Automated Report Generation with Python and Scheduled Email Delivery
Automates report creation and distribution using Python, Pandas, and scheduled email delivery.
Build a Python script that automates the entire report generation pipeline: data extraction from a database, processing with Pandas, chart creation, export to PDF/Excel, and automatic email delivery.
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Prompt objective
Build a Python script that automates the entire report generation pipeline: data extraction from a database, processing with Pandas, chart creation, export to PDF/Excel, and automatic email delivery.
Real use case
The BI analyst at Farma Express Network spends every Monday morning generating the same weekly sales report — three hours of repetitive work that could be automated in a Python script running automatically at 7 AM.
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Replace the placeholders with your own context before you run the prompt. That usually improves the first output more than adding more instructions later.
Prompt
Create a complete Python script to automate the [REPORT TYPE] report for [COMPANY NAME], which is currently generated manually every [FREQUENCY].\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Part 1 — Data Extraction:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`python\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n# Connection to [PostgreSQL/MySQL/BigQuery]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n# Use SQLAlchemy + pandas.read_sql()\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nRequired queries:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n1. [QUERY 1 — e.g., sales by branch for the week]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n2. [QUERY 2 — e.g., current stock vs. minimum stock]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n3. [QUERY 3 — e.g., sales rep performance]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Part 2 — Data Processing with Pandas:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Data cleaning and transformation\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Derived calculations: [KPI 1], [KPI 2], [KPI 3]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Comparisons: current vs. prior period, actual vs. target\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Classification: rankings, ABC categorization, traffic light indicators\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Pivot tables for summaries\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Part 3 — Visualization with Matplotlib/Plotly:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Chart 1: [TYPE] showing [METRIC]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Chart 2: [TYPE] showing [METRIC]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Chart 3: [TYPE] showing [METRIC]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Style: company colors ([COLOR 1], [COLOR 2]), font [FONT]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Save as PNG for email inclusion\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Part 4 — Report Generation:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nOption A — Formatted Excel (openpyxl):\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Multiple sheets with professional formatting\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Embedded charts\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Conditional formatting\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Header with company logo\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nOption B — PDF (reportlab or weasyprint):\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Professional layout with header/footer\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Tables and charts embedded\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Page numbering\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Part 5 — Email Delivery:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`python\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n# SMTP with attachment (smtplib + email.mime)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n# Or [Resend/Brevo/SendGrid] API\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- HTML email body with KPI summary\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Attachment: complete report (Excel + PDF)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Recipient list by role:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n - Executive leadership: executive summary\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n - Managers: area-specific details\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n - Operations: granular data\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Part 6 — Scheduling:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Cron job (Linux): cron expression for [FREQUENCY]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Or Task Scheduler (Windows)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Or GitHub Actions / Cloud Function\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Error handling: notification email on failure\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Execution log with timestamp\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Complete code, commented, with exception handling and environment variables for credentials.**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nLibraries: pandas, sqlalchemy, matplotlib, openpyxl, smtplib, python-dotenv, schedule.
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How to use this prompt
- 1Replace the key placeholders first: REPORT TYPE, COMPANY NAME, FREQUENCY, PostgreSQL/MySQL/BigQuery.
- 2Replace any bracketed placeholders like [this] with your own context.
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