Overview

Dataset statistics

Number of variables6
Number of observations2566
Missing cells8964
Missing cells (%)58.2%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory120.4 KiB
Average record size in memory48.0 B

Variable types

Text5
Categorical1

Dataset

Description전라북도 완주군 읍면별 실외(야외) 운동기구 현황에 대하여 읍면별, 운동기구 종류, 소재지, 업체 등을 공개합니다.
Author전라북도 완주군
URLhttps://www.data.go.kr/data/15037918/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
연번 has 1792 (69.8%) missing valuesMissing
관리번호 has 1793 (69.9%) missing valuesMissing
위치 has 1793 (69.9%) missing valuesMissing
설치장소 has 1793 (69.9%) missing valuesMissing
기구명 has 1793 (69.9%) missing valuesMissing

Reproduction

Analysis started2024-03-14 10:39:20.775537
Analysis finished2024-03-14 10:39:22.471476
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Text

MISSING 

Distinct774
Distinct (%)100.0%
Missing1792
Missing (%)69.8%
Memory size20.2 KiB
2024-03-14T19:39:24.083175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.8656331
Min length1

Characters and Unicode

Total characters2218
Distinct characters13
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

Unique774 ?
Unique (%)100.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5
ValueCountFrequency (%)
194 1
 
0.1%
581 1
 
0.1%
520 1
 
0.1%
522 1
 
0.1%
512 1
 
0.1%
513 1
 
0.1%
514 1
 
0.1%
515 1
 
0.1%
516 1
 
0.1%
517 1
 
0.1%
Other values (764) 764
98.7%
2024-03-14T19:39:26.316696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 259
11.7%
2 258
11.6%
3 258
11.6%
4 257
11.6%
5 257
11.6%
6 257
11.6%
7 225
10.1%
0 150
6.8%
9 147
6.6%
8 147
6.6%
Other values (3) 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2215
99.9%
Other Letter 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 259
11.7%
2 258
11.6%
3 258
11.6%
4 257
11.6%
5 257
11.6%
6 257
11.6%
7 225
10.2%
0 150
6.8%
9 147
6.6%
8 147
6.6%
Other Letter
ValueCountFrequency (%)
용 1
33.3%
진 1
33.3%
읍 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2215
99.9%
Hangul 3
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 259
11.7%
2 258
11.6%
3 258
11.6%
4 257
11.6%
5 257
11.6%
6 257
11.6%
7 225
10.2%
0 150
6.8%
9 147
6.6%
8 147
6.6%
Hangul
ValueCountFrequency (%)
용 1
33.3%
진 1
33.3%
읍 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2215
99.9%
Hangul 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 259
11.7%
2 258
11.6%
3 258
11.6%
4 257
11.6%
5 257
11.6%
6 257
11.6%
7 225
10.2%
0 150
6.8%
9 147
6.6%
8 147
6.6%
Hangul
ValueCountFrequency (%)
용 1
33.3%
진 1
33.3%
읍 1
33.3%

관리번호
Text

MISSING 

Distinct213
Distinct (%)27.6%
Missing1793
Missing (%)69.9%
Memory size20.2 KiB
2024-03-14T19:39:27.107372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length12.670116
Min length5

Characters and Unicode

Total characters9794
Distinct characters192
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st row삼례수도산근린공원
2nd row삼례수도산근린공원
3rd row삼례수도산근린공원
4th row삼례수도산근린공원
5th row삼례수도산근린공원
ValueCountFrequency (%)
이서 68
 
7.3%
설화공원 18
 
1.9%
운주면 17
 
1.8%
비봉면-소농리-문장마을-2 14
 
1.5%
노을공원 14
 
1.5%
지사울공원 13
 
1.4%
장산아파트 12
 
1.3%
삼봉지구 12
 
1.3%
공원 12
 
1.3%
상관면-신리-지큐빌아파트 11
 
1.2%
Other values (223) 740
79.5%
2024-03-14T19:39:28.303052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1601
 
16.3%
리 604
 
6.2%
을 548
 
5.6%
마 547
 
5.6%
면 426
 
4.3%
1 305
 
3.1%
원 229
 
2.3%
산 228
 
2.3%
이 179
 
1.8%
읍 176
 
1.8%
Other values (182) 4951
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7400
75.6%
Dash Punctuation 1601
 
16.3%
Decimal Number 635
 
6.5%
Space Separator 158
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 604
 
8.2%
을 548
 
7.4%
마 547
 
7.4%
면 426
 
5.8%
원 229
 
3.1%
산 228
 
3.1%
이 179
 
2.4%
읍 176
 
2.4%
봉 165
 
2.2%
공 146
 
2.0%
Other values (170) 4152
56.1%
Decimal Number
ValueCountFrequency (%)
1 305
48.0%
0 81
 
12.8%
2 73
 
11.5%
3 32
 
5.0%
4 30
 
4.7%
7 28
 
4.4%
6 27
 
4.3%
9 20
 
3.1%
5 20
 
3.1%
8 19
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 1601
100.0%
Space Separator
ValueCountFrequency (%)
158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7400
75.6%
Common 2394
 
24.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 604
 
8.2%
을 548
 
7.4%
마 547
 
7.4%
면 426
 
5.8%
원 229
 
3.1%
산 228
 
3.1%
이 179
 
2.4%
읍 176
 
2.4%
봉 165
 
2.2%
공 146
 
2.0%
Other values (170) 4152
56.1%
Common
ValueCountFrequency (%)
- 1601
66.9%
1 305
 
12.7%
158
 
6.6%
0 81
 
3.4%
2 73
 
3.0%
3 32
 
1.3%
4 30
 
1.3%
7 28
 
1.2%
6 27
 
1.1%
9 20
 
0.8%
Other values (2) 39
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7400
75.6%
ASCII 2394
 
24.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1601
66.9%
1 305
 
12.7%
158
 
6.6%
0 81
 
3.4%
2 73
 
3.0%
3 32
 
1.3%
4 30
 
1.3%
7 28
 
1.2%
6 27
 
1.1%
9 20
 
0.8%
Other values (2) 39
 
1.6%
Hangul
ValueCountFrequency (%)
리 604
 
8.2%
을 548
 
7.4%
마 547
 
7.4%
면 426
 
5.8%
원 229
 
3.1%
산 228
 
3.1%
이 179
 
2.4%
읍 176
 
2.4%
봉 165
 
2.2%
공 146
 
2.0%
Other values (170) 4152
56.1%

위치
Text

MISSING 

Distinct193
Distinct (%)25.0%
Missing1793
Missing (%)69.9%
Memory size20.2 KiB
2024-03-14T19:39:29.834772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length23
Mean length12.566624
Min length4

Characters and Unicode

Total characters9714
Distinct characters171
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st row삼례읍 삼례리 123-11 일원
2nd row삼례읍 삼례리 123-11 일원
3rd row삼례읍 삼례리 123-11 일원
4th row삼례읍 삼례리 123-11 일원
5th row삼례읍 삼례리 123-11 일원
ValueCountFrequency (%)
봉동읍 114
 
4.7%
삼례읍 84
 
3.5%
옆 82
 
3.4%
경로당 78
 
3.2%
이서면 68
 
2.8%
앞 67
 
2.8%
고산면 58
 
2.4%
소양면 52
 
2.1%
전라북도 49
 
2.0%
상관면 43
 
1.8%
Other values (323) 1736
71.4%
2024-03-14T19:39:31.788672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1686
 
17.4%
1 582
 
6.0%
리 424
 
4.4%
면 361
 
3.7%
- 305
 
3.1%
읍 261
 
2.7%
2 254
 
2.6%
산 246
 
2.5%
로 202
 
2.1%
봉 194
 
2.0%
Other values (161) 5199
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5443
56.0%
Decimal Number 2185
22.5%
Space Separator 1686
 
17.4%
Dash Punctuation 305
 
3.1%
Close Punctuation 37
 
0.4%
Open Punctuation 37
 
0.4%
Other Punctuation 21
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 424
 
7.8%
면 361
 
6.6%
읍 261
 
4.8%
산 246
 
4.5%
로 202
 
3.7%
봉 194
 
3.6%
동 156
 
2.9%
길 128
 
2.4%
경 126
 
2.3%
당 120
 
2.2%
Other values (146) 3225
59.3%
Decimal Number
ValueCountFrequency (%)
1 582
26.6%
2 254
11.6%
7 189
 
8.6%
5 184
 
8.4%
3 175
 
8.0%
9 174
 
8.0%
8 173
 
7.9%
4 155
 
7.1%
0 150
 
6.9%
6 149
 
6.8%
Space Separator
ValueCountFrequency (%)
1686
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 305
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Other Punctuation
ValueCountFrequency (%)
, 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5443
56.0%
Common 4271
44.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 424
 
7.8%
면 361
 
6.6%
읍 261
 
4.8%
산 246
 
4.5%
로 202
 
3.7%
봉 194
 
3.6%
동 156
 
2.9%
길 128
 
2.4%
경 126
 
2.3%
당 120
 
2.2%
Other values (146) 3225
59.3%
Common
ValueCountFrequency (%)
1686
39.5%
1 582
 
13.6%
- 305
 
7.1%
2 254
 
5.9%
7 189
 
4.4%
5 184
 
4.3%
3 175
 
4.1%
9 174
 
4.1%
8 173
 
4.1%
4 155
 
3.6%
Other values (5) 394
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5443
56.0%
ASCII 4271
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1686
39.5%
1 582
 
13.6%
- 305
 
7.1%
2 254
 
5.9%
7 189
 
4.4%
5 184
 
4.3%
3 175
 
4.1%
9 174
 
4.1%
8 173
 
4.1%
4 155
 
3.6%
Other values (5) 394
 
9.2%
Hangul
ValueCountFrequency (%)
리 424
 
7.8%
면 361
 
6.6%
읍 261
 
4.8%
산 246
 
4.5%
로 202
 
3.7%
봉 194
 
3.6%
동 156
 
2.9%
길 128
 
2.4%
경 126
 
2.3%
당 120
 
2.2%
Other values (146) 3225
59.3%

설치장소
Text

MISSING 

Distinct185
Distinct (%)23.9%
Missing1793
Missing (%)69.9%
Memory size20.2 KiB
2024-03-14T19:39:33.191114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length16
Mean length10.430789
Min length3

Characters and Unicode

Total characters8063
Distinct characters207
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st row삼례읍 삼례리 123-11 일원
2nd row삼례읍 삼례리 123-11 일원
3rd row삼례읍 삼례리 123-11 일원
4th row삼례읍 삼례리 123-11 일원
5th row삼례읍 삼례리 123-11 일원
ValueCountFrequency (%)
앞 196
 
9.2%
옆 180
 
8.4%
공터 116
 
5.4%
경로당 105
 
4.9%
이서면 68
 
3.2%
봉동읍 61
 
2.9%
모정 56
 
2.6%
내 51
 
2.4%
삼례읍 43
 
2.0%
경로회관 36
 
1.7%
Other values (237) 1228
57.4%
2024-03-14T19:39:34.938155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1387
 
17.2%
로 310
 
3.8%
1 300
 
3.7%
경 288
 
3.6%
당 264
 
3.3%
리 239
 
3.0%
공 197
 
2.4%
앞 196
 
2.4%
옆 182
 
2.3%
산 178
 
2.2%
Other values (197) 4522
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5541
68.7%
Space Separator 1387
 
17.2%
Decimal Number 923
 
11.4%
Dash Punctuation 109
 
1.4%
Open Punctuation 48
 
0.6%
Close Punctuation 48
 
0.6%
Other Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 310
 
5.6%
경 288
 
5.2%
당 264
 
4.8%
리 239
 
4.3%
공 197
 
3.6%
앞 196
 
3.5%
옆 182
 
3.3%
산 178
 
3.2%
마 160
 
2.9%
원 147
 
2.7%
Other values (181) 3380
61.0%
Decimal Number
ValueCountFrequency (%)
1 300
32.5%
7 95
 
10.3%
8 93
 
10.1%
2 78
 
8.5%
0 69
 
7.5%
3 69
 
7.5%
9 64
 
6.9%
5 56
 
6.1%
6 55
 
6.0%
4 44
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 5
71.4%
· 2
 
28.6%
Space Separator
ValueCountFrequency (%)
1387
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5541
68.7%
Common 2522
31.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 310
 
5.6%
경 288
 
5.2%
당 264
 
4.8%
리 239
 
4.3%
공 197
 
3.6%
앞 196
 
3.5%
옆 182
 
3.3%
산 178
 
3.2%
마 160
 
2.9%
원 147
 
2.7%
Other values (181) 3380
61.0%
Common
ValueCountFrequency (%)
1387
55.0%
1 300
 
11.9%
- 109
 
4.3%
7 95
 
3.8%
8 93
 
3.7%
2 78
 
3.1%
0 69
 
2.7%
3 69
 
2.7%
9 64
 
2.5%
5 56
 
2.2%
Other values (6) 202
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5541
68.7%
ASCII 2520
31.3%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1387
55.0%
1 300
 
11.9%
- 109
 
4.3%
7 95
 
3.8%
8 93
 
3.7%
2 78
 
3.1%
0 69
 
2.7%
3 69
 
2.7%
9 64
 
2.5%
5 56
 
2.2%
Other values (5) 200
 
7.9%
Hangul
ValueCountFrequency (%)
로 310
 
5.6%
경 288
 
5.2%
당 264
 
4.8%
리 239
 
4.3%
공 197
 
3.6%
앞 196
 
3.5%
옆 182
 
3.3%
산 178
 
3.2%
마 160
 
2.9%
원 147
 
2.7%
Other values (181) 3380
61.0%
None
ValueCountFrequency (%)
· 2
100.0%

기구명
Text

MISSING 

Distinct257
Distinct (%)33.2%
Missing1793
Missing (%)69.9%
Memory size20.2 KiB
2024-03-14T19:39:35.880154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length5.7956016
Min length2

Characters and Unicode

Total characters4480
Distinct characters184
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)21.2%

Sample

1st row가로하늘타기
2nd row온몸허리돌리기
3rd row앉아당기기
4th row앉아밀기
5th row하늘걸기
ValueCountFrequency (%)
허리돌리기 107
 
13.3%
하늘걷기 34
 
4.2%
달리기 26
 
3.2%
공중걷기 22
 
2.7%
윗몸일으키기 20
 
2.5%
원그리기 19
 
2.4%
팔관절운동 17
 
2.1%
파도타기 16
 
2.0%
온몸허리돌리기 16
 
2.0%
가로하늘타기 14
 
1.7%
Other values (243) 516
63.9%
2024-03-14T19:39:37.254582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 666
 
14.9%
리 503
 
11.2%
허 186
 
4.2%
돌 185
 
4.1%
+ 107
 
2.4%
걷 103
 
2.3%
신 91
 
2.0%
트 90
 
2.0%
활 90
 
2.0%
차 90
 
2.0%
Other values (174) 2369
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4300
96.0%
Math Symbol 107
 
2.4%
Space Separator 34
 
0.8%
Other Punctuation 30
 
0.7%
Decimal Number 7
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 666
 
15.5%
리 503
 
11.7%
허 186
 
4.3%
돌 185
 
4.3%
걷 103
 
2.4%
신 91
 
2.1%
트 90
 
2.1%
활 90
 
2.1%
차 90
 
2.1%
타 86
 
2.0%
Other values (166) 2210
51.4%
Other Punctuation
ValueCountFrequency (%)
, 14
46.7%
/ 9
30.0%
· 7
23.3%
Math Symbol
ValueCountFrequency (%)
+ 107
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Decimal Number
ValueCountFrequency (%)
3 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4300
96.0%
Common 180
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 666
 
15.5%
리 503
 
11.7%
허 186
 
4.3%
돌 185
 
4.3%
걷 103
 
2.4%
신 91
 
2.1%
트 90
 
2.1%
활 90
 
2.1%
차 90
 
2.1%
타 86
 
2.0%
Other values (166) 2210
51.4%
Common
ValueCountFrequency (%)
+ 107
59.4%
34
 
18.9%
, 14
 
7.8%
/ 9
 
5.0%
3 7
 
3.9%
· 7
 
3.9%
( 1
 
0.6%
) 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4300
96.0%
ASCII 173
 
3.9%
None 7
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 666
 
15.5%
리 503
 
11.7%
허 186
 
4.3%
돌 185
 
4.3%
걷 103
 
2.4%
신 91
 
2.1%
트 90
 
2.1%
활 90
 
2.1%
차 90
 
2.1%
타 86
 
2.0%
Other values (166) 2210
51.4%
ASCII
ValueCountFrequency (%)
+ 107
61.8%
34
 
19.7%
, 14
 
8.1%
/ 9
 
5.2%
3 7
 
4.0%
( 1
 
0.6%
) 1
 
0.6%
None
ValueCountFrequency (%)
· 7
100.0%

데이터기준일
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.2 KiB
<NA>
1793 
2024-02-01
773 

Length

Max length10
Median length4
Mean length5.8074825
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-02-01
2nd row2024-02-01
3rd row2024-02-01
4th row2024-02-01
5th row2024-02-01

Common Values

ValueCountFrequency (%)
<NA> 1793
69.9%
2024-02-01 773
30.1%

Length

2024-03-14T19:39:37.687488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:39:38.010280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1793
69.9%
2024-02-01 773
30.1%

Missing values

2024-03-14T19:39:21.581019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:39:21.940838image/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.
2024-03-14T19:39:22.270506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번관리번호위치설치장소기구명데이터기준일
01삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원가로하늘타기2024-02-01
12삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원온몸허리돌리기2024-02-01
23삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원앉아당기기2024-02-01
34삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원앉아밀기2024-02-01
45삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원하늘걸기2024-02-01
56삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원철봉기2024-02-01
67삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원앉아밀어주기2024-02-01
78삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원팔근육길으기2024-02-01
89삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원노젖기2024-02-01
910삼례수도산근린공원삼례읍 삼례리 123-11 일원삼례읍 삼례리 123-11 일원평행봉2024-02-01
연번관리번호위치설치장소기구명데이터기준일
2556<NA><NA><NA><NA><NA><NA>
2557<NA><NA><NA><NA><NA><NA>
2558<NA><NA><NA><NA><NA><NA>
2559<NA><NA><NA><NA><NA><NA>
2560<NA><NA><NA><NA><NA><NA>
2561<NA><NA><NA><NA><NA><NA>
2562<NA><NA><NA><NA><NA><NA>
2563<NA><NA><NA><NA><NA><NA>
2564<NA><NA><NA><NA><NA><NA>
2565용진읍0001<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

연번관리번호위치설치장소기구명데이터기준일# duplicates
0<NA><NA><NA><NA><NA><NA>1792