Step 01
Load the file and inspect shape, null rates, and header rows before trusting column names.
TACTICAL LAB · PYTHON · INTRO
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.
01 / OBJECTIVE
Technical & financial objective
Take a typical finance extract with mixed headers, blank rows, and inconsistent account codes, and produce a clean, analysis-ready table.
02 / BUSINESS CONTEXT
Operational tension & business context
Month-end manual consolidation and unmapped extract data lead to recurring cycle delays and broken general ledger reconciliations.
03 / INPUT DATA
Source data schema & structure
Sample messy CSV/Excel extract (4,821 rows × 14 columns; headers: Acct, Acct Name, Period, Amt) with mixed headers, empty rows, and unmapped account codes.
Bring your own extract; no starter file required
04 / TOOLING
Required software & prerequisites
Environment: Python 3.10+ with pandas
- Basic Python (pandas installed)
- A sample CSV or Excel extract (any messy P&L or trial-balance style file)
05 / WORKFLOW
Execution procedure
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.
06 / BUILD
Working artifact
Executable syntax / model artifact
>>> df.shape
(4821, 14)
>>> df.columns[:4].tolist()
['Acct', 'Acct Name', 'Period', 'Amt']
>>> df['Amt'].sum()
# → must tie to TB controlNotebook fragment — shape check before rename
07 / VALIDATE
Validation criteria & assertion checks
- Assert total transaction amount ties directly to Trial Balance control total.
- Verify essential account and period fields contain zero unexpected nulls.
- Isolate unmapped account aliases into a structured exception log.
08 / RECONCILE
Tie-out reconciliation method
Sum of transactional amounts (df["Amt"].sum()) must tie out to the trial-balance control total before handoff.
09 / INTERPRET
Interpreting results for finance leadership
Separate clean transactional records for downstream reporting from unmapped rows routed to the exception log.
10 / CONTROL CHECK
Financial statement & reporting implication
Audit risk
Unchecked export formatting errors and unmapped account aliases silently drop or misallocate transactional amounts, corrupting financial statement line items.
11 / EXPECTED OUTPUT
Target deliverable
A reusable cleanup pattern you can adapt to SAP, ERP, or spreadsheet exports — with an exception log you can trust before analysis begins.
12 / FAILURE MODES
Common analyst pitfalls & mistakes
- Trusting raw export column names before inspecting null rates and header rows.
- Dropping empty rows without verifying whether summary rows or period boundaries were lost.
13 / NEXT STEP