Hugging Face's logo

Datasets:
protoneko
/
freelance-samples

Modalities:
Text
Formats:
text
Size:
< 1K
Libraries:
Datasets
Dataset card Data Studio Files
xet
 Community
1
freelance-samples / data_cleaning /README.md
protoneko's picture
protoneko
Upload folder using huggingface_hub
61bb557 verified
preview code
|
Raw History Blame Contribute Delete
4.63 kB

Data Cleaning — Messy CRM Export → Clean, Typed Dataset

A reproducible end-to-end data-cleaning case study. You receive a "customer orders" export from a legacy CRM in the condition real exports arrive in: mixed date formats, duplicate rows, casing and whitespace noise, sentinel nulls, and prices that are text. This project cleans it with pandas and produces a QA report generated from the actual run — every fix is counted, nothing is hand-typed.

The dirty data is generated by a seeded script (make_messy.py, seed 42), so anyone can reproduce the exact same input and verify every number in the QA report.

The mess (real examples from messy_orders.csv)

Problem Example
Mixed date formats 2024-03-01, 03/01/2024, 01/03/2024, Mar 01, 2024, 01-Mar-2024, 2024/03/01
Ambiguous dates 01/03/2024 — Jan 3 or Mar 1?
Casing noise joseph thomas, SHIPPED, SAN DIEGO
Whitespace noise san diego, Pending (trailing), double spaces
Sentinel nulls N/A, n/a, NA, ?, empty
Wrong types $1,234.56 as text, (294.50) accounting-negative, 4.0 where an int is expected, ORD 10001 (space not dash)
Duplicates 25 exact + 15 near-duplicates (same order, different casing)

What the pipeline does

clean_with_pandas.py reads everything as dtype=str (no silent coercion), then:

  1. Strips whitespace on all cells — counted per run.
  2. Converts sentinel nulls to real nulls.
  3. Normalizes order_id (ORD 10001 → ORD-10001).
  4. Title-cases names/cities, lower-cases email/payment/status.
  5. Parses dates against an explicit format list (no guess_date). Ambiguous X/Y/YYYY (both parts ≤ 12) is assumed US month-first and the count is reported — a documented, auditable decision, not a guess.
  6. Casts quantity to int and money columns to float ($/,/() handled; accounting parentheses → negative, i.e. credit notes).
  7. Deduplicates last — a whitespace-padded duplicate is only an exact duplicate after normalization, so order matters. Rows with a real key dedupe on order_id; keyless rows dedupe on the full row (never on a null key).
  8. Validates: flags rows where |total − unit_price × quantity| > 0.02.

It writes clean.csv and QA_report.md (row counts, every issue found/fixed with its rule, the date-format distribution, the ambiguity count, validation results, and a data dictionary).

Run it 

python make_messy.py messy_orders.csv          # reproduce the dirty input (seed 42)
python clean_with_pandas.py messy_orders.csv clean.csv QA_report.md
python make_previews.py                         # render before/after PNGs

Result of the committed run

  • 540 raw rows → 500 clean rows (exactly the 40 injected duplicates removed).
  • 163 whitespace cells stripped; 292 casing fixes; 96 sentinel nulls; 109 money-text values; 15 order_id format fixes; 77 ambiguous dates resolved (assumed month-first, reported); 21 credit-note totals normalized to negative; 21 rows flagged by the total check — all of them credit notes, no true arithmetic errors.

See QA_report.md for the full per-issue table.

Before / after

before_messy.png and after_clean.png show the first 13 rows of each. The contrast is the whole point: mixed-case cities, 29-Nov-2024 / 07/06/2024 / Sep 10, 2024 / 2024-12-28 on the left become a single ISO YYYY-MM-DD on the right; $1,847.64 and (294.50) become 1847.64 and -294.5.

Why this is how I clean data in production

  • Everything is counted, not assumed. The QA report is generated from the run, so a client can audit exactly what changed and how much.
  • Decisions are documented. The month-first tie-break and the parentheses-are-negative rule are stated in the report, so a stakeholder can object to a policy, not discover it in a dashboard.
  • Nothing is silently dropped. Duplicates are removed by an explicit key rule; quality problems are flagged in counts and kept for review.
  • Reproducible. Seeded generator + dtype=str read means the before and after are both verifiable from scratch.

Files

  • make_messy.py — seeded generator of the 540-row dirty input
  • clean_with_pandas.py — the cleaning + QA pipeline
  • make_previews.py — Pillow-based before/after PNG renderer
  • messy_orders.csv — the dirty input (540 rows)
  • clean.csv — the cleaned output (500 rows)
  • QA_report.md — generated QA report
  • before_messy.png / after_clean.png — rendered previews