an.tsouchlos/banking-breakdown
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29
banking_breakdown/__main__.py
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@ -1,9 +1,34 @@
import pandas as pd
from banking_breakdown import document_builder
from banking_breakdown import statement_parser
import subprocess
import os
import shutil
import pandas as pd
from banking_breakdown import types
import numpy as np
def main():
report_data = statement_parser.parse_statement("res/banking_statement_2023.csv")
categories = ["A", "B", "C", "D", "E", "F", "G"]
values = np.array([10, 12, 53, 12, 90, 23, 32])
values = values / values.sum() * 100
total_value = np.random.normal(size=10) + 4
net_income = np.diff(total_value)
category_overview_df = pd.DataFrame(
{"category": categories, "value": values.astype('int32')})
t = np.linspace(0, total_value.size, total_value.size)
total_value_df = pd.DataFrame({"t": t, "value": total_value})
t = np.linspace(0, net_income.size, net_income.size)
net_income_df = pd.DataFrame({"t": t, "value": net_income})
report_data = types.ReportData(category_overview=category_overview_df,
net_income=net_income_df,
total_value=total_value_df)
document_builder.build_document(report_data)

2
banking_breakdown/document_builder.py
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@ -15,8 +15,6 @@ def _serialize_report_data(report_data: types.ReportData):
report_data.category_overview.to_csv('build/category_overview.csv',
index=False)
report_data.total_value.to_csv('build/total_value.csv', index=False)
report_data.detailed_balance.to_csv('build/detailed_balance.csv',
index=False)
def _compile_document():

80
banking_breakdown/statement_parser.py
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@ -1,80 +0,0 @@
import typing
import pandas as pd
from banking_breakdown import types
import json
import re
import numpy as np
def _read_regex_dict(regex_file: str = "res/category_regexes.json"):
with open(regex_file, 'r') as f:
return json.load(f)
def _tag_with_category(df: pd.DataFrame) -> pd.DataFrame:
regex_dict = _read_regex_dict()
return df
def _compute_total_balance(df: pd.DataFrame) -> pd.DataFrame:
stripped_df = pd.DataFrame(
{'t': df["Buchungstag"], 'value': df["Saldo nach Buchung"]})
stripped_df.index = stripped_df['t']
gb = stripped_df.groupby(pd.Grouper(freq='M'))
result = gb.tail(1)['value'].reset_index()
#result['t'] = result['t'].apply(lambda dt: dt.replace(day=1))
return result
def _compute_net_income(df: pd.DataFrame) -> pd.DataFrame:
stripped_df = pd.DataFrame({'t': df["Buchungstag"], 'value': df["Betrag"]})
result = stripped_df.resample(rule='M', on="t").sum().reset_index()
#result['t'] = result['t'].apply(lambda dt: dt.replace(day=1))
return result
def _compute_category_overview(df: pd.DataFrame) -> pd.DataFrame:
categories = ["A", "B", "C", "D", "E", "F", "G"]
values = np.array([10, 12, 53, 12, 90, 23, 32])
values = values / values.sum() * 100
values = np.round(values, decimals=1)
values[-1] += 100 - np.sum(values)
category_overview_df = pd.DataFrame(
{"category": categories, "value": values})
return category_overview_df
def _compute_detailed_balance(df: pd.DataFrame) -> pd.DataFrame:
return pd.DataFrame({'t': df["Buchungstag"],
'value': df["Saldo nach Buchung"]})
def parse_statement(filename: str) -> types.ReportData:
df = pd.read_csv(filename, delimiter=';', decimal=",")
df["Buchungstag"] = pd.to_datetime(df["Buchungstag"], format='%d.%m.%Y')
category_overview_df = _compute_category_overview(df)
total_balance_df = _compute_total_balance(df)
net_income_df = _compute_net_income(df)
detailed_balance_df = _compute_detailed_balance(df)
return types.ReportData(category_overview_df,
net_income_df,
total_balance_df,
detailed_balance_df)
def main():
report_data = parse_statement("../res/banking_statement_2023.csv")
if __name__ == "__main__":
main()

1
banking_breakdown/types.py
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@ -7,4 +7,3 @@ class ReportData:
category_overview: pd.DataFrame
net_income: pd.DataFrame
total_value: pd.DataFrame
detailed_balance: pd.DataFrame

51
res/report.tex
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@ -4,7 +4,6 @@
\usepackage{amsmath}
\usepackage{pgfplots}
\pgfplotsset{compat=newest}
\usetikzlibrary{pgfplots.dateplot}
% Other packages
\usepackage[a4paper, total={12cm, 25cm}]{geometry}
@ -31,10 +30,6 @@
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\makeatletter
\newcommand*\shortyear[1]{\expandafter\@gobbletwo\number\numexpr#1\relax}
\makeatother
\newcommand{\slice}[6]{
\pgfmathparse{0.5*#1+0.5*#2}
\let\midangle\pgfmathresult
@ -141,34 +136,15 @@
\begin{tikzpicture}
\begin{axis}[
date coordinates in=x,
width=\textwidth,
height=0.375\textwidth,
ylabel={Net income in €},
y label style={at={(-0.1,0.5)},anchor=south},
xticklabel=\month.\shortyear{\year},
xtick=data,
xticklabel style={
rotate=60,
anchor=near xticklabel,
},
grid,
enlarge x limits=0.03,
]
% Dummy plot to set x axis ticks
\addplot[draw=none]
table[col sep=comma, x=t, y=value]
{net_income.csv};
% Dummy plot to set x axis scale
\addplot[draw=none]
table[col sep=comma, x=t, y=value]
{detailed_balance.csv};
\addplot[ybar, color=scol2, fill=scol2, line width=1pt]
\addplot+[ybar, color=scol2, fill=scol2, line width=1pt]
table[col sep=comma, x=t, y=value, discard if lt={value}{0}]
{net_income.csv};
\addplot[ybar, color=scol0, fill=scol0, line width=1pt]
\addplot+[ybar, color=scol0, fill=scol0, line width=1pt]
table[col sep=comma, x=t, y=value, discard if gt={value}{0}]
{net_income.csv};
\end{axis}
@ -179,31 +155,15 @@
\begin{tikzpicture}
\begin{axis}[
date coordinates in=x,
width=\textwidth,
height=0.375\textwidth,
area style,
ylabel={Total balance in €},
y label style={at={(-0.1,0.5)},anchor=south},
xticklabel=\month.\shortyear{\year},
xtick=data,
enlarge x limits=0.03,
xticklabel style={
rotate=60,
anchor=near xticklabel,
},
grid,
]
% Dummy plot to set x axis ticks
\addplot[draw=none]
\addplot+[mark=none, color=scol1, line width=1pt]
table[col sep=comma, x=t, y=value]
{total_value.csv};
\addplot[scol3, mark=none, line width=1pt]
table[col sep=comma, x=t, y=value]
{detailed_balance.csv};
\addplot[scol1, mark=none, line width=1pt]
table[col sep=comma, x=t, y=value]
{total_value.csv};
{total_value.csv} \closedcycle;
\end{axis}
\end{tikzpicture}
\end{subfigure}
@ -211,6 +171,5 @@
\caption{Development of account balance over time}
\end{figure}
\end{document}