Overview

Dataset statistics

Number of variables26
Number of observations64
Missing cells58
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory221.1 B

Variable types

Categorical13
Numeric8
Text3
Boolean2

Dataset

Description충청남도 재난대응용 배수펌프장 현황(펌프장명, 주소, 설치년도, 설치목적, 모터 펌프규모(kW(HP) X 대), 엔진 펌프규모(kW(HP) X 대), 한전 전원공급방식(1회선 또는 2회선), 비상발전기(kW(HP) X 대), 한전 계약전력(kW, 고압/저압), 사용전력량(kWH)/년 (최대/최소,고압/저압), 전기요금(원)/년 (최대/최소,고압/저압)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=411&beforeMenuCd=DOM_000000201001001000&publicdatapk=15032206

Alerts

정전대비 예비전기시설(이중선로) has constant value ""Constant
보강년도 is highly imbalanced (74.9%)Imbalance
비고 is highly imbalanced (53.4%)Imbalance
계획빈도(년) has 2 (3.1%) missing valuesMissing
유수지용량(m3) has 13 (20.3%) missing valuesMissing
정전대비 예비전기시설(이중선로) has 43 (67.2%) missing valuesMissing
연번 has unique valuesUnique
시설명 has unique valuesUnique
위 치 (상세주소 작성) has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:48:10.725278
Analysis finished2024-01-09 22:48:11.068125
Duration0.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

Distinct12
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size644.0 B
천안
15 
부여
13 
논산
7 
홍성
6 
공주
4 
Other values (7)
19 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
천안 15
23.4%
부여 13
20.3%
논산 7
10.9%
홍성 6
 
9.4%
공주 4
 
6.2%
아산 4
 
6.2%
예산 4
 
6.2%
보령 3
 
4.7%
당진 3
 
4.7%
청양 2
 
3.1%
Other values (2) 3
 
4.7%

Length

2024-01-10T07:48:11.116362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
천안 15
23.4%
부여 13
20.3%
논산 7
10.9%
홍성 6
 
9.4%
공주 4
 
6.2%
아산 4
 
6.2%
예산 4
 
6.2%
보령 3
 
4.7%
당진 3
 
4.7%
청양 2
 
3.1%
Other values (2) 3
 
4.7%

연번
Real number (ℝ)

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.5
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-01-10T07:48:11.222587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.15
Q116.75
median32.5
Q348.25
95-th percentile60.85
Maximum64
Range63
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.618987
Coefficient of variation (CV)0.5728919
Kurtosis-1.2
Mean32.5
Median Absolute Deviation (MAD)16
Skewness0
Sum2080
Variance346.66667
MonotonicityStrictly increasing
2024-01-10T07:48:11.339837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
34 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
43 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
64 1
1.6%
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%

시설명
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-01-10T07:48:11.563821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length7.484375
Min length5

Characters and Unicode

Total characters479
Distinct characters85
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)100.0%

Sample

1st row원성2-1지구 배수펌프장
2nd row원성2-2지구 배수펌프장
3rd row연춘리 배수펌프장
4th row송정리 배수펌프장
5th row봉명동 배수펌프장
ValueCountFrequency (%)
배수펌프장 26
27.7%
펌프장 2
 
2.1%
배수장 2
 
2.1%
원성2-1지구 1
 
1.1%
외리배수장 1
 
1.1%
북고2배수장 1
 
1.1%
북고1배수장 1
 
1.1%
홍산배수장 1
 
1.1%
석동배수장 1
 
1.1%
규암배수장 1
 
1.1%
Other values (57) 57
60.6%
2024-01-10T07:48:11.901616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 63
13.2%
장 63
13.2%
배 61
12.7%
펌 37
 
7.7%
프 37
 
7.7%
30
 
6.3%
성 15
 
3.1%
1 12
 
2.5%
구 12
 
2.5%
지 11
 
2.3%
Other values (75) 138
28.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 416
86.8%
Space Separator 30
 
6.3%
Decimal Number 22
 
4.6%
Dash Punctuation 9
 
1.9%
Uppercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 63
15.1%
장 63
15.1%
배 61
14.7%
펌 37
 
8.9%
프 37
 
8.9%
성 15
 
3.6%
구 12
 
2.9%
지 11
 
2.6%
정 8
 
1.9%
원 5
 
1.2%
Other values (66) 104
25.0%
Decimal Number
ValueCountFrequency (%)
1 12
54.5%
2 7
31.8%
3 1
 
4.5%
4 1
 
4.5%
5 1
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 416
86.8%
Common 61
 
12.7%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 63
15.1%
장 63
15.1%
배 61
14.7%
펌 37
 
8.9%
프 37
 
8.9%
성 15
 
3.6%
구 12
 
2.9%
지 11
 
2.6%
정 8
 
1.9%
원 5
 
1.2%
Other values (66) 104
25.0%
Common
ValueCountFrequency (%)
30
49.2%
1 12
 
19.7%
- 9
 
14.8%
2 7
 
11.5%
3 1
 
1.6%
4 1
 
1.6%
5 1
 
1.6%
Latin
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 416
86.8%
ASCII 63
 
13.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 63
15.1%
장 63
15.1%
배 61
14.7%
펌 37
 
8.9%
프 37
 
8.9%
성 15
 
3.6%
구 12
 
2.9%
지 11
 
2.6%
정 8
 
1.9%
원 5
 
1.2%
Other values (66) 104
25.0%
ASCII
ValueCountFrequency (%)
30
47.6%
1 12
 
19.0%
- 9
 
14.3%
2 7
 
11.1%
L 1
 
1.6%
3 1
 
1.6%
4 1
 
1.6%
5 1
 
1.6%
G 1
 
1.6%

위 치 (상세주소 작성)
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-01-10T07:48:12.204310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length21
Mean length16.390625
Min length10

Characters and Unicode

Total characters1049
Distinct characters117
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

Unique64 ?
Unique (%)100.0%

Sample

1st row동남구 원성동 622
2nd row동남구 원성동 620
3rd row동남구 북면 연춘리 245
4th row동남구 병천면 송정리 520-1
5th row동남구 봉명동 194-40
ValueCountFrequency (%)
천안시 10
 
4.0%
서북구 9
 
3.6%
논산시 7
 
2.8%
동남구 6
 
2.4%
홍성군 6
 
2.4%
규암면 6
 
2.4%
성정동 6
 
2.4%
예산군 4
 
1.6%
부여군 4
 
1.6%
공주시 4
 
1.6%
Other values (160) 186
75.0%
2024-01-10T07:48:12.613075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
186
 
17.7%
1 54
 
5.1%
- 37
 
3.5%
리 33
 
3.1%
시 33
 
3.1%
0 31
 
3.0%
9 29
 
2.8%
산 28
 
2.7%
4 27
 
2.6%
2 25
 
2.4%
Other values (107) 566
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 558
53.2%
Decimal Number 258
24.6%
Space Separator 186
 
17.7%
Dash Punctuation 37
 
3.5%
Open Punctuation 5
 
0.5%
Close Punctuation 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 33
 
5.9%
시 33
 
5.9%
산 28
 
5.0%
성 24
 
4.3%
면 24
 
4.3%
천 22
 
3.9%
읍 20
 
3.6%
동 20
 
3.6%
군 18
 
3.2%
로 18
 
3.2%
Other values (93) 318
57.0%
Decimal Number
ValueCountFrequency (%)
1 54
20.9%
0 31
12.0%
9 29
11.2%
4 27
10.5%
2 25
9.7%
7 21
 
8.1%
3 20
 
7.8%
6 20
 
7.8%
5 19
 
7.4%
8 12
 
4.7%
Space Separator
ValueCountFrequency (%)
186
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 558
53.2%
Common 491
46.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 33
 
5.9%
시 33
 
5.9%
산 28
 
5.0%
성 24
 
4.3%
면 24
 
4.3%
천 22
 
3.9%
읍 20
 
3.6%
동 20
 
3.6%
군 18
 
3.2%
로 18
 
3.2%
Other values (93) 318
57.0%
Common
ValueCountFrequency (%)
186
37.9%
1 54
 
11.0%
- 37
 
7.5%
0 31
 
6.3%
9 29
 
5.9%
4 27
 
5.5%
2 25
 
5.1%
7 21
 
4.3%
3 20
 
4.1%
6 20
 
4.1%
Other values (4) 41
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 558
53.2%
ASCII 491
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
186
37.9%
1 54
 
11.0%
- 37
 
7.5%
0 31
 
6.3%
9 29
 
5.9%
4 27
 
5.5%
2 25
 
5.1%
7 21
 
4.3%
3 20
 
4.1%
6 20
 
4.1%
Other values (4) 41
 
8.4%
Hangul
ValueCountFrequency (%)
리 33
 
5.9%
시 33
 
5.9%
산 28
 
5.0%
성 24
 
4.3%
면 24
 
4.3%
천 22
 
3.9%
읍 20
 
3.6%
동 20
 
3.6%
군 18
 
3.2%
로 18
 
3.2%
Other values (93) 318
57.0%

준공년도
Real number (ℝ)

Distinct30
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.125
Minimum1970
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-01-10T07:48:12.733598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1989.3
Q11999.75
median2007
Q32011
95-th percentile2018.85
Maximum2022
Range52
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation9.8553023
Coefficient of variation (CV)0.0049150563
Kurtosis1.7918217
Mean2005.125
Median Absolute Deviation (MAD)6
Skewness-0.92815267
Sum128328
Variance97.126984
MonotonicityNot monotonic
2024-01-10T07:48:12.839519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2007 7
 
10.9%
2009 5
 
7.8%
2001 4
 
6.2%
2016 3
 
4.7%
2012 3
 
4.7%
2011 3
 
4.7%
2002 3
 
4.7%
1999 3
 
4.7%
1997 3
 
4.7%
2006 3
 
4.7%
Other values (20) 27
42.2%
ValueCountFrequency (%)
1970 1
 
1.6%
1979 1
 
1.6%
1989 2
3.1%
1991 2
3.1%
1992 1
 
1.6%
1993 1
 
1.6%
1996 1
 
1.6%
1997 3
4.7%
1998 1
 
1.6%
1999 3
4.7%
ValueCountFrequency (%)
2022 1
 
1.6%
2021 2
3.1%
2019 1
 
1.6%
2018 1
 
1.6%
2017 2
3.1%
2016 3
4.7%
2015 2
3.1%
2012 3
4.7%
2011 3
4.7%
2010 2
3.1%

보강년도
Categorical

IMBALANCE 

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size644.0 B
<NA>
59 
2018
 
3
2012
 
1
2001
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)3.1%

Sample

1st row2018
2nd row2018
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 59
92.2%
2018 3
 
4.7%
2012 1
 
1.6%
2001 1
 
1.6%

Length

2024-01-10T07:48:12.940898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:13.036003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 59
92.2%
2018 3
 
4.7%
2012 1
 
1.6%
2001 1
 
1.6%

계획빈도(년)
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)9.7%
Missing2
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean34.83871
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-01-10T07:48:13.135272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q120
median30
Q330
95-th percentile100
Maximum100
Range90
Interquartile range (IQR)10

Descriptive statistics

Standard deviation23.660296
Coefficient of variation (CV)0.67913814
Kurtosis2.6656326
Mean34.83871
Median Absolute Deviation (MAD)10
Skewness1.8893439
Sum2160
Variance559.80962
MonotonicityNot monotonic
2024-01-10T07:48:13.241351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20 25
39.1%
30 21
32.8%
50 7
 
10.9%
100 5
 
7.8%
80 2
 
3.1%
10 2
 
3.1%
(Missing) 2
 
3.1%
ValueCountFrequency (%)
10 2
 
3.1%
20 25
39.1%
30 21
32.8%
50 7
 
10.9%
80 2
 
3.1%
100 5
 
7.8%
ValueCountFrequency (%)
100 5
 
7.8%
80 2
 
3.1%
50 7
 
10.9%
30 21
32.8%
20 25
39.1%
10 2
 
3.1%

펌프(HP)
Real number (ℝ)

Distinct41
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.19375
Minimum10
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-01-10T07:48:13.355335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15.75
Q150
median148.5
Q3336.85
95-th percentile1240.75
Maximum1500
Range1490
Interquartile range (IQR)286.85

Descriptive statistics

Standard deviation360.55024
Coefficient of variation (CV)1.286789
Kurtosis4.9533707
Mean280.19375
Median Absolute Deviation (MAD)111
Skewness2.2563294
Sum17932.4
Variance129996.48
MonotonicityNot monotonic
2024-01-10T07:48:13.474597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
40.0 4
 
6.2%
200.0 4
 
6.2%
95.0 4
 
6.2%
300.0 3
 
4.7%
10.0 3
 
4.7%
75.0 3
 
4.7%
150.0 3
 
4.7%
50.0 2
 
3.1%
500.0 2
 
3.1%
30.0 2
 
3.1%
Other values (31) 34
53.1%
ValueCountFrequency (%)
10.0 3
4.7%
15.0 1
 
1.6%
20.0 2
3.1%
25.0 1
 
1.6%
30.0 2
3.1%
35.0 1
 
1.6%
40.0 4
6.2%
41.0 1
 
1.6%
50.0 2
3.1%
60.0 1
 
1.6%
ValueCountFrequency (%)
1500.0 2
3.1%
1474.0 1
1.6%
1300.0 1
1.6%
905.0 1
1.6%
670.0 1
1.6%
600.0 2
3.1%
550.0 1
1.6%
533.0 1
1.6%
500.0 2
3.1%
475.0 1
1.6%

펌프(기)
Real number (ℝ)

Distinct7
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.734375
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-01-10T07:48:13.573437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2629292
Coefficient of variation (CV)0.46187124
Kurtosis1.4399885
Mean2.734375
Median Absolute Deviation (MAD)1
Skewness1.0596562
Sum175
Variance1.5949901
MonotonicityNot monotonic
2024-01-10T07:48:13.660768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 23
35.9%
3 21
32.8%
1 8
 
12.5%
4 5
 
7.8%
5 5
 
7.8%
7 1
 
1.6%
6 1
 
1.6%
ValueCountFrequency (%)
1 8
 
12.5%
2 23
35.9%
3 21
32.8%
4 5
 
7.8%
5 5
 
7.8%
6 1
 
1.6%
7 1
 
1.6%
ValueCountFrequency (%)
7 1
 
1.6%
6 1
 
1.6%
5 5
 
7.8%
4 5
 
7.8%
3 21
32.8%
2 23
35.9%
1 8
 
12.5%

계약용량(KW)
Real number (ℝ)

Distinct46
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1043.4219
Minimum10
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-01-10T07:48:13.776099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15.9
Q173
median275
Q31500
95-th percentile3510
Maximum10000
Range9990
Interquartile range (IQR)1427

Descriptive statistics

Standard deviation1760.9445
Coefficient of variation (CV)1.687663
Kurtosis13.194026
Mean1043.4219
Median Absolute Deviation (MAD)253
Skewness3.3098217
Sum66779
Variance3100925.6
MonotonicityNot monotonic
2024-01-10T07:48:13.892351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1500 4
 
6.2%
200 3
 
4.7%
86 2
 
3.1%
73 2
 
3.1%
2500 2
 
3.1%
500 2
 
3.1%
140 2
 
3.1%
950 2
 
3.1%
1750 2
 
3.1%
3000 2
 
3.1%
Other values (36) 41
64.1%
ValueCountFrequency (%)
10 2
3.1%
15 2
3.1%
21 2
3.1%
23 1
1.6%
30 2
3.1%
37 1
1.6%
39 1
1.6%
47 1
1.6%
50 1
1.6%
60 1
1.6%
ValueCountFrequency (%)
10000 1
1.6%
8000 1
1.6%
4250 1
1.6%
3600 1
1.6%
3000 2
3.1%
2650 1
1.6%
2500 2
3.1%
2000 1
1.6%
1750 2
3.1%
1700 1
1.6%

처리능력(m3_분)
Real number (ℝ)

Distinct55
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.20984
Minimum6.4
Maximum8100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-01-10T07:48:14.012326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile14.15
Q143.8325
median192.5
Q3604.5
95-th percentile2549
Maximum8100
Range8093.6
Interquartile range (IQR)560.6675

Descriptive statistics

Standard deviation1276.4071
Coefficient of variation (CV)1.9782821
Kurtosis19.910541
Mean645.20984
Median Absolute Deviation (MAD)168.4
Skewness4.1013763
Sum41293.43
Variance1629215
MonotonicityNot monotonic
2024-01-10T07:48:14.369176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200.0 3
 
4.7%
180.0 3
 
4.7%
120.0 2
 
3.1%
19.7 2
 
3.1%
240.0 2
 
3.1%
60.0 2
 
3.1%
300.0 2
 
3.1%
660.0 1
 
1.6%
350.0 1
 
1.6%
1680.0 1
 
1.6%
Other values (45) 45
70.3%
ValueCountFrequency (%)
6.4 1
1.6%
9.82 1
1.6%
12.5 1
1.6%
14.0 1
1.6%
15.0 1
1.6%
19.7 2
3.1%
20.0 1
1.6%
28.2 1
1.6%
31.28 1
1.6%
32.5 1
1.6%
ValueCountFrequency (%)
8100.0 1
 
1.6%
4900.0 1
 
1.6%
3458.0 1
 
1.6%
2600.0 1
 
1.6%
2260.0 1
 
1.6%
1680.0 1
 
1.6%
1245.0 1
 
1.6%
1200.0 3
4.7%
1080.0 1
 
1.6%
894.0 1
 
1.6%

유수지용량(m3)
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)82.4%
Missing13
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean17070.708
Minimum10
Maximum469131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-01-10T07:48:14.485868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q1275
median2513
Q37750
95-th percentile33000
Maximum469131
Range469121
Interquartile range (IQR)7475

Descriptive statistics

Standard deviation66337.385
Coefficient of variation (CV)3.886036
Kurtosis45.486358
Mean17070.708
Median Absolute Deviation (MAD)2476.216
Skewness6.6107781
Sum870606.1
Variance4.4006486 × 109
MonotonicityNot monotonic
2024-01-10T07:48:14.596141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10.0 5
 
7.8%
20000.0 2
 
3.1%
2450.0 2
 
3.1%
30000.0 2
 
3.1%
4000.0 2
 
3.1%
20.0 2
 
3.1%
860.0 1
 
1.6%
45.0 1
 
1.6%
3745.0 1
 
1.6%
230.0 1
 
1.6%
Other values (32) 32
50.0%
(Missing) 13
20.3%
ValueCountFrequency (%)
10.0 5
7.8%
20.0 2
 
3.1%
21.318 1
 
1.6%
36.784 1
 
1.6%
45.0 1
 
1.6%
128.0 1
 
1.6%
194.0 1
 
1.6%
230.0 1
 
1.6%
320.0 1
 
1.6%
500.0 1
 
1.6%
ValueCountFrequency (%)
469131.0 1
1.6%
94000.0 1
1.6%
36000.0 1
1.6%
30000.0 2
3.1%
23000.0 1
1.6%
20000.0 2
3.1%
19990.0 1
1.6%
19312.0 1
1.6%
18000.0 1
1.6%
9046.0 1
1.6%

수문(기)
Categorical

Distinct6
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size644.0 B
1
22 
2
22 
4
8 
<NA>
7 
3
4 

Length

Max length4
Median length1
Mean length1.328125
Min length1

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row<NA>
2nd row1
3rd row<NA>
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
1 22
34.4%
2 22
34.4%
4 8
 
12.5%
<NA> 7
 
10.9%
3 4
 
6.2%
6 1
 
1.6%

Length

2024-01-10T07:48:14.713053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:14.812278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 22
34.4%
2 22
34.4%
4 8
 
12.5%
na 7
 
10.9%
3 4
 
6.2%
6 1
 
1.6%

정전대비 예비전기시설(비상발전기최대유지시간)
Categorical

Distinct6
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size644.0 B
<NA>
41 
2
9 
10
8 
12
 
3
1
 
2

Length

Max length4
Median length4
Mean length3.09375
Min length1

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 41
64.1%
2 9
 
14.1%
10 8
 
12.5%
12 3
 
4.7%
1 2
 
3.1%
0 1
 
1.6%

Length

2024-01-10T07:48:14.919262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:15.015873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 41
64.1%
2 9
 
14.1%
10 8
 
12.5%
12 3
 
4.7%
1 2
 
3.1%
0 1
 
1.6%

정전대비 예비전기시설(이중선로)
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)4.8%
Missing43
Missing (%)67.2%
Memory size260.0 B
True
21 
(Missing)
43 
ValueCountFrequency (%)
True 21
32.8%
(Missing) 43
67.2%
2024-01-10T07:48:15.099448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

수혜지역
Text

Distinct53
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-01-10T07:48:15.286410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length6.09375
Min length3

Characters and Unicode

Total characters390
Distinct characters96
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

Unique47 ?
Unique (%)73.4%

Sample

1st row원성2동
2nd row원성2동
3rd row북면 연춘리
4th row병천면 송정리
5th row봉명동
ValueCountFrequency (%)
성정동 6
 
6.0%
규암면 3
 
3.0%
원성2동 3
 
3.0%
강경읍 3
 
3.0%
성환읍 3
 
3.0%
성환리 2
 
2.0%
서천읍 2
 
2.0%
부여읍 2
 
2.0%
염치읍 2
 
2.0%
장암면 2
 
2.0%
Other values (68) 72
72.0%
2024-01-10T07:48:15.657023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 44
 
11.3%
37
 
9.5%
동 25
 
6.4%
, 25
 
6.4%
읍 20
 
5.1%
면 18
 
4.6%
성 17
 
4.4%
암 14
 
3.6%
정 10
 
2.6%
산 9
 
2.3%
Other values (86) 171
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 320
82.1%
Space Separator 37
 
9.5%
Other Punctuation 26
 
6.7%
Decimal Number 3
 
0.8%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 44
 
13.8%
동 25
 
7.8%
읍 20
 
6.2%
면 18
 
5.6%
성 17
 
5.3%
암 14
 
4.4%
정 10
 
3.1%
산 9
 
2.8%
천 8
 
2.5%
규 6
 
1.9%
Other values (80) 149
46.6%
Other Punctuation
ValueCountFrequency (%)
, 25
96.2%
. 1
 
3.8%
Space Separator
ValueCountFrequency (%)
37
100.0%
Decimal Number
ValueCountFrequency (%)
2 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 320
82.1%
Common 70
 
17.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 44
 
13.8%
동 25
 
7.8%
읍 20
 
6.2%
면 18
 
5.6%
성 17
 
5.3%
암 14
 
4.4%
정 10
 
3.1%
산 9
 
2.8%
천 8
 
2.5%
규 6
 
1.9%
Other values (80) 149
46.6%
Common
ValueCountFrequency (%)
37
52.9%
, 25
35.7%
2 3
 
4.3%
( 2
 
2.9%
) 2
 
2.9%
. 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 320
82.1%
ASCII 70
 
17.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 44
 
13.8%
동 25
 
7.8%
읍 20
 
6.2%
면 18
 
5.6%
성 17
 
5.3%
암 14
 
4.4%
정 10
 
3.1%
산 9
 
2.8%
천 8
 
2.5%
규 6
 
1.9%
Other values (80) 149
46.6%
ASCII
ValueCountFrequency (%)
37
52.9%
, 25
35.7%
2 3
 
4.3%
( 2
 
2.9%
) 2
 
2.9%
. 1
 
1.4%

방류하천명
Categorical

Distinct29
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size644.0 B
금강
8 
성정천
6 
논산천
5 
무한천
4 
곡교천
4 
Other values (24)
37 

Length

Max length4
Median length3
Mean length2.8125
Min length2

Unique

Unique15 ?
Unique (%)23.4%

Sample

1st row원성천
2nd row삼룡천
3rd row병천천
4th row녹동천
5th row천안천

Common Values

ValueCountFrequency (%)
금강 8
 
12.5%
성정천 6
 
9.4%
논산천 5
 
7.8%
무한천 4
 
6.2%
곡교천 4
 
6.2%
은산천 3
 
4.7%
성환천 3
 
4.7%
광천천 3
 
4.7%
대천천 3
 
4.7%
판교천 2
 
3.1%
Other values (19) 23
35.9%

Length

2024-01-10T07:48:15.797643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금강 8
 
12.5%
성정천 6
 
9.4%
논산천 5
 
7.8%
무한천 4
 
6.2%
곡교천 4
 
6.2%
은산천 3
 
4.7%
성환천 3
 
4.7%
광천천 3
 
4.7%
대천천 3
 
4.7%
금강천 2
 
3.1%
Other values (19) 23
35.9%

기관명
Categorical

Distinct15
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size644.0 B
부여군
13 
천안시 서북구
9 
논산시
7 
천안시 동남구
5 
공주시
4 
Other values (10)
26 

Length

Max length7
Median length3
Mean length3.90625
Min length3

Unique

Unique2 ?
Unique (%)3.1%

Sample

1st row천안시 동남구
2nd row천안시 동남구
3rd row천안시 동남구
4th row천안시 동남구
5th row천안시 동남구

Common Values

ValueCountFrequency (%)
부여군 13
20.3%
천안시 서북구 9
14.1%
논산시 7
10.9%
천안시 동남구 5
 
7.8%
공주시 4
 
6.2%
아산시 4
 
6.2%
홍성군 4
 
6.2%
예산군 4
 
6.2%
보령시 3
 
4.7%
당진시 3
 
4.7%
Other values (5) 8
12.5%

Length

2024-01-10T07:48:15.933973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
천안시 15
19.2%
부여군 13
16.7%
서북구 9
11.5%
논산시 7
9.0%
홍성군 6
 
7.7%
동남구 5
 
6.4%
공주시 4
 
5.1%
아산시 4
 
5.1%
예산군 4
 
5.1%
보령시 3
 
3.8%
Other values (4) 8
10.3%

부서
Categorical

Distinct16
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
건설과
12 
안전총괄과
12 
서북구 건설과
10 
안전관리과
7 
동남구 건설과
5 
Other values (11)
18 

Length

Max length7
Median length6
Mean length4.796875
Min length3

Unique

Unique7 ?
Unique (%)10.9%

Sample

1st row동남구 건설과
2nd row동남구 건설과
3rd row동남구 건설과
4th row동남구 건설과
5th row동남구 건설과

Common Values

ValueCountFrequency (%)
건설과 12
18.8%
안전총괄과 12
18.8%
서북구 건설과 10
15.6%
안전관리과 7
10.9%
동남구 건설과 5
7.8%
수도과 5
7.8%
규암면 2
 
3.1%
건설정책과 2
 
3.1%
맑은물사업소 2
 
3.1%
맑은물관리과 1
 
1.6%
Other values (6) 6
9.4%

Length

2024-01-10T07:48:16.067880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건설과 27
34.2%
안전총괄과 12
15.2%
서북구 10
 
12.7%
안전관리과 7
 
8.9%
동남구 5
 
6.3%
수도과 5
 
6.3%
규암면 2
 
2.5%
건설정책과 2
 
2.5%
맑은물사업소 2
 
2.5%
맑은물관리과 1
 
1.3%
Other values (6) 6
 
7.6%

사무실
Categorical

Distinct25
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Memory size644.0 B
041-521-6362
9 
041-830-2373
7 
041-746-6320
7 
041-521-4361
5 
041-536-8479
4 
Other values (20)
32 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique12 ?
Unique (%)18.8%

Sample

1st row041-521-4361
2nd row041-521-4361
3rd row041-521-4361
4th row041-521-4361
5th row041-521-4361

Common Values

ValueCountFrequency (%)
041-521-6362 9
14.1%
041-830-2373 7
 
10.9%
041-746-6320 7
 
10.9%
041-521-4361 5
 
7.8%
041-536-8479 4
 
6.2%
041-630-1385 3
 
4.7%
041-630-1594 3
 
4.7%
041-930-3011 3
 
4.7%
041-840-8337 3
 
4.7%
041-830-6371 2
 
3.1%
Other values (15) 18
28.1%

Length

2024-01-10T07:48:16.177590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
041-521-6362 9
14.1%
041-746-6320 7
 
10.9%
041-830-2373 7
 
10.9%
041-521-4361 5
 
7.8%
041-536-8479 4
 
6.2%
041-630-1385 3
 
4.7%
041-630-1594 3
 
4.7%
041-930-3011 3
 
4.7%
041-840-8337 3
 
4.7%
041-950-6821 2
 
3.1%
Other values (15) 18
28.1%

운영기간
Categorical

Distinct12
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size644.0 B
상시
14 
05-15~10-15
14 
23.6~9.
14 
05-01~10-31
7 
05-02~10-31
4 
Other values (7)
11 

Length

Max length16
Median length11
Mean length8.21875
Min length2

Unique

Unique4 ?
Unique (%)6.2%

Sample

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

Common Values

ValueCountFrequency (%)
상시 14
21.9%
05-15~10-15 14
21.9%
23.6~9. 14
21.9%
05-01~10-31 7
10.9%
05-02~10-31 4
 
6.2%
04-01~09-30 3
 
4.7%
05-15~10-31 2
 
3.1%
2023-05-15~10-31 2
 
3.1%
5~10월 1
 
1.6%
06-01~09-30 1
 
1.6%
Other values (2) 2
 
3.1%

Length

2024-01-10T07:48:16.301525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상시 14
21.9%
05-15~10-15 14
21.9%
23.6~9 14
21.9%
05-01~10-31 7
10.9%
05-02~10-31 4
 
6.2%
04-01~09-30 3
 
4.7%
05-15~10-31 2
 
3.1%
2023-05-15~10-31 2
 
3.1%
5~10월 1
 
1.6%
06-01~09-30 1
 
1.6%
Other values (2) 2
 
3.1%

수전일 또는 예정일
Categorical

Distinct10
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
상시
15 
2023-06-01
15 
2023-05-15
11 
2023-05-01
10 
2023-05-02
4 
Other values (5)
9 

Length

Max length10
Median length10
Mean length8.125
Min length2

Unique

Unique2 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
상시 15
23.4%
2023-06-01 15
23.4%
2023-05-15 11
17.2%
2023-05-01 10
15.6%
2023-05-02 4
 
6.2%
2023-04-15 3
 
4.7%
2020-04-06 2
 
3.1%
2023-05-20 2
 
3.1%
2018-06-01 1
 
1.6%
2023-04-06 1
 
1.6%

Length

2024-01-10T07:48:16.413161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:16.530289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상시 15
23.4%
2023-06-01 15
23.4%
2023-05-15 11
17.2%
2023-05-01 10
15.6%
2023-05-02 4
 
6.2%
2023-04-15 3
 
4.7%
2020-04-06 2
 
3.1%
2023-05-20 2
 
3.1%
2018-06-01 1
 
1.6%
2023-04-06 1
 
1.6%

운영인력교대주기
Categorical

Distinct7
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size644.0 B
3교대
24 
1교대
17 
<NA>
8 
2교대
7 
비상주
4 
Other values (2)
4 

Length

Max length4
Median length3
Mean length3.09375
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row3교대
2nd row3교대
3rd row3교대
4th row3교대
5th row3교대

Common Values

ValueCountFrequency (%)
3교대 24
37.5%
1교대 17
26.6%
<NA> 8
 
12.5%
2교대 7
 
10.9%
비상주 4
 
6.2%
없음 3
 
4.7%
상시대기 1
 
1.6%

Length

2024-01-10T07:48:16.652967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:16.757723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3교대 24
37.5%
1교대 17
26.6%
na 8
 
12.5%
2교대 7
 
10.9%
비상주 4
 
6.2%
없음 3
 
4.7%
상시대기 1
 
1.6%

운영인력상주여부
Boolean

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size196.0 B
False
48 
True
16 
ValueCountFrequency (%)
False 48
75.0%
True 16
 
25.0%
2024-01-10T07:48:16.847452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

전화번호
Categorical

Distinct29
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size644.0 B
041-521-6362
9 
041-830-2373
8 
041-521-4361
5 
041-540-2023
4 
041-630-1385
 
3
Other values (24)
35 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique16 ?
Unique (%)25.0%

Sample

1st row041-521-4361
2nd row041-521-4361
3rd row041-521-4361
4th row041-521-4361
5th row041-521-4361

Common Values

ValueCountFrequency (%)
041-521-6362 9
14.1%
041-830-2373 8
 
12.5%
041-521-4361 5
 
7.8%
041-540-2023 4
 
6.2%
041-630-1385 3
 
4.7%
041-630-1594 3
 
4.7%
041-931-1422 3
 
4.7%
041-840-8337 3
 
4.7%
041-746-6347 2
 
3.1%
041-350-4642 2
 
3.1%
Other values (19) 22
34.4%

Length

2024-01-10T07:48:16.935046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
041-521-6362 9
14.1%
041-830-2373 8
 
12.5%
041-521-4361 5
 
7.8%
041-540-2023 4
 
6.2%
041-630-1385 3
 
4.7%
041-630-1594 3
 
4.7%
041-931-1422 3
 
4.7%
041-840-8337 3
 
4.7%
041-339-7776 2
 
3.1%
041-830-6371 2
 
3.1%
Other values (19) 22
34.4%

비고
Categorical

IMBALANCE 

Distinct5
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size644.0 B
기존
51 
신규
6 
배수펌프장 운영 대행 용역 사업 진행중
 
4
배수펌프장 운영 대행 용역 시행중
 
2
<NA>
 
1

Length

Max length21
Median length2
Mean length3.71875
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
기존 51
79.7%
신규 6
 
9.4%
배수펌프장 운영 대행 용역 사업 진행중 4
 
6.2%
배수펌프장 운영 대행 용역 시행중 2
 
3.1%
<NA> 1
 
1.6%

Length

2024-01-10T07:48:17.042702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:17.144110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기존 51
55.4%
신규 6
 
6.5%
배수펌프장 6
 
6.5%
운영 6
 
6.5%
대행 6
 
6.5%
용역 6
 
6.5%
사업 4
 
4.3%
진행중 4
 
4.3%
시행중 2
 
2.2%
na 1
 
1.1%

Sample

시군연번시설명위 치 (상세주소 작성)준공년도보강년도계획빈도(년)펌프(HP)펌프(기)계약용량(KW)처리능력(m3_분)유수지용량(m3)수문(기)정전대비 예비전기시설(비상발전기최대유지시간)정전대비 예비전기시설(이중선로)수혜지역방류하천명기관명부서사무실운영기간수전일 또는 예정일운영인력교대주기운영인력상주여부전화번호비고
0천안1원성2-1지구 배수펌프장동남구 원성동 6222007201810050.036028.2<NA><NA><NA><NA>원성2동원성천천안시 동남구동남구 건설과041-521-4361상시상시3교대N041-521-4361기존
1천안2원성2-2지구 배수펌프장동남구 원성동 62020072018100150.027944.021.3181<NA><NA>원성2동삼룡천천안시 동남구동남구 건설과041-521-4361상시상시3교대N041-521-4361기존
2천안3연춘리 배수펌프장동남구 북면 연춘리 2452010<NA><NA>40.027040.0<NA><NA><NA><NA>북면 연춘리병천천천안시 동남구동남구 건설과041-521-4361상시상시3교대N041-521-4361기존
3천안4송정리 배수펌프장동남구 병천면 송정리 520-12010<NA><NA>80.0220045.0<NA>1<NA><NA>병천면 송정리녹동천천안시 동남구동남구 건설과041-521-4361상시상시3교대N041-521-4361기존
4천안5봉명동 배수펌프장동남구 봉명동 194-402015<NA>5030.022112.536.784<NA><NA><NA>봉명동천안천천안시 동남구동남구 건설과041-521-4361상시상시3교대N041-521-4361기존
5천안6성정지구 배수펌프장천안시 서북구 성정동 602-222004<NA>2015.022320.010.0210<NA>성정동성정천천안시 서북구서북구 건설과041-521-6362상시상시3교대N041-521-6362기존
6천안7성정1-1지구 배수펌프장천안시 서북구 성정동 157-420072018100300.0425094.0194.0210<NA>성정동성정천천안시 서북구서북구 건설과041-521-6362상시상시3교대N041-521-6362기존
7천안8성정1-2지구 배수펌프장천안시 서북구 성정동 1902007<NA>2010.02109.8210.0210<NA>성정동성정천천안시 서북구서북구 건설과041-521-6362상시상시3교대N041-521-6362기존
8천안9성정1-3지구 배수펌프장천안시 서북구 성정동 203-282007<NA>2020.022131.2810.0210<NA>성정동성정천천안시 서북구서북구 건설과041-521-6362상시상시3교대N041-521-6362기존
9천안10성정1-4지구 배수펌프장천안시 서북구 성정동 609-1192007<NA>2010.021019.710.0210<NA>성정동성정천천안시 서북구서북구 건설과041-521-6362상시상시3교대N041-521-6362기존
시군연번시설명위 치 (상세주소 작성)준공년도보강년도계획빈도(년)펌프(HP)펌프(기)계약용량(KW)처리능력(m3_분)유수지용량(m3)수문(기)정전대비 예비전기시설(비상발전기최대유지시간)정전대비 예비전기시설(이중선로)수혜지역방류하천명기관명부서사무실운영기간수전일 또는 예정일운영인력교대주기운영인력상주여부전화번호비고
54홍성55상촌배수펌프장홍성군 갈산면 갈산로102번길 112009<NA>20500.031500500.0320.01<NA>Y상촌리갈산천홍성군안전관리과041-630-138505-15~10-152023-05-013교대Y041-630-1385기존
55홍성56옥계배수펌프장홍성군 장곡면 무한로 865번길 182017<NA>3095.0214034.02270.011<NA>옥계리무한천홍성군안전관리과041-630-138505-15~10-152023-05-01비상주N041-630-1385기존
56예산57창소배수펌프장예산군 신암면 충서로 1313-442003<NA>20400.041500800.094000.04<NA>Y예산읍 창소리, 신암면종경리무한천예산군안전관리과041-339-775005-01~10-312023-05-011교대N041-339-8942기존
57예산58산성배수펌프장예산군 예산읍 무한산성 1길 742000<NA>20275.0517501200.06000.04<NA>Y예산읍 산성리무한천예산군예산읍041-339-840005-01~10-312023-05-011교대N041-339-8941신규
58예산59삽교배수펌프장예산군 삽교읍 충의로 671-291999<NA>10175.03750300.036000.02<NA>Y삽교읍 두리삽교천예산군삽교읍041-339-848105-01~10-312023-05-011교대N041-339-7776신규
59예산60광시배수펌프장예산군 광시면 예당로 154-132011<NA>2025.021514.0<NA><NA>2<NA>광시면 광시리, 하장대리무한천예산군광시면041-339-860001-01~12-312023-05-011교대N041-339-7776기존
60천안61원성삼룡 배수펌프장천안시 동남구 구성동 4962022<NA>3050.0237120.0<NA>1<NA><NA>원성2동삼룡천천안시서북구 건설과041-521-4368상시상시3교대N041-521-4368신규
61홍성62어사 배수펌프장홍성군 서부면 어사리 490-82009<NA>2095.02306.4128.01<NA><NA>어사리해안홍성군안전관리과041-630-159405-15~10-152023-05-15비상주N041-630-1594신규
62홍성63옹암1 배수펌프장홍성군 광천읍 옹암리 329-62021<NA>3095.0217060.0860.01<NA><NA>광천읍 옹암리광천천홍성군안전관리과041-630-159405-15~10-152023-05-15비상주N041-630-1594신규
63홍성64옹암2 배수펌프장홍성군 광천읍 옹암리 459-42021<NA>3095.017339.045.01<NA><NA>광천읍 옹암리광천천홍성군안전관리과041-630-159405-15~10-152023-05-15비상주N041-630-1594신규