Add detailed balance plot

banking_breakdown/__main__.py

@@ -1,48 +1,9 @@
 from banking_breakdown import document_builder
-import pandas as pd
-from banking_breakdown import types
-import numpy as np
-
-
-def _generate_dummy_data() -> types.ReportData:
-    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})
-
-    t = ["2023-01-01",
-         "2023-02-01",
-         "2023-03-01",
-         "2023-04-01",
-         "2023-05-01",
-         "2023-06-01",
-         "2023-07-01",
-         "2023-08-01",
-         "2023-09-01",
-         "2023-10-01",
-         "2023-11-01",
-         "2023-12-01"]
-
-    net_income = 200 * np.random.normal(size=len(t)) + 100
-    net_income_df = pd.DataFrame({"t": t, "value": net_income})
-
-    total_value = np.cumsum(net_income)
-
-    total_value_df = pd.DataFrame({"t": t, "value": total_value})
-
-    report_data = types.ReportData(category_overview=category_overview_df,
-                                   net_income=net_income_df,
-                                   total_value=total_value_df)
-
-    return report_data
+from banking_breakdown import statement_parser
 
 
 def main():
-    report_data = _generate_dummy_data()
+    report_data = statement_parser.parse_statement("res/banking_statement_2023.csv")
     document_builder.build_document(report_data)
 
 

banking_breakdown/document_builder.py

@@ -15,6 +15,8 @@ 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():

banking_breakdown/statement_parser.py

@@ -0,0 +1,80 @@
+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()

banking_breakdown/types.py

@@ -7,3 +7,4 @@ class ReportData:
     category_overview: pd.DataFrame
     net_income: pd.DataFrame
     total_value: pd.DataFrame
+    detailed_balance: pd.DataFrame