NumPy: Find the missing data in a given array

NumPy: Basic Exercise-43 with Solution

Write a NumPy program to find missing data in a given array.

Sample Solution :

Python Code :

# Importing the NumPy library with an alias 'np'
import numpy as np
 
# Creating a NumPy array 'nums' with provided values, including NaN (Not a Number)
nums = np.array([[3, 2, np.nan, 1],
                 [10, 12, 10, 9],
                 [5, np.nan, 1, np.nan]])

# Printing a message indicating the original array 'nums'
print("Original array:")
print(nums)

# Printing a message indicating finding the missing data (NaN) in the array using np.isnan()
# This function returns a boolean array of the same shape as 'nums', where True represents NaN values
print("\nFind the missing data of the said array:")
print(np.isnan(nums)) 

Sample Output:

Original array:
[[ 3.  2. nan  1.]
 [10. 12. 10.  9.]
 [ 5. nan  1. nan]]

Find the missing data of the said array:
[[False False  True False]
 [False False False False]
 [False  True False  True]]

Explanation:

The above example creates a NumPy array containing NaN values and prints a boolean array indicating which elements in the original array are NaN values.

nums = np.array(...): Here np.array(...) creates a 2D NumPy array named 'nums' with 3 rows and 4 columns. Some of the elements in the array are NaN (Not a Number).

print(np.isnan(nums)): Here np.isnan() function returns a boolean array of the same shape as 'nums', where each element indicates whether the corresponding element in 'nums' is NaN or not. Finally, it prints the resulting boolean array.

Python-Numpy Code Editor:

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