Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.9 KiB
Average record size in memory108.3 B

Variable types

Text3
Numeric3
Categorical4
DateTime1
Boolean2

Dataset

Description해당 파일 데이터는 신용보증기금의 전자결재집단결재자정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092959/fileData.do

Alerts

결재일자 has constant value ""Constant
삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
직위코드 is highly overall correlated with 직급코드High correlation
직급코드 is highly overall correlated with 직위코드High correlation
찬성투표여부 is highly imbalanced (83.7%)Imbalance

Reproduction

Analysis started2023-12-12 09:22:17.470850
Analysis finished2023-12-12 09:22:19.716283
Duration2.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전자결재개별결재ID
Text

Distinct158
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:22:20.013060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)2.6%

Sample

1st row9dnUh8DfQ7
2nd row9dnUh8DfQ7
3rd row9dnS9zeRzI
4th row9dnS9zeRzI
5th row9dnS9GI9g1
ValueCountFrequency (%)
9dnarirx3p 6
 
1.2%
9dnajhl0tl 6
 
1.2%
9dnaiuxpl4 6
 
1.2%
9dnairoyvo 6
 
1.2%
9dnaimamxl 6
 
1.2%
9dnaib25m0 6
 
1.2%
9dnaifwxj3 6
 
1.2%
9dnah3taxn 6
 
1.2%
9dnah2pdtz 6
 
1.2%
9dnaym1gyt 6
 
1.2%
Other values (148) 440
88.0%
2023-12-12T18:22:20.527590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 585
 
11.7%
9 566
 
11.3%
d 523
 
10.5%
A 248
 
5.0%
m 103
 
2.1%
J 96
 
1.9%
M 91
 
1.8%
L 87
 
1.7%
S 83
 
1.7%
l 83
 
1.7%
Other values (52) 2535
50.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2341
46.8%
Uppercase Letter 1660
33.2%
Decimal Number 999
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 585
25.0%
d 523
22.3%
m 103
 
4.4%
l 83
 
3.5%
r 77
 
3.3%
s 73
 
3.1%
y 70
 
3.0%
x 64
 
2.7%
u 62
 
2.6%
e 60
 
2.6%
Other values (16) 641
27.4%
Uppercase Letter
ValueCountFrequency (%)
A 248
 
14.9%
J 96
 
5.8%
M 91
 
5.5%
L 87
 
5.2%
S 83
 
5.0%
I 79
 
4.8%
O 72
 
4.3%
H 71
 
4.3%
U 70
 
4.2%
D 69
 
4.2%
Other values (16) 694
41.8%
Decimal Number
ValueCountFrequency (%)
9 566
56.7%
0 74
 
7.4%
5 71
 
7.1%
6 55
 
5.5%
7 50
 
5.0%
4 45
 
4.5%
2 42
 
4.2%
8 36
 
3.6%
1 33
 
3.3%
3 27
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4001
80.0%
Common 999
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 585
 
14.6%
d 523
 
13.1%
A 248
 
6.2%
m 103
 
2.6%
J 96
 
2.4%
M 91
 
2.3%
L 87
 
2.2%
S 83
 
2.1%
l 83
 
2.1%
I 79
 
2.0%
Other values (42) 2023
50.6%
Common
ValueCountFrequency (%)
9 566
56.7%
0 74
 
7.4%
5 71
 
7.1%
6 55
 
5.5%
7 50
 
5.0%
4 45
 
4.5%
2 42
 
4.2%
8 36
 
3.6%
1 33
 
3.3%
3 27
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 585
 
11.7%
9 566
 
11.3%
d 523
 
10.5%
A 248
 
5.0%
m 103
 
2.1%
J 96
 
1.9%
M 91
 
1.8%
L 87
 
1.7%
S 83
 
1.7%
l 83
 
1.7%
Other values (52) 2535
50.7%

일련번호
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.566
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:22:20.689080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5769011
Coefficient of variation (CV)0.61453669
Kurtosis-0.39603701
Mean2.566
Median Absolute Deviation (MAD)1
Skewness0.85560364
Sum1283
Variance2.4866172
MonotonicityNot monotonic
2023-12-12T18:22:20.811166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 158
31.6%
2 145
29.0%
3 71
14.2%
4 47
 
9.4%
5 40
 
8.0%
6 39
 
7.8%
ValueCountFrequency (%)
1 158
31.6%
2 145
29.0%
3 71
14.2%
4 47
 
9.4%
5 40
 
8.0%
6 39
 
7.8%
ValueCountFrequency (%)
6 39
 
7.8%
5 40
 
8.0%
4 47
 
9.4%
3 71
14.2%
2 145
29.0%
1 158
31.6%

직위코드
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
I03
166 
C02
159 
K01
60 
B10
43 
B20
40 
Other values (4)
32 

Length

Max length3
Median length3
Mean length2.976
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC02
2nd rowK01
3rd rowC02
4th rowC02
5th rowC02

Common Values

ValueCountFrequency (%)
I03 166
33.2%
C02 159
31.8%
K01 60
 
12.0%
B10 43
 
8.6%
B20 40
 
8.0%
C36 14
 
2.8%
B22 6
 
1.2%
6
 
1.2%
C05 6
 
1.2%

Length

2023-12-12T18:22:20.980774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:22:21.142144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i03 166
33.6%
c02 159
32.2%
k01 60
 
12.1%
b10 43
 
8.7%
b20 40
 
8.1%
c36 14
 
2.8%
b22 6
 
1.2%
c05 6
 
1.2%

직급코드
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A3
338 
A4
58 
A2
50 
A1
43 
 
6

Length

Max length2
Median length2
Mean length1.988
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA4
2nd rowA5
3rd rowA3
4th rowA3
5th rowA3

Common Values

ValueCountFrequency (%)
A3 338
67.6%
A4 58
 
11.6%
A2 50
 
10.0%
A1 43
 
8.6%
6
 
1.2%
A5 5
 
1.0%

Length

2023-12-12T18:22:21.303016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:22:21.424579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a3 338
68.4%
a4 58
 
11.7%
a2 50
 
10.1%
a1 43
 
8.7%
a5 5
 
1.0%

결재일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T18:22:21.564981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:22:21.677914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

결재시분초
Date

Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 08:27:19
Maximum2023-12-12 20:32:24
2023-12-12T18:22:21.790138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:21.939879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

찬성투표여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
True
488 
False
 
12
ValueCountFrequency (%)
True 488
97.6%
False 12
 
2.4%
2023-12-12T18:22:22.082723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T18:22:22.194854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-12T18:22:22.328324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:22:22.439739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

처리시각
Text

Distinct158
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:22:22.879170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
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

Unique13 ?
Unique (%)2.6%

Sample

1st row58:26.6
2nd row58:26.6
3rd row38:25.2
4th row38:25.2
5th row34:15.2
ValueCountFrequency (%)
18:31.9 6
 
1.2%
19:02.1 6
 
1.2%
35:44.9 6
 
1.2%
37:21.6 6
 
1.2%
40:58.8 6
 
1.2%
44:57.9 6
 
1.2%
46:46.7 6
 
1.2%
50:24.5 6
 
1.2%
52:04.0 6
 
1.2%
53:51.3 6
 
1.2%
Other values (148) 440
88.0%
2023-12-12T18:22:23.483649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 371
10.6%
0 353
10.1%
5 327
9.3%
4 311
8.9%
2 298
8.5%
1 294
8.4%
6 180
 
5.1%
9 152
 
4.3%
Other values (2) 214
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 371
14.8%
0 353
14.1%
5 327
13.1%
4 311
12.4%
2 298
11.9%
1 294
11.8%
6 180
7.2%
9 152
6.1%
8 118
 
4.7%
7 96
 
3.8%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 371
10.6%
0 353
10.1%
5 327
9.3%
4 311
8.9%
2 298
8.5%
1 294
8.4%
6 180
 
5.1%
9 152
 
4.3%
Other values (2) 214
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 371
10.6%
0 353
10.1%
5 327
9.3%
4 311
8.9%
2 298
8.5%
1 294
8.4%
6 180
 
5.1%
9 152
 
4.3%
Other values (2) 214
6.1%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3560.782
Minimum2710
Maximum5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:22:23.694084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2710
5-th percentile3136
Q13264
median3264
Q33779
95-th percentile4512
Maximum5848
Range3138
Interquartile range (IQR)515

Descriptive statistics

Standard deviation452.14754
Coefficient of variation (CV)0.12697984
Kurtosis2.7412753
Mean3560.782
Median Absolute Deviation (MAD)128
Skewness1.5286093
Sum1780391
Variance204437.4
MonotonicityNot monotonic
2023-12-12T18:22:23.872918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3264 233
46.6%
3485 18
 
3.6%
3564 12
 
2.4%
3136 9
 
1.8%
4208 8
 
1.6%
3718 8
 
1.6%
3038 7
 
1.4%
3176 6
 
1.2%
3504 6
 
1.2%
3708 6
 
1.2%
Other values (74) 187
37.4%
ValueCountFrequency (%)
2710 2
 
0.4%
3038 7
 
1.4%
3044 4
 
0.8%
3059 4
 
0.8%
3136 9
 
1.8%
3176 6
 
1.2%
3264 233
46.6%
3337 2
 
0.4%
3353 2
 
0.4%
3363 2
 
0.4%
ValueCountFrequency (%)
5848 2
0.4%
5073 2
0.4%
5025 2
0.4%
4870 2
0.4%
4755 1
0.2%
4730 2
0.4%
4667 2
0.4%
4588 2
0.4%
4584 2
0.4%
4577 2
0.4%

최초처리시각
Text

Distinct158
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:22:24.308986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
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

Unique13 ?
Unique (%)2.6%

Sample

1st row58:26.6
2nd row58:26.6
3rd row38:25.2
4th row38:25.2
5th row34:15.2
ValueCountFrequency (%)
18:31.9 6
 
1.2%
19:02.1 6
 
1.2%
35:44.9 6
 
1.2%
37:21.6 6
 
1.2%
40:58.8 6
 
1.2%
44:57.9 6
 
1.2%
46:46.7 6
 
1.2%
50:24.5 6
 
1.2%
52:04.0 6
 
1.2%
53:51.3 6
 
1.2%
Other values (148) 440
88.0%
2023-12-12T18:22:24.926598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 371
10.6%
0 353
10.1%
5 327
9.3%
4 311
8.9%
2 298
8.5%
1 294
8.4%
6 180
 
5.1%
9 152
 
4.3%
Other values (2) 214
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 371
14.8%
0 353
14.1%
5 327
13.1%
4 311
12.4%
2 298
11.9%
1 294
11.8%
6 180
7.2%
9 152
6.1%
8 118
 
4.7%
7 96
 
3.8%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 371
10.6%
0 353
10.1%
5 327
9.3%
4 311
8.9%
2 298
8.5%
1 294
8.4%
6 180
 
5.1%
9 152
 
4.3%
Other values (2) 214
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 371
10.6%
0 353
10.1%
5 327
9.3%
4 311
8.9%
2 298
8.5%
1 294
8.4%
6 180
 
5.1%
9 152
 
4.3%
Other values (2) 214
6.1%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3560.782
Minimum2710
Maximum5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:22:25.155273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2710
5-th percentile3136
Q13264
median3264
Q33779
95-th percentile4512
Maximum5848
Range3138
Interquartile range (IQR)515

Descriptive statistics

Standard deviation452.14754
Coefficient of variation (CV)0.12697984
Kurtosis2.7412753
Mean3560.782
Median Absolute Deviation (MAD)128
Skewness1.5286093
Sum1780391
Variance204437.4
MonotonicityNot monotonic
2023-12-12T18:22:25.301378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3264 233
46.6%
3485 18
 
3.6%
3564 12
 
2.4%
3136 9
 
1.8%
4208 8
 
1.6%
3718 8
 
1.6%
3038 7
 
1.4%
3176 6
 
1.2%
3504 6
 
1.2%
3708 6
 
1.2%
Other values (74) 187
37.4%
ValueCountFrequency (%)
2710 2
 
0.4%
3038 7
 
1.4%
3044 4
 
0.8%
3059 4
 
0.8%
3136 9
 
1.8%
3176 6
 
1.2%
3264 233
46.6%
3337 2
 
0.4%
3353 2
 
0.4%
3363 2
 
0.4%
ValueCountFrequency (%)
5848 2
0.4%
5073 2
0.4%
5025 2
0.4%
4870 2
0.4%
4755 1
0.2%
4730 2
0.4%
4667 2
0.4%
4588 2
0.4%
4584 2
0.4%
4577 2
0.4%

Interactions

2023-12-12T18:22:18.596864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:17.933725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:18.278707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:19.061387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:18.026332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:18.390550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:19.183144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:18.139150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:22:18.487545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:22:25.404854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호직위코드직급코드찬성투표여부처리직원번호최초처리직원번호
일련번호1.0000.7530.7080.0510.3670.367
직위코드0.7531.0000.9800.1270.6470.647
직급코드0.7080.9801.0000.0000.5460.546
찬성투표여부0.0510.1270.0001.0000.3570.357
처리직원번호0.3670.6470.5460.3571.0001.000
최초처리직원번호0.3670.6470.5460.3571.0001.000
2023-12-12T18:22:25.558360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
찬성투표여부직급코드직위코드
찬성투표여부1.0000.0000.125
직급코드0.0001.0000.881
직위코드0.1250.8811.000
2023-12-12T18:22:25.695504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호처리직원번호최초처리직원번호직위코드직급코드찬성투표여부
일련번호1.000-0.415-0.4150.4930.3250.036
처리직원번호-0.4151.0001.0000.3610.3390.263
최초처리직원번호-0.4151.0001.0000.3610.3390.263
직위코드0.4930.3610.3611.0000.8810.125
직급코드0.3250.3390.3390.8811.0000.000
찬성투표여부0.0360.2630.2630.1250.0001.000

Missing values

2023-12-12T18:22:19.382435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:22:19.619246image/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.

Sample

전자결재개별결재ID일련번호직위코드직급코드결재일자결재시분초찬성투표여부삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnUh8DfQ71C02A400:00.010:53:52YN158:26.6447558:26.64475
19dnUh8DfQ72K01A500:00.010:58:26YN158:26.6447558:26.64475
29dnS9zeRzI1C02A300:00.016:54:49YN138:25.2271038:25.22710
39dnS9zeRzI2C02A300:00.017:38:25YN138:25.2271038:25.22710
49dnS9GI9g11C02A300:00.016:56:23YN134:15.2390634:15.23906
59dnS9GI9g14I03A300:00.017:34:15YN134:15.2390634:15.23906
69dnS9GI9g13C36A300:00.017:05:49YN134:15.2390634:15.23906
79dnS9GI9g12C36A300:00.016:58:29YN134:15.2390634:15.23906
89dnS7BomGH1C02A300:00.016:26:15YN136:16.6365036:16.63650
99dnS7BomGH3C02A300:00.016:36:16YN136:16.6365036:16.63650
전자결재개별결재ID일련번호직위코드직급코드결재일자결재시분초찬성투표여부삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dnAJOKHMk6I03A300:00.015:15:40YN115:40.9326415:40.93264
4919dnAJOKHMk5I03A300:00.017:51:11YN115:40.9326415:40.93264
4929dnAJOKHMk4I03A300:00.017:50:58YN115:40.9326415:40.93264
4939dnAJOKHMk3I03A300:00.017:50:24YN115:40.9326415:40.93264
4949dnAJOKHMk2B20A200:00.014:31:03YN115:40.9326415:40.93264
4959dnCh8kvZ21B10A100:00.014:53:27YN113:43.2326413:43.23264
4969dnCh8kvZ25I03A300:00.015:08:22YN113:43.2326413:43.23264
4979dnCh8kvZ24I03A300:00.015:07:57YN113:43.2326413:43.23264
4989dnCh8kvZ23I03A300:00.014:55:01YN113:43.2326413:43.23264
4999dnCh8kvZ22B20A200:00.014:54:40YN113:43.2326413:43.23264