How do I access the ith column of a NumPy multidimensional array

Given:

test = np.array([[1, 2], [3, 4], [5, 6]])

test[i] gives the ith row (e.g. [1, 2]). How do I access the ith column? (e.g. [1, 3, 5]). Also, would this be an expensive operation?

Answers

To access the ith column of a NumPy array, you can use slicing. In your example:

import numpy as np

test = np.array([[1, 2], [3, 4], [5, 6]])

To access the ith column, you can do:

column_i = test[:, i]  # Replace i with the index of the column you want

For example, if you want to access the first column (i = 0):

column_0 = test[:, 0]
print(column_0)  # Output: [1 3 5]

Explanation:

• test[:, i] means:
  • The : selects all rows.
  • The i selects the ith column.

Performance:

This is not an expensive operation in NumPy because it is essentially a view (a reference) on the original data, not a deep copy. NumPy's slicing operations are very efficient and done in constant time (O(1)), so there is no significant overhead for accessing columns.

If you modify the column_i array (using in-place operations), it will modify the original test array as well since they share the same memory. If you want a copy instead of a view, you can explicitly copy the column like this:

column_0_copy = test[:, 0].copy()