OK-donor-to-CSV

import pandas as pd
import re
import usaddress
import datetime

# Replace 'your_file.csv' with the actual path to your CSV file
csv_file_path = 'your_file.csv'

# Read the CSV file into a DataFrame
df = pd.read_csv(csv_file_path)

def remove_after_last_int(s):
  # Find the last occurrence of a digit in the string
  match = re.search(r'\d(?=[^\d]*$)', s)
  # If there is a match, return the substring up to and including the digit
  if match:
    return s[:match.end()]
  # Otherwise, return the original string
  else:
    return s

def replace_carriage_returns(s):
    return s.replace('\n', ' ')

def remove_commas(s):
    return s.replace(',', '')

def insert_ok_comma(s):
    return s.replace('Ok 7', 'OK, 7')

def append_zip_comma(s):
    s = s + ","
    return s

def concatenate_except_recipient(tuples):
    # Filter out tuples where the second element is 'Recipient'
    filtered_tuples = [t for t in tuples if t[1] != 'Recipient']
    # Concatenate the first elements of the remaining tuples
    result = ' '.join(t[0] for t in filtered_tuples)
    return result

# Function to process each donor string
def process_string(donor_str):
    match = re.search(r'\d', donor_str)  # Find the first integer in the string
    if match:
        donorName = donor_str[:match.start()]  # Get all characters up to the first integer
    else:
        donorName = donor_str  # If no integer found, use the whole string

    # Remove spaces and carriage returns
    donorName = donorName.replace(' ', '').replace('\n', '')
    return donorName

# Function to convert rawDate to the required format
def convert_date(raw_date):
    return datetime.datetime.fromtimestamp((raw_date - 25569) * 86400).strftime('%m/%d/%Y')


# Remove the job title
df['donorNoJob'] = df['Donor'].apply(remove_after_last_int)
df['donorNoJob'] = df['donorNoJob'].apply(replace_carriage_returns)

# Apply the function to the 'Donor' column
df['donorName'] = df['Donor'].apply(process_string)

# Apply the function to the "Date" column
df['Date'] = df['Date'].apply(convert_date)

print("Date, Lname, Fname, Address, Zip, Amount")

for ind in df.index:
  donorAddress = remove_after_last_int(df['donorNoJob'][ind])
  rawAddress = usaddress.parse(donorAddress)
  fullAddress = concatenate_except_recipient(rawAddress)
  noCommaAddress = remove_commas(fullAddress)
  stateCommaAddress = insert_ok_comma(noCommaAddress)
  finalAddress = append_zip_comma(stateCommaAddress)
  cleanDonor = process_string(df['donorName'][ind])
  cleanDate = (df['Date'][ind])
  cleanAmount = "{:.2f}".format(df['Amount'][ind])

  print(cleanDate + "," + cleanDonor + "," + finalAddress + cleanAmount)