PDF zu interaktivem Dashboard Prompt

import dash from dash import dcc, html, Input, Output, callback import plotly.graph_objs as go import plotly.express as px import pandas as pd import numpy as np from datetime import datetime # ============================================================================== # DATA PREPARATION # ============================================================================== # Simulated data representing key figures from a typical financial/operational PDF # In production, replace this with actual data extraction from your PDF np.random.seed(42) years = list(range(2019, 2025)) quarters = ['Q1', 'Q2', 'Q3', 'Q4'] # Generate revenue data with growth trend revenue_base = 1000000 revenue_growth = 0.15 revenue_data = [] for year in years: for quarter in quarters: seasonal_factor = {'Q1': 0.9, 'Q2': 1.1, 'Q3': 1.2, 'Q4': 1.0}[quarter] base = revenue_base * (1 + revenue_growth) ** (year - 2019) revenue_data.append({ 'Year': year, 'Quarter': quarter, 'Period': f'{year}-{quarter}', 'Revenue': base * seasonal_factor * (1 + np.random.uniform(-0.05, 0.05)), 'Expenses': base * 0.7 * seasonal_factor * (1 + np.random.uniform(-0.05, 0.05)), 'Profit': base * 0.3 * seasonal_factor * (1 + np.random.uniform(-0.08, 0.08)), 'Customers': int(5000 * (1 + 0.2) ** (year - 2019) * (1 + np.random.uniform(-0.1, 0.1))), 'MarketShare': 15 + (year - 2019) * 2 + np.random.uniform(-1, 1) }) df = pd.DataFrame(revenue_data) # Calculate year-over-year metrics df['YoY_Growth'] = df.groupby('Quarter')['Revenue'].pct_change() * 100 # Create summary DataFrame for annual comparisons annual_df = df.groupby('Year').agg({ 'Revenue': 'sum', 'Expenses': 'sum', 'Profit': 'sum', 'Customers': 'mean', 'MarketShare': 'mean' }).reset_index() annual_df['Profit_Margin'] = (annual_df['Profit'] / annual_df['Revenue']) * 100 annual_df['YoY_Revenue_Growth'] = annual_df['Revenue'].pct_change() * 100 # ============================================================================== # DASHBOARD INITIALIZATION # ============================================================================== app = dash.Dash(__name__) server = app.server # For Replit compatibility # Modern color palette colors = { 'background': '#F8F9FA', 'text': '#2C3E50', 'primary': '#3498DB', 'secondary': '#2ECC71', 'accent': '#E74C3C', 'grid': '#E5E8E8' } # ============================================================================== # LAYOUT # ============================================================================== app.layout = html.Div( style={ 'backgroundColor': colors['background'], 'fontFamily': 'Segoe UI, Roboto, Helvetica Neue, Arial, sans-serif', 'padding': '20px', 'maxWidth': '1400px', 'margin': '0 auto' }, children=[ # Header html.Div([ html.H1('Company Performance Dashboard', style={'color': colors['text'], 'marginBottom': '5px'}), html.P('Interactive analysis of key business metrics (2019-2024)', style={'color': '#7F8C8D', 'marginTop': '0'}) ], style={'textAlign': 'center', 'marginBottom': '30px'}), # Filters html.Div([ html.Label('Select Year:', style={'fontWeight': 'bold', 'color': colors['text']}), dcc.Dropdown( id='year-filter', options=[{'label': str(year), 'value': year} for year in years], value=years[-1], multi=False, clearable=False, style={'width': '200px', 'marginLeft': '10px'} ), html.Label('Select Metric:', style={'fontWeight': 'bold', 'color': colors['text'], 'marginLeft': '30px'}), dcc.Dropdown( id='metric-filter', options=[ {'label': 'Revenue', 'value': 'Revenue'}, {'label': 'Expenses', 'value': 'Expenses'}, {'label': 'Profit', 'value': 'Profit'}, {'label': 'Customers', 'value': 'Customers'}, {'label': 'Market Share', 'value': 'MarketShare'} ], value='Revenue', multi=False, clearable=False, style={'width': '200px', 'marginLeft': '10px'} ) ], style={'display': 'flex', 'alignItems': 'center', 'marginBottom': '30px', 'flexWrap': 'wrap'}), # Chart 1: Quarterly Trend with Year-over-Year Comparison html.Div([ html.H3('Quarterly Performance Trend', style={'color': colors['text'], 'marginBottom': '15px'}), dcc.Graph(id='quarterly-trend-chart') ], style={'marginBottom': '40px'}), # Chart 2: Annual Revenue vs Expenses (Bar Chart) html.Div([ html.H3('Annual Revenue vs Expenses', style={'color': colors['text'], 'marginBottom': '15px'}), dcc.Graph(id='annual-bar-chart') ], style={'marginBottom': '40px'}), # Chart 3: Profit Margin Evolution (Line Chart) html.Div([ html.H3('Profit Margin Evolution', style={'color': colors['text'], 'marginBottom': '15px'}), dcc.Graph(id='profit-margin-chart') ], style={'marginBottom': '40px'}), # Chart 4: Customer Growth (Area Chart) html.Div([ html.H3('Customer Base Growth', style={'color': colors['text'], 'marginBottom': '15px'}), dcc.Graph(id='customer-growth-chart') ], style={'marginBottom': '40px'}), # Chart 5: Market Share Comparison (Pie Chart) html.Div([ html.H3('Market Share Distribution', style={'color': colors['text'], 'marginBottom': '15px'}), dcc.Graph(id='market-share-chart') ], style={'marginBottom': '40px'}), # Footer html.Footer( 'Data source: Company financial reports | Dashboard created with Plotly Dash', style={'textAlign': 'center', 'color': '#7F8C8D', 'marginTop': '30px'} ) ] ) # ============================================================================== # CALLBACKS # ============================================================================== @callback( Output('quarterly-trend-chart', 'figure'), [Input('year-filter', 'value'), Input('metric-filter', 'value')] ) def update_quarterly_trend(selected_year, selected_metric): """Update quarterly trend chart based on selected year and metric""" # Filter data for selected year and previous year for comparison current_year_data = df[df['Year'] == selected_year] previous_year_data = df[df['Year'] == selected_year - 1] if selected_year > years[0] else pd.DataFrame() fig = go.Figure() # Current year data fig.add_trace(go.Scatter( x=current_year_data['Quarter'], y=current_year_data[selected_metric], mode='lines+markers', name=f'{selected_year}', line=dict(color=colors['primary'], width=3), marker=dict(size=10, color=colors['primary']), hovertemplate=f'{selected_metric}: %{{y:,.0f}}<br>Quarter: %{{x}}<extra></extra>' )) # Previous year data for comparison if not previous_year_data.empty: fig.add_trace(go.Scatter( x=previous_year_data['Quarter'], y=previous_year_data[selected_metric], mode='lines+markers', name=f'{selected_year - 1}', line=dict(color=colors['secondary'], width=2, dash='dash'), marker=dict(size=8, color=colors['secondary']), hovertemplate=f'{selected_metric}: %{{y:,.0f}}<br>Quarter: %{{x}}<extra></extra>' )) # Calculate YoY growth for annotation if not previous_year_data.empty: current_total = current_year_data[selected_metric].sum() previous_total = previous_year_data[selected_metric].sum() growth = ((current_total - previous_total) / previous_total) * 100 fig.add_annotation( text=f'YoY Growth: {growth:.1f}%', xref='paper', yref='paper', x=0.02, y=0.98, showarrow=False, font=dict(size=14, color=colors['accent']), bgcolor='rgba(255,255,255,0.8)', bordercolor=colors['accent'], borderwidth=2 ) fig.update_layout( template='plotly_white', paper_bgcolor=colors['background'], plot_bgcolor=colors['background'], font=dict(color=colors['text'], family='Segoe UI, Roboto'), xaxis=dict(gridcolor=colors['grid'], title='Quarter'), yaxis=dict(gridcolor=colors['grid'], title=selected_metric), hovermode='x unified', legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1), margin=dict(l=60, r=30, t=50, b=50) ) return fig @callback( Output('annual-bar-chart', 'figure'), [Input('metric-filter', 'value')] ) def update_annual_bar_chart(selected_metric): """Update annual bar chart comparing revenue and expenses""" fig = go.Figure() # Revenue bars fig.add_trace(go.Bar( x=annual_df['Year'], y=annual_df['Revenue'], name='Revenue', marker_color=colors['primary'], hovertemplate='Revenue: %{y:,.0f}<extra></extra>' )) # Expenses bars fig.add_trace(go.Bar( x=annual_df['Year'], y=annual_df['Expenses'], name='Expenses', marker_color=colors['accent'], hovertemplate='Expenses: %{y:,.0f}<extra></extra>' )) # Add profit line overlay fig.add_trace(go.Scatter( x=annual_df['Year'], y=annual_df['Profit'], name='Profit', mode='lines+markers', line=dict(color=colors['secondary'], width=3), marker=dict(size=10), yaxis='y2', hovertemplate='Profit: %{y:,.0f}<extra></extra>' )) fig.update_layout( template='plotly_white', paper_bgcolor=colors['background'], plot_bgcolor=colors['background'], font=dict(color=colors['text'], family='Segoe UI, Roboto'), barmode='group', xaxis=dict(gridcolor=colors['grid'], title='Year'), yaxis=dict(gridcolor=colors['grid'], title='Amount ($)'), yaxis2=dict( title='Profit ($)', overlaying='y', side='right', showgrid=False ), hovermode='x unified', legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1), margin=dict(l=60, r=60, t=50, b=50) ) return fig @callback( Output('profit-margin-chart', 'figure'), [Input('year-filter', 'value')] ) def update_profit_margin_chart(selected_year): """Update profit margin evolution chart""" # Filter data up to selected year filtered_df = annual_df[annual_df['Year'] <= selected_year] fig = go.Figure() fig.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Profit_Margin'], mode='lines+markers', name='Profit Margin', line=dict(color=colors['primary'], width=3), marker=dict(size=12, color=colors['primary']), fill='tozeroy', fillcolor='rgba(52, 152, 219, 0.2)', hovertemplate='Year: %{x}<br>Profit Margin: %{y:.1f}%<extra></extra>' )) # Add average line avg_margin = filtered_df['Profit_Margin'].mean() fig.add_hline( y=avg_margin, line_dash="dash", line_color=colors['accent'], annotation_text=f'Average: {avg_margin:.1f}%', annotation_position="top right" ) fig.update_layout( template='plotly_white', paper_bgcolor=colors['background'], plot_bgcolor=colors['background'], font=dict(color=colors['text'], family='Segoe UI, Roboto'), xaxis=dict(gridcolor=colors['grid'], title='Year', dtick=1), yaxis=dict(gridcolor=colors['grid'], title='Profit Margin (%)', ticksuffix='%'), hovermode='x', showlegend=False, margin=dict(l=60, r=30, t=50, b=50) ) return fig @callback( Output('customer-growth-chart', 'figure'), [Input('year-filter', 'value')] ) def update_customer_growth_chart(selected_year): """Update customer growth area chart""" # Filter data up to selected year filtered_df = annual_df[annual_df['Year'] <= selected_year] fig = go.Figure() fig.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Customers'], mode='lines+markers', name='Customers', line=dict(color=colors['secondary'], width=3), marker=dict(size=12, color=colors['secondary']), fill='tozeroy', fillcolor='rgba(46, 204, 113, 0.2)', hovertemplate='Year: %{x}<br>Customers: %{y:,.0f}<extra></extra>' )) # Add growth rate as secondary axis growth_rate = filtered_df['Customers'].pct_change() * 100 fig.add_trace(go.Scatter( x=filtered_df['Year'], y=growth_rate, name='Growth Rate', mode='lines', line=dict(color=colors['accent'], width=2, dash='dot'), yaxis='y2', hovertemplate='Year: %{x}<br>Growth: %{y:.1f}%<extra></extra>' )) fig.update_layout( template='plotly_white', paper_bgcolor=colors['background'], plot_bgcolor=colors['background'], font=dict(color=colors['text'], family='Segoe UI, Roboto'), xaxis=dict(gridcolor=colors['grid'], title='Year', dtick=1), yaxis=dict(gridcolor=colors['grid'], title='Number of Customers'), yaxis2=dict( title='Growth Rate (%)', overlaying='y', side='right', showgrid=False, ticksuffix='%' ), hovermode='x unified', legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1), margin=dict(l=60, r=60, t=50, b=50) ) return fig @callback( Output('market-share-chart', 'figure'), [Input('year-filter', 'value')] ) def update_market_share_chart(selected_year): """Update market share pie chart""" # Get data for selected year year_data = annual_df[annual_df['Year'] == selected_year] if year_data.empty: return go.Figure() market_share = year_data['MarketShare'].iloc[0] competitors_share = 100 - market_share fig = go.Figure(data=[go.Pie( labels=['Our Company', 'Competitors'], values=[market_share, competitors_share], hole=0.4, marker=dict(colors=[colors['primary'], '#BDC3C7']), textinfo='label+percent', hovertemplate='%{label}: %{value:.1f}%<extra></extra>' )]) fig.update_layout( template='plotly_white', paper_bgcolor=colors['background'], plot_bgcolor=colors['background'], font=dict(color=colors['text'], family='Segoe UI, Roboto'), annotations=[dict( text=f'{selected_year}', x=0.5, y=0.5, font_size=20, showarrow=False )], showlegend=True, legend=dict(orientation='h', yanchor='bottom', y=-0.2), margin=dict(l=30, r=30, t=50, b=50) ) return fig # ============================================================================== # MAIN EXECUTION # ============================================================================== if __name__ == '__main__': # For Replit compatibility app.run(host='0.0.0.0', port=8080, debug=False)