Overview

Dataset statistics

Number of variables3
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory26.9 B

Variable types

Text2
Categorical1

Dataset

Description샘플 데이터
Author신용보증재단
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=26

Alerts

서비스_업종_코드(SVC_INDUTY_CD) has unique valuesUnique
서비스_업종_명(SVC_INDUTY_CD_NM) has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:52:33.773450
Analysis finished2023-12-10 14:52:34.079930
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

서비스_업종_코드(SVC_INDUTY_CD)
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-10T23:52:34.251286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters360
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

Unique45 ?
Unique (%)100.0%

Sample

1st rowCS100001
2nd rowCS100002
3rd rowCS100003
4th rowCS100004
5th rowCS100005
ValueCountFrequency (%)
cs100001 1
 
2.2%
cs200014 1
 
2.2%
cs200016 1
 
2.2%
cs200017 1
 
2.2%
cs200018 1
 
2.2%
cs300001 1
 
2.2%
cs300002 1
 
2.2%
cs300003 1
 
2.2%
cs300004 1
 
2.2%
cs300005 1
 
2.2%
Other values (35) 35
77.8%
2023-12-10T23:52:34.637953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 165
45.8%
C 45
 
12.5%
S 45
 
12.5%
1 33
 
9.2%
2 23
 
6.4%
3 22
 
6.1%
4 5
 
1.4%
5 5
 
1.4%
6 5
 
1.4%
7 5
 
1.4%
Other values (2) 7
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
75.0%
Uppercase Letter 90
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
61.1%
1 33
 
12.2%
2 23
 
8.5%
3 22
 
8.1%
4 5
 
1.9%
5 5
 
1.9%
6 5
 
1.9%
7 5
 
1.9%
8 4
 
1.5%
9 3
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 45
50.0%
S 45
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 270
75.0%
Latin 90
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 165
61.1%
1 33
 
12.2%
2 23
 
8.5%
3 22
 
8.1%
4 5
 
1.9%
5 5
 
1.9%
6 5
 
1.9%
7 5
 
1.9%
8 4
 
1.5%
9 3
 
1.1%
Latin
ValueCountFrequency (%)
C 45
50.0%
S 45
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 165
45.8%
C 45
 
12.5%
S 45
 
12.5%
1 33
 
9.2%
2 23
 
6.4%
3 22
 
6.1%
4 5
 
1.4%
5 5
 
1.4%
6 5
 
1.4%
7 5
 
1.4%
Other values (2) 7
 
1.9%

상위_서비스_업종(UPPER_SVC_INDUTY_NM)
Categorical

Distinct3
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size492.0 B
서비스
18 
소매
17 
외식
10 

Length

Max length3
Median length2
Mean length2.4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서비스 18
40.0%
소매 17
37.8%
외식 10
22.2%

Length

2023-12-10T23:52:34.801183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:52:34.916599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서비스 18
40.0%
소매 17
37.8%
외식 10
22.2%

서비스_업종_명(SVC_INDUTY_CD_NM)
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-10T23:52:35.135139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.7777778
Min length3

Characters and Unicode

Total characters215
Distinct characters113
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row한식음식점
2nd row중식음식점
3rd row일식음식점
4th row양식음식점
5th row분식전문점
ValueCountFrequency (%)
한식음식점 1
 
2.2%
스포츠클럽 1
 
2.2%
두발미용업 1
 
2.2%
네일숍 1
 
2.2%
피부관리실 1
 
2.2%
슈퍼마켓 1
 
2.2%
편의점 1
 
2.2%
컴퓨터·주변기기 1
 
2.2%
핸드폰 1
 
2.2%
식료품 1
 
2.2%
Other values (35) 35
77.8%
2023-12-10T23:52:35.521023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 11
 
5.1%
점 11
 
5.1%
· 10
 
4.7%
품 8
 
3.7%
의 7
 
3.3%
원 6
 
2.8%
가 5
 
2.3%
음 5
 
2.3%
일 4
 
1.9%
업 4
 
1.9%
Other values (103) 144
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 201
93.5%
Other Punctuation 10
 
4.7%
Uppercase Letter 2
 
0.9%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 11
 
5.5%
점 11
 
5.5%
품 8
 
4.0%
의 7
 
3.5%
원 6
 
3.0%
가 5
 
2.5%
음 5
 
2.5%
일 4
 
2.0%
업 4
 
2.0%
전 4
 
2.0%
Other values (98) 136
67.7%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%
Other Punctuation
ValueCountFrequency (%)
· 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 201
93.5%
Common 12
 
5.6%
Latin 2
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 11
 
5.5%
점 11
 
5.5%
품 8
 
4.0%
의 7
 
3.5%
원 6
 
3.0%
가 5
 
2.5%
음 5
 
2.5%
일 4
 
2.0%
업 4
 
2.0%
전 4
 
2.0%
Other values (98) 136
67.7%
Common
ValueCountFrequency (%)
· 10
83.3%
( 1
 
8.3%
) 1
 
8.3%
Latin
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 201
93.5%
None 10
 
4.7%
ASCII 4
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 11
 
5.5%
점 11
 
5.5%
품 8
 
4.0%
의 7
 
3.5%
원 6
 
3.0%
가 5
 
2.5%
음 5
 
2.5%
일 4
 
2.0%
업 4
 
2.0%
전 4
 
2.0%
Other values (98) 136
67.7%
None
ValueCountFrequency (%)
· 10
100.0%
ASCII
ValueCountFrequency (%)
( 1
25.0%
) 1
25.0%
C 1
25.0%
P 1
25.0%

Correlations

2023-12-10T23:52:35.646866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서비스_업종_코드(SVC_INDUTY_CD)상위_서비스_업종(UPPER_SVC_INDUTY_NM)서비스_업종_명(SVC_INDUTY_CD_NM)
서비스_업종_코드(SVC_INDUTY_CD)1.0001.0001.000
상위_서비스_업종(UPPER_SVC_INDUTY_NM)1.0001.0001.000
서비스_업종_명(SVC_INDUTY_CD_NM)1.0001.0001.000

Missing values

2023-12-10T23:52:33.948177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:52:34.042290image/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

서비스_업종_코드(SVC_INDUTY_CD)상위_서비스_업종(UPPER_SVC_INDUTY_NM)서비스_업종_명(SVC_INDUTY_CD_NM)
0CS100001외식한식음식점
1CS100002외식중식음식점
2CS100003외식일식음식점
3CS100004외식양식음식점
4CS100005외식분식전문점
5CS100006외식패스트푸드점
6CS100007외식치킨전문점
7CS100008외식제과점
8CS100009외식커피·음료
9CS100010외식호프·간이주점
서비스_업종_코드(SVC_INDUTY_CD)상위_서비스_업종(UPPER_SVC_INDUTY_NM)서비스_업종_명(SVC_INDUTY_CD_NM)
35CS300008소매패션용품
36CS300009소매의약·의료용품
37CS300010소매서적·문구
38CS300011소매화장품
39CS300012소매오락·운동
40CS300013소매섬유제품
41CS300014소매화초·애완
42CS300015소매가구·가전
43CS300016소매주방·가정용품
44CS300017소매통신판매업