Overview

Dataset statistics

Number of variables7
Number of observations42
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory63.1 B

Variable types

Numeric4
Text3

Dataset

Description가뭄 분석 정보 제공을 위한 K-water 관리 광역 정수장에 대한 시설용량, 취수구분, 전처리시설 등 시설 제원정보 데이터 항목을 제공합니다
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049844/fileData.do

Alerts

관리번호 is highly overall correlated with 읍면동 코드High correlation
읍면동 코드 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
정수용량 is highly overall correlated with 읍면동 코드High correlation
건설일 has 1 (2.4%) missing valuesMissing
관리번호 has unique valuesUnique
정수장 명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:38:13.833803
Analysis finished2023-12-12 11:38:17.402159
Duration3.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.910814 × 109
Minimum1.9 × 109
Maximum2.017 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T20:38:17.541500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9 × 109
5-th percentile1.9 × 109
Q11.9000001 × 109
median1.9000001 × 109
Q31.9000001 × 109
95-th percentile2.011156 × 109
Maximum2.017 × 109
Range1.1699999 × 108
Interquartile range (IQR)77

Descriptive statistics

Standard deviation33744075
Coefficient of variation (CV)0.017659529
Kurtosis6.5176798
Mean1.910814 × 109
Median Absolute Deviation (MAD)39.5
Skewness2.8639525
Sum8.0254187 × 1010
Variance1.1386626 × 1015
MonotonicityStrictly increasing
2023-12-12T20:38:17.809814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1900000010 1
 
2.4%
1900000134 1
 
2.4%
1900000108 1
 
2.4%
1900000113 1
 
2.4%
1900000117 1
 
2.4%
1900000124 1
 
2.4%
1900000126 1
 
2.4%
1900000127 1
 
2.4%
1900000129 1
 
2.4%
1900000130 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1900000010 1
2.4%
1900000013 1
2.4%
1900000020 1
2.4%
1900000025 1
2.4%
1900000033 1
2.4%
1900000036 1
2.4%
1900000037 1
2.4%
1900000046 1
2.4%
1900000047 1
2.4%
1900000049 1
2.4%
ValueCountFrequency (%)
2017000002 1
2.4%
2015007936 1
2.4%
2011163592 1
2.4%
2011011568 1
2.4%
1900001007 1
2.4%
1900000155 1
2.4%
1900000146 1
2.4%
1900000144 1
2.4%
1900000142 1
2.4%
1900000134 1
2.4%

정수장 명
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T20:38:18.197073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.4761905
Min length5

Characters and Unicode

Total characters230
Distinct characters61
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row온산정수장
2nd row송전정수장
3rd row황지정수장
4th row충주정수장
5th row일산정수장
ValueCountFrequency (%)
온산정수장 1
 
2.4%
운문정수장 1
 
2.4%
금산정수장 1
 
2.4%
덕정정수장 1
 
2.4%
평림정수장 1
 
2.4%
대불정수장 1
 
2.4%
구미정수장(광역 1
 
2.4%
고령정수장(광역 1
 
2.4%
학야정수장 1
 
2.4%
자인정수장 1
 
2.4%
Other values (32) 32
76.2%
2023-12-12T20:38:18.814244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 43
18.7%
수 43
18.7%
장 42
18.3%
산 8
 
3.5%
) 5
 
2.2%
( 5
 
2.2%
역 4
 
1.7%
성 4
 
1.7%
광 4
 
1.7%
주 4
 
1.7%
Other values (51) 68
29.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 220
95.7%
Close Punctuation 5
 
2.2%
Open Punctuation 5
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 43
19.5%
수 43
19.5%
장 42
19.1%
산 8
 
3.6%
역 4
 
1.8%
성 4
 
1.8%
광 4
 
1.8%
주 4
 
1.8%
고 3
 
1.4%
화 3
 
1.4%
Other values (49) 62
28.2%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 220
95.7%
Common 10
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 43
19.5%
수 43
19.5%
장 42
19.1%
산 8
 
3.6%
역 4
 
1.8%
성 4
 
1.8%
광 4
 
1.8%
주 4
 
1.8%
고 3
 
1.4%
화 3
 
1.4%
Other values (49) 62
28.2%
Common
ValueCountFrequency (%)
) 5
50.0%
( 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 220
95.7%
ASCII 10
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 43
19.5%
수 43
19.5%
장 42
19.1%
산 8
 
3.6%
역 4
 
1.8%
성 4
 
1.8%
광 4
 
1.8%
주 4
 
1.8%
고 3
 
1.4%
화 3
 
1.4%
Other values (49) 62
28.2%
ASCII
ValueCountFrequency (%)
) 5
50.0%
( 5
50.0%

건설일
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)58.5%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean1997.0488
Minimum1977
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T20:38:19.103802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1977
5-th percentile1980
Q11992
median1998
Q32002
95-th percentile2009
Maximum2017
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9161405
Coefficient of variation (CV)0.0044646583
Kurtosis0.057279916
Mean1997.0488
Median Absolute Deviation (MAD)5
Skewness-0.2654621
Sum81879
Variance79.497561
MonotonicityNot monotonic
2023-12-12T20:38:19.343905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1998 6
 
14.3%
2002 3
 
7.1%
1994 2
 
4.8%
1996 2
 
4.8%
1992 2
 
4.8%
1999 2
 
4.8%
1988 2
 
4.8%
2009 2
 
4.8%
1987 2
 
4.8%
2003 2
 
4.8%
Other values (14) 16
38.1%
ValueCountFrequency (%)
1977 1
2.4%
1979 1
2.4%
1980 1
2.4%
1983 1
2.4%
1987 2
4.8%
1988 2
4.8%
1989 1
2.4%
1992 2
4.8%
1993 1
2.4%
1994 2
4.8%
ValueCountFrequency (%)
2017 1
 
2.4%
2012 1
 
2.4%
2009 2
4.8%
2008 1
 
2.4%
2007 1
 
2.4%
2005 2
4.8%
2003 2
4.8%
2002 3
7.1%
2001 2
4.8%
1999 2
4.8%

주소
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T20:38:19.918251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length20.857143
Min length16

Characters and Unicode

Total characters876
Distinct characters128
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

Unique42 ?
Unique (%)100.0%

Sample

1st row울산광역시 울주군 온산읍 학남리 산37-1
2nd row강원도 횡성군 횡성읍 송전리 5-1
3rd row강원도 태백시 황연동 산174-2
4th row충청북도 충주시 용탄동 305
5th row경기도 고양시 덕양구 대장동 223-1
ValueCountFrequency (%)
경기도 10
 
4.9%
경상남도 6
 
2.9%
충청남도 6
 
2.9%
경상북도 5
 
2.4%
전라남도 5
 
2.4%
전라북도 4
 
2.0%
남양주시 2
 
1.0%
거제시 2
 
1.0%
고양시 2
 
1.0%
안산시 2
 
1.0%
Other values (158) 161
78.5%
2023-12-12T20:38:20.713876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
175
 
20.0%
도 42
 
4.8%
산 31
 
3.5%
시 30
 
3.4%
리 28
 
3.2%
1 27
 
3.1%
경 22
 
2.5%
남 22
 
2.5%
2 22
 
2.5%
면 21
 
2.4%
Other values (118) 456
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 545
62.2%
Space Separator 175
 
20.0%
Decimal Number 136
 
15.5%
Dash Punctuation 20
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 42
 
7.7%
산 31
 
5.7%
시 30
 
5.5%
리 28
 
5.1%
경 22
 
4.0%
남 22
 
4.0%
면 21
 
3.9%
동 17
 
3.1%
상 15
 
2.8%
군 14
 
2.6%
Other values (106) 303
55.6%
Decimal Number
ValueCountFrequency (%)
1 27
19.9%
2 22
16.2%
6 14
10.3%
4 13
9.6%
7 13
9.6%
3 12
8.8%
8 10
 
7.4%
0 10
 
7.4%
5 9
 
6.6%
9 6
 
4.4%
Space Separator
ValueCountFrequency (%)
175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 545
62.2%
Common 331
37.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 42
 
7.7%
산 31
 
5.7%
시 30
 
5.5%
리 28
 
5.1%
경 22
 
4.0%
남 22
 
4.0%
면 21
 
3.9%
동 17
 
3.1%
상 15
 
2.8%
군 14
 
2.6%
Other values (106) 303
55.6%
Common
ValueCountFrequency (%)
175
52.9%
1 27
 
8.2%
2 22
 
6.6%
- 20
 
6.0%
6 14
 
4.2%
4 13
 
3.9%
7 13
 
3.9%
3 12
 
3.6%
8 10
 
3.0%
0 10
 
3.0%
Other values (2) 15
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 545
62.2%
ASCII 331
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
175
52.9%
1 27
 
8.2%
2 22
 
6.6%
- 20
 
6.0%
6 14
 
4.2%
4 13
 
3.9%
7 13
 
3.9%
3 12
 
3.6%
8 10
 
3.0%
0 10
 
3.0%
Other values (2) 15
 
4.5%
Hangul
ValueCountFrequency (%)
도 42
 
7.7%
산 31
 
5.7%
시 30
 
5.5%
리 28
 
5.1%
경 22
 
4.0%
남 22
 
4.0%
면 21
 
3.9%
동 17
 
3.1%
상 15
 
2.8%
군 14
 
2.6%
Other values (106) 303
55.6%

읍면동 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44532430
Minimum31710250
Maximum48330330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T20:38:21.000641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31710250
5-th percentile41271671
Q141740364
median44945555
Q347055092
95-th percentile48308368
Maximum48330330
Range16620080
Interquartile range (IQR)5314728.8

Descriptive statistics

Standard deviation3256188.7
Coefficient of variation (CV)0.073119494
Kurtosis4.2502662
Mean44532430
Median Absolute Deviation (MAD)2294736.5
Skewness-1.4518784
Sum1.870362 × 109
Variance1.0602765 × 1013
MonotonicityNot monotonic
2023-12-12T20:38:21.305741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
41360250 2
 
4.8%
31710250 1
 
2.4%
48123510 1
 
2.4%
46840320 1
 
2.4%
47190253 1
 
2.4%
47830340 1
 
2.4%
47113340 1
 
2.4%
47290330 1
 
2.4%
47820350 1
 
2.4%
48270330 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
31710250 1
2.4%
41131650 1
2.4%
41271570 1
2.4%
41273590 1
2.4%
41281610 1
2.4%
41285540 1
2.4%
41360250 2
4.8%
41465580 1
2.4%
41480253 1
2.4%
41590310 1
2.4%
ValueCountFrequency (%)
48330330 1
2.4%
48310590 1
2.4%
48310370 1
2.4%
48270330 1
2.4%
48240340 1
2.4%
48123510 1
2.4%
47830340 1
2.4%
47820350 1
2.4%
47290330 1
2.4%
47190253 1
2.4%

정수용량
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211504.76
Minimum16000
Maximum916000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T20:38:21.546427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16000
5-th percentile20350
Q158600
median125000
Q3275250
95-th percentile694800
Maximum916000
Range900000
Interquartile range (IQR)216650

Descriptive statistics

Standard deviation217996.97
Coefficient of variation (CV)1.0306953
Kurtosis2.4853309
Mean211504.76
Median Absolute Deviation (MAD)90000
Skewness1.6649186
Sum8883200
Variance4.7522677 × 1010
MonotonicityNot monotonic
2023-12-12T20:38:21.784233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
70000 2
 
4.8%
250000 2
 
4.8%
100000 2
 
4.8%
130000 2
 
4.8%
30000 2
 
4.8%
16000 2
 
4.8%
281000 1
 
2.4%
80000 1
 
2.4%
431000 1
 
2.4%
44000 1
 
2.4%
Other values (26) 26
61.9%
ValueCountFrequency (%)
16000 2
4.8%
20000 1
2.4%
27000 1
2.4%
30000 2
4.8%
40000 1
2.4%
44000 1
2.4%
45000 1
2.4%
52000 1
2.4%
57500 1
2.4%
61900 1
2.4%
ValueCountFrequency (%)
916000 1
2.4%
786000 1
2.4%
700000 1
2.4%
596000 1
2.4%
450000 1
2.4%
431000 1
2.4%
421000 1
2.4%
414000 1
2.4%
325000 1
2.4%
285200 1
2.4%

전처리 시설
Text

Distinct34
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T20:38:22.136338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length5.547619
Min length3

Characters and Unicode

Total characters233
Distinct characters63
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)66.7%

Sample

1st row원동취수장
2nd row횡성댐
3rd row광동댐
4th row충주댐취수장
5th row자양취수장
ValueCountFrequency (%)
팔당2취수장 3
 
7.1%
팔당1취수장 3
 
7.1%
밀양댐 2
 
4.8%
현도취수장 2
 
4.8%
부안댐취수장 2
 
4.8%
팔당3취수장 2
 
4.8%
운문취수장 1
 
2.4%
구천취수장 1
 
2.4%
연초댐 1
 
2.4%
본포취수장 1
 
2.4%
Other values (24) 24
57.1%
2023-12-12T20:38:22.748818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 36
15.5%
취 35
15.0%
수 35
15.0%
댐 17
 
7.3%
팔 8
 
3.4%
당 8
 
3.4%
산 3
 
1.3%
주 3
 
1.3%
) 3
 
1.3%
동 3
 
1.3%
Other values (53) 82
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 218
93.6%
Decimal Number 8
 
3.4%
Close Punctuation 3
 
1.3%
Open Punctuation 3
 
1.3%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 36
16.5%
취 35
16.1%
수 35
16.1%
댐 17
 
7.8%
팔 8
 
3.7%
당 8
 
3.7%
산 3
 
1.4%
주 3
 
1.4%
동 3
 
1.4%
부 3
 
1.4%
Other values (47) 67
30.7%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
2 3
37.5%
3 2
25.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 218
93.6%
Common 15
 
6.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 36
16.5%
취 35
16.1%
수 35
16.1%
댐 17
 
7.8%
팔 8
 
3.7%
당 8
 
3.7%
산 3
 
1.4%
주 3
 
1.4%
동 3
 
1.4%
부 3
 
1.4%
Other values (47) 67
30.7%
Common
ValueCountFrequency (%)
) 3
20.0%
( 3
20.0%
1 3
20.0%
2 3
20.0%
3 2
13.3%
, 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 218
93.6%
ASCII 15
 
6.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 36
16.5%
취 35
16.1%
수 35
16.1%
댐 17
 
7.8%
팔 8
 
3.7%
당 8
 
3.7%
산 3
 
1.4%
주 3
 
1.4%
동 3
 
1.4%
부 3
 
1.4%
Other values (47) 67
30.7%
ASCII
ValueCountFrequency (%)
) 3
20.0%
( 3
20.0%
1 3
20.0%
2 3
20.0%
3 2
13.3%
, 1
 
6.7%

Interactions

2023-12-12T20:38:16.397937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:14.372631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:15.074066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:15.761140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:16.542021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:14.572308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:15.245901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:15.923376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:16.702444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:14.757830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:15.437177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:16.094844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:16.905921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:14.914958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:15.619819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:38:16.254937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:38:22.945039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호정수장 명건설일주소읍면동 코드정수용량전처리 시설
관리번호1.0001.0000.9471.0000.2580.0000.000
정수장 명1.0001.0001.0001.0001.0001.0001.000
건설일0.9471.0001.0001.0000.3450.0000.775
주소1.0001.0001.0001.0001.0001.0001.000
읍면동 코드0.2581.0000.3451.0001.0000.4470.971
정수용량0.0001.0000.0001.0000.4471.0000.000
전처리 시설0.0001.0000.7751.0000.9710.0001.000
2023-12-12T20:38:23.168308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호건설일읍면동 코드정수용량
관리번호1.0000.3500.596-0.477
건설일0.3501.0000.074-0.081
읍면동 코드0.5960.0741.000-0.563
정수용량-0.477-0.081-0.5631.000

Missing values

2023-12-12T20:38:17.091196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:38:17.320545image/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

관리번호정수장 명건설일주소읍면동 코드정수용량전처리 시설
01900000010온산정수장1994울산광역시 울주군 온산읍 학남리 산37-131710250281000원동취수장
11900000013송전정수장2003강원도 횡성군 횡성읍 송전리 5-142730250100000횡성댐
21900000020황지정수장1989강원도 태백시 황연동 산174-24219052570000광동댐
31900000025충주정수장1998충청북도 충주시 용탄동 30543130640250000충주댐취수장
41900000033일산정수장1992경기도 고양시 덕양구 대장동 223-141281610250000자양취수장
51900000036덕소정수장1999경기도 남양주시 와부읍 도곡리 산102-141360250450000덕소취수장
61900000037와부정수장1988경기도 남양주시 와부읍 덕소7리 산4841360250130000팔당2취수장
71900000046반월정수장1980경기도 안산시 상록구 부곡동 산41-941271570190000팔당1취수장
81900000047시흥정수장1993경기도 안산시 단원구 선부2동 116941273590258000팔당2취수장
91900000049성남정수장1988경기도 성남시 수정구 사송동 산88-541131650786000팔당2취수장
관리번호정수장 명건설일주소읍면동 코드정수용량전처리 시설
321900000134양산정수장2001경상남도 양산시 상북면 상삼리 2134833033080000밀양댐
331900000142반송정수장1977경상남도 창원시 반림동 25번지48123510120000본포취수장
341900000144연초정수장1979경상남도 거제시 연초면 덕치리 1071114831037016000연초댐
351900000146구천정수장1987경상남도 거제시 삼거동 529-44831059020000구천취수장
361900000155사천정수장2002경상남도 사천시 축동면 배춘리 18번지48240340325000남강취수장
371900001007공주정수장2009충청남도 공주시 월송동 산42-74415059030000현도취수장
382011011568고양정수장2009경기도 고양시 일산동구 산황동 300번지41285540210000팔당1취수장
392011163592파주정수장(공업)<NA>경기도 파주시 파주읍 봉암리 260-541480253222000팔당1취수장
402015007936금산정수장2012충청남도 금산군 남일면 신정리 796-64471035027000용담댐취수장(금산무주)
412017000002화성정수장2017경기도 화성시 매송면 천천리 1841590310178000팔당3취수장