Overview

Dataset statistics

Number of variables6
Number of observations275
Missing cells113
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 KiB
Average record size in memory48.5 B

Variable types

Categorical1
Text4
DateTime1

Dataset

Description경상남도 거제시 체육시설업현황(상호명, 주소, 위도, 경도, 전화번호, 기준일자) 등에 대한 정보를 제공합니다.
Author경상남도 거제시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3079326

Alerts

기준일 has constant value ""Constant
시설전화번호 has 113 (41.1%) missing valuesMissing
신고번호 has unique valuesUnique

Reproduction

Analysis started2024-04-16 22:52:01.933234
Analysis finished2024-04-16 22:52:02.322185
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct8
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
당구장업
112 
체육도장업
71 
체력단련장업
40 
골프연습장업
36 
수영장업
 
9
Other values (3)
 
7

Length

Max length7
Median length6
Mean length4.8436364
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
당구장업 112
40.7%
체육도장업 71
25.8%
체력단련장업 40
 
14.5%
골프연습장업 36
 
13.1%
수영장업 9
 
3.3%
무도학원업 3
 
1.1%
요트장업 2
 
0.7%
종합체육시설업 2
 
0.7%

Length

2024-04-17T07:52:02.376244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T07:52:02.469751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당구장업 112
40.7%
체육도장업 71
25.8%
체력단련장업 40
 
14.5%
골프연습장업 36
 
13.1%
수영장업 9
 
3.3%
무도학원업 3
 
1.1%
요트장업 2
 
0.7%
종합체육시설업 2
 
0.7%

신고번호
Text

UNIQUE 

Distinct275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-17T07:52:02.656463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters4675
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique275 ?
Unique (%)100.0%

Sample

1st row30101-1996-000001
2nd row30101-2012-000001
3rd row30101-2013-000001
4th row30101-2016-000001
5th row30101-2016-000002
ValueCountFrequency (%)
30101-1996-000001 1
 
0.4%
30108-2010-000019 1
 
0.4%
30108-2009-000030 1
 
0.4%
30108-2010-000009 1
 
0.4%
30108-2010-000010 1
 
0.4%
30108-2010-000013 1
 
0.4%
30108-2010-000016 1
 
0.4%
30108-2014-000008 1
 
0.4%
30108-2010-000025 1
 
0.4%
30106-2015-000008 1
 
0.4%
Other values (265) 265
96.4%
2024-04-17T07:52:02.945940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2191
46.9%
1 631
 
13.5%
- 550
 
11.8%
2 401
 
8.6%
3 332
 
7.1%
8 171
 
3.7%
6 103
 
2.2%
9 100
 
2.1%
5 99
 
2.1%
4 50
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4125
88.2%
Dash Punctuation 550
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2191
53.1%
1 631
 
15.3%
2 401
 
9.7%
3 332
 
8.0%
8 171
 
4.1%
6 103
 
2.5%
9 100
 
2.4%
5 99
 
2.4%
4 50
 
1.2%
7 47
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2191
46.9%
1 631
 
13.5%
- 550
 
11.8%
2 401
 
8.6%
3 332
 
7.1%
8 171
 
3.7%
6 103
 
2.2%
9 100
 
2.1%
5 99
 
2.1%
4 50
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2191
46.9%
1 631
 
13.5%
- 550
 
11.8%
2 401
 
8.6%
3 332
 
7.1%
8 171
 
3.7%
6 103
 
2.2%
9 100
 
2.1%
5 99
 
2.1%
4 50
 
1.1%

상호
Text

Distinct270
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-17T07:52:03.190436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length7.2654545
Min length2

Characters and Unicode

Total characters1998
Distinct characters303
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique265 ?
Unique (%)96.4%

Sample

1st row삼성문화관수영장
2nd row오션사이드수영장
3rd row삼성중공업 거제호텔 수영장
4th row찬스아이수영장
5th row이야짐
ValueCountFrequency (%)
당구클럽 6
 
1.7%
아일랜드 5
 
1.4%
태권도장 4
 
1.1%
당구장 4
 
1.1%
용인대 4
 
1.1%
태권도 4
 
1.1%
당구 3
 
0.8%
스포츠센터 3
 
0.8%
수영장 3
 
0.8%
한화리조트거제벨버디어 3
 
0.8%
Other values (299) 315
89.0%
2024-04-17T07:52:03.553082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 120
 
6.0%
구 107
 
5.4%
당 105
 
5.3%
79
 
4.0%
스 71
 
3.6%
도 68
 
3.4%
권 56
 
2.8%
태 56
 
2.8%
클 47
 
2.4%
럽 47
 
2.4%
Other values (293) 1242
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1821
91.1%
Space Separator 79
 
4.0%
Uppercase Letter 46
 
2.3%
Decimal Number 16
 
0.8%
Close Punctuation 11
 
0.6%
Open Punctuation 11
 
0.6%
Lowercase Letter 11
 
0.6%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 120
 
6.6%
구 107
 
5.9%
당 105
 
5.8%
스 71
 
3.9%
도 68
 
3.7%
권 56
 
3.1%
태 56
 
3.1%
클 47
 
2.6%
럽 47
 
2.6%
프 34
 
1.9%
Other values (253) 1110
61.0%
Uppercase Letter
ValueCountFrequency (%)
M 8
17.4%
B 7
15.2%
S 3
 
6.5%
P 3
 
6.5%
C 3
 
6.5%
J 3
 
6.5%
N 2
 
4.3%
Y 2
 
4.3%
G 2
 
4.3%
A 2
 
4.3%
Other values (10) 11
23.9%
Lowercase Letter
ValueCountFrequency (%)
o 2
18.2%
y 2
18.2%
k 1
9.1%
m 1
9.1%
t 1
9.1%
i 1
9.1%
d 1
9.1%
w 1
9.1%
e 1
9.1%
Decimal Number
ValueCountFrequency (%)
0 5
31.2%
7 4
25.0%
2 3
18.8%
3 2
 
12.5%
6 1
 
6.2%
4 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
· 1
33.3%
Space Separator
ValueCountFrequency (%)
79
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1821
91.1%
Common 120
 
6.0%
Latin 57
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 120
 
6.6%
구 107
 
5.9%
당 105
 
5.8%
스 71
 
3.9%
도 68
 
3.7%
권 56
 
3.1%
태 56
 
3.1%
클 47
 
2.6%
럽 47
 
2.6%
프 34
 
1.9%
Other values (253) 1110
61.0%
Latin
ValueCountFrequency (%)
M 8
 
14.0%
B 7
 
12.3%
S 3
 
5.3%
P 3
 
5.3%
C 3
 
5.3%
J 3
 
5.3%
o 2
 
3.5%
N 2
 
3.5%
Y 2
 
3.5%
G 2
 
3.5%
Other values (19) 22
38.6%
Common
ValueCountFrequency (%)
79
65.8%
) 11
 
9.2%
( 11
 
9.2%
0 5
 
4.2%
7 4
 
3.3%
2 3
 
2.5%
. 2
 
1.7%
3 2
 
1.7%
· 1
 
0.8%
6 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1821
91.1%
ASCII 176
 
8.8%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 120
 
6.6%
구 107
 
5.9%
당 105
 
5.8%
스 71
 
3.9%
도 68
 
3.7%
권 56
 
3.1%
태 56
 
3.1%
클 47
 
2.6%
럽 47
 
2.6%
프 34
 
1.9%
Other values (253) 1110
61.0%
ASCII
ValueCountFrequency (%)
79
44.9%
) 11
 
6.2%
( 11
 
6.2%
M 8
 
4.5%
B 7
 
4.0%
0 5
 
2.8%
7 4
 
2.3%
S 3
 
1.7%
2 3
 
1.7%
P 3
 
1.7%
Other values (29) 42
23.9%
None
ValueCountFrequency (%)
· 1
100.0%

주소
Text

Distinct269
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-17T07:52:03.820691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length48
Mean length30.272727
Min length19

Characters and Unicode

Total characters8325
Distinct characters188
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique263 ?
Unique (%)95.6%

Sample

1st row경상남도 거제시 장평3로 80-45, 1층 (장평동)
2nd row경상남도 거제시 장승로 145 (장승포동)
3rd row경상남도 거제시 장평3로 80-37 (장평동)
4th row경상남도 거제시 수양로 456-7, 지하1층 (수월동)
5th row경상남도 거제시 일운면 지세포3길 99
ValueCountFrequency (%)
경상남도 275
 
15.9%
거제시 275
 
15.9%
2층 66
 
3.8%
고현동 63
 
3.6%
장평동 41
 
2.4%
3층 38
 
2.2%
옥포동 36
 
2.1%
아주동 27
 
1.6%
4층 18
 
1.0%
상동동 17
 
1.0%
Other values (453) 875
50.5%
2024-04-17T07:52:04.207789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1456
 
17.5%
제 351
 
4.2%
거 345
 
4.1%
상 331
 
4.0%
1 321
 
3.9%
동 316
 
3.8%
, 283
 
3.4%
남 282
 
3.4%
도 280
 
3.4%
경 278
 
3.3%
Other values (178) 4082
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4587
55.1%
Space Separator 1456
 
17.5%
Decimal Number 1436
 
17.2%
Other Punctuation 284
 
3.4%
Close Punctuation 241
 
2.9%
Open Punctuation 241
 
2.9%
Dash Punctuation 58
 
0.7%
Math Symbol 18
 
0.2%
Lowercase Letter 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 351
 
7.7%
거 345
 
7.5%
상 331
 
7.2%
동 316
 
6.9%
남 282
 
6.1%
도 280
 
6.1%
경 278
 
6.1%
시 277
 
6.0%
로 246
 
5.4%
층 156
 
3.4%
Other values (159) 1725
37.6%
Decimal Number
ValueCountFrequency (%)
1 321
22.4%
2 256
17.8%
3 219
15.3%
0 150
10.4%
4 146
10.2%
5 100
 
7.0%
7 67
 
4.7%
8 67
 
4.7%
6 60
 
4.2%
9 50
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 283
99.6%
. 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1456
100.0%
Close Punctuation
ValueCountFrequency (%)
) 241
100.0%
Open Punctuation
ValueCountFrequency (%)
( 241
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Math Symbol
ValueCountFrequency (%)
~ 18
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4587
55.1%
Common 3734
44.9%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 351
 
7.7%
거 345
 
7.5%
상 331
 
7.2%
동 316
 
6.9%
남 282
 
6.1%
도 280
 
6.1%
경 278
 
6.1%
시 277
 
6.0%
로 246
 
5.4%
층 156
 
3.4%
Other values (159) 1725
37.6%
Common
ValueCountFrequency (%)
1456
39.0%
1 321
 
8.6%
, 283
 
7.6%
2 256
 
6.9%
) 241
 
6.5%
( 241
 
6.5%
3 219
 
5.9%
0 150
 
4.0%
4 146
 
3.9%
5 100
 
2.7%
Other values (7) 321
 
8.6%
Latin
ValueCountFrequency (%)
e 2
50.0%
B 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4587
55.1%
ASCII 3738
44.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1456
39.0%
1 321
 
8.6%
, 283
 
7.6%
2 256
 
6.8%
) 241
 
6.4%
( 241
 
6.4%
3 219
 
5.9%
0 150
 
4.0%
4 146
 
3.9%
5 100
 
2.7%
Other values (9) 325
 
8.7%
Hangul
ValueCountFrequency (%)
제 351
 
7.7%
거 345
 
7.5%
상 331
 
7.2%
동 316
 
6.9%
남 282
 
6.1%
도 280
 
6.1%
경 278
 
6.1%
시 277
 
6.0%
로 246
 
5.4%
층 156
 
3.4%
Other values (159) 1725
37.6%

시설전화번호
Text

MISSING 

Distinct157
Distinct (%)96.9%
Missing113
Missing (%)41.1%
Memory size2.3 KiB
2024-04-17T07:52:04.441030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.944444
Min length8

Characters and Unicode

Total characters1935
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)93.8%

Sample

1st row055-630-5230
2nd row055-680-1000
3rd row055-631-2114
4th row055-638-0510
5th row1670-9977
ValueCountFrequency (%)
055-681-7787 2
 
1.2%
1670-9977 2
 
1.2%
055-682-7000 2
 
1.2%
055-632-7004 2
 
1.2%
055-682-4368 2
 
1.2%
055-638-4789 1
 
0.6%
055-633-8709 1
 
0.6%
055-682-1607 1
 
0.6%
055-687-9629 1
 
0.6%
055-635-9886 1
 
0.6%
Other values (147) 147
90.7%
2024-04-17T07:52:04.779745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 402
20.8%
- 321
16.6%
0 269
13.9%
6 231
11.9%
3 160
 
8.3%
8 142
 
7.3%
7 126
 
6.5%
1 88
 
4.5%
2 85
 
4.4%
4 62
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1614
83.4%
Dash Punctuation 321
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 402
24.9%
0 269
16.7%
6 231
14.3%
3 160
 
9.9%
8 142
 
8.8%
7 126
 
7.8%
1 88
 
5.5%
2 85
 
5.3%
4 62
 
3.8%
9 49
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1935
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 402
20.8%
- 321
16.6%
0 269
13.9%
6 231
11.9%
3 160
 
8.3%
8 142
 
7.3%
7 126
 
6.5%
1 88
 
4.5%
2 85
 
4.4%
4 62
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 402
20.8%
- 321
16.6%
0 269
13.9%
6 231
11.9%
3 160
 
8.3%
8 142
 
7.3%
7 126
 
6.5%
1 88
 
4.5%
2 85
 
4.4%
4 62
 
3.2%

기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2019-10-31 00:00:00
Maximum2019-10-31 00:00:00
2024-04-17T07:52:04.873046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:52:04.957805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2024-04-17T07:52:02.212567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T07:52:02.291569image/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

업종신고번호상호주소시설전화번호기준일
0수영장업30101-1996-000001삼성문화관수영장경상남도 거제시 장평3로 80-45, 1층 (장평동)055-630-52302019-10-31
1수영장업30101-2012-000001오션사이드수영장경상남도 거제시 장승로 145 (장승포동)055-680-10002019-10-31
2수영장업30101-2013-000001삼성중공업 거제호텔 수영장경상남도 거제시 장평3로 80-37 (장평동)055-631-21142019-10-31
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267당구장업30108-2019-000012우리동네 당구장경상남도 거제시 장평3로4길 43, 2층 (장평동)055-635-38882019-10-31
268무도학원업30111-1994-000001장승포무도학원경상남도 거제시 능포로 146, 102호 (능포동, 동림상가)055-681-59822019-10-31
269무도학원업30111-2011-000001TOP 댄스스포츠경상남도 거제시 고현로 112 (고현동)055-637-19352019-10-31
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271요트장업30121-2009-000001거제요트학교경상남도 거제시 일운면 지세포해안로 41055-682-11002019-10-31
272요트장업30121-2016-000001(주)거제해양복합리조트경상남도 거제시 동부면 함박금길 85 (가배랑리조트수련원)055-637-54542019-10-31
273종합체육시설업30126-2013-000001거제도해수보양온천경상남도 거제시 수양로 507, 거제도해수온천 1동 3~5층, 5동 1층 (양정동)<NA>2019-10-31
274종합체육시설업30126-2015-000001(주)웰리브 스포츠센터경상남도 거제시 거제대로 3567 (아주동)055-682-18852019-10-31