Overview

Dataset statistics

Number of variables8
Number of observations156
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory71.8 B

Variable types

DateTime1
Numeric7

Dataset

Description대구광역시 상수도사업본부의 정수장별 낙동강 본류 월간 취수량 데이터입니다. 2011년부터 낙동강 본류 정수장별, 월별 생활용수 및 공업용수 취수량을 제공합니다
Author대구광역시
URLhttps://www.data.go.kr/data/15086307/fileData.do

Alerts

합계 is highly overall correlated with 생활용수 계 and 3 other fieldsHigh correlation
생활용수 계 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
매곡(생활용수) is highly overall correlated with 합계 and 4 other fieldsHigh correlation
문산(생활용수) is highly overall correlated with 공업용수 계 and 2 other fieldsHigh correlation
공업용수 계 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
죽곡(공업용수) is highly overall correlated with 합계 and 4 other fieldsHigh correlation
구지(공업용수) is highly overall correlated with 매곡(생활용수) and 3 other fieldsHigh correlation
연월 has unique valuesUnique
합계 has unique valuesUnique
생활용수 계 has unique valuesUnique
매곡(생활용수) has unique valuesUnique
문산(생활용수) has unique valuesUnique
공업용수 계 has unique valuesUnique
죽곡(공업용수) has unique valuesUnique
구지(공업용수) has 98 (62.8%) zerosZeros

Reproduction

Analysis started2024-03-15 02:49:07.282894
Analysis finished2024-03-15 02:49:22.287580
Duration15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2011-01-01 00:00:00
Maximum2023-12-01 00:00:00
2024-03-15T11:49:22.516698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:22.976359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19984208
Minimum15635825
Maximum23123648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-15T11:49:23.418556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15635825
5-th percentile16792687
Q118910574
median20139868
Q321242139
95-th percentile22616778
Maximum23123648
Range7487823
Interquartile range (IQR)2331565.2

Descriptive statistics

Standard deviation1668342.7
Coefficient of variation (CV)0.083483053
Kurtosis-0.31962372
Mean19984208
Median Absolute Deviation (MAD)1159546
Skewness-0.36207198
Sum3.1175364 × 109
Variance2.7833673 × 1012
MonotonicityNot monotonic
2024-03-15T11:49:23.876989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20969095 1
 
0.6%
18901673 1
 
0.6%
19731742 1
 
0.6%
21140931 1
 
0.6%
20961170 1
 
0.6%
19439818 1
 
0.6%
20048005 1
 
0.6%
19284601 1
 
0.6%
19611107 1
 
0.6%
16762716 1
 
0.6%
Other values (146) 146
93.6%
ValueCountFrequency (%)
15635825 1
0.6%
16105239 1
0.6%
16367942 1
0.6%
16390614 1
0.6%
16412720 1
0.6%
16514421 1
0.6%
16757465 1
0.6%
16762716 1
0.6%
16802677 1
0.6%
16936502 1
0.6%
ValueCountFrequency (%)
23123648 1
0.6%
23030073 1
0.6%
23027760 1
0.6%
22979527 1
0.6%
22921232 1
0.6%
22804816 1
0.6%
22709781 1
0.6%
22668484 1
0.6%
22599542 1
0.6%
22571223 1
0.6%

생활용수 계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16590358
Minimum12956230
Maximum19597684
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-15T11:49:24.330917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12956230
5-th percentile14178328
Q115945348
median16589472
Q317485264
95-th percentile18753142
Maximum19597684
Range6641454
Interquartile range (IQR)1539916.5

Descriptive statistics

Standard deviation1343353.8
Coefficient of variation (CV)0.08097196
Kurtosis0.35290579
Mean16590358
Median Absolute Deviation (MAD)725489
Skewness-0.34224285
Sum2.5880959 × 109
Variance1.8045995 × 1012
MonotonicityNot monotonic
2024-03-15T11:49:24.611986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17223385 1
 
0.6%
16181994 1
 
0.6%
16558483 1
 
0.6%
17856679 1
 
0.6%
17869639 1
 
0.6%
16571852 1
 
0.6%
16724909 1
 
0.6%
16199801 1
 
0.6%
16542304 1
 
0.6%
13837983 1
 
0.6%
Other values (146) 146
93.6%
ValueCountFrequency (%)
12956230 1
0.6%
13158639 1
0.6%
13192331 1
0.6%
13202217 1
0.6%
13362048 1
0.6%
13837983 1
0.6%
14138229 1
0.6%
14161855 1
0.6%
14183819 1
0.6%
14199616 1
0.6%
ValueCountFrequency (%)
19597684 1
0.6%
19570637 1
0.6%
19496746 1
0.6%
19255312 1
0.6%
19173813 1
0.6%
18914756 1
0.6%
18876058 1
0.6%
18811551 1
0.6%
18733672 1
0.6%
18715060 1
0.6%

매곡(생활용수)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12867679
Minimum9308976
Maximum15993120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-15T11:49:24.899735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9308976
5-th percentile9670957
Q111971885
median13048679
Q313810530
95-th percentile14902934
Maximum15993120
Range6684144
Interquartile range (IQR)1838645.5

Descriptive statistics

Standard deviation1449723.3
Coefficient of variation (CV)0.11266393
Kurtosis0.13733187
Mean12867679
Median Absolute Deviation (MAD)867900
Skewness-0.56363778
Sum2.007358 × 109
Variance2.1016977 × 1012
MonotonicityNot monotonic
2024-03-15T11:49:25.282267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14111237 1
 
0.6%
11982270 1
 
0.6%
12870790 1
 
0.6%
13534140 1
 
0.6%
13585600 1
 
0.6%
12789700 1
 
0.6%
12884860 1
 
0.6%
12743810 1
 
0.6%
12956610 1
 
0.6%
9315710 1
 
0.6%
Other values (146) 146
93.6%
ValueCountFrequency (%)
9308976 1
0.6%
9315710 1
0.6%
9421060 1
0.6%
9437340 1
0.6%
9461630 1
0.6%
9509500 1
0.6%
9538240 1
0.6%
9619780 1
0.6%
9688016 1
0.6%
9755960 1
0.6%
ValueCountFrequency (%)
15993120 1
0.6%
15860288 1
0.6%
15555269 1
0.6%
15504894 1
0.6%
15375584 1
0.6%
15049024 1
0.6%
15019808 1
0.6%
14912384 1
0.6%
14899784 1
0.6%
14899042 1
0.6%

문산(생활용수)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3722678.9
Minimum262132
Maximum5388368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-15T11:49:25.550223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum262132
5-th percentile518739.75
Q13259932
median3642424.5
Q34754757.2
95-th percentile5121817.5
Maximum5388368
Range5126236
Interquartile range (IQR)1494825.2

Descriptive statistics

Standard deviation1199446.7
Coefficient of variation (CV)0.32219988
Kurtosis1.8911452
Mean3722678.9
Median Absolute Deviation (MAD)679677
Skewness-1.3009181
Sum5.8073791 × 108
Variance1.4386723 × 1012
MonotonicityNot monotonic
2024-03-15T11:49:25.925580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3112148 1
 
0.6%
4199724 1
 
0.6%
3687693 1
 
0.6%
4322539 1
 
0.6%
4284039 1
 
0.6%
3782152 1
 
0.6%
3840049 1
 
0.6%
3455991 1
 
0.6%
3585694 1
 
0.6%
4522273 1
 
0.6%
Other values (146) 146
93.6%
ValueCountFrequency (%)
262132 1
0.6%
283159 1
0.6%
303760 1
0.6%
396071 1
0.6%
447569 1
0.6%
447759 1
0.6%
473220 1
0.6%
506571 1
0.6%
522796 1
0.6%
523573 1
0.6%
ValueCountFrequency (%)
5388368 1
0.6%
5380100 1
0.6%
5236371 1
0.6%
5231562 1
0.6%
5223911 1
0.6%
5193082 1
0.6%
5182388 1
0.6%
5172384 1
0.6%
5104962 1
0.6%
5075213 1
0.6%

공업용수 계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3393849.6
Minimum1974532
Maximum4491930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-15T11:49:26.233093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1974532
5-th percentile2455580.5
Q12812036.5
median3486930
Q33906815
95-th percentile4310630
Maximum4491930
Range2517398
Interquartile range (IQR)1094778.5

Descriptive statistics

Standard deviation628792.82
Coefficient of variation (CV)0.18527422
Kurtosis-1.1118284
Mean3393849.6
Median Absolute Deviation (MAD)530780.5
Skewness-0.15299699
Sum5.2944053 × 108
Variance3.9538041 × 1011
MonotonicityNot monotonic
2024-03-15T11:49:26.703741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3745710 1
 
0.6%
2719679 1
 
0.6%
3173259 1
 
0.6%
3284252 1
 
0.6%
3091531 1
 
0.6%
2867966 1
 
0.6%
3323096 1
 
0.6%
3084800 1
 
0.6%
3068803 1
 
0.6%
2924733 1
 
0.6%
Other values (146) 146
93.6%
ValueCountFrequency (%)
1974532 1
0.6%
2167683 1
0.6%
2228759 1
0.6%
2233777 1
0.6%
2273777 1
0.6%
2298565 1
0.6%
2435060 1
0.6%
2452102 1
0.6%
2456740 1
0.6%
2466522 1
0.6%
ValueCountFrequency (%)
4491930 1
0.6%
4416223 1
0.6%
4355460 1
0.6%
4353450 1
0.6%
4349380 1
0.6%
4316110 1
0.6%
4312700 1
0.6%
4312190 1
0.6%
4310110 1
0.6%
4294360 1
0.6%

죽곡(공업용수)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct156
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3383949.9
Minimum1958606
Maximum4491930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-15T11:49:27.270396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1958606
5-th percentile2432485.5
Q12790594
median3485350
Q33906815
95-th percentile4310630
Maximum4491930
Range2533324
Interquartile range (IQR)1116221

Descriptive statistics

Standard deviation638900.93
Coefficient of variation (CV)0.18880331
Kurtosis-1.1320825
Mean3383949.9
Median Absolute Deviation (MAD)540153.5
Skewness-0.15940882
Sum5.2789618 × 108
Variance4.081944 × 1011
MonotonicityNot monotonic
2024-03-15T11:49:27.731494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3745710 1
 
0.6%
2689706 1
 
0.6%
3137496 1
 
0.6%
3284250 1
 
0.6%
3040227 1
 
0.6%
2850048 1
 
0.6%
3319120 1
 
0.6%
3065408 1
 
0.6%
3040766 1
 
0.6%
2898451 1
 
0.6%
Other values (146) 146
93.6%
ValueCountFrequency (%)
1958606 1
0.6%
2148102 1
0.6%
2211983 1
0.6%
2212484 1
0.6%
2247202 1
0.6%
2280169 1
0.6%
2409640 1
0.6%
2421858 1
0.6%
2436028 1
0.6%
2437638 1
0.6%
ValueCountFrequency (%)
4491930 1
0.6%
4416223 1
0.6%
4355460 1
0.6%
4353450 1
0.6%
4349380 1
0.6%
4316110 1
0.6%
4312700 1
0.6%
4312190 1
0.6%
4310110 1
0.6%
4294360 1
0.6%

구지(공업용수)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9899.7179
Minimum0
Maximum57529
Zeros98
Zeros (%)62.8%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-15T11:49:28.152270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324897.25
95-th percentile33425.25
Maximum57529
Range57529
Interquartile range (IQR)24897.25

Descriptive statistics

Standard deviation14121.091
Coefficient of variation (CV)1.4264135
Kurtosis-0.20781912
Mean9899.7179
Median Absolute Deviation (MAD)0
Skewness1.016357
Sum1544356
Variance1.9940522 × 108
MonotonicityNot monotonic
2024-03-15T11:49:28.586647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98
62.8%
5111 1
 
0.6%
33744 1
 
0.6%
19547 1
 
0.6%
25666 1
 
0.6%
29258 1
 
0.6%
26575 1
 
0.6%
28590 1
 
0.6%
29927 1
 
0.6%
33752 1
 
0.6%
Other values (49) 49
31.4%
ValueCountFrequency (%)
0 98
62.8%
2 1
 
0.6%
3976 1
 
0.6%
5111 1
 
0.6%
15926 1
 
0.6%
16458 1
 
0.6%
16776 1
 
0.6%
17918 1
 
0.6%
18237 1
 
0.6%
18396 1
 
0.6%
ValueCountFrequency (%)
57529 1
0.6%
51304 1
0.6%
41339 1
0.6%
41001 1
0.6%
37184 1
0.6%
35763 1
0.6%
33752 1
0.6%
33744 1
0.6%
33319 1
0.6%
31617 1
0.6%

Interactions

2024-03-15T11:49:19.675014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:09.493675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:11.650073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:13.096055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:14.848023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:16.228227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:17.904316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:19.940486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:09.893301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:11.941546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:13.356298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:15.036825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:16.444334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:18.174901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:20.145452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:10.201504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:12.203622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:13.621162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:15.190257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:16.602962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:18.430521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:20.409762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:10.489936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:12.423620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:13.843529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:15.446513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:16.906291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:18.691850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:20.653090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:10.753347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:12.574400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:14.001646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:15.739394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:17.158343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:18.932338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:20.904239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:11.088451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:12.772424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:14.272274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:15.898966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:17.405787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:19.179687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:21.156204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:11.367014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:12.937084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:14.556962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:16.070129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:17.651206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:49:19.425440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T11:49:28.846834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계생활용수 계매곡(생활용수)문산(생활용수)공업용수 계죽곡(공업용수)구지(공업용수)
합계1.0000.9270.8780.4330.7590.7650.364
생활용수 계0.9271.0000.8440.5370.4730.4970.118
매곡(생활용수)0.8780.8441.0000.5930.6110.6400.459
문산(생활용수)0.4330.5370.5931.0000.5470.5430.500
공업용수 계0.7590.4730.6110.5471.0001.0000.613
죽곡(공업용수)0.7650.4970.6400.5431.0001.0000.598
구지(공업용수)0.3640.1180.4590.5000.6130.5981.000
2024-03-15T11:49:29.140651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계생활용수 계매곡(생활용수)문산(생활용수)공업용수 계죽곡(공업용수)구지(공업용수)
합계1.0000.9210.785-0.0100.6830.682-0.429
생활용수 계0.9211.0000.6620.2540.3770.376-0.155
매곡(생활용수)0.7850.6621.000-0.4320.6420.643-0.603
문산(생활용수)-0.0100.254-0.4321.000-0.534-0.5370.656
공업용수 계0.6830.3770.642-0.5341.0001.000-0.750
죽곡(공업용수)0.6820.3760.643-0.5371.0001.000-0.757
구지(공업용수)-0.429-0.155-0.6030.656-0.750-0.7571.000

Missing values

2024-03-15T11:49:21.502660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T11:49:22.116902image/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

연월합계생활용수 계매곡(생활용수)문산(생활용수)공업용수 계죽곡(공업용수)구지(공업용수)
02011-012096909517223385141112373112148374571037457100
12011-021891208615760736128444762916260315135031513500
22011-032162630817210085141062893103796441622344162230
32011-042131796717026377139317793094598429159042915900
42011-052138678617111816141986002913216427497042749700
52011-062145696717237354144708822766472421961342196130
62011-072210669017835450148990422936408427124042712400
72011-082124071617307306145262782781028393341039334100
82011-092050716416750824140843762666448375634037563400
92011-102131539217161892137769963384896415350041535000
연월합계생활용수 계매곡(생활용수)문산(생활용수)공업용수 계죽곡(공업용수)구지(공업용수)
1462023-0318864658159301691115584047743292934489290698127508
1472023-0418863105161319751132712048048552731130270499826132
1482023-0520602221178758321281310450627282726389269477231617
1492023-0620247503174894341266772848217062758069272827929790
1502023-0721219284185664361354750450189322652848262474328105
1512023-0820431451177714301284904049223902660021261868241339
1522023-0919345300167670371193846448285732578263254917929084
1532023-1019382573166743101186344048108702708263268036827895
1542023-1118906037161676221145017647174462738415271410824307
1552023-1219083696165520961116372853883682531600250591225688