Overview

Dataset statistics

Number of variables2
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory484.0 B
Average record size in memory22.0 B

Variable types

Text2

Dataset

Description샘플 데이터
Author신한카드
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=50

Alerts

업종코드(UPJONG) has unique valuesUnique
업종분류코드명(UPJONG_NM) has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:52:55.466857
Analysis finished2023-12-10 14:52:55.796182
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종코드(UPJONG)
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-10T23:52:55.953844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters88
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

Unique22 ?
Unique (%)100.0%

Sample

1st rowsb01
2nd rowsb02
3rd rowsb03
4th rowsb04
5th rowsb05
ValueCountFrequency (%)
sb01 1
 
4.5%
sb02 1
 
4.5%
sb21 1
 
4.5%
sb20 1
 
4.5%
sb19 1
 
4.5%
sb18 1
 
4.5%
sb17 1
 
4.5%
sb16 1
 
4.5%
sb15 1
 
4.5%
sb14 1
 
4.5%
Other values (12) 12
54.5%
2023-12-10T23:52:56.398015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 22
25.0%
b 22
25.0%
1 13
14.8%
0 11
12.5%
2 6
 
6.8%
3 2
 
2.3%
4 2
 
2.3%
5 2
 
2.3%
6 2
 
2.3%
7 2
 
2.3%
Other values (2) 4
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44
50.0%
Decimal Number 44
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13
29.5%
0 11
25.0%
2 6
13.6%
3 2
 
4.5%
4 2
 
4.5%
5 2
 
4.5%
6 2
 
4.5%
7 2
 
4.5%
8 2
 
4.5%
9 2
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
s 22
50.0%
b 22
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44
50.0%
Common 44
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13
29.5%
0 11
25.0%
2 6
13.6%
3 2
 
4.5%
4 2
 
4.5%
5 2
 
4.5%
6 2
 
4.5%
7 2
 
4.5%
8 2
 
4.5%
9 2
 
4.5%
Latin
ValueCountFrequency (%)
s 22
50.0%
b 22
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 22
25.0%
b 22
25.0%
1 13
14.8%
0 11
12.5%
2 6
 
6.8%
3 2
 
2.3%
4 2
 
2.3%
5 2
 
2.3%
6 2
 
2.3%
7 2
 
2.3%
Other values (2) 4
 
4.5%

업종분류코드명(UPJONG_NM)
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-10T23:52:56.658316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.4545455
Min length2

Characters and Unicode

Total characters98
Distinct characters55
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row한식
2nd row일식/중식/양식
3rd row제과점
4th row커피전문점
5th row패스트푸드
ValueCountFrequency (%)
한식 1
 
4.5%
일식/중식/양식 1
 
4.5%
자동차 1
 
4.5%
가전/가구 1
 
4.5%
의료 1
 
4.5%
교육용품 1
 
4.5%
유아교육 1
 
4.5%
학원 1
 
4.5%
가정생활/서비스 1
 
4.5%
미용 1
 
4.5%
Other values (12) 12
54.5%
2023-12-10T23:52:57.039920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 11
 
11.2%
식 6
 
6.1%
유 4
 
4.1%
스 4
 
4.1%
용 3
 
3.1%
화 3
 
3.1%
품 3
 
3.1%
교 3
 
3.1%
문 3
 
3.1%
가 3
 
3.1%
Other values (45) 55
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
88.8%
Other Punctuation 11
 
11.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 6
 
6.9%
유 4
 
4.6%
스 4
 
4.6%
용 3
 
3.4%
화 3
 
3.4%
품 3
 
3.4%
교 3
 
3.4%
문 3
 
3.4%
가 3
 
3.4%
의 2
 
2.3%
Other values (44) 53
60.9%
Other Punctuation
ValueCountFrequency (%)
/ 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
88.8%
Common 11
 
11.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 6
 
6.9%
유 4
 
4.6%
스 4
 
4.6%
용 3
 
3.4%
화 3
 
3.4%
품 3
 
3.4%
교 3
 
3.4%
문 3
 
3.4%
가 3
 
3.4%
의 2
 
2.3%
Other values (44) 53
60.9%
Common
ValueCountFrequency (%)
/ 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
88.8%
ASCII 11
 
11.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 11
100.0%
Hangul
ValueCountFrequency (%)
식 6
 
6.9%
유 4
 
4.6%
스 4
 
4.6%
용 3
 
3.4%
화 3
 
3.4%
품 3
 
3.4%
교 3
 
3.4%
문 3
 
3.4%
가 3
 
3.4%
의 2
 
2.3%
Other values (44) 53
60.9%

Correlations

2023-12-10T23:52:57.130797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종코드(UPJONG)업종분류코드명(UPJONG_NM)
업종코드(UPJONG)1.0001.000
업종분류코드명(UPJONG_NM)1.0001.000

Missing values

2023-12-10T23:52:55.621042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:52:55.746983image/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

업종코드(UPJONG)업종분류코드명(UPJONG_NM)
0sb01한식
1sb02일식/중식/양식
2sb03제과점
3sb04커피전문점
4sb05패스트푸드
5sb06기타요식
6sb07유흥
7sb08유통
8sb09음/식료품
9sb10의류/잡화
업종코드(UPJONG)업종분류코드명(UPJONG_NM)
12sb13여행/교통
13sb14미용
14sb15가정생활/서비스
15sb16학원
16sb17유아교육
17sb18교육용품
18sb19의료
19sb20가전/가구
20sb21자동차
21sb22주유