7]],
             [["chosen","not chosen"], ["chosen","not chosen"]],
             [["not chosen","chosen"], ["not chosen","chosen"]])

//array([[&#39;chosen&#39;, &#39;chosen&#39;], [&#39;chosen&#39;, &#39;chosen&#39;]], dtype=&#39;<U10&#39;)

The first row a> 5True, then take the first value of the first row, ace523c7b8482a09a9795861645b3d5b5=0 and data<=2, np.ones_like(data)the value of the corresponding coordinates will be returned if it is satisfied, no If satisfied, the value of np.zeros_like(data) corresponding coordinates will be returned. Of course, x and y can be changed to other values, as long as they have the same size as the conditions.

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