A powerful Python automation tool that transforms any data source into professional PowerPoint presentations and PDF reports with auto-generated visualizations.
Tech Stack: Python Pandas Matplotlib python-pptx ReportLab MySQL PostgreSQL REST APIs Google Sheets YAML Schedule
- 🔌 Multiple Data Sources — CSV, Excel, JSON, MySQL, PostgreSQL, REST APIs, Google Sheets
- 📈 Smart Visualizations — Auto-generates bar, line, and pie charts based on your data
- 📑 PowerPoint Generation — Professional presentations with branded slides
- 📄 PDF Export — Clean PDF reports with embedded charts
- 📧 Email Delivery — Automated email sending with attachments
- ⏰ Scheduling — Daily, weekly, or interval-based automation
- ⚙️ Fully Configurable — YAML-based settings with smart auto-detection
- PowerPoint: Professional slides with title, summary, and chart slides
- PDF: Executive summary with embedded visualizations
# Clone the repository
git clone https://github.com/SaadZafarMayo/python-report-automation.git
cd python-report-automation
# Install dependencies
pip install -r requirements.txt
# Run the report generator
python main.pypython-report-automation/
│
├── main.py # Entry point - run this to generate reports
├── run_scheduled.py # Run this for scheduled automation
├── config.yaml # Configuration file (customize here!)
├── requirements.txt # Python dependencies
├── README.md
│
├── src/ # Source code modules
│ ├── __init__.py
│ ├── data_loader.py # Multi-source data loading
│ ├── visualizer.py # Chart generation (matplotlib)
│ ├── ppt_generator.py # PowerPoint creation
│ ├── pdf_exporter.py # PDF report generation
│ ├── email_sender.py # Email delivery
│ ├── scheduler.py # Scheduling utilities
│ └── config_loader.py # Configuration management
│
├── sample_data/ # Sample data files
│ └── sales_data.json
│
└── output/ # Generated reports
├── charts/
├── presentations/
└── pdf/
| Source | Format | Connection Example |
|---|---|---|
| CSV | .csv |
sample_data/sales.csv |
| Excel | .xlsx, .xls |
reports/data.xlsx |
| JSON | .json |
sample_data/data.json |
| MySQL | Connection string | mysql+pymysql://user:pass@localhost:3306/db |
| PostgreSQL | Connection string | postgresql://user:pass@localhost:5432/db |
| SQLite | Connection string | sqlite:///database.db |
| REST API | URL | https://api.example.com/data |
| Google Sheets | Sheet ID | 1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms |
All settings are managed in config.yaml. The file is fully commented with examples.
data:
# File-based sources
default_file: "sample_data/sales_data.json"
# Database (uncomment to use)
# default_file: "mysql+pymysql://user:password@localhost:3306/database"
# source_type: "sql"
# table: "sales_table"
# query: "SELECT * FROM sales WHERE year = 2024" # Optional: custom query
# REST API (uncomment to use)
# default_file: "https://api.example.com/data"
# source_type: "api"charts:
bar_chart:
enabled: true
category_column: "auto" # Or specify: "region", "company", etc.
value_column: "auto" # Or specify: "revenue", "sales", etc.
aggregation: "sum" # Options: sum, mean, count, max, min
title: "auto" # Or custom: "Sales by Region"
top_n: 15 # Show top N categories
pie_chart:
enabled: true
category_column: "auto"
value_column: "auto"
aggregation: "sum"
title: "auto"
top_n: 10 # Ideal: 3-10 categories for pie charts
line_chart:
enabled: true
x_column: "auto"
y_columns: "auto"
title: "auto"email:
enabled: true
smtp_server: "smtp.gmail.com"
smtp_port: 587
sender_email: "your-email@gmail.com"
sender_password: "your-app-password" # Use App Password for Gmail
recipients:
- "recipient1@example.com"
- "recipient2@example.com"schedule:
enabled: true
frequency: "weekly" # Options: daily, weekly, interval
time: "09:00" # 24-hour format
day: "monday" # For weekly
interval_minutes: 60 # For interval
⚠️ Never commit sensitive credentials to GitHub!
Instead of hardcoding passwords in config.yaml, use environment variables:
# Set environment variables
export EMAIL_PASSWORD="your-app-password"
export DB_PASSWORD="your-db-password"Then reference them in your code or use a .env file with python-dotenv:
pip install python-dotenv# In your code
from dotenv import load_dotenv
import os
load_dotenv()
password = os.getenv("EMAIL_PASSWORD")# Never commit these
.env
credentials.json
config_local.yaml
from main import generate_report
# Generate report using config.yaml settings
outputs = generate_report()
# Generate and send via email
outputs = generate_report(send_email=True)from src.data_loader import load_data
# CSV / Excel / JSON (auto-detected)
df = load_data("data.csv")
df = load_data("report.xlsx")
df = load_data("data.json")
# MySQL Database
df = load_data(
"mysql+pymysql://root:password@localhost:3306/mydb",
source_type="sql",
table="sales"
)
# With Custom Query
df = load_data(
"mysql+pymysql://root:password@localhost:3306/mydb",
source_type="sql",
query="SELECT * FROM sales WHERE year = 2024"
)
# REST API
df = load_data(
"https://api.coingecko.com/api/v3/coins/markets?vs_currency=usd",
source_type="api"
)
# Google Sheets
df = load_data(
"your-sheet-id",
source_type="google_sheets",
credentials_file="credentials.json"
)# Configure schedule in config.yaml, then run:
python run_scheduled.pyOr programmatically:
from src.scheduler import schedule_weekly, run_scheduler
from main import generate_report
schedule_weekly(generate_report, day="monday", time_str="09:00")
run_scheduler() # Runs indefinitely- Enable 2-Factor Authentication on your Google account
- Generate an App Password: https://myaccount.google.com/apppasswords
- Use the App Password in
config.yaml(not your regular password)
pip install pymysqldata:
default_file: "mysql+pymysql://user:password@localhost:3306/database"
source_type: "sql"
table: "your_table"pip install psycopg2-binarydata:
default_file: "postgresql://user:password@localhost:5432/database"
source_type: "sql"
table: "your_table"- Go to Google Cloud Console
- Create a new project & enable Google Sheets API
- Create Service Account credentials
- Download JSON key as
credentials.json - Share your Google Sheet with the service account email
The tool intelligently analyzes your data:
- Numeric columns → Used for chart values (revenue, sales, counts)
- Categorical columns → Used for grouping (region, company, category)
- Pie charts → Automatically selects categories with 3-10 unique values
- Titles → Auto-generated from column names
Works with any data structure — sales, analytics, surveys, inventory, and more.
| Package | Purpose |
|---|---|
pandas |
Data manipulation |
matplotlib |
Chart generation |
python-pptx |
PowerPoint creation |
reportlab |
PDF generation |
schedule |
Task scheduling |
PyYAML |
Configuration |
requests |
REST API calls |
sqlalchemy |
Database connections |
pymysql |
MySQL driver |
gspread |
Google Sheets |
Contributions are welcome! Feel free to:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- 🤖 AI Integration (Gemini) — Auto-generate insights and executive summaries
- 💬 Natural Language Queries — Ask questions about your data in plain English
- 📊 Smart Chart Recommendations — AI-powered visualization suggestions
- 📱 Dashboard Generation — Interactive web dashboards
- 🔄 Real-time Data Sync — Live data updates from APIs
- 📈 Tableau Integration — Pull data directly from Tableau Server/Cloud
- 📊 Power BI Integration — Connect to Power BI datasets and reports
- Built with Python and open-source libraries
- Inspired by the need for automated reporting workflows
Made with ❤️ for data automation