| # 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 |
|
|
| ```bash |
| 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 |
|
|
