How to write a loop function for Levene test in Python

I want to perform a Levene and one-way ANOVA test, with the outcome being continuous and a predictor with n groups. For example:

import pandas as pd
import scipy.stats as sps               

# create the df
test_df = pd.DataFrame({
    'score': [5,15,1,51,1,51,5,15,16,3,1,81,6,1,2],
    'groups': ['a','b','c','a','b','c','a','b','c','a','b','c','a','b','c']})

factor = test_df['groups'].unique()            # create a list of individual factors 
arr1 = []                                      # to store the outcome by groups
for i in range(len(factor)):
    variable = test_df[test_df['groups'] == factor[i]]['score']
                                               # Make individual values by group
    arr1.append(variable)                      # append these to arr1  
    levene = sps.levene(arr1)         # the problem is here. The code does not recognize my arr1 has multiple arrays in it.

Ideally, I would like to be able to do something like this sps.levene(arr1[0],...,arr1[n]), the loop would do the following:

1. append each array within arr1 into a list
2. perform one sps.levene test with the list.

Can someone please help me how to solve this issue?

>Solution :

You need to unpack the elements of arr1 when passing them to levene.
You can achieve this by using the * operator:

import pandas as pd
import scipy.stats as sps               

# create the df
test_df = pd.DataFrame({
    'score': [5,15,1,51,1,51,5,15,16,3,1,81,6,1,2],
    'groups': ['a','b','c','a','b','c','a','b','c','a','b','c','a','b','c']})

factor = test_df['groups'].unique()            # create a list of individual factors 
arr1 = []                                      # to store the outcome by groups
for i in range(len(factor)):
    variable = test_df[test_df['groups'] == factor[i]]['score']
    arr1.append(variable)                      # append these to arr1  

# Perform Levene's test
levene_result = sps.levene(*arr1)
print("Levene's test p-value:", levene_result.pvalue)

Output:

Levene's test p-value: 0.253368701021017