Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells11
Missing cells (%)< 0.1%
Duplicate rows13
Duplicate rows (%)0.1%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Categorical2
Numeric3
Text2
DateTime1

Dataset

Description한국농어촌공사 관리 농업생산기반시설물 안전진단 정보입니다.(내용) 시설구분, 시설코드, 시설명, 점검일자, 종합평가, 주소, 좌표
Author한국농어촌공사
URLhttps://www.data.go.kr/data/15034316/fileData.do

Alerts

Dataset has 13 (0.1%) duplicate rowsDuplicates
시설코드 is highly overall correlated with 중부원점좌표(y)High correlation
중부원점좌표(y) is highly overall correlated with 시설코드High correlation
시설구분 is highly imbalanced (51.7%)Imbalance
중부원점좌표(x) is highly skewed (γ1 = 56.82037053)Skewed

Reproduction

Analysis started2023-12-23 07:00:51.258075
Analysis finished2023-12-23 07:01:01.582768
Duration10.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설구분
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
저수지
7655 
배수장
1527 
방조제
 
440
양배수장
 
240
양수장
 
138

Length

Max length4
Median length3
Mean length3.024
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row저수지
2nd row배수장
3rd row저수지
4th row저수지
5th row저수지

Common Values

ValueCountFrequency (%)
저수지 7655
76.5%
배수장 1527
 
15.3%
방조제 440
 
4.4%
양배수장 240
 
2.4%
양수장 138
 
1.4%

Length

2023-12-23T07:01:01.926657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:01:02.695548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저수지 7655
76.5%
배수장 1527
 
15.3%
방조제 440
 
4.4%
양배수장 240
 
2.4%
양수장 138
 
1.4%

시설코드
Real number (ℝ)

HIGH CORRELATION 

Distinct3364
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5168031 × 109
Minimum2.64403 × 109
Maximum5.01301 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-23T07:01:03.554765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.64403 × 109
5-th percentile4.1220395 × 109
Q14.42004 × 109
median4.6710101 × 109
Q34.7230108 × 109
95-th percentile4.87404 × 109
Maximum5.01301 × 109
Range2.36898 × 109
Interquartile range (IQR)3.0297077 × 108

Descriptive statistics

Standard deviation4.1078112 × 108
Coefficient of variation (CV)0.090945102
Kurtosis8.8007038
Mean4.5168031 × 109
Median Absolute Deviation (MAD)1.5299993 × 108
Skewness-2.8307568
Sum4.5168031 × 1013
Variance1.6874113 × 1017
MonotonicityNot monotonic
2023-12-23T07:01:04.678760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4481010007 11
 
0.1%
2729010002 10
 
0.1%
4811010037 10
 
0.1%
4278010015 10
 
0.1%
4579010011 10
 
0.1%
4376010004 10
 
0.1%
4713010433 10
 
0.1%
4874010022 10
 
0.1%
4729010065 10
 
0.1%
4315010049 10
 
0.1%
Other values (3354) 9899
99.0%
ValueCountFrequency (%)
2644030002 3
 
< 0.1%
2644030003 4
 
< 0.1%
2644040002 2
 
< 0.1%
2671010056 2
 
< 0.1%
2671010067 3
 
< 0.1%
2671010085 3
 
< 0.1%
2671010097 1
 
< 0.1%
2714010005 10
0.1%
2714010023 3
 
< 0.1%
2723010006 3
 
< 0.1%
ValueCountFrequency (%)
5013010001 3
< 0.1%
4971010003 2
< 0.1%
4971010002 4
< 0.1%
4971010001 1
 
< 0.1%
4889040018 3
< 0.1%
4889040017 2
< 0.1%
4889040016 3
< 0.1%
4889040015 3
< 0.1%
4889040013 3
< 0.1%
4889040012 4
< 0.1%

시설명
Text

Distinct2448
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-23T07:01:06.943802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.179
Min length1

Characters and Unicode

Total characters21790
Distinct characters365
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique617 ?
Unique (%)6.2%

Sample

1st row옥녀
2nd row무촌
3rd row대정
4th row노천
5th row전당
ValueCountFrequency (%)
화산 37
 
0.4%
옥계 32
 
0.3%
오봉 31
 
0.3%
대곡 30
 
0.3%
성산 28
 
0.3%
백운 28
 
0.3%
덕산 28
 
0.3%
마산 26
 
0.3%
연화 25
 
0.2%
대룡 25
 
0.2%
Other values (2438) 9712
97.1%
2023-12-23T07:01:09.433522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 884
 
4.1%
동 648
 
3.0%
곡 575
 
2.6%
천 497
 
2.3%
신 457
 
2.1%
대 451
 
2.1%
성 420
 
1.9%
정 373
 
1.7%
용 322
 
1.5%
암 319
 
1.5%
Other values (355) 16844
77.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20858
95.7%
Decimal Number 507
 
2.3%
Open Punctuation 206
 
0.9%
Close Punctuation 206
 
0.9%
Uppercase Letter 6
 
< 0.1%
Other Punctuation 4
 
< 0.1%
Space Separator 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 884
 
4.2%
동 648
 
3.1%
곡 575
 
2.8%
천 497
 
2.4%
신 457
 
2.2%
대 451
 
2.2%
성 420
 
2.0%
정 373
 
1.8%
용 322
 
1.5%
암 319
 
1.5%
Other values (343) 15912
76.3%
Decimal Number
ValueCountFrequency (%)
2 256
50.5%
1 191
37.7%
3 49
 
9.7%
4 9
 
1.8%
6 1
 
0.2%
5 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 206
100.0%
Close Punctuation
ValueCountFrequency (%)
) 206
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20858
95.7%
Common 926
 
4.2%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 884
 
4.2%
동 648
 
3.1%
곡 575
 
2.8%
천 497
 
2.4%
신 457
 
2.2%
대 451
 
2.2%
성 420
 
2.0%
정 373
 
1.8%
용 322
 
1.5%
암 319
 
1.5%
Other values (343) 15912
76.3%
Common
ValueCountFrequency (%)
2 256
27.6%
( 206
22.2%
) 206
22.2%
1 191
20.6%
3 49
 
5.3%
4 9
 
1.0%
. 4
 
0.4%
2
 
0.2%
- 1
 
0.1%
6 1
 
0.1%
Latin
ValueCountFrequency (%)
A 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20858
95.7%
ASCII 932
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 884
 
4.2%
동 648
 
3.1%
곡 575
 
2.8%
천 497
 
2.4%
신 457
 
2.2%
대 451
 
2.2%
성 420
 
2.0%
정 373
 
1.8%
용 322
 
1.5%
암 319
 
1.5%
Other values (343) 15912
76.3%
ASCII
ValueCountFrequency (%)
2 256
27.5%
( 206
22.1%
) 206
22.1%
1 191
20.5%
3 49
 
5.3%
4 9
 
1.0%
A 6
 
0.6%
. 4
 
0.4%
2
 
0.2%
- 1
 
0.1%
Other values (2) 2
 
0.2%

점검일자
Date

Distinct248
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1995-12-01 00:00:00
Maximum2023-12-31 00:00:00
2023-12-23T07:01:10.326754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:01:11.077076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

종합평가
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
C
6746 
B
1726 
D
934 
<NA>
 
507
A
 
86

Length

Max length4
Median length1
Mean length1.1521
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 6746
67.5%
B 1726
 
17.3%
D 934
 
9.3%
<NA> 507
 
5.1%
A 86
 
0.9%
E 1
 
< 0.1%

Length

2023-12-23T07:01:12.153432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:01:13.622219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 6746
67.5%
b 1726
 
17.3%
d 934
 
9.3%
na 507
 
5.1%
a 86
 
0.9%
e 1
 
< 0.1%

주소
Text

Distinct2468
Distinct (%)24.7%
Missing11
Missing (%)0.1%
Memory size156.2 KiB
2023-12-23T07:01:15.264192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length12.202022
Min length2

Characters and Unicode

Total characters121886
Distinct characters333
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique553 ?
Unique (%)5.5%

Sample

1st row군산시 개사동,내초동
2nd row경상남도 진주시 사봉면 무촌리 1448-1
3rd row전라북도 고창군 공음면
4th row강원도 홍천군 동면 노천리
5th row전주시 전미동1가
ValueCountFrequency (%)
경상북도 1407
 
4.5%
전라남도 1017
 
3.2%
경상남도 523
 
1.7%
전라북도 406
 
1.3%
충청남도 390
 
1.2%
경남 330
 
1.0%
충남 310
 
1.0%
진주시 217
 
0.7%
영암군 212
 
0.7%
나주시 208
 
0.7%
Other values (2797) 26490
84.1%
2023-12-23T07:01:19.068824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21565
 
17.7%
면 7300
 
6.0%
리 7011
 
5.8%
군 5445
 
4.5%
도 4827
 
4.0%
시 4683
 
3.8%
남 3617
 
3.0%
경 2861
 
2.3%
산 2833
 
2.3%
북 2558
 
2.1%
Other values (323) 59186
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 98749
81.0%
Space Separator 21565
 
17.7%
Decimal Number 1062
 
0.9%
Other Punctuation 348
 
0.3%
Dash Punctuation 160
 
0.1%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 7300
 
7.4%
리 7011
 
7.1%
군 5445
 
5.5%
도 4827
 
4.9%
시 4683
 
4.7%
남 3617
 
3.7%
경 2861
 
2.9%
산 2833
 
2.9%
북 2558
 
2.6%
상 2456
 
2.5%
Other values (308) 55158
55.9%
Decimal Number
ValueCountFrequency (%)
1 243
22.9%
2 173
16.3%
3 126
11.9%
4 111
10.5%
6 89
 
8.4%
5 80
 
7.5%
0 67
 
6.3%
7 63
 
5.9%
8 56
 
5.3%
9 54
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 327
94.0%
. 21
 
6.0%
Space Separator
ValueCountFrequency (%)
21565
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 160
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 98749
81.0%
Common 23137
 
19.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 7300
 
7.4%
리 7011
 
7.1%
군 5445
 
5.5%
도 4827
 
4.9%
시 4683
 
4.7%
남 3617
 
3.7%
경 2861
 
2.9%
산 2833
 
2.9%
북 2558
 
2.6%
상 2456
 
2.5%
Other values (308) 55158
55.9%
Common
ValueCountFrequency (%)
21565
93.2%
, 327
 
1.4%
1 243
 
1.1%
2 173
 
0.7%
- 160
 
0.7%
3 126
 
0.5%
4 111
 
0.5%
6 89
 
0.4%
5 80
 
0.3%
0 67
 
0.3%
Other values (5) 196
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 98749
81.0%
ASCII 23137
 
19.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21565
93.2%
, 327
 
1.4%
1 243
 
1.1%
2 173
 
0.7%
- 160
 
0.7%
3 126
 
0.5%
4 111
 
0.5%
6 89
 
0.4%
5 80
 
0.3%
0 67
 
0.3%
Other values (5) 196
 
0.8%
Hangul
ValueCountFrequency (%)
면 7300
 
7.4%
리 7011
 
7.1%
군 5445
 
5.5%
도 4827
 
4.9%
시 4683
 
4.7%
남 3617
 
3.7%
경 2861
 
2.9%
산 2833
 
2.9%
북 2558
 
2.6%
상 2456
 
2.5%
Other values (308) 55158
55.9%

중부원점좌표(x)
Real number (ℝ)

SKEWED 

Distinct3363
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250393.11
Minimum99908.891
Maximum14084840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-23T07:01:20.094976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99908.891
5-th percentile144968.59
Q1182446.43
median228228.11
Q3315020.29
95-th percentile389796.68
Maximum14084840
Range13984931
Interquartile range (IQR)132573.86

Descriptive statistics

Standard deviation210484.64
Coefficient of variation (CV)0.84061673
Kurtosis3732.1121
Mean250393.11
Median Absolute Deviation (MAD)58882.366
Skewness56.820371
Sum2.5039311 × 109
Variance4.4303785 × 1010
MonotonicityNot monotonic
2023-12-23T07:01:20.890148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194928.3399 11
 
0.1%
340076.6799 10
 
0.1%
352595.2193 10
 
0.1%
225311.9525 10
 
0.1%
173597.0374 10
 
0.1%
269739.2987 10
 
0.1%
406560.7437 10
 
0.1%
340125.1066 10
 
0.1%
366163.5769 10
 
0.1%
290307.8028 10
 
0.1%
Other values (3353) 9899
99.0%
ValueCountFrequency (%)
99908.89083 7
0.1%
106890.4032 5
0.1%
107532.3236 1
 
< 0.1%
110773.1045 1
 
< 0.1%
110951.5722 3
< 0.1%
111026.6529 1
 
< 0.1%
111964.7924 2
 
< 0.1%
112181.6943 1
 
< 0.1%
112427.4989 2
 
< 0.1%
112985.367 1
 
< 0.1%
ValueCountFrequency (%)
14084839.62 2
< 0.1%
432002.2119 2
< 0.1%
430561.7608 4
< 0.1%
430204.4101 3
< 0.1%
420363.0171 2
< 0.1%
420100.5139 4
< 0.1%
418667.5183 3
< 0.1%
418593.555 2
< 0.1%
417462.0689 2
< 0.1%
416678.115 1
 
< 0.1%

중부원점좌표(y)
Real number (ℝ)

HIGH CORRELATION 

Distinct3363
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370406.15
Minimum80757.54
Maximum4524914.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-23T07:01:21.682664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80757.54
5-th percentile222372.28
Q1292273.3
median357381.31
Q3442364.19
95-th percentile571567.92
Maximum4524914.8
Range4444157.3
Interquartile range (IQR)150090.89

Descriptive statistics

Standard deviation117729.62
Coefficient of variation (CV)0.31783926
Kurtosis308.7721
Mean370406.15
Median Absolute Deviation (MAD)73583.894
Skewness9.068013
Sum3.7040615 × 109
Variance1.3860262 × 1010
MonotonicityNot monotonic
2023-12-23T07:01:22.469743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
454339.5604 11
 
0.1%
357381.3116 10
 
0.1%
302561.815 10
 
0.1%
630238.7007 10
 
0.1%
314241.3149 10
 
0.1%
472751.8119 10
 
0.1%
373727.2749 10
 
0.1%
323658.9088 10
 
0.1%
368485.4065 10
 
0.1%
508070.3338 10
 
0.1%
Other values (3353) 9899
99.0%
ValueCountFrequency (%)
80757.53967 2
< 0.1%
90249.94063 3
< 0.1%
97701.24086 1
 
< 0.1%
97774.51403 4
< 0.1%
189936.776 1
 
< 0.1%
190218.7441 1
 
< 0.1%
190544.0976 1
 
< 0.1%
190722.8156 3
< 0.1%
193796.9847 1
 
< 0.1%
195588.8559 2
< 0.1%
ValueCountFrequency (%)
4524914.81 2
 
< 0.1%
648305.9888 7
0.1%
635719.7885 7
0.1%
633241.8284 5
0.1%
631488.3614 6
0.1%
631116.469 8
0.1%
630828.6095 2
 
< 0.1%
630238.7007 10
0.1%
628852.8876 6
0.1%
628737.0605 1
 
< 0.1%

Interactions

2023-12-23T07:00:58.335228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:55.126656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:56.772833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:58.769120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:55.792234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:57.247334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:59.310576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:56.313345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:57.786392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:01:23.049655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분시설코드종합평가중부원점좌표(x)중부원점좌표(y)
시설구분1.0000.2910.2810.0510.119
시설코드0.2911.0000.1050.0710.646
종합평가0.2810.1051.0000.0000.060
중부원점좌표(x)0.0510.0710.0001.0001.000
중부원점좌표(y)0.1190.6460.0601.0001.000
2023-12-23T07:01:23.646501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분종합평가
시설구분1.0000.108
종합평가0.1081.000
2023-12-23T07:01:24.227256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설코드중부원점좌표(x)중부원점좌표(y)시설구분종합평가
시설코드1.0000.400-0.5700.1910.067
중부원점좌표(x)0.4001.0000.1310.0630.000
중부원점좌표(y)-0.5700.1311.0000.0890.045
시설구분0.1910.0630.0891.0000.108
종합평가0.0670.0000.0450.1081.000

Missing values

2023-12-23T07:01:00.135074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:01:01.231527image/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

시설구분시설코드시설명점검일자종합평가주소중부원점좌표(x)중부원점좌표(y)
3727저수지4513010007옥녀2018-12-15B군산시 개사동,내초동166953.5135372494.9712
8985배수장4817040017무촌2020-10-12C경상남도 진주시 사봉면 무촌리 1448-1314327.4372287950.5222
6559저수지4579010116대정2023-10-31C전라북도 고창군 공음면154788.8301312842.0457
908저수지4272010045노천2015-11-30C강원도 홍천군 동면 노천리290467.1345566195.1839
3968저수지4511010055전당2015-09-18C전주시 전미동1가211765.3106365406.1709
5337저수지4678010126학동2021-12-31C전라남도 보성군 노동면 학동리206209.5981250849.5876
10063저수지4884010112고현대곡2016-09-09C경상남도 남해군 고현면 대곡리280399.8017252973.7972
9121저수지4886010053중방곡2011-10-13D산청군 오부면 방곡리280043.6227319733.3416
8635배수장4825040017신문2018-10-12C경남 김해시 장유면367129.808289618.9016
10245저수지4888010101무창1999-12-31D거창군 남상면 무촌리282464.6487337983.8019
시설구분시설코드시설명점검일자종합평가주소중부원점좌표(x)중부원점좌표(y)
1671저수지4377010047양덕2002-12-31C삼성면243628.5004491666.3425
9716저수지4827010197소태2013-11-30C밀양시 청도면 소태리346964.1116331356.3923
9267저수지4872010150입사2017-11-10C경남 의령군 궁유면 운계리311882.8004314262.9499
916저수지4280010011월운2014-12-12C강원도 양구군 동면 월운리291398.2342625022.1737
4425저수지4573010059덕산2014-12-12C전라북도 무주군 안성면 덕산리263809.4842363609.2257
4155저수지4579010158석남2013-08-22C고창군 상하면 석남리 283152421.3018314860.966
6301저수지4684010183일로22022-10-31C전라남도 무안군 몽탄면 당호리154107.8175254680.248
10384방조제4421090007서산A지구2023-10-31C충남 서산시 간월도리, 창리, 홍성군 궁리150069.8866444760.362
5778저수지4681010012서산2011-11-30C전라남도 강진군 강진읍 서산리174459.2213228281.2175
7779저수지4790010105방화2013-06-28<NA>경상북도 예천군 지보면324461.1013442078.9709

Duplicate rows

Most frequently occurring

시설구분시설코드시설명점검일자종합평가주소중부원점좌표(x)중부원점좌표(y)# duplicates
0배수장4476040051토정2020-10-12C충청남도 부여군 세도면 청포리196854.3602395923.43622
1배수장4476040051토정2020-10-27C충청남도 부여군 세도면 청포리196854.3602395923.43622
2저수지2729010002도원2021-10-29C대구광역시 달서구 도원동340076.6799357381.31162
3저수지4213010067손곡2019-12-31C원주시270760.0653514065.25192
4저수지4376010004신항2015-11-13C괴산군 괴산읍269739.2987472751.81192
5저수지4423010045탑정2016-09-09B충청남도 논산시 부적면216850.8632398017.98612
6저수지4481010007송석2021-10-31<NA>예산군 대술면 관내일원194928.3399454339.56042
7저수지4481010007송석2021-12-31C예산군 대술면 관내일원194928.3399454339.56042
8저수지4577010172구림2020-10-12C전라북도 순창군 구림면 월정리204736.069316060.09272
9저수지4874010022옥천2017-11-10C경남 창녕군 계성면340125.1066323658.90882