NumPy: Compute pearson product-moment correlation coefficients of two given arrays

Last update on March 25 2025 12:21:14 (UTC/GMT +8 hours)

10. Pearson Product-Moment Correlation Coefficients

Write a NumPy program to compute pearson product-moment correlation coefficients of two given arrays.

Sample Solution:

Python Code:

# Importing the NumPy library
import numpy as np

# Creating an array 'x' containing elements [0, 1, 3]
x = np.array([0, 1, 3])

# Creating an array 'y' containing elements [2, 4, 5]
y = np.array([2, 4, 5])

# Displaying the original array 'x'
print("\nOriginal array1:")
print(x)

# Displaying the original array 'y'
print("\nOriginal array1:")
print(y)

# Calculating the Pearson product-moment correlation coefficients of arrays 'x' and 'y' using np.corrcoef()
print("\nPearson product-moment correlation coefficients of the said arrays:\n", np.corrcoef(x, y))

Sample Output:

Original array1:
[0 1 3]

Original array1:
[2 4 5]

Pearson product-moment correlation coefficients of the said arrays:
 [[1.         0.92857143]
 [0.92857143 1.        ]]

Explanation:

In the above code –

x = np.array([0, 1, 3]): This creates a NumPy array x with values [0, 1, 3].

y = np.array([2, 4, 5]): This creates a NumPy array y with values [2, 4, 5].

print(np.corrcoef(x, y)): This computes the correlation matrix between x and y, and prints it to the console.

For more Practice: Solve these Related Problems:

• Write a function that computes the Pearson correlation coefficient matrix of two 1D arrays using np.corrcoef.
• Create a program that calculates the Pearson coefficient manually and compares it with the result from np.corrcoef.
• Implement a solution that computes Pearson correlation for multi-dimensional arrays along a specified axis.
• Develop a method that tests Pearson correlation on datasets with known relationships to verify the coefficient’s accuracy.

Python-Numpy Code Editor:

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