NumPy: Calculate averages without NaNs along a given array
Calculate averages without NaN.
Write a NumPy program to calculate averages without NaNs along a given array.
Sample Solution:
Python Code:
# Importing the NumPy library and aliasing it as 'np'
import numpy as np
# Creating a NumPy array with some NaN values
arr1 = np.array([[10, 20, 30], [40, 50, np.nan], [np.nan, 6, np.nan], [np.nan, np.nan, np.nan]])
# Displaying the original array
print("Original array:")
print(arr1)
# Masking the array 'arr1' where the elements are NaN
temp = np.ma.masked_array(arr1, np.isnan(arr1))
# Calculating the mean along the rows (axis=1) without considering NaN values
result = np.mean(temp, axis=1)
# Displaying the averages without NaNs along the rows and replacing masked values with NaN
print("Averages without NaNs along the said array:")
print(result.filled(np.nan))
Sample Output:
Original array: [[10. 20. 30.] [40. 50. nan] [nan 6. nan] [nan nan nan]] Averages without NaNs along the said array: [20. 45. 6. nan]
Explanation:
arr1 = np.array([[10, 20 ,30], [40, 50, np.nan], [np.nan, 6, np.nan], [np.nan, np.nan, np.nan]])
The above code creates a NumPy array 'arr1' with the given values, including some NaN values.
temp = np.ma.masked_array(arr1,np.isnan(arr1)): This code creates a masked array 'temp' from 'arr1' using NumPy's Masked Array module (np.ma). The mask is created using np.isnan(arr1), which generates a boolean array indicating whether each element in 'arr1' is NaN or not. In 'temp', the NaN values are masked and will be excluded from the calculations.
result = np.mean(temp, axis=1): This code calculates the mean of 'temp' along axis 1 (row-wise mean). Since 'temp' is a masked array, the NaN values are excluded from the mean calculation.
print(result.filled(np.nan)): Replace the masked values in 'result' with NaN using the filled() method and print the final result. This will display the row-wise mean of 'arr1', excluding the NaN values.
Pictorial Presentation:
Python-Numpy Code Editor: