The bones of your books, examined and scored.
Before a single figure is interpreted, we assess whether the underlying records can be trusted to carry it. This review measures the structural health of 6 source tables — their completeness, their uniqueness, and the small inconsistencies that quietly distort everything downstream.
Completeness & Uniqueness
The share of required cells that carry a value across every table. What is missing is listed below and backfilled where a reliable match exists.
The share of rows that are distinct. Exact duplicates are removed in the cleaned file, with the originals preserved for your record.
Per-Table Integrity
| Source file | Status | Score |
|---|---|---|
arabic_ledger.csv |
Needs attention | 72.59/100 |
swiss_de_ledger.csv |
Needs attention | 72.59/100 |
turkiye_vendors.csv |
Clean | 100/100 |
thai_saraban.csv |
Clean | 96.83/100 |
india_invoices.csv |
Needs attention | 72.56/100 |
japan_billing.xlsx:請求一覧 |
Needs attention | 67.38/100 |
Structural Findings
arabic_ledger.csv: 169 missing values detected (الحالة: 169)
swiss_de_ledger.csv: 193 missing values detected (Status: 193)
thai_saraban.csv: 190 missing values detected (สถานะ: 190)
india_invoices.csv: 220 missing values detected (gstin_status: 220)
japan_billing.xlsx:請求一覧: 366 missing values detected (支払期日: 282, 備考: 84)
arabic_ledger.csv: column 'المبلغ' looks monetary (digit-heavy values) but could not be parsed as numeric at all -- likely an unrecognized currency convention, digit script, or separator. Currently read as text, not a number: sums, sorting, and any numeric analysis on this column will not work until it's parsed.
swiss_de_ledger.csv: column 'betrag' looks monetary (digit-heavy values) but could not be parsed as numeric at all -- likely an unrecognized currency convention, digit script, or separator. Currently read as text, not a number: sums, sorting, and any numeric analysis on this column will not work until it's parsed.
thai_saraban.csv: 8 value(s) in column 'วันที่' could not be read as date and were left blank rather than guessed at ('10 ก.ค. 2568', '19 ก.ค. 2568', '12 ก.ค. 2568', '26 ก.ค. 2568', '18 ก.ค. 2568', +3 more)
thai_saraban.csv: column 'วันที่' parsed as dates with a median year of 2568 -- every value parsed successfully, so nothing else in this report would have flagged it, but a year this far out usually means the source used a non-Gregorian calendar (Thai Buddhist Era, Gregorian + 543 years, is the most common case) that was read as literal years. Treat these dates as unverified until confirmed.
india_invoices.csv: column 'amount' looks monetary (digit-heavy values) but could not be parsed as numeric at all -- likely an unrecognized currency convention, digit script, or separator. Currently read as text, not a number: sums, sorting, and any numeric analysis on this column will not work until it's parsed.
japan_billing.xlsx:請求一覧: column '金額（税込）' looks monetary (digit-heavy values) but could not be parsed as numeric at all -- likely an unrecognized currency convention, digit script, or separator. Currently read as text, not a number: sums, sorting, and any numeric analysis on this column will not work until it's parsed.
reportvsc.txt: not a recognized tabular/document format -- no data extracted from this file