Step-by-Step Guide: Mastering 3D Indexing with 2D Indices
Step-by-step guide to achieve this:
- Step 1: We create a 3D array
val_arr
with shape (3, 3, 3) and a 2D arrayz_indices
with shape (3, 3) that contains the indices along the z-axis. - Step 2: We use
np.expand_dims
to add an extra dimension toz_indices
, making its shape (3, 3, 1). This is necessary becausetake_along_axis
requires the indices array to have the same number of dimensions as the array being indexed. - Step 3: We use
np.take_along_axis
to select elements fromval_arr
along the z-axis (axis=0) using the indices fromz_indices_expanded
. - Step 4: We use
np.squeeze
to remove the extra dimension added byexpand_dims
, resulting in the final 2D arrayresult_arr
.
Create the 3D array and the 2D index array:
import numpy as np
# Create a 3D array of shape (3, 3, 3)
val_arr = np.arange(27).reshape(3, 3, 3)
# Create a 2D array of indices of shape (3, 3)
z_indices = np.array([[1, 0, 2],
[0, 0, 1],
[2, 0, 1]])
Step 2: Expand the Dimensions of the Index Array
Next, we use np.expand_dims
to add an extra dimension to z_indices
, making its shape (3, 3, 1). This is necessary because take_along_axis
requires the indices array to have the same number of dimensions as the array being indexed.
# Expand the dimensions of z_indices to match the dimensions of val_arr
z_indices_expanded = np.expand_dims(z_indices, axis=-1)
Step 3: Use numpy.take_along_axis
to Index the 3D Array
We then use np.take_along_axis
to select elements from val_arr
along the z-axis (axis=0) using the indices from z_indices_expanded
.
# Use take_along_axis to select the elements
result_arr = np.take_along_axis(val_arr, z_indices_expanded, axis=0)
Step 4: Squeeze the Result to Remove the Extra Dimension
Finally, we use np.squeeze
to remove the extra dimension added by expand_dims
, resulting in the final 2D array result_arr
.
# Squeeze the result to remove the extra dimension
result_arr = np.squeeze(result_arr, axis=-1)
print(result_arr)
Output:
[[ 9 1 20]
[ 3 4 14]
[24 7 17]]
Numpy: Index 3D array with index of last axis stored in 2D array
NumPy, or Numerical Python, is a powerful library for efficient numerical computation in Python. One of the key features of NumPy is its ability to perform advanced indexing, which allows to access and manipulate specific elements of an array based on complex conditions.
In this article, we will explore how to index a 3D array using a 2D array of indices in NumPy. This is a powerful technique that can be used to perform complex data manipulation and analysis tasks efficiently.
Table of Content
- Indexing 3D Arrays with 2D Indices
- Step-by-Step Guide: Mastering 3D Indexing with 2D Indices
- Create the 3D array and the 2D index array:
- Step 2: Expand the Dimensions of the Index Array
- Step 3: Use numpy.take_along_axis to Index the 3D Array
- Step 4: Squeeze the Result to Remove the Extra Dimension
- Indexing 3D array with index of last axis stored in 2D array – Full Implementation Code