NumPy: Calculate percentiles for a sequence or single-dimensional NumPy array

Last update on March 24 2025 13:39:12 (UTC/GMT +8 hours)

Calculate Percentiles of Array

Write a NumPy program to calculate percentiles for a sequence or single-dimensional NumPy array.

Sample Solution:

Python Code:

# Importing the NumPy library and aliasing it as 'np'
import numpy as np

# Creating a NumPy array 'nums' containing integers
nums = np.array([1, 2, 3, 4, 5])

# Printing the 50th percentile (median) of the array 'nums'
print("50th percentile (median):")
p = np.percentile(nums, 50)
print(p)

# Printing the 40th percentile of the array 'nums'
print("40th percentile:")
p = np.percentile(nums, 40)
print(p)

# Printing the 90th percentile of the array 'nums'
print("90th percentile:")
p = np.percentile(nums, 90)
print(p)

Sample Output:

50th percentile (median):
3.0
40th percentile:
2.6
90th percentile:
4.6

Explanation:

In the above code -

nums = np.array([...]): This line creates a NumPy array named 'nums' containing integer values 1 to 5.

p = np.percentile(nums, 50): Calculate the 50th percentile (also known as the median) of the 'nums' array, which is the value that separates the lower 50% from the upper 50% of the data.

p = np.percentile(nums, 40): Calculate the 40th percentile of the 'nums' array, which is the value that separates the lower 40% from the upper 60% of the data.

p = np.percentile(nums, 90): Calculate the 90th percentile of the 'nums' array, which is the value that separates the lower 90% from the upper 10% of the data.

For more Practice: Solve these Related Problems:

• Write a NumPy program to compute the 25th, 50th, and 75th percentiles of a 1D array using np.percentile.
• Create a function that takes a percentile value as input and returns the corresponding array element.
• Test the percentile function on a sorted array and verify the median calculation matches np.median.
• Implement a solution that calculates percentiles for each column in a 2D array independently.

Python-Numpy Code Editor: