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The plot also shows a strong positive correlation between the variables as they are in increasing mode. Strong Positive Correlation – Scatter Chart Now, let’s take a look at a scatter chart for the above array using following code. The correlation coefficient between x and y is : 0.82477049 print("The correlation coefficient between x and y is :",np.corrcoef(x,y)) If we only wanted to return the correlation coefficient between the two variables, we will use the following code.
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The correlation coefficient between these two variables is 0.82477, which is a strong positive correlation.īy default, this function returns a matrix of correlation coefficients. The above code returns below output: //output The numpy library corrcoef() function accepts x and y array as input parameters and returns correlation matrix of x and y as a result. In the above example, we have created two x and y array using numpy library random function. Print("The correlation between x and y is : \n ",correlation) # Create the second array using first array by adding some noise # Create a random array of 500 integers between 0 and 50 # Using seed function to generate the same random number every time with the same seed value
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Let’s understand how to calculate the correlation between two variables with given below python code #import modules We’re interested in the values of correlation of x with y (so position (1, 0) or (0, 1)). In python, Numpy library provides corrcoef() function to calculate the correlation between two variables.Ĭorrcoef() a function that returns a matrix of correlations of x with x, x with y, y with x, and y with y. If you don’t have numpy package installed on your system, installed it using the below commands on the window system pip install numpy