NumPy: Find the missing data in a given array
Find Missing Data in Array
Write a NumPy program to find missing data in a given array.
This problem involves writing a NumPy program to identify missing data within a given array. The task requires utilizing NumPy's capabilities to detect "NaN" (Not a Number) values, which typically represent missing data. By applying functions like "isnan()", the program can efficiently locate and highlight the positions of missing data within the array, facilitating data cleaning and preprocessing tasks.
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))
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: