PyCafe - Dash - Microfinance Sales Analysis with Dash

app.py
dashdebug.py
requirements.txt
dashdebug.py
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import dash
from dash import Input, Output, html, dcc
import dash_bootstrap_components as dbc
import plotly.express as px
import pandas as pd
import numpy as np

# Sample sales dataset
data = {
    'Transaction_ID': range(1, 101),
    'Date': pd.date_range(start='2023-01-01', periods=100, freq='D'),
    'Product_Category': ['Loan', 'Savings', 'Insurance'] * 33 + ['Loan'],
    'Region': ['North', 'South', 'East', 'West'] * 25,
    'Sale_Amount': [round(abs(x), 2) for x in pd.Series(np.random.randn(100) * 1000 + 5000)]
}

df = pd.DataFrame(data)

# Initialize Dash app
app = dash.Dash(__name__)

# Custom CSS style (moved here from html.Style)
card_style = {
    'border': '1px solid #ddd',
    'borderRadius': '10px',
    'padding': '20px',
    'margin': '10px',
    'textAlign': 'center',
    'boxShadow': '2px 2px 12px #aaa',
    'flex': '1'
}

# App layout
app.layout = html.Div([
    html.H1("Microfinance Sales Dashboard", style={'textAlign': 'center', 'color': 'blue'}),

    # Filters
    html.Div([
        html.Div([
            html.Label("Select Region:"),
            dcc.Dropdown(
                id='region-filter',
                options=[{'label': region, 'value': region} for region in df['Region'].unique()],
                multi=True,
                value=df['Region'].unique().tolist()
            ),
        ], style={'flex': '1'}),

        html.Div([
            html.Label("Select Product Category:"),
            dcc.Dropdown(
                id='category-filter',
                options=[{'label': category, 'value': category} for category in df['Product_Category'].unique()],
                multi=True,
                value=df['Product_Category'].unique().tolist()
            ),
        ], style={'flex': '1'}),

        html.Div([
            html.Label("Select Date Range:"),
            dcc.DatePickerRange(
                id='date-filter',
                start_date=df['Date'].min(),
                end_date=df['Date'].max(),
                display_format='YYYY-MM-DD'
            ),
        ], style={'flex': '1'}),
    ], style={'display': 'flex', 'gap': '20px', 'margin': '20px'}),

    # Metrics Cards
    html.Div([
        html.Div([
            html.H3("Total Sales"),
            html.P(id='total-sales')
        ], style=card_style),

        html.Div([
            html.H3("Average Sale Amount"),
            html.P(id='avg-sales')
        ], style=card_style),

        html.Div([
            html.H3("Total Transactions"),
            html.P(id='transaction-count')
        ], style=card_style),
    ], style={'display': 'flex', 'justifyContent': 'space-around'}),

    # Visualizations with full-screen support
    html.Div([
        dcc.Graph(id='sales-trend', config={'displayModeBar': True, 'displaylogo': False}),
        dcc.Graph(id='sales-category', config={'displayModeBar': True, 'displaylogo': False}),
        dcc.Graph(id='sales-region', config={'displayModeBar': True, 'displaylogo': False}),
    ]),
])

# Callback to update charts and metrics
@app.callback(
    [Output('sales-trend', 'figure'),
     Output('sales-category', 'figure'),
     Output('sales-region', 'figure'),
     Output('total-sales', 'children'),
     Output('avg-sales', 'children'),
     Output('transaction-count', 'children')],
    [Input('region-filter', 'value'),
     Input('category-filter', 'value'),
     Input('date-filter', 'start_date'),
     Input('date-filter', 'end_date')]
)
def update_dashboard(selected_regions, selected_categories, start_date, end_date):
    # Filter data
    filtered_df = df[
        (df['Region'].isin(selected_regions)) &
        (df['Product_Category'].isin(selected_categories)) &
        (df['Date'] >= pd.to_datetime(start_date)) &
        (df['Date'] <= pd.to_datetime(end_date))
    ]

    # Metrics
    total_sales = filtered_df['Sale_Amount'].sum()
    avg_sales = filtered_df['Sale_Amount'].mean()
    transaction_count = filtered_df.shape[0]

    # Sales trend over time
    fig_trend = px.line(filtered_df, x='Date', y='Sale_Amount', title='Sales Trend Over Time')

    # Sales by product category
    fig_category = px.bar(filtered_df, x='Product_Category', y='Sale_Amount', title='Sales by Product Category',
                          color='Product_Category')

    # Sales by region (pie chart)
    fig_region = px.pie(filtered_df, names='Region', values='Sale_Amount', title='Sales by Region')

    # Metric card content
    total_sales_text = f"${total_sales:,.2f}"
    avg_sales_text = f"${avg_sales:,.2f}" if transaction_count > 0 else "$0.00"
    transaction_count_text = f"{transaction_count}"

    return fig_trend, fig_category, fig_region, total_sales_text, avg_sales_text, transaction_count_text


# Run app
if __name__ == '__main__':
    app.run_server(debug=False)
Py.Cafe

dash-microfinance-sales-analysis

Microfinance Sales Analysis with Dash
