Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows16
Duplicate rows (%)3.2%
Total size in memory32.4 KiB
Average record size in memory66.3 B

Variable types

Categorical2
Boolean1
Numeric1
Text4

Dataset

Description해당 파일 데이터는 신용보증기금의 보증부문 중 원장별 조건변경과 관련된 자료를 확인하실 수 있으니 참고하시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092658/fileData.do

Alerts

업무구분코드 has constant value ""Constant
Dataset has 16 (3.2%) duplicate rowsDuplicates
원장조변역할관계코드 is highly imbalanced (93.3%)Imbalance
삭제여부 is highly imbalanced (74.9%)Imbalance

Reproduction

Analysis started2023-12-12 09:25:24.227878
Analysis finished2023-12-12 09:25:24.855438
Duration0.63 seconds
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

2023-12-12T18:25:24.945398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:25:25.045856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

원장조변역할관계코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
496 
2
 
4

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 (%)
1 496
99.2%
2 4
 
0.8%

Length

2023-12-12T18:25:25.159115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:25:25.265659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 496
99.2%
2 4
 
0.8%

삭제여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
479 
True
 
21
ValueCountFrequency (%)
False 479
95.8%
True 21
 
4.2%
2023-12-12T18:25:25.348099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.394
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:25:25.437152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0123308
Coefficient of variation (CV)0.72620573
Kurtosis20.448409
Mean1.394
Median Absolute Deviation (MAD)0
Skewness3.6887166
Sum697
Variance1.0248136
MonotonicityNot monotonic
2023-12-12T18:25:25.546068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 412
82.4%
3 52
 
10.4%
2 19
 
3.8%
5 13
 
2.6%
4 2
 
0.4%
7 1
 
0.2%
11 1
 
0.2%
ValueCountFrequency (%)
1 412
82.4%
2 19
 
3.8%
3 52
 
10.4%
4 2
 
0.4%
5 13
 
2.6%
7 1
 
0.2%
11 1
 
0.2%
ValueCountFrequency (%)
11 1
 
0.2%
7 1
 
0.2%
5 13
 
2.6%
4 2
 
0.4%
3 52
 
10.4%
2 19
 
3.8%
1 412
82.4%

처리시각
Text

Distinct465
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:25:25.923378image/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

Unique445 ?
Unique (%)89.0%

Sample

1st row12:27.6
2nd row12:20.8
3rd row12:17.9
4th row12:17.3
5th row12:07.1
ValueCountFrequency (%)
17:12.5 8
 
1.6%
49:15.1 6
 
1.2%
00:39.4 4
 
0.8%
58:50.2 3
 
0.6%
38:33.1 3
 
0.6%
15:19.3 3
 
0.6%
08:14.3 2
 
0.4%
25:03.9 2
 
0.4%
45:05.8 2
 
0.4%
57:56.7 2
 
0.4%
Other values (455) 465
93.0%
2023-12-12T18:25:26.453164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 359
10.3%
3 320
9.1%
0 317
9.1%
4 304
8.7%
2 302
8.6%
5 297
8.5%
8 170
 
4.9%
7 161
 
4.6%
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 (%)
1 359
14.4%
3 320
12.8%
0 317
12.7%
4 304
12.2%
2 302
12.1%
5 297
11.9%
8 170
6.8%
7 161
6.4%
9 157
6.3%
6 113
 
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 (%)
: 500
14.3%
. 500
14.3%
1 359
10.3%
3 320
9.1%
0 317
9.1%
4 304
8.7%
2 302
8.6%
5 297
8.5%
8 170
 
4.9%
7 161
 
4.6%
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%
1 359
10.3%
3 320
9.1%
0 317
9.1%
4 304
8.7%
2 302
8.6%
5 297
8.5%
8 170
 
4.9%
7 161
 
4.6%
Other values (2) 270
7.7%

처리직원번호
Text

Distinct236
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:25:26.853297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.468
Min length4

Characters and Unicode

Total characters2234
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

Unique119 ?
Unique (%)23.8%

Sample

1st row9C669
2nd row4492
3rd row5463
4th row9C669
5th row4685
ValueCountFrequency (%)
9c667 28
 
5.6%
9c776 17
 
3.4%
4398 11
 
2.2%
5064 8
 
1.6%
4510 7
 
1.4%
9c624 6
 
1.2%
9c770 6
 
1.2%
5108 6
 
1.2%
4803 6
 
1.2%
9c729 5
 
1.0%
Other values (226) 400
80.0%
2023-12-12T18:25:27.350370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 359
16.1%
6 328
14.7%
7 266
11.9%
C 234
10.5%
5 212
9.5%
4 194
8.7%
0 160
7.2%
3 139
 
6.2%
8 115
 
5.1%
2 115
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
89.5%
Uppercase Letter 234
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 359
17.9%
6 328
16.4%
7 266
13.3%
5 212
10.6%
4 194
9.7%
0 160
8.0%
3 139
 
7.0%
8 115
 
5.8%
2 115
 
5.8%
1 112
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
C 234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
89.5%
Latin 234
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
9 359
17.9%
6 328
16.4%
7 266
13.3%
5 212
10.6%
4 194
9.7%
0 160
8.0%
3 139
 
7.0%
8 115
 
5.8%
2 115
 
5.8%
1 112
 
5.6%
Latin
ValueCountFrequency (%)
C 234
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 359
16.1%
6 328
14.7%
7 266
11.9%
C 234
10.5%
5 212
9.5%
4 194
8.7%
0 160
7.2%
3 139
 
6.2%
8 115
 
5.1%
2 115
 
5.1%

최초처리시각
Text

Distinct464
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:25:27.706482image/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

Unique442 ?
Unique (%)88.4%

Sample

1st row12:27.6
2nd row12:20.8
3rd row12:17.9
4th row12:17.3
5th row12:07.1
ValueCountFrequency (%)
17:12.5 8
 
1.6%
49:15.1 5
 
1.0%
00:39.4 4
 
0.8%
15:19.3 3
 
0.6%
58:50.2 3
 
0.6%
38:33.1 3
 
0.6%
36:17.7 2
 
0.4%
24:38.1 2
 
0.4%
21:17.7 2
 
0.4%
25:03.9 2
 
0.4%
Other values (454) 466
93.2%
2023-12-12T18:25:28.289448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 353
10.1%
2 316
9.0%
3 315
9.0%
0 313
8.9%
4 296
8.5%
5 294
8.4%
8 170
 
4.9%
7 168
 
4.8%
Other values (2) 275
7.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 353
14.1%
2 316
12.6%
3 315
12.6%
0 313
12.5%
4 296
11.8%
5 294
11.8%
8 170
6.8%
7 168
6.7%
9 154
6.2%
6 121
 
4.8%
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%
1 353
10.1%
2 316
9.0%
3 315
9.0%
0 313
8.9%
4 296
8.5%
5 294
8.4%
8 170
 
4.9%
7 168
 
4.8%
Other values (2) 275
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 353
10.1%
2 316
9.0%
3 315
9.0%
0 313
8.9%
4 296
8.5%
5 294
8.4%
8 170
 
4.9%
7 168
 
4.8%
Other values (2) 275
7.9%

최초처리직원번호
Text

Distinct237
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:25:28.844431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.47
Min length4

Characters and Unicode

Total characters2235
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

Unique120 ?
Unique (%)24.0%

Sample

1st row9C669
2nd row4492
3rd row5463
4th row9C669
5th row4685
ValueCountFrequency (%)
9c667 28
 
5.6%
9c776 17
 
3.4%
4398 11
 
2.2%
5064 8
 
1.6%
9c770 6
 
1.2%
5108 6
 
1.2%
4510 6
 
1.2%
9c624 6
 
1.2%
4803 6
 
1.2%
9c745 5
 
1.0%
Other values (227) 401
80.2%
2023-12-12T18:25:29.515304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 362
16.2%
6 331
14.8%
7 267
11.9%
C 235
10.5%
5 212
9.5%
4 197
8.8%
0 158
7.1%
3 137
 
6.1%
2 114
 
5.1%
8 111
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
89.5%
Uppercase Letter 235
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 362
18.1%
6 331
16.6%
7 267
13.4%
5 212
10.6%
4 197
9.8%
0 158
7.9%
3 137
 
6.9%
2 114
 
5.7%
8 111
 
5.5%
1 111
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
C 235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
89.5%
Latin 235
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
9 362
18.1%
6 331
16.6%
7 267
13.4%
5 212
10.6%
4 197
9.8%
0 158
7.9%
3 137
 
6.9%
2 114
 
5.7%
8 111
 
5.5%
1 111
 
5.5%
Latin
ValueCountFrequency (%)
C 235
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 362
16.2%
6 331
14.8%
7 267
11.9%
C 235
10.5%
5 212
9.5%
4 197
8.8%
0 158
7.1%
3 137
 
6.1%
2 114
 
5.1%
8 111
 
5.0%

Interactions

2023-12-12T18:25:24.451665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:25:29.638229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원장조변역할관계코드삭제여부최종수정수
원장조변역할관계코드1.0000.0000.000
삭제여부0.0001.0001.000
최종수정수0.0001.0001.000
2023-12-12T18:25:29.760747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원장조변역할관계코드삭제여부
원장조변역할관계코드1.0000.000
삭제여부0.0001.000
2023-12-12T18:25:29.865719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수원장조변역할관계코드삭제여부
최종수정수1.0000.0000.296
원장조변역할관계코드0.0001.0000.000
삭제여부0.2960.0001.000

Missing values

2023-12-12T18:25:24.597013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:25:24.780138image/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

업무구분코드원장조변역할관계코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0G1N112:27.69C66912:27.69C669
1G1N112:20.8449212:20.84492
2G1N112:17.9546312:17.95463
3G1N112:17.39C66912:17.39C669
4G1N112:07.1468512:07.14685
5G1N111:38.2601011:38.26010
6G1N111:38.2601011:38.26010
7G1N111:26.8603011:26.86030
8G1N110:42.5499410:42.54994
9G1N110:42.2617810:42.26178
업무구분코드원장조변역할관계코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490G1N106:49.49C67706:49.49C677
491G1Y206:48.7510805:22.75108
492G1N106:44.89C66706:44.89C667
493G1N106:13.79C66206:13.79C662
494G1N106:11.09C76106:11.09C761
495G1N105:57.79C69505:57.79C695
496G1N104:19.2487004:19.24870
497G1N104:14.6575804:14.65758
498G1N304:14.4609359:28.76093
499G1N103:17.39C64403:17.39C644

Duplicate rows

Most frequently occurring

업무구분코드원장조변역할관계코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호# duplicates
4G1N117:12.5506417:12.550648
13G1N149:15.1451049:15.145105
0G1N100:39.4484300:39.448434
2G1N115:19.3613215:19.361323
11G1N138:33.1613638:33.161363
15G1N158:50.29C72958:50.29C7293
1G1N111:38.2601011:38.260102
3G1N115:58.19C66715:58.19C6672
5G1N118:03.2480318:03.248032
6G1N118:28.49C73918:28.49C7392