Filling NaNs in a financial dataset

[snippsat]

Do you do something to create merged_df_nan i mean for me it dos not work if i just read sample.xlsx?
Ohh 500 columns is hell of DataFrame,not gone look for error in that.

import pandas as pd
from tqdm import tqdm

def average_col(merged_df_nan):
    #Filling NaN values for columns with less than 20k NaNs with the average of the period before and after
    na_counts = merged_df_nan.isna().sum()
    columns_to_fill2 = na_counts[(na_counts < 20000) & (~na_counts.index.isin(['Text', 'gvkey', 'datadate', 'prccq', 'naics', 'sic', 'processed_text', 'stemmed_text', 'sentiment_score', 'fyr', 'indfmt','consol','popsrc','tic','conm','apdedateq','fdateq','rdq']))].index

    #Iterate over the filtered columns and fill NA values with the average of previous and next non-NA values
    for column in tqdm(columns_to_fill2, desc = 'Columns done'):
        na_mask = merged_df_nan[column].isna()
        for index, row in merged_df_nan.iterrows():
            if na_mask[index]:
                fyearq = row['fyearq']
                fqtr = row['fqtr']
                cik = row['cik']

                #Looking for the values and taking the average of them
                prev_value = merged_df_nan.loc[(merged_df_nan['cik'] == cik) & ((merged_df_nan['fyearq'] < fyearq) | ((merged_df_nan['fyearq'] == fyearq) & (merged_df_nan['fqtr'] < fqtr)))].sort_values(['fyearq', 'fqtr'], ascending=[False, False]).head(1)[column].item()
                next_value = merged_df_nan.loc[(merged_df_nan['cik'] == cik) & ((merged_df_nan['fyearq'] > fyearq) | ((merged_df_nan['fyearq'] == fyearq) & (merged_df_nan['fqtr'] > fqtr)))].sort_values(['fyearq', 'fqtr'], ascending=[True, True]).head(1)[column].item()
                average_value = np.mean([prev_value, next_value])
                merged_df_nan.loc[index, column] = average_value
    return merged_df_nan

if __name__ == '__main__':
    merged_df_nan = pd.read_excel('sample.xlsx')
    res =  average_col(merged_df_nan)

Error:
Traceback (most recent call last):
  File "G:\div_code\hex\game\av.py", line 28, in <module>
    res =  average_col(merged_df_nan)
  File "G:\div_code\hex\game\av.py", line 20, in average_col
    prev_value = merged_df_nan.loc[(merged_df_nan['cik'] == cik) & ((merged_df_nan['fyearq'] < fyearq) | ((merged_df_nan['fyearq'] == fyearq) & (merged_df_nan['fqtr'] < fqtr)))].sort_values(['fyearq', 'fqtr'], ascending=[False, False]).head(1)[column].item()
  File "C:\Python310\lib\site-packages\pandas\core\base.py", line 349, in item
    raise ValueError("can only convert an array of size 1 to a Python scalar")
ValueError: can only convert an array of size 1 to a Python scalar