Overview

Dataset statistics

Number of variables3
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows124
Duplicate rows (%)24.8%
Total size in memory12.3 KiB
Average record size in memory25.3 B

Variable types

Categorical1
Text2

Dataset

Description해당 파일 데이터는 신용보증기금의 기타 행정정보 이용로그에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093048/fileData.do

Alerts

최종수정수 has constant value ""Constant
Dataset has 124 (24.8%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 02:00:56.260173
Analysis finished2023-12-12 02:00:56.490367
Duration0.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

최종수정수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-12T11:00:56.569607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:00:56.695626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

처리직원번호
Text

Distinct206
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:00:57.123960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.156
Min length4

Characters and Unicode

Total characters2078
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

Unique82 ?
Unique (%)16.4%

Sample

1st row4120
2nd row3927
3rd row9C713
4th row6077
5th row5928
ValueCountFrequency (%)
4005 9
 
1.8%
9c656 8
 
1.6%
4051 8
 
1.6%
5892 7
 
1.4%
5975 7
 
1.4%
4134 7
 
1.4%
3984 7
 
1.4%
5416 7
 
1.4%
3602 6
 
1.2%
9c647 6
 
1.2%
Other values (196) 428
85.6%
2023-12-12T11:00:57.825586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 302
14.5%
5 256
12.3%
9 254
12.2%
6 223
10.7%
3 187
9.0%
0 183
8.8%
1 173
8.3%
7 154
7.4%
8 134
6.4%
2 134
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
96.2%
Uppercase Letter 78
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 302
15.1%
5 256
12.8%
9 254
12.7%
6 223
11.2%
3 187
9.3%
0 183
9.2%
1 173
8.6%
7 154
7.7%
8 134
6.7%
2 134
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
96.2%
Latin 78
 
3.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 302
15.1%
5 256
12.8%
9 254
12.7%
6 223
11.2%
3 187
9.3%
0 183
9.2%
1 173
8.6%
7 154
7.7%
8 134
6.7%
2 134
6.7%
Latin
ValueCountFrequency (%)
C 78
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 302
14.5%
5 256
12.3%
9 254
12.2%
6 223
10.7%
3 187
9.0%
0 183
8.8%
1 173
8.3%
7 154
7.4%
8 134
6.4%
2 134
6.4%

최초처리직원번호
Text

Distinct206
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:00:58.329372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.156
Min length4

Characters and Unicode

Total characters2078
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

Unique82 ?
Unique (%)16.4%

Sample

1st row4120
2nd row3927
3rd row9C713
4th row6077
5th row5928
ValueCountFrequency (%)
4005 9
 
1.8%
9c656 8
 
1.6%
4051 8
 
1.6%
5892 7
 
1.4%
5975 7
 
1.4%
4134 7
 
1.4%
3984 7
 
1.4%
5416 7
 
1.4%
3602 6
 
1.2%
9c647 6
 
1.2%
Other values (196) 428
85.6%
2023-12-12T11:00:58.998143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 302
14.5%
5 256
12.3%
9 254
12.2%
6 223
10.7%
3 187
9.0%
0 183
8.8%
1 173
8.3%
7 154
7.4%
8 134
6.4%
2 134
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
96.2%
Uppercase Letter 78
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 302
15.1%
5 256
12.8%
9 254
12.7%
6 223
11.2%
3 187
9.3%
0 183
9.2%
1 173
8.6%
7 154
7.7%
8 134
6.7%
2 134
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
96.2%
Latin 78
 
3.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 302
15.1%
5 256
12.8%
9 254
12.7%
6 223
11.2%
3 187
9.3%
0 183
9.2%
1 173
8.6%
7 154
7.7%
8 134
6.7%
2 134
6.7%
Latin
ValueCountFrequency (%)
C 78
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 302
14.5%
5 256
12.3%
9 254
12.2%
6 223
10.7%
3 187
9.0%
0 183
8.8%
1 173
8.3%
7 154
7.4%
8 134
6.4%
2 134
6.4%

Missing values

2023-12-12T11:00:56.357057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:00:56.454568image/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

최종수정수처리직원번호최초처리직원번호
0141204120
1139273927
219C7139C713
3160776077
4159285928
5154735473
619C7089C708
7154075407
8142234223
9158935893
최종수정수처리직원번호최초처리직원번호
49019C7139C713
49119C6569C656
492154405440
493155395539
494154825482
49519C6919C691
496151685168
49719C6429C642
498159255925
499140514051

Duplicate rows

Most frequently occurring

최종수정수처리직원번호최초처리직원번호# duplicates
181400540059
211405140518
10819C6569C6568
171398439847
251413441347
721541654167
831589258927
871597559757
71360236026
201404940496