Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.8 KiB
Average record size in memory110.3 B

Variable types

Categorical5
DateTime3
Numeric3
Text2

Dataset

Description해당 데이터는 신용보증기금 보증부문의 위탁대출금리와 관련된 내용을 확인하실 수 있는 자료이니 참고하시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092636/fileData.do

Alerts

업무구분코드 has constant value ""Constant
대출금리적용일자 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
실행해지기표일자 is highly overall correlated with 대출금리비율 and 1 other fieldsHigh correlation
최종수정수 is highly overall correlated with 이력일련번호High correlation
이력일련번호 is highly overall correlated with 최종수정수High correlation
실행해지기표일련번호 is highly overall correlated with 실행해지기표일자High correlation
대출금리비율 is highly overall correlated with 실행해지기표일자High correlation
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
실행해지기표일자 is highly imbalanced (74.0%)Imbalance
실행해지기표일련번호 is highly imbalanced (87.3%)Imbalance
이력일련번호 is highly imbalanced (97.9%)Imbalance
최종수정수 is highly imbalanced (96.3%)Imbalance

Reproduction

Analysis started2023-12-12 23:37:28.411542
Analysis finished2023-12-12 23:37:29.767846
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업무구분코드
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
G 500
100.0%

Length

2023-12-13T08:37:29.830114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:37:29.928043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

실행해지기표일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
478 
00:00.0
 
22

Length

Max length26
Median length26
Mean length25.164
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 478
95.6%
00:00.0 22
 
4.4%

Length

2023-12-13T08:37:30.028460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:37:30.145300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 478
48.9%
00:00:00.000000 478
48.9%
00:00.0 22
 
2.2%

실행해지기표일련번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
478 
2
 
19
5
 
1
4
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 478
95.6%
2 19
 
3.8%
5 1
 
0.2%
4 1
 
0.2%
3 1
 
0.2%

Length

2023-12-13T08:37:30.247341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:37:30.334339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 478
95.6%
2 19
 
3.8%
5 1
 
0.2%
4 1
 
0.2%
3 1
 
0.2%

이력일련번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
499 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 499
99.8%
3 1
 
0.2%

Length

2023-12-13T08:37:30.442511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:37:30.530191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 499
99.8%
3 1
 
0.2%

대출금리적용일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T08:37:30.620102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:30.757098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대출금리비율
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9775
Minimum2.37
Maximum6.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T08:37:30.888159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.37
5-th percentile2.85
Q12.9
median2.9
Q32.9
95-th percentile3.562
Maximum6.54
Range4.17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39598631
Coefficient of variation (CV)0.13299288
Kurtosis45.908531
Mean2.9775
Median Absolute Deviation (MAD)0
Skewness6.248809
Sum1488.75
Variance0.15680516
MonotonicityNot monotonic
2023-12-13T08:37:31.032681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2.9 352
70.4%
2.85 27
 
5.4%
2.89 25
 
5.0%
2.87 19
 
3.8%
2.88 16
 
3.2%
2.84 9
 
1.8%
2.86 7
 
1.4%
3.99 6
 
1.2%
3.27 2
 
0.4%
6.54 2
 
0.4%
Other values (34) 35
 
7.0%
ValueCountFrequency (%)
2.37 1
 
0.2%
2.58 1
 
0.2%
2.65 1
 
0.2%
2.8 1
 
0.2%
2.81 1
 
0.2%
2.83 1
 
0.2%
2.84 9
 
1.8%
2.85 27
5.4%
2.86 7
 
1.4%
2.87 19
3.8%
ValueCountFrequency (%)
6.54 2
 
0.4%
6.44 1
 
0.2%
5.72 1
 
0.2%
5.27 1
 
0.2%
5.25 1
 
0.2%
4.04 1
 
0.2%
4.0 1
 
0.2%
3.99 6
1.2%
3.96 1
 
0.2%
3.95 1
 
0.2%

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T08:37:31.154214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:31.266554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효종료일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T08:37:31.374100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:31.483871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
497 
3
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 497
99.4%
3 2
 
0.4%
2 1
 
0.2%

Length

2023-12-13T08:37:31.596358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:37:31.686024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 497
99.4%
3 2
 
0.4%
2 1
 
0.2%

처리시각
Text

Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T08:37:32.026964image/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

Unique492 ?
Unique (%)98.4%

Sample

1st row06:35.8
2nd row59:11.6
3rd row58:58.4
4th row54:25.4
5th row53:58.5
ValueCountFrequency (%)
00:47.5 2
 
0.4%
17:42.4 2
 
0.4%
16:34.8 2
 
0.4%
01:09.5 2
 
0.4%
03:25.3 1
 
0.2%
13:04.3 1
 
0.2%
03:41.0 1
 
0.2%
06:50.7 1
 
0.2%
07:37.1 1
 
0.2%
08:05.8 1
 
0.2%
Other values (486) 486
97.2%
2023-12-13T08:37:32.500726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 406
11.6%
0 365
10.4%
4 346
9.9%
3 279
8.0%
2 279
8.0%
5 244
7.0%
6 162
 
4.6%
8 149
 
4.3%
Other values (2) 270
7.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 406
16.2%
0 365
14.6%
4 346
13.8%
3 279
11.2%
2 279
11.2%
5 244
9.8%
6 162
 
6.5%
8 149
 
6.0%
7 142
 
5.7%
9 128
 
5.1%
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%
1 406
11.6%
0 365
10.4%
4 346
9.9%
3 279
8.0%
2 279
8.0%
5 244
7.0%
6 162
 
4.6%
8 149
 
4.3%
Other values (2) 270
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 406
11.6%
0 365
10.4%
4 346
9.9%
3 279
8.0%
2 279
8.0%
5 244
7.0%
6 162
 
4.6%
8 149
 
4.3%
Other values (2) 270
7.7%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99010.95
Minimum99001
Maximum99024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T08:37:32.680884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99001
5-th percentile99001
Q199006
median99007
Q399016
95-th percentile99023
Maximum99024
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.3628747
Coefficient of variation (CV)7.4364247 × 10-5
Kurtosis-1.2536809
Mean99010.95
Median Absolute Deviation (MAD)6
Skewness0.30952832
Sum49505475
Variance54.211924
MonotonicityNot monotonic
2023-12-13T08:37:32.821843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
99016 136
27.2%
99006 128
25.6%
99023 75
15.0%
99007 53
 
10.6%
99002 45
 
9.0%
99001 44
 
8.8%
99015 7
 
1.4%
99014 4
 
0.8%
99019 4
 
0.8%
99008 2
 
0.4%
ValueCountFrequency (%)
99001 44
 
8.8%
99002 45
 
9.0%
99006 128
25.6%
99007 53
 
10.6%
99008 2
 
0.4%
99014 4
 
0.8%
99015 7
 
1.4%
99016 136
27.2%
99019 4
 
0.8%
99023 75
15.0%
ValueCountFrequency (%)
99024 2
 
0.4%
99023 75
15.0%
99019 4
 
0.8%
99016 136
27.2%
99015 7
 
1.4%
99014 4
 
0.8%
99008 2
 
0.4%
99007 53
 
10.6%
99006 128
25.6%
99002 45
 
9.0%

최초처리시각
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T08:37:33.172846image/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

Unique490 ?
Unique (%)98.0%

Sample

1st row06:35.8
2nd row59:11.6
3rd row58:58.4
4th row54:25.4
5th row53:58.5
ValueCountFrequency (%)
17:42.4 2
 
0.4%
27:24.5 2
 
0.4%
16:34.8 2
 
0.4%
00:47.5 2
 
0.4%
01:09.5 2
 
0.4%
13:04.3 1
 
0.2%
13:11.9 1
 
0.2%
12:45.6 1
 
0.2%
03:41.0 1
 
0.2%
06:50.7 1
 
0.2%
Other values (485) 485
97.0%
2023-12-13T08:37:33.690039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 405
11.6%
0 364
10.4%
4 343
9.8%
2 282
8.1%
3 280
8.0%
5 248
7.1%
6 161
 
4.6%
8 147
 
4.2%
Other values (2) 270
7.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 405
16.2%
0 364
14.6%
4 343
13.7%
2 282
11.3%
3 280
11.2%
5 248
9.9%
6 161
 
6.4%
8 147
 
5.9%
7 144
 
5.8%
9 126
 
5.0%
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%
1 405
11.6%
0 364
10.4%
4 343
9.8%
2 282
8.1%
3 280
8.0%
5 248
7.1%
6 161
 
4.6%
8 147
 
4.2%
Other values (2) 270
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 405
11.6%
0 364
10.4%
4 343
9.8%
2 282
8.1%
3 280
8.0%
5 248
7.1%
6 161
 
4.6%
8 147
 
4.2%
Other values (2) 270
7.7%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99010.95
Minimum99001
Maximum99024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T08:37:33.820935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99001
5-th percentile99001
Q199006
median99007
Q399016
95-th percentile99023
Maximum99024
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.3628747
Coefficient of variation (CV)7.4364247 × 10-5
Kurtosis-1.2536809
Mean99010.95
Median Absolute Deviation (MAD)6
Skewness0.30952832
Sum49505475
Variance54.211924
MonotonicityNot monotonic
2023-12-13T08:37:33.928822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
99016 136
27.2%
99006 128
25.6%
99023 75
15.0%
99007 53
 
10.6%
99002 45
 
9.0%
99001 44
 
8.8%
99015 7
 
1.4%
99014 4
 
0.8%
99019 4
 
0.8%
99008 2
 
0.4%
ValueCountFrequency (%)
99001 44
 
8.8%
99002 45
 
9.0%
99006 128
25.6%
99007 53
 
10.6%
99008 2
 
0.4%
99014 4
 
0.8%
99015 7
 
1.4%
99016 136
27.2%
99019 4
 
0.8%
99023 75
15.0%
ValueCountFrequency (%)
99024 2
 
0.4%
99023 75
15.0%
99019 4
 
0.8%
99016 136
27.2%
99015 7
 
1.4%
99014 4
 
0.8%
99008 2
 
0.4%
99007 53
 
10.6%
99006 128
25.6%
99002 45
 
9.0%

Interactions

2023-12-13T08:37:29.278836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:28.818552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:29.038440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:29.353202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:28.882814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:29.121224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:29.442439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:28.960723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:29.204666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:37:34.013491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실행해지기표일자실행해지기표일련번호이력일련번호대출금리비율최종수정수처리직원번호최초처리직원번호
실행해지기표일자1.0001.0000.0000.8910.0000.2450.245
실행해지기표일련번호1.0001.0000.0000.6340.0000.2410.241
이력일련번호0.0000.0001.0000.0001.0000.0000.000
대출금리비율0.8910.6340.0001.0000.0000.6860.686
최종수정수0.0000.0001.0000.0001.0000.0000.000
처리직원번호0.2450.2410.0000.6860.0001.0001.000
최초처리직원번호0.2450.2410.0000.6860.0001.0001.000
2023-12-13T08:37:34.128769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실행해지기표일자최종수정수이력일련번호실행해지기표일련번호
실행해지기표일자1.0000.0000.0000.997
최종수정수0.0001.0000.9990.000
이력일련번호0.0000.9991.0000.000
실행해지기표일련번호0.9970.0000.0001.000
2023-12-13T08:37:34.233995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대출금리비율처리직원번호최초처리직원번호실행해지기표일자실행해지기표일련번호이력일련번호최종수정수
대출금리비율1.000-0.361-0.3610.7130.4550.0000.000
처리직원번호-0.3611.0001.0000.4480.2040.0000.000
최초처리직원번호-0.3611.0001.0000.4480.2040.0000.000
실행해지기표일자0.7130.4480.4481.0000.9970.0000.000
실행해지기표일련번호0.4550.2040.2040.9971.0000.0000.000
이력일련번호0.0000.0000.0000.0000.0001.0000.999
최종수정수0.0000.0000.0000.0000.0000.9991.000

Missing values

2023-12-13T08:37:29.553205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:37:29.703891image/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

업무구분코드실행해지기표일자실행해지기표일련번호이력일련번호대출금리적용일자대출금리비율유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0106:35.89900606:35.899006
1G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0159:11.69900759:11.699007
2G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0158:58.49900258:58.499002
3G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0154:25.49900654:25.499006
4G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0153:58.59900653:58.599006
5G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0146:44.49900246:44.499002
6G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0146:37.99900646:37.999006
7G0001-01-01 00:00:00.0000000100:00.03.8800:00.000:00.0143:26.19901543:26.199015
8G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0137:24.99900237:24.999002
9G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0133:21.29900733:21.299007
업무구분코드실행해지기표일자실행해지기표일련번호이력일련번호대출금리적용일자대출금리비율유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490G0001-01-01 00:00:00.0000000100:00.02.8900:00.000:00.0147:09.39900647:09.399006
491G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0145:14.69900245:14.699002
492G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0136:23.69900636:23.699006
493G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0136:23.29900236:23.299002
494G0001-01-01 00:00:00.0000000100:00.02.8900:00.000:00.0134:50.89900634:50.899006
495G0001-01-01 00:00:00.0000000100:00.02.8800:00.000:00.0132:02.79900632:02.799006
496G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0130:25.19900630:25.199006
497G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0126:48.59900626:48.599006
498G0001-01-01 00:00:00.0000000100:00.02.900:00.000:00.0124:49.09900624:49.099006
499G0001-01-01 00:00:00.0000000100:00.02.8900:00.000:00.0116:23.49900616:23.499006