01 / Overview
Dataset overview
Size, completeness and the main signal to check first.
- Rows
- 60No empty rows
- Columns
- 171,502 cells analyzed
- Missing cells
- 3.2%48 cells
- Duplicate rows
- 0Beyond the first occurrence
Derived · sum of 2 date columns
0
ambiguous dates, kept unresolved.
Values that match more than one configured date order. That is 0.0% of the 179 present date values. Tabalyst does not guess the order.
See date analysisQuality observations
Column types
- Text 10
- Integer 2
- Date 2
- Boolean 1
- Number 2
Semantic types
- No semantic type 13
- Email 1
- Phone 1
- Date 2
Mixed: fewer than 95.0% of present values agree on one type.
02 / Structure
Columns(17)
Inferred and semantic types per column. With issues: missing values or mixed type.
Column | Missing | Distinct | Inferred type | Semantic type | Error | Examples |
|---|---|---|---|---|---|---|
| 1id | – | 60 | text | – | – | C0001C0002C0003 Values 60 C0001(1) C0002(1) C0003(1) C0007(1) C0010(1) C0019(1) C0024(1) C0028(1) C0030(1) C0032(1) C0033(1) C0037(1) C0039(1) C0043(1) C0045(1) C0048(1) C0049(1) C0051(1) C0055(1) C0058(1) |
| 2name | – | 56 | text | – | – | David DuboisFarid DuboisInès Laurent Values 56 David Dubois(2) Farid Dubois(2) Inès Laurent(2) Bruno Dubois(1) Chloé Leroy(1) Chloé Thomas(1) Jules Robert(1) Nadia Bernard(1) Olivier Richard(1) Pauline Simon(1) Quentin Robert(1) Quentin Simon(1) Rose Moreau(1) Rose Petit(1) Ugo Durand(1) Ugo Martin(1) Ugo Richard(1) William Durand(1) William Petit(1) Yasmine Leroy(1) |
| 3email | 6.67%4 | 56 | text | – | aaaaa.aaaaaa99@aaaa.aaaaaaaaaaaa.aaaaaaa99@aaaa.aaaaaaaaaaaaaa.aaaaaa99@aaaa.aaaaaaa Masked values 16 / 56 aaaaa.aaaaaa99@aaaa.aaaaaaa(14) aaaaa.aaaaaaa99@aaaa.aaaaaaa(6) aaaaaaa.aaaaaa99@aaaa.aaaaaaa(6) aaaa.aaaaaaa99@aaaa.aaaaaaa(5) aaaaaaa.aaaaa99@aaaa.aaaaaaa(5) aaaaa.aaaaa99@aaaa.aaaaaaa(4) aaaaa.aaaaaa9@aaaa.aaaaaaa(4) aaaa.aaaaaa99@aaaa.aaaaaaa(3) aaa.aaaaaaa99@aaaa.aaaaaaa(2) aaa.aaaaaa99@aaaa.aaaaaaa(1) aaa.aaaaaa9@aaaa.aaaaaaa(1) aaaa.aaaaa99@aaaa.aaaaaaa(1) aaaa.aaaaa9@aaaa.aaaaaaa(1) aaaa.aaaaaaa9@aaaa.aaaaaaa(1) aaaaa.aaaaaaa9@aaaa.aaaaaaa(1) aaaaaaa.aaaaaaa99@aaaa.aaaaaaa(1) | |
| 4phone | 33.33%20 | 40 | text | phone | – | +99 9 99 99 99 99 Masked values 1 / 40 +99 9 99 99 99 99(40) |
| 5address.city | – | 6 | text | – | – | LyonLilleParis Values 6 Lyon(15) Lille(14) Paris(10) Bordeaux(9) Nantes(8) Toulouse(4) |
| 6address.postal_code | 15.0%9 | 5 | integer | – | – | 690035900075011 Values 5 69003(15) 59000(11) 75011(10) 44000(8) 33000(7) |
| 7signup_date | – | 58 | date | date | – | 2025-01-042025-12-072025-01-01 Values 58 2025-01-04(2) 2025-12-07(2) 2025-01-01(1) 2025-01-05(1) 2025-01-07(1) 2025-01-09(1) 2025-01-23(1) 2025-02-25(1) 2025-05-17(1) 2025-06-01(1) 2025-06-11(1) 2025-07-01(1) 2025-07-14(1) 2025-07-24(1) 2025-08-10(1) 2025-08-11(1) 2025-08-26(1) 2025-09-20(1) 2025-10-19(1) 2025-10-28(1) |
| 8newsletter | 6.67%4 | 2 | boolean | – | – | falsetrue Values 2 false(29) true(27) |
| 9tags[] | – | 5 | text | – | – | returningb2bnewsletter Values 5 returning(20) b2b(15) newsletter(13) vip(12) student(10) |
| 10orders[].order_id | – | 119 | text | – | – | O0002-1O0003-1O0003-2 Values 100 / 119 O0002-1(1) O0003-1(1) O0003-2(1) O0005-4(1) O0007-2(1) O0011-1(1) O0012-3(1) O0014-2(1) O0021-3(1) O0027-1(1) O0029-2(1) O0030-2(1) O0033-1(1) O0036-3(1) O0038-4(1) O0043-3(1) O0046-1(1) O0055-1(1) O0056-2(1) O0057-3(1) |
| 11orders[].ordered_at | – | 92 | date | date | – | 2026-01-072026-01-182026-01-22 Values 92 2026-01-07(4) 2026-01-18(3) 2026-01-22(2) 2026-02-21(2) 2026-01-08(1) 2026-01-19(1) 2026-02-11(1) 2026-03-11(1) 2026-03-19(1) 2026-04-05(1) 2026-05-08(1) 2026-05-14(1) 2026-06-14(1) 2026-06-21(1) 2026-06-27(1) 2026-07-01(1) 2026-07-26(1) 2026-08-04(1) 2026-08-09(1) 2026-08-22(1) |
| 12orders[].status | – | 3 | text | – | – | deliveredshippedcancelled Values 3 delivered(74) shipped(24) cancelled(21) |
| 13orders[].items[].product | – | 5 | text | – | – | NotebookHeadphonesDesk lamp Values 5 Notebook(26) Headphones(25) Desk lamp(24) Backpack(23) Mug(21) |
| 14orders[].items[].quantity | – | 3 | integer | – | – | 312 Values 3 3(41) 1(40) 2(38) |
| 15orders[].items[].unit_price | – | 5 | number | – | – | 4.589.029.9 Values 5 4.5(26) 89.0(25) 29.9(24) 54.0(23) 8.0(21) |
| 16orders[].total | 9.24%11 | 15 | number | – | – | 13.5178.09.0 Values 15 13.5(10) 178.0(10) 9.0(10) 24.0(9) 89.0(9) 89.7(9) 54.0(8) 108.0(7) 29.9(6) 4.5(6) 8.0(6) 16.0(5) 162.0(5) 267.0(4) 59.8(4) |
| 17orders[].currency | – | 1 | text | – | – | EUR Values 1 EUR(119) |
03 / Shape
JSON structure(23)
Every path of the records, containers included, with presence per parent and array lengths.
- Records
- 6060 object
- Paths
- 2317 with values
- Maximum depth
- 5Limit 64
Path | Depth | Native types | Present | Absent | Array length | Empty arrays | Role |
|---|---|---|---|---|---|---|---|
id | 1 | string | 100.0%60 | 0 | – | – | column |
name | 1 | string | 100.0%60 | 0 | – | – | column |
email | 1 | string | 93.33%56 | 4 | – | – | column |
phone | 1 | string | 66.67%40 | 20 | – | – | column |
address | 1 | object | 100.0%60 | 0 | – | – | container |
address.city | 2 | string | 100.0%60 | 0 | – | – | column |
address.postal_code | 2 | string | 85.0%51 | 9 | – | – | column |
signup_date | 1 | string | 100.0%60 | 0 | – | – | column |
newsletter | 1 | nullboolean | 100.0%60 | 0 | – | – | column |
tags | 1 | array | 93.33%56 | 4 | 0–2mean 1.25 | 9 | container |
tags[] | 2 | string | items 70 | – | – | – | column |
orders | 1 | array | 100.0%60 | 0 | 0–4mean 1.98 | 11 | container |
orders[] | 2 | object | items 119 | – | – | – | container |
orders[].order_id | 3 | string | 100.0%119 | 0 | – | – | column |
orders[].ordered_at | 3 | string | 100.0%119 | 0 | – | – | column |
orders[].status | 3 | string | 100.0%119 | 0 | – | – | column |
orders[].items | 3 | array | 100.0%119 | 0 | 1 | 0 | container |
orders[].items[] | 4 | object | items 119 | – | – | – | container |
orders[].items[].product | 5 | string | 100.0%119 | 0 | – | – | column |
orders[].items[].quantity | 5 | integer | 100.0%119 | 0 | – | – | column |
orders[].items[].unit_price | 5 | number | 100.0%119 | 0 | – | – | column |
orders[].total | 3 | nullnumber | 100.0%119 | 0 | – | – | column |
orders[].currency | 3 | string | 100.0%119 | 0 | – | – | column |
04 / Cleaning
Transformations(17)
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 |
|---|---|---|---|---|---|---|---|---|
| 1id | – | – | – | 100.0%60 | – | 60 | 60 | 0 |
| 2name | – | – | – | 100.0%60 | 13.33%8 | 56 | 56 | 0 |
| 3email | – | – | – | – | – | 56 | 56 | 0 |
| 4phone | – | – | – | – | – | 40 | 40 | 0 |
| 5address.city | – | – | – | 100.0%60 | – | 6 | 6 | 0 |
| 6address.postal_code | – | – | – | – | – | 5 | 5 | 0 |
| 7signup_date | – | – | – | – | – | 58 | 58 | 0 |
| 8newsletter | – | – | – | – | – | 2 | 2 | – |
| 9tags[] | – | – | – | – | – | 5 | 5 | 0 |
| 10orders[].order_id | – | – | – | 100.0%119 | – | 119 | 119 | 0 |
| 11orders[].ordered_at | – | – | – | – | – | 92 | 92 | 0 |
| 12orders[].status | – | – | – | – | – | 3 | 3 | 0 |
| 13orders[].items[].product | – | – | – | 100.0%119 | – | 5 | 5 | 0 |
| 14orders[].items[].quantity | – | – | – | – | – | 3 | 3 | – |
| 15orders[].items[].unit_price | – | – | – | – | – | 5 | 5 | – |
| 16orders[].total | – | – | – | – | – | 15 | 15 | – |
| 17orders[].currency | – | – | – | 100.0%119 | – | 1 | 1 | 0 |
05 / Numeric
Numeric analysis(4)
Range and distribution statistics for accepted numeric values.
Column | Range | Minimum | Maximum | Mean | Median | Distinct | Examples |
|---|---|---|---|---|---|---|---|
| 6address.postal_code | 42,011 | 33,000 | 75,011 | 59,159.902 | 59,000 | 5 | 690035900075011 Values 5 69003(15) 59000(11) 75011(10) 44000(8) 33000(7) |
| 14orders[].items[].quantity | 2 | 1 | 3 | 2.0084 | 2 | 3 | 312 Values 3 3(41) 1(40) 2(38) |
| 15orders[].items[].unit_price | 84.5 | 4.5 | 89 | 37.5597 | 29.9 | 5 | 4.589.029.9 Values 5 4.5(26) 89.0(25) 29.9(24) 54.0(23) 8.0(21) |
| 16orders[].total | 262.5 | 4.5 | 267 | 69.1565 | 54 | 15 | 13.5178.09.0 Values 15 13.5(10) 178.0(10) 9.0(10) 24.0(9) 89.0(9) 89.7(9) 54.0(8) 108.0(7) 29.9(6) 4.5(6) 8.0(6) 16.0(5) 162.0(5) 267.0(4) 59.8(4) |
06 / Dates
Date analysis(2)
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 |
|---|---|---|---|---|---|---|---|---|
| 7signup_date | valid | 100.0%60 | – | – | – | 58 | 1 variant 1 variant YYYY-MM-DD60 (100.0%) Invalid date0 (0.0%) Not a date0 (0.0%) | |
| 11orders[].ordered_at | valid | 100.0%119 | – | – | – | 92 | 1 variant 1 variant YYYY-MM-DD119 (100.0%) Invalid date0 (0.0%) Not a date0 (0.0%) |
07 / Text
String analysis(10)
Length classes, fixed widths and representative values.
Column | Class | Fixed | Min | Max | Mean | Median | Lengths | Distinct | Examples |
|---|---|---|---|---|---|---|---|---|---|
| 1id | very short | 5 | – | – | – | – | 60 | C0001 Lengths 1 5 charactersC0001 / C0002 / C0003 100.0%60 | |
| 2name | short | – | 10 | 15 | 12.23 | 12.0 | 56 | David DuboisKarim LaurentBruno Leroy Lengths 6 12 charactersDavid Dubois / Farid Dubois / Inès Laurent 43.33%26 13 charactersKarim Laurent / Bruno Bernard / Jules Richard 21.67%13 11 charactersBruno Leroy / Chloé Leroy / David Simon 15.0%9 14 charactersPauline Robert / Quentin Dubois / Quentin Robert 10.0%6 10 charactersInès Leroy / Rose Petit / Ugo Durand 6.67%4 15 charactersOlivier Richard / Yasmine Bernard 3.33%2 | |
| 3email | medium | – | 24 | 30 | 27.04 | 27.0 | 56 | aaaaa.aaaaaa99@aaaa.aaaaaaaaaaaa.aaaaa99@aaaa.aaaaaaaaaaaa.aaaaaaa99@aaaa.aaaaaaa Lengths 7 27 charactersaaaaa.aaaaaa99@aaaa.aaaaaaa / aaaa.aaaaaaa99@aaaa.aaaaaaa / aaaaa.aaaaaaa9@aaaa.aaaaaaa 35.71%20 26 charactersaaaaa.aaaaa99@aaaa.aaaaaaa / aaaaa.aaaaaa9@aaaa.aaaaaaa / aaaa.aaaaaa99@aaaa.aaaaaaa 25.0%14 28 charactersaaaaa.aaaaaaa99@aaaa.aaaaaaa / aaaaaaa.aaaaa99@aaaa.aaaaaaa 19.64%11 29 charactersaaaaaaa.aaaaaa99@aaaa.aaaaaaa 10.71%6 24 charactersaaa.aaaaaa9@aaaa.aaaaaaa / aaaa.aaaaa9@aaaa.aaaaaaa 3.57%2 25 charactersaaa.aaaaaa99@aaaa.aaaaaaa / aaaa.aaaaa99@aaaa.aaaaaaa 3.57%2 30 charactersaaaaaaa.aaaaaaa99@aaaa.aaaaaaa 1.79%1 | |
| 4phone | short | 17 | – | – | – | – | 40 | +99 9 99 99 99 99 Lengths 1 17 characters+99 9 99 99 99 99 100.0%40 | |
| 5address.city | short | – | 4 | 8 | 5.53 | 5.0 | 6 | LilleLyonBordeaux Lengths 4 5 charactersLille / Paris 40.0%24 4 charactersLyon 25.0%15 8 charactersBordeaux / Toulouse 21.67%13 6 charactersNantes 13.33%8 | |
| 9tags[] | short | – | 3 | 10 | 6.59 | 7.0 | 5 | b2breturningnewsletter Lengths 4 3 charactersb2b / vip 38.57%27 9 charactersreturning 28.57%20 10 charactersnewsletter 18.57%13 7 charactersstudent 14.29%10 | |
| 10orders[].order_id | short | 7 | – | – | – | – | 119 | O0002-1 Lengths 1 7 charactersO0002-1 / O0003-1 / O0003-2 100.0%119 | |
| 12orders[].status | short | – | 7 | 9 | 8.6 | 9.0 | 3 | deliveredshipped Lengths 2 9 charactersdelivered / cancelled 79.83%95 7 charactersshipped 20.17%24 | |
| 13orders[].items[].product | short | – | 3 | 10 | 7.74 | 8.0 | 5 | NotebookHeadphonesDesk lamp Lengths 4 8 charactersNotebook / Backpack 41.18%49 10 charactersHeadphones 21.01%25 9 charactersDesk lamp 20.17%24 3 charactersMug 17.65%21 | |
| 17orders[].currency | very short | 3 | – | – | – | – | 1 | EUR Lengths 1 3 charactersEUR 100.0%119 |
08 / Detectors
Detectors and formats(6)
What each detector recognized per column, with the formats it found. Primary: the interpretation shown as semantic type.
Column | Detector | Matched | Ambiguous | Invalid | Formats |
|---|---|---|---|---|---|
| 3email | email primary | 100.0%56 | 0 | 0 | – |
| 4phone | phone primary | 100.0%40 | 0 | 0 | +33 9 99 99 99 99 1 format +33 9 99 99 99 9940 (100.0%) |
| 6address.postal_code | number | 100.0%51 | 0 | 0 | 0 1 format 051 (100.0%) |
| 6address.postal_code | postal_code | 100.0%51 | 0 | 0 | 99999 1 format 9999951 (100.0%) |
| 7signup_date | date primary | 100.0%60 | 0 | 0 | YYYY-MM-DD 1 format YYYY-MM-DD60 (100.0%) |
| 11orders[].ordered_at | date primary | 100.0%119 | 0 | 0 | YYYY-MM-DD 1 format YYYY-MM-DD119 (100.0%) |
10 / Raw values
Data sample(20)
First 20 records with original row numbers and raw values.
Row | idtext | nametext | emailtext | phonetext | address. | address. | signup_ | newsletterboolean | tags[]text | orders[]. | orders[]. | orders[]. | orders[]. | orders[]. | orders[]. | orders[]. | orders[]. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | C0001 | Tania Robert | aaaaa.aaaaaa9@aaaa.aaaaaaa | +99 9 99 99 99 99 | Lyon | 69003 | 2025-03-04 | true | returning | absent | absent | absent | absent | absent | absent | absent | absent |
| 2 | C0002 | Samir Thomas | absent | absent | Nantes | 44000 | 2025-07-06 | false | returning, newsletter | O0002-1 | 2026-01-07 | delivered | Desk lamp | 1 | 29.9 | 29.9 | EUR |
| 3 | C0003 | Farid Dubois | aaaaa.aaaaaa9@aaaa.aaaaaaa | +99 9 99 99 99 99 | Lyon | 69003 | 2025-12-07 | true | vip | O0003-1, O0003-2 | 2026-03-09, 2026-01-20 | delivered, delivered | Headphones, Backpack | 1, 2 | 89.0, 54.0 | 89.0, 108.0 | EUR, EUR |
| 4 | C0004 | Chloé Robert | aaaaa.aaaaaa9@aaaa.aaaaaaa | +99 9 99 99 99 99 | Lille | 59000 | 2025-01-09 | false | student, newsletter | absent | absent | absent | absent | absent | absent | absent | absent |
| 5 | C0005 | Rose Petit | aaaa.aaaaa9@aaaa.aaaaaaa | +99 9 99 99 99 99 | Bordeaux | 33000 | 2025-04-04 | true | student | O0005-1, O0005-2, O0005-3, O0005-4 | 2026-05-25, 2026-06-28, 2026-02-07, 2026-06-05 | shipped, delivered, delivered, delivered | Backpack, Notebook, Notebook, Mug | 3, 3, 2, 3 | 54.0, 4.5, 4.5, 8.0 | 162.0, 13.5, 9.0, 24.0 | EUR, EUR, EUR, EUR |
| 6 | C0006 | Inès Laurent | aaaa.aaaaaaa9@aaaa.aaaaaaa | +99 9 99 99 99 99 | Toulouse | absent | 2025-12-10 | false | absent | O0006-1, O0006-2, O0006-3, O0006-4 | 2026-01-08, 2026-07-05, 2026-06-27, 2026-03-21 | cancelled, delivered, delivered, shipped | Mug, Backpack, Notebook, Desk lamp | 2, 2, 2, 3 | 8.0, 54.0, 4.5, 29.9 | 16.0, 108.0, 9.0, 89.7 | EUR, EUR, EUR, EUR |
| 7 | C0007 | David Dubois | aaaaa.aaaaaa9@aaaa.aaaaaaa | absent | Lille | 59000 | 2025-08-20 | true | newsletter | O0007-1, O0007-2 | 2026-05-16, 2026-07-27 | shipped, cancelled | Headphones, Desk lamp | 2, 1 | 89.0, 29.9 | 178.0, null | EUR, EUR |
| 8 | C0008 | Ugo Durand | aaa.aaaaaa9@aaaa.aaaaaaa | absent | Lille | 59000 | 2025-09-28 | false | returning | absent | absent | absent | absent | absent | absent | absent | absent |
| 9 | C0009 | Jules Richard | aaaaa.aaaaaaa9@aaaa.aaaaaaa | +99 9 99 99 99 99 | Nantes | 44000 | 2025-10-28 | null | student, returning | O0009-1, O0009-2 | 2026-06-12, 2026-07-12 | delivered, delivered | Headphones, Backpack | 2, 3 | 89.0, 54.0 | 178.0, 162.0 | EUR, EUR |
| 10 | C0010 | William Durand | aaaaaaa.aaaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Lyon | 69003 | 2025-08-10 | true | newsletter, b2b | O0010-1, O0010-2, O0010-3, O0010-4 | 2026-07-10, 2026-01-07, 2026-03-08, 2026-04-06 | cancelled, delivered, delivered, delivered | Mug, Backpack, Mug, Notebook | 3, 3, 2, 2 | 8.0, 54.0, 8.0, 4.5 | null, 162.0, 16.0, 9.0 | EUR, EUR, EUR, EUR |
| 11 | C0011 | Emma Bernard | aaaa.aaaaaaa99@aaaa.aaaaaaa | absent | Lyon | 69003 | 2025-07-15 | true | absent | O0011-1 | 2026-06-13 | delivered | Backpack | 1 | 54.0 | 54.0 | EUR |
| 12 | C0012 | Nadia Bernard | aaaaa.aaaaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Paris | 75011 | 2025-09-11 | false | b2b | O0012-1, O0012-2, O0012-3 | 2026-06-15, 2026-07-19, 2026-02-12 | delivered, shipped, delivered | Headphones, Desk lamp, Desk lamp | 3, 3, 1 | 89.0, 29.9, 29.9 | 267.0, 89.7, 29.9 | EUR, EUR, EUR |
| 13 | C0013 | Marc Bernard | aaaa.aaaaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Bordeaux | 33000 | 2025-07-08 | false | newsletter, returning | O0013-1 | 2026-05-24 | delivered | Mug | 3 | 8.0 | 24.0 | EUR |
| 14 | C0014 | Pauline Robert | aaaaaaa.aaaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Lille | absent | 2025-11-20 | false | absent | O0014-1, O0014-2, O0014-3 | 2026-05-08, 2026-08-09, 2026-08-20 | shipped, delivered, shipped | Notebook, Notebook, Backpack | 2, 1, 2 | 4.5, 4.5, 54.0 | 9.0, 4.5, 108.0 | EUR, EUR, EUR |
| 15 | C0015 | Quentin Simon | aaaaaaa.aaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Nantes | 44000 | 2025-06-20 | true | student | O0015-1, O0015-2 | 2026-01-01, 2026-02-22 | delivered, cancelled | Mug, Mug | 1, 2 | 8.0, 8.0 | 8.0, 16.0 | EUR, EUR |
| 16 | C0016 | Nadia Petit | aaaaa.aaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Lille | 59000 | 2025-12-07 | false | absent | absent | absent | absent | absent | absent | absent | absent | absent |
| 17 | C0017 | William Petit | aaaaaaa.aaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Lille | 59000 | 2025-08-11 | false | vip, returning | O0017-1 | 2026-02-22 | delivered | Backpack | 1 | 54.0 | 54.0 | EUR |
| 18 | C0018 | Karim Laurent | aaaaa.aaaaaaa99@aaaa.aaaaaaa | absent | Lille | 59000 | 2025-01-04 | false | vip, b2b | O0018-1, O0018-2 | 2026-03-20, 2026-02-20 | delivered, delivered | Headphones, Headphones | 1, 1 | 89.0, 89.0 | 89.0, 89.0 | EUR, EUR |
| 19 | C0019 | Bruno Leroy | aaaaa.aaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Bordeaux | absent | 2025-07-20 | false | returning | O0019-1, O0019-2, O0019-3 | 2026-06-02, 2026-05-18, 2026-04-09 | delivered, cancelled, delivered | Headphones, Backpack, Backpack | 2, 1, 1 | 89.0, 54.0, 54.0 | 178.0, null, 54.0 | EUR, EUR, EUR |
| 20 | C0020 | Quentin Dubois | aaaaaaa.aaaaaa99@aaaa.aaaaaaa | +99 9 99 99 99 99 | Toulouse | absent | 2025-06-03 | false | absent | O0020-1, O0020-2, O0020-3 | 2026-08-16, 2026-01-18, 2026-07-22 | delivered, shipped, delivered | Desk lamp, Notebook, Notebook | 2, 3, 1 | 29.9, 4.5, 4.5 | 59.8, 13.5, 4.5 | EUR, EUR, EUR |
11 / Method
Analysis settings
Rules used for this analysis, so the result can be reproduced.
- Scope
- All 60 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-256
ece53017421aa38fc11f6f7f07a7ad0168ffe606b098618c8fb1e69c59df1a59