Calculating Pearson correlation and significance in Python

Pearson coefficient calculation using pandas in python: I would suggest trying this approach since your data contains lists. It will be easy to interact with your data and manipulate it from the console since you can visualise your data structure and update it as you wish. You can also export the data set and save it and add new data out of the python console for later analysis. This code is simpler and contains less lines of code. I am assuming you need a few quick lines of code to screen your data for further analysis

Example:

data = {'list 1':[2,4,6,8],'list 2':[4,16,36,64]}

import pandas as pd #To Convert your lists to pandas data frames convert your lists into pandas dataframes

df = pd.DataFrame(data, columns = ['list 1','list 2'])

from scipy import stats # For in-built method to get PCC

pearson_coef, p_value = stats.pearsonr(df["list 1"], df["list 2"]) #define the columns to perform calculations on
print("Pearson Correlation Coefficient: ", pearson_coef, "and a P-value of:", p_value) # Results 

However, you did not post your data for me to see the size of the data set or the transformations that might be needed before the analysis.

I have a very simple and easy to understand solution for this. For two arrays of equal length, Pearson coefficient can be easily computed as follows:

def manual_pearson(a,b):
"""
Accepts two arrays of equal length, and computes correlation coefficient. 
Numerator is the sum of product of (a - a_avg) and (b - b_avg), 
while denominator is the product of a_std and b_std multiplied by 
length of array. 
"""
  a_avg, b_avg = np.average(a), np.average(b)
  a_stdev, b_stdev = np.std(a), np.std(b)
  n = len(a)
  denominator = a_stdev * b_stdev * n
  numerator = np.sum(np.multiply(a-a_avg, b-b_avg))
  p_coef = numerator/denominator
  return p_coef

Starting in Python 3.10, the Pearson’s correlation coefficient (statistics.correlation) is directly available in the standard library:

from statistics import correlation

# a = [15, 12, 8, 8, 7, 7, 7, 6, 5, 3]
# b = [10, 25, 17, 11, 13, 17, 20, 13, 9, 15]
correlation(a, b)
# 0.1449981545806852