Overview

Dataset statistics

Number of variables18
Number of observations2960
Missing cells4168
Missing cells (%)7.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory442.4 KiB
Average record size in memory153.0 B

Variable types

Categorical5
Text4
Numeric7
DateTime2

Dataset

Description경기도 양주시 계약정보공개에 따라 대금지급 현황에 관련한 데이터로 계약명, 검수일시, 검수액, 수수료, 총계약금액 등을 포함하고 있습니다.
Author경기도 양주시
URLhttps://www.data.go.kr/data/15063367/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
당년년도 is highly overall correlated with 계약번호High correlation
계약번호 is highly overall correlated with 당년년도High correlation
검수액 is highly overall correlated with 지출결의요청액 and 2 other fieldsHigh correlation
지출결의요청액 is highly overall correlated with 검수액 and 2 other fieldsHigh correlation
총계약금액 is highly overall correlated with 검수액 and 2 other fieldsHigh correlation
최초계약금액 is highly overall correlated with 검수액 and 2 other fieldsHigh correlation
종류 is highly overall correlated with 검수유형High correlation
검수유형 is highly overall correlated with 종류High correlation
입회자1 has 1355 (45.8%) missing valuesMissing
입회자2 has 2813 (95.0%) missing valuesMissing
수수료 has 1794 (60.6%) zerosZeros
지출결의요청액 has 42 (1.4%) zerosZeros
선급금액 has 2507 (84.7%) zerosZeros

Reproduction

Analysis started2023-12-12 21:34:23.419214
Analysis finished2023-12-12 21:34:30.873933
Duration7.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
물품
1564 
공사
833 
용역
563 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
물품 1564
52.8%
공사 833
28.1%
용역 563
 
19.0%

Length

2023-12-13T06:34:30.934560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:34:31.037857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물품 1564
52.8%
공사 833
28.1%
용역 563
 
19.0%

계약명
Text

Distinct2681
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2023-12-13T06:34:31.314350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length48
Mean length28.133784
Min length7

Characters and Unicode

Total characters83276
Distinct characters657
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2533 ?
Unique (%)85.6%

Sample

1st row장흥 문턱없는 아트화장실 신축공사 관급자재(창호)
2nd row장흥 문턱없는 아트화장실 신축공사 관급자재(냉·난방기)
3rd row장흥 문턱없는 아트화장실 전기 관급자재(분전반)
4th row[용역]마전동 도시계획도로(소로2-39호선) 개설사업 실시설계용역
5th row회천청소년문화의집 인테리어 공사 실시설계 용역 시행
ValueCountFrequency (%)
구입 443
 
3.1%
일원 370
 
2.6%
및 215
 
1.5%
2021년 206
 
1.4%
등 183
 
1.3%
2022년 174
 
1.2%
용역 166
 
1.2%
외 136
 
0.9%
시행 124
 
0.9%
관급자재 114
 
0.8%
Other values (3788) 12260
85.2%
2023-12-13T06:34:31.791320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11439
 
13.7%
사 2530
 
3.0%
( 1895
 
2.3%
) 1894
 
2.3%
공 1691
 
2.0%
2 1667
 
2.0%
도 1639
 
2.0%
관 1302
 
1.6%
시 1247
 
1.5%
- 1246
 
1.5%
Other values (647) 56726
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59173
71.1%
Space Separator 11439
 
13.7%
Decimal Number 5285
 
6.3%
Open Punctuation 2499
 
3.0%
Close Punctuation 2497
 
3.0%
Dash Punctuation 1246
 
1.5%
Uppercase Letter 788
 
0.9%
Other Punctuation 236
 
0.3%
Lowercase Letter 41
 
< 0.1%
Math Symbol 39
 
< 0.1%
Other values (4) 33
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 2530
 
4.3%
공 1691
 
2.9%
도 1639
 
2.8%
관 1302
 
2.2%
시 1247
 
2.1%
지 1203
 
2.0%
로 1117
 
1.9%
구 1088
 
1.8%
급 1006
 
1.7%
정 983
 
1.7%
Other values (575) 45367
76.7%
Uppercase Letter
ValueCountFrequency (%)
C 181
23.0%
V 88
11.2%
T 85
10.8%
S 67
 
8.5%
E 66
 
8.4%
P 61
 
7.7%
D 52
 
6.6%
L 39
 
4.9%
I 29
 
3.7%
B 19
 
2.4%
Other values (13) 101
12.8%
Lowercase Letter
ValueCountFrequency (%)
o 10
24.4%
w 4
 
9.8%
e 4
 
9.8%
p 3
 
7.3%
i 3
 
7.3%
s 2
 
4.9%
n 2
 
4.9%
d 2
 
4.9%
c 2
 
4.9%
y 2
 
4.9%
Other values (5) 7
17.1%
Decimal Number
ValueCountFrequency (%)
2 1667
31.5%
1 1101
20.8%
0 673
12.7%
3 521
 
9.9%
6 292
 
5.5%
5 285
 
5.4%
4 280
 
5.3%
7 163
 
3.1%
9 162
 
3.1%
8 141
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 167
70.8%
· 35
 
14.8%
. 12
 
5.1%
' 8
 
3.4%
: 6
 
2.5%
/ 4
 
1.7%
" 3
 
1.3%
* 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 1895
75.8%
[ 583
 
23.3%
「 11
 
0.4%
『 10
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 1894
75.9%
] 582
 
23.3%
」 11
 
0.4%
』 10
 
0.4%
Math Symbol
ValueCountFrequency (%)
~ 38
97.4%
> 1
 
2.6%
Space Separator
ValueCountFrequency (%)
11439
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1246
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Letter Number
ValueCountFrequency (%)
Ⅸ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59173
71.1%
Common 23272
 
27.9%
Latin 831
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 2530
 
4.3%
공 1691
 
2.9%
도 1639
 
2.8%
관 1302
 
2.2%
시 1247
 
2.1%
지 1203
 
2.0%
로 1117
 
1.9%
구 1088
 
1.8%
급 1006
 
1.7%
정 983
 
1.7%
Other values (575) 45367
76.7%
Latin
ValueCountFrequency (%)
C 181
21.8%
V 88
10.6%
T 85
10.2%
S 67
 
8.1%
E 66
 
7.9%
P 61
 
7.3%
D 52
 
6.3%
L 39
 
4.7%
I 29
 
3.5%
B 19
 
2.3%
Other values (29) 144
17.3%
Common
ValueCountFrequency (%)
11439
49.2%
( 1895
 
8.1%
) 1894
 
8.1%
2 1667
 
7.2%
- 1246
 
5.4%
1 1101
 
4.7%
0 673
 
2.9%
[ 583
 
2.5%
] 582
 
2.5%
3 521
 
2.2%
Other values (23) 1671
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59173
71.1%
ASCII 24022
28.8%
None 77
 
0.1%
Number Forms 2
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11439
47.6%
( 1895
 
7.9%
) 1894
 
7.9%
2 1667
 
6.9%
- 1246
 
5.2%
1 1101
 
4.6%
0 673
 
2.8%
[ 583
 
2.4%
] 582
 
2.4%
3 521
 
2.2%
Other values (54) 2421
 
10.1%
Hangul
ValueCountFrequency (%)
사 2530
 
4.3%
공 1691
 
2.9%
도 1639
 
2.8%
관 1302
 
2.2%
시 1247
 
2.1%
지 1203
 
2.0%
로 1117
 
1.9%
구 1088
 
1.8%
급 1006
 
1.7%
정 983
 
1.7%
Other values (575) 45367
76.7%
None
ValueCountFrequency (%)
· 35
45.5%
」 11
 
14.3%
「 11
 
14.3%
『 10
 
13.0%
』 10
 
13.0%
Number Forms
ValueCountFrequency (%)
Ⅸ 2
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
50.0%
‘ 1
50.0%

계약번호
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
202100000000
1616 
202200000000
1161 
202000000000
 
138
201900000000
 
42
201800000000
 
3

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202000000000
2nd row202000000000
3rd row202000000000
4th row202100000000
5th row202100000000

Common Values

ValueCountFrequency (%)
202100000000 1616
54.6%
202200000000 1161
39.2%
202000000000 138
 
4.7%
201900000000 42
 
1.4%
201800000000 3
 
0.1%

Length

2023-12-13T06:34:31.915107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:34:32.018709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202100000000 1616
54.6%
202200000000 1161
39.2%
202000000000 138
 
4.7%
201900000000 42
 
1.4%
201800000000 3
 
0.1%

검수회차
Real number (ℝ)

Distinct30
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5827703
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-12-13T06:34:32.410414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum30
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6449031
Coefficient of variation (CV)1.6710594
Kurtosis45.176147
Mean1.5827703
Median Absolute Deviation (MAD)0
Skewness6.3595391
Sum4685
Variance6.9955124
MonotonicityNot monotonic
2023-12-13T06:34:32.549203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 2633
89.0%
2 118
 
4.0%
3 48
 
1.6%
4 33
 
1.1%
5 20
 
0.7%
6 13
 
0.4%
7 12
 
0.4%
8 10
 
0.3%
10 7
 
0.2%
9 7
 
0.2%
Other values (20) 59
 
2.0%
ValueCountFrequency (%)
1 2633
89.0%
2 118
 
4.0%
3 48
 
1.6%
4 33
 
1.1%
5 20
 
0.7%
6 13
 
0.4%
7 12
 
0.4%
8 10
 
0.3%
9 7
 
0.2%
10 7
 
0.2%
ValueCountFrequency (%)
30 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 4
0.1%
22 4
0.1%
21 4
0.1%

검수유형
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
완납급
1515 
준공금
1207 
노무비
 
95
기성금
 
94
부분급
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row완납급
2nd row완납급
3rd row완납급
4th row준공금
5th row준공금

Common Values

ValueCountFrequency (%)
완납급 1515
51.2%
준공금 1207
40.8%
노무비 95
 
3.2%
기성금 94
 
3.2%
부분급 49
 
1.7%

Length

2023-12-13T06:34:32.713394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:34:32.835991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
완납급 1515
51.2%
준공금 1207
40.8%
노무비 95
 
3.2%
기성금 94
 
3.2%
부분급 49
 
1.7%

검수일시
Date

Distinct275
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
Minimum2021-08-24 00:00:00
Maximum2022-07-20 00:00:00
2023-12-13T06:34:33.009337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:33.209339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

검수액
Real number (ℝ)

HIGH CORRELATION 

Distinct2678
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36757682
Minimum60200
Maximum1.776775 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-12-13T06:34:33.410236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60200
5-th percentile1018179.5
Q14937425
median13438905
Q324425895
95-th percentile1.3444487 × 108
Maximum1.776775 × 109
Range1.7767148 × 109
Interquartile range (IQR)19488470

Descriptive statistics

Standard deviation1.0619518 × 108
Coefficient of variation (CV)2.8890608
Kurtosis84.488325
Mean36757682
Median Absolute Deviation (MAD)8788815
Skewness8.1656479
Sum1.0880274 × 1011
Variance1.1277416 × 1016
MonotonicityNot monotonic
2023-12-13T06:34:33.597626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19800000 19
 
0.6%
10800000 9
 
0.3%
19701000 8
 
0.3%
20367000 7
 
0.2%
4500000 7
 
0.2%
18000000 7
 
0.2%
5940000 6
 
0.2%
7920000 6
 
0.2%
20460000 6
 
0.2%
2700000 5
 
0.2%
Other values (2668) 2880
97.3%
ValueCountFrequency (%)
60200 1
< 0.1%
76230 1
< 0.1%
91800 1
< 0.1%
103000 1
< 0.1%
151000 1
< 0.1%
171000 1
< 0.1%
178500 1
< 0.1%
183150 1
< 0.1%
185000 1
< 0.1%
186000 1
< 0.1%
ValueCountFrequency (%)
1776775000 1
< 0.1%
1518037000 1
< 0.1%
1288370000 1
< 0.1%
1276000000 1
< 0.1%
1241677800 1
< 0.1%
1135580600 1
< 0.1%
1060359000 1
< 0.1%
1048427100 1
< 0.1%
1000000000 1
< 0.1%
998000000 1
< 0.1%

수수료
Real number (ℝ)

ZEROS 

Distinct1096
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57275.679
Minimum0
Maximum7992990
Zeros1794
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-12-13T06:34:33.783334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324957.5
95-th percentile253652
Maximum7992990
Range7992990
Interquartile range (IQR)24957.5

Descriptive statistics

Standard deviation284699.49
Coefficient of variation (CV)4.9706872
Kurtosis375.11909
Mean57275.679
Median Absolute Deviation (MAD)0
Skewness17.095625
Sum1.6953601 × 108
Variance8.1053798 × 1010
MonotonicityNot monotonic
2023-12-13T06:34:34.001133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1794
60.6%
23080 3
 
0.1%
2510 3
 
0.1%
12830 3
 
0.1%
6590 2
 
0.1%
69010 2
 
0.1%
13510 2
 
0.1%
20900 2
 
0.1%
21650 2
 
0.1%
10580 2
 
0.1%
Other values (1086) 1145
38.7%
ValueCountFrequency (%)
0 1794
60.6%
320 1
 
< 0.1%
470 1
 
< 0.1%
550 1
 
< 0.1%
810 1
 
< 0.1%
890 1
 
< 0.1%
900 1
 
< 0.1%
910 1
 
< 0.1%
1020 1
 
< 0.1%
1070 1
 
< 0.1%
ValueCountFrequency (%)
7992990 1
< 0.1%
6027890 1
< 0.1%
5977490 1
< 0.1%
4397350 1
< 0.1%
4314470 1
< 0.1%
2994660 1
< 0.1%
2957990 1
< 0.1%
1697980 1
< 0.1%
1622260 1
< 0.1%
1568120 1
< 0.1%

지출결의요청액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2705
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28244600
Minimum0
Maximum1.306037 × 109
Zeros42
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-12-13T06:34:34.167359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile765060
Q14421850
median11880000
Q320432250
95-th percentile98457518
Maximum1.306037 × 109
Range1.306037 × 109
Interquartile range (IQR)16010400

Descriptive statistics

Standard deviation72632632
Coefficient of variation (CV)2.5715582
Kurtosis112.11739
Mean28244600
Median Absolute Deviation (MAD)8162000
Skewness9.0530784
Sum8.3604017 × 1010
Variance5.2754993 × 1015
MonotonicityNot monotonic
2023-12-13T06:34:34.317023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
1.4%
19800000 17
 
0.6%
10800000 9
 
0.3%
19701000 8
 
0.3%
20367000 7
 
0.2%
9000000 7
 
0.2%
5940000 6
 
0.2%
20460000 6
 
0.2%
18000000 5
 
0.2%
7920000 5
 
0.2%
Other values (2695) 2848
96.2%
ValueCountFrequency (%)
0 42
1.4%
60520 1
 
< 0.1%
76230 1
 
< 0.1%
92270 1
 
< 0.1%
103550 1
 
< 0.1%
151810 1
 
< 0.1%
152860 1
 
< 0.1%
171000 1
 
< 0.1%
179410 1
 
< 0.1%
183150 1
 
< 0.1%
ValueCountFrequency (%)
1306037000 1
< 0.1%
1266775000 1
< 0.1%
1089400000 1
< 0.1%
892418000 1
< 0.1%
857203000 1
< 0.1%
816784600 1
< 0.1%
739279000 1
< 0.1%
725356500 1
< 0.1%
678898000 1
< 0.1%
615429100 1
< 0.1%

계약일자
Date

Distinct435
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
Minimum2018-04-26 00:00:00
Maximum2022-07-15 00:00:00
2023-12-13T06:34:34.463850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:34.620384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

당년년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2021
1616 
2022
1161 
2020
 
138
2019
 
42
2018
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 1616
54.6%
2022 1161
39.2%
2020 138
 
4.7%
2019 42
 
1.4%
2018 3
 
0.1%

Length

2023-12-13T06:34:34.756570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:34:34.855910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 1616
54.6%
2022 1161
39.2%
2020 138
 
4.7%
2019 42
 
1.4%
2018 3
 
0.1%

총계약금액
Real number (ℝ)

HIGH CORRELATION 

Distinct2447
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7375973 × 108
Minimum60200
Maximum8.2698 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-12-13T06:34:34.968943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60200
5-th percentile1090585
Q15428395
median16385200
Q336057450
95-th percentile5.9904027 × 108
Maximum8.2698 × 109
Range8.2697398 × 109
Interquartile range (IQR)30629055

Descriptive statistics

Standard deviation7.8195107 × 108
Coefficient of variation (CV)4.5001859
Kurtosis54.673238
Mean1.7375973 × 108
Median Absolute Deviation (MAD)11823500
Skewness7.0671415
Sum5.143288 × 1011
Variance6.1144748 × 1017
MonotonicityNot monotonic
2023-12-13T06:34:35.095446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19800000 23
 
0.8%
6212400000 14
 
0.5%
2600991000 14
 
0.5%
7249280000 11
 
0.4%
18000000 11
 
0.4%
1169505300 10
 
0.3%
10800000 10
 
0.3%
3664288000 9
 
0.3%
377336020 9
 
0.3%
19701000 8
 
0.3%
Other values (2437) 2841
96.0%
ValueCountFrequency (%)
60200 1
< 0.1%
91800 1
< 0.1%
103000 1
< 0.1%
151000 1
< 0.1%
171000 1
< 0.1%
178500 1
< 0.1%
183150 1
< 0.1%
185000 1
< 0.1%
186000 1
< 0.1%
190000 1
< 0.1%
ValueCountFrequency (%)
8269800000 3
 
0.1%
7249280000 11
0.4%
6212400000 14
0.5%
3970230000 7
0.2%
3675993190 6
0.2%
3664288000 9
0.3%
2694633000 7
0.2%
2600991000 14
0.5%
2176117000 4
 
0.1%
1776775000 1
 
< 0.1%

선급금액
Real number (ℝ)

ZEROS 

Distinct296
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39836196
Minimum0
Maximum2.32 × 109
Zeros2507
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-12-13T06:34:35.209588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.6210514 × 108
Maximum2.32 × 109
Range2.32 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9292381 × 108
Coefficient of variation (CV)4.8429277
Kurtosis58.237859
Mean39836196
Median Absolute Deviation (MAD)0
Skewness7.1347561
Sum1.1791514 × 1011
Variance3.7219598 × 1016
MonotonicityNot monotonic
2023-12-13T06:34:35.334293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2507
84.7%
1281270000 14
 
0.5%
872800000 14
 
0.5%
1830000000 11
 
0.4%
228800000 10
 
0.3%
284489470 9
 
0.3%
137000000 9
 
0.3%
500000000 7
 
0.2%
1128900000 6
 
0.2%
77220000 6
 
0.2%
Other values (286) 367
 
12.4%
ValueCountFrequency (%)
0 2507
84.7%
422100 1
 
< 0.1%
483000 1
 
< 0.1%
557900 1
 
< 0.1%
844800 1
 
< 0.1%
1022000 1
 
< 0.1%
1218000 1
 
< 0.1%
1338120 1
 
< 0.1%
1573600 1
 
< 0.1%
1971200 1
 
< 0.1%
ValueCountFrequency (%)
2320000000 3
 
0.1%
1830000000 11
0.4%
1281270000 14
0.5%
1195600000 2
 
0.1%
1128900000 6
0.2%
965749400 1
 
< 0.1%
872800000 14
0.5%
802130000 1
 
< 0.1%
800000000 4
 
0.1%
700000000 1
 
< 0.1%

최초계약금액
Real number (ℝ)

HIGH CORRELATION 

Distinct2381
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83523804
Minimum60200
Maximum4 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.1 KiB
2023-12-13T06:34:35.455182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60200
5-th percentile1079151.5
Q15400000
median16132500
Q335340000
95-th percentile4.8642917 × 108
Maximum4 × 109
Range3.9999398 × 109
Interquartile range (IQR)29940000

Descriptive statistics

Standard deviation2.633425 × 108
Coefficient of variation (CV)3.1529036
Kurtosis66.591197
Mean83523804
Median Absolute Deviation (MAD)11533500
Skewness6.8150819
Sum2.4723046 × 1011
Variance6.9349274 × 1016
MonotonicityNot monotonic
2023-12-13T06:34:35.587622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19800000 23
 
0.8%
20460000 15
 
0.5%
1035675000 14
 
0.5%
20367000 14
 
0.5%
869882470 14
 
0.5%
502200000 11
 
0.4%
18000000 11
 
0.4%
500000000 10
 
0.3%
10800000 10
 
0.3%
329912540 9
 
0.3%
Other values (2371) 2829
95.6%
ValueCountFrequency (%)
60200 1
< 0.1%
91800 1
< 0.1%
103000 1
< 0.1%
151000 1
< 0.1%
171000 1
< 0.1%
178500 1
< 0.1%
183150 1
< 0.1%
185000 1
< 0.1%
186000 1
< 0.1%
190000 1
< 0.1%
ValueCountFrequency (%)
4000000000 3
 
0.1%
2122377470 7
0.2%
1708000000 2
 
0.1%
1656498880 4
0.1%
1611236000 4
0.1%
1533600000 9
0.3%
1526627000 1
 
< 0.1%
1459629000 2
 
0.1%
1379642000 1
 
< 0.1%
1250700000 7
0.2%

검사자
Text

Distinct224
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2023-12-13T06:34:35.925525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9989865
Min length2

Characters and Unicode

Total characters8877
Distinct characters130
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)1.7%

Sample

1st row이상용
2nd row이상용
3rd row이상용
4th row서형기
5th row고영식
ValueCountFrequency (%)
우찬국 180
 
6.1%
김기원 167
 
5.6%
김민성 155
 
5.2%
하창수 143
 
4.8%
황선미 100
 
3.4%
홍기준 97
 
3.3%
이승엽 96
 
3.2%
주현준 69
 
2.3%
박동식 68
 
2.3%
여종훈 66
 
2.2%
Other values (214) 1819
61.5%
2023-12-13T06:34:36.370220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 712
 
8.0%
민 344
 
3.9%
기 336
 
3.8%
성 284
 
3.2%
이 278
 
3.1%
영 270
 
3.0%
황 264
 
3.0%
우 230
 
2.6%
수 228
 
2.6%
준 217
 
2.4%
Other values (120) 5714
64.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8877
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 712
 
8.0%
민 344
 
3.9%
기 336
 
3.8%
성 284
 
3.2%
이 278
 
3.1%
영 270
 
3.0%
황 264
 
3.0%
우 230
 
2.6%
수 228
 
2.6%
준 217
 
2.4%
Other values (120) 5714
64.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8877
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 712
 
8.0%
민 344
 
3.9%
기 336
 
3.8%
성 284
 
3.2%
이 278
 
3.1%
영 270
 
3.0%
황 264
 
3.0%
우 230
 
2.6%
수 228
 
2.6%
준 217
 
2.4%
Other values (120) 5714
64.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8877
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 712
 
8.0%
민 344
 
3.9%
기 336
 
3.8%
성 284
 
3.2%
이 278
 
3.1%
영 270
 
3.0%
황 264
 
3.0%
우 230
 
2.6%
수 228
 
2.6%
준 217
 
2.4%
Other values (120) 5714
64.4%

입회자1
Text

MISSING 

Distinct125
Distinct (%)7.8%
Missing1355
Missing (%)45.8%
Memory size23.3 KiB
2023-12-13T06:34:36.639318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9993769
Min length2

Characters and Unicode

Total characters4814
Distinct characters102
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)2.4%

Sample

1st row백인선
2nd row박은희
3rd row박은희
4th row이관희
5th row손보경
ValueCountFrequency (%)
양경수 197
 
12.3%
김범찬 146
 
9.1%
조홍기 78
 
4.9%
장대식 71
 
4.4%
조전희 66
 
4.1%
손보경 61
 
3.8%
김현석 48
 
3.0%
임창준 40
 
2.5%
어연선 40
 
2.5%
김자영 40
 
2.5%
Other values (115) 818
51.0%
2023-12-13T06:34:36.991113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 391
 
8.1%
경 294
 
6.1%
수 220
 
4.6%
양 198
 
4.1%
조 183
 
3.8%
범 146
 
3.0%
찬 146
 
3.0%
이 144
 
3.0%
성 137
 
2.8%
희 128
 
2.7%
Other values (92) 2827
58.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4814
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 391
 
8.1%
경 294
 
6.1%
수 220
 
4.6%
양 198
 
4.1%
조 183
 
3.8%
범 146
 
3.0%
찬 146
 
3.0%
이 144
 
3.0%
성 137
 
2.8%
희 128
 
2.7%
Other values (92) 2827
58.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4814
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 391
 
8.1%
경 294
 
6.1%
수 220
 
4.6%
양 198
 
4.1%
조 183
 
3.8%
범 146
 
3.0%
찬 146
 
3.0%
이 144
 
3.0%
성 137
 
2.8%
희 128
 
2.7%
Other values (92) 2827
58.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4814
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 391
 
8.1%
경 294
 
6.1%
수 220
 
4.6%
양 198
 
4.1%
조 183
 
3.8%
범 146
 
3.0%
찬 146
 
3.0%
이 144
 
3.0%
성 137
 
2.8%
희 128
 
2.7%
Other values (92) 2827
58.7%

입회자2
Text

MISSING 

Distinct67
Distinct (%)45.6%
Missing2813
Missing (%)95.0%
Memory size23.3 KiB
2023-12-13T06:34:37.252881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9931973
Min length2

Characters and Unicode

Total characters440
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)26.5%

Sample

1st row박선희
2nd row성상오
3rd row이승엽
4th row박영재
5th row홍기준
ValueCountFrequency (%)
홍기준 13
 
8.8%
주현준 11
 
7.5%
김현석 10
 
6.8%
권광중 9
 
6.1%
정하늘 6
 
4.1%
박지아 4
 
2.7%
김동규 4
 
2.7%
김상현 4
 
2.7%
김수연 4
 
2.7%
정혜영 4
 
2.7%
Other values (57) 78
53.1%
2023-12-13T06:34:37.636796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 47
 
10.7%
현 29
 
6.6%
준 27
 
6.1%
홍 17
 
3.9%
기 14
 
3.2%
주 14
 
3.2%
영 12
 
2.7%
석 12
 
2.7%
지 11
 
2.5%
정 11
 
2.5%
Other values (73) 246
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 440
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 47
 
10.7%
현 29
 
6.6%
준 27
 
6.1%
홍 17
 
3.9%
기 14
 
3.2%
주 14
 
3.2%
영 12
 
2.7%
석 12
 
2.7%
지 11
 
2.5%
정 11
 
2.5%
Other values (73) 246
55.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 440
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 47
 
10.7%
현 29
 
6.6%
준 27
 
6.1%
홍 17
 
3.9%
기 14
 
3.2%
주 14
 
3.2%
영 12
 
2.7%
석 12
 
2.7%
지 11
 
2.5%
정 11
 
2.5%
Other values (73) 246
55.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 440
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 47
 
10.7%
현 29
 
6.6%
준 27
 
6.1%
홍 17
 
3.9%
기 14
 
3.2%
주 14
 
3.2%
영 12
 
2.7%
석 12
 
2.7%
지 11
 
2.5%
정 11
 
2.5%
Other values (73) 246
55.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
2022-09-20
2960 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-20
2nd row2022-09-20
3rd row2022-09-20
4th row2022-09-20
5th row2022-09-20

Common Values

ValueCountFrequency (%)
2022-09-20 2960
100.0%

Length

2023-12-13T06:34:37.768057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:34:37.899660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-20 2960
100.0%

Interactions

2023-12-13T06:34:29.521269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:24.920921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.601550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:26.629335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.442976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.122799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.777334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:29.624699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.019216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.734583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:26.737404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.535217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.227682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.867268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:29.763561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.127879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.851264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:26.860574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.648198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.320624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.961437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:29.883573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.223858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:26.232377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:26.964141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.746699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.408467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:29.056417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:30.002642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.321696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:26.355247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.052868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.847878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.503511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:29.214480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:30.118656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.407402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:26.446280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.154883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.925969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.585278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:29.320191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:30.243384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:25.489951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:26.528682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:27.271755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.019079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:28.677549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:34:29.406151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:34:37.959941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류계약번호검수회차검수유형검수액수수료지출결의요청액당년년도총계약금액선급금액최초계약금액입회자2
종류1.0000.1250.2480.7300.1270.0550.2200.1250.2880.2240.2590.963
계약번호0.1251.0000.7010.4680.3620.0000.2481.0000.5610.5400.4520.741
검수회차0.2480.7011.0000.6600.5490.0000.5040.7010.7590.7000.6440.609
검수유형0.7300.4680.6601.0000.4210.0640.3210.4680.5210.4520.4800.256
검수액0.1270.3620.5490.4211.0000.6830.8470.3620.5940.6340.6220.901
수수료0.0550.0000.0000.0640.6831.0000.4670.0000.2380.3000.4590.502
지출결의요청액0.2200.2480.5040.3210.8470.4671.0000.2480.5570.5660.5550.567
당년년도0.1251.0000.7010.4680.3620.0000.2481.0000.5610.5400.4520.741
총계약금액0.2880.5610.7590.5210.5940.2380.5570.5611.0000.9760.9000.734
선급금액0.2240.5400.7000.4520.6340.3000.5660.5400.9761.0000.8410.643
최초계약금액0.2590.4520.6440.4800.6220.4590.5550.4520.9000.8411.0000.899
입회자20.9630.7410.6090.2560.9010.5020.5670.7410.7340.6430.8991.000
2023-12-13T06:34:38.102655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류당년년도검수유형계약번호
종류1.0000.0940.7230.094
당년년도0.0941.0000.1901.000
검수유형0.7230.1901.0000.190
계약번호0.0941.0000.1901.000
2023-12-13T06:34:38.196814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검수회차검수액수수료지출결의요청액총계약금액선급금액최초계약금액종류계약번호검수유형당년년도
검수회차1.0000.155-0.0010.1210.4510.4390.4380.1530.3640.3330.364
검수액0.1551.0000.0340.9390.8900.2930.8830.0750.1580.1870.158
수수료-0.0010.0341.000-0.0540.0390.0760.0370.0370.0000.0410.000
지출결의요청액0.1210.939-0.0541.0000.8190.0940.8110.0980.1450.1910.145
총계약금액0.4510.8900.0390.8191.0000.4600.9900.1910.3870.3530.387
선급금액0.4390.2930.0760.0940.4601.0000.4560.1450.3690.2970.369
최초계약금액0.4380.8830.0370.8110.9900.4561.0000.1800.3100.3330.310
종류0.1530.0750.0370.0980.1910.1450.1801.0000.0940.7230.094
계약번호0.3640.1580.0000.1450.3870.3690.3100.0941.0000.1901.000
검수유형0.3330.1870.0410.1910.3530.2970.3330.7230.1901.0000.190
당년년도0.3640.1580.0000.1450.3870.3690.3100.0941.0000.1901.000

Missing values

2023-12-13T06:34:30.397553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:34:30.668389image/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.
2023-12-13T06:34:30.814398image/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

종류계약명계약번호검수회차검수유형검수일시검수액수수료지출결의요청액계약일자당년년도총계약금액선급금액최초계약금액검사자입회자1입회자2데이터기준일자
0물품장흥 문턱없는 아트화장실 신축공사 관급자재(창호)2020000000001완납급2021-08-2462989503401063329602020-11-272020633296006332960이상용<NA><NA>2022-09-20
1물품장흥 문턱없는 아트화장실 신축공사 관급자재(냉·난방기)2020000000001완납급2021-08-241142555061690114872402020-11-29202011425550011425550이상용<NA><NA>2022-09-20
2물품장흥 문턱없는 아트화장실 전기 관급자재(분전반)2020000000001완납급2021-08-2431260001688031428802020-12-052020312600003126000이상용<NA><NA>2022-09-20
3용역[용역]마전동 도시계획도로(소로2-39호선) 개설사업 실시설계용역2021000000001준공금2021-08-24120630000120630002021-05-25202112063000012063000서형기<NA><NA>2022-09-20
4용역회천청소년문화의집 인테리어 공사 실시설계 용역 시행2021000000001준공금2021-08-24391950000391950002021-06-11202139195000039195000고영식백인선<NA>2022-09-20
5용역시립미술관 특별기획전 진진묘 대여작품 운송 및 설치2021000000001준공금2021-08-244940100049401002021-08-182021494010004940100김명훈<NA><NA>2022-09-20
6물품고읍저류지 공영주차장 건립공사 관급자재(토목)-철근2021000000001완납급2021-08-24259370900119895002021-05-172021611147560349332500499046440박동식<NA><NA>2022-09-20
7물품[관급]시도35호선(가납-연곡간)도로확포장공사(3-1구간)(2차)-PVC이중벽관22021000000002부분급2021-08-2459797760322910601206702021-06-2520211279214600127921460이승엽<NA><NA>2022-09-20
8물품2021년 하반기 인사발령에 따른 사무 집기 구입2021000000001완납급2021-08-2432290001743032464302021-07-282021322900003229000강성아박은희<NA>2022-09-20
9물품2021년 하반기 인사발령에 따른 사무 집기 구입2021000000001완납급2021-08-2457340003096057649602021-07-282021573400005734000강성아박은희<NA>2022-09-20
종류계약명계약번호검수회차검수유형검수일시검수액수수료지출결의요청액계약일자당년년도총계약금액선급금액최초계약금액검사자입회자1입회자2데이터기준일자
2950물품2022년 버스승강장 시설물 설치(안내판)2022000000001완납급2022-07-1850000000220590152205902022-06-082022500000003500000050000000엄재혁나태인<NA>2022-09-20
2951물품종합관광안내표지판 교체(1개소_25사단 신병교육대)2022000000001완납급2022-07-182376000023760002022-06-282022237600002376000최유림조전희<NA>2022-09-20
2952물품신병교육대 등산로 안내판 구입2022000000001완납급2022-07-184050000040500002022-06-282022405000004050000허재희최미정<NA>2022-09-20
2953공사회암사지 관광안내소 배수시설 설치공사 계약의뢰2022000000001준공금2022-07-192501700025017002022-07-052022250170002501700최유림<NA><NA>2022-09-20
2954용역덕정 도시재생 골목길 정비사업 경관조명설치 실시설계 용역2022000000001준공금2022-07-19260271000260271002022-04-18202226027100026027100김태영<NA><NA>2022-09-20
2955용역2022 양주 다문화 축제 행사대행2022000000001준공금2022-07-19770000000770000002022-06-23202277000000077000000이봉숙조전희정혜영2022-09-20
2956물품폭염저감시설(그늘막) 천막 교체2022000000001완납급2022-07-19107910000107910002022-06-03202210791000010791000강진영유선규<NA>2022-09-20
2957물품투명페트병 전용 수거함 구입2022000000001완납급2022-07-191026000052630103126302022-06-23202210260000010260000우승옥장석출<NA>2022-09-20
2958용역율정-봉양간 도로 확포장공사(2구간) GIS DB 구축용역2020000000001준공금2022-07-201432970000429891002020-05-212020143297000100307900143297000김민성백인선<NA>2022-09-20
2959물품양주시 장애인보호작업장 화물트럭 조달구입2022000000001완납급2022-07-2042170000216330423863302022-05-02202242170000042170000윤종민이창수<NA>2022-09-20