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