Overview

Dataset statistics

Number of variables9
Number of observations1404
Missing cells851
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory105.7 KiB
Average record size in memory77.1 B

Variable types

Numeric4
Categorical2
Text3

Dataset

Description경상남도 공사계약대장 시스템의 총설계변경 데이터입니다. 변경금액일, 증감액, 총공사금액, 낙찰금액등의 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15049516

Alerts

부서코드 has constant value ""Constant
공사년도 is highly overall correlated with 낙찰금액High correlation
공사번호 is highly overall correlated with 공사구분High correlation
낙찰금액 is highly overall correlated with 공사년도High correlation
공사구분 is highly overall correlated with 공사번호High correlation
공사구분 is highly imbalanced (60.1%)Imbalance
변경계약일 has 230 (16.4%) missing valuesMissing
증감액 has 241 (17.2%) missing valuesMissing
총공사금액(당초) has 32 (2.3%) missing valuesMissing
총공사금액(변경) has 248 (17.7%) missing valuesMissing
낙찰금액 has 100 (7.1%) missing valuesMissing
총공사금액(당초) has 22 (1.6%) zerosZeros
낙찰금액 has 519 (37.0%) zerosZeros

Reproduction

Analysis started2023-12-11 00:42:28.882446
Analysis finished2023-12-11 00:42:31.826653
Duration2.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.4929
Minimum1999
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-12-11T09:42:31.899211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2003
Q12006
median2008
Q32011
95-th percentile2014
Maximum2019
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5713807
Coefficient of variation (CV)0.0017781396
Kurtosis-0.68485122
Mean2008.4929
Median Absolute Deviation (MAD)3
Skewness0.27136102
Sum2819924
Variance12.75476
MonotonicityNot monotonic
2023-12-11T09:42:32.062067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2006 206
14.7%
2005 153
10.9%
2009 136
9.7%
2010 135
9.6%
2011 122
8.7%
2013 106
7.5%
2004 100
7.1%
2014 96
6.8%
2008 84
6.0%
2007 81
 
5.8%
Other values (10) 185
13.2%
ValueCountFrequency (%)
1999 2
 
0.1%
2000 1
 
0.1%
2001 4
 
0.3%
2002 9
 
0.6%
2003 66
 
4.7%
2004 100
7.1%
2005 153
10.9%
2006 206
14.7%
2007 81
 
5.8%
2008 84
6.0%
ValueCountFrequency (%)
2019 6
 
0.4%
2018 7
 
0.5%
2016 6
 
0.4%
2015 28
 
2.0%
2014 96
6.8%
2013 106
7.5%
2012 56
4.0%
2011 122
8.7%
2010 135
9.6%
2009 136
9.7%

공사구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
공사
1293 
용역
 
111

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공사 1293
92.1%
용역 111
 
7.9%

Length

2023-12-11T09:42:32.222519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:32.359177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사 1293
92.1%
용역 111
 
7.9%

공사번호
Real number (ℝ)

HIGH CORRELATION 

Distinct258
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.56339
Minimum1
Maximum518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-12-11T09:42:32.495703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q130
median57
Q397
95-th percentile403
Maximum518
Range517
Interquartile range (IQR)67

Descriptive statistics

Standard deviation112.45093
Coefficient of variation (CV)1.1891592
Kurtosis3.9909511
Mean94.56339
Median Absolute Deviation (MAD)31
Skewness2.195952
Sum132767
Variance12645.211
MonotonicityNot monotonic
2023-12-11T09:42:32.659169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 23
 
1.6%
18 22
 
1.6%
39 21
 
1.5%
85 17
 
1.2%
33 17
 
1.2%
35 17
 
1.2%
28 17
 
1.2%
22 16
 
1.1%
23 16
 
1.1%
26 16
 
1.1%
Other values (248) 1222
87.0%
ValueCountFrequency (%)
1 11
0.8%
2 10
0.7%
3 10
0.7%
4 5
 
0.4%
5 10
0.7%
6 13
0.9%
7 6
0.4%
8 9
0.6%
9 3
 
0.2%
10 14
1.0%
ValueCountFrequency (%)
518 2
0.1%
516 4
0.3%
514 1
 
0.1%
508 1
 
0.1%
505 2
0.1%
503 1
 
0.1%
502 1
 
0.1%
495 1
 
0.1%
494 1
 
0.1%
475 3
0.2%

부서코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
1
1404 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1404
100.0%

Length

2023-12-11T09:42:32.833355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:42:32.961550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1404
100.0%

변경계약일
Text

MISSING 

Distinct747
Distinct (%)63.6%
Missing230
Missing (%)16.4%
Memory size11.1 KiB
2023-12-11T09:42:33.239843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.8943782
Min length1

Characters and Unicode

Total characters11616
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

Unique470 ?
Unique (%)40.0%

Sample

1st row200008
2nd row2000-07-14
3rd row2001-11-13
4th row2001-07-04
5th row2001-07-31
ValueCountFrequency (%)
12
 
1.0%
2005-12-07 7
 
0.6%
2008-12-22 6
 
0.5%
2007-12-14 6
 
0.5%
2008-12-29 6
 
0.5%
2007-04-02 5
 
0.4%
2007-12-20 5
 
0.4%
2010-12-23 5
 
0.4%
2011-06-27 5
 
0.4%
2010-04-23 5
 
0.4%
Other values (737) 1112
94.7%
2023-12-11T09:42:33.655529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3150
27.1%
- 2322
20.0%
2 2070
17.8%
1 1693
14.6%
6 413
 
3.6%
4 363
 
3.1%
5 346
 
3.0%
7 339
 
2.9%
9 329
 
2.8%
3 322
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9294
80.0%
Dash Punctuation 2322
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3150
33.9%
2 2070
22.3%
1 1693
18.2%
6 413
 
4.4%
4 363
 
3.9%
5 346
 
3.7%
7 339
 
3.6%
9 329
 
3.5%
3 322
 
3.5%
8 269
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 2322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11616
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3150
27.1%
- 2322
20.0%
2 2070
17.8%
1 1693
14.6%
6 413
 
3.6%
4 363
 
3.1%
5 346
 
3.0%
7 339
 
2.9%
9 329
 
2.8%
3 322
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3150
27.1%
- 2322
20.0%
2 2070
17.8%
1 1693
14.6%
6 413
 
3.6%
4 363
 
3.1%
5 346
 
3.0%
7 339
 
2.9%
9 329
 
2.8%
3 322
 
2.8%

증감액
Text

MISSING 

Distinct968
Distinct (%)83.2%
Missing241
Missing (%)17.2%
Memory size11.1 KiB
2023-12-11T09:42:33.882065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.4385211
Min length1

Characters and Unicode

Total characters9814
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

Unique835 ?
Unique (%)71.8%

Sample

1st row-11935000
2nd row219450000
3rd row18779000
4th row24798000
5th row108480000
ValueCountFrequency (%)
0 36
 
3.1%
12
 
1.0%
488000000 5
 
0.4%
349700000 5
 
0.4%
95205000 4
 
0.3%
228545000 3
 
0.3%
119000000 3
 
0.3%
1922697000 3
 
0.3%
333000000 3
 
0.3%
20136000 3
 
0.3%
Other values (955) 1086
93.4%
2023-12-11T09:42:34.233890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4443
45.3%
1 799
 
8.1%
3 660
 
6.7%
2 659
 
6.7%
4 570
 
5.8%
5 514
 
5.2%
8 509
 
5.2%
9 494
 
5.0%
7 481
 
4.9%
6 473
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9602
97.8%
Dash Punctuation 212
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4443
46.3%
1 799
 
8.3%
3 660
 
6.9%
2 659
 
6.9%
4 570
 
5.9%
5 514
 
5.4%
8 509
 
5.3%
9 494
 
5.1%
7 481
 
5.0%
6 473
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9814
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4443
45.3%
1 799
 
8.1%
3 660
 
6.7%
2 659
 
6.7%
4 570
 
5.8%
5 514
 
5.2%
8 509
 
5.2%
9 494
 
5.0%
7 481
 
4.9%
6 473
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9814
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4443
45.3%
1 799
 
8.1%
3 660
 
6.7%
2 659
 
6.7%
4 570
 
5.8%
5 514
 
5.2%
8 509
 
5.2%
9 494
 
5.0%
7 481
 
4.9%
6 473
 
4.8%

총공사금액(당초)
Real number (ℝ)

MISSING  ZEROS 

Distinct1014
Distinct (%)73.9%
Missing32
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean1.7226356 × 1010
Minimum0
Maximum3.27 × 1011
Zeros22
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-12-11T09:42:34.428495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.1756135 × 108
Q12.6221246 × 109
median4.488515 × 109
Q39.14156 × 109
95-th percentile9.1003 × 1010
Maximum3.27 × 1011
Range3.27 × 1011
Interquartile range (IQR)6.5194354 × 109

Descriptive statistics

Standard deviation4.0120718 × 1010
Coefficient of variation (CV)2.3290311
Kurtosis26.924054
Mean1.7226356 × 1010
Median Absolute Deviation (MAD)2.477975 × 109
Skewness4.7457198
Sum2.363456 × 1013
Variance1.609672 × 1021
MonotonicityNot monotonic
2023-12-11T09:42:34.571639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
1.6%
6540300000 5
 
0.4%
127000000000 5
 
0.4%
22076901000 5
 
0.4%
8403400000 4
 
0.3%
7381549000 4
 
0.3%
42195994000 4
 
0.3%
6890000000 4
 
0.3%
3139195000 4
 
0.3%
25008139000 4
 
0.3%
Other values (1004) 1311
93.4%
(Missing) 32
 
2.3%
ValueCountFrequency (%)
0 22
1.6%
2329000 1
 
0.1%
2958340 1
 
0.1%
3916000 1
 
0.1%
4950000 1
 
0.1%
5176000 1
 
0.1%
5220000 1
 
0.1%
52743000 1
 
0.1%
52965780 1
 
0.1%
80196000 1
 
0.1%
ValueCountFrequency (%)
327000000000 1
 
0.1%
326000000000 1
 
0.1%
323000000000 1
 
0.1%
316000000000 1
 
0.1%
315000000000 3
0.2%
305000000000 1
 
0.1%
296000000000 1
 
0.1%
295000000000 1
 
0.1%
288000000000 1
 
0.1%
270000000000 1
 
0.1%

총공사금액(변경)
Text

MISSING 

Distinct948
Distinct (%)82.0%
Missing248
Missing (%)17.7%
Memory size11.1 KiB
2023-12-11T09:42:34.779141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.7517301
Min length1

Characters and Unicode

Total characters11273
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique794 ?
Unique (%)68.7%

Sample

1st row1366067000
2nd row1378002000
3rd row-
4th row1537608000
5th row-
ValueCountFrequency (%)
0 20
 
1.7%
17
 
1.5%
6890000000 5
 
0.4%
3234400000 4
 
0.3%
7378000000 4
 
0.3%
42195994000 4
 
0.3%
7659000000 3
 
0.3%
3384700000 3
 
0.3%
11365300000 3
 
0.3%
2535650000 3
 
0.3%
Other values (938) 1090
94.3%
2023-12-11T09:42:35.107812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4495
39.9%
1 895
 
7.9%
2 824
 
7.3%
3 795
 
7.1%
4 758
 
6.7%
5 708
 
6.3%
8 672
 
6.0%
6 661
 
5.9%
9 654
 
5.8%
7 646
 
5.7%
Other values (4) 165
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11108
98.5%
Other Punctuation 49
 
0.4%
Uppercase Letter 49
 
0.4%
Math Symbol 49
 
0.4%
Dash Punctuation 18
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4495
40.5%
1 895
 
8.1%
2 824
 
7.4%
3 795
 
7.2%
4 758
 
6.8%
5 708
 
6.4%
8 672
 
6.0%
6 661
 
6.0%
9 654
 
5.9%
7 646
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 49
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 49
100.0%
Math Symbol
ValueCountFrequency (%)
+ 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11224
99.6%
Latin 49
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4495
40.0%
1 895
 
8.0%
2 824
 
7.3%
3 795
 
7.1%
4 758
 
6.8%
5 708
 
6.3%
8 672
 
6.0%
6 661
 
5.9%
9 654
 
5.8%
7 646
 
5.8%
Other values (3) 116
 
1.0%
Latin
ValueCountFrequency (%)
E 49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4495
39.9%
1 895
 
7.9%
2 824
 
7.3%
3 795
 
7.1%
4 758
 
6.7%
5 708
 
6.3%
8 672
 
6.0%
6 661
 
5.9%
9 654
 
5.8%
7 646
 
5.7%
Other values (4) 165
 
1.5%

낙찰금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct322
Distinct (%)24.7%
Missing100
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean8.587097 × 109
Minimum0
Maximum2.7 × 1011
Zeros519
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-12-11T09:42:35.269240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5907167 × 109
Q34.099666 × 109
95-th percentile4.6306597 × 1010
Maximum2.7 × 1011
Range2.7 × 1011
Interquartile range (IQR)4.099666 × 109

Descriptive statistics

Standard deviation2.7388713 × 1010
Coefficient of variation (CV)3.1895194
Kurtosis43.794565
Mean8.587097 × 109
Median Absolute Deviation (MAD)1.5907167 × 109
Skewness6.0226895
Sum1.1197574 × 1013
Variance7.5014157 × 1020
MonotonicityNot monotonic
2023-12-11T09:42:35.463465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 519
37.0%
6540300000 12
 
0.9%
1474720000 9
 
0.6%
3951400000 9
 
0.6%
23457811000 9
 
0.6%
3139195000 9
 
0.6%
1803060000 8
 
0.6%
19812000000 8
 
0.6%
15969047000 8
 
0.6%
36245482870 8
 
0.6%
Other values (312) 705
50.2%
(Missing) 100
 
7.1%
ValueCountFrequency (%)
0 519
37.0%
2178700 2
 
0.1%
2329000 1
 
0.1%
2958340 1
 
0.1%
3916000 1
 
0.1%
4950000 1
 
0.1%
5176000 1
 
0.1%
5220000 1
 
0.1%
37426980 1
 
0.1%
52743000 1
 
0.1%
ValueCountFrequency (%)
270000000000 5
0.4%
252000000000 1
 
0.1%
178000000000 3
0.2%
150000000000 1
 
0.1%
144000000000 2
 
0.1%
135000000000 6
0.4%
125000000000 1
 
0.1%
122000000000 6
0.4%
117000000000 1
 
0.1%
112000000000 4
0.3%

Interactions

2023-12-11T09:42:30.560741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:29.253088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:29.649650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:30.021258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:30.688835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:29.379286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:29.752841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:30.133259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:30.806979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:29.466081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:29.838341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:30.293678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:31.242233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:29.564922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:29.931623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:42:30.459691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:42:35.577921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사구분공사번호총공사금액(당초)낙찰금액
공사년도1.0000.3330.6280.1550.275
공사구분0.3331.0000.7840.0760.046
공사번호0.6280.7841.0000.1560.118
총공사금액(당초)0.1550.0760.1561.0000.821
낙찰금액0.2750.0460.1180.8211.000
2023-12-11T09:42:35.682844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사번호총공사금액(당초)낙찰금액공사구분
공사년도1.0000.1000.064-0.5690.255
공사번호0.1001.000-0.112-0.0730.618
총공사금액(당초)0.064-0.1121.0000.1820.075
낙찰금액-0.569-0.0730.1821.0000.034
공사구분0.2550.6180.0750.0341.000

Missing values

2023-12-11T09:42:31.390811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:42:31.569171image/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.
2023-12-11T09:42:31.741436image/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

공사년도공사구분공사번호부서코드변경계약일증감액총공사금액(당초)총공사금액(변경)낙찰금액
01999공사761200008-11935000137800200013660670001158552000
11999공사7612000-07-14219450000115855200013780020001158552000
22000공사6812001-11-13187790003077188000-3058409000
32001공사1812001-07-0424798000151281000015376080001512810000
42001공사4112001-07-311084800003608728000-3500248000
52001공사3312001-11-13187790003077188000-3058409000
62001공사3312002-05-131680970003245285000-3077188000
72002공사1812002-12-201334911003222800000<NA>3089308900
82002공사1912004-10-222394130004595273000<NA>4355860000
92002공사3512002-12-30203560000334520100035487610003228761000
공사년도공사구분공사번호부서코드변경계약일증감액총공사금액(당초)총공사금액(변경)낙찰금액
13942018공사5312018-07-09-21280000681887000660607000681887000
13952018공사6512018-12-203106000135847680013615828001358476800
13962018공사6412018-11-06-570000280010400027995340002800104000
13972018공사6312018-11-20146731000290967999030564109902909679990
13982019공사1012019-06-11-13628000665311000651683000681887000
13992019공사1012019-05-154704000660607000665311000681887000
14002019공사1112018-12-203106000135847680013615828001358476800
14012019공사1112019-06-25-16950800135926680013423160001358476800
14022019공사1112019-05-27-2316000136158280013592668000
14032019공사7212019-06-280884216000088421600000