Overview

Dataset statistics

Number of variables14
Number of observations965
Missing cells425
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.2 KiB
Average record size in memory121.1 B

Variable types

Numeric8
Categorical3
Text3

Dataset

Description경상남도 공사계약대장시스템의 하도급변경내역 데이터입니다. 하도순번, 변경계약일, 변경하도부 등의 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15049514

Alerts

공사구분 has constant value ""Constant
부서코드 has constant value ""Constant
공사년도 is highly overall correlated with 변경공사종료일High correlation
하도순번 is highly overall correlated with 순번High correlation
순번 is highly overall correlated with 하도순번High correlation
변경하도부분금액 is highly overall correlated with 변경하도금액High correlation
변경하도금액 is highly overall correlated with 변경하도부분금액High correlation
변경공사종료일 is highly overall correlated with 공사년도High correlation
변경하도부분금액 has 98 (10.2%) missing valuesMissing
변경하도금액 has 83 (8.6%) missing valuesMissing
변경공사시작일 has 88 (9.1%) missing valuesMissing
변경공사종료일 has 57 (5.9%) missing valuesMissing
하도비율 has 99 (10.3%) missing valuesMissing
변경공사종료일 is highly skewed (γ1 = -21.23636491)Skewed
변경하도부분금액 has 12 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-11 00:34:22.316714
Analysis finished2023-12-11 00:34:28.695190
Duration6.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공사년도
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.0518
Minimum2003
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-11T09:34:28.737082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2008
Q12010
median2011
Q32014
95-th percentile2018
Maximum2019
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3287085
Coefficient of variation (CV)0.0016543851
Kurtosis0.30253143
Mean2012.0518
Median Absolute Deviation (MAD)2
Skewness0.028170738
Sum1941630
Variance11.0803
MonotonicityNot monotonic
2023-12-11T09:34:28.824241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2010 217
22.5%
2011 129
13.4%
2013 126
13.1%
2012 100
10.4%
2018 85
 
8.8%
2009 85
 
8.8%
2017 67
 
6.9%
2014 46
 
4.8%
2015 36
 
3.7%
2003 21
 
2.2%
Other values (7) 53
 
5.5%
ValueCountFrequency (%)
2003 21
 
2.2%
2004 11
 
1.1%
2005 6
 
0.6%
2006 1
 
0.1%
2007 3
 
0.3%
2008 14
 
1.5%
2009 85
 
8.8%
2010 217
22.5%
2011 129
13.4%
2012 100
10.4%
ValueCountFrequency (%)
2019 7
 
0.7%
2018 85
 
8.8%
2017 67
 
6.9%
2016 11
 
1.1%
2015 36
 
3.7%
2014 46
 
4.8%
2013 126
13.1%
2012 100
10.4%
2011 129
13.4%
2010 217
22.5%

공사구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
공사
965 

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 965
100.0%

Length

2023-12-11T09:34:28.919574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:34:28.986289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사 965
100.0%

공사번호
Real number (ℝ)

Distinct190
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.45803
Minimum1
Maximum596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-11T09:34:29.070654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.2
Q134
median93
Q3219
95-th percentile505
Maximum596
Range595
Interquartile range (IQR)185

Descriptive statistics

Standard deviation171.13005
Coefficient of variation (CV)1.0731981
Kurtosis-0.28644532
Mean159.45803
Median Absolute Deviation (MAD)63
Skewness1.1315651
Sum153877
Variance29285.495
MonotonicityNot monotonic
2023-12-11T09:34:29.179031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 34
 
3.5%
11 25
 
2.6%
49 25
 
2.6%
105 23
 
2.4%
7 23
 
2.4%
505 22
 
2.3%
62 15
 
1.6%
107 15
 
1.6%
435 14
 
1.5%
142 14
 
1.5%
Other values (180) 755
78.2%
ValueCountFrequency (%)
1 8
 
0.8%
2 1
 
0.1%
4 5
 
0.5%
5 11
1.1%
7 23
2.4%
10 1
 
0.1%
11 25
2.6%
12 9
 
0.9%
13 14
1.5%
14 12
1.2%
ValueCountFrequency (%)
596 6
 
0.6%
530 4
 
0.4%
529 14
1.5%
524 5
 
0.5%
518 2
 
0.2%
516 1
 
0.1%
514 3
 
0.3%
510 4
 
0.4%
507 1
 
0.1%
505 22
2.3%

부서코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
1
965 

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 (%)
1 965
100.0%

Length

2023-12-11T09:34:29.278765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:34:29.343179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 965
100.0%

하도순번
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.973057
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-11T09:34:29.408226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile10
Maximum34
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0833255
Coefficient of variation (CV)1.3734434
Kurtosis23.00689
Mean2.973057
Median Absolute Deviation (MAD)1
Skewness4.2814083
Sum2869
Variance16.673547
MonotonicityNot monotonic
2023-12-11T09:34:29.502119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 443
45.9%
2 228
23.6%
3 95
 
9.8%
4 54
 
5.6%
5 35
 
3.6%
6 21
 
2.2%
7 19
 
2.0%
8 10
 
1.0%
9 8
 
0.8%
11 8
 
0.8%
Other values (17) 44
 
4.6%
ValueCountFrequency (%)
1 443
45.9%
2 228
23.6%
3 95
 
9.8%
4 54
 
5.6%
5 35
 
3.6%
6 21
 
2.2%
7 19
 
2.0%
8 10
 
1.0%
9 8
 
0.8%
10 5
 
0.5%
ValueCountFrequency (%)
34 3
0.3%
32 1
 
0.1%
31 2
0.2%
29 1
 
0.1%
25 2
0.2%
24 2
0.2%
23 1
 
0.1%
21 1
 
0.1%
19 1
 
0.1%
18 1
 
0.1%

순번
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0746114
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-11T09:34:29.597033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile13
Maximum25
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1960053
Coefficient of variation (CV)1.0297928
Kurtosis5.4287781
Mean4.0746114
Median Absolute Deviation (MAD)2
Skewness2.2438914
Sum3932
Variance17.606461
MonotonicityNot monotonic
2023-12-11T09:34:29.691960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 282
29.2%
2 190
19.7%
3 128
13.3%
4 93
 
9.6%
5 59
 
6.1%
6 46
 
4.8%
7 34
 
3.5%
8 25
 
2.6%
9 17
 
1.8%
10 12
 
1.2%
Other values (15) 79
 
8.2%
ValueCountFrequency (%)
1 282
29.2%
2 190
19.7%
3 128
13.3%
4 93
 
9.6%
5 59
 
6.1%
6 46
 
4.8%
7 34
 
3.5%
8 25
 
2.6%
9 17
 
1.8%
10 12
 
1.2%
ValueCountFrequency (%)
25 1
 
0.1%
24 1
 
0.1%
23 2
 
0.2%
22 3
0.3%
21 4
0.4%
20 4
0.4%
19 5
0.5%
18 5
0.5%
17 4
0.4%
16 5
0.5%
Distinct456
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2023-12-11T09:34:29.933827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.811399
Min length1

Characters and Unicode

Total characters9468
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

Unique218 ?
Unique (%)22.6%

Sample

1st row2010-07-30
2nd row2010-09-28
3rd row2010-07-30
4th row2010-09-30
5th row2010-09-30
ValueCountFrequency (%)
2013-03-12 17
 
1.8%
2011-08-27 11
 
1.1%
2012-04-05 10
 
1.0%
2018-08-14 9
 
0.9%
9
 
0.9%
2012-12-31 8
 
0.8%
2010-11-30 8
 
0.8%
2018-10-12 8
 
0.8%
2015-11-25 7
 
0.7%
2011-10-06 7
 
0.7%
Other values (446) 871
90.3%
2023-12-11T09:34:30.343841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2193
23.2%
1 2026
21.4%
- 1873
19.8%
2 1668
17.6%
3 467
 
4.9%
8 267
 
2.8%
5 220
 
2.3%
9 211
 
2.2%
4 190
 
2.0%
7 187
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7595
80.2%
Dash Punctuation 1873
 
19.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2193
28.9%
1 2026
26.7%
2 1668
22.0%
3 467
 
6.1%
8 267
 
3.5%
5 220
 
2.9%
9 211
 
2.8%
4 190
 
2.5%
7 187
 
2.5%
6 166
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 1873
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2193
23.2%
1 2026
21.4%
- 1873
19.8%
2 1668
17.6%
3 467
 
4.9%
8 267
 
2.8%
5 220
 
2.3%
9 211
 
2.2%
4 190
 
2.0%
7 187
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2193
23.2%
1 2026
21.4%
- 1873
19.8%
2 1668
17.6%
3 467
 
4.9%
8 267
 
2.8%
5 220
 
2.3%
9 211
 
2.2%
4 190
 
2.0%
7 187
 
2.0%
Distinct425
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2023-12-11T09:34:30.636630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9233161
Min length1

Characters and Unicode

Total characters9576
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

Unique194 ?
Unique (%)20.1%

Sample

1st row2010-10-06
2nd row2010-11-23
3rd row2010-10-06
4th row2010-11-23
5th row2010-11-23
ValueCountFrequency (%)
2013-06-26 15
 
1.6%
2013-12-05 12
 
1.2%
2011-09-28 11
 
1.1%
2018-11-05 10
 
1.0%
2012-05-08 10
 
1.0%
2018-08-29 9
 
0.9%
2011-03-04 8
 
0.8%
2013-02-21 8
 
0.8%
2010-12-22 8
 
0.8%
8
 
0.8%
Other values (415) 866
89.7%
2023-12-11T09:34:31.259583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2183
22.8%
1 1973
20.6%
- 1920
20.1%
2 1722
18.0%
3 420
 
4.4%
8 276
 
2.9%
9 251
 
2.6%
5 229
 
2.4%
4 209
 
2.2%
6 206
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7656
79.9%
Dash Punctuation 1920
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2183
28.5%
1 1973
25.8%
2 1722
22.5%
3 420
 
5.5%
8 276
 
3.6%
9 251
 
3.3%
5 229
 
3.0%
4 209
 
2.7%
6 206
 
2.7%
7 187
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 1920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2183
22.8%
1 1973
20.6%
- 1920
20.1%
2 1722
18.0%
3 420
 
4.4%
8 276
 
2.9%
9 251
 
2.6%
5 229
 
2.4%
4 209
 
2.2%
6 206
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2183
22.8%
1 1973
20.6%
- 1920
20.1%
2 1722
18.0%
3 420
 
4.4%
8 276
 
2.9%
9 251
 
2.6%
5 229
 
2.4%
4 209
 
2.2%
6 206
 
2.2%

변경하도부분금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct740
Distinct (%)85.4%
Missing98
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean1.9259419 × 109
Minimum0
Maximum6.6806719 × 1010
Zeros12
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-11T09:34:31.392132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44360600
Q12.1289986 × 108
median5.72465 × 108
Q31.2025775 × 109
95-th percentile8.843131 × 109
Maximum6.6806719 × 1010
Range6.6806719 × 1010
Interquartile range (IQR)9.8967768 × 108

Descriptive statistics

Standard deviation4.9952432 × 109
Coefficient of variation (CV)2.5936624
Kurtosis52.42444
Mean1.9259419 × 109
Median Absolute Deviation (MAD)4.24135 × 108
Skewness6.1928412
Sum1.6697917 × 1012
Variance2.4952455 × 1019
MonotonicityNot monotonic
2023-12-11T09:34:31.521063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
1.2%
1164300000 4
 
0.4%
57970000 4
 
0.4%
3687200000 3
 
0.3%
238700000 3
 
0.3%
1268462000 3
 
0.3%
1088890000 3
 
0.3%
275000000 3
 
0.3%
7942000000 3
 
0.3%
1477000000 3
 
0.3%
Other values (730) 826
85.6%
(Missing) 98
 
10.2%
ValueCountFrequency (%)
0 12
1.2%
6130000 1
 
0.1%
7485000 1
 
0.1%
8657000 1
 
0.1%
8835000 1
 
0.1%
11099000 1
 
0.1%
15180000 1
 
0.1%
17067000 1
 
0.1%
18222080 1
 
0.1%
19715840 1
 
0.1%
ValueCountFrequency (%)
66806718691 1
0.1%
47145320575 1
0.1%
35697825422 1
0.1%
32547700000 1
0.1%
31620050000 1
0.1%
30582483153 1
0.1%
30130359343 1
0.1%
28829900000 1
0.1%
28588890000 1
0.1%
28501300000 1
0.1%

변경하도금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct759
Distinct (%)86.1%
Missing83
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean1.5987446 × 109
Minimum0
Maximum3.978337 × 1010
Zeros8
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-11T09:34:31.640000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41454378
Q11.9448 × 108
median5.1505858 × 108
Q31.0565125 × 109
95-th percentile7.632185 × 109
Maximum3.978337 × 1010
Range3.978337 × 1010
Interquartile range (IQR)8.620325 × 108

Descriptive statistics

Standard deviation3.7345974 × 109
Coefficient of variation (CV)2.3359562
Kurtosis30.172414
Mean1.5987446 × 109
Median Absolute Deviation (MAD)3.7652 × 108
Skewness4.9573347
Sum1.4100927 × 1012
Variance1.3947218 × 1019
MonotonicityNot monotonic
2023-12-11T09:34:31.775372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.8%
956600000 4
 
0.4%
492800000 4
 
0.4%
1184810000 3
 
0.3%
548460000 3
 
0.3%
73040000 3
 
0.3%
16608900 3
 
0.3%
572000000 3
 
0.3%
1392600000 3
 
0.3%
225500000 3
 
0.3%
Other values (749) 845
87.6%
(Missing) 83
 
8.6%
ValueCountFrequency (%)
0 8
0.8%
5517000 1
 
0.1%
6683000 1
 
0.1%
7328000 1
 
0.1%
8800000 1
 
0.1%
9988000 1
 
0.1%
13090000 1
 
0.1%
14000000 1
 
0.1%
15000000 1
 
0.1%
16168900 1
 
0.1%
ValueCountFrequency (%)
39783370000 1
0.1%
27234900000 1
0.1%
26753100000 1
0.1%
26177800000 1
0.1%
25825593200 1
0.1%
25788400000 1
0.1%
23010900000 1
0.1%
22885500000 1
0.1%
22419100000 1
0.1%
22203500000 1
0.1%

변경공사시작일
Text

MISSING 

Distinct380
Distinct (%)43.3%
Missing88
Missing (%)9.1%
Memory size7.7 KiB
2023-12-11T09:34:32.055929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.986317
Min length5

Characters and Unicode

Total characters8758
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

Unique171 ?
Unique (%)19.5%

Sample

1st row2008-12-01
2nd row2004-10-04
3rd row2004-10-04
4th row2005-06-13
5th row2005-06-26
ValueCountFrequency (%)
2010-05-13 12
 
1.4%
2018-05-16 12
 
1.4%
2010-08-11 12
 
1.4%
2012-03-12 11
 
1.3%
2007-06-14 8
 
0.9%
2013-03-11 8
 
0.9%
2013-03-08 8
 
0.9%
2013-08-30 8
 
0.9%
2014-08-28 7
 
0.8%
2018-02-06 7
 
0.8%
Other values (370) 784
89.4%
2023-12-11T09:34:32.436627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2441
27.9%
- 1746
19.9%
1 1458
16.6%
2 1444
16.5%
3 373
 
4.3%
8 280
 
3.2%
5 268
 
3.1%
4 219
 
2.5%
9 190
 
2.2%
6 171
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7012
80.1%
Dash Punctuation 1746
 
19.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2441
34.8%
1 1458
20.8%
2 1444
20.6%
3 373
 
5.3%
8 280
 
4.0%
5 268
 
3.8%
4 219
 
3.1%
9 190
 
2.7%
6 171
 
2.4%
7 168
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 1746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2441
27.9%
- 1746
19.9%
1 1458
16.6%
2 1444
16.5%
3 373
 
4.3%
8 280
 
3.2%
5 268
 
3.1%
4 219
 
2.5%
9 190
 
2.2%
6 171
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2441
27.9%
- 1746
19.9%
1 1458
16.6%
2 1444
16.5%
3 373
 
4.3%
8 280
 
3.2%
5 268
 
3.1%
4 219
 
2.5%
9 190
 
2.2%
6 171
 
2.0%

변경공사종료일
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct324
Distinct (%)35.7%
Missing57
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean20095830
Minimum2013103
Maximum20416042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-11T09:34:32.563935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013103
5-th percentile20101130
Q120111223
median20130630
Q320150247
95-th percentile20190628
Maximum20416042
Range18402939
Interquartile range (IQR)39023.75

Descriptive statistics

Standard deviation850141.03
Coefficient of variation (CV)0.042304349
Kurtosis450.48375
Mean20095830
Median Absolute Deviation (MAD)19410.5
Skewness-21.236365
Sum1.8247014 × 1010
Variance7.2273977 × 1011
MonotonicityNot monotonic
2023-12-11T09:34:32.736777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20131231 51
 
5.3%
20141231 26
 
2.7%
20111231 25
 
2.6%
20101130 21
 
2.2%
20130709 18
 
1.9%
20140817 10
 
1.0%
20121231 10
 
1.0%
20110506 9
 
0.9%
20161231 9
 
0.9%
20111221 9
 
0.9%
Other values (314) 720
74.6%
(Missing) 57
 
5.9%
ValueCountFrequency (%)
2013103 1
 
0.1%
2030105 1
 
0.1%
20100222 1
 
0.1%
20100228 2
0.2%
20100331 2
0.2%
20100423 1
 
0.1%
20100531 1
 
0.1%
20100623 2
0.2%
20100630 2
0.2%
20100731 4
0.4%
ValueCountFrequency (%)
20416042 1
 
0.1%
20211231 1
 
0.1%
20201219 1
 
0.1%
20200817 6
0.6%
20200704 2
 
0.2%
20200324 1
 
0.1%
20200323 8
0.8%
20191231 3
 
0.3%
20191128 8
0.8%
20191125 1
 
0.1%

하도비율
Real number (ℝ)

MISSING 

Distinct496
Distinct (%)57.3%
Missing99
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean87.783326
Minimum0
Maximum218.01
Zeros7
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2023-12-11T09:34:32.871409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile82
Q183
median85.99
Q390.0275
95-th percentile100.5775
Maximum218.01
Range218.01
Interquartile range (IQR)7.0275

Descriptive statistics

Standard deviation13.764153
Coefficient of variation (CV)0.1567969
Kurtosis31.01642
Mean87.783326
Median Absolute Deviation (MAD)3.4
Skewness0.40961594
Sum76020.36
Variance189.45192
MonotonicityNot monotonic
2023-12-11T09:34:32.999883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0 27
 
2.8%
85.0 13
 
1.3%
87.0 10
 
1.0%
95.0 9
 
0.9%
82.0 9
 
0.9%
89.99 9
 
0.9%
84.99 8
 
0.8%
82.5 7
 
0.7%
0.0 7
 
0.7%
87.99 6
 
0.6%
Other values (486) 761
78.9%
(Missing) 99
 
10.3%
ValueCountFrequency (%)
0.0 7
0.7%
10.22 1
 
0.1%
57.0 1
 
0.1%
65.02 1
 
0.1%
65.34 1
 
0.1%
65.61 1
 
0.1%
67.46 1
 
0.1%
68.43 1
 
0.1%
68.47 1
 
0.1%
69.98 1
 
0.1%
ValueCountFrequency (%)
218.01 1
0.1%
188.88 1
0.1%
188.78 1
0.1%
158.41 1
0.1%
151.9 1
0.1%
144.21 1
0.1%
141.4 1
0.1%
139.04 1
0.1%
135.11 1
0.1%
133.73 1
0.1%

적용방법
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
직불
582 
대지급
232 
-
151 

Length

Max length3
Median length2
Mean length2.0839378
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row대지급
3rd row-
4th row대지급
5th row대지급

Common Values

ValueCountFrequency (%)
직불 582
60.3%
대지급 232
 
24.0%
- 151
 
15.6%

Length

2023-12-11T09:34:33.140890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:34:33.253652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직불 582
60.3%
대지급 232
 
24.0%
151
 
15.6%

Interactions

2023-12-11T09:34:27.740795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:22.800015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.472317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.088186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.730570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.335651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.324206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.058756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.816303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:22.883633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.548562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.170371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.807038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.419357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.407469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.147789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.895025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:22.964742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.621383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.254675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.879419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.514022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.509948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.244684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.968774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.056746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.700404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.347444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.952954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.616110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.599374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.339197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:28.040911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.145953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.771532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.420171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.020303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.708493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.678711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.413267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:28.128941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.243006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.846607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.496042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.099241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.813915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.779432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.486346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:28.208766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.325236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.934239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.579115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.184083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.157413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.890135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.579844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:28.283623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:23.404676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.016879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:24.658295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:25.270838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.254290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:26.978451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:34:27.663268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:34:33.330764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사번호하도순번순번변경하도부분금액변경하도금액변경공사종료일하도비율적용방법
공사년도1.0000.5470.4880.1650.1670.1940.0000.0000.577
공사번호0.5471.0000.1320.0400.1350.2110.1260.0000.338
하도순번0.4880.1321.0000.6260.4030.1600.0000.1490.321
순번0.1650.0400.6261.0000.1850.1210.0000.0930.354
변경하도부분금액0.1670.1350.4030.1851.0000.9020.0000.2260.313
변경하도금액0.1940.2110.1600.1210.9021.0000.0000.0000.290
변경공사종료일0.0000.1260.0000.0000.0000.0001.0000.0000.000
하도비율0.0000.0000.1490.0930.2260.0000.0001.0000.336
적용방법0.5770.3380.3210.3540.3130.2900.0000.3361.000
2023-12-11T09:34:33.458804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사번호하도순번순번변경하도부분금액변경하도금액변경공사종료일하도비율적용방법
공사년도1.000-0.398-0.153-0.126-0.181-0.1880.844-0.0170.420
공사번호-0.3981.000-0.117-0.0350.0790.078-0.4370.0270.157
하도순번-0.153-0.1171.0000.5030.0390.0390.0020.0410.202
순번-0.126-0.0350.5031.0000.1030.0910.0910.0340.230
변경하도부분금액-0.1810.0790.0390.1031.0000.985-0.083-0.0710.221
변경하도금액-0.1880.0780.0390.0910.9851.000-0.085-0.0180.192
변경공사종료일0.844-0.4370.0020.091-0.083-0.0851.000-0.0610.000
하도비율-0.0170.0270.0410.034-0.071-0.018-0.0611.0000.157
적용방법0.4200.1570.2020.2300.2210.1920.0000.1571.000

Missing values

2023-12-11T09:34:28.380687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:34:28.523215image/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.
2023-12-11T09:34:28.634431image/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

공사년도공사구분공사번호부서코드하도순번순번변경계약일변경신고일변경하도부분금액변경하도금액변경공사시작일변경공사종료일하도비율적용방법
02003공사105124122010-07-302010-10-06668067186916830780000<NA><NA>10.22-
12003공사105131212010-09-282010-11-23745595403561831000002008-12-012010113082.93대지급
22003공사10518142010-07-302010-10-061260980183210348800000<NA>2010113082.07-
32003공사105111152010-09-302010-11-231165560000088088000002004-10-042010113075.58대지급
42003공사105112162010-09-302010-11-23560450000042555700002004-10-042010113075.93대지급
52003공사105121172010-09-302010-11-239413800007170900002005-06-132010113076.17직불
62003공사105119182010-09-302010-11-239779000008289600002005-06-262010113084.77대지급
72003공사105132192010-09-302010-11-234545200002982100002010-01-072010113065.61대지급
82003공사105118202010-09-282010-11-23170406118516830000002005-03-072010113098.76대지급
92003공사105134102010-08-022010-10-0518622003781652200000<NA>2010113088.72-
공사년도공사구분공사번호부서코드하도순번순번변경계약일변경신고일변경하도부분금액변경하도금액변경공사시작일변경공사종료일하도비율적용방법
9552017공사1321182018-10-122018-11-051513740001362350002018-05-162019112890.0직불
9562017공사1321272018-10-122018-11-05613000055170002018-05-162019112890.0직불
9572017공사1321362018-10-122018-11-05264497500023803990002017-12-182019112890.0직불
9582017공사1321452018-10-122018-11-05189223500017029800002017-12-182019112890.0직불
9592018공사731112018-12-182018-12-19478358100446532900<NA><NA>93.35직불
9602017공사1321332018-08-142018-08-29<NA><NA>2017-12-1820191128<NA>-
9612017공사1321222018-08-142018-08-29<NA><NA>2018-05-1620191128<NA>-
9622017공사1321112018-08-142018-08-29<NA><NA>2018-05-1620191128<NA>-
9632018공사731222018-12-182018-12-1912723084001069103200<NA><NA>84.03직불
9642017공사1321442018-08-142018-08-29<NA><NA>2017-12-1820191128<NA>-