📊 Automated Report Generator

A complete system for automatically generating Excel reports with advanced analysis, graphs, and professional formatting. Transforms raw data into presentation-ready executive reports.

🚀 Features

• 📈 Automatic Report Generation: Creates complete Excel reports automatically
• 🎨 Professional Formatting: Applies styles, colors, and automatic formatting
• 📊 Integrated Charts: Automatically generates bar and line charts
• 🔍 Advanced Analytics: Calculates KPIs, trends, and statistics
• 📋 Multiple Worksheets: Organizes data into themed worksheets
• 💼 Executive Summary: Creates a summary spreadsheet with key insights
• 🎯 Top Performers: Identify top sellers, products, and regions
• 📅 Time-Lapse Analysis: Tracks trends over time
• 🔄 Simulated Data: Generates sample data for demonstration purposes

🛠️ Technologies Used

• Python 3.7+
• pandas: Data manipulation and analysis Data
• openpyxl: Creating and formatting Excel files
• numpy: Numerical and statistical calculations
• matplotlib: Basic visualizations
• seaborn: Advanced statistical graphics
• dataclasses: Typed data structures

📋 Prerequisites

Python 3.7 or higher
pip (Python package manager)

🔧 Installation

1. Clone the repository:

git clone https://github.com/your-user/automated-report-generator.git
cd automated-report-generator

2. Install the dependencies:

pip install -r requirements.txt

Or install manually:

pip install pandas openpyxl numpy matplotlib seaborn

🚀 How to Use

Basic Execution

python main.py

Usage Examples

1. Generate Sales Report

from report_generator import ExcelReportGenerator, ReportConfig, DataGenerator

# Configuration
config = ReportConfig(
title="Sales Report Q1",
output_path="relatorio_vendas_q1.xlsx",
company_name="My Company"
)

# Generate Data
sales_data = DataGenerator.generate_sales_data(90)

# Create Report
generator = ExcelReportGenerator(config)
generator.generate_complete_report({"Sales": sales_data})

2. Financial Report

# Generate Financial Data
financial_data = DataGenerator.generate_financial_data(12)

# Configure Report
config = ReportConfig(
title="Annual Financial Report",
output_path="relatorio_financeiro_2024.xlsx",
include_charts=True,
include_summary=True
)

# Generate Report
data_dict = {
"Financial Data": financial_data,
"Monthly Analysis": financial_data.groupby('mes').sum()
}

generator = ExcelReportGenerator(config)
generator.generate_complete_report(data_dict)

3. Custom Analysis

from report_generator import ReportAnalyzer

# Analyze Data
analyzer = ReportAnalyzer(sales_data)

# Summary Statistics
stats = analyzer.get_summary_stats()
print(f"Total records: {stats['total_records']}")

# Top performers
top_sellers = analyzer.get_top_performers('seller', 'net_value', 5)
top_products = analyzer.get_top_performers('product', 'net_value', 5)

# Trend analysis
trend = analyzer.get_trend_analysis('data', 'net_value')

⚙️ Configuration

ReportConfig Parameters

config = ReportConfig(
title="Report Title", # Main Title
output_path="path/file.xlsx", # Output Path
include_charts=True, # Include charts
include_summary=True, # Include executive summary
auto_format=True, # Automatic formatting
company_name="Company Name" # Company name
)

📁 Project Structure

automated-report-generator/
├── main.py # Main file
├── report_generator.py # Generator classes
├── requirements.txt # Dependencies
├── README.md # Documentation
├── reports/ # Generated reports
│ ├── sales_report.xlsx
│ ├── financial_report.xlsx
│ └── executive_report.xlsx
├── data/ # Input data
│ ├── sales_data.csv
│ └── financial_data.csv
└── templates/ # Report Templates
└── template_padrao.xlsx

📊 Generated Report Types

1. Sales Report

• Detailed Sales: All sales records
• Top Sellers: Ranking by performance
• Top Products: Best-selling products
• Analysis by Region: Geographic distribution
• Monthly Trends: Evolution over time

2. Financial Report

• Financial Data: Revenue, costs, and profits
• Margin Analysis: Gross and net margin
• Monthly Growth: Growth rate
• Cost Analysis: Distribution of Expenses
• Projections: Future Trends

3. Executive Summary

Main KPIs: Key metrics Overall Statistics: Data summary Insights: Points of attention Recommendations: Data-based suggestions

🎨 Automatic Formatting Applied Styles

Headings: Blue background, white text, bold Borders: Thin borders on all cells Alignment: Centered for headers Width: Auto-adjust based on content Colors: Professional color scheme

Automatic Charts

Bar Charts: For comparisons Line Charts: For time trends Positioning: Automatic next to data Styles: Predefined and professional

📈 Available Analyses Basic Statistics pythonsummary = analyzer.get_summary_stats()

Returns: total_records, date_range, numeric_summary

Top Performers pythontop_items = analyzer.get_top_performers('column', 'value', 10)

Returns: ranking ordered by value

Trend Analysis pythontrend = analyzer.get_trend_analysis('data', 'value')

Returns: data aggregated by period with growth rate

🔍 Example of Generated Data Sales Data python{ 'date': '2024-01-15', 'product': 'Product A', 'salesperson': 'John Smith', 'quantity': 5, 'unit_price': 299.99, 'net_value': 1274.95, 'region': 'South' } Financial Data python{ 'month': '2024-01', 'revenue': 150000.00, 'costs': 90000.00, 'net_profit': 45000.00, 'net_margin': 30.00 } 🎯 Use Cases For Businesses

Monthly Reports: Automation of recurring reports Performance Analysis: KPI tracking Presentations: Reports for executive meetings

For Analysts

Data Analysis: Quickly transform data into insights Visualizations: Automated professional charts Ad-hoc Reports: Custom analyses

For Developers

Integration: Easy integration with existing systems Customization: Modular and extensible code Automation: Programmatic report generation

🤝 Contributing

Fork the project Create a branch for your feature (git checkout -b feature/NewFunction) Commit your changes (git commit -m 'Add new feature') Push to the branch (git push origin feature/NewFunction) Open a Pull Request

📝 Upcoming Features

Web Dashboard: Web interface for configuration Multiple Formats: PDF, PowerPoint support Data Connectors: Database integration Custom Templates: Template system Scheduling: Scheduled automatic execution Notifications: Automatic email sending APIs: REST endpoints for integration Interactive Reports: Dynamic Dashboards

🐛 Troubleshooting Error: "ModuleNotFoundError" bashpip install -r requirements.txt Error: "Permission denied" bash# Make sure the Excel file is not open

Or change the output path

Error: "Invalid data format" bash# Make sure the data is in pandas DataFrame format

Use DataGenerator for generating sample data

📄 License This project is licensed under the MIT License. See the LICENSE file for more details. 👨‍💻 Author Your Name

GitHub: @Jbreccio LinkedIn: www.linkedin.com/in/josebreccio-dev-35b8292a4 Email: oibreccio@hotmail.com

🙏 Thanks

Python community for the excellent documentation Openpyxl library developers Pandas community for making data analysis easier Everyone who contributed feedback and suggestions

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