Added Python script for visualizations of expenditure

Visualisations of expenditure.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[6]:
+
+
+#Orignal data based on fake generated data.
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# In[11]:
+
+
+def plot_financial_overview_for_user_interactive(file_path):
+    import pandas as pd
+    import matplotlib.pyplot as plt
+
+    # Load the data from the specified Excel file
+    data = pd.read_excel(file_path)
+
+    # Ensure 'Date' is in datetime format
+    data['Date'] = pd.to_datetime(data['Date'])
+
+    # Prompt the user to enter a User ID
+    user_id = int(input("Please enter a User ID to display the financial overview: "))
+    print(f"You have selected User ID: {user_id}")  # Confirming the user's selection
+
+
+    # Add a 'User ID' field if it does not exist in the dataset
+    if 'User ID' not in data.columns:
+        data['User ID'] = range(1, len(data) + 1)
+
+    # Filter data for the specified user ID
+    user_data = data[data['User ID'] == user_id]
+
+    # Check if there is data available for the selected user ID
+    if user_data.empty:
+        print(f"No data available for User ID: {user_id}")
+        return
+
+    # Define color scheme for the plot
+    colors = {"Credit": "#0077bb", "Debit": "#d9d9d9", "Balance": "#343a40", "Text": "#abb5be"}
+
+    # Plotting bars for Credit, Debit, and Balance
+    plt.figure(figsize=(14, 7))
+    bar_width = 0.15  # Width of the bars
+    r1 = range(len(user_data))
+    r2 = [x + bar_width for x in r1]
+    r3 = [x + 2 * bar_width for x in r1]
+
+    plt.bar(r1, user_data['Credit'], color=colors['Credit'], width=bar_width, edgecolor='grey', label='Credit')
+    plt.bar(r2, user_data['Debit'], color=colors['Debit'], width=bar_width, edgecolor='grey', label='Debit')
+    plt.bar(r3, user_data['Balance'], color=colors['Balance'], width=bar_width, edgecolor='grey', label='Balance')
+
+    # Adding x-axis labels, title, and other plot details
+    plt.xlabel('Date', fontweight='bold')
+    plt.xticks([r + bar_width for r in range(len(user_data))], user_data['Date'].dt.strftime('%Y-%m-%d'))
+    plt.ylabel('Amount')
+    plt.title(f'Financial Overview for User ID: {user_id}')
+
+    # Display additional category information in a text box
+    category_info = f"Categories:\nCategory 1: {user_data['Category 1'].unique()}\nCategory 2: {user_data['Category 2'].unique()}\nCategory 3: {user_data['Category 3'].unique()}"
+    plt.figtext(0.915, 0.5, category_info, ha="center", va="center", fontsize=10, bbox={"boxstyle":"round", "facecolor":colors['Text']})
+
+    plt.subplots_adjust(right=0.85)
+    plt.legend()
+    plt.show()
+
+# Example usage:
+# plot_financial_overview_for_user_interactive('fake_bank_account_transactions2 1.xlsx')
+
+
+# In[8]:
+
+
+plot_financial_overview_for_user(2, 'fake_bank_account_transactions2 1.xlsx')
+
+
+# In[13]:
+
+
+def plot_category_summary(file_path):
+    import pandas as pd
+    import matplotlib.pyplot as plt
+
+    # Load the data from the specified Excel file
+    data = pd.read_excel(file_path)
+
+    # Calculate totals for Category 1
+    category1_totals = data.groupby(['Category 1']).agg({'Debit': 'sum', 'Credit': 'sum'})
+    category2_totals = data.groupby(['Category 2']).agg({'Debit': 'sum', 'Credit': 'sum'})
+
+    # Define color scheme
+    colors = ["#0077bb", "#d9d9d9"]  # Blue for Credit, Gray for Debit
+
+    # Plotting for Category 1
+    plt.figure(figsize=(10, 6))
+    category1_totals.plot(kind='bar', color=colors, width=0.8)
+    plt.title('Income vs Expenditure for Category 1')
+    plt.ylabel('Amount')
+    plt.xlabel('Category')
+    plt.xticks(rotation=0)
+    plt.grid(True, linestyle='--', alpha=0.6)
+    plt.legend(['Debit', 'Credit'])
+    plt.tight_layout()
+
+    # Plotting for Category 2
+    plt.figure(figsize=(10, 6))
+    category2_totals.plot(kind='bar', color=colors, width=0.8)
+    plt.title('Income vs Expenditure for Category 2')
+    plt.ylabel('Amount')
+    plt.xlabel('Category')
+    plt.xticks(rotation=45)
+    plt.grid(True, linestyle='--', alpha=0.6)
+    plt.legend(['Debit', 'Credit'])
+    plt.tight_layout()
+
+    plt.show()
+
+# Example usage:
+plot_category_summary('fake_bank_account_transactions2 1.xlsx')
+
+
+# In[15]:
+
+
+def plot_by_category_selection_interactive(file_path):
+    import pandas as pd
+    import matplotlib.pyplot as plt
+
+    # Load the data from the specified Excel file
+    data = pd.read_excel(file_path)
+
+    # Get unique values for Category 1 and Category 2 for user to choose from
+    category1_options = data['Category 1'].unique()
+    category2_options = data['Category 2'].unique()
+
+    # Display options and prompt user for input
+    print("Available Category 1 options:", category1_options)
+    category1_selection = input("Select a category from Category 1: ")
+
+    print("Available Category 2 options:", category2_options)
+    category2_selection = input("Select a category from Category 2: ")
+
+    # Filter data based on user selection
+    filtered_data = data[(data['Category 1'] == category1_selection) & (data['Category 2'] == category2_selection)]
+
+    if filtered_data.empty:
+        print(f"No data available for Category 1: {category1_selection} and Category 2: {category2_selection}")
+        return
+
+    # Group by Category 3 and calculate totals
+    category3_totals = filtered_data.groupby('Category 3').agg({'Debit': 'sum', 'Credit': 'sum'})
+
+    # Define color scheme
+    colors = ["#0077bb", "#d9d9d9"]  # Blue for Credit, Gray for Debit
+
+    # Plotting results
+    plt.figure(figsize=(10, 6))
+    category3_totals.plot(kind='bar', color=colors, width=0.8)
+    plt.title(f'Income vs Expenditure for {category1_selection} - {category2_selection}')
+    plt.ylabel('Amount')
+    plt.xlabel('Category 3')
+    plt.xticks(rotation=45)
+    plt.grid(True, linestyle='--', alpha=0.6)
+    plt.legend(['Debit', 'Credit'])
+    plt.tight_layout()
+
+    plt.show()
+
+
+plot_by_category_selection_interactive('fake_bank_account_transactions2 1.xlsx')
+
+
+# In[ ]:
+
+
+
+