Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells86
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory966.8 KiB
Average record size in memory99.0 B

Variable types

Numeric3
Text2
Categorical3
DateTime3

Dataset

Description부산광역시 상수도사업본부에서 상하수도 요금 계산 및 징수를 위해 운영하는 수용가정보시스템에 사용되는 계량기 정보 자료입니다.
Author부산광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15100349/fileData.do

Alerts

급수관종류 is highly imbalanced (63.0%)Imbalance
검침코드명 is highly imbalanced (72.7%)Imbalance
급수관연장 is highly skewed (γ1 = 23.18328722)Skewed
연번 has unique valuesUnique
급수관연장 has 2287 (22.9%) zerosZeros

Reproduction

Analysis started2024-03-14 11:22:12.270295
Analysis finished2024-03-14 11:22:17.115600
Duration4.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24162.937
Minimum5
Maximum48074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T20:22:17.333439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile2452.6
Q112245.5
median24103
Q336106
95-th percentile45835.15
Maximum48074
Range48069
Interquartile range (IQR)23860.5

Descriptive statistics

Standard deviation13879.571
Coefficient of variation (CV)0.57441571
Kurtosis-1.1958644
Mean24162.937
Median Absolute Deviation (MAD)11917.5
Skewness-0.0049501719
Sum2.4162937 × 108
Variance1.9264248 × 108
MonotonicityNot monotonic
2024-03-14T20:22:17.776621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32827 1
 
< 0.1%
12669 1
 
< 0.1%
47345 1
 
< 0.1%
27433 1
 
< 0.1%
4391 1
 
< 0.1%
10047 1
 
< 0.1%
46425 1
 
< 0.1%
30615 1
 
< 0.1%
43415 1
 
< 0.1%
18722 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
26 1
< 0.1%
29 1
< 0.1%
32 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
37 1
< 0.1%
39 1
< 0.1%
ValueCountFrequency (%)
48074 1
< 0.1%
48072 1
< 0.1%
48070 1
< 0.1%
48068 1
< 0.1%
48067 1
< 0.1%
48062 1
< 0.1%
48057 1
< 0.1%
48053 1
< 0.1%
48052 1
< 0.1%
48051 1
< 0.1%

고객번호
Text

Distinct5588
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T20:22:19.276424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3186 ?
Unique (%)31.9%

Sample

1st row*37*17
2nd row*50*54
3rd row*47*08
4th row*08*84
5th row*54*05
ValueCountFrequency (%)
19*26 14
 
0.1%
22*96 14
 
0.1%
22*25 13
 
0.1%
13*63 13
 
0.1%
76*14 12
 
0.1%
85*44 12
 
0.1%
21*71 11
 
0.1%
00*88 11
 
0.1%
93*61 11
 
0.1%
20*63 11
 
0.1%
Other values (5578) 9878
98.8%
2024-03-14T20:22:20.890082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 20000
33.3%
0 4625
 
7.7%
1 4309
 
7.2%
2 4195
 
7.0%
9 4128
 
6.9%
3 4059
 
6.8%
5 4046
 
6.7%
7 3847
 
6.4%
4 3710
 
6.2%
8 3552
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40000
66.7%
Other Punctuation 20000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4625
11.6%
1 4309
10.8%
2 4195
10.5%
9 4128
10.3%
3 4059
10.1%
5 4046
10.1%
7 3847
9.6%
4 3710
9.3%
8 3552
8.9%
6 3529
8.8%
Other Punctuation
ValueCountFrequency (%)
* 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 20000
33.3%
0 4625
 
7.7%
1 4309
 
7.2%
2 4195
 
7.0%
9 4128
 
6.9%
3 4059
 
6.8%
5 4046
 
6.7%
7 3847
 
6.4%
4 3710
 
6.2%
8 3552
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 20000
33.3%
0 4625
 
7.7%
1 4309
 
7.2%
2 4195
 
7.0%
9 4128
 
6.9%
3 4059
 
6.8%
5 4046
 
6.7%
7 3847
 
6.4%
4 3710
 
6.2%
8 3552
 
5.9%

본관종류
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
스테인레스관
3737 
PFP
3077 
닥타일주철관(시멘트)
2419 
에폭시라이닝관
 
329
기타
 
240
Other values (7)
 
198

Length

Max length15
Median length11
Mean length6.3127
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPFP
2nd rowPFP
3rd row스테인레스관
4th rowPFP
5th rowPFP

Common Values

ValueCountFrequency (%)
스테인레스관 3737
37.4%
PFP 3077
30.8%
닥타일주철관(시멘트) 2419
24.2%
에폭시라이닝관 329
 
3.3%
기타 240
 
2.4%
닥타일주철관(에폭시) 183
 
1.8%
<NA> 6
 
0.1%
PE관 3
 
< 0.1%
회주철 2
 
< 0.1%
HI-3P 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2024-03-14T20:22:21.163086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
스테인레스관 3737
37.4%
pfp 3077
30.8%
닥타일주철관(시멘트 2419
24.2%
에폭시라이닝관 329
 
3.3%
기타 240
 
2.4%
닥타일주철관(에폭시 183
 
1.8%
na 6
 
0.1%
pe관 3
 
< 0.1%
회주철 2
 
< 0.1%
hi-3p 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

본관구경
Text

Distinct390
Distinct (%)3.9%
Missing17
Missing (%)0.2%
Memory size156.2 KiB
2024-03-14T20:22:22.396954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length18
Mean length3.7945507
Min length2

Characters and Unicode

Total characters37881
Distinct characters24
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)1.7%

Sample

1st row65
2nd row65*40
3rd row25
4th row50
5th row25
ValueCountFrequency (%)
25 1207
 
12.1%
100 1128
 
11.3%
40 681
 
6.8%
150 564
 
5.6%
20 498
 
5.0%
50 444
 
4.4%
200 365
 
3.7%
15 362
 
3.6%
65 359
 
3.6%
13 250
 
2.5%
Other values (380) 4125
41.3%
2024-03-14T20:22:23.802942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12881
34.0%
5 6662
17.6%
1 5200
13.7%
* 4305
 
11.4%
2 4266
 
11.3%
4 1776
 
4.7%
3 1277
 
3.4%
6 1137
 
3.0%
8 352
 
0.9%
7 6
 
< 0.1%
Other values (14) 19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33558
88.6%
Other Punctuation 4307
 
11.4%
Other Letter 8
 
< 0.1%
Dash Punctuation 3
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12881
38.4%
5 6662
19.9%
1 5200
15.5%
2 4266
 
12.7%
4 1776
 
5.3%
3 1277
 
3.8%
6 1137
 
3.4%
8 352
 
1.0%
7 6
 
< 0.1%
9 1
 
< 0.1%
Other Letter
ValueCountFrequency (%)
확 1
12.5%
인 1
12.5%
함 1
12.5%
옥 1
12.5%
내 1
12.5%
선 1
12.5%
연 1
12.5%
결 1
12.5%
Other Punctuation
ValueCountFrequency (%)
* 4305
> 99.9%
. 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37872
> 99.9%
Hangul 8
 
< 0.1%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12881
34.0%
5 6662
17.6%
1 5200
13.7%
* 4305
 
11.4%
2 4266
 
11.3%
4 1776
 
4.7%
3 1277
 
3.4%
6 1137
 
3.0%
8 352
 
0.9%
7 6
 
< 0.1%
Other values (5) 10
 
< 0.1%
Hangul
ValueCountFrequency (%)
확 1
12.5%
인 1
12.5%
함 1
12.5%
옥 1
12.5%
내 1
12.5%
선 1
12.5%
연 1
12.5%
결 1
12.5%
Latin
ValueCountFrequency (%)
P 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37873
> 99.9%
Hangul 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12881
34.0%
5 6662
17.6%
1 5200
13.7%
* 4305
 
11.4%
2 4266
 
11.3%
4 1776
 
4.7%
3 1277
 
3.4%
6 1137
 
3.0%
8 352
 
0.9%
7 6
 
< 0.1%
Other values (6) 11
 
< 0.1%
Hangul
ValueCountFrequency (%)
확 1
12.5%
인 1
12.5%
함 1
12.5%
옥 1
12.5%
내 1
12.5%
선 1
12.5%
연 1
12.5%
결 1
12.5%

급수관종류
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
스테인레스관
7471 
PFP
1196 
기타
1147 
닥타일주철관(시멘트)
 
109
닥타일주철관(에폭시)
 
60
Other values (4)
 
17

Length

Max length11
Median length6
Mean length5.2669
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row스테인레스관
2nd row스테인레스관
3rd row스테인레스관
4th row스테인레스관
5th row기타

Common Values

ValueCountFrequency (%)
스테인레스관 7471
74.7%
PFP 1196
 
12.0%
기타 1147
 
11.5%
닥타일주철관(시멘트) 109
 
1.1%
닥타일주철관(에폭시) 60
 
0.6%
에폭시라이닝관 12
 
0.1%
<NA> 3
 
< 0.1%
회주철 1
 
< 0.1%
PE관 1
 
< 0.1%

Length

2024-03-14T20:22:24.041034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:22:24.243173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
스테인레스관 7471
74.7%
pfp 1196
 
12.0%
기타 1147
 
11.5%
닥타일주철관(시멘트 109
 
1.1%
닥타일주철관(에폭시 60
 
0.6%
에폭시라이닝관 12
 
0.1%
na 3
 
< 0.1%
회주철 1
 
< 0.1%
pe관 1
 
< 0.1%

계량기구경
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.471
Minimum15
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T20:22:24.443738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q115
median15
Q315
95-th percentile40
Maximum300
Range285
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.283682
Coefficient of variation (CV)0.7733031
Kurtosis101.12332
Mean18.471
Median Absolute Deviation (MAD)0
Skewness8.4658948
Sum184710
Variance204.02356
MonotonicityNot monotonic
2024-03-14T20:22:24.721886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
15 8320
83.2%
20 580
 
5.8%
25 554
 
5.5%
40 188
 
1.9%
50 147
 
1.5%
80 82
 
0.8%
100 55
 
0.5%
32 40
 
0.4%
150 19
 
0.2%
200 9
 
0.1%
Other values (2) 6
 
0.1%
ValueCountFrequency (%)
15 8320
83.2%
20 580
 
5.8%
25 554
 
5.5%
32 40
 
0.4%
40 188
 
1.9%
50 147
 
1.5%
80 82
 
0.8%
100 55
 
0.5%
150 19
 
0.2%
200 9
 
0.1%
ValueCountFrequency (%)
300 2
 
< 0.1%
250 4
 
< 0.1%
200 9
 
0.1%
150 19
 
0.2%
100 55
 
0.5%
80 82
 
0.8%
50 147
 
1.5%
40 188
 
1.9%
32 40
 
0.4%
25 554
5.5%

급수관연장
Real number (ℝ)

SKEWED  ZEROS 

Distinct58
Distinct (%)0.6%
Missing9
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.1516365
Minimum0
Maximum368
Zeros2287
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T20:22:25.096091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile11
Maximum368
Range368
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.6970365
Coefficient of variation (CV)2.4422349
Kurtosis891.26567
Mean3.1516365
Median Absolute Deviation (MAD)1
Skewness23.183287
Sum31488
Variance59.244371
MonotonicityNot monotonic
2024-03-14T20:22:25.530660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3756
37.6%
0 2287
22.9%
2 613
 
6.1%
4 539
 
5.4%
3 524
 
5.2%
6 514
 
5.1%
5 480
 
4.8%
8 254
 
2.5%
7 208
 
2.1%
10 149
 
1.5%
Other values (48) 667
 
6.7%
ValueCountFrequency (%)
0 2287
22.9%
1 3756
37.6%
2 613
 
6.1%
3 524
 
5.2%
4 539
 
5.4%
5 480
 
4.8%
6 514
 
5.1%
7 208
 
2.1%
8 254
 
2.5%
9 134
 
1.3%
ValueCountFrequency (%)
368 1
< 0.1%
312 1
< 0.1%
240 1
< 0.1%
155 1
< 0.1%
130 1
< 0.1%
112 1
< 0.1%
99 1
< 0.1%
96 1
< 0.1%
90 1
< 0.1%
80 1
< 0.1%

계량기설치일자
Date

Distinct319
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-01-02 00:00:00
Maximum2023-12-31 00:00:00
2024-03-14T20:22:25.885744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:26.130678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

검침일자
Date

Distinct181
Distinct (%)1.8%
Missing60
Missing (%)0.6%
Memory size156.2 KiB
Minimum2015-02-04 00:00:00
Maximum2024-01-10 00:00:00
2024-03-14T20:22:26.369452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:26.610138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

검침코드명
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정상
8221 
계량기교체
1239 
전화검침
 
232
급수중지
 
161
<NA>
 
63
Other values (6)
 
84

Length

Max length5
Median length2
Mean length2.4796
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
정상 8221
82.2%
계량기교체 1239
 
12.4%
전화검침 232
 
2.3%
급수중지 161
 
1.6%
<NA> 63
 
0.6%
자가검침 34
 
0.3%
이사정산 34
 
0.3%
정수처분 12
 
0.1%
ARS검침 2
 
< 0.1%
인터넷 1
 
< 0.1%

Length

2024-03-14T20:22:26.880183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정상 8221
82.2%
계량기교체 1239
 
12.4%
전화검침 232
 
2.3%
급수중지 161
 
1.6%
na 63
 
0.6%
자가검침 34
 
0.3%
이사정산 34
 
0.3%
정수처분 12
 
0.1%
ars검침 2
 
< 0.1%
인터넷 1
 
< 0.1%

자료등록일시
Date

Distinct1948
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-01-06 01:00:06
Maximum2024-01-06 01:38:16
2024-03-14T20:22:27.107580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:27.354809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-03-14T20:22:14.831876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:13.252664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:14.044612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:15.096862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:13.509496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:14.303287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:15.368487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:13.771383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:22:14.558028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:22:27.508490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번본관종류급수관종류계량기구경급수관연장검침코드명
연번1.0000.2860.4420.0620.0000.213
본관종류0.2861.0000.5800.2400.0400.078
급수관종류0.4420.5801.0000.6530.3000.072
계량기구경0.0620.2400.6531.0000.4430.084
급수관연장0.0000.0400.3000.4431.0000.029
검침코드명0.2130.0780.0720.0840.0291.000
2024-03-14T20:22:27.692993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수관종류검침코드명본관종류
급수관종류1.0000.0340.320
검침코드명0.0341.0000.033
본관종류0.3200.0331.000
2024-03-14T20:22:27.852115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번계량기구경급수관연장본관종류급수관종류검침코드명
연번1.000-0.014-0.1840.1260.2290.067
계량기구경-0.0141.0000.3110.1200.4240.042
급수관연장-0.1840.3111.0000.0190.1040.014
본관종류0.1260.1200.0191.0000.3200.033
급수관종류0.2290.4240.1040.3201.0000.034
검침코드명0.0670.0420.0140.0330.0341.000

Missing values

2024-03-14T20:22:15.766949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:22:16.511217image/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.
2024-03-14T20:22:16.896610image/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

연번고객번호본관종류본관구경급수관종류계량기구경급수관연장계량기설치일자검침일자검침코드명자료등록일시
3282632827*37*17PFP65스테인레스관1582023-04-282023-12-05정상2024-01-06 01:34:09
2123921240*50*54PFP65*40스테인레스관1512023-08-092023-12-06정상2024-01-06 01:23:33
1999519996*47*08스테인레스관25스테인레스관1512023-07-272023-11-29정상2024-01-06 01:23:16
29362937*08*84PFP50스테인레스관1512023-03-102023-12-05정상2024-01-06 01:02:54
3684436845*54*05PFP25기타1502023-01-252023-12-30정상2024-01-06 01:36:04
4429344294*20*07닥타일주철관(에폭시)150닥타일주철관(에폭시)8092023-11-032024-01-02정상2024-01-06 01:37:53
95739574*92*99닥타일주철관(시멘트)150스테인레스관1592023-10-122023-12-04계량기교체2024-01-06 01:11:08
1219612197*27*30스테인레스관65*25스테인레스관1512023-07-142023-12-30정상2024-01-06 01:14:04
91859186*13*89닥타일주철관(시멘트)200*40*20스테인레스관1512023-02-032023-11-30정상2024-01-06 01:11:38
2863328634*96*80스테인레스관15스테인레스관1512023-11-212024-01-02계량기교체2024-01-06 01:32:00
연번고객번호본관종류본관구경급수관종류계량기구경급수관연장계량기설치일자검침일자검침코드명자료등록일시
3176731768*22*40스테인레스관100*40스테인레스관1532023-10-132023-12-05계량기교체2024-01-06 01:33:38
3801238013*08*24스테인레스관20기타1502023-06-302023-12-01정상2024-01-06 01:36:44
3474834749*15*71스테인레스관25스테인레스관1502023-12-312023-11-06정상2024-01-06 01:34:24
1481614817*92*90닥타일주철관(시멘트)100PFP15182023-04-192023-11-06정상2024-01-06 01:17:35
67466747*44*08에폭시라이닝관50PFP5012023-05-122023-12-30정상2024-01-06 01:07:14
4749147492*54*93스테인레스관20스테인레스관1502023-11-062023-12-08계량기교체2024-01-06 01:38:16
60686069*63*84닥타일주철관(시멘트)100스테인레스관1572023-05-112024-01-03정상2024-01-06 01:07:33
25072508*18*41PFP100*40스테인레스관1512023-05-172023-12-29정상2024-01-06 01:03:32
3955239553*01*29스테인레스관25스테인레스관1502023-11-142023-12-27계량기교체2024-01-06 01:37:12
3706737068*00*46스테인레스관25스테인레스관1502023-05-032024-01-03정상2024-01-06 01:36:57