NumPy: Compute the median of flattened given array
From Wikipedia: The median is the value separating the higher half from the lower half of a data sample (a population or a probability distribution). For a data set, it may be thought of as the "middle" value. For example, in the data set {1, 3, 3, 6, 7, 8, 9}, the median is 6, the fourth largest, and also the fifth smallest, number in the sample. For a continuous probability distribution, the median is the value such that a number is equally likely to fall above or below it.
The median is a commonly used measure of the properties of a data set in statistics and probability theory. The basic advantage of the median in describing data compared to the mean (often simply described as the "average") is that it is not skewed so much by extremely large or small values, and so it may give a better idea of a "typical" value.
Write a NumPy program to compute the median of flattened given array.
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 median of the array 'x' using np.median()
r1 = np.median(x)
# Displaying the calculated median of the array 'x'
print("\nMedian of said array:")
print(r1)
Sample Output:
Original array: [[ 0 1 2 3 4 5] [ 6 7 8 9 10 11]] Median of said array: 5.5
Explanation:
In the above code –
x = np.arange(12).reshape((2, 6)): This line creates a 2-dimensional NumPy array x with shape (2, 6) and values 0 to 11 arranged row-wise. That is, the first row of x has values [0, 1, 2, 3, 4, 5] and the second row has values [6, 7, 8, 9, 10, 11].
r1 = np.median(x): This line calculates the median value of all the elements in the array using the np.median() function and stores it in the variable r1.
Python-Numpy Code Editor: