Financial Summary — Python

Automation that rebuilds the Excel Summary (12 KPIs) and generates a Word report section — deterministic, repeatable output every run

Repository Quick start

Python file summary

This isn’t a demo or a bundle of screenshots. It’s a Python routine that runs from an Excel workbook: using the supplied inputs, the Summary sheet compiles a table of twelve key ratios over three years; trends are calculated, the latest period is set against the first, and each result is checked against its norm. The script then produces a Word report in a standard financial-analysis layout – a tidy table with norms, brief notes on movements and a clear conclusion. What’s shown here is a small public excerpt from a much deeper analysis, shared to convey the method and standard of the work.

Why this project is different

Deterministic pipeline: same numbers on every run (audit‑friendly)
Templated Word report section (Jinja2 for text placeholders, python-docx for document assembly)
Defensive KPI rules and norm bands — a stable methodology, not “magic”
Drop‑in reuse: a new Excel with the same mapping yields the same outputs

Deterministic outputs • Templated Word report section • Defensive KPI rules • Drop‑in reuse across datasets

Who uses this

Executive teams and finance leads — decision-ready view of the 12 KPIs
Finance analysts — standardise a portfolio of KPIs
BI teams — guarantee the same numbers across Excel, Power BI and Word
Insolvency and restructuring professionals — licensed insolvency practitioners (administrators, liquidators, CVA supervisors) and turnaround advisers; reproducible exhibits for formal papers

Limitations and not a fit

Requires a structured Excel template (sheet names and layout are expected)
Current narrative focuses on ratios (easily extensible to other topics)

Highlights

One-click run: Summary (Excel) + Word report section generated together.
Excel Summary (12 KPIs): liquidity, financial stability, and profitability — with trend arrows and YoY change.
Word report section (4 KPIs): cash ratio, current ratio, quick ratio, months to repay — table + narrative.
Consistent formatting: fonts, paddings, widths, captions, spacing — no manual polishing.

Word variants

Option 1 — v1 (Python only): section as generated by the script (table + narrative).
Option 2 — v2 (Python + Power BI visuals): same numbers , but with selected charts embedded for portfolio presentation.

Why Word v2? For the portfolio we embed clean charts captured from the companion Power BI dashboard (Page 2 — v2, flat/print).

For a similar interpretation in Power BI — interactive dashboarding and print‑friendly report visuals from the same dataset — see: live page · repository

What you get

Summary — before → after

Summary sheet — empty/initial — Summary — empty/initial

Summary sheet — generated & formatted — Summary — generated & formatted

The numbers shown are anonymised; the workflow is real.

Option 1 — Word

Word report section — page 1 (Python only)

Word report section — page 2 (Python only)

Option 2 — Word

Word report section — page 1 (with Power BI visuals)

Word report section — page 2 (with Power BI visuals)

Tech stack and run

Python: 3.11+
Libraries: pandas, openpyxl/xlrd, jinja2, python-docx, matplotlib
Build: static GitHub Pages; optional GitHub Actions for auto-regeneration
Data: anonymised Excel sample; generated Word report section

How to run

Shell

          
          cd run
          python -m venv .venv
          # Windows:
          .venv\Scripts\activate
          # macOS/Linux:
          source .venv/bin/activate
          # Install & run:
          pip install -r ../requirements.txt
          python summary.py

Result: the workbook in run/ gets its Summary sheet rebuilt, and the file analysis_of_liquidity_ratios.docx appears in run/.

Data and privacy

This portfolio case uses anonymised sample data. No client-identifying information is included.

Get in touch

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