NumPy: Compute the 80th percentile for all elements in a given array along the second axis
Write a NumPy program to compute the 80th percentile for all elements in a given array along the second axis.
Sample Solution:
Python Code:
# Importing the NumPy library
import numpy as np
# Creating a 2x6 array 'x' using arange and reshape
x = np.arange(12).reshape((2, 6))
# Displaying the original array 'x'
print("\nOriginal array:")
print(x)
# Calculating the 80th percentile along the second axis using np.percentile()
r1 = np.percentile(x, 80, 1)
# Displaying the 80th percentile for all elements along the second axis of the array 'x'
print("\n80th percentile for all elements of the said array along the second axis:")
print(r1)
Sample Output:
Original array: [[ 0 1 2 3 4 5] [ 6 7 8 9 10 11]] 80th percentile for all elements of the said array along the second axis: [ 4. 10.]
Explanation:
In the above code –
np.percentile(x, 80, 1): In this code the first argument x is the input array. The second argument 80 is the percentile to compute, i.e., the value below which 80% of the data falls. The third argument 1 specifies that the operation is performed along axis 1, i.e., each row of the array.
r1 = np.percentile(x, 80, 1): This code calculates the 80th percentile of x along the second axis (axis=1), which means the percentiles will be calculated for each row of the array. The resulting array will have the same shape as x but with the second dimension reduced to one.
Python-Numpy Code Editor: