Overview

Dataset statistics

Number of variables6
Number of observations247
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory48.5 B

Variable types

Categorical2
Text4

Dataset

Description상가(상권)정보 업종 코드를 나타내는 데이터로 세분화된 업종 분류 기준으로 업종 코드를 나눠 업종 코드 항목을 제공합니다. [데이터 변경 안내] 1. 상권업종분류 : 표준산업분류 기반 업종분류 개편(837개 -> 247개) 2. 표준산업분류 : 9차→10차 3. 상가업소번호 : 상가업소번호를 새롭게 생성하여 과거 데이터와 연계 불가 자세한 업종분류의 개편사항은 상권정보시스템 공지사항에서 확인하시기 바랍니다. (붙임 1 참고) https://sg.sbiz.or.kr/godo/noticeInfo/announcementView.sg?id=5158&page=1
URLhttps://www.data.go.kr/data/15067631/fileData.do

Alerts

대분류명 is highly overall correlated with 대분류코드High correlation
대분류코드 is highly overall correlated with 대분류명High correlation
소분류코드 has unique valuesUnique
소분류명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:47:19.286088
Analysis finished2023-12-12 05:47:19.900664
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류코드
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
G2
64 
I2
43 
M1
30 
N1
24 
S2
22 
Other values (5)
64 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowG2
2nd rowG2
3rd rowG2
4th rowG2
5th rowG2

Common Values

ValueCountFrequency (%)
G2 64
25.9%
I2 43
17.4%
M1 30
12.1%
N1 24
 
9.7%
S2 22
 
8.9%
R1 21
 
8.5%
P1 18
 
7.3%
Q1 18
 
7.3%
I1 6
 
2.4%
L1 1
 
0.4%

Length

2023-12-12T14:47:19.973305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:47:20.108201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g2 64
25.9%
i2 43
17.4%
m1 30
12.1%
n1 24
 
9.7%
s2 22
 
8.9%
r1 21
 
8.5%
p1 18
 
7.3%
q1 18
 
7.3%
i1 6
 
2.4%
l1 1
 
0.4%

대분류명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
소매업
64 
음식점업
43 
전문, 과학 및 기술 서비스업
30 
사업시설 관리, 사업 지원 및 임대 서비스업
24 
수리 및 개인 서비스업
22 
Other values (5)
64 

Length

Max length24
Median length16
Mean length9.3967611
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
소매업 64
25.9%
음식점업 43
17.4%
전문, 과학 및 기술 서비스업 30
12.1%
사업시설 관리, 사업 지원 및 임대 서비스업 24
 
9.7%
수리 및 개인 서비스업 22
 
8.9%
예술, 스포츠 및 여가관련 서비스업 21
 
8.5%
교육 서비스업 18
 
7.3%
보건의료업 18
 
7.3%
숙박업 6
 
2.4%
부동산업 1
 
0.4%

Length

2023-12-12T14:47:20.279289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:47:20.395555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서비스업 115
16.9%
및 97
14.3%
소매업 64
 
9.4%
음식점업 43
 
6.3%
전문 30
 
4.4%
과학 30
 
4.4%
기술 30
 
4.4%
사업 24
 
3.5%
임대 24
 
3.5%
지원 24
 
3.5%
Other values (11) 198
29.2%

중분류코드
Text

Distinct75
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T14:47:20.694113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters988
Distinct characters19
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

Unique25 ?
Unique (%)10.1%

Sample

1st rowG202
2nd rowG202
3rd rowG203
4th rowG204
5th rowG204
ValueCountFrequency (%)
p106 15
 
6.1%
i201 14
 
5.7%
q102 12
 
4.9%
g209 11
 
4.5%
r103 10
 
4.0%
r104 10
 
4.0%
g205 9
 
3.6%
i210 9
 
3.6%
g213 6
 
2.4%
m103 6
 
2.4%
Other values (65) 145
58.7%
2023-12-12T14:47:21.080261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 217
22.0%
0 203
20.5%
2 165
16.7%
G 64
 
6.5%
I 49
 
5.0%
3 31
 
3.1%
M 30
 
3.0%
4 29
 
2.9%
6 28
 
2.8%
5 25
 
2.5%
Other values (9) 147
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 741
75.0%
Uppercase Letter 247
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 217
29.3%
0 203
27.4%
2 165
22.3%
3 31
 
4.2%
4 29
 
3.9%
6 28
 
3.8%
5 25
 
3.4%
9 21
 
2.8%
7 12
 
1.6%
8 10
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
G 64
25.9%
I 49
19.8%
M 30
12.1%
N 24
 
9.7%
S 22
 
8.9%
R 21
 
8.5%
P 18
 
7.3%
Q 18
 
7.3%
L 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 741
75.0%
Latin 247
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 217
29.3%
0 203
27.4%
2 165
22.3%
3 31
 
4.2%
4 29
 
3.9%
6 28
 
3.8%
5 25
 
3.4%
9 21
 
2.8%
7 12
 
1.6%
8 10
 
1.3%
Latin
ValueCountFrequency (%)
G 64
25.9%
I 49
19.8%
M 30
12.1%
N 24
 
9.7%
S 22
 
8.9%
R 21
 
8.5%
P 18
 
7.3%
Q 18
 
7.3%
L 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 217
22.0%
0 203
20.5%
2 165
16.7%
G 64
 
6.5%
I 49
 
5.0%
3 31
 
3.1%
M 30
 
3.0%
4 29
 
2.9%
6 28
 
2.8%
5 25
 
2.5%
Other values (9) 147
14.9%

중분류명
Text

Distinct75
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T14:47:21.409635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length21
Mean length11.331984
Min length2

Characters and Unicode

Total characters2799
Distinct characters160
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)10.1%

Sample

1st row자동차 부품 및 내장품 소매업
2nd row자동차 부품 및 내장품 소매업
3rd row모터사이클 및 부품 소매업
4th row종합 소매업
5th row종합 소매업
ValueCountFrequency (%)
및 101
 
12.3%
소매업 61
 
7.4%
기타 57
 
6.9%
서비스업 57
 
6.9%
음식점업 36
 
4.4%
교육기관 16
 
1.9%
한식 14
 
1.7%
의원 12
 
1.5%
기술 12
 
1.5%
섬유 11
 
1.3%
Other values (139) 444
54.1%
2023-12-12T14:47:21.906162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
574
20.5%
업 219
 
7.8%
기 107
 
3.8%
및 101
 
3.6%
식 77
 
2.8%
비 75
 
2.7%
스 69
 
2.5%
소 67
 
2.4%
서 64
 
2.3%
매 64
 
2.3%
Other values (150) 1382
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2164
77.3%
Space Separator 574
 
20.5%
Other Punctuation 61
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 219
 
10.1%
기 107
 
4.9%
및 101
 
4.7%
식 77
 
3.6%
비 75
 
3.5%
스 69
 
3.2%
소 67
 
3.1%
서 64
 
3.0%
매 64
 
3.0%
품 63
 
2.9%
Other values (148) 1258
58.1%
Space Separator
ValueCountFrequency (%)
574
100.0%
Other Punctuation
ValueCountFrequency (%)
, 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2164
77.3%
Common 635
 
22.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 219
 
10.1%
기 107
 
4.9%
및 101
 
4.7%
식 77
 
3.6%
비 75
 
3.5%
스 69
 
3.2%
소 67
 
3.1%
서 64
 
3.0%
매 64
 
3.0%
품 63
 
2.9%
Other values (148) 1258
58.1%
Common
ValueCountFrequency (%)
574
90.4%
, 61
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2160
77.2%
ASCII 635
 
22.7%
Compat Jamo 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
574
90.4%
, 61
 
9.6%
Hangul
ValueCountFrequency (%)
업 219
 
10.1%
기 107
 
5.0%
및 101
 
4.7%
식 77
 
3.6%
비 75
 
3.5%
스 69
 
3.2%
소 67
 
3.1%
서 64
 
3.0%
매 64
 
3.0%
품 63
 
2.9%
Other values (147) 1254
58.1%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%

소분류코드
Text

UNIQUE 

Distinct247
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T14:47:22.265343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1482
Distinct characters19
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

Unique247 ?
Unique (%)100.0%

Sample

1st rowG20201
2nd rowG20202
3rd rowG20301
4th rowG20404
5th rowG20405
ValueCountFrequency (%)
g20201 1
 
0.4%
q10101 1
 
0.4%
n10805 1
 
0.4%
n10901 1
 
0.4%
n10999 1
 
0.4%
n11001 1
 
0.4%
n11002 1
 
0.4%
n11003 1
 
0.4%
n11004 1
 
0.4%
n11099 1
 
0.4%
Other values (237) 237
96.0%
2023-12-12T14:47:22.737858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 407
27.5%
1 309
20.9%
2 215
14.5%
9 72
 
4.9%
3 67
 
4.5%
G 64
 
4.3%
I 49
 
3.3%
4 48
 
3.2%
5 39
 
2.6%
6 38
 
2.6%
Other values (9) 174
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1235
83.3%
Uppercase Letter 247
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 407
33.0%
1 309
25.0%
2 215
17.4%
9 72
 
5.8%
3 67
 
5.4%
4 48
 
3.9%
5 39
 
3.2%
6 38
 
3.1%
7 23
 
1.9%
8 17
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
G 64
25.9%
I 49
19.8%
M 30
12.1%
N 24
 
9.7%
S 22
 
8.9%
R 21
 
8.5%
P 18
 
7.3%
Q 18
 
7.3%
L 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1235
83.3%
Latin 247
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 407
33.0%
1 309
25.0%
2 215
17.4%
9 72
 
5.8%
3 67
 
5.4%
4 48
 
3.9%
5 39
 
3.2%
6 38
 
3.1%
7 23
 
1.9%
8 17
 
1.4%
Latin
ValueCountFrequency (%)
G 64
25.9%
I 49
19.8%
M 30
12.1%
N 24
 
9.7%
S 22
 
8.9%
R 21
 
8.5%
P 18
 
7.3%
Q 18
 
7.3%
L 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 407
27.5%
1 309
20.9%
2 215
14.5%
9 72
 
4.9%
3 67
 
4.5%
G 64
 
4.3%
I 49
 
3.3%
4 48
 
3.2%
5 39
 
2.6%
6 38
 
2.6%
Other values (9) 174
11.7%

소분류명
Text

UNIQUE 

Distinct247
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T14:47:23.022549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length8.1497976
Min length2

Characters and Unicode

Total characters2013
Distinct characters300
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)100.0%

Sample

1st row타이어 소매업
2nd row자동차 부품 소매업
3rd row모터사이클 및 부품 소매업
4th row슈퍼마켓
5th row편의점
ValueCountFrequency (%)
소매업 54
 
10.6%
기타 28
 
5.5%
서비스업 19
 
3.7%
의원 10
 
2.0%
대여업 9
 
1.8%
및 8
 
1.6%
외 6
 
1.2%
수리업 6
 
1.2%
전문 6
 
1.2%
그 6
 
1.2%
Other values (299) 356
70.1%
2023-12-12T14:47:23.442183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
261
 
13.0%
업 123
 
6.1%
/ 100
 
5.0%
소 67
 
3.3%
매 57
 
2.8%
기 57
 
2.8%
스 37
 
1.8%
타 32
 
1.6%
비 32
 
1.6%
원 32
 
1.6%
Other values (290) 1215
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1645
81.7%
Space Separator 261
 
13.0%
Other Punctuation 105
 
5.2%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 123
 
7.5%
소 67
 
4.1%
매 57
 
3.5%
기 57
 
3.5%
스 37
 
2.2%
타 32
 
1.9%
비 32
 
1.9%
원 32
 
1.9%
사 30
 
1.8%
장 28
 
1.7%
Other values (284) 1150
69.9%
Other Punctuation
ValueCountFrequency (%)
/ 100
95.2%
· 3
 
2.9%
, 2
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%
Space Separator
ValueCountFrequency (%)
261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1645
81.7%
Common 366
 
18.2%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 123
 
7.5%
소 67
 
4.1%
매 57
 
3.5%
기 57
 
3.5%
스 37
 
2.2%
타 32
 
1.9%
비 32
 
1.9%
원 32
 
1.9%
사 30
 
1.8%
장 28
 
1.7%
Other values (284) 1150
69.9%
Common
ValueCountFrequency (%)
261
71.3%
/ 100
 
27.3%
· 3
 
0.8%
, 2
 
0.5%
Latin
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1645
81.7%
ASCII 365
 
18.1%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
261
71.5%
/ 100
 
27.4%
, 2
 
0.5%
C 1
 
0.3%
P 1
 
0.3%
Hangul
ValueCountFrequency (%)
업 123
 
7.5%
소 67
 
4.1%
매 57
 
3.5%
기 57
 
3.5%
스 37
 
2.2%
타 32
 
1.9%
비 32
 
1.9%
원 32
 
1.9%
사 30
 
1.8%
장 28
 
1.7%
Other values (284) 1150
69.9%
None
ValueCountFrequency (%)
· 3
100.0%

Correlations

2023-12-12T14:47:23.521968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드대분류명중분류코드중분류명
대분류코드1.0001.0001.0001.000
대분류명1.0001.0001.0001.000
중분류코드1.0001.0001.0001.000
중분류명1.0001.0001.0001.000
2023-12-12T14:47:23.599517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류명대분류코드
대분류명1.0001.000
대분류코드1.0001.000
2023-12-12T14:47:23.680656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류코드대분류명
대분류코드1.0001.000
대분류명1.0001.000

Missing values

2023-12-12T14:47:19.729888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:47:19.851811image/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

대분류코드대분류명중분류코드중분류명소분류코드소분류명
0G2소매업G202자동차 부품 및 내장품 소매업G20201타이어 소매업
1G2소매업G202자동차 부품 및 내장품 소매업G20202자동차 부품 소매업
2G2소매업G203모터사이클 및 부품 소매업G20301모터사이클 및 부품 소매업
3G2소매업G204종합 소매업G20404슈퍼마켓
4G2소매업G204종합 소매업G20405편의점
5G2소매업G204종합 소매업G20499그 외 기타 종합 소매업
6G2소매업G205식료품 소매업G20501곡물/곡분 소매업
7G2소매업G205식료품 소매업G20502가축 사료 소매업
8G2소매업G205식료품 소매업G20503정육점
9G2소매업G205식료품 소매업G20504건어물/젓갈 소매업
대분류코드대분류명중분류코드중분류명소분류코드소분류명
237S2수리 및 개인 서비스업S207이용 및 미용업S20703네일숍
238S2수리 및 개인 서비스업S208욕탕, 마사지 및 기타 신체 관리 서비스업S20801목욕탕/사우나
239S2수리 및 개인 서비스업S208욕탕, 마사지 및 기타 신체 관리 서비스업S20802마사지/안마
240S2수리 및 개인 서비스업S208욕탕, 마사지 및 기타 신체 관리 서비스업S20803체형/비만 관리
241S2수리 및 개인 서비스업S209세탁업S20901세탁소
242S2수리 및 개인 서비스업S209세탁업S20902셀프 빨래방
243S2수리 및 개인 서비스업S210장례식장 및 관련 서비스업S21001장례식장
244S2수리 및 개인 서비스업S210장례식장 및 관련 서비스업S21002화장터/묘지/납골당
245S2수리 및 개인 서비스업S211기타 개인서비스S21101예식장업
246S2수리 및 개인 서비스업S211기타 개인서비스S21105결혼 상담 서비스업