Apply np.exp, np.log, and np.sqrt ufuncs to transform a NumPy array

Last update on March 26 2025 13:18:20 (UTC/GMT +8 hours)

8. Ufunc Chain: exp, log, and sqrt Transformations

Chaining ufuncs:

Write a NumPy program that creates a NumPy array and applies a sequence of ufuncs (np.exp, np.log, and np.sqrt) to transform the array.

Sample Solution:

Python Code:

import numpy as np

# Create a 1D NumPy array of integers
array_1d = np.array([1, 2, 3, 4, 5])

# Apply the np.exp ufunc to the array
exp_array = np.exp(array_1d)

# Apply the np.log ufunc to the resulting array from np.exp
log_array = np.log(exp_array)

# Apply the np.sqrt ufunc to the resulting array from np.log
sqrt_array = np.sqrt(log_array)

# Print the original array and the resulting arrays after each transformation
print('Original 1D array:', array_1d)
print('Array after applying np.exp:', exp_array)
print('Array after applying np.log:', log_array)
print('Array after applying np.sqrt:', sqrt_array)

Output:

Original 1D array: [1 2 3 4 5]
Array after applying np.exp: [  2.71828183   7.3890561   20.08553692  54.59815003 148.4131591 ]
Array after applying np.log: [1. 2. 3. 4. 5.]
Array after applying np.sqrt: [1.         1.41421356 1.73205081 2.         2.23606798]

Explanation:

• Import Libraries:
• Imported numpy as "np" for array creation and manipulation.
• Create 1D NumPy Array:
• Create a 1D NumPy array named 'array_1d' with integers [1, 2, 3, 4, 5].
• Apply np.exp ufunc:
• Applied the np.exp "ufunc" to the 'array_1d' to compute the exponential of each element, resulting in 'exp_array'.
• Apply np.log ufunc:
• Applied the np.log "ufunc" to the 'exp_array' to compute the natural logarithm of each element, resulting in log_array.
• Apply np.sqrt ufunc:
• Applied the np.sqrt "ufunc" to the 'log_array' to compute the square root of each element, resulting in 'sqrt_array'.
• Print Results:
• Print the original array and the resulting arrays after each transformation to verify the operations.

For more Practice: Solve these Related Problems:

• Write a Numpy program to apply np.exp, np.log, and np.sqrt sequentially on an array and then compare the final output with the original array.
• Write a Numpy program to transform an array using np.exp, then np.log, and finally np.sqrt, ensuring proper handling of edge cases.
• Write a Numpy program to chain np.exp, np.log, and np.sqrt on an array and use np.allclose to verify reversibility.
• Write a Numpy program to combine np.exp, np.log, and np.sqrt in a single pipeline using function composition on an input array.

Python-Numpy Code Editor:

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