Handling NaN values in NumPy arrays using np.nan_to_num

Last update on December 21 2024 07:36:56 (UTC/GMT +8 hours)

NumPy: Universal Functions Exercise-17 with Solution

ufunc and NaN Handling:

Write a NumPy program that creates an array with NaN values and uses np.nan_to_num to replace NaN with a specified number.

Sample Solution:

Python Code:

import numpy as np

# Create a NumPy array with NaN values
array_with_nan = np.array([1, 2, np.nan, 4, np.nan, 6])

# Replace NaN values with a specified number, e.g., 0
array_without_nan = np.nan_to_num(array_with_nan, nan=0.0)

# Print the original and modified arrays
print("Original Array with NaN values:")
print(array_with_nan)

print("\nArray after replacing NaN values:")
print(array_without_nan)

Output:

Original Array with NaN values:
[ 1.  2. nan  4. nan  6.]

Array after replacing NaN values:
[1. 2. 0. 4. 0. 6.]

Explanation:

• Import NumPy Library:
• Import the NumPy library to handle array operations.
• Create Array with NaN Values:
• Define a NumPy array array_with_nan that includes some NaN values.
• Replace NaN Values:
• Use "np.nan_to_num()" to replace NaN values in the array with a specified number (e.g., 0.0).
• Print Original and Modified Arrays:
• Output both the original array with NaN values and the modified array after replacing NaNs to verify the operation.

Python-Numpy Code Editor: