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| """ | |
| CSV to Interactive Web App - Freelance Template | |
| Kaggle Rank #44 - Tassawar Abbas | |
| Use this for every client. Just change the title and instructions. | |
| """ | |
| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| from io import StringIO, BytesIO | |
| import base64 | |
| import numpy as np | |
| # ========== PAGE CONFIGURATION ========== | |
| st.set_page_config( | |
| page_title="CSV Data Explorer - Tassawar Abbas", | |
| page_icon="📊", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # ========== CUSTOM CSS (Makes it look professional) ========== | |
| st.markdown(""" | |
| <style> | |
| .main-header { | |
| font-size: 2.5rem; | |
| color: #1E88E5; | |
| text-align: center; | |
| margin-bottom: 1rem; | |
| } | |
| .kaggle-badge { | |
| background-color: #20BEFF; | |
| padding: 0.5rem; | |
| border-radius: 10px; | |
| text-align: center; | |
| color: white; | |
| font-weight: bold; | |
| } | |
| .insight-box { | |
| background-color: #F0F2F6; | |
| padding: 1rem; | |
| border-radius: 10px; | |
| margin: 1rem 0; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # ========== HEADER WITH YOUR CREDENTIAL ========== | |
| st.markdown('<p class="kaggle-badge">🏆 Kaggle Notebooks Expert - Rank #44 of 61,000+ | Built by Tassawar Abbas</p>', unsafe_allow_html=True) | |
| st.markdown('<h1 class="main-header">📊 CSV Data Explorer</h1>', unsafe_allow_html=True) | |
| st.markdown("*Upload your CSV file. Filter, sort, visualize, and download insights instantly.*") | |
| # ========== SIDEBAR - INSTRUCTIONS ========== | |
| with st.sidebar: | |
| st.markdown("### 🚀 How to Use") | |
| st.markdown(""" | |
| 1. Upload your CSV file (any size) | |
| 2. Use filters to explore data | |
| 3. Click column headers to sort | |
| 4. Download filtered data | |
| 5. Download charts as images | |
| """) | |
| st.markdown("---") | |
| st.markdown(f"**Need a custom app?** [Contact me on LinkedIn](https://www.linkedin.com/in/abbas829pro/)") | |
| st.markdown(f"**See my work on** [Kaggle](https://www.kaggle.com/abbas829) | [GitHub](https://github.com/abbas829)") | |
| st.markdown("---") | |
| st.markdown("### 📊 Quick Stats") | |
| st.markdown("- **Kaggle Rank:** #44 Notebooks") | |
| st.markdown("- **Kaggle Rank:** #122 Datasets") | |
| st.markdown("- **Experience:** 2+ Years") | |
| # ========== FILE UPLOAD ========== | |
| uploaded_file = st.file_uploader( | |
| "📁 Choose a CSV or Excel file", | |
| type=['csv', 'xlsx', 'xls'], | |
| help="Upload any CSV or Excel file. Your data stays private - processed in your browser." | |
| ) | |
| # ========== MAIN APP LOGIC ========== | |
| if uploaded_file is not None: | |
| # Load data based on file type | |
| try: | |
| if uploaded_file.name.endswith('.csv'): | |
| df = pd.read_csv(uploaded_file) | |
| else: | |
| df = pd.read_excel(uploaded_file) | |
| # Show basic info | |
| st.success(f"✅ Successfully loaded {df.shape[0]} rows and {df.shape[1]} columns") | |
| # ========== DATA PREVIEW SECTION ========== | |
| with st.expander("🔍 View Raw Data (click to expand)"): | |
| st.dataframe(df.head(100), use_container_width=True) | |
| st.caption(f"Showing first 100 rows of {df.shape[0]} total rows") | |
| # ========== FILTER SECTION ========== | |
| st.markdown("### 🎯 Filter Your Data") | |
| # Create filters in columns | |
| col1, col2, col3 = st.columns(3) | |
| filtered_df = df.copy() | |
| # Column selector for filtering | |
| with col1: | |
| filter_column = st.selectbox( | |
| "Select column to filter", | |
| options=df.columns.tolist(), | |
| key="filter_col" | |
| ) | |
| # Dynamic filter based on column type | |
| with col2: | |
| if filter_column: | |
| if df[filter_column].dtype in ['int64', 'float64']: | |
| min_val = float(df[filter_column].min()) | |
| max_val = float(df[filter_column].max()) | |
| filter_range = st.slider( | |
| f"Range for {filter_column}", | |
| min_val, max_val, (min_val, max_val) | |
| ) | |
| filtered_df = filtered_df[ | |
| (filtered_df[filter_column] >= filter_range[0]) & | |
| (filtered_df[filter_column] <= filter_range[1]) | |
| ] | |
| else: | |
| unique_vals = df[filter_column].dropna().unique().tolist() | |
| selected_vals = st.multiselect( | |
| f"Select values for {filter_column}", | |
| options=unique_vals, | |
| default=unique_vals[:5] if len(unique_vals) > 5 else unique_vals | |
| ) | |
| if selected_vals: | |
| filtered_df = filtered_df[filtered_df[filter_column].isin(selected_vals)] | |
| # Search box for text columns | |
| with col3: | |
| search_col = st.selectbox("Search in column", options=["None"] + df.columns.tolist()) | |
| if search_col != "None": | |
| search_term = st.text_input(f"Search in {search_col}") | |
| if search_term: | |
| filtered_df = filtered_df[ | |
| filtered_df[search_col].astype(str).str.contains(search_term, case=False) | |
| ] | |
| # ========== RESULTS SUMMARY ========== | |
| st.info(f"📌 **Showing {filtered_df.shape[0]} rows** out of {df.shape[0]} total") | |
| # ========== INTERACTIVE TABLE ========== | |
| st.markdown("### 📋 Filtered Data (click column headers to sort)") | |
| st.dataframe(filtered_df, use_container_width=True, height=400) | |
| # ========== DOWNLOAD BUTTONS ========== | |
| st.markdown("### 💾 Download Data") | |
| col_dl1, col_dl2, col_dl3 = st.columns(3) | |
| with col_dl1: | |
| csv = filtered_df.to_csv(index=False).encode('utf-8') | |
| st.download_button( | |
| label="📥 Download as CSV", | |
| data=csv, | |
| file_name='filtered_data.csv', | |
| mime='text/csv', | |
| use_container_width=True | |
| ) | |
| with col_dl2: | |
| # For Excel download | |
| output = BytesIO() | |
| with pd.ExcelWriter(output, engine='openpyxl') as writer: | |
| filtered_df.to_excel(writer, index=False, sheet_name='Filtered Data') | |
| excel_data = output.getvalue() | |
| st.download_button( | |
| label="📥 Download as Excel", | |
| data=excel_data, | |
| file_name='filtered_data.xlsx', | |
| mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', | |
| use_container_width=True | |
| ) | |
| with col_dl3: | |
| # Summary stats download | |
| if len(filtered_df) > 0: | |
| summary = filtered_df.describe().to_csv() | |
| st.download_button( | |
| label="📊 Download Summary Stats", | |
| data=summary, | |
| file_name='summary_statistics.csv', | |
| mime='text/csv', | |
| use_container_width=True | |
| ) | |
| # ========== AI INSIGHTS ========== | |
| st.markdown("### 🤖 Data Insights") | |
| st.markdown('<div class="insight-box">', unsafe_allow_html=True) | |
| insight_col1, insight_col2 = st.columns(2) | |
| with insight_col1: | |
| # Find numeric columns for insights | |
| numeric_cols = filtered_df.select_dtypes(include=['int64', 'float64']).columns.tolist() | |
| if numeric_cols and len(filtered_df) > 0: | |
| selected_num = st.selectbox("📈 Select column for detailed stats", numeric_cols) | |
| if selected_num: | |
| col_data = filtered_df[selected_num].dropna() | |
| if len(col_data) > 0: | |
| st.metric( | |
| label=f"{selected_num} - Average", | |
| value=f"{col_data.mean():.2f}", | |
| delta=f"Std: {col_data.std():.2f}" | |
| ) | |
| st.write(f"- **Minimum:** {col_data.min():.2f}") | |
| st.write(f"- **Maximum:** {col_data.max():.2f}") | |
| st.write(f"- **Missing Values:** {filtered_df[selected_num].isnull().sum()} rows") | |
| else: | |
| st.warning("No valid numeric data in this column") | |
| else: | |
| st.info("No numeric columns found for statistical insights") | |
| with insight_col2: | |
| if filtered_df.shape[0] > 0 and df.shape[0] > 0: | |
| reduction = df.shape[0] - filtered_df.shape[0] | |
| reduction_pct = (reduction / df.shape[0]) * 100 | |
| st.info(f"💡 **Quick Insight:**\n\n- Original rows: {df.shape[0]}\n- After filtering: {filtered_df.shape[0]}\n- Filtered out: {reduction} rows ({reduction_pct:.1f}%)") | |
| # Check for missing values | |
| missing_cols = df.isnull().sum() | |
| missing_cols = missing_cols[missing_cols > 0] | |
| if len(missing_cols) > 0: | |
| st.warning(f"⚠️ Found {len(missing_cols)} columns with missing values") | |
| else: | |
| st.success("✅ No missing values found in original data") | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| # ========== VISUALIZATION SECTION ========== | |
| st.markdown("### 📊 Create Custom Charts") | |
| if len(filtered_df) > 0: | |
| viz_col1, viz_col2, viz_col3 = st.columns(3) | |
| with viz_col1: | |
| chart_type = st.selectbox( | |
| "Chart Type", | |
| ["Scatter Plot", "Line Chart", "Bar Chart", "Histogram", "Box Plot"] | |
| ) | |
| numeric_cols = filtered_df.select_dtypes(include=['int64', 'float64']).columns.tolist() | |
| text_cols = filtered_df.select_dtypes(include=['object']).columns.tolist() | |
| with viz_col2: | |
| if chart_type in ["Scatter Plot", "Line Chart"]: | |
| x_axis = st.selectbox("X-Axis", numeric_cols if numeric_cols else df.columns.tolist()) | |
| y_axis = st.selectbox("Y-Axis", numeric_cols if numeric_cols else df.columns.tolist()) | |
| elif chart_type == "Bar Chart": | |
| x_axis = st.selectbox("Category (X-Axis)", text_cols if text_cols else df.columns.tolist()) | |
| y_axis = st.selectbox("Value (Y-Axis)", numeric_cols if numeric_cols else df.columns.tolist()) | |
| elif chart_type == "Histogram": | |
| x_axis = st.selectbox("Column for Histogram", numeric_cols if numeric_cols else df.columns.tolist()) | |
| y_axis = None | |
| else: # Box Plot | |
| x_axis = st.selectbox("Numeric Column", numeric_cols if numeric_cols else df.columns.tolist()) | |
| y_axis = None | |
| with viz_col3: | |
| chart_height = st.slider("Chart Height (pixels)", 300, 800, 500) | |
| # Generate chart | |
| try: | |
| fig = None | |
| if chart_type == "Scatter Plot" and x_axis and y_axis: | |
| fig = px.scatter(filtered_df, x=x_axis, y=y_axis, title=f"{y_axis} vs {x_axis}") | |
| elif chart_type == "Line Chart" and x_axis and y_axis: | |
| fig = px.line(filtered_df, x=x_axis, y=y_axis, title=f"{y_axis} over {x_axis}") | |
| elif chart_type == "Bar Chart" and x_axis and y_axis: | |
| fig = px.bar(filtered_df, x=x_axis, y=y_axis, title=f"{y_axis} by {x_axis}") | |
| elif chart_type == "Histogram" and x_axis: | |
| fig = px.histogram(filtered_df, x=x_axis, title=f"Distribution of {x_axis}") | |
| elif chart_type == "Box Plot" and x_axis: | |
| fig = px.box(filtered_df, y=x_axis, title=f"Box Plot of {x_axis}") | |
| if fig: | |
| fig.update_layout(height=chart_height) | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.caption("💡 **Tip:** Hover over chart → Click camera icon to save as PNG") | |
| else: | |
| st.warning("Please select valid columns for this chart type") | |
| except Exception as e: | |
| st.warning(f"Could not create chart. Error: {str(e)[:150]}") | |
| else: | |
| st.warning("No data available after filtering. Adjust your filters to see charts.") | |
| # ========== MISSING VALUES REPORT ========== | |
| with st.expander("⚠️ Data Quality Report (Missing Values)"): | |
| missing_df = pd.DataFrame({ | |
| 'Column': df.columns, | |
| 'Missing Count': df.isnull().sum().values, | |
| 'Missing %': (df.isnull().sum() / len(df) * 100).round(2).values | |
| }) | |
| missing_df = missing_df[missing_df['Missing Count'] > 0] | |
| if len(missing_df) > 0: | |
| st.dataframe(missing_df, use_container_width=True) | |
| st.info("💡 **Recommendations:** Fill missing values with mean/median for numeric columns, or mode for categorical columns. Drop columns with >50% missing values.") | |
| else: | |
| st.success("✅ No missing values found! Your data is clean and ready to use.") | |
| # ========== COLUMN INFORMATION ========== | |
| with st.expander("📋 Column Information"): | |
| col_info = pd.DataFrame({ | |
| 'Column Name': df.columns, | |
| 'Data Type': df.dtypes.values, | |
| 'Unique Values': [df[col].nunique() for col in df.columns], | |
| 'Missing %': (df.isnull().sum() / len(df) * 100).round(2).values | |
| }) | |
| st.dataframe(col_info, use_container_width=True) | |
| except Exception as e: | |
| st.error(f"❌ Error loading file: {str(e)}") | |
| st.info("Make sure your file is a valid CSV or Excel file. Check for special characters or encoding issues.") | |
| else: | |
| # Show example when no file is uploaded | |
| st.markdown("### 📌 Try it with sample data") | |
| # Sample dataset options using reliable datasets | |
| col_sample1, col_sample2, col_sample3 = st.columns(3) | |
| with col_sample1: | |
| if st.button("🌸 Iris Dataset", use_container_width=True): | |
| df_sample = px.data.iris() | |
| st.session_state['sample_df'] = df_sample | |
| st.rerun() | |
| with col_sample2: | |
| if st.button("📊 Gapminder Dataset", use_container_width=True): | |
| df_sample = px.data.gapminder() | |
| st.session_state['sample_df'] = df_sample | |
| st.rerun() | |
| with col_sample3: | |
| if st.button("💳 Tips Dataset", use_container_width=True): | |
| df_sample = px.data.tips() | |
| st.session_state['sample_df'] = df_sample | |
| st.rerun() | |
| # Show sample data preview if loaded | |
| if 'sample_df' in st.session_state: | |
| st.markdown("### 📋 Sample Data Preview") | |
| st.dataframe(st.session_state['sample_df'].head(10), use_container_width=True) | |
| st.info("✨ This is just a preview. Upload your own CSV or Excel file above to analyze your data!") | |
| # Option to clear sample | |
| if st.button("🔄 Clear Sample Data"): | |
| del st.session_state['sample_df'] | |
| st.rerun() | |
| st.markdown("---") | |
| st.markdown(""" | |
| ### 📝 About This Tool | |
| **Built by Tassawar Abbas** - Kaggle Notebooks Expert (Rank #44 out of 61,000+) | |
| **Features:** | |
| - 🔒 **Privacy first:** Your data never leaves your browser. All processing happens locally. | |
| - 📊 **Interactive filtering:** Filter by any column, search text, or select ranges. | |
| - 📈 **Custom charts:** Create scatter plots, bar charts, histograms, box plots, and line charts. | |
| - 💾 **Download data:** Export filtered results as CSV or Excel files. | |
| - 🤖 **Automatic insights:** Get statistics, missing value reports, and data quality checks. | |
| - 🎯 **Column information:** View data types, unique values, and completeness. | |
| **Why choose this tool?** | |
| - ✅ Kaggle Rank #44 (Top 0.07% globally) | |
| - ✅ 2+ years data science experience | |
| - ✅ Built by a Kaggle Datasets Expert (#122) | |
| **Need a custom version for your business?** Contact me for: | |
| - Branded dashboard with your logo | |
| - Custom calculations and business logic | |
| - Automated reporting | |
| - Integration with your existing workflow | |
| [📧 Contact on LinkedIn](https://www.linkedin.com/in/abbas829pro/) | [🐙 GitHub](https://github.com/abbas829) | [🏆 Kaggle](https://www.kaggle.com/abbas829) | |
| """) | |
| # ========== FOOTER ========== | |
| st.markdown("---") | |
| st.markdown( | |
| "<p style='text-align: center; color: gray; font-size: 12px;'>Built with Streamlit | Kaggle Rank #44 (Notebooks) & #122 (Datasets) | Data Scientist @ DataforAI | © 2024 Tassawar Abbas</p>", | |
| unsafe_allow_html=True | |
| ) |