Python : appened the value of created_date based on the condition

i am trying to write one function which will create a new column called last_created_date, and appened the value in that column as per condition.

so this is the data.

so over here i want to derive last created date column per rake.every pair of rounded_geo_lat, rounded_geo_lng pair corrospondes to distance column first pair will have distance_1_lat_lng, 2nd pair will have distance_2_lat_lng, 3rd pair will have distance_3_lat_lng and so on..

note: when we iterating distance column corrosponding to lat,lng pair we always will exclude self value in distance column and will go down till last value of that rake device.

now ,case 1: while iterating down thorugh distnace column, there is no value less then 5 (<5) then last_created_Date will be null. (look at distance_1_lat_lng) and break the loop else continue

case 2: while iterating down thorugh distnace column from current pair of lat,lng when there is only ONE value less then 0.1 (<0.1) (look at distance_2_lat_lng) then pick up the created_date for that corrosponding value and add it to the last created date column. and break the loop else continue

case 3: while iterating down thorugh distnace column from current pair of lat,lng when there is multiple value less then 0.1 (<0.1) (look at distance_3_lat_lng) then pick up the created date of last occuring value and put it in last_Created_Date for that pair of lat_lng and break the loop else continue

case 4: while iterating down thorugh distnace column from current pair of lat,lng when there is value in the column which is > 0.1 and < 5 then put the created date of first occurence of that value as last_Created_Date.

i have written this function but while writing this above condition it is not giving expected result.

def find_and_append_created_dates(df_0):    df_0["last_created_dates"] = None  # Add the new column with initial values of None    # Iterate over unique rake devices    for rake_device in df_0['rake_device'].unique():        rake_device_df = df_0[df_0['rake_device'] == rake_device]        # Iterate over distance columns for the current rake device        for i in range(len(rake_device_df)):            distance_column = f"distance_{i+1}_lat_lng"            # Iterate over rows below the current row            for j in range(i + 1, len(rake_device_df)):                distance = rake_device_df[distance_column].iloc[j]            # Iterate over distance columns            for distance_column in rake_device_df.filter(like='distance_').columns:                # Iterate downward from the row after the current row                has_value_less_than_5 = False                for j in range(i + 1, len(rake_device_df)):                    distance = rake_device_df[distance_column].iloc[j]                    if pd.isna(distance):                        continue                    # Condition 1: No value less than 5 in the entire column                    if distance < 5:                        has_value_less_than_5 = True  # Mark that a value less than 5 exists                    # Condition 2: First occurrence of value less than 0.1                    if distance < 0.1 and not found_date:                        found_date = rake_device_df.loc[rake_device_df.index[j], "created_date"]                        break  # Stop iterating if you find a value less than 0.1                # If no value less than 5 was found, assign NULL and break the loop                if not has_value_less_than_5:                    found_date = None                    break            # Assign the found_date (or None if not found) to the last_created_dates column            df.at[rake_device_df.index[i], "last_created_dates"] = found_date    return dffind_and_append_created_dates(df)print(df)

Column last_created_date in the table i gave u is the expected output, we want to derive that column only, based on table above