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 PayPal(institution_base.Institution):
"""docs here!"""
_this_institution_name = "paypal"
def __init__(self) -> None:
super().__init__()
def _third_party_cleaning(
self, input_df: pd.DataFrame, account_name: str
) -> pd.DataFrame:
"""docs here!"""
def is_transfer_finder(row):
name_is_nan = pd.isna(row["Name"])
try:
regex_flag_redundant = re.search(
"Authorization|Order", str(row["Type"])
)
except Exception:
return "consider"
if regex_flag_redundant:
return "redundant"
elif name_is_nan:
return "transfer"
elif not(name_is_nan or regex_flag_redundant):
return "expense"
else:
return "consider"
def description_finder(row):
if pd.isna(row["Name"]):
return str(row["Type"])
else:
return f"{str(row['Name'])}: {row['Type']}"
def amount_finder(val):
return float(str(val).replace(",", ""))
input_df["_new_Description"] = input_df.apply(description_finder, axis=1)
input_df["_new_Amount"] = input_df["Amount"].map(amount_finder)
input_df["_new_Date"] = input_df["Date"].copy(deep=True)
input_df["_new_InstitutionCategory"] = pd.Series([np.nan] * len(input_df))
input_df["_new_MyCategory"] = pd.Series([np.nan] * len(input_df))
input_df["_new_Institution"] = pd.Series([f"PayPal {account_name}"] * len(input_df))
input_df["_new_IsTransfer"] = input_df.apply(is_transfer_finder, axis=1)
return input_df