Chapter 14 — Documentation

Project Documentation Pack

Ready-to-use templates and standards for README, data dictionary, validation log, and handover notes.

14.0 Why this chapter matters

Good analysis without documentation is hard to trust and hard to reuse. Use this chapter as your final quality gate before sharing a notebook, dashboard, or model output.

DataXForgeAuto-generate docs: Data Dictionary Generator · Metadata Extractor · Auto Schema Detector.

14.1 Required documentation set

Document	Minimum content	Owner	Update frequency
README	Business goal, data source, setup steps, outputs, limitations	Analyst	Every major update
Data dictionary	Column name, meaning, type, units, allowed values, null policy	Analyst + data owner	When schema changes
Cleaning log	What changed, why changed, impact on row count/metrics	Analyst	Every cleaning operation
Validation log	Checks run, pass/fail result, unresolved issues	Analyst / reviewer	Before reporting
Handover note	How to rerun analysis, dependencies, known risks, next steps	Project owner	At delivery

14.2 Validation checklist before publish

Business question and target KPI are written in one sentence
Data source and extraction date are documented
All filters/cleaning rules are explained
Key assumptions are stated and justified
At least one limitation and one risk are reported
Charts include units, time range, and interpretation note
Result files are reproducible from the notebook/script
Privacy-sensitive columns are masked or removed

14.3 Markdown starter templates

Generate a starter file: Pick a template, then copy it or download it as a .md file to drop into a new project.

README.md skeleton

# Project Title

## Business Question
- What decision this analysis supports

## Data Source
- File/table name
- Date range
- Row granularity

## Method Overview
1. Data loading
2. EDA
3. Cleaning and transformation
4. Analysis/modeling
5. Reporting

## Key Findings
- Insight 1
- Insight 2

## Limitations
- Limitation 1

## Repro Steps
- Install dependencies
- Run notebook/script order

Data dictionary skeleton

| column_name | description | dtype | unit | allowed_values | null_policy |
|---|---|---|---|---|---|
| customer_id | unique customer key | string | n/a | unique | never null |
| order_date | transaction date | datetime | yyyy-mm-dd | valid dates | drop if null |
| revenue | order revenue | float | USD | >= 0 | fill 0 if missing |

Use the template generator at the top of this section to copy or save these documents quickly for every new project.

Common mistakes to avoid

Skipping business context before running technical steps
Not writing assumptions and limitations explicitly
Treating one metric as the full story

Quick cheatsheet

df.info() -> Structure and non-null counts

df.describe() -> Numeric summary statistics

df.isnull().sum() -> Missing-value counts by column

df.groupby() -> Segmented aggregation

pd.merge() -> Join multiple datasets