Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory52.4 B

Variable types

Categorical4
Text2

Dataset

Description샘플 데이터
Author한국신용데이터
URLhttps://bigdata-region.kr/#/dataset/3d329063-82a2-44ae-a87b-8233916af566

Alerts

업종대분류명 is highly overall correlated with 업종대분류코드 and 2 other fieldsHigh correlation
업종대분류코드 is highly overall correlated with 업종대분류명 and 2 other fieldsHigh correlation
업종중분류명 is highly overall correlated with 업종대분류코드 and 2 other fieldsHigh correlation
업종중분류코드 is highly overall correlated with 업종대분류코드 and 2 other fieldsHigh correlation
업종소분류코드 has unique valuesUnique
업종소분류명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:55:43.983659
Analysis finished2023-12-10 13:55:44.952237
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종대분류코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
A
25 
B
5 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 25
83.3%
B 5
 
16.7%

Length

2023-12-10T22:55:45.060888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:45.244476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 25
83.3%
b 5
 
16.7%

업종대분류명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
유통업
25 
서비스업
5 

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유통업
2nd row유통업
3rd row유통업
4th row유통업
5th row유통업

Common Values

ValueCountFrequency (%)
유통업 25
83.3%
서비스업 5
 
16.7%

Length

2023-12-10T22:55:45.422667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:45.593128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유통업 25
83.3%
서비스업 5
 
16.7%

업종중분류코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
A02
21 
B01
5 
A01
4 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA01
2nd rowA01
3rd rowA01
4th rowA01
5th rowA02

Common Values

ValueCountFrequency (%)
A02 21
70.0%
B01 5
 
16.7%
A01 4
 
13.3%

Length

2023-12-10T22:55:46.110424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:46.262606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a02 21
70.0%
b01 5
 
16.7%
a01 4
 
13.3%

업종중분류명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
전문유통
21 
교육서비스업
5 
종합유통
4 

Length

Max length6
Median length4
Mean length4.3333333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종합유통
2nd row종합유통
3rd row종합유통
4th row종합유통
5th row전문유통

Common Values

ValueCountFrequency (%)
전문유통 21
70.0%
교육서비스업 5
 
16.7%
종합유통 4
 
13.3%

Length

2023-12-10T22:55:46.450157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:46.630327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전문유통 21
70.0%
교육서비스업 5
 
16.7%
종합유통 4
 
13.3%

업종소분류코드
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:55:46.982466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters180
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowA01A01
2nd rowA01A02
3rd rowA01A03
4th rowA01A99
5th rowA02A01
ValueCountFrequency (%)
a01a01 1
 
3.3%
a01a02 1
 
3.3%
b01a04 1
 
3.3%
b01a03 1
 
3.3%
b01a02 1
 
3.3%
b01a01 1
 
3.3%
a02a99 1
 
3.3%
a02a20 1
 
3.3%
a02a19 1
 
3.3%
a02a18 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:55:47.514432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 55
30.6%
0 49
27.2%
2 26
14.4%
1 23
12.8%
9 6
 
3.3%
B 5
 
2.8%
3 4
 
2.2%
4 3
 
1.7%
5 3
 
1.7%
6 2
 
1.1%
Other values (2) 4
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120
66.7%
Uppercase Letter 60
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49
40.8%
2 26
21.7%
1 23
19.2%
9 6
 
5.0%
3 4
 
3.3%
4 3
 
2.5%
5 3
 
2.5%
6 2
 
1.7%
7 2
 
1.7%
8 2
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
A 55
91.7%
B 5
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 120
66.7%
Latin 60
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49
40.8%
2 26
21.7%
1 23
19.2%
9 6
 
5.0%
3 4
 
3.3%
4 3
 
2.5%
5 3
 
2.5%
6 2
 
1.7%
7 2
 
1.7%
8 2
 
1.7%
Latin
ValueCountFrequency (%)
A 55
91.7%
B 5
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 55
30.6%
0 49
27.2%
2 26
14.4%
1 23
12.8%
9 6
 
3.3%
B 5
 
2.8%
3 4
 
2.2%
4 3
 
1.7%
5 3
 
1.7%
6 2
 
1.1%
Other values (2) 4
 
2.2%

업종소분류명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:55:47.848849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length10
Mean length6.2666667
Min length2

Characters and Unicode

Total characters188
Distinct characters98
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row마트/슈퍼마켓
2nd row이커머스/전자상거래
3rd row편의점
4th row기타 종합유통
5th row가구
ValueCountFrequency (%)
기타 2
 
5.4%
및 2
 
5.4%
마트/슈퍼마켓 1
 
2.7%
화원 1
 
2.7%
음식료품 1
 
2.7%
유통 1
 
2.7%
의료용품/기기 1
 
2.7%
자동차용품/부품 1
 
2.7%
주방용품 1
 
2.7%
패션/의류/잡화 1
 
2.7%
Other values (25) 25
67.6%
2023-12-10T22:55:48.492897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 17
 
9.0%
품 10
 
5.3%
기 8
 
4.3%
7
 
3.7%
용 7
 
3.7%
원 6
 
3.2%
유 5
 
2.7%
자 4
 
2.1%
구 4
 
2.1%
전 4
 
2.1%
Other values (88) 116
61.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 164
87.2%
Other Punctuation 17
 
9.0%
Space Separator 7
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 10
 
6.1%
기 8
 
4.9%
용 7
 
4.3%
원 6
 
3.7%
유 5
 
3.0%
자 4
 
2.4%
구 4
 
2.4%
전 4
 
2.4%
의 3
 
1.8%
스 3
 
1.8%
Other values (86) 110
67.1%
Other Punctuation
ValueCountFrequency (%)
/ 17
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 164
87.2%
Common 24
 
12.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 10
 
6.1%
기 8
 
4.9%
용 7
 
4.3%
원 6
 
3.7%
유 5
 
3.0%
자 4
 
2.4%
구 4
 
2.4%
전 4
 
2.4%
의 3
 
1.8%
스 3
 
1.8%
Other values (86) 110
67.1%
Common
ValueCountFrequency (%)
/ 17
70.8%
7
29.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 164
87.2%
ASCII 24
 
12.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 17
70.8%
7
29.2%
Hangul
ValueCountFrequency (%)
품 10
 
6.1%
기 8
 
4.9%
용 7
 
4.3%
원 6
 
3.7%
유 5
 
3.0%
자 4
 
2.4%
구 4
 
2.4%
전 4
 
2.4%
의 3
 
1.8%
스 3
 
1.8%
Other values (86) 110
67.1%

Correlations

2023-12-10T22:55:48.696199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류코드업종대분류명업종중분류코드업종중분류명업종소분류코드업종소분류명
업종대분류코드1.0000.9811.0001.0001.0001.000
업종대분류명0.9811.0001.0001.0001.0001.000
업종중분류코드1.0001.0001.0001.0001.0001.000
업종중분류명1.0001.0001.0001.0001.0001.000
업종소분류코드1.0001.0001.0001.0001.0001.000
업종소분류명1.0001.0001.0001.0001.0001.000
2023-12-10T22:55:48.901318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류명업종대분류코드업종중분류명업종중분류코드
업종대분류명1.0000.8750.9820.982
업종대분류코드0.8751.0000.9820.982
업종중분류명0.9820.9821.0001.000
업종중분류코드0.9820.9821.0001.000
2023-12-10T22:55:49.090150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류코드업종대분류명업종중분류코드업종중분류명
업종대분류코드1.0000.8750.9820.982
업종대분류명0.8751.0000.9820.982
업종중분류코드0.9820.9821.0001.000
업종중분류명0.9820.9821.0001.000

Missing values

2023-12-10T22:55:44.692740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:55:44.883852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

업종대분류코드업종대분류명업종중분류코드업종중분류명업종소분류코드업종소분류명
0A유통업A01종합유통A01A01마트/슈퍼마켓
1A유통업A01종합유통A01A02이커머스/전자상거래
2A유통업A01종합유통A01A03편의점
3A유통업A01종합유통A01A99기타 종합유통
4A유통업A02전문유통A02A01가구
5A유통업A02전문유통A02A02가전/디지털기기
6A유통업A02전문유통A02A03건축/철물/난방자재 및 기계공구
7A유통업A02전문유통A02A04귀금속/시계
8A유통업A02전문유통A02A05농업용품
9A유통업A02전문유통A02A06담배/전자담배
업종대분류코드업종대분류명업종중분류코드업종중분류명업종소분류코드업종소분류명
20A유통업A02전문유통A02A17주방용품
21A유통업A02전문유통A02A18패션/의류/잡화
22A유통업A02전문유통A02A19화원
23A유통업A02전문유통A02A20화장품
24A유통업A02전문유통A02A99기타 전문유통
25B서비스업B01교육서비스업B01A01독서실/스터디카페
26B서비스업B01교육서비스업B01A02예체능학원
27B서비스업B01교육서비스업B01A03외국어학원
28B서비스업B01교육서비스업B01A04유치원/유아원/어린이집
29B서비스업B01교육서비스업B01A05교습학원