Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.4 KiB
Average record size in memory66.3 B

Variable types

Text3
Categorical4
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 시스템메뉴 도움말 정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093173/fileData.do

Alerts

삭제여부 has constant value ""Constant
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
최종수정수 is highly imbalanced (68.7%)Imbalance

Reproduction

Analysis started2023-12-12 16:22:20.691320
Analysis finished2023-12-12 16:22:21.488444
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

리소스ID
Text

Distinct269
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:22:21.818247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4000
Distinct characters13
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

Unique38 ?
Unique (%)7.6%

Sample

1st rowRES35672
2nd rowRES35672
3rd rowRES35684
4th rowRES35684
5th rowRES35683
ValueCountFrequency (%)
res35672 2
 
0.4%
res40795 2
 
0.4%
res40797 2
 
0.4%
res42959 2
 
0.4%
res42958 2
 
0.4%
res42957 2
 
0.4%
res42956 2
 
0.4%
res42955 2
 
0.4%
res42954 2
 
0.4%
res51986 2
 
0.4%
Other values (259) 480
96.0%
2023-12-13T01:22:22.301599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 500
12.5%
E 500
12.5%
S 500
12.5%
4 476
11.9%
3 425
10.6%
5 316
7.9%
0 264
6.6%
6 230
5.8%
7 197
 
4.9%
2 184
 
4.6%
Other values (3) 408
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
62.5%
Uppercase Letter 1500
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 476
19.0%
3 425
17.0%
5 316
12.6%
0 264
10.6%
6 230
9.2%
7 197
7.9%
2 184
 
7.4%
9 165
 
6.6%
1 124
 
5.0%
8 119
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
R 500
33.3%
E 500
33.3%
S 500
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2500
62.5%
Latin 1500
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 476
19.0%
3 425
17.0%
5 316
12.6%
0 264
10.6%
6 230
9.2%
7 197
7.9%
2 184
 
7.4%
9 165
 
6.6%
1 124
 
5.0%
8 119
 
4.8%
Latin
ValueCountFrequency (%)
R 500
33.3%
E 500
33.3%
S 500
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 500
12.5%
E 500
12.5%
S 500
12.5%
4 476
11.9%
3 425
10.6%
5 316
7.9%
0 264
6.6%
6 230
5.8%
7 197
 
4.9%
2 184
 
4.6%
Other values (3) 408
10.2%

파일종류코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
269 
1
231 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 269
53.8%
1 231
46.2%

Length

2023-12-13T01:22:22.440604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:22.518787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 269
53.8%
1 231
46.2%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-13T01:22:22.584970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
446 
2
 
34
3
 
16
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 446
89.2%
2 34
 
6.8%
3 16
 
3.2%
4 4
 
0.8%

Length

2023-12-13T01:22:22.740331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:22.826744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 446
89.2%
2 34
 
6.8%
3 16
 
3.2%
4 4
 
0.8%

처리시각
Text

Distinct242
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:22:23.119173image/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

Unique3 ?
Unique (%)0.6%

Sample

1st row38:08.8
2nd row38:08.8
3rd row49:29.7
4th row49:29.7
5th row49:00.3
ValueCountFrequency (%)
25:53.7 4
 
0.8%
32:54.2 4
 
0.8%
25:54.2 4
 
0.8%
25:54.5 4
 
0.8%
32:54.0 3
 
0.6%
25:54.3 3
 
0.6%
25:53.6 3
 
0.6%
32:54.1 3
 
0.6%
25:53.8 3
 
0.6%
25:53.5 3
 
0.6%
Other values (232) 466
93.2%
2023-12-13T01:22:23.542201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 371
10.6%
2 362
10.3%
1 352
10.1%
3 294
8.4%
4 286
8.2%
0 272
7.8%
7 152
 
4.3%
9 139
 
4.0%
Other values (2) 272
7.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 371
14.8%
2 362
14.5%
1 352
14.1%
3 294
11.8%
4 286
11.4%
0 272
10.9%
7 152
6.1%
9 139
 
5.6%
8 138
 
5.5%
6 134
 
5.4%
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%
5 371
10.6%
2 362
10.3%
1 352
10.1%
3 294
8.4%
4 286
8.2%
0 272
7.8%
7 152
 
4.3%
9 139
 
4.0%
Other values (2) 272
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 371
10.6%
2 362
10.3%
1 352
10.1%
3 294
8.4%
4 286
8.2%
0 272
7.8%
7 152
 
4.3%
9 139
 
4.0%
Other values (2) 272
7.8%

처리직원번호
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4293
216 
4509
136 
ISU
38 
4448
32 
5559
30 
Other values (4)
48 

Length

Max length5
Median length4
Mean length4.164
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4509
2nd row4509
3rd row4509
4th row4509
5th row4509

Common Values

ValueCountFrequency (%)
4293 216
43.2%
4509 136
27.2%
ISU 38
 
7.6%
4448 32
 
6.4%
5559 30
 
6.0%
EXF08 16
 
3.2%
GRN 16
 
3.2%
EXB32 12
 
2.4%
4444 4
 
0.8%

Length

2023-12-13T01:22:23.675348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:23.776527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4293 216
43.2%
4509 136
27.2%
isu 38
 
7.6%
4448 32
 
6.4%
5559 30
 
6.0%
exf08 16
 
3.2%
grn 16
 
3.2%
exb32 12
 
2.4%
4444 4
 
0.8%

최초처리시각
Text

Distinct244
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:22:24.128206image/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

Unique7 ?
Unique (%)1.4%

Sample

1st row38:44.4
2nd row38:44.4
3rd row49:29.7
4th row49:29.7
5th row49:00.3
ValueCountFrequency (%)
25:53.7 4
 
0.8%
32:54.2 4
 
0.8%
25:54.2 4
 
0.8%
25:54.5 4
 
0.8%
32:54.3 3
 
0.6%
25:53.6 3
 
0.6%
32:54.1 3
 
0.6%
25:53.5 3
 
0.6%
25:53.8 3
 
0.6%
25:54.3 3
 
0.6%
Other values (234) 466
93.2%
2023-12-13T01:22:24.648611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 374
10.7%
2 369
10.5%
1 350
10.0%
3 314
9.0%
4 291
8.3%
0 261
7.5%
7 138
 
3.9%
8 136
 
3.9%
Other values (2) 267
7.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 374
15.0%
2 369
14.8%
1 350
14.0%
3 314
12.6%
4 291
11.6%
0 261
10.4%
7 138
 
5.5%
8 136
 
5.4%
6 134
 
5.4%
9 133
 
5.3%
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%
5 374
10.7%
2 369
10.5%
1 350
10.0%
3 314
9.0%
4 291
8.3%
0 261
7.5%
7 138
 
3.9%
8 136
 
3.9%
Other values (2) 267
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 374
10.7%
2 369
10.5%
1 350
10.0%
3 314
9.0%
4 291
8.3%
0 261
7.5%
7 138
 
3.9%
8 136
 
3.9%
Other values (2) 267
7.6%

최초처리직원번호
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4293
216 
4509
136 
ISU
38 
4448
32 
5559
30 
Other values (5)
48 

Length

Max length5
Median length4
Mean length4.164
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4509
2nd row4509
3rd row4509
4th row4509
5th row4509

Common Values

ValueCountFrequency (%)
4293 216
43.2%
4509 136
27.2%
ISU 38
 
7.6%
4448 32
 
6.4%
5559 30
 
6.0%
EXF08 18
 
3.6%
GRN 16
 
3.2%
EXB32 6
 
1.2%
admin 4
 
0.8%
4444 4
 
0.8%

Length

2023-12-13T01:22:24.836436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:24.997592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4293 216
43.2%
4509 136
27.2%
isu 38
 
7.6%
4448 32
 
6.4%
5559 30
 
6.0%
exf08 18
 
3.6%
grn 16
 
3.2%
exb32 6
 
1.2%
admin 4
 
0.8%
4444 4
 
0.8%

Correlations

2023-12-13T01:22:25.108741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
파일종류코드최종수정수처리직원번호최초처리직원번호
파일종류코드1.0000.0000.2360.302
최종수정수0.0001.0000.3790.398
처리직원번호0.2360.3791.0000.994
최초처리직원번호0.3020.3980.9941.000
2023-12-13T01:22:25.243840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수처리직원번호파일종류코드최초처리직원번호
최종수정수1.0000.2500.0000.246
처리직원번호0.2501.0000.2340.982
파일종류코드0.0000.2341.0000.230
최초처리직원번호0.2460.9820.2301.000
2023-12-13T01:22:25.341726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
파일종류코드최종수정수처리직원번호최초처리직원번호
파일종류코드1.0000.0000.2340.230
최종수정수0.0001.0000.2500.246
처리직원번호0.2340.2501.0000.982
최초처리직원번호0.2300.2460.9821.000

Missing values

2023-12-13T01:22:21.301491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:22:21.439764image/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

리소스ID파일종류코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0RES356721N338:08.8450938:44.44509
1RES356722N338:08.8450938:44.44509
2RES356841N149:29.7450949:29.74509
3RES356842N149:29.7450949:29.74509
4RES356831N149:00.3450949:00.34509
5RES356832N149:00.3450949:00.34509
6RES356821N148:21.6450948:21.64509
7RES356822N148:21.6450948:21.64509
8RES356801N147:43.3450947:43.34509
9RES356802N147:43.3450947:43.34509
리소스ID파일종류코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490RES452422N125:53.7ISU25:53.7ISU
491RES451052N125:53.7ISU25:53.7ISU
492RES452412N125:53.7ISU25:53.7ISU
493RES451042N125:53.6ISU25:53.6ISU
494RES452392N125:53.6ISU25:53.6ISU
495RES451022N125:53.6ISU25:53.6ISU
496RES451492N125:53.5ISU25:53.5ISU
497RES451432N125:53.5ISU25:53.5ISU
498RES451382N125:53.5ISU25:53.5ISU
499RES451372N125:53.4ISU25:53.4ISU