Python Automation Hub

Watch a complete FP&A workflow, then download the files to run it yourself.

This hub shows how I use Python for recurring finance work: reading source files, validating data contracts, calculating KPIs, writing Excel and Word outputs, and preparing stakeholder handoffs. Everything here uses synthetic files and generic labels, so the logic is visible while company data stays private.

inspectable Python scripts

finance automation cases

end-to-end video demo

company records exposed

01 Watch the demo

See raw Excel data become a finance review pack.

02 Download the package

Get the input, script, dependencies, and instructions.

03 Run the workflow

Regenerate clean data, analysis, and the report.

04 Inspect controls

Review tie-outs, mappings, and the approval boundary.

Featured walkthrough

Raw data in. Decision-ready outputs out.

Video + runnable ZIP

Full workflow

Excel input → Python → clean data, variance analysis, and management report.

Synthetic data 4/4 controls pass

Case 03 · End-to-end monthly pack

FP&A Monthly Reporting Demo

One script standardizes 360 fictional transaction rows, calculates revenue and profit variances, builds control checks, and drafts an action-oriented Word report.

Decision: isolate growth, margin, and EBITDA actions by region
Proof: video, source code, synthetic input, and visible control logic
Boundary: finance review remains required before distribution

Download package Open full case

More runnable workflows

Two focused samples for recurring FP&A handoffs.

Source code + README + sample data

Case 01 · Monthly reporting

Monthly Reporting Pipeline

Shows a full reporting flow: ingest monthly files, standardize fields, map service/office dimensions, summarize revenue and gross profit, compare actuals to plan, and generate an FP&A brief.

Code: 4 scripts for config, merge, ratio pack, and brief generation
Output: summary CSV, actual-vs-plan pack, markdown insight brief
Skill signal: pandas ETL, validation, aggregation, business rules

Open case README

Case 02 · KPI distribution

Quick KPI File Distribution

Shows the quick-check workflow: refresh actuals into a master table, validate KPI achievement, flag watch items, and split stakeholder-ready files by rule.

Code: 3 scripts for refresh, split, and orchestration
Output: refreshed master file and owner-specific CSV extracts
Skill signal: data joins, exceptions, file automation, handoff logic

Open case README

What this proves

Python capability shown through finance work, not generic toy scripts.

Readable scripting

Small, named scripts with clear entry points, reusable config, and business-language variables.

Data contract control

Required-column checks, duplicate-key checks, unmapped-value checks, and explicit output naming.

FP&A outputs

KPI packs, actual-vs-target comparisons, watch items, and management brief outputs.

Direct code index

Open the actual scripts from one place.

8 Python files

run_fpa_automation.pyExcel-to-Word monthly reporting workflow config.pyMonthly pipeline settings and mappings merge_monthly_reports.pyRead, validate, clean, and aggregate build_ratio_pack.pyActual-vs-plan KPI calculations generate_insight_brief.pyMarkdown management narrative refresh_actuals.pyUpdate master KPI actuals split_stakeholder_files.pyCreate stakeholder-specific files run_pipeline.pyRun refresh and split together

Privacy boundary

Public enough to inspect, private enough to protect old company data.

Clean-room portfolio sample

What is included

Generic workflow names, synthetic CSVs, public-safe scripts, and process README files.

What is excluded

Production workbooks, company folder paths, client names, internal stakeholder names, and real values.

How to read it

Judge the workflow structure, validation mindset, and finance logic rather than the fictional figures.