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Numpy arrays are generally indexed in three ways:

Basic index and slicing

Boolean index

Magic index

All three methods can intercept part of the data in the original array, but they are essentially different when they are used, especially when dealing with a large amount of data, not understanding the nature of the index will lead to inaccurate final prediction results.

Basic index and slicing

Code first:

Import numpy as np

Arr = np.arange (25) .reshape (5jue 5)

Arr

Arr1 = arr [: 3]

Arr1

Arr1 [1] =-1

Arr

You can see from the above results that the first three lines of arr are assigned to arr1, then the value of the second line of arr1 is changed to-1, and finally arr changes. The reason is that in the underlying index, only the view of the original array is returned, not a copy of the original array, and any modification to the view will affect the original array.

The solution is to copy the original array using the ndarray built-in method copy ().

Arr = np.arange (25) .reshape (5jue 5)

Arr

Arr1 = arr.copy ()

Arr1 [1] =-1

Arr

Boolean index

Code first:

Bool_ = [True,False,True,False,False]

Arr1 = arr [bool_]

Arr1

Arr1 [1] =-1

Arr

You can find that there is no similar problem with the Boolean index, because when using a Boolean index, a copy of the data is always generated, so changing the selected data has no effect on the original array.

Magic index

Index = [3, 4, 1, 2]

Arr1 = arr [index]

Arr1

Arr1 [1] =-1

Arr

Magic indexes are the same as Boolean indexes, where changes to the selected data do not affect the original array.

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