Overview

Dataset statistics

Number of variables10
Number of observations98
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory84.3 B

Variable types

Text2
Categorical7
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 시스템 인터페이스 대외 수신서비스 관계에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093210/fileData.do

Alerts

삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
처리일자 is highly overall correlated with 사용자메소드명 and 4 other fieldsHigh correlation
최초처리시각 is highly overall correlated with 사용자메소드명 and 4 other fieldsHigh correlation
처리시각 is highly overall correlated with 사용자메소드명 and 4 other fieldsHigh correlation
사용자메소드명 is highly overall correlated with 처리일자 and 4 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 사용자메소드명 and 4 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 사용자메소드명 and 4 other fieldsHigh correlation
처리직원번호 is highly imbalanced (70.9%)Imbalance
최초처리직원번호 is highly imbalanced (70.9%)Imbalance
인터페이스ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:50:07.855516
Analysis finished2023-12-12 00:50:08.869478
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

인터페이스ID
Text

UNIQUE 

Distinct98
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
2023-12-12T09:50:09.077812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length17.010204
Min length17

Characters and Unicode

Total characters1667
Distinct characters29
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

Unique98 ?
Unique (%)100.0%

Sample

1st rowIF-KTNET-GRN-00030
2nd rowIF-BNK1-GRN-01730
3rd rowIF-BNK1-GRN-01720
4th rowIF-BNK1-GRN-01710
5th rowIF-NIC-CUST-00010
ValueCountFrequency (%)
if-ktnet-grn-00030 1
 
1.0%
if-bnk1-grn-01450 1
 
1.0%
if-bnk1-grn-01470 1
 
1.0%
if-bnk1-grn-01480 1
 
1.0%
if-bnk1-grn-01490 1
 
1.0%
if-vir-acng-00310 1
 
1.0%
if-vir-acng-00320 1
 
1.0%
if-vir-acng-00330 1
 
1.0%
if-bnk1-grn-01290 1
 
1.0%
if-bnk1-grn-01320 1
 
1.0%
Other values (88) 88
89.8%
2023-12-12T09:50:09.607089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 294
17.6%
0 233
14.0%
N 151
9.1%
1 133
8.0%
I 130
 
7.8%
F 108
 
6.5%
K 88
 
5.3%
G 69
 
4.1%
R 68
 
4.1%
B 64
 
3.8%
Other values (19) 329
19.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 807
48.4%
Decimal Number 566
34.0%
Dash Punctuation 294
 
17.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 151
18.7%
I 130
16.1%
F 108
13.4%
K 88
10.9%
G 69
8.6%
R 68
8.4%
B 64
7.9%
V 21
 
2.6%
E 20
 
2.5%
D 18
 
2.2%
Other values (8) 70
8.7%
Decimal Number
ValueCountFrequency (%)
0 233
41.2%
1 133
23.5%
2 47
 
8.3%
3 36
 
6.4%
6 30
 
5.3%
5 24
 
4.2%
7 24
 
4.2%
4 17
 
3.0%
8 14
 
2.5%
9 8
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 860
51.6%
Latin 807
48.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 151
18.7%
I 130
16.1%
F 108
13.4%
K 88
10.9%
G 69
8.6%
R 68
8.4%
B 64
7.9%
V 21
 
2.6%
E 20
 
2.5%
D 18
 
2.2%
Other values (8) 70
8.7%
Common
ValueCountFrequency (%)
- 294
34.2%
0 233
27.1%
1 133
15.5%
2 47
 
5.5%
3 36
 
4.2%
6 30
 
3.5%
5 24
 
2.8%
7 24
 
2.8%
4 17
 
2.0%
8 14
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 294
17.6%
0 233
14.0%
N 151
9.1%
1 133
8.0%
I 130
 
7.8%
F 108
 
6.5%
K 88
 
5.3%
G 69
 
4.1%
R 68
 
4.1%
B 64
 
3.8%
Other values (19) 329
19.7%

서비스명
Text

Distinct84
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size916.0 B
2023-12-12T09:50:09.875638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1078
Distinct characters24
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

Unique77 ?
Unique (%)78.6%

Sample

1st rowBOA523_00SO
2nd rowBOC559_00SO
3rd rowBOC558_00SO
4th rowBOC557_00SO
5th rowUXG007_00SO
ValueCountFrequency (%)
jxa044_00so 5
 
5.1%
ixe005_00so 4
 
4.1%
ixe001_00so 4
 
4.1%
boc553_00so 2
 
2.0%
boa515_00so 2
 
2.0%
boa514_00so 2
 
2.0%
aae601_03so 2
 
2.0%
boa523_00so 1
 
1.0%
boc521_00so 1
 
1.0%
boc522_00so 1
 
1.0%
Other values (74) 74
75.5%
2023-12-12T09:50:10.297394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 233
21.6%
O 162
15.0%
_ 98
9.1%
S 98
9.1%
5 86
 
8.0%
B 64
 
5.9%
C 59
 
5.5%
4 58
 
5.4%
1 36
 
3.3%
A 31
 
2.9%
Other values (14) 153
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 490
45.5%
Uppercase Letter 490
45.5%
Connector Punctuation 98
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 162
33.1%
S 98
20.0%
B 64
 
13.1%
C 59
 
12.0%
A 31
 
6.3%
X 29
 
5.9%
J 18
 
3.7%
E 14
 
2.9%
I 9
 
1.8%
U 3
 
0.6%
Other values (3) 3
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 233
47.6%
5 86
 
17.6%
4 58
 
11.8%
1 36
 
7.3%
3 22
 
4.5%
2 19
 
3.9%
6 14
 
2.9%
7 9
 
1.8%
9 7
 
1.4%
8 6
 
1.2%
Connector Punctuation
ValueCountFrequency (%)
_ 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 588
54.5%
Latin 490
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 162
33.1%
S 98
20.0%
B 64
 
13.1%
C 59
 
12.0%
A 31
 
6.3%
X 29
 
5.9%
J 18
 
3.7%
E 14
 
2.9%
I 9
 
1.8%
U 3
 
0.6%
Other values (3) 3
 
0.6%
Common
ValueCountFrequency (%)
0 233
39.6%
_ 98
16.7%
5 86
 
14.6%
4 58
 
9.9%
1 36
 
6.1%
3 22
 
3.7%
2 19
 
3.2%
6 14
 
2.4%
7 9
 
1.5%
9 7
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 233
21.6%
O 162
15.0%
_ 98
9.1%
S 98
9.1%
5 86
 
8.0%
B 64
 
5.9%
C 59
 
5.5%
4 58
 
5.4%
1 36
 
3.3%
A 31
 
2.9%
Other values (14) 153
14.2%

사용자메소드명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size916.0 B
pcBkgrnTl
58 
rcvKedTabuAcngDta
18 
pcMbizTl
 
5
pcElcOncrAcrcIsuOsidTl
 
4
pcElcNoteInfoOsidTl
 
4
Other values (7)
9 

Length

Max length25
Median length9
Mean length11.765306
Min length1

Unique

Unique5 ?
Unique (%)5.1%

Sample

1st rowrcptlndrExpAtrs
2nd rowpcBkgrnTl
3rd rowpcBkgrnTl
4th rowpcBkgrnTl
5th rowrcvRettRgstInfo

Common Values

ValueCountFrequency (%)
pcBkgrnTl 58
59.2%
rcvKedTabuAcngDta 18
 
18.4%
pcMbizTl 5
 
5.1%
pcElcOncrAcrcIsuOsidTl 4
 
4.1%
pcElcNoteInfoOsidTl 4
 
4.1%
2
 
2.0%
rcptFibaVrtlActMnrcTl 2
 
2.0%
rcptlndrExpAtrs 1
 
1.0%
rcvRettRgstInfo 1
 
1.0%
updSrncResult 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2023-12-12T09:50:10.509450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pcbkgrntl 58
60.4%
rcvkedtabuacngdta 18
 
18.8%
pcmbiztl 5
 
5.2%
pcelconcracrcisuosidtl 4
 
4.2%
pcelcnoteinfoosidtl 4
 
4.2%
rcptfibavrtlactmnrctl 2
 
2.1%
rcptlndrexpatrs 1
 
1.0%
rcvrettrgstinfo 1
 
1.0%
updsrncresult 1
 
1.0%
rcptfibavrtlactrcvnsrchtl 1
 
1.0%

처리일자
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Memory size916.0 B
14:25.0
25 
42:34.0
13 
17:41.0
13 
47:16.0
8 
17:42.0
5 
Other values (22)
34 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique17 ?
Unique (%)17.3%

Sample

1st row25:37.0
2nd row37:10.0
3rd row16:15.0
4th row28:11.0
5th row23:29.0

Common Values

ValueCountFrequency (%)
14:25.0 25
25.5%
42:34.0 13
13.3%
17:41.0 13
13.3%
47:16.0 8
 
8.2%
17:42.0 5
 
5.1%
40:33.0 5
 
5.1%
14:26.0 4
 
4.1%
41:12.0 4
 
4.1%
47:19.0 2
 
2.0%
01:47.0 2
 
2.0%
Other values (17) 17
17.3%

Length

2023-12-12T09:50:10.693120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14:25.0 25
25.5%
42:34.0 13
13.3%
17:41.0 13
13.3%
47:16.0 8
 
8.2%
17:42.0 5
 
5.1%
40:33.0 5
 
5.1%
14:26.0 4
 
4.1%
41:12.0 4
 
4.1%
01:47.0 2
 
2.0%
47:19.0 2
 
2.0%
Other values (17) 17
17.3%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size230.0 B
False
98 
ValueCountFrequency (%)
False 98
100.0%
2023-12-12T09:50:10.811657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
1
98 

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 98
100.0%

Length

2023-12-12T09:50:10.923473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:50:11.048287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 98
100.0%

처리시각
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Memory size916.0 B
47:16.4
7 
17:41.9
7 
14:25.9
7 
42:34.7
 
6
14:25.8
 
6
Other values (32)
65 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique20 ?
Unique (%)20.4%

Sample

1st row25:37.2
2nd row37:10.5
3rd row16:15.2
4th row28:11.3
5th row23:29.4

Common Values

ValueCountFrequency (%)
47:16.4 7
 
7.1%
17:41.9 7
 
7.1%
14:25.9 7
 
7.1%
42:34.7 6
 
6.1%
14:25.8 6
 
6.1%
14:26.0 6
 
6.1%
17:42.0 6
 
6.1%
40:33.7 5
 
5.1%
14:25.6 5
 
5.1%
41:12.9 4
 
4.1%
Other values (27) 39
39.8%

Length

2023-12-12T09:50:11.196402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
47:16.4 7
 
7.1%
14:25.9 7
 
7.1%
17:41.9 7
 
7.1%
42:34.7 6
 
6.1%
14:25.8 6
 
6.1%
14:26.0 6
 
6.1%
17:42.0 6
 
6.1%
40:33.7 5
 
5.1%
14:25.6 5
 
5.1%
41:12.9 4
 
4.1%
Other values (27) 39
39.8%

처리직원번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
0
93 
5391
 
5

Length

Max length4
Median length1
Mean length1.1530612
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 93
94.9%
5391 5
 
5.1%

Length

2023-12-12T09:50:11.342314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:50:11.449801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 93
94.9%
5391 5
 
5.1%

최초처리시각
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Memory size916.0 B
47:16.4
7 
17:41.9
7 
14:25.9
7 
42:34.7
 
6
14:25.8
 
6
Other values (32)
65 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique20 ?
Unique (%)20.4%

Sample

1st row25:37.2
2nd row37:10.5
3rd row16:15.2
4th row28:11.3
5th row23:29.4

Common Values

ValueCountFrequency (%)
47:16.4 7
 
7.1%
17:41.9 7
 
7.1%
14:25.9 7
 
7.1%
42:34.7 6
 
6.1%
14:25.8 6
 
6.1%
14:26.0 6
 
6.1%
17:42.0 6
 
6.1%
40:33.7 5
 
5.1%
14:25.6 5
 
5.1%
41:12.9 4
 
4.1%
Other values (27) 39
39.8%

Length

2023-12-12T09:50:11.575998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
47:16.4 7
 
7.1%
14:25.9 7
 
7.1%
17:41.9 7
 
7.1%
42:34.7 6
 
6.1%
14:25.8 6
 
6.1%
14:26.0 6
 
6.1%
17:42.0 6
 
6.1%
40:33.7 5
 
5.1%
14:25.6 5
 
5.1%
41:12.9 4
 
4.1%
Other values (27) 39
39.8%

최초처리직원번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
0
93 
5391
 
5

Length

Max length4
Median length1
Mean length1.1530612
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 93
94.9%
5391 5
 
5.1%

Length

2023-12-12T09:50:11.751182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:50:11.883411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 93
94.9%
5391 5
 
5.1%

Correlations

2023-12-12T09:50:11.971237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인터페이스ID서비스명사용자메소드명처리일자처리시각처리직원번호최초처리시각최초처리직원번호
인터페이스ID1.0001.0001.0001.0001.0001.0001.0001.000
서비스명1.0001.0001.0000.9470.9261.0000.9261.000
사용자메소드명1.0001.0001.0000.9900.9920.7190.9920.719
처리일자1.0000.9470.9901.0000.9991.0000.9991.000
처리시각1.0000.9260.9920.9991.0001.0001.0001.000
처리직원번호1.0001.0000.7191.0001.0001.0001.0000.986
최초처리시각1.0000.9260.9920.9991.0001.0001.0001.000
최초처리직원번호1.0001.0000.7191.0001.0000.9861.0001.000
2023-12-12T09:50:12.132324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리직원번호사용자메소드명처리일자최초처리직원번호최초처리시각처리시각
처리직원번호1.0000.5390.8600.8930.7970.797
사용자메소드명0.5391.0000.8350.5390.7710.771
처리일자0.8600.8351.0000.8600.9020.902
최초처리직원번호0.8930.5390.8601.0000.7970.797
최초처리시각0.7970.7710.9020.7971.0001.000
처리시각0.7970.7710.9020.7971.0001.000
2023-12-12T09:50:12.252242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용자메소드명처리일자처리시각처리직원번호최초처리시각최초처리직원번호
사용자메소드명1.0000.8350.7710.5390.7710.539
처리일자0.8351.0000.9020.8600.9020.860
처리시각0.7710.9021.0000.7971.0000.797
처리직원번호0.5390.8600.7971.0000.7970.893
최초처리시각0.7710.9021.0000.7971.0000.797
최초처리직원번호0.5390.8600.7970.8930.7971.000

Missing values

2023-12-12T09:50:08.582499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:50:08.788370image/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-KTNET-GRN-00030BOA523_00SOrcptlndrExpAtrs25:37.0N125:37.2539125:37.25391
1IF-BNK1-GRN-01730BOC559_00SOpcBkgrnTl37:10.0N137:10.5539137:10.55391
2IF-BNK1-GRN-01720BOC558_00SOpcBkgrnTl16:15.0N116:15.2539116:15.25391
3IF-BNK1-GRN-01710BOC557_00SOpcBkgrnTl28:11.0N128:11.3539128:11.35391
4IF-NIC-CUST-00010UXG007_00SOrcvRettRgstInfo23:29.0N123:29.4539123:29.45391
5IF-MNBZ-GRN-02371BOA516_01SOpcMbizTl01:47.0N101:47.7001:47.70
6IF-MNBZ-GRN-02360BOA515_00SOpcMbizTl01:47.0N101:47.7001:47.70
7IF-MNBZ-GRN-02350BOA514_00SOpcMbizTl01:34.0N101:34.5001:34.50
8IF-FEP-ACNG-00084ACE601_14SOupdSrncResult27:52.0N127:52.6027:52.60
9IF-GRN-MNBZ-02350BOA514_00SOpcMbizTl07:10.0N107:10.7007:10.70
인터페이스ID서비스명사용자메소드명처리일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
88IF-BNK1-GRN-01260BOC511_00SOpcBkgrnTl14:25.0N114:25.7014:25.70
89IF-BNK1-GRN-01250BOC510_00SOpcBkgrnTl14:25.0N114:25.7014:25.70
90IF-BNK1-GRN-01230BOC508_00SOpcBkgrnTl14:25.0N114:25.7014:25.70
91IF-BNK1-GRN-01220BOC507_00SOpcBkgrnTl14:25.0N114:25.6014:25.60
92IF-BNK1-GRN-01210BOC506_00SOpcBkgrnTl14:25.0N114:25.6014:25.60
93IF-BNK1-GRN-01200BOC505_00SOpcBkgrnTl14:25.0N114:25.6014:25.60
94IF-BNK1-GRN-01190BOC504_00SOpcBkgrnTl14:25.0N114:25.6014:25.60
95IF-BNK1-GRN-01180BOC503_00SOpcBkgrnTl14:25.0N114:25.6014:25.60
96IF-BNK1-GRN-01170BOC502_00SOpcBkgrnTl14:25.0N114:25.5014:25.50
97IF-KFTC-ISU-00860KEI002_00SOrcvOsidTl18:29.0N118:29.4018:29.40