Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells2862
Missing cells (%)3.2%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory820.3 KiB
Average record size in memory84.0 B

Variable types

Numeric3
Categorical3
Text3

Dataset

Description경상남도 공사계약대장시스템의 기성준공 데이터입니다. 공사년도, 공사구분, 검사일자, 기성금액, 기성율 등의 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15049526

Alerts

부서코드 has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
구분 is highly imbalanced (70.3%)Imbalance
기성금액 has 881 (8.8%) missing valuesMissing
기성율 has 1977 (19.8%) missing valuesMissing

Reproduction

Analysis started2023-12-11 00:33:39.913864
Analysis finished2023-12-11 00:33:42.017628
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공사년도
Real number (ℝ)

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.3593
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:33:42.079347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1993
Q12003
median2008
Q32010
95-th percentile2016
Maximum2019
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.3842055
Coefficient of variation (CV)0.0031819851
Kurtosis0.12346912
Mean2006.3593
Median Absolute Deviation (MAD)3
Skewness-0.62043628
Sum20063593
Variance40.758079
MonotonicityNot monotonic
2023-12-11T09:33:42.229587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2010 1216
 
12.2%
2009 851
 
8.5%
2008 771
 
7.7%
2007 714
 
7.1%
2011 674
 
6.7%
2006 575
 
5.8%
2003 518
 
5.2%
2004 500
 
5.0%
2005 459
 
4.6%
2012 331
 
3.3%
Other values (20) 3391
33.9%
ValueCountFrequency (%)
1990 137
1.4%
1991 183
1.8%
1992 120
1.2%
1993 149
1.5%
1994 107
1.1%
1995 98
1.0%
1996 146
1.5%
1997 179
1.8%
1998 154
1.5%
1999 215
2.1%
ValueCountFrequency (%)
2019 51
 
0.5%
2018 192
 
1.9%
2017 202
 
2.0%
2016 245
 
2.5%
2015 274
 
2.7%
2014 145
 
1.5%
2013 93
 
0.9%
2012 331
 
3.3%
2011 674
6.7%
2010 1216
12.2%

공사구분
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공사
5233 
용역
4127 
기타
 
448
구매
 
192

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공사 5233
52.3%
용역 4127
41.3%
기타 448
 
4.5%
구매 192
 
1.9%

Length

2023-12-11T09:33:42.386893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:33:42.514827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공사 5233
52.3%
용역 4127
41.3%
기타 448
 
4.5%
구매 192
 
1.9%

공사번호
Real number (ℝ)

Distinct545
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.4044
Minimum1
Maximum623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:33:42.637992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q134
median67
Q3123
95-th percentile321
Maximum623
Range622
Interquartile range (IQR)89

Descriptive statistics

Standard deviation102.03469
Coefficient of variation (CV)1.0264605
Kurtosis4.7578762
Mean99.4044
Median Absolute Deviation (MAD)39
Skewness2.0792189
Sum994044
Variance10411.078
MonotonicityNot monotonic
2023-12-11T09:33:42.834530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 106
 
1.1%
14 103
 
1.0%
43 99
 
1.0%
30 96
 
1.0%
49 95
 
0.9%
7 92
 
0.9%
35 91
 
0.9%
29 90
 
0.9%
9 90
 
0.9%
47 89
 
0.9%
Other values (535) 9049
90.5%
ValueCountFrequency (%)
1 80
0.8%
2 56
0.6%
3 76
0.8%
4 67
0.7%
5 81
0.8%
6 83
0.8%
7 92
0.9%
8 77
0.8%
9 90
0.9%
10 82
0.8%
ValueCountFrequency (%)
623 1
< 0.1%
620 1
< 0.1%
619 1
< 0.1%
618 1
< 0.1%
617 1
< 0.1%
615 1
< 0.1%
614 1
< 0.1%
607 1
< 0.1%
604 1
< 0.1%
601 1
< 0.1%

부서코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

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 10000
100.0%

Length

2023-12-11T09:33:42.990381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:33:43.087127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

검사일자
Text

Distinct4097
Distinct (%)41.0%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-11T09:33:43.365087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9984994
Min length5

Characters and Unicode

Total characters99945
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

Unique1920 ?
Unique (%)19.2%

Sample

1st row2010-05-25
2nd row1991-05-06
3rd row2008-04-07
4th row2009-12-15
5th row2010-11-29
ValueCountFrequency (%)
2010-12-30 31
 
0.3%
2003-12-30 27
 
0.3%
2009-12-28 21
 
0.2%
2006-12-26 18
 
0.2%
2005-12-21 17
 
0.2%
2010-02-05 16
 
0.2%
2010-07-06 16
 
0.2%
2007-04-03 16
 
0.2%
2010-06-30 15
 
0.2%
2011-03-03 15
 
0.2%
Other values (4087) 9804
98.1%
2023-12-11T09:33:43.843492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26847
26.9%
- 19984
20.0%
2 16188
16.2%
1 14808
14.8%
9 5992
 
6.0%
6 2869
 
2.9%
8 2831
 
2.8%
3 2784
 
2.8%
7 2783
 
2.8%
4 2467
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79961
80.0%
Dash Punctuation 19984
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26847
33.6%
2 16188
20.2%
1 14808
18.5%
9 5992
 
7.5%
6 2869
 
3.6%
8 2831
 
3.5%
3 2784
 
3.5%
7 2783
 
3.5%
4 2467
 
3.1%
5 2392
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 19984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99945
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26847
26.9%
- 19984
20.0%
2 16188
16.2%
1 14808
14.8%
9 5992
 
6.0%
6 2869
 
2.9%
8 2831
 
2.8%
3 2784
 
2.8%
7 2783
 
2.8%
4 2467
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99945
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26847
26.9%
- 19984
20.0%
2 16188
16.2%
1 14808
14.8%
9 5992
 
6.0%
6 2869
 
2.9%
8 2831
 
2.8%
3 2784
 
2.8%
7 2783
 
2.8%
4 2467
 
2.5%

구분
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
준공
5170 
기성
4757 
-
 
55
주공
 
8
정산
 
2
Other values (7)
 
8

Length

Max length9
Median length2
Mean length1.997
Min length1

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row준공
2nd row준공
3rd row기성
4th row준공
5th row기성

Common Values

ValueCountFrequency (%)
준공 5170
51.7%
기성 4757
47.6%
- 55
 
0.5%
주공 8
 
0.1%
정산 2
 
< 0.1%
타절정산 2
 
< 0.1%
선진엔지나어링 1
 
< 0.1%
보완시공지시 1
 
< 0.1%
208204000 1
 
< 0.1%
선진엔지니어링 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2023-12-11T09:33:44.020145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
준공 5170
51.7%
기성 4757
47.6%
55
 
0.5%
주공 8
 
0.1%
정산 2
 
< 0.1%
타절정산 2
 
< 0.1%
선진엔지나어링 1
 
< 0.1%
보완시공지시 1
 
< 0.1%
208204000 1
 
< 0.1%
선진엔지니어링 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

기성금액
Real number (ℝ)

MISSING 

Distinct6412
Distinct (%)70.3%
Missing881
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean7.5856168 × 108
Minimum0
Maximum1.65 × 1011
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:33:44.177332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile435000
Q17000000
median99493750
Q34.498775 × 108
95-th percentile2.0301731 × 109
Maximum1.65 × 1011
Range1.65 × 1011
Interquartile range (IQR)4.428775 × 108

Descriptive statistics

Standard deviation5.2067194 × 109
Coefficient of variation (CV)6.8639368
Kurtosis441.35103
Mean7.5856168 × 108
Median Absolute Deviation (MAD)98106750
Skewness19.363258
Sum6.917324 × 1012
Variance2.7109927 × 1019
MonotonicityNot monotonic
2023-12-11T09:33:44.322109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330000 54
 
0.5%
1070000 48
 
0.5%
650000 39
 
0.4%
1500000 36
 
0.4%
400000 34
 
0.3%
6000000 28
 
0.3%
4000000 28
 
0.3%
200000 27
 
0.3%
20000000 26
 
0.3%
300000000 26
 
0.3%
Other values (6402) 8773
87.7%
(Missing) 881
 
8.8%
ValueCountFrequency (%)
0 7
0.1%
9 1
 
< 0.1%
1061 1
 
< 0.1%
80850 1
 
< 0.1%
82500 4
< 0.1%
83000 1
 
< 0.1%
87840 1
 
< 0.1%
87860 2
 
< 0.1%
93800 1
 
< 0.1%
94000 1
 
< 0.1%
ValueCountFrequency (%)
165000000000 1
< 0.1%
147000000000 1
< 0.1%
137000000000 1
< 0.1%
133000000000 1
< 0.1%
121000000000 1
< 0.1%
117000000000 1
< 0.1%
108000000000 1
< 0.1%
102000000000 1
< 0.1%
92115432000 1
< 0.1%
90149860000 1
< 0.1%

기성율
Text

MISSING 

Distinct2342
Distinct (%)29.2%
Missing1977
Missing (%)19.8%
Memory size156.2 KiB
2023-12-11T09:33:44.693367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.6192197
Min length1

Characters and Unicode

Total characters29037
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1937 ?
Unique (%)24.1%

Sample

1st row100
2nd row25
3rd row100
4th row90.7
5th row100
ValueCountFrequency (%)
100 4525
56.4%
50 82
 
1.0%
16.67 62
 
0.8%
25 52
 
0.6%
8.33 49
 
0.6%
75 46
 
0.6%
83.33 43
 
0.5%
41.67 42
 
0.5%
66.67 42
 
0.5%
33.33 41
 
0.5%
Other values (2332) 3039
37.9%
2023-12-11T09:33:45.274184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9789
33.7%
1 5655
19.5%
. 3054
 
10.5%
3 1588
 
5.5%
6 1530
 
5.3%
7 1393
 
4.8%
5 1361
 
4.7%
8 1241
 
4.3%
4 1204
 
4.1%
2 1172
 
4.0%
Other values (4) 1050
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25976
89.5%
Other Punctuation 3055
 
10.5%
Other Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9789
37.7%
1 5655
21.8%
3 1588
 
6.1%
6 1530
 
5.9%
7 1393
 
5.4%
5 1361
 
5.2%
8 1241
 
4.8%
4 1204
 
4.6%
2 1172
 
4.5%
9 1043
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 3054
> 99.9%
, 1
 
< 0.1%
Other Letter
ValueCountFrequency (%)
월 3
50.0%
분 3
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29031
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9789
33.7%
1 5655
19.5%
. 3054
 
10.5%
3 1588
 
5.5%
6 1530
 
5.3%
7 1393
 
4.8%
5 1361
 
4.7%
8 1241
 
4.3%
4 1204
 
4.1%
2 1172
 
4.0%
Other values (2) 1044
 
3.6%
Hangul
ValueCountFrequency (%)
월 3
50.0%
분 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29031
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9789
33.7%
1 5655
19.5%
. 3054
 
10.5%
3 1588
 
5.5%
6 1530
 
5.3%
7 1393
 
4.8%
5 1361
 
4.7%
8 1241
 
4.3%
4 1204
 
4.1%
2 1172
 
4.0%
Other values (2) 1044
 
3.6%
Hangul
ValueCountFrequency (%)
월 3
50.0%
분 3
50.0%

확인공무원
Text

Distinct2181
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T09:33:45.656322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length6.0387
Min length1

Characters and Unicode

Total characters60387
Distinct characters313
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1205 ?
Unique (%)12.0%

Sample

1st row감사관실 정연보
2nd row신귀득, 이수재
3rd row지방행정주사보 김병범
4th row허진영
5th row8급 조영해
ValueCountFrequency (%)
1455
 
9.4%
지방시설사무관 450
 
2.9%
토목5급 302
 
2.0%
강병철 262
 
1.7%
지방행정사무관 247
 
1.6%
김영택 235
 
1.5%
지방행정주사 175
 
1.1%
이채건 167
 
1.1%
김영근 163
 
1.1%
김이규 156
 
1.0%
Other values (1659) 11811
76.6%
2023-12-11T09:33:46.105313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5452
 
9.0%
김 2578
 
4.3%
지 2559
 
4.2%
방 2366
 
3.9%
정 2172
 
3.6%
이 1976
 
3.3%
사 1809
 
3.0%
급 1637
 
2.7%
- 1455
 
2.4%
주 1327
 
2.2%
Other values (303) 37056
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50375
83.4%
Space Separator 5452
 
9.0%
Decimal Number 1775
 
2.9%
Dash Punctuation 1455
 
2.4%
Other Punctuation 1079
 
1.8%
Close Punctuation 119
 
0.2%
Open Punctuation 97
 
0.2%
Uppercase Letter 35
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 2578
 
5.1%
지 2559
 
5.1%
방 2366
 
4.7%
정 2172
 
4.3%
이 1976
 
3.9%
사 1809
 
3.6%
급 1637
 
3.2%
주 1327
 
2.6%
행 1193
 
2.4%
관 1170
 
2.3%
Other values (281) 31588
62.7%
Decimal Number
ValueCountFrequency (%)
5 721
40.6%
7 457
25.7%
6 368
20.7%
8 154
 
8.7%
1 29
 
1.6%
2 19
 
1.1%
0 11
 
0.6%
4 8
 
0.5%
3 5
 
0.3%
9 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
, 1059
98.1%
. 12
 
1.1%
/ 4
 
0.4%
: 3
 
0.3%
？ 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
E 17
48.6%
G 11
31.4%
L 7
20.0%
Space Separator
ValueCountFrequency (%)
5452
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1455
100.0%
Close Punctuation
ValueCountFrequency (%)
) 119
100.0%
Open Punctuation
ValueCountFrequency (%)
( 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50375
83.4%
Common 9977
 
16.5%
Latin 35
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 2578
 
5.1%
지 2559
 
5.1%
방 2366
 
4.7%
정 2172
 
4.3%
이 1976
 
3.9%
사 1809
 
3.6%
급 1637
 
3.2%
주 1327
 
2.6%
행 1193
 
2.4%
관 1170
 
2.3%
Other values (281) 31588
62.7%
Common
ValueCountFrequency (%)
5452
54.6%
- 1455
 
14.6%
, 1059
 
10.6%
5 721
 
7.2%
7 457
 
4.6%
6 368
 
3.7%
8 154
 
1.5%
) 119
 
1.2%
( 97
 
1.0%
1 29
 
0.3%
Other values (9) 66
 
0.7%
Latin
ValueCountFrequency (%)
E 17
48.6%
G 11
31.4%
L 7
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50371
83.4%
ASCII 10011
 
16.6%
Compat Jamo 4
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5452
54.5%
- 1455
 
14.5%
, 1059
 
10.6%
5 721
 
7.2%
7 457
 
4.6%
6 368
 
3.7%
8 154
 
1.5%
) 119
 
1.2%
( 97
 
1.0%
1 29
 
0.3%
Other values (11) 100
 
1.0%
Hangul
ValueCountFrequency (%)
김 2578
 
5.1%
지 2559
 
5.1%
방 2366
 
4.7%
정 2172
 
4.3%
이 1976
 
3.9%
사 1809
 
3.6%
급 1637
 
3.2%
주 1327
 
2.6%
행 1193
 
2.4%
관 1170
 
2.3%
Other values (277) 31584
62.7%
None
ValueCountFrequency (%)
？ 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅅ 1
25.0%
ㄱ 1
25.0%
ㅣ 1
25.0%
ㅗ 1
25.0%

Interactions

2023-12-11T09:33:41.333176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:33:40.703875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:33:40.992644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:33:41.428676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:33:40.792710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:33:41.150633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:33:41.535304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:33:40.881123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:33:41.240086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:33:46.233047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사구분공사번호구분기성금액
공사년도1.0000.5520.4720.1960.169
공사구분0.5521.0000.3540.2970.045
공사번호0.4720.3541.0000.1770.059
구분0.1960.2970.1771.0000.000
기성금액0.1690.0450.0590.0001.000
2023-12-11T09:33:46.361893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사구분구분
공사구분1.0000.141
구분0.1411.000
2023-12-11T09:33:46.478732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사년도공사번호기성금액공사구분구분
공사년도1.0000.305-0.2700.3670.065
공사번호0.3051.000-0.0970.2190.075
기성금액-0.270-0.0971.0000.0270.000
공사구분0.3670.2190.0271.0000.141
구분0.0650.0750.0000.1411.000

Missing values

2023-12-11T09:33:41.663186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:33:41.840752image/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.
2023-12-11T09:33:41.960021image/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

공사년도공사구분공사번호부서코드검사일자구분기성금액기성율확인공무원
92452010용역24912010-05-25준공27000000100감사관실 정연보
341990공사2011991-05-06준공<NA><NA>신귀득, 이수재
61272008용역512008-04-07기성1055000025지방행정주사보 김병범
80722009기타6012009-12-15준공8357450100허진영
88772010용역8312010-11-29기성24800090.78급 조영해
50402006용역7212007-12-14준공16015000100시설5급 김대형
79782009용역23412009-10-01기성750000050지방행정주사 곽기출
22692001공사3912001-12-27준공756980000100문재화
123942018공사3612019-05-10준공2312500000100감리단
58652007용역20512008-07-10준공469642000100지방시설사무관 김대형
공사년도공사구분공사번호부서코드검사일자구분기성금액기성율확인공무원
61362008용역1112008-10-06기성107000075지방행정서기 김지애
27942003공사3412003-07-10기성13137000036.1곽수관
110162014공사112014-02-04준공3600000100-
80322009용역26812009-12-01기성547200050자방행정주사 류효종
59842007용역26512007-11-08준공129236000100지방행정5급 진윤생
88012010용역912011-06-08기성52776000060안병천
122532018공사13412018-12-14기성23199000066.54-
63872008용역10312008-12-04기성316667075지방행정서기 김지애
34792004공사3412005-04-28기성30000000048.99황상업
20042000공사6612001-01-16기성38520000047.9이병희

Duplicate rows

Most frequently occurring

공사년도공사구분공사번호부서코드검사일자구분기성금액기성율확인공무원# duplicates
02005용역13612005-11-01기성<NA><NA>상동2