Create a Python dashboard for budgeting

Creating a Python dashboard for budgeting typically involves using libraries like Dash by Plotly, Streamlit, or Panel to create interactive web apps, combined with data handling libraries like Pandas. Here’s a straightforward example of a budgeting dashboard using Dash that lets you input budget categories and expenses, then visualizes the data.

python
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import dash
from dash import dcc, html, Input, Output, State
import pandas as pd
import plotly.express as px

# Initialize the Dash app
app = dash.Dash(__name__)
app.title = "Budget Dashboard"

# Sample initial budget data
initial_data = {
    "Category": ["Rent", "Food", "Transport", "Entertainment", "Savings"],
    "Budgeted": [1200, 400, 150, 200, 300],
    "Spent": [0, 0, 0, 0, 0]
}

df = pd.DataFrame(initial_data)

# App layout
app.layout = html.Div([
    html.H1("Personal Budget Dashboard", style={'textAlign': 'center'}),
    
    # Table showing budget and expenses
    html.Div(id='budget-table'),
    
    # Input fields to add/update expenses
    html.Div([
        html.Label("Category:"),
        dcc.Dropdown(
            id='category-dropdown',
            options=[{'label': cat, 'value': cat} for cat in df['Category']],
            value='Food',
            clearable=False,
            style={'width': '200px'}
        ),
        html.Label("Amount Spent:"),
        dcc.Input(id='amount-input', type='number', min=0, step=0.01, value=0),
        html.Button('Add Expense', id='add-expense-btn', n_clicks=0)
    ], style={'marginTop': '20px'}),
    
    # Graph showing budget vs spent
    dcc.Graph(id='budget-graph'),
    
    # Store data in hidden div to keep state between callbacks
    dcc.Store(id='budget-data', data=df.to_dict('records'))
])


# Callback to update the table and graph whenever data changes
@app.callback(
    Output('budget-table', 'children'),
    Output('budget-graph', 'figure'),
    Output('budget-data', 'data'),
    Input('add-expense-btn', 'n_clicks'),
    State('category-dropdown', 'value'),
    State('amount-input', 'value'),
    State('budget-data', 'data')
)
def update_budget(n_clicks, category, amount, data):
    if n_clicks > 0 and amount is not None:
        df = pd.DataFrame(data)
        # Update the 'Spent' value for the selected category
        df.loc[df['Category'] == category, 'Spent'] += amount
    else:
        df = pd.DataFrame(data)

    # Create a simple table
    table = html.Table([
        html.Thead([
            html.Tr([html.Th(col) for col in df.columns])
        ]),
        html.Tbody([
            html.Tr([
                html.Td(df.iloc[i][col]) for col in df.columns
            ]) for i in range(len(df))
        ])
    ], style={'width': '50%', 'margin': 'auto', 'border': '1px solid black', 'borderCollapse': 'collapse',
              'textAlign': 'center'})

    # Create bar chart comparing budgeted vs spent
    fig = px.bar(df, x='Category', y=['Budgeted', 'Spent'], barmode='group',
                 title="Budgeted vs Spent per Category",
                 labels={"value": "Amount ($)", "Category": "Budget Category"})

    return table, fig, df.to_dict('records')


if __name__ == '__main__':
    app.run_server(debug=True)

How this works:

• The dashboard starts with predefined budget categories and budgeted amounts.

• You select a category and enter the amount spent, then click Add Expense to update spending.

• The table shows updated spending per category.

• A bar chart dynamically compares budgeted vs spent amounts.

To run this:

1. Install necessary libraries:

bash
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pip install dash pandas plotly

2. Save the code to a .py file (e.g., budget_dashboard.py) and run it:

bash
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python budget_dashboard.py

3. Open the local URL Dash outputs (usually http://127.0.0.1:8050) in your browser.

If you want, I can help you extend this with more features like monthly breakdowns, savings goals, or exporting data.