Code Editor for app.py

import streamlit as st
import pandas as pd
import plotly.express as px

# Set page config
st.set_page_config(page_title="EU Economic Comparison", layout="wide")

# Title and description
st.title("European Economic Indicators Comparison")
st.write("Compare key economic indicators across European countries")

# Load the data
@st.cache_data
def load_data():
    # World Bank data for EU countries (2021)
    data = {
        'Country': [
            'Germany', 'France', 'Italy', 'Spain', 'Netherlands', 
            'Poland', 'Sweden', 'Belgium', 'Austria', 'Ireland',
            'Denmark', 'Finland', 'Greece', 'Portugal', 'Czech Republic'
        ],
        'GDP_Billions': [
            4223.12, 2937.47, 2099.88, 1425.28, 1012.84,
            674.05, 627.44, 594.10, 477.08, 498.56,
            398.30, 297.30, 214.87, 253.66, 281.78
        ],
        'GDP_Per_Capita': [
            50788, 45028, 35551, 30103, 58061,
            17840, 60239, 51096, 53637, 99239,
            68007, 53654, 20192, 24711, 26378
        ],
        'Unemployment_Rate': [
            3.5, 7.9, 9.5, 14.8, 4.2,
            3.4, 8.8, 6.3, 6.0, 6.2,
            5.1, 7.7, 14.8, 6.9, 2.8
        ],
        'Inflation_Rate': [
            3.2, 2.1, 1.9, 3.0, 2.7,
            5.1, 2.2, 3.2, 2.8, 2.4,
            1.9, 2.2, 1.2, 1.3, 3.8
        ]
    }
    return pd.DataFrame(data)

df = load_data()

# Sidebar controls
st.sidebar.header("Select Indicators")
indicator = st.sidebar.selectbox(
    "Choose Economic Indicator",
    ["GDP_Billions", "GDP_Per_Capita", "Unemployment_Rate", "Inflation_Rate"],
    format_func=lambda x: x.replace("_", " ")
)

selected_countries = st.sidebar.multiselect(
    "Select Countries to Compare",
    df['Country'].tolist(),
    default=df['Country'].head(5).tolist()
)

# Main content
if not selected_countries:
    st.warning("Please select at least one country from the sidebar.")
else:
    # Filter data based on selection
    filtered_df = df[df['Country'].isin(selected_countries)]
    
    # Create visualization
    fig = px.bar(
        filtered_df,
        x='Country',
        y=indicator,
        title=f"{indicator.replace('_', ' ')} by Country",
        color='Country'
    )
    fig.update_layout(
        xaxis_title="Country",
        yaxis_title=indicator.replace('_', ' '),
        showlegend=False
    )
    st.plotly_chart(fig, use_container_width=True)
    
    # Display data table
    st.subheader("Detailed Data")
    st.dataframe(
        filtered_df.style.highlight_max(axis=0, props='background-color: #90EE90'),
        hide_index=True
    )
    
    # Summary statistics
    st.subheader("Summary Statistics")
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric(
            "Highest Value",
            f"{filtered_df[indicator].max():,.2f}",
            f"Country: {filtered_df.loc[filtered_df[indicator].idxmax(), 'Country']}"
        )
    
    with col2:
        st.metric(
            "Lowest Value",
            f"{filtered_df[indicator].min():,.2f}",
            f"Country: {filtered_df.loc[filtered_df[indicator].idxmin(), 'Country']}"
        )
    
    with col3:
        st.metric(
            "Average",
            f"{filtered_df[indicator].mean():,.2f}"
        )

# Footer
st.markdown("---")
st.markdown(
    """
    Data source: Sample data based on World Bank indicators (2021)  
    Note: This is simplified demo data for illustration purposes.
    """
)