Calculating distance between rows of Pandas dataframe and adding to list

The question I'm asking is similar to the one I posted here a while ago: Comparing 2 Pandas dataframes row by row and performing a calculation on each row

I got a very helpful answer to that question and I'm trying to use that information to help me answer my current question.

Task: Group a dataframe by columns trial, RECORDING_SESSION_LABEL, and IP_INDEX. For each group, I need to calculate the Euclidean distance between a row and all rows above it (so from Row 2 to Row n) using the values in columns CURRENT_FIX_X and CURRENT_FIX_Y. If the distance is less than 58.93, I need to add the value of CURRENT_FIX_INDEX from the row I'm comparing to (not against) to a list, and then concatenate that list into a string and add it to a new column (refix_list) so the string is in the new column of the row I'm comparing against.

Example: I'm on Row 7, so I'm comparing the distance of Row 7 to Rows 6, 5, 4, 3, 2, and 1 of that group. If the distance between Row 7 and Rows 5, 3, and 1 are less than 58.93, I want a comma-separated string that contains the CURRENT_FIX_INDEX value of each of those 3 rows in the refix_list column at Row 7.

Problem: I have code that I'm working with, and I'm not sure if it's working because I get a 'ValueError: Length of values (0) does not match length of index (297)' when I try to print the df so I know there's an issue either creating the list or more likely, concatenating it into a string and assigning it to the specific row.

Here's the code I'm working with (with sample data for 1 participant):

import pandas as pd
import numpy as np

data_df = {
    'IP_INDEX': [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 4, 1, 1, 2, 3, 3, 3, 4, 4, 4, 4],
    'RECORDING_SESSION_LABEL': ['a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a'],
    'trial': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
    'CURRENT_FIX_INDEX': [1, 2, 3, 1, 2, 3, 4, 5, 1, 2, 3, 1, 1, 2, 1, 1, 2, 3, 1, 2, 3, 4],
    'CURRENT_FIX_X': [550, 575, 250, 300, 500, 475, 275, 550, 675, 650, 800, 325, 450, 400, 375, 650, 700, 675, 825, 400, 375, 150],
    'CURRENT_FIX_Y': [275, 250, 600, 650, 300, 325, 675, 300, 850, 875, 250, 625, 225, 150, 675, 250, 300, 275, 150, 225, 250, 650]

}

# Create DF1
df = pd.DataFrame(data_df)

# Define a function to calculate Euclidean distance
def euclidean_distance(x1, y1, x2, y2):
    return np.sqrt((x1 - x2)**2 + (y1 - y2)**2)

# Grouping the DataFrame by RECORDING_SESSION_LABEL, trial, and IP_INDEX
grouped = df.groupby(['RECORDING_SESSION_LABEL', 'trial', 'IP_INDEX'])

# List to store CURRENT_FIX_INDEX for each row
index_list = []
refix_values = []

# Iterate over each group
for group_name, group_df in grouped:
    # Sort the group_df by some unique column
    group_df = group_df.sort_values(by='trial')
    
    # Calculate Euclidean distance for each row
    for i, row in group_df.iterrows():
        current_x = row['CURRENT_FIX_X']
        current_y = row['CURRENT_FIX_Y']
        
        # Calculate distance with every row above it
        for j, prev_row in group_df.iloc[:i].iterrows():
            current_index = prev_row['CURRENT_FIX_INDEX']
            prev_x = prev_row['CURRENT_FIX_X']
            prev_y = prev_row['CURRENT_FIX_Y']
            
            distance = euclidean_distance(current_x, current_y, prev_x, prev_y)
            
            # If distance is less than or equal to 58.93, store CURRENT_FIX_INDEX
            if distance <= 58.93:
                index_list.append(current_index)
    refix_values.append(','.join(map(str, index_list))) #Add list of matching INDEX values to list of lists

df['refix_list'] = []

# Iterate over the DataFrame to access each row and its index
for index, row in df.iterrows():
    # Assign the list to the current row in the specified column
    df.at[index, refix_list] = refix_values

print(df)

Expected Output:

From my limited knowledge, I'm guessing the issue is in the last block of code, but I'm not positive. Any help is appreciated!