Overview

Dataset statistics

Number of variables3
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory852.0 B
Average record size in memory28.4 B

Variable types

Text3

Dataset

Description(주)한국가스기술공사는 천연가스 설비의 유지보수 사업 및 수소 충전소 운영사업 수행등을 위하여 전국에 사업장을 소재하고 있으며, 사업장별 관할 세무서 현황자료입니다.
URLhttps://www.data.go.kr/data/15103203/fileData.do

Alerts

사업장명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:46:55.799215
Analysis finished2023-12-12 11:46:56.233391
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:46:56.416375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13.5
Mean length8.0666667
Min length2

Characters and Unicode

Total characters242
Distinct characters68
Distinct categories6 ?
Distinct scripts3 ?
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 row본사
2nd row평택기지지사
3rd row인천기지지사
4th row통영기지지사
5th row삼척기지지사
ValueCountFrequency (%)
수소충전소 10
 
19.2%
부안군 2
 
3.8%
본사 1
 
1.9%
버스수소충전소 1
 
1.9%
평창 1
 
1.9%
대관령 1
 
1.9%
전주시 1
 
1.9%
삼천 1
 
1.9%
서울 1
 
1.9%
강서 1
 
1.9%
Other values (32) 32
61.5%
2023-12-12T20:46:56.857521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 28
 
11.6%
22
 
9.1%
지 19
 
7.9%
전 17
 
7.0%
사 16
 
6.6%
충 15
 
6.2%
수 14
 
5.8%
기 7
 
2.9%
주 5
 
2.1%
시 5
 
2.1%
Other values (58) 94
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 214
88.4%
Space Separator 22
 
9.1%
Uppercase Letter 3
 
1.2%
Decimal Number 1
 
0.4%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 28
 
13.1%
지 19
 
8.9%
전 17
 
7.9%
사 16
 
7.5%
충 15
 
7.0%
수 14
 
6.5%
기 7
 
3.3%
주 5
 
2.3%
시 5
 
2.3%
천 5
 
2.3%
Other values (51) 83
38.8%
Uppercase Letter
ValueCountFrequency (%)
L 1
33.3%
N 1
33.3%
G 1
33.3%
Space Separator
ValueCountFrequency (%)
22
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 214
88.4%
Common 25
 
10.3%
Latin 3
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 28
 
13.1%
지 19
 
8.9%
전 17
 
7.9%
사 16
 
7.5%
충 15
 
7.0%
수 14
 
6.5%
기 7
 
3.3%
주 5
 
2.3%
시 5
 
2.3%
천 5
 
2.3%
Other values (51) 83
38.8%
Common
ValueCountFrequency (%)
22
88.0%
1 1
 
4.0%
( 1
 
4.0%
) 1
 
4.0%
Latin
ValueCountFrequency (%)
L 1
33.3%
N 1
33.3%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 214
88.4%
ASCII 28
 
11.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 28
 
13.1%
지 19
 
8.9%
전 17
 
7.9%
사 16
 
7.5%
충 15
 
7.0%
수 14
 
6.5%
기 7
 
3.3%
주 5
 
2.3%
시 5
 
2.3%
천 5
 
2.3%
Other values (51) 83
38.8%
ASCII
ValueCountFrequency (%)
22
78.6%
1 1
 
3.6%
( 1
 
3.6%
L 1
 
3.6%
N 1
 
3.6%
G 1
 
3.6%
) 1
 
3.6%

사업장 소재지
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:46:57.074091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.4666667
Min length3

Characters and Unicode

Total characters104
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)53.3%

Sample

1st row대전광역시
2nd row평택시
3rd row인천광역시
4th row통영시
5th row삼척시
ValueCountFrequency (%)
대전광역시 2
 
6.7%
제주시 2
 
6.7%
서울특별시 2
 
6.7%
부안군 2
 
6.7%
평택시 2
 
6.7%
인천광역시 2
 
6.7%
삼척시 2
 
6.7%
경산시 1
 
3.3%
안성시 1
 
3.3%
충주시 1
 
3.3%
Other values (13) 13
43.3%
2023-12-12T20:46:57.464398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 27
26.0%
광 6
 
5.8%
주 6
 
5.8%
안 5
 
4.8%
역 5
 
4.8%
산 4
 
3.8%
천 4
 
3.8%
군 4
 
3.8%
서 3
 
2.9%
전 3
 
2.9%
Other values (24) 37
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 27
26.0%
광 6
 
5.8%
주 6
 
5.8%
안 5
 
4.8%
역 5
 
4.8%
산 4
 
3.8%
천 4
 
3.8%
군 4
 
3.8%
서 3
 
2.9%
전 3
 
2.9%
Other values (24) 37
35.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 27
26.0%
광 6
 
5.8%
주 6
 
5.8%
안 5
 
4.8%
역 5
 
4.8%
산 4
 
3.8%
천 4
 
3.8%
군 4
 
3.8%
서 3
 
2.9%
전 3
 
2.9%
Other values (24) 37
35.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 27
26.0%
광 6
 
5.8%
주 6
 
5.8%
안 5
 
4.8%
역 5
 
4.8%
산 4
 
3.8%
천 4
 
3.8%
군 4
 
3.8%
서 3
 
2.9%
전 3
 
2.9%
Other values (24) 37
35.6%

관할세무서
Text

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T20:46:57.677559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0666667
Min length5

Characters and Unicode

Total characters152
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)63.3%

Sample

1st row북대전세무서
2nd row평택세무서
3rd row연수세무서
4th row통영세무서
5th row삼척세무서
ValueCountFrequency (%)
평택세무서 3
 
10.0%
강서세무서 2
 
6.7%
정읍세무서 2
 
6.7%
삼척세무서 2
 
6.7%
제주세무서 2
 
6.7%
화성세무서 1
 
3.3%
북대전세무서 1
 
3.3%
김해세무서 1
 
3.3%
충주세무서 1
 
3.3%
전주세무서 1
 
3.3%
Other values (14) 14
46.7%
2023-12-12T20:46:58.039183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 34
22.4%
세 30
19.7%
무 30
19.7%
산 5
 
3.3%
주 5
 
3.3%
택 3
 
2.0%
전 3
 
2.0%
천 3
 
2.0%
평 3
 
2.0%
강 3
 
2.0%
Other values (26) 33
21.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 152
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 34
22.4%
세 30
19.7%
무 30
19.7%
산 5
 
3.3%
주 5
 
3.3%
택 3
 
2.0%
전 3
 
2.0%
천 3
 
2.0%
평 3
 
2.0%
강 3
 
2.0%
Other values (26) 33
21.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 152
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 34
22.4%
세 30
19.7%
무 30
19.7%
산 5
 
3.3%
주 5
 
3.3%
택 3
 
2.0%
전 3
 
2.0%
천 3
 
2.0%
평 3
 
2.0%
강 3
 
2.0%
Other values (26) 33
21.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 152
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 34
22.4%
세 30
19.7%
무 30
19.7%
산 5
 
3.3%
주 5
 
3.3%
택 3
 
2.0%
전 3
 
2.0%
천 3
 
2.0%
평 3
 
2.0%
강 3
 
2.0%
Other values (26) 33
21.7%

Correlations

2023-12-12T20:46:58.151172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장명사업장 소재지관할세무서
사업장명1.0001.0001.000
사업장 소재지1.0001.0000.993
관할세무서1.0000.9931.000

Missing values

2023-12-12T20:46:56.069918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:46:56.178784image/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

사업장명사업장 소재지관할세무서
0본사대전광역시북대전세무서
1평택기지지사평택시평택세무서
2인천기지지사인천광역시연수세무서
3통영기지지사통영시통영세무서
4삼척기지지사삼척시삼척세무서
5제주(LNG)지사제주시제주세무서
6서울지사서울특별시강서세무서
7인천지사인천광역시서인천세무서
8경기지사안산시안산세무서
9강원지사원주시원주세무서
사업장명사업장 소재지관할세무서
20신사업개발부삼척시삼척세무서
21부안군 수소충전소부안군정읍세무서
22평창 대관령 수소충전소평창군강릉세무서
23전주시 삼천 수소충전소전주시전주세무서
24서울 강서 버스수소충전소서울특별시강서세무서
25평택 수소생산기지평택시평택세무서
26충주시 수소버스충전소충주시충주세무서
27제주도 함덕 그린수소충전소제주시제주세무서
28보령 1호 수소충전소보령시보령세무서
29부안군 곰소 수소충전소부안군정읍세무서