Correlation Plot Using Matplotlib In Python

Overview:    

  • Correlation measures the relationship or association between two variables or two datasets
  • Correlation measures both the vigor of the association as well as the direction of association between two variables.
  • The measure of Correlation is represented by ρ (rho) or simply ‘r’ which is also called as the "Correlation Coefficient"
  • Correlation captures the linear relationship between two variables and it ranges from -1 to 0 to +1
  • A perfect positive measure of correlation yields a value of +1, this means that if variable 1 increases or decreases by x%, then variable 2 also increases or decreases by x% respectively.
  • A perfect negative measure of correlation yields a value of -1, this means that if variable 1 increases by x%, then variable 2 decreases by x%. Hence a negative sign denotes an inverse association between the two variables in study. 

Example:

import matplotlib.pyplot as plot

import numpy as np

 

# Angle of collision - variable 1 in correlation example

xData = np.array([24.40,10.25,20.05,22.00,16.90,7.80,15.00,22.80,34.90,13.30])

 

# Energy lost - variable 2 in correlation example

yData = np.array([-4.40,0.25,-0.05,2.00,6.90,-0.80,5.00,2.80,-4.90,3.30])

 

# Draw the scatter plot

lines = plot.xcorr(xData, yData, maxlags=9, usevlines=True)

plot.title('Hypothetical Data: Angle of collision vs Energy lost')

plot.xlabel('Angle of collision')

plot.ylabel('Energy lost')    

plot.grid(True)

plot.axhline(0, color='red', lw=2)

plot.show()

 

Output:

Correlation plot using matplotlib in Python

 

 


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