NumPy: Efficient Selection of Specific Column Indexes per Row

Data selection is a crucial operation in data analysis. When working with NumPy arrays, selecting specific columns from each row can be a common task. This selection can be accomplished with various methods, but selecting columns based on a list of indexes per row requires a more efficient approach.

Using Boolean Arrays for Direct Selection

If you have a boolean array indicating the columns to be selected, you can use direct selection to extract the desired values efficiently. Boolean arrays can be created by comparing a list of indexes with the range of columns. For example, given a matrix X and a list of indexes Y as described in the question, you can create a boolean array b as follows:

import numpy as np

X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
Y = np.array([1, 0, 2])
b = np.arange(X.shape[1])[np.isin(np.arange(X.shape[1]), Y)]

With the boolean array b, direct selection can be performed:

result = X[np.arange(X.shape[0]), b]

This method provides a fast way to select specific columns based on the boolean array.

Alternate Methods

Alternatively, you can use np.arange and direct selection based on the index list Y. This approach involves creating an array of indices and selecting from the matrix X accordingly:

result = X[np.arange(X.shape[0]), Y]

Conclusion

Selecting specific column indexes per row in NumPy can be done efficiently using boolean arrays. This method provides fast and straightforward selection of columns based on a list of indexes. For large arrays of data, this approach will offer significant performance benefits over iteration-based methods.