Financial Summary

Role & Impact

Role: Financial/BI Analyst — Python automation (data prep, KPIs, docs).
Method: Script rebuilds the Excel Summary (12 KPIs) and composes Word §3.2 with templated narrative.
Reusability: Parameterised config; Jinja2 & python-docx templates; drop‑in for new datasets.
Transparency: Deterministic outputs; versioned code; anonymised sample; clean repo structure.

Highlights

One-click run: Summary (Excel) + Word 3.2 generated together.
Excel Summary (12 KPIs): liquidity, leverage, profitability — with trend arrows and YoY change.
Word 3.2 (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 (why two options?)

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 (gallery)

Summary — before → after

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

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

Option 1 — Word (v1, Python only)

Option 2 — Word (v2, Python + Power BI visuals)

Word 3.2 — page 1 (with Power BI visuals)

Word 3.2 — page 2 (with Power BI visuals)

Tech Stack & 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

How to run (short)

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 3.2_analysis_of_liquidity_ratios.docx appears in run/.

Links

Repository: github.com/IrinaTok11/fin-health-python
Methodology: Methodology page
Architecture: overview (GitHub)
KPI catalog: table (GitHub)
Case study: narrative (GitHub)
Power BI portfolio: live page · repository

Data & Privacy

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

Financial Summary — Python