Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.5 KiB
Average record size in memory93.3 B

Variable types

Categorical3
DateTime3
Numeric3
Text2

Dataset

Description해당 파일 데이터는 신용보증기금 보증부문의 보증부대출과 관련된 금리 정보를 확인하실 수 있는 자료이니 참고바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092637/fileData.do

Alerts

업무구분코드 has constant value ""Constant
대출금리적용일자 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
이력일련번호 is highly imbalanced (97.4%)Imbalance
최종수정수 is highly imbalanced (96.3%)Imbalance

Reproduction

Analysis started2023-12-12 17:22:53.649949
Analysis finished2023-12-12 17:22:55.457516
Duration1.81 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

2023-12-13T02:22:55.560506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:22:55.710279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

이력일련번호
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
498 
2
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 498
99.6%
2 1
 
0.2%
3 1
 
0.2%

Length

2023-12-13T02:22:55.834670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:22:55.946615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 498
99.6%
2 1
 
0.2%
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
2023-12-13T02:22:56.041310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:56.149757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대출금리비율
Real number (ℝ)

Distinct223
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.37772
Minimum0.19
Maximum6.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T02:22:56.334364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile2.34
Q12.98
median3.325
Q33.715
95-th percentile4.632
Maximum6.09
Range5.9
Interquartile range (IQR)0.735

Descriptive statistics

Standard deviation0.78033059
Coefficient of variation (CV)0.23102288
Kurtosis2.9346814
Mean3.37772
Median Absolute Deviation (MAD)0.365
Skewness-0.012311279
Sum1688.86
Variance0.60891583
MonotonicityNot monotonic
2023-12-13T02:22:56.536189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.22 9
 
1.8%
3.24 8
 
1.6%
3.12 8
 
1.6%
3.42 7
 
1.4%
3.68 6
 
1.2%
3.01 6
 
1.2%
3.32 6
 
1.2%
3.62 6
 
1.2%
3.59 6
 
1.2%
3.46 6
 
1.2%
Other values (213) 432
86.4%
ValueCountFrequency (%)
0.19 1
0.2%
0.2 1
0.2%
0.29 1
0.2%
0.43 1
0.2%
0.44 1
0.2%
0.98 1
0.2%
1.2 2
0.4%
1.67 1
0.2%
1.69 1
0.2%
1.89 1
0.2%
ValueCountFrequency (%)
6.09 1
0.2%
5.93 2
0.4%
5.87 1
0.2%
5.74 1
0.2%
5.57 1
0.2%
5.55 1
0.2%
5.53 2
0.4%
5.45 1
0.2%
5.43 1
0.2%
5.34 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
2023-12-13T02:22:56.666900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:56.775125image/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
2023-12-13T02:22:56.881637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:57.020058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
497 
2
 
2
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 (%)
1 497
99.4%
2 2
 
0.4%
3 1
 
0.2%

Length

2023-12-13T02:22:57.143243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:22:57.244947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 497
99.4%
2 2
 
0.4%
3 1
 
0.2%

처리시각
Text

Distinct494
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T02:22:57.633017image/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

Unique488 ?
Unique (%)97.6%

Sample

1st row11:08.3
2nd row03:07.0
3rd row47:38.1
4th row47:18.4
5th row44:26.9
ValueCountFrequency (%)
58:18.7 2
 
0.4%
54:45.1 2
 
0.4%
13:22.9 2
 
0.4%
03:25.5 2
 
0.4%
23:14.1 2
 
0.4%
00:38.7 2
 
0.4%
00:53.7 1
 
0.2%
01:00.4 1
 
0.2%
00:57.4 1
 
0.2%
00:49.7 1
 
0.2%
Other values (484) 484
96.8%
2023-12-13T02:22:58.412167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 519
14.8%
: 500
14.3%
. 500
14.3%
4 346
9.9%
1 308
8.8%
3 291
8.3%
5 290
8.3%
2 260
7.4%
6 137
 
3.9%
8 119
 
3.4%
Other values (2) 230
6.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 519
20.8%
4 346
13.8%
1 308
12.3%
3 291
11.6%
5 290
11.6%
2 260
10.4%
6 137
 
5.5%
8 119
 
4.8%
9 117
 
4.7%
7 113
 
4.5%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 519
14.8%
: 500
14.3%
. 500
14.3%
4 346
9.9%
1 308
8.8%
3 291
8.3%
5 290
8.3%
2 260
7.4%
6 137
 
3.9%
8 119
 
3.4%
Other values (2) 230
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 519
14.8%
: 500
14.3%
. 500
14.3%
4 346
9.9%
1 308
8.8%
3 291
8.3%
5 290
8.3%
2 260
7.4%
6 137
 
3.9%
8 119
 
3.4%
Other values (2) 230
6.6%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99016.258
Minimum99001
Maximum99024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T02:22:58.530426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99001
5-th percentile99002
Q199006
median99023
Q399023
95-th percentile99023
Maximum99024
Range23
Interquartile range (IQR)17

Descriptive statistics

Standard deviation8.0037213
Coefficient of variation (CV)8.0832396 × 10-5
Kurtosis-1.2607141
Mean99016.258
Median Absolute Deviation (MAD)0
Skewness-0.63671694
Sum49508129
Variance64.059555
MonotonicityNot monotonic
2023-12-13T02:22:58.649241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
99023 259
51.8%
99006 101
 
20.2%
99016 72
 
14.4%
99002 28
 
5.6%
99001 12
 
2.4%
99015 8
 
1.6%
99008 6
 
1.2%
99014 5
 
1.0%
99005 3
 
0.6%
99007 2
 
0.4%
Other values (3) 4
 
0.8%
ValueCountFrequency (%)
99001 12
 
2.4%
99002 28
 
5.6%
99005 3
 
0.6%
99006 101
20.2%
99007 2
 
0.4%
99008 6
 
1.2%
99014 5
 
1.0%
99015 8
 
1.6%
99016 72
14.4%
99017 1
 
0.2%
ValueCountFrequency (%)
99024 1
 
0.2%
99023 259
51.8%
99019 2
 
0.4%
99017 1
 
0.2%
99016 72
 
14.4%
99015 8
 
1.6%
99014 5
 
1.0%
99008 6
 
1.2%
99007 2
 
0.4%
99006 101
 
20.2%

최초처리시각
Text

Distinct492
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T02:22:58.956877image/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

Unique485 ?
Unique (%)97.0%

Sample

1st row11:08.3
2nd row03:07.0
3rd row47:38.1
4th row47:18.4
5th row44:26.9
ValueCountFrequency (%)
03:25.5 3
 
0.6%
23:14.1 2
 
0.4%
23:06.3 2
 
0.4%
00:38.7 2
 
0.4%
58:18.7 2
 
0.4%
54:45.1 2
 
0.4%
13:22.9 2
 
0.4%
00:51.0 1
 
0.2%
00:57.4 1
 
0.2%
00:59.4 1
 
0.2%
Other values (482) 482
96.4%
2023-12-13T02:22:59.518077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 520
14.9%
: 500
14.3%
. 500
14.3%
4 344
9.8%
1 306
8.7%
3 294
8.4%
5 290
8.3%
2 263
7.5%
6 137
 
3.9%
8 118
 
3.4%
Other values (2) 228
6.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 520
20.8%
4 344
13.8%
1 306
12.2%
3 294
11.8%
5 290
11.6%
2 263
10.5%
6 137
 
5.5%
8 118
 
4.7%
9 116
 
4.6%
7 112
 
4.5%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 520
14.9%
: 500
14.3%
. 500
14.3%
4 344
9.8%
1 306
8.7%
3 294
8.4%
5 290
8.3%
2 263
7.5%
6 137
 
3.9%
8 118
 
3.4%
Other values (2) 228
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 520
14.9%
: 500
14.3%
. 500
14.3%
4 344
9.8%
1 306
8.7%
3 294
8.4%
5 290
8.3%
2 263
7.5%
6 137
 
3.9%
8 118
 
3.4%
Other values (2) 228
6.5%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99016.258
Minimum99001
Maximum99024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T02:22:59.704399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99001
5-th percentile99002
Q199006
median99023
Q399023
95-th percentile99023
Maximum99024
Range23
Interquartile range (IQR)17

Descriptive statistics

Standard deviation8.0037213
Coefficient of variation (CV)8.0832396 × 10-5
Kurtosis-1.2607141
Mean99016.258
Median Absolute Deviation (MAD)0
Skewness-0.63671694
Sum49508129
Variance64.059555
MonotonicityNot monotonic
2023-12-13T02:22:59.857922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
99023 259
51.8%
99006 101
 
20.2%
99016 72
 
14.4%
99002 28
 
5.6%
99001 12
 
2.4%
99015 8
 
1.6%
99008 6
 
1.2%
99014 5
 
1.0%
99005 3
 
0.6%
99007 2
 
0.4%
Other values (3) 4
 
0.8%
ValueCountFrequency (%)
99001 12
 
2.4%
99002 28
 
5.6%
99005 3
 
0.6%
99006 101
20.2%
99007 2
 
0.4%
99008 6
 
1.2%
99014 5
 
1.0%
99015 8
 
1.6%
99016 72
14.4%
99017 1
 
0.2%
ValueCountFrequency (%)
99024 1
 
0.2%
99023 259
51.8%
99019 2
 
0.4%
99017 1
 
0.2%
99016 72
 
14.4%
99015 8
 
1.6%
99014 5
 
1.0%
99008 6
 
1.2%
99007 2
 
0.4%
99006 101
 
20.2%

Interactions

2023-12-13T02:22:54.729292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:53.980853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:54.316520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:54.848409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:54.077258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:54.454425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:54.977703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:54.190002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:54.581364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:22:59.973359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호대출금리비율최종수정수처리직원번호최초처리직원번호
이력일련번호1.0000.0000.8240.0000.000
대출금리비율0.0001.0000.0000.2700.270
최종수정수0.8240.0001.0000.0000.000
처리직원번호0.0000.2700.0001.0001.000
최초처리직원번호0.0000.2700.0001.0001.000
2023-12-13T02:23:00.139027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호최종수정수
이력일련번호1.0000.496
최종수정수0.4961.000
2023-12-13T02:23:00.271088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대출금리비율처리직원번호최초처리직원번호이력일련번호최종수정수
대출금리비율1.0000.2040.2040.0000.000
처리직원번호0.2041.0001.0000.0000.000
최초처리직원번호0.2041.0001.0000.0000.000
이력일련번호0.0000.0000.0001.0000.496
최종수정수0.0000.0000.0000.4961.000

Missing values

2023-12-13T02:22:55.135352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:22:55.371158image/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

업무구분코드이력일련번호대출금리적용일자대출금리비율유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0G100:00.03.5900:00.000:00.0111:08.39900111:08.399001
1G100:00.03.4200:00.000:00.0103:07.09901603:07.099016
2G100:00.04.5300:00.000:00.0147:38.19900847:38.199008
3G100:00.00.4300:00.000:00.0147:18.49901647:18.499016
4G100:00.03.2100:00.000:00.0144:26.99900644:26.999006
5G100:00.02.8800:00.000:00.0143:21.19900243:21.199002
6G100:00.04.3500:00.000:00.0143:21.09900243:21.099002
7G100:00.03.1300:00.000:00.0139:05.29900639:05.299006
8G100:00.03.3900:00.000:00.0138:52.29901638:52.299016
9G100:00.03.600:00.000:00.0135:14.19901435:14.199014
업무구분코드이력일련번호대출금리적용일자대출금리비율유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490G100:00.02.2700:00.000:00.0140:55.19901640:55.199016
491G100:00.05.5500:00.000:00.0140:53.69901640:53.699016
492G100:00.04.500:00.000:00.0140:51.89901640:51.899016
493G100:00.06.0900:00.000:00.0140:50.49901640:50.499016
494G100:00.04.4800:00.000:00.0140:47.39901640:47.399016
495G100:00.03.500:00.000:00.0140:45.59901640:45.599016
496G100:00.03.9400:00.000:00.0140:43.79901640:43.799016
497G100:00.04.4700:00.000:00.0140:42.39901640:42.399016
498G100:00.03.4900:00.000:00.0140:41.19901640:41.199016
499G100:00.03.6800:00.000:00.0140:39.89901640:39.899016