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| import streamlit as st | |
| import pandas as pd | |
| def render_visualization(results, key_prefix): | |
| """Render data tables and visualizations from SQL results.""" | |
| if not results: | |
| return | |
| # Convert to DataFrame | |
| df = pd.DataFrame(results) | |
| with st.expander("📊 Results & Visualization", expanded=False): | |
| tab_data, tab_summary, tab_viz = st.tabs(["📄 Data", "🧮 Summary", "📈 Visualize"]) | |
| with tab_data: | |
| col_search, col_info = st.columns([3, 1]) | |
| with col_search: | |
| search_term = st.text_input( | |
| "🔍 Filter Data", | |
| placeholder="Type to search...", | |
| label_visibility="collapsed", | |
| key=f"{key_prefix}_search" | |
| ) | |
| if search_term: | |
| # Filter dataframe (case-insensitive) across all columns | |
| mask = df.astype(str).apply( | |
| lambda x: x.str.contains(search_term, case=False, na=False) | |
| ).any(axis=1) | |
| filtered_df = df[mask] | |
| with col_info: | |
| st.caption(f"Showing {len(filtered_df)} / {len(df)} rows") | |
| st.dataframe(filtered_df, width="stretch") | |
| else: | |
| with col_info: | |
| st.caption(f"Total rows: {len(df)}") | |
| st.dataframe(df, width="stretch") | |
| with tab_summary: | |
| numeric_cols = df.select_dtypes(include=['number']).columns.tolist() | |
| if not numeric_cols: | |
| st.info("No numeric columns found for summary statistics.") | |
| else: | |
| st.markdown("### 📊 Quick Statistics") | |
| # Create metrics for each numeric column | |
| for col in numeric_cols: | |
| st.markdown(f"**{col}**") | |
| c1, c2, c3, c4 = st.columns(4) | |
| series = df[col] | |
| with c1: | |
| st.metric("Total", f"{series.sum():,.2f}") | |
| with c2: | |
| st.metric("Average", f"{series.mean():,.2f}") | |
| with c3: | |
| st.metric("Min", f"{series.min():,.2f}") | |
| with c4: | |
| st.metric("Max", f"{series.max():,.2f}") | |
| st.divider() | |
| with tab_viz: | |
| numeric_cols = df.select_dtypes(include=['number']).columns.tolist() | |
| categorical_cols = df.select_dtypes(exclude=['number']).columns.tolist() | |
| if not numeric_cols: | |
| st.info("No numeric data found to visualize.") | |
| else: | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| chart_type = st.selectbox( | |
| "Chart Type", | |
| ["Bar", "Line", "Area", "Scatter"], | |
| key=f"{key_prefix}_chart_type" | |
| ) | |
| with col2: | |
| # Default X axis logic | |
| x_options = df.columns.tolist() | |
| # Try to find a good categorical column for X axis | |
| default_x = categorical_cols[0] if categorical_cols else x_options[0] | |
| # Find index safely | |
| try: | |
| def_index = x_options.index(default_x) | |
| except ValueError: | |
| def_index = 0 | |
| x_axis = st.selectbox( | |
| "X Axis", | |
| x_options, | |
| index=def_index, | |
| key=f"{key_prefix}_x_axis" | |
| ) | |
| with col3: | |
| y_axis = st.selectbox( | |
| "Y Axis", | |
| numeric_cols, | |
| index=0, | |
| key=f"{key_prefix}_y_axis" | |
| ) | |
| if chart_type == "Bar": | |
| st.bar_chart(df, x=x_axis, y=y_axis) | |
| elif chart_type == "Line": | |
| st.line_chart(df, x=x_axis, y=y_axis) | |
| elif chart_type == "Area": | |
| st.area_chart(df, x=x_axis, y=y_axis) | |
| elif chart_type == "Scatter": | |
| st.scatter_chart(df, x=x_axis, y=y_axis) | |