A clean file.A complete record.

Quality summary

Scored by the engine after cleaning: completeness, uniqueness and validity.

Quality score

94.8 · grade A

Rows

52 in → 40 out

Duplicate rows removed

8

Date columns standardised

4, 155/155 values parsed, 0 failed

Record of changes

Every step the engine ran, in order, with the rows and columns it touched.

1. 1

   Step 1: Standardize Headers

   Lowercase, strip special chars, underscores for spaces

   Rows 52 → 52 · Columns 8 → 8 · 2 Oct 2026, 04:05 pm UTC

   Columns affected: Invoice ID → invoice_id, Customer Name → customer_name, Invoice Date → invoice_date, Due Date → due_date, Paid Date → paid_date, Posting Date → posting_date, Amount (INR) → amount_inr, Status → status

2. 2

   Step 2: Drop Empty Rows

   Removed 4 rows where all values were null/empty

   Rows 52 → 48 · Columns 8 → 8 · 2 Oct 2026, 04:05 pm UTC

   Columns affected: invoice_id, customer_name, invoice_date, due_date, paid_date, posting_date, amount_inr, status

3. 3

   Step 3: Remove Duplicates

   Removed 8 duplicate rows (subset: all columns, mode: exact)

   Rows 48 → 40 · Columns 8 → 8 · 2 Oct 2026, 04:05 pm UTC

   Columns affected: invoice_id, customer_name, invoice_date, due_date, paid_date, posting_date, amount_inr, status

4. 4

   Step 4: Parse Dates

   Parsed 4 column(s) · 155/155 values parsed, 0 failed

   Rows 40 → 40 · Columns 8 → 8 · 2 Oct 2026, 04:05 pm UTC

   Columns affected: invoice_date, due_date, paid_date, posting_date

5. 5

   Step 5: Normalize Numbers

   Normalized 1 column(s) · 40/40 values parsed, 0 failed

   Rows 40 → 40 · Columns 8 → 8 · 2 Oct 2026, 04:05 pm UTC

   Columns affected: amount_inr

Before and after

Cleaned values, with the original underneath wherever the engine changed it. Rows are matched on Invoice ID between the raw file and the cleaned output.