Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells33
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory908.2 KiB
Average record size in memory93.0 B

Variable types

Numeric4
Text2
DateTime1
Categorical3

Dataset

Description부산광역시 상수도사업본부에서 상하수도 요금 계산 및 징수를 위해 운영하는 수용가정보시스템에 사용되는 계량기 정보(급수관 변경이력 정보) 자료입니다.
Author부산광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15083665/fileData.do

Alerts

부설년도 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
본관관종 is highly overall correlated with 부설년도High correlation
급수관관종 is highly overall correlated with 급수관구경 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 부설년도High correlation
순번 is highly overall correlated with 부설년도High correlation
급수관구경 is highly overall correlated with 부설년도 and 1 other fieldsHigh correlation
연장 is highly overall correlated with 부설년도High correlation
본관관종 is highly imbalanced (51.2%)Imbalance
급수관관종 is highly imbalanced (65.3%)Imbalance
연번 has unique valuesUnique
순번 has 2963 (29.6%) zerosZeros
연장 has 2304 (23.0%) zerosZeros

Reproduction

Analysis started2024-03-14 12:41:54.315978
Analysis finished2024-03-14 12:42:00.096083
Duration5.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6122.8889
Minimum2
Maximum12270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T21:42:00.241654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile625.95
Q13072.75
median6124.5
Q39161.25
95-th percentile11637.1
Maximum12270
Range12268
Interquartile range (IQR)6088.5

Descriptive statistics

Standard deviation3528.7182
Coefficient of variation (CV)0.5763159
Kurtosis-1.1918998
Mean6122.8889
Median Absolute Deviation (MAD)3044
Skewness0.0045347072
Sum61228889
Variance12451852
MonotonicityNot monotonic
2024-03-14T21:42:00.499834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4487 1
 
< 0.1%
9981 1
 
< 0.1%
10459 1
 
< 0.1%
338 1
 
< 0.1%
8468 1
 
< 0.1%
3178 1
 
< 0.1%
9239 1
 
< 0.1%
3819 1
 
< 0.1%
5092 1
 
< 0.1%
1324 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
12270 1
< 0.1%
12269 1
< 0.1%
12268 1
< 0.1%
12267 1
< 0.1%
12266 1
< 0.1%
12265 1
< 0.1%
12263 1
< 0.1%
12262 1
< 0.1%
12260 1
< 0.1%
12259 1
< 0.1%

고객번호
Text

Distinct5124
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T21:42:01.878053image/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

Unique3217 ?
Unique (%)32.2%

Sample

1st row*09*89
2nd row*17*83
3rd row*66*93
4th row*08*41
5th row*45*64
ValueCountFrequency (%)
47*55 42
 
0.4%
03*89 37
 
0.4%
34*40 37
 
0.4%
09*82 36
 
0.4%
33*05 35
 
0.4%
48*39 34
 
0.3%
48*01 34
 
0.3%
68*30 32
 
0.3%
19*91 30
 
0.3%
76*14 30
 
0.3%
Other values (5114) 9653
96.5%
2024-03-14T21:42:03.546446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 20000
33.3%
1 5032
 
8.4%
0 4615
 
7.7%
9 4463
 
7.4%
2 3898
 
6.5%
3 3820
 
6.4%
4 3817
 
6.4%
7 3706
 
6.2%
5 3655
 
6.1%
8 3537
 
5.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5032
12.6%
0 4615
11.5%
9 4463
11.2%
2 3898
9.7%
3 3820
9.6%
4 3817
9.5%
7 3706
9.3%
5 3655
9.1%
8 3537
8.8%
6 3457
8.6%
Other Punctuation
ValueCountFrequency (%)
* 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 20000
33.3%
1 5032
 
8.4%
0 4615
 
7.7%
9 4463
 
7.4%
2 3898
 
6.5%
3 3820
 
6.4%
4 3817
 
6.4%
7 3706
 
6.2%
5 3655
 
6.1%
8 3537
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 20000
33.3%
1 5032
 
8.4%
0 4615
 
7.7%
9 4463
 
7.4%
2 3898
 
6.5%
3 3820
 
6.4%
4 3817
 
6.4%
7 3706
 
6.2%
5 3655
 
6.1%
8 3537
 
5.9%

순번
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0863
Minimum0
Maximum13
Zeros2963
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T21:42:04.139553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4128063
Coefficient of variation (CV)0.78177959
Kurtosis-0.76335898
Mean3.0863
Median Absolute Deviation (MAD)2
Skewness0.12079156
Sum30863
Variance5.8216345
MonotonicityNot monotonic
2024-03-14T21:42:04.518615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 2963
29.6%
5 1665
16.7%
4 1652
16.5%
3 1504
15.0%
6 908
 
9.1%
2 575
 
5.8%
7 380
 
3.8%
8 133
 
1.3%
1 120
 
1.2%
9 55
 
0.5%
Other values (4) 45
 
0.4%
ValueCountFrequency (%)
0 2963
29.6%
1 120
 
1.2%
2 575
 
5.8%
3 1504
15.0%
4 1652
16.5%
5 1665
16.7%
6 908
 
9.1%
7 380
 
3.8%
8 133
 
1.3%
9 55
 
0.5%
ValueCountFrequency (%)
13 2
 
< 0.1%
12 6
 
0.1%
11 13
 
0.1%
10 24
 
0.2%
9 55
 
0.5%
8 133
 
1.3%
7 380
 
3.8%
6 908
9.1%
5 1665
16.7%
4 1652
16.5%

부설일자
Date

Distinct353
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-01-01 00:00:00
Maximum2023-12-29 00:00:00
2024-03-14T21:42:04.894519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:42:05.316046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

급수관구경
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.1667
Minimum15
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T21:42:05.676216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q115
median15
Q315
95-th percentile32
Maximum200
Range185
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.582491
Coefficient of variation (CV)0.58252136
Kurtosis72.630954
Mean18.1667
Median Absolute Deviation (MAD)0
Skewness7.0979608
Sum181667
Variance111.98911
MonotonicityNot monotonic
2024-03-14T21:42:06.038296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
15 7992
79.9%
20 727
 
7.3%
25 706
 
7.1%
40 227
 
2.3%
50 175
 
1.8%
32 81
 
0.8%
80 44
 
0.4%
100 21
 
0.2%
150 18
 
0.2%
65 7
 
0.1%
ValueCountFrequency (%)
15 7992
79.9%
20 727
 
7.3%
25 706
 
7.1%
32 81
 
0.8%
40 227
 
2.3%
50 175
 
1.8%
65 7
 
0.1%
80 44
 
0.4%
100 21
 
0.2%
150 18
 
0.2%
ValueCountFrequency (%)
200 2
 
< 0.1%
150 18
 
0.2%
100 21
 
0.2%
80 44
 
0.4%
65 7
 
0.1%
50 175
 
1.8%
40 227
 
2.3%
32 81
 
0.8%
25 706
7.1%
20 727
7.3%

부설년도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7543 
2023
2457 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 7543
75.4%
2023 2457
 
24.6%

Length

2024-03-14T21:42:06.423324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:42:06.720822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7543
75.4%
2023 2457
 
24.6%

본관구경
Text

Distinct278
Distinct (%)2.8%
Missing22
Missing (%)0.2%
Memory size156.2 KiB
2024-03-14T21:42:07.851991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length3.8364402
Min length2

Characters and Unicode

Total characters38280
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)1.0%

Sample

1st row25
2nd row100*50
3rd row100*15
4th row100
5th row15
ValueCountFrequency (%)
15 1442
 
14.5%
25 1090
 
10.9%
40 682
 
6.8%
100 581
 
5.8%
20 485
 
4.9%
40*15 382
 
3.8%
50 362
 
3.6%
150 358
 
3.6%
100*15 322
 
3.2%
50*15 309
 
3.1%
Other values (267) 3965
39.7%
2024-03-14T21:42:09.461117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10771
28.1%
5 8944
23.4%
1 6422
16.8%
* 4822
12.6%
2 3999
 
10.4%
4 1772
 
4.6%
6 915
 
2.4%
3 383
 
1.0%
8 243
 
0.6%
- 5
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33450
87.4%
Other Punctuation 4822
 
12.6%
Dash Punctuation 5
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10771
32.2%
5 8944
26.7%
1 6422
19.2%
2 3999
 
12.0%
4 1772
 
5.3%
6 915
 
2.7%
3 383
 
1.1%
8 243
 
0.7%
9 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
* 4822
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38277
> 99.9%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10771
28.1%
5 8944
23.4%
1 6422
16.8%
* 4822
12.6%
2 3999
 
10.4%
4 1772
 
4.6%
6 915
 
2.4%
3 383
 
1.0%
8 243
 
0.6%
- 5
 
< 0.1%
Latin
ValueCountFrequency (%)
x 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10771
28.1%
5 8944
23.4%
1 6422
16.8%
* 4822
12.6%
2 3999
 
10.4%
4 1772
 
4.6%
6 915
 
2.4%
3 383
 
1.0%
8 243
 
0.6%
- 5
 
< 0.1%
Other values (2) 4
 
< 0.1%

본관관종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
스테인레스관
5816 
PFP
2394 
닥타일주철관(시멘트)
1053 
닥타일주철관(에폭시)
642 
기타
 
53
Other values (5)
 
42

Length

Max length11
Median length6
Mean length6.109
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row스테인레스관
2nd rowPFP
3rd row스테인레스관
4th row닥타일주철관(시멘트)
5th row스테인레스관

Common Values

ValueCountFrequency (%)
스테인레스관 5816
58.2%
PFP 2394
23.9%
닥타일주철관(시멘트) 1053
 
10.5%
닥타일주철관(에폭시) 642
 
6.4%
기타 53
 
0.5%
에폭시라이닝관 33
 
0.3%
PE관 6
 
0.1%
PVC 1
 
< 0.1%
HI-3P 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2024-03-14T21:42:09.880936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:42:10.228086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
스테인레스관 5816
58.2%
pfp 2394
23.9%
닥타일주철관(시멘트 1053
 
10.5%
닥타일주철관(에폭시 642
 
6.4%
기타 53
 
0.5%
에폭시라이닝관 33
 
0.3%
pe관 6
 
0.1%
pvc 1
 
< 0.1%
hi-3p 1
 
< 0.1%
na 1
 
< 0.1%

급수관관종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
스테인레스관
8211 
PFP
1027 
기타
 
679
닥타일주철관(에폭시)
 
73
<NA>
 
6

Length

Max length11
Median length6
Mean length5.4576
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
스테인레스관 8211
82.1%
PFP 1027
 
10.3%
기타 679
 
6.8%
닥타일주철관(에폭시) 73
 
0.7%
<NA> 6
 
0.1%
닥타일주철관(시멘트) 4
 
< 0.1%

Length

2024-03-14T21:42:10.641082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:42:10.956750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
스테인레스관 8211
82.1%
pfp 1027
 
10.3%
기타 679
 
6.8%
닥타일주철관(에폭시 73
 
0.7%
na 6
 
0.1%
닥타일주철관(시멘트 4
 
< 0.1%

연장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)0.7%
Missing11
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.4622084
Minimum0
Maximum312
Zeros2304
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T21:42:11.329242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile8
Maximum312
Range312
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.1304072
Coefficient of variation (CV)3.7082186
Kurtosis445.33188
Mean2.4622084
Median Absolute Deviation (MAD)0
Skewness17.690774
Sum24595
Variance83.364336
MonotonicityNot monotonic
2024-03-14T21:42:11.760747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5480
54.8%
0 2304
23.0%
2 417
 
4.2%
3 361
 
3.6%
6 275
 
2.8%
4 253
 
2.5%
5 206
 
2.1%
8 110
 
1.1%
7 94
 
0.9%
12 87
 
0.9%
Other values (64) 402
 
4.0%
ValueCountFrequency (%)
0 2304
23.0%
1 5480
54.8%
2 417
 
4.2%
3 361
 
3.6%
4 253
 
2.5%
5 206
 
2.1%
6 275
 
2.8%
7 94
 
0.9%
8 110
 
1.1%
9 41
 
0.4%
ValueCountFrequency (%)
312 2
< 0.1%
274 1
< 0.1%
186 1
< 0.1%
180 2
< 0.1%
155 1
< 0.1%
150 1
< 0.1%
140 1
< 0.1%
138 1
< 0.1%
130 1
< 0.1%
126 1
< 0.1%

Interactions

2024-03-14T21:41:58.361597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:55.107033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:56.185852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:57.258418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:58.628525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:55.375386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:56.452872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:57.530321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:58.913641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:55.642447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:56.717958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:57.810405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:59.167410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:55.919255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:56.992880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:41:58.089071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:42:12.025601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번순번급수관구경본관관종급수관관종연장
연번1.0000.0640.0000.0780.1160.000
순번0.0641.0000.0910.1610.4840.070
급수관구경0.0000.0911.0000.2520.7000.414
본관관종0.0780.1610.2521.0000.4160.067
급수관관종0.1160.4840.7000.4161.0000.231
연장0.0000.0700.4140.0670.2311.000
2024-03-14T21:42:12.295590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부설년도본관관종급수관관종
부설년도1.0001.0001.000
본관관종1.0001.0000.255
급수관관종1.0000.2551.000
2024-03-14T21:42:12.550146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번순번급수관구경연장부설년도본관관종급수관관종
연번1.0000.0090.0350.0281.0000.0360.048
순번0.0091.0000.0080.2891.0000.0740.221
급수관구경0.0350.0081.0000.3641.0000.1350.548
연장0.0280.2890.3641.0001.0000.0330.144
부설년도1.0001.0001.0001.0001.0001.0001.000
본관관종0.0360.0740.1350.0331.0001.0000.255
급수관관종0.0480.2210.5480.1441.0000.2551.000

Missing values

2024-03-14T21:41:59.406382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:41:59.669999image/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.
2024-03-14T21:41:59.889690image/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

연번고객번호순번부설일자급수관구경부설년도본관구경본관관종급수관관종연장
44864487*09*8902023-02-2315202325스테인레스관스테인레스관0
60026003*17*8312023-02-0950<NA>100*50PFPPFP0
42864287*66*9322023-01-0615<NA>100*15스테인레스관스테인레스관1
81988199*08*4142023-02-2220<NA>100닥타일주철관(시멘트)스테인레스관7
1050110502*45*6432023-07-3115<NA>15스테인레스관스테인레스관1
23332334*56*0402023-04-2615202325스테인레스관스테인레스관0
27482749*97*4102023-11-1515202320스테인레스관기타0
72477248*02*6342023-06-2120<NA>150닥타일주철관(에폭시)스테인레스관1
1000210003*11*7732023-10-0215<NA>80닥타일주철관(시멘트)스테인레스관1
40074008*09*6472023-05-0215<NA>100*40*15스테인레스관스테인레스관1
연번고객번호순번부설일자급수관구경부설년도본관구경본관관종급수관관종연장
65386539*03*8902023-03-1515202340PFP스테인레스관0
1104411045*00*5152023-10-1815<NA>15스테인레스관스테인레스관1
77667767*55*2352023-06-2815<NA>15스테인레스관스테인레스관1
36273628*01*3532023-08-1015<NA>100닥타일주철관(시멘트)스테인레스관1
82688269*53*7442023-09-2515<NA>20*15스테인레스관스테인레스관1
90399040*70*7402023-11-0615202325스테인레스관스테인레스관0
789790*77*5842023-02-2315<NA>100닥타일주철관(시멘트)스테인레스관1
84778478*62*0452023-11-0215<NA>40PFP스테인레스관1
50755076*87*4602023-06-1415202340PFP스테인레스관0
37113712*50*7942023-03-0215<NA>15스테인레스관스테인레스관1