Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://www.tutorialspoint.com/python/python_statistics_correlation_function.htm below:

Python statistics.correlation() Function

The Python statistics.correlation function determines Pearson's correlation coefficient for two given inputs. This function measures the strength and direction of a linear relationship. The correlation coefficients are indicators of the strength of the linear relationship between two different variables, i.e., x and y.

A linear correlation coefficient that is greater than zero indicates a positive relationship; otherwise, this variable signifies a negative relationship. The resulting coefficient measures the strength of a monotonic relationship.

Inputs must be of the same length and need not be constant; otherwise, a StatisticsError is raised. A value close to zero indicates a weak relationship between the two variables being compared. The mathematical representation of the Statistics Correlation is as follows −

Following is the basic syntax for the statistics.Correlation function.

statistics.Correlation(x, y, /, *, method = 'linear')

This function takes x and y parameters to represent the correlation coefficients to be determined.

The Correlation function returns Pearson's Correlation for the given inputs.

Now we are using statistics.correlation() function to determines Pearson's correlation coefficient for the two given x and y inputs.

import numpy as np
x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [5, 6, 7, 8, 9, 1, 2, 3, 4]
z = np.corrcoef(x, y)
print("The Pearson's coefficient inputs are:" ,z)

The result is produced as follows −

The Pearson's coefficient inputs are: [[ 1.  -0.5]
 [-0.5  1. ]]
 

In the below example, we are giving a single variable using statistics.correlation function, then this returns NaN(Not a Number).

import numpy as np
import warnings
warnings.filterwarnings('ignore')
x = [2]
y = [4]
z = np.corrcoef(x, y)
print("The Pearson's coefficient inputs are:", z)

The output is obtained as follows −

The Pearson's coefficient inputs are: [[nan nan]
 [nan nan]]

Here, we are calculating the correlation between the given numbers using statistics.Correlation function.

import numpy as np
x = np.array([1, 2, 3, 4, 5, 6])
y = np.array([10, 20, 30, 40, 50, 60])

def Pearson_correlation(x, y):
    if len(x) == len(y):
        sum_xy = sum((x - x.mean()) * (y - y.mean()))
        sum_x_squared = sum((x - x.mean()) ** 2)
        sum_y_squared = sum((y - y.mean()) ** 2)
        corr = sum_xy / np.sqrt(sum_x_squared * sum_y_squared)
        return corr
    else:
        return "Arrays must be of the same length."
print(Pearson_correlation(x, y))
print(Pearson_correlation(x, x))

We will get the output as follows −

1.0
1.0

python_statistics_module.htm

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4