Change net income figure; Add table

banking_breakdown/__main__.py

@@ -1,6 +1,5 @@
-from banking_breakdown import document_builder
-from banking_breakdown import statement_parser
-from banking_breakdown import ui
+from banking_breakdown import ui, regex_categorizer, statement_parser, \
+    document_builder
 
 import argparse
 
@@ -10,6 +9,9 @@ def categorize_func(args):
 
     df = pd.read_csv(args.i, delimiter=args.d)
 
+    if args.f is not None:
+        df = regex_categorizer.assign_categories(df, args.f)
+
     import signal
     signal.signal(signal.SIGINT, signal.SIG_DFL)
 
@@ -17,7 +19,8 @@ def categorize_func(args):
 
 
 def report_func(args):
-    print("Report")
+    report_data = statement_parser.parse_statement(args.i)
+    document_builder.build_document(report_data)
 
 
 #

banking_breakdown/document_builder.py

@@ -14,6 +14,8 @@ def _serialize_report_data(report_data: types.ReportData):
     report_data.net_income.to_csv('build/net_income.csv', index=False)
     report_data.category_overview.to_csv('build/category_overview.csv',
                                          index=False)
+    report_data.expenses_by_category.to_csv('build/expenses_by_category.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)

banking_breakdown/regex_categorizer.py

@@ -0,0 +1,54 @@
+import pandas as pd
+import json
+
+
+def _is_str_column(s: pd.Series):
+    """Check if the type of a pandas DataFrame column is str.
+
+    Taken from https://stackoverflow.com/a/67001213/3433817.
+    """
+    if isinstance(s.dtype, pd.StringDtype):
+        # The series was explicitly created as a string series (Pandas>=1.0.0)
+        return True
+    elif s.dtype == 'object':
+        # Object series, check each value
+        return all((v is None) or isinstance(v, str) for v in s)
+    else:
+        return False
+
+
+def _read_regex_dict(regex_file: str):
+    with open(regex_file, 'r') as f:
+        return json.load(f)
+
+
+def assign_categories(df: pd.DataFrame, regex_file: str) -> pd.DataFrame:
+    if 'category' not in df.columns:
+        df['category'] = [' '] * len(df.index)
+
+    regex_dict = _read_regex_dict(regex_file)
+
+    df = df.fillna('')
+
+    for column in df.columns:
+        if not _is_str_column(df[column]):
+            continue
+
+        for category in regex_dict:
+            for regex in regex_dict[category]:
+                matched = df[column].str.contains(regex, regex=True)
+                df.loc[matched, 'category'] = category
+
+    return df
+
+
+def main():
+    df = pd.read_csv('../res/bank_statement_2023_categorized.csv')
+
+    df = assign_categories(df, regex_file='../res/regexes.json')
+
+    print(df['category'])
+
+
+if __name__ == "__main__":
+    main()

banking_breakdown/statement_parser.py

@@ -6,94 +6,117 @@ 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["Valutadatum"], '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()
-#
-#     return result
-#
-#
-# def _compute_net_income(df: pd.DataFrame) -> pd.DataFrame:
-#     stripped_df = pd.DataFrame({'t': df["Valutadatum"], 'value': df["Betrag"]})
-#
-#     stripped_df.index = stripped_df['t']
-#     gb = stripped_df.groupby(pd.Grouper(freq='M'))
-#
-#     result = gb["value"].sum().reset_index()
-#     return result
-#
-#
-# def _compute_category_overview(df: pd.DataFrame) -> pd.DataFrame:
-#     categories = ["Social life", "Other", "Food", "Hobbies",
-#                   "Rent \\& Utilities", "Education", "Transportation"]
-#     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["Valutadatum"],
-#                          'value': df["Saldo nach Buchung"]})
-#
-#
-# def parse_statement(filename: str) -> types.ReportData:
-#     df = pd.read_csv(filename, delimiter=';', decimal=",")
-#     df["Valutadatum"] = pd.to_datetime(df["Valutadatum"], 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/bank_statement_2023.csv")
-#
-#
-# if __name__ == "__main__":
-#     main()
+def _escape_string(to_escape: str):
+    return to_escape.translate(str.maketrans({"&": r"\&"}))
 
 
-def get_stripped_statement(filename: str) -> pd.DataFrame:
-    # df = pd.read_csv(filename, delimiter=';', decimal=",")
-    df = pd.read_csv(filename, delimiter=';')
-    df["Valutadatum"] = (pd.to_datetime(df["Valutadatum"], format='%d.%m.%Y')
-                         .dt.strftime('%Y-%m-%d'))
+def _compute_total_balance(df: pd.DataFrame) -> pd.DataFrame:
+    stripped_df = pd.DataFrame(
+        {'t': df["t"], 'value': df["balance"]})
 
-    result = pd.DataFrame({'t': df["Valutadatum"],
-                           'other party': df["Name Zahlungsbeteiligter"],
-                           'value': df["Betrag"],
-                           'balance': df["Saldo nach Buchung"],
-                           'category': [''] * len(df["Valutadatum"]),
-                           'description': df["Buchungstext"],
-                           'purpose': df["Verwendungszweck"]
-                           })
+    stripped_df.index = stripped_df['t']
+    gb = stripped_df.groupby(pd.Grouper(freq='M'))
+
+    result = gb.tail(1)['value'].reset_index()
 
     return result
+
+
+def _compute_net_income(df: pd.DataFrame) -> pd.DataFrame:
+    df.index = df['t']
+    income_df = df.loc[df['value'] > 0]
+    expenses_df = df.loc[df['value'] < 0]
+
+    income_df = income_df.groupby(pd.Grouper(freq='M'))[
+        'value'].sum().reset_index().round(decimals=2)
+    expenses_df = expenses_df.groupby(pd.Grouper(freq='M'))[
+        'value'].sum().reset_index().round(decimals=2)
+
+    t = income_df['t']
+    income = income_df['value'].round(decimals=2)
+    expenses = expenses_df['value'].round(decimals=2)
+    net = (income + expenses).round(decimals=2)
+
+    result_df = pd.DataFrame(
+        {'t': t, 'income': income, 'expenses': expenses, 'net': net})
+
+    return result_df
+
+
+def _compute_category_overview(df: pd.DataFrame) -> pd.DataFrame:
+    df = df.loc[df['value'] < 0]
+    df = df.drop('t', axis=1)
+    df = df.groupby(['category']).sum().reset_index()
+    values = (df['value'] / df['value'].sum() * 100).to_numpy()
+    values[-1] += 100 - np.sum(values)
+    values = np.round(values, decimals=1)
+    categories = [_escape_string(category) for category in df['category']]
+
+    category_overview_df = pd.DataFrame(
+        {"category": categories, "value": values})
+
+    category_overview_df = category_overview_df.sort_values('value',
+                                                            ascending=False)
+
+    return category_overview_df
+
+
+def _compute_expenses_by_category(complete_df: pd.DataFrame) -> pd.DataFrame:
+    complete_df = complete_df.loc[complete_df['value'] < 0].copy()
+    complete_df['value'] = -complete_df['value']
+    complete_df.index = complete_df['t']
+
+    complete_gb = complete_df.groupby(pd.Grouper(freq='M'))
+
+    categories = complete_df['category'].unique()
+    data_dict = {category: [] for category in categories}
+
+    for (month_date, month_df) in complete_gb:
+        month_df = month_df.drop('t', axis=1).reset_index().drop('t', axis=1)
+
+        category_df = month_df.groupby(['category']).sum().reset_index()
+
+        for _, row in category_df.iterrows():
+            data_dict[row['category']].append(row['value'])
+
+        non_listed = list(set(categories) - set(category_df['category']))
+        for category in non_listed:
+            data_dict[category].append(0)
+
+    result = pd.DataFrame(data_dict)
+    result = result.reindex(result.mean().sort_values(ascending=False).index,
+                            axis=1)
+    result = result.round(decimals=2)
+    result['t'] = complete_gb.tail(1).drop('t', axis=1).reset_index()['t']
+
+    return result
+
+
+def _compute_detailed_balance(df: pd.DataFrame) -> pd.DataFrame:
+    return pd.DataFrame({'t': df["t"],
+                         'value': df["balance"]})
+
+
+def parse_statement(filename: str) -> types.ReportData:
+    df = pd.read_csv(filename)
+    df["t"] = pd.to_datetime(df["t"], format='%Y-%m-%d')
+
+    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)
+    expenses_by_category_df = _compute_expenses_by_category(df)
+
+    return types.ReportData(category_overview_df,
+                            expenses_by_category_df,
+                            net_income_df,
+                            total_balance_df,
+                            detailed_balance_df, )
+
+
+def main():
+    report_data = parse_statement("../res/bank_statement_2023_categorized.csv")
+
+
+if __name__ == "__main__":
+    main()

banking_breakdown/test.json

@@ -0,0 +1,5 @@
+{
+  "asdf": [
+    "Kinemic"
+  ]
+}

banking_breakdown/types.py

@@ -5,6 +5,7 @@ import pandas as pd
 @dataclass
 class ReportData:
     category_overview: pd.DataFrame
+    expenses_by_category: pd.DataFrame
     net_income: pd.DataFrame
     total_value: pd.DataFrame
     detailed_balance: pd.DataFrame

banking_breakdown/ui/custom_ui_items.py

@@ -35,16 +35,16 @@ class HeaderContextMenu(QMenu):
     """Context menu appearing when right-clicking the header of the QTableView.
     """
 
-    def __init__(self, column, pandas_model: PandasModel, callback=None,
+    def __init__(self, column_index, pandas_model: PandasModel, callback=None,
                  parent=None):
         super(HeaderContextMenu, self).__init__()
 
-        self._column = column
         self._pandas_model = pandas_model
         self._callback = callback
 
+        self._column_index = column_index
         self._column_text \
-            = self._pandas_model.headerData(self._column,
+            = self._pandas_model.headerData(self._column_index,
                                             Qt.Orientation.Horizontal)
 
         # Define assign action
@@ -85,9 +85,11 @@ class HeaderContextMenu(QMenu):
             return
 
         if (new_name != self._column_text) and (new_name != ''):
-            df = self._pandas_model.get_dataframe()
-            df = df.rename(columns={self._column_text: new_name})
-            self._pandas_model.set_dataframe(df)
+            try:
+                self._pandas_model.rename_column(self._column_text, new_name)
+            except:
+                QMessageBox.warning(self, "No action performed",
+                                    "An error occurred.")
 
             if self._callback:
                 self._callback()
@@ -98,10 +100,7 @@ class HeaderContextMenu(QMenu):
                                       f" column '{self._column_text}'?")
 
         if button == QMessageBox.StandardButton.Yes:
-            df = self._pandas_model.get_dataframe()
-            df = df.iloc[:, [j for j, c
-                             in enumerate(df.columns) if j != self._column]]
-            self._pandas_model.set_dataframe(df)
+            self._pandas_model.delete_column_by_index(self._column_index)
 
             if self._callback:
                 self._callback()
@@ -119,14 +118,7 @@ class HeaderContextMenu(QMenu):
         if not flag:
             return
 
-        column_titles = list(df.columns)
-        index1, index2 = column_titles.index(
-            self._column_text), column_titles.index(other_name)
-        column_titles[index1], column_titles[index2] \
-            = column_titles[index2], column_titles[index1]
-
-        df = df.reindex(columns=column_titles)
-        self._pandas_model.set_dataframe(df)
+        self._pandas_model.switch_columns(self._column_text, other_name)
 
         if self._callback:
             self._callback()
@@ -139,14 +131,12 @@ class HeaderContextMenu(QMenu):
         if not flag:
             return
 
-        df = self._pandas_model.get_dataframe()
         try:
-            df[self._column_text] \
-                = pd.to_datetime(df[self._column_text], format=date_format)
+            self._pandas_model.assign_date_column(self._column_text,
+                                                  date_format)
         except:
             QMessageBox.warning(self, "No action performed",
                                 "An error occurred.")
-        self._pandas_model.set_dataframe(df)
 
         if self._callback:
             self._callback()
@@ -160,19 +150,12 @@ class HeaderContextMenu(QMenu):
         if not flag:
             return
 
-        df = self._pandas_model.get_dataframe()
-
         try:
-            if decimal_sep == ',':
-                df[self._column_text] \
-                    = df[self._column_text].str.replace(',', '.').astype(float)
-            else:
-                df[self._column_text] = df[self._column_text].astype(float)
+            self._pandas_model.assign_float_column(self._column_text,
+                                                   decimal_sep)
         except:
             QMessageBox.warning(self, "No action performed",
                                 "An error occurred.")
 
-        self._pandas_model.set_dataframe(df)
-
         if self._callback:
             self._callback()

banking_breakdown/ui/main_window.py

@@ -45,6 +45,7 @@ class MainWindow(QMainWindow):
         self._proxy_model.setSourceModel(self._pandas_model)
         self._table_view.setModel(self._proxy_model)
         self._proxy_model.setSortRole(Qt.ItemDataRole.EditRole)
+        self._proxy_model.setDynamicSortFilter(False)
 
         # Set event handlers
 
@@ -98,6 +99,13 @@ class MainWindow(QMainWindow):
                                len(col))
                 self._table_view.setColumnWidth(i, max_char * 10)
 
+    def _assign_category_to_selected_transactions(self, category: str):
+        indexes = self._table_view.selectionModel().selectedRows()
+        row_indices = [self._table_view.model().mapToSource(index).row()
+                       for index in indexes]
+
+        self._pandas_model.assign_category(category, row_indices)
+
     #
     # List data updates
     #
@@ -107,16 +115,11 @@ class MainWindow(QMainWindow):
             self._list_widget.addItem(category)
 
     def _update_categories_from_dataframe(self):
-        df = self._pandas_model.get_dataframe()
-
-        if 'category' not in df.columns:
-            df['category'] = [' '] * len(df.index)
-
-        df_categories = df['category'].unique()
+        df_categories = self._pandas_model.get_categories()
         current_categories = [self._list_widget.item(x).text() for x
                               in range(self._list_widget.count())]
-
         missing = list(set(df_categories) - set(current_categories))
+
         self._add_categories([category for category
                               in missing if category != ' '])
 
@@ -135,19 +138,19 @@ class MainWindow(QMainWindow):
             warning_item.hide()
             self._warning_layout.removeItem(warning_item)
 
-        df = self._pandas_model.get_dataframe()
+        columns = self._pandas_model.get_columns()
 
-        if 't' not in df.columns:
+        if 't' not in columns:
             self._add_warning_item(
                 "The column 't' does not exist. Please rename the column"
                 " containing the dates of the transactions to 't'.")
 
-        if 'value' not in df.columns:
+        if 'value' not in columns:
             self._add_warning_item(
                 "The column 'value' does not exist. Please rename the column"
                 " containing the values of the transactions to 'value'.")
 
-        if 'balance' not in df.columns:
+        if 'balance' not in columns:
             self._add_warning_item(
                 "The column 'balance' does not exist. Please rename the column"
                 " containing the balance after each transaction to 'balance'")
@@ -159,7 +162,7 @@ class MainWindow(QMainWindow):
     def _handle_header_right_click(self, pos):
         column = self._table_view.horizontalHeader().logicalIndexAt(pos)
 
-        context = HeaderContextMenu(parent=self, column=column,
+        context = HeaderContextMenu(parent=self, column_index=column,
                                     pandas_model=self._pandas_model,
                                     callback=self._dataframe_update_callback)
         context.exec(self.sender().mapToGlobal(pos))
@@ -187,35 +190,19 @@ class MainWindow(QMainWindow):
                                       f"Are you sure you want to delete"
                                       f" category '{selected_item.text()}'?")
 
-        df = self._pandas_model.get_dataframe()
-
-        if 'category' not in df.columns:
-            df['category'] = [' '] * len(df.index)
-
         if button == QMessageBox.StandardButton.Yes:
-            df.loc[df['category'] == selected_item.text(), 'category'] = ' '
+            self._pandas_model.delete_category(selected_item.text())
             self._list_widget.takeItem(self._list_widget.row(selected_item))
-            self._pandas_model.set_dataframe(df)
 
     def _handle_clear_click(self):
-        self._assign_category(' ')
-
-    def _assign_category(self, category: str):
-        indexes = self._table_view.selectionModel().selectedRows()
-
-        row_indices = [self._table_view.model().mapToSource(index).row()
-                       for index in indexes]
-
-        df = self._pandas_model.get_dataframe()
-        df.loc[row_indices, 'category'] = category
-        self._pandas_model.set_dataframe(df)
+        self._assign_category_to_selected_transactions(' ')
 
     def _handle_apply_click(self):
         category = self._list_widget.selectedItems()[0].text()
-        self._assign_category(category)
+        self._assign_category_to_selected_transactions(category)
 
     def _handle_item_double_click(self, item):
-        self._assign_category(item.text())
+        self._assign_category_to_selected_transactions(item.text())
 
     def _handle_save(self):
         filename, _ = QFileDialog.getSaveFileName(self, 'Save File')

banking_breakdown/ui/pandas_model.py

@@ -1,3 +1,5 @@
+import typing
+
 import numpy
 import pandas as pd
 from PyQt6 import QtCore
@@ -20,9 +22,10 @@ class PandasModel(QtCore.QAbstractTableModel):
 
         self._data = pd.DataFrame()
         self._data_str = pd.DataFrame()
-        self._horizontalHeaders = None
 
+    #
     # Overloaded functions
+    #
 
     def rowCount(self, parent=None):
         return len(self._data_str.values)
@@ -55,14 +58,112 @@ class PandasModel(QtCore.QAbstractTableModel):
                 and (role == Qt.ItemDataRole.DisplayRole)):
             return super().headerData(section, orientation, role)
 
-        return self._horizontalHeaders[section]
+        return self._data_str.columns[section]
 
-    # Other functions
+    #
+    # Manipulate categories
+    #
+
+    def assign_category(self, category, row_indices):
+        if 'category' not in self._data.columns:
+            self.create_column('category')
+
+        self._data.loc[row_indices, 'category'] = category
+        self._data_str = _get_str_dataframe(self._data)
+
+        for row_index in row_indices:
+            start_index = self.index(row_index, 0)
+            stop_index = self.index(row_index, len(self._data.columns) - 1)
+            self.dataChanged.emit(start_index, stop_index)
+
+    def delete_category(self, category):
+        if 'category' not in self._data.columns:
+            self.create_column('category')
+
+        row_indices = self._data.loc[self._data['category'] == category].index
+        self.assign_category(' ', row_indices)
+
+    def get_categories(self) -> typing.List[str]:
+        if 'category' not in self._data.columns:
+            self.create_column('category')
+
+        return self._data['category'].unique()
+
+    #
+    # Manipulate columns
+    #
+
+    def create_column(self, column, initial_value=' '):
+        self._data[column] = [initial_value] * len(self._data.index)
+        self._data_str = _get_str_dataframe(self._data)
+        self.layoutAboutToBeChanged.emit()
+        self.layoutChanged.emit()
+
+    def delete_column_by_index(self, column_index):
+        self._data \
+            = self._data.iloc[:, [j for j, c in enumerate(self._data.columns)
+                                  if j != column_index]]
+        self._data_str = _get_str_dataframe(self._data)
+
+        self.layoutAboutToBeChanged.emit()
+        self.layoutChanged.emit()
+
+    def rename_column(self, old_name, new_name):
+        if new_name in self._data.columns:
+            raise Exception(
+                f"A column with the name '{new_name}' already exists.")
+
+        self._data = self._data.rename(columns={old_name: new_name})
+        self._data_str = _get_str_dataframe(self._data)
+
+        column_index = self._data.columns.get_loc(new_name)
+        self.headerDataChanged.emit(Qt.Orientation.Horizontal,
+                                    column_index, column_index)
+
+    def switch_columns(self, column1, column2):
+        column_titles = list(self._data.columns)
+
+        index1, index2 \
+            = column_titles.index(column1), column_titles.index(column2)
+
+        column_titles[index1], column_titles[index2] \
+            = column_titles[index2], column_titles[index1]
+
+        self._data = self._data.reindex(columns=column_titles)
+        self._data_str = _get_str_dataframe(self._data)
+
+        self.layoutAboutToBeChanged.emit()
+        self.layoutChanged.emit()
+
+    def get_columns(self) -> typing.List[str]:
+        return list(self._data.columns)
+
+    def assign_float_column(self, column, decimal_sep):
+        if decimal_sep == ',':
+            self._data[column] \
+                = self._data[column].str.replace(',', '.').astype(float)
+        else:
+            self._data[column] = self._data[column].astype(float)
+
+        self._data_str = _get_str_dataframe(self._data)
+
+        column_index = self._data.columns.get_loc(column)
+        start_index = self.index(0, column_index)
+        stop_index = self.index(len(self._data.index), column_index)
+        self.dataChanged.emit(start_index, stop_index)
+
+    def assign_date_column(self, column, date_format):
+        self._data[column] \
+            = pd.to_datetime(self._data[column], format=date_format)
+        self._data_str = _get_str_dataframe(self._data)
+
+    #
+    # Directly access dataframe
+    #
 
     def set_dataframe(self, df):
         self._data = df
         self._data_str = _get_str_dataframe(df)
-        self._horizontalHeaders = list(df.columns)
 
         self.layoutAboutToBeChanged.emit()
         self.layoutChanged.emit()

res/report.tex

@@ -157,7 +157,7 @@
 			]
 				% Dummy plot to set x axis ticks
 				\addplot[draw=none]
-					table[col sep=comma, x=t, y=value]
+					table[col sep=comma, x=t, y=net]
 						{net_income.csv};
 
 				% Dummy plot to set x axis scale
@@ -165,11 +165,17 @@
 					table[col sep=comma, x=t, y expr=0]
 						{detailed_balance.csv};
 
-				\addplot[ybar, color=scol2, fill=scol2, line width=1pt]
-					table[col sep=comma, x=t, y=value, discard if lt={value}{0}]
+				\addplot[ybar, bar width=0.4cm, draw=none, fill=scol2!30, line width=1pt]
+					table[col sep=comma, x=t, y=income]
 						{net_income.csv};
-				\addplot[ybar, color=scol0, fill=scol0, line width=1pt]
-					table[col sep=comma, x=t, y=value, discard if gt={value}{0}]
+				\addplot[ybar, bar width=0.4cm, draw=none, fill=scol0!30, line width=1pt]
+					table[col sep=comma, x=t, y=expenses]
+						{net_income.csv};
+                \addplot[ybar, bar width=0.3cm, draw=none, fill=scol0, line width=1pt]
+					table[col sep=comma, x=t, y=net, discard if gt={net}{0}]
+						{net_income.csv};
+                \addplot[ybar, bar width=0.3cm, draw=none, fill=scol2, line width=1pt]
+					table[col sep=comma, x=t, y=net, discard if lt={net}{0}]
 						{net_income.csv};
 			\end{axis}
 		\end{tikzpicture}
@@ -211,6 +217,105 @@
 	\caption{Development of account balance over time}
 \end{figure}
 
+\begin{figure}
+	\centering
+
+	
+	\csvautotabular{net_income.csv}
+\end{figure}
+
+
+\begin{figure}[H]
+	\centering
+
+	% Read table
+	\pgfplotstableread[col sep=comma]{expenses_by_category.csv}\expbycattable
+	\pgfplotstablegetcolsof{\expbycattable}
+	\pgfmathtruncatemacro\NumCols{\pgfplotsretval-1}
+
+	\begin{subfigure}[c]{\textwidth}
+		\centering
+
+		\begin{tikzpicture}
+			\begin{axis}[
+				stack plots=y,
+				area style,
+				date coordinates in=x,
+				width=\textwidth,
+				height=0.375\textwidth,
+				xticklabel=\month.\shortyear{\year},
+				xtick=data,
+				enlargelimits=false,
+				xticklabel style={
+					rotate=60,
+					anchor=near xticklabel,
+				},
+				legend columns=5,
+				legend style={at={(0.5,-0.6)},anchor=south},
+				ylabel={Expenses in €},
+				ymin=0,
+			]
+				% For each
+				\pgfplotsinvokeforeach{0,...,\NumCols/2 -1}{
+					% Define color
+					\pgfmathparse{1000 / (\NumCols/2 -1) * #1}
+					\extractcolormapcolor{tempcol#1}{\pgfmathresult}
+
+					% Add plot
+					\addplot+[tempcol#1]
+						table[col sep=comma, x=t, y index=#1]
+							{\expbycattable} \closedcycle;
+
+					% Add legend entry (https://tex.stackexchange.com/a/405018)
+					\pgfplotstablegetcolumnnamebyindex{#1}\of{\expbycattable}\to\pgfplotsretval
+					\expandafter\addlegendentry\expandafter{\pgfplotsretval}
+				}
+			\end{axis}
+		\end{tikzpicture}
+	\end{subfigure}\\[1em]
+	\begin{subfigure}[c]{\textwidth}
+		\centering
+
+		\begin{tikzpicture}
+			\begin{axis}[
+				stack plots=y,
+				area style,
+				date coordinates in=x,
+				width=\textwidth,
+				height=0.375\textwidth,
+				xticklabel=\month.\shortyear{\year},
+				xtick=data,
+				enlargelimits=false,
+				xticklabel style={
+					rotate=60,
+					anchor=near xticklabel,
+				},
+				legend columns=5,
+				legend style={at={(0.5,-0.6)},anchor=south},
+				ylabel={Expenses in €},
+				ymin=0,
+			]
+				% For each
+				\pgfplotsinvokeforeach{\NumCols/2,...,\NumCols-1}{
+					% Define color
+					\pgfmathparse{1000 * (#1 - \NumCols/2) / (\NumCols-1 - \NumCols/2)}
+					\extractcolormapcolor{tempcol#1}{\pgfmathresult}
+
+					% Add plot
+					\addplot+[tempcol#1]
+						table[col sep=comma, x=t, y index=#1]
+							{\expbycattable} \closedcycle;
+
+					% Add legend entry (https://tex.stackexchange.com/a/405018)
+					\pgfplotstablegetcolumnnamebyindex{#1}\of{\expbycattable}\to\pgfplotsretval
+					\expandafter\addlegendentry\expandafter{\pgfplotsretval}
+				}
+			\end{axis}
+		\end{tikzpicture}
+	\end{subfigure}
+
+	\caption{Expenses by category}
+\end{figure}
 
 \end{document}