This project demonstrates how to build a fully automated Excel reporting workflow using PyXLL, allowing Python code to run inside Excel as native macros.
The system:
- ✅ Imports CSV data directly into Excel sheets
- ✅ Builds structured report tabs automatically
- ✅ Embeds chart images
- ✅ Creates native Excel PivotTables and charts
- ✅ Runs entirely from a single Excel macro button
- ✅ Enables controlled integration of Python, data pipelines, and AI into Excel
This approach is especially powerful for financial workflows, analytics automation, and controlled AI augmentation inside Excel environments.
Traditional libraries like openpyxl operate by:
- Opening Excel files offline
- Modifying the workbook structure in memory
- Saving a new file back to disk
- No live Excel interactivity
- No access to Excel’s real-time UI, pivots, charts, macros, or calculation engine
PyXLL is architecturally different:
- 🧠 Python runs inside the live Excel process
- 🔗 Full access to Excel’s COM object model (like VBA, but in Python)
- ⚡ Can manipulate:
- Worksheets
- PivotTables
- Charts
- Shapes / Images
- Active workbooks
- User interactions
- 🎯 Macros run Python directly
- 🔒 Ideal for enterprise workflows where Excel remains the primary UI
Think of PyXLL as:
“Replacing VBA with full Python — without leaving Excel.”
This makes it uniquely suited for:
- Financial modeling automation
- Data engineering inside Excel
- AI-assisted workflows
- Controlled AI pipelines
- High-performance analytics tooling
This project demonstrates how to build a fully automated Excel reporting workflow using PyXLL, allowing Python code to run inside Excel as native macros.
The system:
- ✅ Imports CSV data directly into Excel sheets
- ✅ Builds structured report tabs automatically
- ✅ Embeds chart images
- ✅ Creates native Excel PivotTables and charts
- ✅ Runs entirely from a single Excel macro button
- ✅ Enables controlled integration of Python, data pipelines, and AI into Excel
This approach is especially powerful for financial workflows, analytics automation, and controlled AI augmentation inside Excel environments.
Traditional libraries like openpyxl operate by:
- Opening Excel files offline
- Modifying the workbook structure in memory
- Saving a new file back to disk
- No live Excel interactivity
- No access to Excel’s real-time UI, pivots, charts, macros, or calculation engine
PyXLL is architecturally different:
- 🧠 Python runs inside the live Excel process
- 🔗 Full access to Excel’s COM object model (like VBA, but in Python)
- ⚡ Can manipulate:
- Worksheets
- PivotTables
- Charts
- Shapes / Images
- Active workbooks
- User interactions
- 🎯 Macros trigger Python directly — no file round-tripping
- 🔒 Ideal for enterprise workflows where Excel remains the primary UI
Think of PyXLL as:
“Replacing VBA with full Python — without leaving Excel.”
This makes it uniquely suited for:
- Financial modeling automation
- Data engineering inside Excel
- AI-assisted workflows
- Audit-safe controlled pipelines
- High-performance analytics tooling
pip install pyxll
⚠️ PyXLL requires a valid license for production use. Trial licenses are available.
Run once after installing PyXLL:
pyxll installThis registers the PyXLL Excel add-in and enables Excel to load Python macros.
Restart Excel after running this command.
Add your project folder to the PyXLL config file so Excel can import your Python code:
[PYTHONPATH]
paths =
C:\path\to\your\projectRestart Excel after modifying the config.
This project exposes a Python macro:
@xl_macro
def build_pyfi_excel_report():
...To trigger it from Excel, use a VBA macro:
Sub Button1_Click()
Application.Run "build_pyfi_excel_report"
End SubAttach this macro to a button inside Excel.
When clicked:
- ✔️ All Python code executes
- ✔️ CSVs are imported
- ✔️ Sheets are rebuilt
- ✔️ Images inserted
- ✔️ PivotTables and charts generated
- ✔️ Final workbook updated live
No external scripts. No manual refresh. No file exports.
The macro builds the following sheets automatically:
Your original Excel sheet remains untouched.
- Imports
labeled_data.csv - Auto-fits columns
- Serves as the master dataset for pivots and analytics
- Imports
summary.csv - Displays formatted textual summary data
- Imports PNG charts:
- spending_by_spender.png
- spending_by_category.png
- spending_by_vendor.png
- Automatically positions and scales images
- Builds native Excel PivotTables:
- Spending by Spender
- Spending by Category
- Spending by Vendor
- Generates Excel charts directly from the pivots:
- Pie charts
- Clustered bar chart
- Fully interactive inside Excel
This architecture enables:
- Automated reporting pipelines
- Reproducible dashboards
- Zero manual refresh work
- Reduced operational risk
- AI models can run inside Python
- Results injected directly into Excel
- Human oversight preserved
- No uncontrolled spreadsheet logic
- Audit-friendly workflows
- Python handles:
- Data cleaning
- Feature engineering
- Machine learning
- Forecasting
- Optimization
- Excel remains the business-facing UI
- Keeps Excel as the delivery layer
- Avoids shadow IT spreadsheets
- Centralizes logic in version-controlled Python code
- Easier validation and governance
This makes the system ideal for:
- Quant teams
- Financial analysts
- Trading operations
- Risk teams
- Consulting workflows
- AI-augmented Excel tooling
- Automated P&L reporting
- Transaction classification pipelines
- Spend analytics
- AI-driven categorization
- Forecast generation
- Scenario analysis
- Data quality monitoring
- Model explainability inside Excel
- PyXLL requires Excel on Windows.
- Charts and pivots rely on Excel’s live object model.
- Always restart Excel after config changes.
See PyXLL licensing for runtime deployment.
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This software and associated documentation files are created for PyFi.
Unauthorized copying, distribution, modification, or use of this software, in whole or in part, is strictly prohibited without prior written permission from PyFi.