import re
import logging
import numpy as np
import pandas as pd
from mymoney.institutions import institution_base
logging.basicConfig(
level=logging.INFO,
format="%(name)s\t[%(asctime)s] %(levelname)s: %(message)s",
datefmt="%b/%d/%y %I:%M:%S %p",
# filename="logs.log",
)
[docs]
class AmEx(institution_base.Institution):
"""docs here!"""
_this_institution_name = "amex"
def __init__(self) -> None:
super().__init__()
def _credit_cleaning(
self, input_df: pd.DataFrame, account_name: str
) -> pd.DataFrame:
"""docs here!"""
# row["_new_Description"] == "YOUR CASH REWARD/REFUND IS": for cash back payments
# row["Extended Details"].startswith("Amex Offer Credit"): for offer reedems
def is_transfer_finder(row):
try:
regex_flag_paypal = re.search(r"PAYPAL", str(row["_new_Description"]))
regex_flag_payment = re.search(r"\w* PAYMENT - THANK YOU", str(row["_new_Description"]))
except Exception:
return "consider"
if (regex_flag_payment or regex_flag_paypal):
return "transfer"
elif (not regex_flag_payment and not regex_flag_paypal):
return "expense"
else:
return "consider"
input_df["_new_Description"] = input_df["Description"].copy(deep=True)
input_df["_new_Amount"] = -input_df["Amount"]
input_df["_new_Date"] = input_df["Date"].copy(deep=True)
input_df["_new_InstitutionCategory"] = input_df["Category"].copy(deep=True)
input_df["_new_MyCategory"] = input_df["Category"].copy(deep=True)
input_df["_new_Institution"] = pd.Series([f"AmEx {account_name}"] * len(input_df))
input_df["_new_IsTransfer"] = input_df.apply(is_transfer_finder, axis=1)
# TODO: input_df["_new_IsCompatible"] =
return input_df