numpy provides some advanced indexing techniques that can be indexed through integer arrays or boolean arrays. These advanced indexing techniques can be used to get specific elements, rows, or columns in an array.
Integer array indexing: You can use an array of integers to get specific elements in an array. This array of integers represents the index position of the element to be fetched. Example:
import numpy as np
arr=np.array([1,2,3,4,5])
idx=np.array([0,2,4]) The index position of the element to be fetched.
result=arr[idx]
print(result) output: [1 3 5].
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Boolean array index: You can use a Boolean array to get the elements in the array that meet the criteria. This boolean array must be the same length as the original array, where true means the element to be fetched and false means not to fetch. Example:
import numpy as np
arr=np.array([1,2,3,4,5])
mask=np.array([true,false,true,false,true]) An element that satisfies the criteria.
result=arr[mask]
print(result) output: [1 3 5].
Integer and Boolean Array Combinatorial Indexes: You can use both integer and Boolean arrays to get elements in an array. Example:
import numpy as np
arr=np.array([1,2,3,4,5])
idx=np.array([0,2,4]) The index position of the element to be fetched.
mask=np.array([true,false,true,false,true]) An element that satisfies the criteria.
result=arr[idx][mask]
print(result) output: [1 5].
These advanced indexing techniques can be applied to multidimensional arrays, and specific elements, rows, or columns in a multidimensional array can be obtained by using multiple integer arrays or Boolean arrays in the index.
Understanding and mastering these advanced indexing techniques can help you operate and handle numpy arrays more flexibly. You can refer to Numpy's official documentation and other tutorials to learn more about advanced indexing Xi.