Why Real-Time Dashboards Matter
Real-time dashboards provide instant visibility into program performance, enabling timely decisions and interventions. This tutorial shows you how to build a live-updating M&E dashboard using Streamlit that automatically refreshes with new data.

# data_connector.py
import pandas as pd
import os
from datetime import datetime
import numpy as np
class DataConnector:
    """Simple data connector with auto-refresh."""
    
    def __init__(self, file_path='indicator_data.xlsx'):
        self.file_path = file_path
        self.last_refresh = None
        self.cached_data = None
    
    def load_data(self):
        """Load data with caching."""
        if not os.path.exists(self.file_path):
            self._create_sample_data()
        
        df = pd.read_excel(self.file_path)
        df = self._prepare_data(df)
        self.cached_data = df
        self.last_refresh = datetime.now()
        return df
    
    def _prepare_data(self, df):
        """Clean and prepare data."""
        if 'date' in df.columns:
            df['date'] = pd.to_datetime(df['date'])
        
        if 'target' in df.columns and 'actual' in df.columns:
            df['achievement'] = (df['actual'] / df['target'] * 100).round(1)
        
        if 'status' not in df.columns and 'achievement' in df.columns:
            df['status'] = pd.cut(
                df['achievement'], 
                bins=[0, 70, 90, 100], 
                labels=['off_track', 'at_risk', 'on_track']
            )
        
        return df
    
    def get_metrics(self, df):
        """Calculate key metrics."""
        return {
            'total': len(df),
            'avg_achievement': df['achievement'].mean() if 'achievement' in df else 0,
            'on_track': len(df[df['status'] == 'on_track']) if 'status' in df else 0,
            'at_risk': len(df[df['status'] == 'at_risk']) if 'status' in df else 0,
            'off_track': len(df[df['status'] == 'off_track']) if 'status' in df else 0,
            'last_refresh': datetime.now().strftime('%H:%M:%S')
        }
    
    def _create_sample_data(self):
        """Create sample data."""
        indicators = ['Vaccination', 'Education', 'Health', 'WASH', 'Nutrition']
        categories = ['Health', 'Education', 'Health', 'WASH', 'Nutrition']
        
        data = []
        for i, indicator in enumerate(indicators):
            target = np.random.randint(100, 500)
            achievement = np.random.uniform(55, 98)
            actual = int(target * achievement / 100)
            data.append({
                'indicator_name': indicator,
                'target': target,
                'actual': actual,
                'achievement': round(achievement, 1),
                'category': categories[i],
                'date': '2024-01-15'
            })
        
        df = pd.DataFrame(data)
        df.to_excel(self.file_path, index=False)

# dashboard.py
import streamlit as st
import pandas as pd
import plotly.express as px
from data_connector import DataConnector
st.set_page_config(page_title="M&E Dashboard", layout="wide")
# Initialize connector
@st.cache_resource
def get_connector():
    return DataConnector('indicator_data.xlsx')
connector = get_connector()
df = connector.load_data()
metrics = connector.get_metrics(df)
# Header
st.title("📊 M&E Performance Dashboard")
st.caption(f"Last updated: {metrics['last_refresh']}")
# Metrics Row
col1, col2, col3, col4, col5 = st.columns(5)
col1.metric("Total", metrics['total'])
col2.metric("On Track", metrics['on_track'], delta="✓")
col3.metric("At Risk", metrics['at_risk'], delta="⚠️")
col4.metric("Off Track", metrics['off_track'], delta="❌")
col5.metric("Avg Achievement", f"{metrics['avg_achievement']:.1f}%")
st.divider()
# Charts
col1, col2 = st.columns(2)
with col1:
    st.subheader("Status Distribution")
    status_counts = df['status'].value_counts()
    fig = px.pie(
        values=status_counts.values,
        names=status_counts.index,
        color=status_counts.index,
        color_discrete_map={
            'on_track': '#28a745',
            'at_risk': '#ffc107',
            'off_track': '#dc3545'
        }
    )
    st.plotly_chart(fig, use_container_width=True)
with col2:
    st.subheader("Category Performance")
    if 'category' in df.columns:
        cat_avg = df.groupby('category')['achievement'].mean().sort_values()
        fig = px.bar(
            x=cat_avg.values,
            y=cat_avg.index,
            orientation='h',
            color=cat_avg.values,
            color_continuous_scale='RdYlGn',
            labels={'x': 'Achievement (%)', 'y': ''}
        )
        st.plotly_chart(fig, use_container_width=True)
# Full width charts
st.subheader("Target vs Actual")
if 'target' in df.columns and 'actual' in df.columns:
    fig = px.bar(
        df,
        x='indicator_name',
        y=['target', 'actual'],
        barmode='group',
        labels={'value': 'Values', 'variable': ''}
    )
    st.plotly_chart(fig, use_container_width=True)
# Data table
with st.expander("View Data"):
    st.dataframe(df)
# Sidebar filters
st.sidebar.header("Filters")
status_filter = st.sidebar.multiselect(
    "Status",
    options=df['status'].unique(),
    default=df['status'].unique()
)
if 'category' in df.columns:
    cat_filter = st.sidebar.multiselect(
        "Category",
        options=df['category'].unique(),
        default=df['category'].unique()
    )
# Auto-refresh
if st.sidebar.button("Refresh Data"):
    st.cache_data.clear()
    st.rerun()
st.sidebar.markdown("---")
st.sidebar.caption("Data refreshes automatically on load")

# run.py
import subprocess
import os
def main():
    print("Starting M&E Dashboard...")
    print("Open http://localhost:8501 in your browser")
    subprocess.run(["streamlit", "run", "dashboard.py"])
if __name__ == "__main__":
    main()

Master Real-Time Dashboard Development

The AI Agents for Evaluators Certificate teaches you to build complete M&E solutions including real-time dashboards, automated reporting, and stakeholder management.

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