Overview

Dataset statistics

Number of variables7
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.9 KiB
Average record size in memory61.3 B

Variable types

Categorical6
Text1

Dataset

Description해당 파일 데이터는 신용보증기금의 공통기타업종코드정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093185/fileData.do

Alerts

이력일련번호 has constant value ""Constant
업종차수 has constant value ""Constant
최종수정수 has constant value ""Constant
처리직원번호 has constant value ""Constant
대중소분류구분코드 is highly imbalanced (75.3%)Imbalance
업종코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:06:31.682483
Analysis finished2023-12-12 07:06:32.098108
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

이력일련번호
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

2023-12-12T16:06:32.166610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:32.283618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

업종차수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
10
500 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 500
100.0%

Length

2023-12-12T16:06:32.387477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:32.494378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 500
100.0%

업종코드
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T16:06:32.911036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.264
Min length1

Characters and Unicode

Total characters2632
Distinct characters21
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

Unique500 ?
Unique (%)100.0%

Sample

1st rowU99009
2nd rowU99001
3rd rowU9900
4th rowU990
5th rowU99
ValueCountFrequency (%)
u99009 1
 
0.2%
n7599 1
 
0.2%
n7529 1
 
0.2%
n75290 1
 
0.2%
n753 1
 
0.2%
n7531 1
 
0.2%
n75310 1
 
0.2%
n7532 1
 
0.2%
n75320 1
 
0.2%
n7533 1
 
0.2%
Other values (490) 490
98.0%
2023-12-12T16:06:33.524037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 404
15.3%
9 324
12.3%
2 298
11.3%
0 199
 
7.6%
7 185
 
7.0%
8 164
 
6.2%
6 161
 
6.1%
3 144
 
5.5%
5 138
 
5.2%
4 115
 
4.4%
Other values (11) 500
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2132
81.0%
Uppercase Letter 500
 
19.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 90
18.0%
S 71
14.2%
N 69
13.8%
R 67
13.4%
P 59
11.8%
Q 43
8.6%
O 40
8.0%
K 24
 
4.8%
L 19
 
3.8%
T 12
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 404
18.9%
9 324
15.2%
2 298
14.0%
0 199
9.3%
7 185
8.7%
8 164
7.7%
6 161
 
7.6%
3 144
 
6.8%
5 138
 
6.5%
4 115
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2132
81.0%
Latin 500
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 90
18.0%
S 71
14.2%
N 69
13.8%
R 67
13.4%
P 59
11.8%
Q 43
8.6%
O 40
8.0%
K 24
 
4.8%
L 19
 
3.8%
T 12
 
2.4%
Common
ValueCountFrequency (%)
1 404
18.9%
9 324
15.2%
2 298
14.0%
0 199
9.3%
7 185
8.7%
8 164
7.7%
6 161
 
7.6%
3 144
 
6.8%
5 138
 
6.5%
4 115
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 404
15.3%
9 324
12.3%
2 298
11.3%
0 199
 
7.6%
7 185
 
7.0%
8 164
 
6.2%
6 161
 
6.1%
3 144
 
5.5%
5 138
 
5.2%
4 115
 
4.4%
Other values (11) 500
19.0%

대중소분류구분코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3
469 
2
 
21
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 469
93.8%
2 21
 
4.2%
1 10
 
2.0%

Length

2023-12-12T16:06:33.716384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:33.845292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 469
93.8%
2 21
 
4.2%
1 10
 
2.0%

매핑구분코드
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
272 
221 
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 272
54.4%
221
44.2%
2 7
 
1.4%

Length

2023-12-12T16:06:34.316599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:34.461070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 272
97.5%
2 7
 
2.5%

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

2023-12-12T16:06:34.575141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:34.686775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

처리직원번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3513
500 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3513
2nd row3513
3rd row3513
4th row3513
5th row3513

Common Values

ValueCountFrequency (%)
3513 500
100.0%

Length

2023-12-12T16:06:34.812808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:06:34.904498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3513 500
100.0%

Correlations

2023-12-12T16:06:34.987301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대중소분류구분코드매핑구분코드
대중소분류구분코드1.0000.486
매핑구분코드0.4861.000
2023-12-12T16:06:35.099717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
매핑구분코드대중소분류구분코드
매핑구분코드1.0000.195
대중소분류구분코드0.1951.000
2023-12-12T16:06:35.187021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대중소분류구분코드매핑구분코드
대중소분류구분코드1.0000.195
매핑구분코드0.1951.000

Missing values

2023-12-12T16:06:31.892573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:06:32.046445image/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

이력일련번호업종차수업종코드대중소분류구분코드매핑구분코드최종수정수처리직원번호
0110U990093113513
1110U990013113513
2110U9900313513
3110U990313513
4110U99213513
5110U113513
6110T982003113513
7110T9820313513
8110T982313513
9110T981003113513
이력일련번호업종차수업종코드대중소분류구분코드매핑구분코드최종수정수처리직원번호
490110K661313513
491110K66213513
492110K653033113513
493110K653023113513
494110K653013113513
495110K6530313513
496110K653313513
497110K652003113513
498110K6520313513
499110K652313513