sales.xlsx | Tabalyst

01 / Overview

Dataset overview

Size, completeness and the main signal to check first.

Rows: 120No empty rows
Columns: 91,080 cells analyzed
Missing cells: 1.2%13 cells
Duplicate rows: 0Beyond the first occurrence

Derived · sum of 1 date columns

ambiguous dates, kept unresolved.

Values that match more than one configured date order. That is 0.0% of the 120 present date values. Tabalyst does not guess the order.

See date analysis

Quality observations

warning: Missing cells13
info: Cells changed by trimming surrounding whitespace0
info: Cells changed by collapsing repeated internal whitespace0

Column types

Text 5
Date 1
Integer 1
Number 1
Boolean 1

Semantic types

No semantic type 7
Date 1
Email 1

Mixed: fewer than 95.0% of present values agree on one type.

02 / Structure

Columns(9)

Inferred and semantic types per column. With issues: missing values or mixed type.

Column	Missing	Distinct	Inferred type	Semantic type	Error	Examples
1order_id	–	120	text	–	–	SO-0001SO-0002SO-0003
2order_date	–	75	date	date	–	2026-01-032026-01-052026-01-07
3customer	–	48	text	–	–	Emma LaurentHugo LaurentAna Martin
4email	–	120	text	email	–	aaaaa.aaaaaa99@aaaa.aaaaaaaaaaaa.aaaaaaa99@aaaa.aaaaaaaaaaa.aaaaaa99@aaaa.aaaaaaa
5region	–	5	text	–	–	WestCenterEast
6channel	–	3	text	–	–	webstorephone
7quantity	–	12	integer	–	–	7111
8unit_price	–	119	number	–	–	23.69100.44101.24
9paid	10.83%13	2	boolean	–	–	truefalse

04 / Cleaning

Transformations(9)

Occurrences changed by each normalization stage, and the spellings it groups. Raw preview values remain unchanged.

Column	Unicode composed	Trimmed	Whitespace collapsed	Case folded	Accents removed	Distinct	Compared distinct	Variant groups
1order_id	–	–	–	100.0%120	–	120	120	0
2order_date	–	–	–	–	–	75	75	0
3customer	–	–	–	100.0%120	–	48	48	0
4email	–	–	–	–	–	120	120	0
5region	–	–	–	100.0%120	–	5	5	0
6channel	–	–	–	–	–	3	3	0
7quantity	–	–	–	–	–	12	12	–
8unit_price	–	–	–	–	–	119	119	–
9paid	–	–	–	–	–	2	2	–

05 / Numeric

Numeric analysis(2)

Range and distribution statistics for accepted numeric values.

Column	Range	Minimum	Maximum	Mean	Median	Distinct	Examples
7quantity	11	1	12	6.5	7	12	7111
8unit_price	170.83	8.36	179.19	98.1998	105.87	119	23.69100.44101.24

06 / Dates

Date analysis(1)

Strict date parsing keeps ambiguous, invalid and non-date values separate. Ambiguous values are never resolved from the other values of the column.

Column	Status	Breakdown	Valid	Ambiguous	Invalid	Other	Distinct	Formats
2order_date	valid		100.0%120	–	–	–	75	1 variant

07 / Text

String analysis(5)

Length classes, fixed widths and representative values.

Column	Class	Fixed	Min	Max	Mean	Median	Distinct	Examples
1order_id	short	7	–	–	–	–	120	SO-0001
3customer	short	–	9	14	11.84	12.0	48	Emma LaurentBruno BernardEmma Martin
4email	medium	–	24	29	26.94	27.0	120	aaaaa.aaaaaa99@aaaa.aaaaaaaaaaaa.aaaaaaa99@aaaa.aaaaaaaaaaa.aaaaaa99@aaaa.aaaaaaa
5region	short	–	4	6	4.77	5.0	5	WestNorthCenter
6channel	very short	–	3	5	4.25	5.0	3	storeweb

08 / Detectors

Detectors and formats(2)

What each detector recognized per column, with the formats it found. Primary: the interpretation shown as semantic type.

Column	Detector	Matched	Ambiguous	Invalid	Formats
2order_date	date primary	100.0%120	0	0	YYYY-MM-DD
4email	email primary	100.0%120	0	0	–

10 / Raw values

Data sample(20)

First 20 records with original row numbers and raw values.

Row	order_idtext	order_datedate	customertext	emailtext	regiontext	channeltext	quantityinteger	unit_pricenumber	paidboolean
1	SO-0001	2026-01-03	Bruno Martin	aaaaa.aaaaaa9@aaaa.aaaaaaa	South	web	4	28.56	false
2	SO-0002	2026-01-05	Bruno Laurent	aaaaa.aaaaaaa9@aaaa.aaaaaaa	East	web	1	20.49	true
3	SO-0003	2026-01-07	Ana Laurent	aaa.aaaaaaa9@aaaa.aaaaaaa	West	phone	11	127.43	true
4	SO-0004	2026-01-09	Hugo Laurent	aaaa.aaaaaaa9@aaaa.aaaaaaa	South	web	3	126.87	true
5	SO-0005	2026-01-11	Chloe Bernard	aaaaa.aaaaaaa9@aaaa.aaaaaaa	South	web	2	70.87	true
6	SO-0006	2026-01-13	Farid Laurent	aaaaa.aaaaaaa9@aaaa.aaaaaaa	South	web	12	84.86	false
7	SO-0007	2026-01-15	Gaelle Martin	aaaaaa.aaaaaa9@aaaa.aaaaaaa	Center	store	11	112.86	true
8	SO-0008	2026-01-17	David Simon	aaaaa.aaaaa9@aaaa.aaaaaaa	North	web	11	44.11	true
9	SO-0009	2026-01-19	Bruno Michel	aaaaa.aaaaaa9@aaaa.aaaaaaa	West	web	7	52.92	empty
10	SO-0010	2026-01-21	Farid Bernard	aaaaa.aaaaaaa99@aaaa.aaaaaaa	South	store	4	121.95	true
11	SO-0011	2026-01-23	Bruno Laurent	aaaaa.aaaaaaa99@aaaa.aaaaaaa	West	phone	12	47.09	true
12	SO-0012	2026-01-25	Emma Simon	aaaa.aaaaa99@aaaa.aaaaaaa	Center	web	11	61.07	true
13	SO-0013	2026-01-27	Ana Bernard	aaa.aaaaaaa99@aaaa.aaaaaaa	North	store	7	51.12	true
14	SO-0014	2026-01-29	Farid Bernard	aaaaa.aaaaaaa99@aaaa.aaaaaaa	East	store	11	84.76	true
15	SO-0015	2026-01-31	David Simon	aaaaa.aaaaa99@aaaa.aaaaaaa	Center	phone	5	135.47	true
16	SO-0016	2026-02-02	Gaelle Dubois	aaaaaa.aaaaaa99@aaaa.aaaaaaa	West	web	9	90.86	true
17	SO-0017	2026-02-04	Bruno Bernard	aaaaa.aaaaaaa99@aaaa.aaaaaaa	West	phone	7	108.97	true
18	SO-0018	2026-02-06	Hugo Laurent	aaaa.aaaaaaa99@aaaa.aaaaaaa	South	phone	1	123.73	empty
19	SO-0019	2026-02-08	Bruno Simon	aaaaa.aaaaa99@aaaa.aaaaaaa	Center	store	11	63.87	true
20	SO-0020	2026-02-10	Chloe Moreau	aaaaa.aaaaaa99@aaaa.aaaaaaa	North	phone	12	50.36	true

11 / Method

Analysis settings

Rules used for this analysis, so the result can be reproduced.

Scope: All 120 records
Missing values: absent, null, empty, blank, marker; markers: "N/A" "NULL"
Marker comparison: Whitespace trimmed; case-sensitive
Value normalization: Unicode composition (NFC): enabled; trim: enabled; collapse internal whitespace: enabled; comparison also with case folding: enabled, accents removed: enabled. Raw preview values are preserved.
Duplicate comparison: Exact raw values across every column; up to 2,000,000 distinct records compared
Type inference: At least 95.0% agreement across present values; numbers with dot or comma decimals, configured dates and true/false. Leading-zero identifiers remain text.
Date detection: Orders: YMD, MDY, DMY; separators: "-" "/" "." ; month names: en, fr; ambiguous order: unresolved, column evidence shown but never applied
Semantic types: Dates, or the only detector matching at least 95.0% of present values
Adaptive detection: Every detector on the first 10,000 distinct values of each column; detectors that recognized none of them, or at most 0.1% of them and none of the last 5,000, then skip the others, except about 1 value in 100. Number and date detection always test every value
Enumerations: At most 49 distinct values among at least 500 present values
Sensitive values: Masked in examples and the data sample
String lengths: Very short through 5; short through 20; medium through 50; long through 255; otherwise very long. Up to 10 examples per length are retained when the maximum is 50 or less.
Value representation: Complete through 50 distinct values; otherwise a reproducible sample of 100 distinct values
Row numbering: Data records start at 1, excluding the header. Quoted multiline values count as one record.
Preview selection: First 20 records; raw values preserved
JSON format: 0.1.0a - revision 11 (experimental)

SOURCE SHA-2565241c490e48405f13486fea429fe6ca22c5aabd07187fb9225357df94483a671