Overview

Dataset statistics

Number of variables12
Number of observations36
Missing cells54
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory106.7 B

Variable types

Numeric6
Categorical2
DateTime4

Dataset

Description경상남도 공사계약대장시스템의 지체상금 데이터입니다. 준공기간, 준공검사일, 지연배상금, 지체일수, 지체배상금 등의 데이터를 포함하고있습니다.
Author경상남도
URLhttps://www.data.go.kr/data/15049517/fileData.do

Alerts

부서코드 has constant value ""Constant
공사년도 is highly overall correlated with 미이행금액High correlation
미이행금액 is highly overall correlated with 공사년도 and 2 other fieldsHigh correlation
지연배상금율 is highly overall correlated with 미이행금액High correlation
지체일수 is highly overall correlated with 지연배상금High correlation
지연배상금 is highly overall correlated with 미이행금액 and 1 other fieldsHigh correlation
미이행금액 has 28 (77.8%) missing valuesMissing
징수시작기한 has 10 (27.8%) missing valuesMissing
징수마감기한 has 16 (44.4%) missing valuesMissing
미이행금액 has 1 (2.8%) zerosZeros
지연배상금율 has 16 (44.4%) zerosZeros
지체일수 has 1 (2.8%) zerosZeros
지연배상금 has 9 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-12 00:58:19.665058
Analysis finished2023-12-12 00:58:22.832889
Duration3.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.8333
Minimum2005
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:58:22.871101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005.75
Q12007
median2009
Q32010.25
95-th percentile2011.25
Maximum2019
Range14
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.6779523
Coefficient of variation (CV)0.0013330883
Kurtosis4.4977183
Mean2008.8333
Median Absolute Deviation (MAD)2
Skewness1.4077404
Sum72318
Variance7.1714286
MonotonicityIncreasing
2023-12-12T09:58:22.976375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2007 7
19.4%
2010 7
19.4%
2011 7
19.4%
2006 5
13.9%
2008 3
8.3%
2009 3
8.3%
2005 2
 
5.6%
2012 1
 
2.8%
2019 1
 
2.8%
ValueCountFrequency (%)
2005 2
 
5.6%
2006 5
13.9%
2007 7
19.4%
2008 3
8.3%
2009 3
8.3%
2010 7
19.4%
2011 7
19.4%
2012 1
 
2.8%
2019 1
 
2.8%
ValueCountFrequency (%)
2019 1
 
2.8%
2012 1
 
2.8%
2011 7
19.4%
2010 7
19.4%
2009 3
8.3%
2008 3
8.3%
2007 7
19.4%
2006 5
13.9%
2005 2
 
5.6%

공사구분
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
용역
23 
공사
6 
기타
4 
구매
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
용역 23
63.9%
공사 6
 
16.7%
기타 4
 
11.1%
구매 3
 
8.3%

Length

2023-12-12T09:58:23.094776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:58:23.182674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
용역 23
63.9%
공사 6
 
16.7%
기타 4
 
11.1%
구매 3
 
8.3%

공사번호
Real number (ℝ)

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.77778
Minimum9
Maximum542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:58:23.276702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile28.75
Q154.5
median151
Q3237.25
95-th percentile443.5
Maximum542
Range533
Interquartile range (IQR)182.75

Descriptive statistics

Standard deviation130.19833
Coefficient of variation (CV)0.75355949
Kurtosis1.0726443
Mean172.77778
Median Absolute Deviation (MAD)93
Skewness1.0534242
Sum6220
Variance16951.606
MonotonicityNot monotonic
2023-12-12T09:58:23.379368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
111 2
 
5.6%
112 1
 
2.8%
430 1
 
2.8%
236 1
 
2.8%
223 1
 
2.8%
542 1
 
2.8%
34 1
 
2.8%
33 1
 
2.8%
50 1
 
2.8%
53 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
9 1
2.8%
16 1
2.8%
33 1
2.8%
34 1
2.8%
40 1
2.8%
42 1
2.8%
47 1
2.8%
50 1
2.8%
53 1
2.8%
55 1
2.8%
ValueCountFrequency (%)
542 1
2.8%
484 1
2.8%
430 1
2.8%
307 1
2.8%
277 1
2.8%
276 1
2.8%
262 1
2.8%
255 1
2.8%
241 1
2.8%
236 1
2.8%

부서코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
1
36 

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 36
100.0%

Length

2023-12-12T09:58:23.480165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:58:23.592029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 36
100.0%

미이행금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)100.0%
Missing28
Missing (%)77.8%
Infinite0
Infinite (%)0.0%
Mean873232.5
Minimum0
Maximum4594500
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:58:23.690445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2303
Q136842.5
median58890
Q3596302.5
95-th percentile3734550
Maximum4594500
Range4594500
Interquartile range (IQR)559460

Descriptive statistics

Standard deviation1673078.4
Coefficient of variation (CV)1.9159598
Kurtosis3.7289122
Mean873232.5
Median Absolute Deviation (MAD)37995
Skewness2.0374968
Sum6985860
Variance2.7991913 × 1012
MonotonicityNot monotonic
2023-12-12T09:58:23.777273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4594500 1
 
2.8%
2137500 1
 
2.8%
82570 1
 
2.8%
70780 1
 
2.8%
47000 1
 
2.8%
6580 1
 
2.8%
46930 1
 
2.8%
0 1
 
2.8%
(Missing) 28
77.8%
ValueCountFrequency (%)
0 1
2.8%
6580 1
2.8%
46930 1
2.8%
47000 1
2.8%
70780 1
2.8%
82570 1
2.8%
2137500 1
2.8%
4594500 1
2.8%
ValueCountFrequency (%)
4594500 1
2.8%
2137500 1
2.8%
82570 1
2.8%
70780 1
2.8%
47000 1
2.8%
46930 1
2.8%
6580 1
2.8%
0 1
2.8%

준공기한
Date

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum2006-03-15 00:00:00
Maximum2019-07-25 00:00:00
2023-12-12T09:58:23.869088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:23.968478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

준공검사일자
Date

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum2006-03-16 00:00:00
Maximum2019-08-05 00:00:00
2023-12-12T09:58:24.060022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:24.154294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

징수시작기한
Date

MISSING 

Distinct25
Distinct (%)96.2%
Missing10
Missing (%)27.8%
Memory size420.0 B
Minimum2006-06-03 00:00:00
Maximum2019-07-26 00:00:00
2023-12-12T09:58:24.240166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:24.325181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

징수마감기한
Date

MISSING 

Distinct19
Distinct (%)95.0%
Missing16
Missing (%)44.4%
Memory size420.0 B
Minimum2006-06-12 00:00:00
Maximum2019-08-05 00:00:00
2023-12-12T09:58:24.403240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:24.483905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

지연배상금율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14058333
Minimum0
Maximum2.5
Zeros16
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:58:24.560256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0025
Q30.15
95-th percentile0.25
Maximum2.5
Range2.5
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.41665449
Coefficient of variation (CV)2.9637545
Kurtosis31.613397
Mean0.14058333
Median Absolute Deviation (MAD)0.0025
Skewness5.4727069
Sum5.061
Variance0.17360096
MonotonicityNot monotonic
2023-12-12T09:58:24.647885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 16
44.4%
0.25 7
19.4%
0.0025 4
 
11.1%
0.1 3
 
8.3%
0.15 3
 
8.3%
2.5 1
 
2.8%
0.001 1
 
2.8%
0.05 1
 
2.8%
ValueCountFrequency (%)
0.0 16
44.4%
0.001 1
 
2.8%
0.0025 4
 
11.1%
0.05 1
 
2.8%
0.1 3
 
8.3%
0.15 3
 
8.3%
0.25 7
19.4%
2.5 1
 
2.8%
ValueCountFrequency (%)
2.5 1
 
2.8%
0.25 7
19.4%
0.15 3
 
8.3%
0.1 3
 
8.3%
0.05 1
 
2.8%
0.0025 4
 
11.1%
0.001 1
 
2.8%
0.0 16
44.4%

지체일수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.194444
Minimum0
Maximum84
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T09:58:24.739147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median7.5
Q321.5
95-th percentile45.75
Maximum84
Range84
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation18.421756
Coefficient of variation (CV)1.2124008
Kurtosis4.9691229
Mean15.194444
Median Absolute Deviation (MAD)5.5
Skewness2.0906071
Sum547
Variance339.36111
MonotonicityNot monotonic
2023-12-12T09:58:24.829097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 4
 
11.1%
1 4
 
11.1%
5 3
 
8.3%
12 3
 
8.3%
4 3
 
8.3%
6 2
 
5.6%
35 2
 
5.6%
41 1
 
2.8%
0 1
 
2.8%
11 1
 
2.8%
Other values (12) 12
33.3%
ValueCountFrequency (%)
0 1
 
2.8%
1 4
11.1%
2 1
 
2.8%
3 4
11.1%
4 3
8.3%
5 3
8.3%
6 2
5.6%
9 1
 
2.8%
11 1
 
2.8%
12 3
8.3%
ValueCountFrequency (%)
84 1
2.8%
60 1
2.8%
41 1
2.8%
39 1
2.8%
35 2
5.6%
29 1
2.8%
24 1
2.8%
23 1
2.8%
21 1
2.8%
16 1
2.8%

지연배상금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1852069.7
Minimum-1692000
Maximum19369010
Zeros9
Zeros (%)25.0%
Negative1
Negative (%)2.8%
Memory size456.0 B
2023-12-12T09:58:24.927417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1692000
5-th percentile0
Q10
median195580
Q31003515
95-th percentile11439368
Maximum19369010
Range21061010
Interquartile range (IQR)1003515

Descriptive statistics

Standard deviation4371350.6
Coefficient of variation (CV)2.3602516
Kurtosis7.8690787
Mean1852069.7
Median Absolute Deviation (MAD)195580
Skewness2.8410458
Sum66674510
Variance1.9108706 × 1013
MonotonicityNot monotonic
2023-12-12T09:58:25.027293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 9
25.0%
19369010 1
 
2.8%
12074250 1
 
2.8%
191660 1
 
2.8%
1429500 1
 
2.8%
2734870 1
 
2.8%
199500 1
 
2.8%
18500 1
 
2.8%
-1692000 1
 
2.8%
80830 1
 
2.8%
Other values (18) 18
50.0%
ValueCountFrequency (%)
-1692000 1
 
2.8%
0 9
25.0%
11250 1
 
2.8%
17290 1
 
2.8%
18500 1
 
2.8%
72000 1
 
2.8%
80830 1
 
2.8%
86250 1
 
2.8%
156890 1
 
2.8%
191660 1
 
2.8%
ValueCountFrequency (%)
19369010 1
2.8%
12074250 1
2.8%
11227740 1
2.8%
10930390 1
2.8%
2734870 1
2.8%
2124000 1
2.8%
2033730 1
2.8%
1430540 1
2.8%
1429500 1
2.8%
861520 1
2.8%

Interactions

2023-12-12T09:58:22.119452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:19.938031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.324680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.929521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.337595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.726684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:22.198467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.009846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.600158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.009279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.404183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.798277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:22.268961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.072313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.660369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.071622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.469520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.864957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:22.341033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.137015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.718048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.131482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.535907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.931453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:22.417176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.200556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.784278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.200665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.602957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.995414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:22.476820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.266123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:20.856858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.264756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:21.665278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:58:22.052995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:58:25.101019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사구분공사번호미이행금액준공기한준공검사일자징수시작기한징수마감기한지연배상금율지체일수지연배상금
공사년도1.0000.6020.4590.0001.0001.0001.0001.0000.2600.0000.000
공사구분0.6021.0000.3310.0000.9201.0001.0001.0000.0000.3840.562
공사번호0.4590.3311.0000.0000.9921.0001.0001.0000.0000.0000.000
미이행금액0.0000.0000.0001.0001.0001.0001.0001.000NaN0.8880.000
준공기한1.0000.9200.9921.0001.0001.0001.0001.0000.9470.7741.000
준공검사일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
징수시작기한1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
징수마감기한1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지연배상금율0.2600.0000.000NaN0.9471.0001.0001.0001.0000.0000.000
지체일수0.0000.3840.0000.8880.7741.0001.0001.0000.0001.0000.586
지연배상금0.0000.5620.0000.0001.0001.0001.0001.0000.0000.5861.000
2023-12-12T09:58:25.440430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사번호미이행금액지연배상금율지체일수지연배상금공사구분
공사년도1.0000.159-0.8940.0710.250-0.0490.431
공사번호0.1591.000-0.167-0.137-0.101-0.0930.182
미이행금액-0.894-0.1671.000-0.5770.024-0.5770.000
지연배상금율0.071-0.137-0.5771.0000.2660.2730.000
지체일수0.250-0.1010.0240.2661.0000.5460.250
지연배상금-0.049-0.093-0.5770.2730.5461.0000.272
공사구분0.4310.1820.0000.0000.2500.2721.000

Missing values

2023-12-12T09:58:22.564234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:58:22.697892image/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.
2023-12-12T09:58:22.788866image/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

공사년도공사구분공사번호부서코드미이행금액준공기한준공검사일자징수시작기한징수마감기한지연배상금율지체일수지연배상금
02005용역1111<NA>2006-05-092006-06-192006-06-262006-07-100.254119369010
12005용역1121<NA>2006-03-152006-03-16<NA><NA>0.25172000
22006용역421<NA>2007-01-312007-02-05<NA><NA>0.05861520
32006용역401<NA>2006-11-292006-12-152006-12-042006-12-120.0025122124000
42006용역161<NA>2006-05-242006-06-05<NA><NA>0.2512726600
52006용역551<NA>2006-08-072006-08-102006-08-082006-08-100.25311250
62006용역811<NA>2006-10-022006-10-162006-11-072006-11-210.014245000
72007용역127145945002008-05-312008-06-122006-06-032006-06-120.090
82007용역135121375002008-05-012008-05-072008-05-012008-05-070.060
92007용역1611<NA>2008-01-312008-02-042008-02-012008-02-040.254733790
공사년도공사구분공사번호부서코드미이행금액준공기한준공검사일자징수시작기한징수마감기한지연배상금율지체일수지연배상금
262010기타501<NA>2010-09-012010-09-01<NA><NA>0.000
272011구매53102011-04-272011-05-262011-04-282011-05-260.15291430540
282011공사2261<NA>2011-07-252011-07-282011-08-052011-08-220.13156890
292011공사2251<NA>2011-07-252011-07-282011-08-052011-08-220.1380830
302011구매1111<NA>2011-08-212011-09-06<NA><NA>0.1516-1692000
312011기타91<NA>2011-10-132011-10-182011-10-182011-11-020.001518500
322011용역4841<NA>2011-12-152012-01-202011-12-162012-01-050.002521199500
332011구매1571<NA>2012-02-122012-03-232012-02-142012-03-230.15392734870
342012용역2321<NA>2012-09-152012-11-162012-09-182012-11-160.0025601429500
352019공사801<NA>2019-07-252019-08-052019-07-262019-08-050.0511191660