Correlation between two arrays using numpy python tutorial

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tutorial: understanding correlation between two arrays using numpy in python

correlation is a statistical measure that expresses the extent to which two variables are linearly related. in this tutorial, we will explore how to compute the correlation between two arrays using the numpy library in python.

#### prerequisites

to follow this tutorial, you need:

python installed on your machine.
the numpy library, which can be installed via pip if you don't have it:



#### steps to compute correlation

1. *import the numpy library*
2. *create two sample arrays*
3. *compute the correlation coefficient*
4. *interpret the correlation coefficient*

step 1: import the numpy library

first, we need to import the numpy library.



step 2: create two sample arrays

let's create two sample arrays that we want to analyze for correlation:



in this example, we expect a strong positive correlation since `array_y` is simply `array_x` multiplied by 2.

step 3: compute the correlation coefficient

numpy provides a function called `np.corrcoef()` that can be used to compute the correlation coefficient matrix. the correlation coefficient ranges from -1 to 1:

*1* indicates a perfect positive correlation.
*-1* indicates a perfect negative correlation.
*0* indicates no correlation.

here’s how you can compute it:



step 4: interpret the correlation coefficient

after running the above code, you will see the output similar to this:



this indicates a perfect positive correlation, which is expected since `array_y` is a linear function of `array_x`.

complete code example

here is the complete code example for clarity:



additional example: negative correlation

let's look at another example where we have a negative correlation.



in this case, you should see a negative correlation coefficient, indicating that as one variable increases, the other decreases.

conclusion

in this tutorial, we learned how to compute the correlation ...

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