Overview

Dataset statistics

Number of variables18
Number of observations500
Missing cells500
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.3 KiB
Average record size in memory152.3 B

Variable types

Text4
Categorical11
DateTime1
Unsupported1
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 공통전자결재결재자정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093204/fileData.do

Alerts

전자결재부점구분코드 has constant value ""Constant
직위코드 has constant value ""Constant
직급코드 has constant value ""Constant
전자결재미결재사유코드 has constant value ""Constant
삭제여부 is highly overall correlated with 최종수정수High correlation
결재일자 is highly overall correlated with 결재자결재정보코드 and 1 other fieldsHigh correlation
결재자결재정보코드 is highly overall correlated with 결재일자 and 1 other fieldsHigh correlation
최종수정수 is highly overall correlated with 결재일자 and 2 other fieldsHigh correlation
일련번호 is highly overall correlated with 전자결재결재자역할코드High correlation
전자결재결재자역할코드 is highly overall correlated with 일련번호High correlation
전자결재구분코드 is highly imbalanced (94.7%)Imbalance
전자결재전자수기구분코드 is highly imbalanced (84.7%)Imbalance
삭제여부 is highly imbalanced (94.7%)Imbalance
결재의견반송사유내용 has 500 (100.0%) missing valuesMissing
결재의견반송사유내용 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-18 05:03:04.440544
Analysis finished2024-04-18 05:03:08.050798
Duration3.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전자결재개별결재ID
Text

Distinct218
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T14:03:08.220514image/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

Unique2 ?
Unique (%)0.4%

Sample

1st row9dnS0VgFz0
2nd row9dnS0VgFz0
3rd row9dnS0SdHkZ
4th row9dnS0SdHkZ
5th row9dnS0RQJEB
ValueCountFrequency (%)
9dnsvov0mt 4
 
0.8%
9dns0uceho 4
 
0.8%
9dnszpb4lj 3
 
0.6%
9dnoicaea3 3
 
0.6%
9dnsibcrny 3
 
0.6%
9dnsl2awfi 3
 
0.6%
9dnsk5tcgx 3
 
0.6%
9dnoillszz 3
 
0.6%
9dnoccsw66 3
 
0.6%
9dnocskrpa 3
 
0.6%
Other values (208) 468
93.6%
2024-04-18T14:03:08.582821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 536
 
10.7%
9 534
 
10.7%
n 529
 
10.6%
S 488
 
9.8%
0 225
 
4.5%
Z 201
 
4.0%
Y 82
 
1.6%
O 78
 
1.6%
r 64
 
1.3%
X 59
 
1.2%
Other values (52) 2204
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2091
41.8%
Uppercase Letter 1837
36.7%
Decimal Number 1072
21.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 536
25.6%
n 529
25.3%
r 64
 
3.1%
i 56
 
2.7%
u 55
 
2.6%
a 55
 
2.6%
y 52
 
2.5%
e 50
 
2.4%
o 49
 
2.3%
w 48
 
2.3%
Other values (16) 597
28.6%
Uppercase Letter
ValueCountFrequency (%)
S 488
26.6%
Z 201
 
10.9%
Y 82
 
4.5%
O 78
 
4.2%
X 59
 
3.2%
F 57
 
3.1%
C 55
 
3.0%
K 53
 
2.9%
V 53
 
2.9%
N 51
 
2.8%
Other values (16) 660
35.9%
Decimal Number
ValueCountFrequency (%)
9 534
49.8%
0 225
21.0%
5 52
 
4.9%
6 43
 
4.0%
3 41
 
3.8%
4 38
 
3.5%
2 36
 
3.4%
1 36
 
3.4%
7 35
 
3.3%
8 32
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3928
78.6%
Common 1072
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 536
 
13.6%
n 529
 
13.5%
S 488
 
12.4%
Z 201
 
5.1%
Y 82
 
2.1%
O 78
 
2.0%
r 64
 
1.6%
X 59
 
1.5%
F 57
 
1.5%
i 56
 
1.4%
Other values (42) 1778
45.3%
Common
ValueCountFrequency (%)
9 534
49.8%
0 225
21.0%
5 52
 
4.9%
6 43
 
4.0%
3 41
 
3.8%
4 38
 
3.5%
2 36
 
3.4%
1 36
 
3.4%
7 35
 
3.3%
8 32
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 536
 
10.7%
9 534
 
10.7%
n 529
 
10.6%
S 488
 
9.8%
0 225
 
4.5%
Z 201
 
4.0%
Y 82
 
1.6%
O 78
 
1.6%
r 64
 
1.3%
X 59
 
1.2%
Other values (52) 2204
44.1%

일련번호
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
218 
2
216 
3
64 
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 218
43.6%
2 216
43.2%
3 64
 
12.8%
4 2
 
0.4%

Length

2024-04-18T14:03:08.763142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:08.857810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 218
43.6%
2 216
43.2%
3 64
 
12.8%
4 2
 
0.4%

전자결재ID
Text

Distinct218
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T14:03:09.072939image/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

Unique2 ?
Unique (%)0.4%

Sample

1st row9dnS0VgFz0
2nd row9dnS0VgFz0
3rd row9dnS0SdHkZ
4th row9dnS0SdHkZ
5th row9dnS0RQJEB
ValueCountFrequency (%)
9dnsvov0mt 4
 
0.8%
9dns0uceho 4
 
0.8%
9dnszpb4lj 3
 
0.6%
9dnoicaea3 3
 
0.6%
9dnsibcrny 3
 
0.6%
9dnsl2awfi 3
 
0.6%
9dnsk5tcgx 3
 
0.6%
9dnoillszz 3
 
0.6%
9dnoccsw66 3
 
0.6%
9dnocskrpa 3
 
0.6%
Other values (208) 468
93.6%
2024-04-18T14:03:09.466402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 534
 
10.7%
d 533
 
10.7%
n 529
 
10.6%
S 485
 
9.7%
0 225
 
4.5%
Z 198
 
4.0%
Y 82
 
1.6%
O 78
 
1.6%
r 64
 
1.3%
X 59
 
1.2%
Other values (52) 2213
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2094
41.9%
Uppercase Letter 1834
36.7%
Decimal Number 1072
21.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 533
25.5%
n 529
25.3%
r 64
 
3.1%
i 56
 
2.7%
y 55
 
2.6%
a 55
 
2.6%
o 55
 
2.6%
u 52
 
2.5%
e 50
 
2.4%
w 48
 
2.3%
Other values (16) 597
28.5%
Uppercase Letter
ValueCountFrequency (%)
S 485
26.4%
Z 198
 
10.8%
Y 82
 
4.5%
O 78
 
4.3%
X 59
 
3.2%
F 57
 
3.1%
C 55
 
3.0%
V 53
 
2.9%
K 53
 
2.9%
M 52
 
2.8%
Other values (16) 662
36.1%
Decimal Number
ValueCountFrequency (%)
9 534
49.8%
0 225
21.0%
5 49
 
4.6%
6 43
 
4.0%
3 41
 
3.8%
4 38
 
3.5%
7 38
 
3.5%
1 36
 
3.4%
2 36
 
3.4%
8 32
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3928
78.6%
Common 1072
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 533
 
13.6%
n 529
 
13.5%
S 485
 
12.3%
Z 198
 
5.0%
Y 82
 
2.1%
O 78
 
2.0%
r 64
 
1.6%
X 59
 
1.5%
F 57
 
1.5%
i 56
 
1.4%
Other values (42) 1787
45.5%
Common
ValueCountFrequency (%)
9 534
49.8%
0 225
21.0%
5 49
 
4.6%
6 43
 
4.0%
3 41
 
3.8%
4 38
 
3.5%
7 38
 
3.5%
1 36
 
3.4%
2 36
 
3.4%
8 32
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 534
 
10.7%
d 533
 
10.7%
n 529
 
10.6%
S 485
 
9.7%
0 225
 
4.5%
Z 198
 
4.0%
Y 82
 
1.6%
O 78
 
1.6%
r 64
 
1.3%
X 59
 
1.2%
Other values (52) 2213
44.3%

전자결재부점구분코드
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

2024-04-18T14:03:09.593860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:09.678124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

전자결재구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
497 
2
 
3

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 (%)
1 497
99.4%
2 3
 
0.6%

Length

2024-04-18T14:03:09.773883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:09.864726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 497
99.4%
2 3
 
0.6%

직위코드
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2024-04-18T14:03:09.966770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:10.054506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

직급코드
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2024-04-18T14:03:10.143029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:10.231068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

전자결재결재자역할코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
218 
3
216 
2
66 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 218
43.6%
3 216
43.2%
2 66
 
13.2%

Length

2024-04-18T14:03:10.323930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:10.414458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 218
43.6%
3 216
43.2%
2 66
 
13.2%

결재일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
405 
0001-01-01 00:00:00.000000
95 

Length

Max length26
Median length7
Mean length10.61
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row0001-01-01 00:00:00.000000
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 405
81.0%
0001-01-01 00:00:00.000000 95
 
19.0%

Length

2024-04-18T14:03:10.516925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:10.609106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 405
68.1%
0001-01-01 95
 
16.0%
00:00:00.000000 95
 
16.0%

결재시분초
Date

Distinct360
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-04-18 00:00:00
Maximum2024-04-18 18:08:08
2024-04-18T14:03:10.707840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T14:03:10.861583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

결재자결재정보코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
405 
3
95 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 405
81.0%
3 95
 
19.0%

Length

2024-04-18T14:03:10.986482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:11.073514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 405
81.0%
3 95
 
19.0%

전자결재미결재사유코드
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2024-04-18T14:03:11.182036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:11.267893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

전자결재전자수기구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
489 
3
 
11

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 (%)
1 489
97.8%
3 11
 
2.2%

Length

2024-04-18T14:03:11.360238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:11.444247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 489
97.8%
3 11
 
2.2%

결재의견반송사유내용
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

삭제여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
497 
True
 
3
ValueCountFrequency (%)
False 497
99.4%
True 3
 
0.6%
2024-04-18T14:03:11.513376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
326 
1
171 
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 326
65.2%
1 171
34.2%
3 3
 
0.6%

Length

2024-04-18T14:03:11.606247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T14:03:11.694867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 326
65.2%
1 171
34.2%
3 3
 
0.6%

처리시각
Text

Distinct215
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T14:03:11.984908image/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

Unique2 ?
Unique (%)0.4%

Sample

1st row41:01.7
2nd row41:01.7
3rd row40:51.7
4th row40:51.7
5th row40:50.3
ValueCountFrequency (%)
27:42.7 5
 
1.0%
24:12.7 5
 
1.0%
31:50.6 4
 
0.8%
34:31.1 4
 
0.8%
26:58.0 4
 
0.8%
36:51.8 3
 
0.6%
37:07.6 3
 
0.6%
37:06.7 3
 
0.6%
37:06.1 3
 
0.6%
37:05.5 3
 
0.6%
Other values (205) 463
92.6%
2024-04-18T14:03:12.429986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 496
14.2%
2 412
11.8%
4 292
8.3%
0 266
7.6%
1 246
7.0%
5 198
 
5.7%
7 180
 
5.1%
8 161
 
4.6%
Other values (2) 249
7.1%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 496
19.8%
2 412
16.5%
4 292
11.7%
0 266
10.6%
1 246
9.8%
5 198
 
7.9%
7 180
 
7.2%
8 161
 
6.4%
9 132
 
5.3%
6 117
 
4.7%
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%
3 496
14.2%
2 412
11.8%
4 292
8.3%
0 266
7.6%
1 246
7.0%
5 198
 
5.7%
7 180
 
5.1%
8 161
 
4.6%
Other values (2) 249
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 496
14.2%
2 412
11.8%
4 292
8.3%
0 266
7.6%
1 246
7.0%
5 198
 
5.7%
7 180
 
5.1%
8 161
 
4.6%
Other values (2) 249
7.1%

처리직원번호
Text

Distinct168
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T14:03:12.753747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.044
Min length4

Characters and Unicode

Total characters2022
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row5091
2nd row5091
3rd row3026
4th row3026
5th row3593
ValueCountFrequency (%)
4755 21
 
4.2%
5493 11
 
2.2%
4720 10
 
2.0%
4920 9
 
1.8%
5241 9
 
1.8%
5715 8
 
1.6%
5731 6
 
1.2%
5801 6
 
1.2%
4510 6
 
1.2%
5091 6
 
1.2%
Other values (158) 408
81.6%
2024-04-18T14:03:13.221984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 360
17.8%
4 321
15.9%
0 195
9.6%
3 188
9.3%
7 185
9.1%
9 171
8.5%
1 166
8.2%
6 162
8.0%
2 153
7.6%
8 107
 
5.3%
Other values (2) 14
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2008
99.3%
Uppercase Letter 14
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 360
17.9%
4 321
16.0%
0 195
9.7%
3 188
9.4%
7 185
9.2%
9 171
8.5%
1 166
8.3%
6 162
8.1%
2 153
7.6%
8 107
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 10
71.4%
A 4
 
28.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2008
99.3%
Latin 14
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 360
17.9%
4 321
16.0%
0 195
9.7%
3 188
9.4%
7 185
9.2%
9 171
8.5%
1 166
8.3%
6 162
8.1%
2 153
7.6%
8 107
 
5.3%
Latin
ValueCountFrequency (%)
C 10
71.4%
A 4
 
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 360
17.8%
4 321
15.9%
0 195
9.6%
3 188
9.3%
7 185
9.1%
9 171
8.5%
1 166
8.2%
6 162
8.0%
2 153
7.6%
8 107
 
5.3%
Other values (2) 14
 
0.7%

Correlations

2024-04-18T14:03:13.340245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호전자결재구분코드전자결재결재자역할코드결재일자결재자결재정보코드전자결재전자수기구분코드삭제여부최종수정수
일련번호1.0000.0000.6950.6080.6080.2920.0000.000
전자결재구분코드0.0001.0000.0000.0660.0660.0000.0000.053
전자결재결재자역할코드0.6950.0001.0000.2590.2590.0000.0000.000
결재일자0.6080.0660.2591.0001.0000.0510.0660.400
결재자결재정보코드0.6080.0660.2591.0001.0000.0510.0660.400
전자결재전자수기구분코드0.2920.0000.0000.0510.0511.0000.0000.120
삭제여부0.0000.0000.0000.0660.0660.0001.0001.000
최종수정수0.0000.0530.0000.4000.4000.1201.0001.000
2024-04-18T14:03:13.477438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전자결재결재자역할코드삭제여부전자결재구분코드결재일자결재자결재정보코드전자결재전자수기구분코드일련번호최종수정수
전자결재결재자역할코드1.0000.0000.0000.4220.4220.0000.7290.000
삭제여부0.0001.0000.0000.0420.0420.0000.0000.999
전자결재구분코드0.0000.0001.0000.0420.0420.0000.0000.087
결재일자0.4220.0420.0421.0000.9930.0320.4200.629
결재자결재정보코드0.4220.0420.0420.9931.0000.0320.4200.629
전자결재전자수기구분코드0.0000.0000.0000.0320.0321.0000.1940.198
일련번호0.7290.0000.0000.4200.4200.1941.0000.000
최종수정수0.0000.9990.0870.6290.6290.1980.0001.000
2024-04-18T14:03:13.595806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호전자결재구분코드전자결재결재자역할코드결재일자결재자결재정보코드전자결재전자수기구분코드삭제여부최종수정수
일련번호1.0000.0000.7290.4200.4200.1940.0000.000
전자결재구분코드0.0001.0000.0000.0420.0420.0000.0000.087
전자결재결재자역할코드0.7290.0001.0000.4220.4220.0000.0000.000
결재일자0.4200.0420.4221.0000.9930.0320.0420.629
결재자결재정보코드0.4200.0420.4220.9931.0000.0320.0420.629
전자결재전자수기구분코드0.1940.0000.0000.0320.0321.0000.0000.198
삭제여부0.0000.0000.0000.0420.0420.0001.0000.999
최종수정수0.0000.0870.0000.6290.6290.1980.9991.000

Missing values

2024-04-18T14:03:07.962468image/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

전자결재개별결재ID일련번호전자결재ID전자결재부점구분코드전자결재구분코드직위코드직급코드전자결재결재자역할코드결재일자결재시분초결재자결재정보코드전자결재미결재사유코드전자결재전자수기구분코드결재의견반송사유내용삭제여부최종수정수처리시각처리직원번호
09dnS0VgFz019dnS0VgFz011100:00.014:41:0111<NA>N141:01.75091
19dnS0VgFz029dnS0VgFz01130001-01-01 00:00:00.0000000:00:0031<NA>N141:01.75091
29dnS0SdHkZ19dnS0SdHkZ11100:00.014:40:2711<NA>N240:51.73026
39dnS0SdHkZ29dnS0SdHkZ11300:00.014:40:5111<NA>N240:51.73026
49dnS0RQJEB19dnS0RQJEB11100:00.014:40:1511<NA>N240:50.33593
59dnS0RQJEB29dnS0RQJEB11300:00.014:40:5011<NA>N240:50.33593
69dnS0RRuMK19dnS0RRuMK11100:00.014:40:2111<NA>N240:43.04866
79dnS0RRuMK29dnS0RRuMK11300:00.014:40:4211<NA>N240:43.04866
89dnS0TKZr619dnS0TKZr611100:00.014:40:3911<NA>N140:39.45091
99dnS0TKZr629dnS0TKZr61130001-01-01 00:00:00.0000000:00:0031<NA>N140:39.45091
전자결재개별결재ID일련번호전자결재ID전자결재부점구분코드전자결재구분코드직위코드직급코드전자결재결재자역할코드결재일자결재시분초결재자결재정보코드전자결재미결재사유코드전자결재전자수기구분코드결재의견반송사유내용삭제여부최종수정수처리시각처리직원번호
4909dnSZyZoe829dnSZyZoe81130001-01-01 00:00:00.0000000:00:0031<NA>N121:01.89C623
4919dnSYlUY1019dnSYlUY1011100:00.014:01:4911<NA>N220:59.35340
4929dnSYlUY1029dnSYlUY1011300:00.014:20:5911<NA>N220:59.35340
4939dnSYpYueB19dnSYpYueB11100:00.014:02:5311<NA>N220:45.75340
4949dnSYpYueB29dnSYpYueB11300:00.014:20:4511<NA>N220:45.75340
4959dnSZwC08A19dnSZwC08A11100:00.014:20:0311<NA>N220:40.45621
4969dnSZwC08A29dnSZwC08A11300:00.014:20:4011<NA>N220:40.45621
4979dnSZy23Xr19dnSZy23Xr11100:00.014:20:3311<NA>N120:33.34831
4989dnSZy23Xr29dnSZy23Xr1130001-01-01 00:00:00.0000000:00:0031<NA>N120:33.34831
4999dnSYeUDL319dnSYeUDL311100:00.014:00:4011<NA>N220:31.13630