Boolean Indexing on higher dimensions in NumPy arrays
NumPy: Advanced Indexing Exercise-19 with Solution
Boolean Indexing on Higher Dimensions:
Write a NumPy program that creates a 5D NumPy array. Use boolean indexing to select elements along specific dimensions based on conditions applied to other dimensions.
Sample Solution:
Python Code:
import numpy as np
# Create a 5D NumPy array of shape (3, 4, 2, 3, 5) with random integers
array_5d = np.random.randint(0, 100, size=(3, 4, 2, 3, 5))
# Define a condition on the entire 5D array
# For example, select elements where the values are greater than 50
condition = array_5d > 50
# Use boolean indexing to select elements along specific dimensions based on the condition
selected_elements = array_5d[condition]
# Print the shape of the original array, the condition array, and the selected elements
print('Original 5D array shape:', array_5d.shape)
print('Condition (elements > 50):\n', condition)
print('Selected elements based on condition:\n', selected_elements)
print('Shape of selected elements:', selected_elements.shape)
Output:
Original 5D array shape: (3, 4, 2, 3, 5)
Condition (elements > 50):
[[[[[False True False False False]
[ True True False True False]
[False True False True True]]
[[ True True True False True]
[False False True True False]
[ True False False False True]]]
[[[ True False False False True]
[ True True False False True]
[False True False False False]]
[[False False False True True]
[ True True True True True]
[ True True True False False]]]
[[[False False False True False]
[ True True False False False]
[ True False False False True]]
[[False False True True True]
[ True False False True True]
[False True True False False]]]
[[[ True False True True True]
[ True False False True False]
[ True False False False True]]
[[False False True False True]
[ True False True True False]
[False False True False False]]]]
[[[[ True True True False False]
[ True True True True True]
[ True True True False False]]
[[False True False False False]
[ True False False True True]
[ True False True True False]]]
[[[False True False True True]
[False False True False True]
[False False False False False]]
[[False True True True False]
[False False False False True]
[False True False True True]]]
[[[ True False False True True]
[ True True False False False]
[ True False False True False]]
[[ True False True False False]
[ True False True True True]
[False False False False False]]]
[[[False True False True True]
[ True False False False False]
[False True True True True]]
[[False False True True False]
[ True True True True True]
[False False False True True]]]]
[[[[False True False True True]
[False False False False False]
[False True False True False]]
[[ True False False False False]
[False False False False True]
[False False False True True]]]
[[[ True False False True True]
[ True True True False True]
[ True True False False True]]
[[False False True True True]
[False False True False False]
[False True True False True]]]
[[[ True False True True True]
[ True False False False False]
[False False True False False]]
[[False True True True True]
[False True False False True]
[False False True True True]]]
[[[False False False False False]
[ True True True False True]
[ True False False True False]]
[[ True True True True True]
[ True False False True True]
[False True True False False]]]]]
Selected elements based on condition:
[99 71 55 88 81 74 69 58 71 51 97 73 69 77 62 68 53 72 67 99 91 51 61 53
68 67 58 51 83 96 78 72 98 60 58 73 67 90 63 52 67 90 64 98 97 73 58 84
68 55 84 73 66 55 62 78 84 62 53 76 73 95 96 63 79 84 59 64 74 82 71 96
57 87 57 98 54 80 93 76 66 55 79 80 58 72 91 59 89 81 55 60 94 87 53 69
52 53 95 99 94 99 74 58 92 81 99 56 70 72 51 54 51 76 51 82 96 77 55 88
77 94 78 79 99 93 85 87 80 66 85 54 71 97 71 86 96 72 67 87 97 93 83 74
69 92 96 68 92 98 72 94 56 99 93 68 56 97 95 98 73 88 71 52 74 78 99 76
71 90 53 54 90 82 67]
Shape of selected elements: (175,)
Explanation:
- Import Libraries:
- Imported numpy as "np" for array creation and manipulation.
- Create 5D NumPy Array:
- Create a 5D NumPy array named array_5d with random integers ranging from 0 to 99 and a shape of (3, 4, 2, 3, 5).
- Define Condition:
- Define a condition to select elements in the array that are greater than 50 using boolean indexing.
- Boolean Indexing:
- Applied boolean indexing to select elements from array_5d that meet the defined condition.
- Print Results:
- Print the shape of the original 5D array, the boolean condition array, and the selected elements, including the shape of the selected elements to verify the operation.
Python-Numpy Code Editor: