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preprocess.py
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preprocess.py
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import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from config import settings
headers = [
"Who paid",
"Amount",
"Currency",
"For whom",
"Split amounts",
"Purpose",
"Category",
"Date & time",
"Exchange rate",
"Converted amount",
"Type",
"Receipt",
]
dtypes = {
"Who paid": "str",
"Amount": "float",
"Currency": "str",
"For whom": "str",
"Split amounts": "str",
"Purpose": "str",
"Category": "str",
"Date & time": "str",
"Exchange rate": "str",
"Converted amount": "float",
"Type": "str",
"Receipt": "str",
}
DATE_COL = "Date & time"
FOR_WHOM_COL = "For whom"
SPLIT_COL = "Split amounts"
USER = settings["user_to_analyse"]
WANTED_COLUMNS = ["Purpose", "Category", "Month", USER]
class SettleUpProcessor:
def __init__(self):
self.workdir = settings["workdir"]
self.users = np.empty(0, dtype=object)
def read_raw_csv(self):
filepath = os.path.join(self.workdir, settings["filename_to_process"])
self.df = pd.read_csv(
filepath,
header=0,
encoding="utf8",
sep=",",
usecols=headers,
names=headers,
dtype=dtypes,
index_col=DATE_COL,
parse_dates=[DATE_COL],
)
# Remove transfers
self.df = self.df.loc[self.df["Type"] == "expense"]
self.df.loc[:, "Month"] = self.df.index.month
def set_users(self, who_df):
"""Get unique users in the expenses"""
for col in who_df.columns:
self.users = np.append(self.users, who_df[col].unique())
self.users = set(self.users)
if None in self.users:
self.users.remove(None)
self.users = list(self.users)
def calc_user_expenses(self):
who_df = self.df[FOR_WHOM_COL].str.split(";", expand=True)
who_df.columns = ["for_" + str(i) for i in range(0, len(who_df.columns))]
ammount_df = self.df[SPLIT_COL].str.split(";", expand=True)
ammount_df.columns = [
"amount_" + str(i) for i in range(0, len(ammount_df.columns))
]
assert len(who_df.columns) == len(ammount_df.columns)
self.set_users(who_df)
self.df = self.df.join(who_df).join(ammount_df)
# calculate expenses by user (order)
for user in self.users:
for i in range(0, len(self.users)):
self.df.loc[self.df[f"for_{i}"] == user, user] = self.df.loc[
self.df[f"for_{i}"] == user, f"amount_{i}"
]
self.df = self.df.astype({user: float})
def export_processed_csv(self):
wanted_filename = (
settings["filename_to_process"].split(".", maxsplit=1)[0]
+ "_processed.xlsx"
)
self.df = self.df.dropna(subset=[USER])
self.df = self.df.loc[:, WANTED_COLUMNS]
self.df = self.df.rename({USER: "Amount"}, axis=1)
filepath = os.path.join(self.workdir, wanted_filename)
self.df.to_excel(filepath)
def total_expenses(self):
print(self.df.loc[:, self.users].sum().head(20))
df_sum = self.df.groupby([pd.Grouper(freq="M")])[self.users].sum()
print(df_sum.head(100))
_, ax = plt.subplots(figsize=(10, 10))
ax = sns.scatterplot(data=df_sum)
ax.set_xlabel("Month")
ax.set_ylabel("€")
ax.set_title("Monthly total expenses")
ax.tick_params(axis="x", labelrotation=45)
plt.savefig(f"{self.workdir}/total_expenses")
plt.close()
def generate_users_plots(self):
self.total_expenses()
if __name__ == "__main__":
processor = SettleUpProcessor()
processor.read_raw_csv()
processor.calc_user_expenses()
processor.generate_users_plots()
processor.export_processed_csv()