# Automated Report Generator A Python-based automated report generation system that reads data from files, performs analysis, and generates formatted PDF reports. ## Project Overview This project creates professional PDF reports from CSV/Excel data files with: - Data analysis and statistics - Visual charts and graphs - Formatted tables and summaries - Professional styling and layout ## Features - **Multi-format Support**: Reads CSV and Excel files - **Automated Analysis**: Statistical summaries, distributions, trends - **Visualizations**: Charts, graphs, and plots using matplotlib - **Professional PDFs**: Clean, formatted reports using ReportLab - **Customizable**: Easy to modify styles and content ## Requirements - Python 3.7+ - pandas - matplotlib - reportlab - openpyxl (for Excel support) ## Installation 1. **Clone or download the project files** 2. **Install dependencies:** ```bash pip install -r requirements.txt ``` 3. **Verify installation:** ```bash python -c "import pandas, matplotlib, reportlab; print('All packages installed successfully!')" ``` ## Usage ### Basic Usage ```bash python report_generator.py ``` This will: - Read data from `sample_data.csv` - Analyze the data - Generate a timestamped PDF report ### Using with Your Own Data 1. **Replace the sample data file** with your CSV/Excel file 2. **Update the file path** in the script: ```python data_file = "your_data_file.csv" # or .xlsx ``` 3. **Run the generator:** ```bash python report_generator.py ``` ### Custom Report Generation ```python from report_generator import ReportGenerator # Create generator instance generator = ReportGenerator("your_data.csv", "custom_report.pdf") # Generate report generator.generate_report() ``` ## File Structure ``` AUTOMATED REPORT GENERATOR/ │ ├── report_generator.py # Main report generation script ├── simple_example.py # Simple usage example ├── sample_data.csv # Sample dataset ├── requirements.txt # Python dependencies └── README.md # This file ``` ## Sample Data Format The included sample data (`sample_data.csv`) contains sales data with columns: - Date: Transaction dates - Product: Product categories - Sales: Number of units sold - Revenue: Sales revenue - Region: Geographic regions - Sales_Rep: Sales representative names ## Generated Report Sections 1. **Title Page**: Report title and metadata 2. **Executive Summary**: Overview and key insights 3. **Data Overview**: Column descriptions and data types 4. **Statistical Summary**: Mean, median, standard deviation, etc. 5. **Categorical Analysis**: Frequency distributions and percentages 6. **Data Visualizations**: Charts and graphs 7. **Sample Data**: Preview of the raw data ## Customization ### Adding New Chart Types ```python def create_custom_chart(self): plt.figure(figsize=(10, 6)) # Your chart code here plt.title('Custom Chart Title') chart_buffer = io.BytesIO() plt.savefig(chart_buffer, format='png', dpi=300, bbox_inches='tight') chart_buffer.seek(0) return chart_buffer ``` ### Modifying Report Styles ```python # Custom title style self.custom_style = ParagraphStyle( 'CustomStyle', fontSize=18, textColor=colors.blue, alignment=TA_CENTER ) ``` ### Adding New Analysis Functions ```python def custom_analysis(self): # Your analysis code here return analysis_results ``` ## Troubleshooting ### Common Issues 1. **Module Import Errors** ```bash pip install --upgrade pandas matplotlib reportlab openpyxl ``` 2. **File Not Found Errors** - Check file path and name - Ensure data file is in the correct directory 3. **Memory Issues with Large Files** - Process data in chunks - Reduce chart resolution - Limit the number of visualizations ### Performance Tips - Use smaller datasets for testing - Optimize chart generation for large datasets - Consider data sampling for very large files ## Example Output The generated PDF report includes: - Professional formatting and styling - Statistical analysis tables - Distribution charts and graphs - Time series visualizations (if date data present) - Sample data preview ## Extension Ideas 1. **Multiple File Processing**: Batch process multiple data files 2. **Web Interface**: Create a web-based interface 3. **Email Integration**: Automatically email reports 4. **Scheduled Reports**: Set up automated report generation 5. **Interactive Charts**: Add interactive visualizations 6. **Template System**: Create multiple report templates ## Contributing Feel free to extend this project by: - Adding new visualization types - Improving the analysis algorithms - Creating new report templates - Adding support for other data formats ## License This project is created for educational purposes as part of the CodTech internship program. ## Contact For questions or improvements, please refer to the project documentation or contact the development team. --- **Note**: This is a complete automated report generation system that demonstrates data reading, analysis, and PDF report creation using Python. # AUTOMATED-REPORT-GENERATOR