Overview

Dataset statistics

Number of variables7
Number of observations108
Missing cells109
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory59.2 B

Variable types

Categorical2
Text3
Numeric2

Dataset

Description세종특별자치시에 위치한 상수도 기반시설 현황에 관한 자료입니다.데이터는 구분, 기반시설명, 시설주소, 설치년도, 가압능력, 용량, 비고로 구성되어 있습니다
Author세종특별자치시
URLhttps://www.data.go.kr/data/15011851/fileData.do

Alerts

구분 is highly overall correlated with 용량(세제곱미터) and 1 other fieldsHigh correlation
비 고 is highly overall correlated with 구분High correlation
용량(세제곱미터) is highly overall correlated with 구분High correlation
가 압 능 력 has 19 (17.6%) missing valuesMissing
용량(세제곱미터) has 90 (83.3%) missing valuesMissing
기반시설명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 11:22:48.573521
Analysis finished2024-03-14 11:22:51.073967
Duration2.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
가압장
90 
배수지
18 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가압장 90
83.3%
배수지 18
 
16.7%

Length

2024-03-14T20:22:51.275355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:22:51.579326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가압장 90
83.3%
배수지 18
 
16.7%

기반시설명
Text

UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size992.0 B
2024-03-14T20:22:52.649736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.6574074
Min length5

Characters and Unicode

Total characters611
Distinct characters103
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

Unique108 ?
Unique (%)100.0%

Sample

1st row제1가압장
2nd row서창 가압장
3rd row신흥 가압장
4th row내판5리가압장
5th row노송 가압장
ValueCountFrequency (%)
가압장 11
 
8.6%
배수지 5
 
3.9%
금암 3
 
2.3%
2가압장 2
 
1.6%
제1가압장 1
 
0.8%
송수1가압장 1
 
0.8%
영대1-2가압장 1
 
0.8%
영대1-1가압장 1
 
0.8%
대박가압장 1
 
0.8%
1가압장 1
 
0.8%
Other values (101) 101
78.9%
2024-03-14T20:22:54.235315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
가 91
14.9%
장 90
14.7%
압 89
14.6%
수 23
 
3.8%
20
 
3.3%
배 18
 
2.9%
지 18
 
2.9%
2 17
 
2.8%
대 13
 
2.1%
1 12
 
2.0%
Other values (93) 220
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 544
89.0%
Decimal Number 43
 
7.0%
Space Separator 20
 
3.3%
Dash Punctuation 2
 
0.3%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 91
16.7%
장 90
16.5%
압 89
16.4%
수 23
 
4.2%
배 18
 
3.3%
지 18
 
3.3%
대 13
 
2.4%
곡 9
 
1.7%
금 8
 
1.5%
교 7
 
1.3%
Other values (82) 178
32.7%
Decimal Number
ValueCountFrequency (%)
2 17
39.5%
1 12
27.9%
3 6
 
14.0%
4 3
 
7.0%
5 2
 
4.7%
6 2
 
4.7%
8 1
 
2.3%
Space Separator
ValueCountFrequency (%)
20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 544
89.0%
Common 67
 
11.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 91
16.7%
장 90
16.5%
압 89
16.4%
수 23
 
4.2%
배 18
 
3.3%
지 18
 
3.3%
대 13
 
2.4%
곡 9
 
1.7%
금 8
 
1.5%
교 7
 
1.3%
Other values (82) 178
32.7%
Common
ValueCountFrequency (%)
20
29.9%
2 17
25.4%
1 12
17.9%
3 6
 
9.0%
4 3
 
4.5%
- 2
 
3.0%
5 2
 
3.0%
6 2
 
3.0%
( 1
 
1.5%
) 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 544
89.0%
ASCII 67
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
가 91
16.7%
장 90
16.5%
압 89
16.4%
수 23
 
4.2%
배 18
 
3.3%
지 18
 
3.3%
대 13
 
2.4%
곡 9
 
1.7%
금 8
 
1.5%
교 7
 
1.3%
Other values (82) 178
32.7%
ASCII
ValueCountFrequency (%)
20
29.9%
2 17
25.4%
1 12
17.9%
3 6
 
9.0%
4 3
 
4.5%
- 2
 
3.0%
5 2
 
3.0%
6 2
 
3.0%
( 1
 
1.5%
) 1
 
1.5%

시설주소
Text

Distinct107
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size992.0 B
2024-03-14T20:22:55.795416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length20.074074
Min length15

Characters and Unicode

Total characters2168
Distinct characters101
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

Unique106 ?
Unique (%)98.1%

Sample

1st row세종특별자치시 고은동 2079
2nd row세종특별자치시 조치원읍 섭골길 11
3rd row세종특별자치시 조치원읍 신흥리 211-6
4th row세종특별자치시 연동면 내판리 437-2
5th row세종특별자치시 연동면 노송리 183-3
ValueCountFrequency (%)
세종특별자치시 108
25.6%
금남면 18
 
4.3%
장군면 18
 
4.3%
전의면 13
 
3.1%
부강면 12
 
2.8%
연서면 9
 
2.1%
전동면 8
 
1.9%
대교리 7
 
1.7%
조치원읍 5
 
1.2%
연기면 5
 
1.2%
Other values (178) 219
51.9%
2024-03-14T20:22:57.722238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
314
 
14.5%
치 114
 
5.3%
세 112
 
5.2%
종 112
 
5.2%
시 110
 
5.1%
특 108
 
5.0%
별 108
 
5.0%
자 108
 
5.0%
리 95
 
4.4%
면 91
 
4.2%
Other values (91) 896
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1386
63.9%
Decimal Number 393
 
18.1%
Space Separator 314
 
14.5%
Dash Punctuation 75
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
치 114
 
8.2%
세 112
 
8.1%
종 112
 
8.1%
시 110
 
7.9%
특 108
 
7.8%
별 108
 
7.8%
자 108
 
7.8%
리 95
 
6.9%
면 91
 
6.6%
동 25
 
1.8%
Other values (79) 403
29.1%
Decimal Number
ValueCountFrequency (%)
1 84
21.4%
2 50
12.7%
3 50
12.7%
4 45
11.5%
6 40
10.2%
7 29
 
7.4%
0 25
 
6.4%
8 25
 
6.4%
5 24
 
6.1%
9 21
 
5.3%
Space Separator
ValueCountFrequency (%)
314
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1386
63.9%
Common 782
36.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
치 114
 
8.2%
세 112
 
8.1%
종 112
 
8.1%
시 110
 
7.9%
특 108
 
7.8%
별 108
 
7.8%
자 108
 
7.8%
리 95
 
6.9%
면 91
 
6.6%
동 25
 
1.8%
Other values (79) 403
29.1%
Common
ValueCountFrequency (%)
314
40.2%
1 84
 
10.7%
- 75
 
9.6%
2 50
 
6.4%
3 50
 
6.4%
4 45
 
5.8%
6 40
 
5.1%
7 29
 
3.7%
0 25
 
3.2%
8 25
 
3.2%
Other values (2) 45
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1386
63.9%
ASCII 782
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
314
40.2%
1 84
 
10.7%
- 75
 
9.6%
2 50
 
6.4%
3 50
 
6.4%
4 45
 
5.8%
6 40
 
5.1%
7 29
 
3.7%
0 25
 
3.2%
8 25
 
3.2%
Other values (2) 45
 
5.8%
Hangul
ValueCountFrequency (%)
치 114
 
8.2%
세 112
 
8.1%
종 112
 
8.1%
시 110
 
7.9%
특 108
 
7.8%
별 108
 
7.8%
자 108
 
7.8%
리 95
 
6.9%
면 91
 
6.6%
동 25
 
1.8%
Other values (79) 403
29.1%

설치년도
Real number (ℝ)

Distinct17
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.3333
Minimum1982
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T20:22:58.135347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile2006
Q12017
median2019
Q32021
95-th percentile2023
Maximum2023
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.4994608
Coefficient of variation (CV)0.0032218081
Kurtosis9.7919326
Mean2017.3333
Median Absolute Deviation (MAD)2
Skewness-2.7830095
Sum217872
Variance42.242991
MonotonicityNot monotonic
2024-03-14T20:22:58.504156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2018 22
20.4%
2020 16
14.8%
2021 15
13.9%
2023 13
12.0%
2019 10
9.3%
2017 6
 
5.6%
2012 4
 
3.7%
2014 4
 
3.7%
2022 4
 
3.7%
2009 3
 
2.8%
Other values (7) 11
10.2%
ValueCountFrequency (%)
1982 1
 
0.9%
1996 3
2.8%
2002 1
 
0.9%
2006 3
2.8%
2009 3
2.8%
2010 1
 
0.9%
2012 4
3.7%
2014 4
3.7%
2015 1
 
0.9%
2016 1
 
0.9%
ValueCountFrequency (%)
2023 13
12.0%
2022 4
 
3.7%
2021 15
13.9%
2020 16
14.8%
2019 10
9.3%
2018 22
20.4%
2017 6
 
5.6%
2016 1
 
0.9%
2015 1
 
0.9%
2014 4
 
3.7%

가 압 능 력
Text

MISSING 

Distinct82
Distinct (%)92.1%
Missing19
Missing (%)17.6%
Memory size992.0 B
2024-03-14T20:22:59.497461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length19.719101
Min length8

Characters and Unicode

Total characters1755
Distinct characters32
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

Unique77 ?
Unique (%)86.5%

Sample

1st row8.4㎥/min× 29mH× 75kw× 3대
2nd row2.8㎥/min× 29mH× 75kw× 3대
3rd row2.083/min× 30mH× 22kw× 3대
4th row0.03㎥/min× 40mH× 1.5kw× 2대
5th row0.149㎥/min× 60mH× 3.7kw× 2대
ValueCountFrequency (%)
2대 7
 
5.1%
2hp× 5
 
3.6%
3대 5
 
3.6%
3ph50㎥/일 4
 
2.9%
70mh× 4
 
2.9%
380v× 4
 
2.9%
1.28㎥/× 3
 
2.2%
2.2kw× 3
 
2.2%
75kw× 2
 
1.4%
29mh× 2
 
1.4%
Other values (96) 99
71.7%
2024-03-14T20:23:00.664118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
× 259
14.8%
2 141
 
8.0%
0 132
 
7.5%
5 93
 
5.3%
H 92
 
5.2%
3 92
 
5.2%
m 91
 
5.2%
1 89
 
5.1%
/ 86
 
4.9%
. 77
 
4.4%
Other values (22) 603
34.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 701
39.9%
Lowercase Letter 268
 
15.3%
Math Symbol 259
 
14.8%
Uppercase Letter 165
 
9.4%
Other Punctuation 163
 
9.3%
Other Letter 95
 
5.4%
Space Separator 51
 
2.9%
Other Symbol 47
 
2.7%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 141
20.1%
0 132
18.8%
5 93
13.3%
3 92
13.1%
1 89
12.7%
7 39
 
5.6%
8 34
 
4.9%
4 32
 
4.6%
6 31
 
4.4%
9 18
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
m 91
34.0%
w 45
16.8%
k 44
16.4%
h 29
 
10.8%
r 26
 
9.7%
x 15
 
5.6%
n 9
 
3.4%
i 9
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
H 92
55.8%
P 53
32.1%
A 15
 
9.1%
V 5
 
3.0%
Other Letter
ValueCountFrequency (%)
일 44
46.3%
톤 39
41.1%
대 12
 
12.6%
Other Punctuation
ValueCountFrequency (%)
/ 86
52.8%
. 77
47.2%
Math Symbol
ValueCountFrequency (%)
× 259
100.0%
Space Separator
ValueCountFrequency (%)
51
100.0%
Other Symbol
ValueCountFrequency (%)
㎥ 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1227
69.9%
Latin 433
 
24.7%
Hangul 95
 
5.4%

Most frequent character per script

Common
ValueCountFrequency (%)
× 259
21.1%
2 141
11.5%
0 132
10.8%
5 93
 
7.6%
3 92
 
7.5%
1 89
 
7.3%
/ 86
 
7.0%
. 77
 
6.3%
51
 
4.2%
㎥ 47
 
3.8%
Other values (7) 160
13.0%
Latin
ValueCountFrequency (%)
H 92
21.2%
m 91
21.0%
P 53
12.2%
w 45
10.4%
k 44
10.2%
h 29
 
6.7%
r 26
 
6.0%
x 15
 
3.5%
A 15
 
3.5%
n 9
 
2.1%
Other values (2) 14
 
3.2%
Hangul
ValueCountFrequency (%)
일 44
46.3%
톤 39
41.1%
대 12
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1354
77.2%
None 259
 
14.8%
Hangul 95
 
5.4%
CJK Compat 47
 
2.7%

Most frequent character per block

None
ValueCountFrequency (%)
× 259
100.0%
ASCII
ValueCountFrequency (%)
2 141
 
10.4%
0 132
 
9.7%
5 93
 
6.9%
H 92
 
6.8%
3 92
 
6.8%
m 91
 
6.7%
1 89
 
6.6%
/ 86
 
6.4%
. 77
 
5.7%
P 53
 
3.9%
Other values (17) 408
30.1%
CJK Compat
ValueCountFrequency (%)
㎥ 47
100.0%
Hangul
ValueCountFrequency (%)
일 44
46.3%
톤 39
41.1%
대 12
 
12.6%

용량(세제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing90
Missing (%)83.3%
Infinite0
Infinite (%)0.0%
Mean6111.3889
Minimum20
Maximum21000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-14T20:23:00.875252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile62.5
Q1375
median1600
Q311500
95-th percentile20150
Maximum21000
Range20980
Interquartile range (IQR)11125

Descriptive statistics

Standard deviation7667.1288
Coefficient of variation (CV)1.2545641
Kurtosis-0.61213675
Mean6111.3889
Median Absolute Deviation (MAD)1555
Skewness1.0123814
Sum110005
Variance58784865
MonotonicityNot monotonic
2024-03-14T20:23:01.064984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15000 1
 
0.9%
4000 1
 
0.9%
10000 1
 
0.9%
2000 1
 
0.9%
70 1
 
0.9%
600 1
 
0.9%
253 1
 
0.9%
3800 1
 
0.9%
12000 1
 
0.9%
800 1
 
0.9%
Other values (8) 8
 
7.4%
(Missing) 90
83.3%
ValueCountFrequency (%)
20 1
0.9%
70 1
0.9%
232 1
0.9%
253 1
0.9%
300 1
0.9%
600 1
0.9%
730 1
0.9%
800 1
0.9%
1200 1
0.9%
2000 1
0.9%
ValueCountFrequency (%)
21000 1
0.9%
20000 1
0.9%
18000 1
0.9%
15000 1
0.9%
12000 1
0.9%
10000 1
0.9%
4000 1
0.9%
3800 1
0.9%
2000 1
0.9%
1200 1
0.9%

비 고
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size992.0 B
2대1조(배수가압)
64 
3대1조(배수가압)
17 
콘크리트 구조(2지)
 
5
3대(1대예비)+(송수가압)
 
3
콘크리트 구조(3지)
 
3
Other values (11)
16 

Length

Max length25
Median length10
Mean length10.546296
Min length10

Unique

Unique7 ?
Unique (%)6.5%

Sample

1st row3대(1대예비)+(송수가압)
2nd row3대(1대예비)+(송수가압)
3rd row3대1조(배수가압)
4th row2대1조(배수가압)
5th row2대1조(배수가압)

Common Values

ValueCountFrequency (%)
2대1조(배수가압) 64
59.3%
3대1조(배수가압) 17
 
15.7%
콘크리트 구조(2지) 5
 
4.6%
3대(1대예비)+(송수가압) 3
 
2.8%
콘크리트 구조(3지) 3
 
2.8%
STS 원형물탱크(2조) 3
 
2.8%
3대1조(송수가압) 2
 
1.9%
콘크리트 구조(4지) 2
 
1.9%
STS 원형물탱크(1조) 2
 
1.9%
4대2조(배수가압) 1
 
0.9%
Other values (6) 6
 
5.6%

Length

2024-03-14T20:23:01.379956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2대1조(배수가압 64
50.4%
3대1조(배수가압 17
 
13.4%
콘크리트 11
 
8.7%
sts 7
 
5.5%
구조(2지 6
 
4.7%
3대(1대예비)+(송수가압 3
 
2.4%
구조(3지 3
 
2.4%
원형물탱크(2조 3
 
2.4%
원형물탱크(1조 2
 
1.6%
구조(4지 2
 
1.6%
Other values (8) 9
 
7.1%

Interactions

2024-03-14T20:22:49.686097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:49.208780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:49.942685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:49.439987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:23:01.533224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분설치년도가 압 능 력용량(세제곱미터)비 고
구분1.0000.432NaNNaN1.000
설치년도0.4321.0000.0000.3600.810
가 압 능 력NaN0.0001.000NaN0.976
용량(세제곱미터)NaN0.360NaN1.0000.505
비 고1.0000.8100.9760.5051.000
2024-03-14T20:23:01.706720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분비 고
구분1.0000.932
비 고0.9321.000
2024-03-14T20:23:01.846828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도용량(세제곱미터)구분비 고
설치년도1.0000.2090.4790.490
용량(세제곱미터)0.2091.0001.0000.223
구분0.4791.0001.0000.932
비 고0.4900.2230.9321.000

Missing values

2024-03-14T20:22:50.262512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:22:50.646037image/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.
2024-03-14T20:22:50.942603image/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

구분기반시설명시설주소설치년도가 압 능 력용량(세제곱미터)비 고
0가압장제1가압장세종특별자치시 고은동 207920168.4㎥/min× 29mH× 75kw× 3대<NA>3대(1대예비)+(송수가압)
1가압장서창 가압장세종특별자치시 조치원읍 섭골길 1120202.8㎥/min× 29mH× 75kw× 3대<NA>3대(1대예비)+(송수가압)
2가압장신흥 가압장세종특별자치시 조치원읍 신흥리 211-620092.083/min× 30mH× 22kw× 3대<NA>3대1조(배수가압)
3가압장내판5리가압장세종특별자치시 연동면 내판리 437-220090.03㎥/min× 40mH× 1.5kw× 2대<NA>2대1조(배수가압)
4가압장노송 가압장세종특별자치시 연동면 노송리 183-320090.149㎥/min× 60mH× 3.7kw× 2대<NA>2대1조(배수가압)
5가압장연기 가압장세종특별자치시 연기면 연기리 67-620062.639㎥/min× 55mH× 37kw× 3대<NA>3대1조(송수가압)
6가압장전동 가압장세종특별자치시 전동면 노장리 5831996100A×6.375㎥/hr×50mH×2.2kw×3<NA>3대(1대예비)+(송수가압)
7가압장봉안 가압장세종특별자치시 장군면 봉안리 산23-120102HP× 380V× 3PH50㎥/일<NA>2대1조(배수가압)
8가압장명학리가압장세종특별자치시 연동면 명학리 89720122HP× 380V× 3PH50㎥/일<NA>2대1조(배수가압)
9가압장응암가압장세종특별자치시 연동면 내판리 62-3201880A×2.3㎥/hr×30mH×0.55kw×2<NA>3대1조(배수가압)
구분기반시설명시설주소설치년도가 압 능 력용량(세제곱미터)비 고
98배수지소정배수지세종특별자치시 소정면 소정리 40-11996<NA>232콘크리트 구조(2지)
99배수지소정2배수지세종특별자치시 소정면 운당리 213-62020<NA>300STS 원형물탱크(2조)
100배수지제2 배수지세종특별자치시 세종동 12022014<NA>15000콘크리트 구조(2지)
101배수지연기배수지세종특별자치시 연기면 연기리 70-172006<NA>3800STS 원형물탱크(2조)
102배수지전동배수지세종특별자치시 전동면 노장리 599-11996<NA>253콘크리트 구조(1지)
103배수지전동2배수지세종특별자치시 전동면 송성리 산118-22022<NA>600STS 패널형탱크(2지)
104배수지청송배수지세종특별자치시 전동면 청송리 290-42020<NA>70STS 원형물탱크(1조)
105배수지전의배수지세종특별자치시 전의면 읍내리 55-442017<NA>2000콘크리트 구조(3지)
106배수지신안배수지세종특별자치시 조치원읍 신안리 478-42021<NA>10000STS 원형물탱크(2조)+콘크리트 구조(2지)
107배수지충령탑배수지세종특별자치시 조치원읍 침산리 1822006<NA>4000STS 원형물탱크(2조)