Python / Intro / Free practice

Clean a Messy Finance Extract with Python

Take a typical finance extract with mixed headers, blank rows, and inconsistent account codes, and produce a clean, analysis-ready table.

Preview / read-only

>>> df.shape
(4821, 14)
>>> df.columns[:4].tolist()
['Acct', 'Acct Name', 'Period', 'Amt']
>>> df['Amt'].sum()
# → must tie to TB control

// Notebook fragment — shape check before rename

Prerequisites

Procedure

Run the drill

01
Load the file and inspect shape, null rates, and header rows before trusting column names.
Step
02
Normalize column names and drop empty rows that arrived from export formatting.
Step
03
Standardize account codes and map known aliases to a single chart-of-accounts key.
Step
04
Add validation checks: totals that must tie, required fields that cannot be blank, duplicates that need review.
Step
05
Write a clean output table plus a short exception log for anything that failed a check.
Step

OUTPUT

A reusable cleanup pattern you can adapt to SAP, ERP, or spreadsheet exports — with an exception log you can trust before analysis begins.

Target state

// Bring your own extract; no starter file required