Overview

Dataset statistics

Number of variables9
Number of observations319
Missing cells563
Missing cells (%)19.6%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory23.8 KiB
Average record size in memory76.4 B

Variable types

Numeric3
Categorical3
Text2
Unsupported1

Dataset

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

Alerts

Dataset has 1 (0.3%) 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 49 (15.4%) missing valuesMissing
명 칭 has 49 (15.4%) missing valuesMissing
위 치 has 48 (15.0%) missing valuesMissing
일일 생산량(톤) has 49 (15.4%) missing valuesMissing
설치년도 has 49 (15.4%) missing valuesMissing
Unnamed: 8 has 319 (100.0%) missing valuesMissing
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 00:12:43.079164
Analysis finished2024-03-14 00:12:44.522210
Duration1.44 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct270
Distinct (%)100.0%
Missing49
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean135.5
Minimum1
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-14T09:12:44.608165image/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
2024-03-14T09:12:44.722247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187 1
 
0.3%
173 1
 
0.3%
174 1
 
0.3%
175 1
 
0.3%
176 1
 
0.3%
177 1
 
0.3%
178 1
 
0.3%
179 1
 
0.3%
180 1
 
0.3%
181 1
 
0.3%
Other values (260) 260
81.5%
(Missing) 49
 
15.4%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
270 1
0.3%
269 1
0.3%
268 1
0.3%
267 1
0.3%
266 1
0.3%
265 1
0.3%
264 1
0.3%
263 1
0.3%
262 1
0.3%
261 1
0.3%

시군명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
전주시
82 
군산시
56 
익산시
53 
<NA>
49 
정읍시
17 
Other values (10)
62 

Length

Max length4
Median length3
Mean length3.153605
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 82
25.7%
군산시 56
17.6%
익산시 53
16.6%
<NA> 49
15.4%
정읍시 17
 
5.3%
완주군 13
 
4.1%
남원시 11
 
3.4%
김제시 10
 
3.1%
무주군 6
 
1.9%
순창군 5
 
1.6%
Other values (5) 17
 
5.3%

Length

2024-03-14T09:12:44.823435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 82
25.7%
군산시 56
17.6%
익산시 53
16.6%
na 49
15.4%
정읍시 17
 
5.3%
완주군 13
 
4.1%
남원시 11
 
3.4%
김제시 10
 
3.1%
무주군 6
 
1.9%
순창군 5
 
1.6%
Other values (5) 17
 
5.3%

시설구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
공공지정
164 
정부지원
63 
<NA>
49 
공공용지정
38 
자치단체
 
5

Length

Max length5
Median length4
Mean length4.1191223
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공지정 164
51.4%
정부지원 63
 
19.7%
<NA> 49
 
15.4%
공공용지정 38
 
11.9%
자치단체 5
 
1.6%

Length

2024-03-14T09:12:44.912092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:12:45.004368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공지정 164
51.4%
정부지원 63
 
19.7%
na 49
 
15.4%
공공용지정 38
 
11.9%
자치단체 5
 
1.6%

명 칭
Text

MISSING 

Distinct263
Distinct (%)97.4%
Missing49
Missing (%)15.4%
Memory size2.6 KiB
2024-03-14T09:12:45.224962image/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 blocks3 ?
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%
2024-03-14T09:12:45.539741image/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 153
 
9.1%
None 2
 
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
19.0%
( 18
11.8%
1 18
11.8%
) 18
11.8%
2 12
7.8%
3 8
 
5.2%
4 7
 
4.6%
6 6
 
3.9%
9 6
 
3.9%
@ 5
 
3.3%
Other values (11) 26
17.0%
None
ValueCountFrequency (%)
㈜ 2
100.0%

위 치
Text

MISSING 

Distinct267
Distinct (%)98.5%
Missing48
Missing (%)15.0%
Memory size2.6 KiB
2024-03-14T09:12:45.795727image/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 blocks2 ?
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 (445) 591
83.6%
2024-03-14T09:12:46.145278image/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 465
 
15.9%
Close Punctuation 49
 
1.7%
Open Punctuation 49
 
1.7%
Dash Punctuation 46
 
1.6%
Other Punctuation 2
 
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%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
? 1
50.0%
Space Separator
ValueCountFrequency (%)
465
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
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 1411
48.2%

Most frequent character per block

ASCII
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%
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%

일일 생산량(톤)
Real number (ℝ)

MISSING 

Distinct64
Distinct (%)23.7%
Missing49
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean142.74778
Minimum20
Maximum550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-14T09:12:46.503752image/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
2024-03-14T09:12:46.635949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150.0 38
11.9%
90.0 38
11.9%
100.0 32
 
10.0%
200.0 23
 
7.2%
80.0 15
 
4.7%
120.0 14
 
4.4%
110.0 10
 
3.1%
70.0 7
 
2.2%
250.0 6
 
1.9%
130.0 6
 
1.9%
Other values (54) 81
25.4%
(Missing) 49
15.4%
ValueCountFrequency (%)
20.0 1
 
0.3%
42.0 1
 
0.3%
50.0 2
 
0.6%
60.0 2
 
0.6%
65.0 1
 
0.3%
66.0 1
 
0.3%
70.0 7
2.2%
75.0 2
 
0.6%
78.0 3
 
0.9%
80.0 15
4.7%
ValueCountFrequency (%)
550.0 1
0.3%
500.0 2
0.6%
483.0 1
0.3%
410.0 1
0.3%
400.0 1
0.3%
360.0 1
0.3%
340.0 1
0.3%
325.0 1
0.3%
324.0 1
0.3%
320.0 1
0.3%

설치년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)15.6%
Missing49
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean1922.6
Minimum89
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-14T09:12:46.790199image/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
2024-03-14T09:12:46.902798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1997 26
 
8.2%
2002 20
 
6.3%
2004 20
 
6.3%
2003 16
 
5.0%
1995 15
 
4.7%
2012 14
 
4.4%
1994 13
 
4.1%
2001 11
 
3.4%
1998 11
 
3.4%
1996 11
 
3.4%
Other values (32) 113
35.4%
(Missing) 49
15.4%
ValueCountFrequency (%)
89 1
 
0.3%
90 1
 
0.3%
93 1
 
0.3%
94 3
0.9%
95 2
0.6%
97 3
0.9%
1972 1
 
0.3%
1976 1
 
0.3%
1981 1
 
0.3%
1982 2
0.6%
ValueCountFrequency (%)
2015 5
 
1.6%
2014 3
 
0.9%
2013 3
 
0.9%
2012 14
4.4%
2011 6
1.9%
2010 4
 
1.3%
2009 9
2.8%
2008 1
 
0.3%
2007 4
 
1.3%
2006 8
2.5%

활용 (음용,생활)
Categorical

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
음용
144 
생활
126 
<NA>
49 

Length

Max length4
Median length2
Mean length2.30721
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
음용 144
45.1%
생활 126
39.5%
<NA> 49
 
15.4%

Length

2024-03-14T09:12:47.015731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:12:47.100917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
음용 144
45.1%
생활 126
39.5%
na 49
 
15.4%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing319
Missing (%)100.0%
Memory size2.9 KiB

Interactions

2024-03-14T09:12:43.877832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:43.480820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:43.675035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:43.952950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:43.540065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:43.735477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:44.029002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:43.605604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:12:43.802418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:12:47.154309image/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
2024-03-14T09:12:47.267109image/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
2024-03-14T09:12:47.364709image/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

2024-03-14T09:12:44.152220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:12:44.274684image/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.
2024-03-14T09:12:44.418805image/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)Unnamed: 8
01전주시정부지원서곡어린이공원완산구 서곡로 22100.02010음용<NA>
12전주시정부지원삼천약수공원완산구 거마서로 5490.01996음용<NA>
23전주시정부지원평화1공원완산구 평화16길16230.02004음용<NA>
34전주시정부지원평화주공3단지완산구 덕적골2길 1090.01996음용<NA>
45전주시정부지원풍년주택완산구 장승배기로 292-1180.01994음용<NA>
56전주시정부지원기린연립완산구 견훤로 100-10160.01986음용<NA>
67전주시정부지원덕진종합경기장덕진구 기린대로 451 (덕진동1가)483.01986생활<NA>
78전주시정부지원한신휴아파트덕진구 안덕원로 251190.01992음용<NA>
89전주시정부지원덕진체련공원덕진구 소리로 54 (덕진동1가)205.01994음용<NA>
910전주시정부지원아중하인교공원덕진구 우아동1가150.02001음용<NA>
연번시군명시설구분명 칭위 치일일 생산량(톤)설치년도활용 (음용,생활)Unnamed: 8
309<NA><NA><NA><NA><NA><NA><NA><NA><NA>
310<NA><NA><NA><NA><NA><NA><NA><NA><NA>
311<NA><NA><NA><NA><NA><NA><NA><NA><NA>
312<NA><NA><NA><NA><NA><NA><NA><NA><NA>
313<NA><NA><NA><NA><NA><NA><NA><NA><NA>
314<NA><NA><NA><NA><NA><NA><NA><NA><NA>
315<NA><NA><NA><NA><NA><NA><NA><NA><NA>
316<NA><NA><NA><NA><NA><NA><NA><NA><NA>
317<NA><NA><NA><NA><NA><NA><NA><NA><NA>
318<NA><NA><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>48