Overview

Dataset statistics

Number of variables8
Number of observations276
Missing cells29
Missing cells (%)1.3%
Duplicate rows1
Duplicate rows (%)0.4%
Total size in memory18.2 KiB
Average record size in memory67.5 B

Variable types

Numeric3
Categorical3
Text2

Dataset

Description민방위비상급수시설현황1510월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202472

Alerts

Dataset has 1 (0.4%) duplicate rowsDuplicates
연번 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
설치년도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
시설구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has 6 (2.2%) missing valuesMissing
명 칭 has 6 (2.2%) missing valuesMissing
위 치 has 5 (1.8%) missing valuesMissing
일일 생산량(톤) has 6 (2.2%) missing valuesMissing
설치년도 has 6 (2.2%) missing valuesMissing

Reproduction

Analysis started2024-03-14 00:12:48.296371
Analysis finished2024-03-14 00:12:49.719871
Duration1.42 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct270
Distinct (%)100.0%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean135.5
Minimum1
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-14T09:12:49.782242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.45
Q168.25
median135.5
Q3202.75
95-th percentile256.55
Maximum270
Range269
Interquartile range (IQR)134.5

Descriptive statistics

Standard deviation78.086491
Coefficient of variation (CV)0.57628406
Kurtosis-1.2
Mean135.5
Median Absolute Deviation (MAD)67.5
Skewness0
Sum36585
Variance6097.5
MonotonicityStrictly increasing
2024-03-14T09:12:49.904547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187 1
 
0.4%
173 1
 
0.4%
174 1
 
0.4%
175 1
 
0.4%
176 1
 
0.4%
177 1
 
0.4%
178 1
 
0.4%
179 1
 
0.4%
180 1
 
0.4%
181 1
 
0.4%
Other values (260) 260
94.2%
(Missing) 6
 
2.2%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%
265 1
0.4%
264 1
0.4%
263 1
0.4%
262 1
0.4%
261 1
0.4%

시군명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
전주시
82 
군산시
56 
익산시
53 
정읍시
17 
완주군
13 
Other values (10)
55 

Length

Max length4
Median length3
Mean length3.0217391
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 82
29.7%
군산시 56
20.3%
익산시 53
19.2%
정읍시 17
 
6.2%
완주군 13
 
4.7%
남원시 11
 
4.0%
김제시 10
 
3.6%
무주군 6
 
2.2%
<NA> 6
 
2.2%
순창군 5
 
1.8%
Other values (5) 17
 
6.2%

Length

2024-03-14T09:12:50.027922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 82
29.7%
군산시 56
20.3%
익산시 53
19.2%
정읍시 17
 
6.2%
완주군 13
 
4.7%
남원시 11
 
4.0%
김제시 10
 
3.6%
무주군 6
 
2.2%
na 6
 
2.2%
순창군 5
 
1.8%
Other values (5) 17
 
6.2%

시설구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
공공지정
164 
정부지원
63 
공공용지정
38 
<NA>
 
6
자치단체
 
5

Length

Max length5
Median length4
Mean length4.1376812
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공지정 164
59.4%
정부지원 63
 
22.8%
공공용지정 38
 
13.8%
<NA> 6
 
2.2%
자치단체 5
 
1.8%

Length

2024-03-14T09:12:50.117850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:12:50.196595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공지정 164
59.4%
정부지원 63
 
22.8%
공공용지정 38
 
13.8%
na 6
 
2.2%
자치단체 5
 
1.8%

명 칭
Text

MISSING 

Distinct263
Distinct (%)97.4%
Missing6
Missing (%)2.2%
Memory size2.3 KiB
2024-03-14T09:12:50.421078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length6.237037
Min length3

Characters and Unicode

Total characters1684
Distinct characters279
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique257 ?
Unique (%)95.2%

Sample

1st row서곡어린이공원
2nd row삼천약수공원
3rd row평화1공원
4th row평화주공3단지
5th row풍년주택
ValueCountFrequency (%)
현대아파트 3
 
1.0%
공설운동장 2
 
0.7%
제일1차아파트 2
 
0.7%
지곡초등학교 2
 
0.7%
수정사우나 2
 
0.7%
삼성아파트 2
 
0.7%
교육문화회관 2
 
0.7%
명동사우나 2
 
0.7%
양석성 1
 
0.3%
함열고등학교 1
 
0.3%
Other values (280) 280
93.6%
2024-03-14T09:12:50.735151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 80
 
4.8%
학 73
 
4.3%
등 56
 
3.3%
초 37
 
2.2%
주 34
 
2.0%
원 34
 
2.0%
고 33
 
2.0%
아 32
 
1.9%
파 32
 
1.9%
공 31
 
1.8%
Other values (269) 1242
73.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1529
90.8%
Decimal Number 66
 
3.9%
Space Separator 29
 
1.7%
Open Punctuation 18
 
1.1%
Close Punctuation 18
 
1.1%
Uppercase Letter 8
 
0.5%
Other Punctuation 6
 
0.4%
Dash Punctuation 5
 
0.3%
Math Symbol 3
 
0.2%
Other Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 80
 
5.2%
학 73
 
4.8%
등 56
 
3.7%
초 37
 
2.4%
주 34
 
2.2%
원 34
 
2.2%
고 33
 
2.2%
아 32
 
2.1%
파 32
 
2.1%
공 31
 
2.0%
Other values (247) 1087
71.1%
Decimal Number
ValueCountFrequency (%)
1 18
27.3%
2 12
18.2%
3 8
12.1%
4 7
 
10.6%
6 6
 
9.1%
9 6
 
9.1%
5 3
 
4.5%
8 2
 
3.0%
7 2
 
3.0%
0 2
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
D 3
37.5%
L 2
25.0%
G 2
25.0%
V 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
@ 5
83.3%
․ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1531
90.9%
Common 145
 
8.6%
Latin 8
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 80
 
5.2%
학 73
 
4.8%
등 56
 
3.7%
초 37
 
2.4%
주 34
 
2.2%
원 34
 
2.2%
고 33
 
2.2%
아 32
 
2.1%
파 32
 
2.1%
공 31
 
2.0%
Other values (248) 1089
71.1%
Common
ValueCountFrequency (%)
29
20.0%
( 18
12.4%
1 18
12.4%
) 18
12.4%
2 12
8.3%
3 8
 
5.5%
4 7
 
4.8%
6 6
 
4.1%
9 6
 
4.1%
@ 5
 
3.4%
Other values (7) 18
12.4%
Latin
ValueCountFrequency (%)
D 3
37.5%
L 2
25.0%
G 2
25.0%
V 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1529
90.8%
ASCII 152
 
9.0%
None 2
 
0.1%
Punctuation 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 80
 
5.2%
학 73
 
4.8%
등 56
 
3.7%
초 37
 
2.4%
주 34
 
2.2%
원 34
 
2.2%
고 33
 
2.2%
아 32
 
2.1%
파 32
 
2.1%
공 31
 
2.0%
Other values (247) 1087
71.1%
ASCII
ValueCountFrequency (%)
29
19.1%
( 18
11.8%
1 18
11.8%
) 18
11.8%
2 12
7.9%
3 8
 
5.3%
4 7
 
4.6%
6 6
 
3.9%
9 6
 
3.9%
@ 5
 
3.3%
Other values (10) 25
16.4%
None
ValueCountFrequency (%)
㈜ 2
100.0%
Punctuation
ValueCountFrequency (%)
․ 1
100.0%

위 치
Text

MISSING 

Distinct267
Distinct (%)98.5%
Missing5
Missing (%)1.8%
Memory size2.3 KiB
2024-03-14T09:12:51.023109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length19
Mean length10.811808
Min length2

Characters and Unicode

Total characters2930
Distinct characters198
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

Unique263 ?
Unique (%)97.0%

Sample

1st row완산구 서곡로 22
2nd row완산구 거마서로 54
3rd row완산구 평화16길16
4th row완산구 덕적골2길 10
5th row완산구 장승배기로 292-11
ValueCountFrequency (%)
덕진구 31
 
4.4%
완산구 23
 
3.3%
완주군 13
 
1.8%
영등동 9
 
1.3%
봉동읍 9
 
1.3%
마동 7
 
1.0%
무주군 6
 
0.8%
20 6
 
0.8%
10 6
 
0.8%
김제시 6
 
0.8%
Other values (444) 590
83.6%
2024-03-14T09:12:51.421492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
465
 
15.9%
1 179
 
6.1%
로 155
 
5.3%
2 127
 
4.3%
길 106
 
3.6%
동 95
 
3.2%
3 91
 
3.1%
5 73
 
2.5%
구 62
 
2.1%
7 58
 
2.0%
Other values (188) 1519
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1519
51.8%
Decimal Number 800
27.3%
Space Separator 466
 
15.9%
Close Punctuation 49
 
1.7%
Open Punctuation 49
 
1.7%
Dash Punctuation 46
 
1.6%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 155
 
10.2%
길 106
 
7.0%
동 95
 
6.3%
구 62
 
4.1%
산 47
 
3.1%
읍 46
 
3.0%
덕 39
 
2.6%
완 36
 
2.4%
진 36
 
2.4%
리 34
 
2.2%
Other values (172) 863
56.8%
Decimal Number
ValueCountFrequency (%)
1 179
22.4%
2 127
15.9%
3 91
11.4%
5 73
9.1%
7 58
 
7.2%
0 57
 
7.1%
4 57
 
7.1%
9 55
 
6.9%
6 54
 
6.8%
8 49
 
6.1%
Space Separator
ValueCountFrequency (%)
465
99.8%
  1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1519
51.8%
Common 1411
48.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 155
 
10.2%
길 106
 
7.0%
동 95
 
6.3%
구 62
 
4.1%
산 47
 
3.1%
읍 46
 
3.0%
덕 39
 
2.6%
완 36
 
2.4%
진 36
 
2.4%
리 34
 
2.2%
Other values (172) 863
56.8%
Common
ValueCountFrequency (%)
465
33.0%
1 179
 
12.7%
2 127
 
9.0%
3 91
 
6.4%
5 73
 
5.2%
7 58
 
4.1%
0 57
 
4.0%
4 57
 
4.0%
9 55
 
3.9%
6 54
 
3.8%
Other values (6) 195
13.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1519
51.8%
ASCII 1410
48.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
465
33.0%
1 179
 
12.7%
2 127
 
9.0%
3 91
 
6.5%
5 73
 
5.2%
7 58
 
4.1%
0 57
 
4.0%
4 57
 
4.0%
9 55
 
3.9%
6 54
 
3.8%
Other values (5) 194
13.8%
Hangul
ValueCountFrequency (%)
로 155
 
10.2%
길 106
 
7.0%
동 95
 
6.3%
구 62
 
4.1%
산 47
 
3.1%
읍 46
 
3.0%
덕 39
 
2.6%
완 36
 
2.4%
진 36
 
2.4%
리 34
 
2.2%
Other values (172) 863
56.8%
None
ValueCountFrequency (%)
  1
100.0%

일일 생산량(톤)
Real number (ℝ)

MISSING 

Distinct64
Distinct (%)23.7%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean142.74778
Minimum20
Maximum550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-14T09:12:51.539895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile70
Q190
median120
Q3168.5
95-th percentile275.5
Maximum550
Range530
Interquartile range (IQR)78.5

Descriptive statistics

Standard deviation76.994342
Coefficient of variation (CV)0.53937332
Kurtosis7.2774083
Mean142.74778
Median Absolute Deviation (MAD)30
Skewness2.2867289
Sum38541.9
Variance5928.1287
MonotonicityNot monotonic
2024-03-14T09:12:51.685688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0 38
13.8%
150.0 38
13.8%
100.0 32
 
11.6%
200.0 23
 
8.3%
80.0 15
 
5.4%
120.0 14
 
5.1%
110.0 10
 
3.6%
70.0 7
 
2.5%
130.0 6
 
2.2%
250.0 6
 
2.2%
Other values (54) 81
29.3%
(Missing) 6
 
2.2%
ValueCountFrequency (%)
20.0 1
 
0.4%
42.0 1
 
0.4%
50.0 2
 
0.7%
60.0 2
 
0.7%
65.0 1
 
0.4%
66.0 1
 
0.4%
70.0 7
2.5%
75.0 2
 
0.7%
78.0 3
 
1.1%
80.0 15
5.4%
ValueCountFrequency (%)
550.0 1
0.4%
500.0 2
0.7%
483.0 1
0.4%
410.0 1
0.4%
400.0 1
0.4%
360.0 1
0.4%
340.0 1
0.4%
325.0 1
0.4%
324.0 1
0.4%
320.0 1
0.4%

설치년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)15.6%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1922.6
Minimum89
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-14T09:12:51.811740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile1981.45
Q11995
median2000
Q32004
95-th percentile2012
Maximum2015
Range1926
Interquartile range (IQR)9

Descriptive statistics

Standard deviation377.60245
Coefficient of variation (CV)0.19640198
Kurtosis19.960375
Mean1922.6
Median Absolute Deviation (MAD)5
Skewness-4.6693067
Sum519102
Variance142583.61
MonotonicityNot monotonic
2024-03-14T09:12:51.927546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1997 26
 
9.4%
2002 20
 
7.2%
2004 20
 
7.2%
2003 16
 
5.8%
1995 15
 
5.4%
2012 14
 
5.1%
1994 13
 
4.7%
1998 11
 
4.0%
2001 11
 
4.0%
1996 11
 
4.0%
Other values (32) 113
40.9%
ValueCountFrequency (%)
89 1
 
0.4%
90 1
 
0.4%
93 1
 
0.4%
94 3
1.1%
95 2
0.7%
97 3
1.1%
1972 1
 
0.4%
1976 1
 
0.4%
1981 1
 
0.4%
1982 2
0.7%
ValueCountFrequency (%)
2015 5
 
1.8%
2014 3
 
1.1%
2013 3
 
1.1%
2012 14
5.1%
2011 6
2.2%
2010 4
 
1.4%
2009 9
3.3%
2008 1
 
0.4%
2007 4
 
1.4%
2006 8
2.9%

활용 (음용,생활)
Categorical

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
음용
144 
생활
126 
<NA>
 
6

Length

Max length4
Median length2
Mean length2.0434783
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
음용 144
52.2%
생활 126
45.7%
<NA> 6
 
2.2%

Length

2024-03-14T09:12:52.049309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:12:52.134830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
음용 144
52.2%
생활 126
45.7%
na 6
 
2.2%

Interactions

2024-03-14T09:12:49.163774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:48.697551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:48.961223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:49.235290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:48.772452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:49.026641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:49.308712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:48.870054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:49.091627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:12:52.186866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군명시설구분일일 생산량(톤)설치년도활용 (음용,생활)
연번1.0000.9150.7980.4380.5600.388
시군명0.9151.0000.7940.0701.0000.336
시설구분0.7980.7941.0000.2210.5120.466
일일\n생산량(톤)0.4380.0700.2211.0000.0000.196
설치년도0.5601.0000.5120.0001.0000.102
활용\n(음용,생활)0.3880.3360.4660.1960.1021.000
2024-03-14T09:12:52.267607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명시설구분활용 (음용,생활)
시군명1.0000.5790.257
시설구분0.5791.0000.314
활용\n(음용,생활)0.2570.3141.000
2024-03-14T09:12:52.568105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번일일 생산량(톤)설치년도시군명시설구분활용 (음용,생활)
연번1.000-0.158-0.2210.6890.6140.293
일일\n생산량(톤)-0.1581.000-0.0450.0240.1320.147
설치년도-0.221-0.0451.0000.9770.3460.065
시군명0.6890.0240.9771.0000.5790.257
시설구분0.6140.1320.3460.5791.0000.314
활용\n(음용,생활)0.2930.1470.0650.2570.3141.000

Missing values

2024-03-14T09:12:49.426168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:12:49.543866image/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.
2024-03-14T09:12:49.647882image/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

연번시군명시설구분명 칭위 치일일 생산량(톤)설치년도활용 (음용,생활)
01전주시정부지원서곡어린이공원완산구 서곡로 22100.02010음용
12전주시정부지원삼천약수공원완산구 거마서로 5490.01996음용
23전주시정부지원평화1공원완산구 평화16길16230.02004음용
34전주시정부지원평화주공3단지완산구 덕적골2길 1090.01996음용
45전주시정부지원풍년주택완산구 장승배기로 292-1180.01994음용
56전주시정부지원기린연립완산구 견훤로 100-10160.01986음용
67전주시정부지원덕진종합경기장덕진구 기린대로 451 (덕진동1가)483.01986생활
78전주시정부지원한신휴아파트덕진구 안덕원로 251190.01992음용
89전주시정부지원덕진체련공원덕진구 소리로 54 (덕진동1가)205.01994음용
910전주시정부지원아중하인교공원덕진구 우아동1가150.02001음용
연번시군명시설구분명 칭위 치일일 생산량(톤)설치년도활용 (음용,생활)
266267부안군정부지원수도사업소부안읍 수정길 9-8 수도사업소150.01999음용
267268부안군정부지원노인여성회관부안읍 매창로 127 노인여성회관120.01997음용
268269부안군정부지원스포츠파크행안면 체육공원길 31 스포츠파크140.02007음용
269270부안군정부지원예술회관부안읍 예술회관길 11 예술회관150.01998생활
270<NA><NA><NA><NA><NA><NA><NA><NA>
271<NA><NA><NA><NA><NA><NA><NA><NA>
272<NA><NA><NA><NA><NA><NA><NA><NA>
273<NA><NA><NA><NA><NA><NA><NA><NA>
274<NA><NA><NA><NA><NA><NA><NA><NA>
275<NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

연번시군명시설구분명 칭위 치일일 생산량(톤)설치년도활용 (음용,생활)# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>5