Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells500
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.1 KiB
Average record size in memory84.3 B

Variable types

Categorical3
Text3
Numeric3
Unsupported1

Dataset

Description해당 파일 데이터는 신용보증기금의 보증접수상품정보이력에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093234/fileData.do

Alerts

업무구분코드 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
조변접수번호 has 500 (100.0%) missing valuesMissing
조변접수번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 04:20:47.014243
Analysis finished2023-12-12 04:20:48.771970
Duration1.76 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

2023-12-12T13:20:48.850463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:20:48.974336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

원장번호
Text

Distinct119
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T13:20:49.237753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6000
Distinct characters34
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

Unique57 ?
Unique (%)11.4%

Sample

1st rowTAW202105034
2nd rowTHD202102068
3rd rowTAM202104524
4th rowTAM202104524
5th rowTBH202103523
ValueCountFrequency (%)
taq202102333 26
 
5.2%
tat202100367 19
 
3.8%
taq202102334 17
 
3.4%
toj202105917 17
 
3.4%
tpl202104366 15
 
3.0%
toe202102895 15
 
3.0%
tig202105406 14
 
2.8%
thg202104016 14
 
2.8%
tad202103823 13
 
2.6%
tav202102612 13
 
2.6%
Other values (109) 337
67.4%
2023-12-12T13:20:49.705796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1270
21.2%
0 1215
20.2%
1 704
11.7%
T 517
8.6%
3 308
 
5.1%
6 221
 
3.7%
A 219
 
3.6%
4 205
 
3.4%
5 189
 
3.1%
8 150
 
2.5%
Other values (24) 1002
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4500
75.0%
Uppercase Letter 1500
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 517
34.5%
A 219
14.6%
H 82
 
5.5%
Q 76
 
5.1%
I 72
 
4.8%
O 50
 
3.3%
E 48
 
3.2%
G 48
 
3.2%
B 46
 
3.1%
M 45
 
3.0%
Other values (14) 297
19.8%
Decimal Number
ValueCountFrequency (%)
2 1270
28.2%
0 1215
27.0%
1 704
15.6%
3 308
 
6.8%
6 221
 
4.9%
4 205
 
4.6%
5 189
 
4.2%
8 150
 
3.3%
7 121
 
2.7%
9 117
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4500
75.0%
Latin 1500
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 517
34.5%
A 219
14.6%
H 82
 
5.5%
Q 76
 
5.1%
I 72
 
4.8%
O 50
 
3.3%
E 48
 
3.2%
G 48
 
3.2%
B 46
 
3.1%
M 45
 
3.0%
Other values (14) 297
19.8%
Common
ValueCountFrequency (%)
2 1270
28.2%
0 1215
27.0%
1 704
15.6%
3 308
 
6.8%
6 221
 
4.9%
4 205
 
4.6%
5 189
 
4.2%
8 150
 
3.3%
7 121
 
2.7%
9 117
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1270
21.2%
0 1215
20.2%
1 704
11.7%
T 517
8.6%
3 308
 
5.1%
6 221
 
3.7%
A 219
 
3.6%
4 205
 
3.4%
5 189
 
3.1%
8 150
 
2.5%
Other values (24) 1002
16.7%

상품코드
Real number (ℝ)

Distinct92
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12505.154
Minimum11010
Maximum13518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T13:20:49.902475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11146
Q111182
median12990.5
Q313345
95-th percentile13514
Maximum13518
Range2508
Interquartile range (IQR)2163

Descriptive statistics

Standard deviation949.72093
Coefficient of variation (CV)0.07594636
Kurtosis-1.4775972
Mean12505.154
Median Absolute Deviation (MAD)484
Skewness-0.50347149
Sum6252577
Variance901969.84
MonotonicityNot monotonic
2023-12-12T13:20:50.107700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13185 60
 
12.0%
13345 36
 
7.2%
11182 32
 
6.4%
11146 31
 
6.2%
13218 17
 
3.4%
13142 16
 
3.2%
12182 16
 
3.2%
13426 14
 
2.8%
13514 14
 
2.8%
12591 11
 
2.2%
Other values (82) 253
50.6%
ValueCountFrequency (%)
11010 4
 
0.8%
11012 2
 
0.4%
11019 2
 
0.4%
11067 1
 
0.2%
11117 5
 
1.0%
11123 1
 
0.2%
11134 3
 
0.6%
11139 2
 
0.4%
11146 31
6.2%
11152 6
 
1.2%
ValueCountFrequency (%)
13518 4
 
0.8%
13517 2
 
0.4%
13516 5
 
1.0%
13515 5
 
1.0%
13514 14
2.8%
13513 4
 
0.8%
13511 3
 
0.6%
13504 4
 
0.8%
13478 6
1.2%
13476 2
 
0.4%

이력일련번호
Real number (ℝ)

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.656
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T13:20:50.273096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile7
Maximum19
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7700199
Coefficient of variation (CV)1.0429292
Kurtosis7.4625038
Mean2.656
Median Absolute Deviation (MAD)0
Skewness2.373803
Sum1328
Variance7.67301
MonotonicityNot monotonic
2023-12-12T13:20:50.421880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 306
61.2%
3 84
 
16.8%
5 52
 
10.4%
7 35
 
7.0%
9 10
 
2.0%
11 6
 
1.2%
13 3
 
0.6%
19 2
 
0.4%
17 1
 
0.2%
15 1
 
0.2%
ValueCountFrequency (%)
1 306
61.2%
3 84
 
16.8%
5 52
 
10.4%
7 35
 
7.0%
9 10
 
2.0%
11 6
 
1.2%
13 3
 
0.6%
15 1
 
0.2%
17 1
 
0.2%
19 2
 
0.4%
ValueCountFrequency (%)
19 2
 
0.4%
17 1
 
0.2%
15 1
 
0.2%
13 3
 
0.6%
11 6
 
1.2%
9 10
 
2.0%
7 35
 
7.0%
5 52
 
10.4%
3 84
 
16.8%
1 306
61.2%

조변접수번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

유효개시일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T13:20:50.572055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:20:50.689502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

유효종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T13:20:50.808169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:20:50.928467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

최종수정수
Real number (ℝ)

Distinct19
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.912
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T13:20:51.061583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum19
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2213367
Coefficient of variation (CV)0.82345007
Kurtosis4.3671999
Mean3.912
Median Absolute Deviation (MAD)2
Skewness1.8362698
Sum1956
Variance10.37701
MonotonicityNot monotonic
2023-12-12T13:20:51.235458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 121
24.2%
2 98
19.6%
3 65
13.0%
4 53
10.6%
5 42
 
8.4%
6 36
 
7.2%
7 35
 
7.0%
8 11
 
2.2%
9 10
 
2.0%
11 6
 
1.2%
Other values (9) 23
 
4.6%
ValueCountFrequency (%)
1 121
24.2%
2 98
19.6%
3 65
13.0%
4 53
10.6%
5 42
 
8.4%
6 36
 
7.2%
7 35
 
7.0%
8 11
 
2.2%
9 10
 
2.0%
10 6
 
1.2%
ValueCountFrequency (%)
19 2
 
0.4%
18 2
 
0.4%
17 1
 
0.2%
16 3
0.6%
15 1
 
0.2%
14 2
 
0.4%
13 3
0.6%
12 3
0.6%
11 6
1.2%
10 6
1.2%

처리시각
Text

Distinct129
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T13:20:51.525788image/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

Unique58 ?
Unique (%)11.6%

Sample

1st row32:42.9
2nd row32:31.6
3rd row32:11.0
4th row32:11.0
5th row31:52.2
ValueCountFrequency (%)
06:25.1 19
 
3.8%
01:14.4 18
 
3.6%
00:16.9 17
 
3.4%
23:57.7 17
 
3.4%
25:54.0 15
 
3.0%
53:05.6 14
 
2.8%
25:03.6 14
 
2.8%
49:58.0 13
 
2.6%
54:18.9 12
 
2.4%
54:44.0 11
 
2.2%
Other values (119) 350
70.0%
2023-12-12T13:20:51.925132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 358
10.2%
0 357
10.2%
4 350
10.0%
1 331
9.5%
2 328
9.4%
3 214
6.1%
9 145
 
4.1%
6 140
 
4.0%
Other values (2) 277
7.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 358
14.3%
0 357
14.3%
4 350
14.0%
1 331
13.2%
2 328
13.1%
3 214
8.6%
9 145
5.8%
6 140
 
5.6%
7 140
 
5.6%
8 137
 
5.5%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 358
10.2%
0 357
10.2%
4 350
10.0%
1 331
9.5%
2 328
9.4%
3 214
6.1%
9 145
 
4.1%
6 140
 
4.0%
Other values (2) 277
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 358
10.2%
0 357
10.2%
4 350
10.0%
1 331
9.5%
2 328
9.4%
3 214
6.1%
9 145
 
4.1%
6 140
 
4.0%
Other values (2) 277
7.9%

처리직원번호
Text

Distinct69
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T13:20:52.181513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.154
Min length4

Characters and Unicode

Total characters2077
Distinct characters11
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

Unique7 ?
Unique (%)1.4%

Sample

1st row99002
2nd row99002
3rd row99002
4th row99002
5th row5214
ValueCountFrequency (%)
5048 43
 
8.6%
5911 19
 
3.8%
4528 17
 
3.4%
4915 15
 
3.0%
9c647 15
 
3.0%
5106 14
 
2.8%
6080 14
 
2.8%
5360 14
 
2.8%
5405 13
 
2.6%
6012 13
 
2.6%
Other values (59) 323
64.6%
2023-12-12T13:20:52.603432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 314
15.1%
0 298
14.3%
4 298
14.3%
6 236
11.4%
1 226
10.9%
9 223
10.7%
3 139
6.7%
8 115
 
5.5%
2 110
 
5.3%
7 91
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2050
98.7%
Uppercase Letter 27
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 314
15.3%
0 298
14.5%
4 298
14.5%
6 236
11.5%
1 226
11.0%
9 223
10.9%
3 139
6.8%
8 115
 
5.6%
2 110
 
5.4%
7 91
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
C 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2050
98.7%
Latin 27
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 314
15.3%
0 298
14.5%
4 298
14.5%
6 236
11.5%
1 226
11.0%
9 223
10.9%
3 139
6.8%
8 115
 
5.6%
2 110
 
5.4%
7 91
 
4.4%
Latin
ValueCountFrequency (%)
C 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2077
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 314
15.1%
0 298
14.3%
4 298
14.3%
6 236
11.4%
1 226
10.9%
9 223
10.7%
3 139
6.7%
8 115
 
5.5%
2 110
 
5.3%
7 91
 
4.4%

Interactions

2023-12-12T13:20:48.045835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:47.313618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:47.677927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:48.160260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:47.424308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:47.805344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:48.288999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:47.546227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:47.941085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:20:52.716070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상품코드이력일련번호최종수정수처리직원번호
상품코드1.0000.0000.2150.743
이력일련번호0.0001.0000.9380.700
최종수정수0.2150.9381.0000.865
처리직원번호0.7430.7000.8651.000
2023-12-12T13:20:52.836789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상품코드이력일련번호최종수정수
상품코드1.000-0.045-0.099
이력일련번호-0.0451.0000.312
최종수정수-0.0990.3121.000

Missing values

2023-12-12T13:20:48.481985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:20:48.701385image/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

업무구분코드원장번호상품코드이력일련번호조변접수번호유효개시일자유효종료일자최종수정수처리시각처리직원번호
0GTAW202105034133451<NA>00:00.000:00.0132:42.999002
1GTHD202102068134261<NA>00:00.000:00.0132:31.699002
2GTAM202104524133453<NA>00:00.000:00.0232:11.099002
3GTAM202104524133451<NA>00:00.000:00.0332:11.099002
4GTBH202103523111461<NA>00:00.000:00.0331:52.25214
5GTBH202103523125863<NA>00:00.000:00.0231:52.25214
6GTBH202103523111671<NA>00:00.000:00.0131:52.25214
7GTBH202103523111523<NA>00:00.000:00.0231:52.25214
8GTBH202103523111463<NA>00:00.000:00.0231:52.25214
9GTBH202103523111393<NA>00:00.000:00.0231:52.25214
업무구분코드원장번호상품코드이력일련번호조변접수번호유효개시일자유효종료일자최종수정수처리시각처리직원번호
490GTAF202103849135143<NA>00:00.000:00.0245:42.65438
491GTIS202101786121821<NA>00:00.000:00.0245:38.39C743
492GTIS202101786129911<NA>00:00.000:00.0245:38.39C743
493GTAN202101958111461<NA>00:00.000:00.0145:28.76128
494GTAN202101958129911<NA>00:00.000:00.0145:28.76128
495GTAN202101958129721<NA>00:00.000:00.0145:28.76128
496GTAN202101958125911<NA>00:00.000:00.0145:28.76128
497GTAN202101958125011<NA>00:00.000:00.0145:28.76128
498GTAN202101958111821<NA>00:00.000:00.0145:28.76128
499GTAN202101958111691<NA>00:00.000:00.0145:28.76128