Overview

Dataset statistics

Number of variables10
Number of observations461
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.6 KiB
Average record size in memory81.3 B

Variable types

Text6
Boolean1
Categorical3

Dataset

Description해당 파일 데이터는 신용보증기금의 시스템 인터페이스 대내 수신 서비스 관계에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093196/fileData.do

Alerts

삭제여부 has constant value ""Constant
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최종수정수 is highly imbalanced (91.4%)Imbalance
처리직원번호 is highly imbalanced (57.1%)Imbalance
최초처리직원번호 is highly imbalanced (52.8%)Imbalance
인터페이스ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:54:42.778433
Analysis finished2023-12-12 21:54:43.353570
Duration0.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

인터페이스ID
Text

UNIQUE 

Distinct461
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-13T06:54:43.537643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length17.084599
Min length16

Characters and Unicode

Total characters7876
Distinct characters30
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

Unique461 ?
Unique (%)100.0%

Sample

1st rowIF-HMPG-ISU-00760
2nd rowIF-HMPG-ISU-00750
3rd rowIF-ECOS-FCT-00020
4th rowIF-MPS-ISU-01031
5th rowIF-MPS-ISU-01041
ValueCountFrequency (%)
if-hmpg-isu-00760 1
 
0.2%
if-hmpg-inv-00150 1
 
0.2%
if-hmpg-crmn-00080 1
 
0.2%
if-hmpg-crmn-00090 1
 
0.2%
if-hmpg-crmn-00100 1
 
0.2%
if-hmpg-crmn-00110 1
 
0.2%
if-hmpg-crmn-00120 1
 
0.2%
if-hmpg-crmn-00130 1
 
0.2%
if-hmpg-crmn-00140 1
 
0.2%
if-hmpg-crmn-00150 1
 
0.2%
Other values (451) 451
97.8%
2023-12-13T06:54:43.969168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1383
17.6%
0 1336
17.0%
I 634
 
8.0%
F 513
 
6.5%
M 450
 
5.7%
P 449
 
5.7%
G 438
 
5.6%
N 352
 
4.5%
H 260
 
3.3%
R 249
 
3.2%
Other values (20) 1812
23.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4195
53.3%
Decimal Number 2298
29.2%
Dash Punctuation 1383
 
17.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 634
15.1%
F 513
12.2%
M 450
10.7%
P 449
10.7%
G 438
10.4%
N 352
8.4%
H 260
6.2%
R 249
 
5.9%
S 234
 
5.6%
C 171
 
4.1%
Other values (9) 445
10.6%
Decimal Number
ValueCountFrequency (%)
0 1336
58.1%
1 196
 
8.5%
2 168
 
7.3%
5 107
 
4.7%
3 104
 
4.5%
4 96
 
4.2%
6 90
 
3.9%
7 74
 
3.2%
9 65
 
2.8%
8 62
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 1383
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4195
53.3%
Common 3681
46.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 634
15.1%
F 513
12.2%
M 450
10.7%
P 449
10.7%
G 438
10.4%
N 352
8.4%
H 260
6.2%
R 249
 
5.9%
S 234
 
5.6%
C 171
 
4.1%
Other values (9) 445
10.6%
Common
ValueCountFrequency (%)
- 1383
37.6%
0 1336
36.3%
1 196
 
5.3%
2 168
 
4.6%
5 107
 
2.9%
3 104
 
2.8%
4 96
 
2.6%
6 90
 
2.4%
7 74
 
2.0%
9 65
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1383
17.6%
0 1336
17.0%
I 634
 
8.0%
F 513
 
6.5%
M 450
 
5.7%
P 449
 
5.7%
G 438
 
5.6%
N 352
 
4.5%
H 260
 
3.3%
R 249
 
3.2%
Other values (20) 1812
23.0%

서비스명
Text

Distinct437
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-13T06:54:44.279458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.995662
Min length9

Characters and Unicode

Total characters5069
Distinct characters32
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

Unique416 ?
Unique (%)90.2%

Sample

1st rowIXA050_01SO
2nd rowIXA050_00SO
3rd rowLOA001_00SO
4th rowIXD120_01SO
5th rowIXD121_01SO
ValueCountFrequency (%)
ixa028_02so 4
 
0.9%
bod043_00so 3
 
0.7%
bob587_00so 2
 
0.4%
iog022_00so 2
 
0.4%
bob558_00so 2
 
0.4%
bod028_00so 2
 
0.4%
bod038_00so 2
 
0.4%
ixa028_00so 2
 
0.4%
bob535_00so 2
 
0.4%
bob538_00so 2
 
0.4%
Other values (427) 438
95.0%
2023-12-13T06:54:44.829772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1254
24.7%
O 661
13.0%
S 512
10.1%
_ 461
 
9.1%
B 332
 
6.5%
1 204
 
4.0%
2 179
 
3.5%
5 170
 
3.4%
A 156
 
3.1%
I 140
 
2.8%
Other values (22) 1000
19.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2333
46.0%
Decimal Number 2275
44.9%
Connector Punctuation 461
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 661
28.3%
S 512
21.9%
B 332
14.2%
A 156
 
6.7%
I 140
 
6.0%
X 134
 
5.7%
D 100
 
4.3%
Z 60
 
2.6%
L 53
 
2.3%
J 45
 
1.9%
Other values (11) 140
 
6.0%
Decimal Number
ValueCountFrequency (%)
0 1254
55.1%
1 204
 
9.0%
2 179
 
7.9%
5 170
 
7.5%
3 94
 
4.1%
6 87
 
3.8%
4 79
 
3.5%
8 72
 
3.2%
7 72
 
3.2%
9 64
 
2.8%
Connector Punctuation
ValueCountFrequency (%)
_ 461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2736
54.0%
Latin 2333
46.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 661
28.3%
S 512
21.9%
B 332
14.2%
A 156
 
6.7%
I 140
 
6.0%
X 134
 
5.7%
D 100
 
4.3%
Z 60
 
2.6%
L 53
 
2.3%
J 45
 
1.9%
Other values (11) 140
 
6.0%
Common
ValueCountFrequency (%)
0 1254
45.8%
_ 461
 
16.8%
1 204
 
7.5%
2 179
 
6.5%
5 170
 
6.2%
3 94
 
3.4%
6 87
 
3.2%
4 79
 
2.9%
8 72
 
2.6%
7 72
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1254
24.7%
O 661
13.0%
S 512
10.1%
_ 461
 
9.1%
B 332
 
6.5%
1 204
 
4.0%
2 179
 
3.5%
5 170
 
3.4%
A 156
 
3.1%
I 140
 
2.8%
Other values (22) 1000
19.7%

사용자메소드명
Text

Distinct242
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-13T06:54:45.136180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length26
Mean length11.518438
Min length1

Characters and Unicode

Total characters5310
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)49.2%

Sample

1st rowinsDcprCtt
2nd rowsrchDcprCtt
3rd rowinsGvbdEaraInfo
4th rowpcIsuRcmdInBoundTI
5th rowpcIsuRcmdInBoundTI
ValueCountFrequency (%)
pcelgrntl 92
 
23.8%
pciejisuinboundtl 20
 
5.2%
pceaisuinboundtl 12
 
3.1%
pcb2bpisuinboundtl 7
 
1.8%
pcisurcmdinboundti 6
 
1.6%
pchmpgtl 5
 
1.3%
srchempinfo 3
 
0.8%
srchwrgriss 2
 
0.5%
srchwrgrissdetl 2
 
0.5%
srchisuacdenppres 2
 
0.5%
Other values (232) 236
61.0%
2023-12-13T06:54:45.575488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 477
 
9.0%
n 424
 
8.0%
c 424
 
8.0%
s 392
 
7.4%
l 350
 
6.6%
t 293
 
5.5%
p 280
 
5.3%
g 194
 
3.7%
h 182
 
3.4%
I 168
 
3.2%
Other values (42) 2126
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4118
77.6%
Uppercase Letter 1104
 
20.8%
Space Separator 77
 
1.5%
Decimal Number 11
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 477
11.6%
n 424
10.3%
c 424
10.3%
s 392
9.5%
l 350
 
8.5%
t 293
 
7.1%
p 280
 
6.8%
g 194
 
4.7%
h 182
 
4.4%
e 159
 
3.9%
Other values (15) 943
22.9%
Uppercase Letter
ValueCountFrequency (%)
I 168
15.2%
T 164
14.9%
E 147
13.3%
B 80
 
7.2%
D 74
 
6.7%
P 67
 
6.1%
A 56
 
5.1%
C 51
 
4.6%
S 46
 
4.2%
R 39
 
3.5%
Other values (15) 212
19.2%
Space Separator
ValueCountFrequency (%)
77
100.0%
Decimal Number
ValueCountFrequency (%)
2 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5222
98.3%
Common 88
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 477
 
9.1%
n 424
 
8.1%
c 424
 
8.1%
s 392
 
7.5%
l 350
 
6.7%
t 293
 
5.6%
p 280
 
5.4%
g 194
 
3.7%
h 182
 
3.5%
I 168
 
3.2%
Other values (40) 2038
39.0%
Common
ValueCountFrequency (%)
77
87.5%
2 11
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 477
 
9.0%
n 424
 
8.0%
c 424
 
8.0%
s 392
 
7.4%
l 350
 
6.6%
t 293
 
5.5%
p 280
 
5.3%
g 194
 
3.7%
h 182
 
3.4%
I 168
 
3.2%
Other values (42) 2126
40.0%

처리일자
Text

Distinct327
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-13T06:54:45.925968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3227
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

Unique303 ?
Unique (%)65.7%

Sample

1st row50:00.0
2nd row49:33.0
3rd row08:21.0
4th row13:03.0
5th row12:55.0
ValueCountFrequency (%)
42:30.0 43
 
9.3%
42:31.0 43
 
9.3%
16:23.0 20
 
4.3%
03:51.0 7
 
1.5%
59:01.0 5
 
1.1%
52:48.0 3
 
0.7%
17:52.0 3
 
0.7%
27:42.0 2
 
0.4%
23:54.0 2
 
0.4%
34:28.0 2
 
0.4%
Other values (317) 331
71.8%
2023-12-13T06:54:46.388983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 691
21.4%
: 461
14.3%
. 461
14.3%
3 308
9.5%
2 298
9.2%
1 263
 
8.1%
4 229
 
7.1%
5 216
 
6.7%
6 82
 
2.5%
9 73
 
2.3%
Other values (2) 145
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2305
71.4%
Other Punctuation 922
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 691
30.0%
3 308
13.4%
2 298
12.9%
1 263
 
11.4%
4 229
 
9.9%
5 216
 
9.4%
6 82
 
3.6%
9 73
 
3.2%
8 73
 
3.2%
7 72
 
3.1%
Other Punctuation
ValueCountFrequency (%)
: 461
50.0%
. 461
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 691
21.4%
: 461
14.3%
. 461
14.3%
3 308
9.5%
2 298
9.2%
1 263
 
8.1%
4 229
 
7.1%
5 216
 
6.7%
6 82
 
2.5%
9 73
 
2.3%
Other values (2) 145
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 691
21.4%
: 461
14.3%
. 461
14.3%
3 308
9.5%
2 298
9.2%
1 263
 
8.1%
4 229
 
7.1%
5 216
 
6.7%
6 82
 
2.5%
9 73
 
2.3%
Other values (2) 145
 
4.5%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size593.0 B
False
461 
ValueCountFrequency (%)
False 461
100.0%
2023-12-13T06:54:46.522964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
453 
2
 
7
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 453
98.3%
2 7
 
1.5%
6 1
 
0.2%

Length

2023-12-13T06:54:46.616210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:46.716967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 453
98.3%
2 7
 
1.5%
6 1
 
0.2%

처리시각
Text

Distinct359
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-13T06:54:47.044935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3227
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

Unique328 ?
Unique (%)71.1%

Sample

1st row50:00.1
2nd row49:33.6
3rd row08:21.9
4th row13:03.6
5th row12:55.4
ValueCountFrequency (%)
16:23.0 20
 
4.3%
03:51.5 7
 
1.5%
42:30.2 6
 
1.3%
42:31.2 5
 
1.1%
42:31.8 5
 
1.1%
42:31.7 5
 
1.1%
42:31.6 5
 
1.1%
42:30.1 5
 
1.1%
42:31.4 5
 
1.1%
42:30.8 5
 
1.1%
Other values (349) 393
85.2%
2023-12-13T06:54:47.544211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 461
14.3%
. 461
14.3%
3 347
10.8%
2 340
10.5%
1 313
9.7%
0 282
8.7%
4 281
8.7%
5 263
8.1%
6 132
 
4.1%
9 122
 
3.8%
Other values (2) 225
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2305
71.4%
Other Punctuation 922
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 347
15.1%
2 340
14.8%
1 313
13.6%
0 282
12.2%
4 281
12.2%
5 263
11.4%
6 132
 
5.7%
9 122
 
5.3%
8 116
 
5.0%
7 109
 
4.7%
Other Punctuation
ValueCountFrequency (%)
: 461
50.0%
. 461
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 461
14.3%
. 461
14.3%
3 347
10.8%
2 340
10.5%
1 313
9.7%
0 282
8.7%
4 281
8.7%
5 263
8.1%
6 132
 
4.1%
9 122
 
3.8%
Other values (2) 225
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 461
14.3%
. 461
14.3%
3 347
10.8%
2 340
10.5%
1 313
9.7%
0 282
8.7%
4 281
8.7%
5 263
8.1%
6 132
 
4.1%
9 122
 
3.8%
Other values (2) 225
7.0%

처리직원번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
319 
5391
135 
EXF08
 
5
5176
 
1
EXF26
 
1

Length

Max length5
Median length1
Mean length1.9370933
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row5391
2nd row5391
3rd row5391
4th row5391
5th row5391

Common Values

ValueCountFrequency (%)
0 319
69.2%
5391 135
29.3%
EXF08 5
 
1.1%
5176 1
 
0.2%
EXF26 1
 
0.2%

Length

2023-12-13T06:54:47.693923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:47.799545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 319
69.2%
5391 135
29.3%
exf08 5
 
1.1%
5176 1
 
0.2%
exf26 1
 
0.2%

최초처리시각
Text

Distinct357
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-13T06:54:48.125062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3227
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

Unique326 ?
Unique (%)70.7%

Sample

1st row50:00.1
2nd row49:33.6
3rd row08:21.9
4th row12:32.2
5th row12:55.4
ValueCountFrequency (%)
16:23.0 20
 
4.3%
03:51.5 7
 
1.5%
42:30.1 6
 
1.3%
42:30.2 6
 
1.3%
42:30.4 5
 
1.1%
42:31.4 5
 
1.1%
42:31.8 5
 
1.1%
42:31.7 5
 
1.1%
42:31.2 5
 
1.1%
42:31.6 5
 
1.1%
Other values (347) 392
85.0%
2023-12-13T06:54:48.587582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 461
14.3%
. 461
14.3%
2 346
10.7%
3 346
10.7%
1 312
9.7%
0 282
8.7%
4 282
8.7%
5 264
8.2%
6 128
 
4.0%
9 119
 
3.7%
Other values (2) 226
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2305
71.4%
Other Punctuation 922
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 346
15.0%
3 346
15.0%
1 312
13.5%
0 282
12.2%
4 282
12.2%
5 264
11.5%
6 128
 
5.6%
9 119
 
5.2%
8 115
 
5.0%
7 111
 
4.8%
Other Punctuation
ValueCountFrequency (%)
: 461
50.0%
. 461
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 461
14.3%
. 461
14.3%
2 346
10.7%
3 346
10.7%
1 312
9.7%
0 282
8.7%
4 282
8.7%
5 264
8.2%
6 128
 
4.0%
9 119
 
3.7%
Other values (2) 226
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 461
14.3%
. 461
14.3%
2 346
10.7%
3 346
10.7%
1 312
9.7%
0 282
8.7%
4 282
8.7%
5 264
8.2%
6 128
 
4.0%
9 119
 
3.7%
Other values (2) 226
7.0%

최초처리직원번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
323 
5391
134 
EXF08
 
3
5176
 
1

Length

Max length5
Median length1
Mean length1.9045553
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row5391
2nd row5391
3rd row5391
4th row5391
5th row5391

Common Values

ValueCountFrequency (%)
0 323
70.1%
5391 134
29.1%
EXF08 3
 
0.7%
5176 1
 
0.2%

Length

2023-12-13T06:54:48.789759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:48.900385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 323
70.1%
5391 134
29.1%
exf08 3
 
0.7%
5176 1
 
0.2%

Correlations

2023-12-13T06:54:48.976221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수처리직원번호최초처리직원번호
최종수정수1.0000.4960.000
처리직원번호0.4961.0000.917
최초처리직원번호0.0000.9171.000
2023-12-13T06:54:49.111137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수최초처리직원번호처리직원번호
최종수정수1.0000.0000.429
최초처리직원번호0.0001.0000.927
처리직원번호0.4290.9271.000
2023-12-13T06:54:49.227611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수처리직원번호최초처리직원번호
최종수정수1.0000.4290.000
처리직원번호0.4291.0000.927
최초처리직원번호0.0000.9271.000

Missing values

2023-12-13T06:54:43.142400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:54:43.293827image/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

인터페이스ID서비스명사용자메소드명처리일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0IF-HMPG-ISU-00760IXA050_01SOinsDcprCtt50:00.0N150:00.1539150:00.15391
1IF-HMPG-ISU-00750IXA050_00SOsrchDcprCtt49:33.0N149:33.6539149:33.65391
2IF-ECOS-FCT-00020LOA001_00SOinsGvbdEaraInfo08:21.0N108:21.9539108:21.95391
3IF-MPS-ISU-01031IXD120_01SOpcIsuRcmdInBoundTI13:03.0N213:03.6539112:32.25391
4IF-MPS-ISU-01041IXD121_01SOpcIsuRcmdInBoundTI12:55.0N112:55.4539112:55.45391
5IF-MPS-ISU-01020IXD110_00SOpcIsuRcmdInBoundTI12:00.0N112:00.4539112:00.45391
6IF-MPS-ISU-00950IXD103_00SOpcIsuRcmdInBoundTI11:36.0N111:36.4539111:36.45391
7IF-MPS-ISU-00940IXD102_00SOpcIsuRcmdInBoundTI11:06.0N111:06.1539111:06.15391
8IF-MPS-ISU-00930IXD101_00SOpcIsuRcmdInBoundTI10:37.0N110:37.9539110:37.95391
9IF-HMPG-FCT-00230LOB023_00SOsrchPuenpLst35:10.0N135:10.8539135:10.85391
인터페이스ID서비스명사용자메소드명처리일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
451IF-BNMP-GRN-00030BOB525_00SOpcElgrnTl42:30.0N142:30.1042:30.10
452IF-BNMP-GRN-00020BOB524_00SOpcElgrnTl42:30.0N142:30.1042:30.10
453IF-BNMP-GRN-00010BOB523_00SOpcElgrnTl42:30.0N142:30.0042:30.00
454IF-EDMS-COMN-00160ZDZ100_45SO27:43.0N127:43.6027:43.60
455IF-EDMS-COMN-00150ZDZ100_44SO27:42.0N127:42.9027:42.90
456IF-EDMS-COMN-00140ZDZ100_43SO27:42.0N127:42.2027:42.20
457IF-EDMS-COMN-00130ZDZ100_42SO27:41.0N127:41.5027:41.50
458IF-EDMS-COMN-00120ZDZ100_41SO27:40.0N127:40.8027:40.80
459IF-EDMS-COMN-00110ZDZ100_40SO27:39.0N127:39.7027:39.70
460IF-HMPG-SUPR-00360KEI102_00SOrcvInsidTl36:32.0N136:32.2036:32.20