Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows88
Duplicate rows (%)17.6%
Total size in memory40.7 KiB
Average record size in memory83.3 B

Variable types

Categorical5
Text3
Boolean2

Dataset

Description해당 파일 데이터는 신용보증기금의 국가결산 배치 정보를 확인하실 수 있는 자료이니 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092665/fileData.do

Alerts

수행여부 has constant value ""Constant
배치오류코드 has constant value ""Constant
오류메시지상세내용 has constant value ""Constant
삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
Dataset has 88 (17.6%) duplicate rowsDuplicates
회계구분코드 is highly imbalanced (56.9%)Imbalance

Reproduction

Analysis started2023-12-11 23:23:09.668097
Analysis finished2023-12-11 23:23:10.609613
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G
398 
S
62 
I
 
32
R
 
4
C
 
4

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 398
79.6%
S 62
 
12.4%
I 32
 
6.4%
R 4
 
0.8%
C 4
 
0.8%

Length

2023-12-12T08:23:10.695027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:10.825411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 398
79.6%
s 62
 
12.4%
i 32
 
6.4%
r 4
 
0.8%
c 4
 
0.8%

결산업무구분코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
293 
3
207 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 293
58.6%
3 207
41.4%

Length

2023-12-12T08:23:10.969089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:11.084114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 293
58.6%
3 207
41.4%

배치프로그램작업내용
Text

Distinct108
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T08:23:11.304366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length8.972
Min length4

Characters and Unicode

Total characters4486
Distinct characters142
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)4.2%

Sample

1st row현금및현금성자산명세서
2nd row내부거래분개전표생성
3rd row단기차입금명세서
4th row자산부채조정명세서
5th row건설중인자산명세서
ValueCountFrequency (%)
통화별잔액 12
 
2.3%
손익계산서 12
 
2.3%
미지급금명세서 7
 
1.3%
선수수익명세서 7
 
1.3%
기타유동부채명세서 7
 
1.3%
지급수수료명세서 7
 
1.3%
유형자산명세서 7
 
1.3%
관리업무비명세서 7
 
1.3%
기타포괄손익누계액명세서(주식 7
 
1.3%
제충당금명세서 7
 
1.3%
Other values (96) 440
84.6%
2023-12-12T08:23:11.771115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 358
 
8.0%
세 346
 
7.7%
명 346
 
7.7%
수 169
 
3.8%
증 160
 
3.6%
금 155
 
3.5%
기 135
 
3.0%
권 123
 
2.7%
자 88
 
2.0%
( 86
 
1.9%
Other values (132) 2520
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4271
95.2%
Open Punctuation 86
 
1.9%
Close Punctuation 86
 
1.9%
Space Separator 20
 
0.4%
Other Punctuation 14
 
0.3%
Uppercase Letter 9
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 358
 
8.4%
세 346
 
8.1%
명 346
 
8.1%
수 169
 
4.0%
증 160
 
3.7%
금 155
 
3.6%
기 135
 
3.2%
권 123
 
2.9%
자 88
 
2.1%
정 85
 
2.0%
Other values (125) 2306
54.0%
Uppercase Letter
ValueCountFrequency (%)
S 3
33.3%
O 3
33.3%
C 3
33.3%
Open Punctuation
ValueCountFrequency (%)
( 86
100.0%
Close Punctuation
ValueCountFrequency (%)
) 86
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4271
95.2%
Common 206
 
4.6%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 358
 
8.4%
세 346
 
8.1%
명 346
 
8.1%
수 169
 
4.0%
증 160
 
3.7%
금 155
 
3.6%
기 135
 
3.2%
권 123
 
2.9%
자 88
 
2.1%
정 85
 
2.0%
Other values (125) 2306
54.0%
Common
ValueCountFrequency (%)
( 86
41.7%
) 86
41.7%
20
 
9.7%
/ 14
 
6.8%
Latin
ValueCountFrequency (%)
S 3
33.3%
O 3
33.3%
C 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4271
95.2%
ASCII 215
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 358
 
8.4%
세 346
 
8.1%
명 346
 
8.1%
수 169
 
4.0%
증 160
 
3.7%
금 155
 
3.6%
기 135
 
3.2%
권 123
 
2.9%
자 88
 
2.1%
정 85
 
2.0%
Other values (125) 2306
54.0%
ASCII
ValueCountFrequency (%)
( 86
40.0%
) 86
40.0%
20
 
9.3%
/ 14
 
6.5%
S 3
 
1.4%
O 3
 
1.4%
C 3
 
1.4%

수행여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
True
500 
ValueCountFrequency (%)
True 500
100.0%
2023-12-12T08:23:11.889803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

배치오류코드
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 500
100.0%

Length

2023-12-12T08:23:12.034016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:12.166647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%

오류메시지상세내용
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
** NOMAL END **
500 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row** NOMAL END **
2nd row** NOMAL END **
3rd row** NOMAL END **
4th row** NOMAL END **
5th row** NOMAL END **

Common Values

ValueCountFrequency (%)
** NOMAL END ** 500
100.0%

Length

2023-12-12T08:23:12.290765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:12.397114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1000
50.0%
nomal 500
25.0%
end 500
25.0%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T08:23:12.492907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

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

2023-12-12T08:23:12.617668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:12.717095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

처리직원번호
Text

Distinct89
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T08:23:12.962735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2500
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

Unique6 ?
Unique (%)1.2%

Sample

1st rowAND35
2nd rowAND34
3rd rowAND33
4th rowAND32
5th rowAND31
ValueCountFrequency (%)
aad25 8
 
1.6%
aad24 8
 
1.6%
aad14 8
 
1.6%
aad02 8
 
1.6%
aad28 8
 
1.6%
aad31 7
 
1.4%
aad34 7
 
1.4%
aad29 7
 
1.4%
aad27 7
 
1.4%
aad33 7
 
1.4%
Other values (79) 425
85.0%
2023-12-12T08:23:13.371917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 793
31.7%
D 500
20.0%
N 207
 
8.3%
2 181
 
7.2%
1 171
 
6.8%
3 158
 
6.3%
0 142
 
5.7%
4 106
 
4.2%
5 58
 
2.3%
8 47
 
1.9%
Other values (3) 137
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
60.0%
Decimal Number 1000
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 181
18.1%
1 171
17.1%
3 158
15.8%
0 142
14.2%
4 106
10.6%
5 58
 
5.8%
8 47
 
4.7%
6 46
 
4.6%
7 46
 
4.6%
9 45
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
A 793
52.9%
D 500
33.3%
N 207
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
60.0%
Common 1000
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 181
18.1%
1 171
17.1%
3 158
15.8%
0 142
14.2%
4 106
10.6%
5 58
 
5.8%
8 47
 
4.7%
6 46
 
4.6%
7 46
 
4.6%
9 45
 
4.5%
Latin
ValueCountFrequency (%)
A 793
52.9%
D 500
33.3%
N 207
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 793
31.7%
D 500
20.0%
N 207
 
8.3%
2 181
 
7.2%
1 171
 
6.8%
3 158
 
6.3%
0 142
 
5.7%
4 106
 
4.2%
5 58
 
2.3%
8 47
 
1.9%
Other values (3) 137
 
5.5%

최초처리직원번호
Text

Distinct89
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T08:23:13.646804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2500
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

Unique6 ?
Unique (%)1.2%

Sample

1st rowAND35
2nd rowAND34
3rd rowAND33
4th rowAND32
5th rowAND31
ValueCountFrequency (%)
aad25 8
 
1.6%
aad24 8
 
1.6%
aad14 8
 
1.6%
aad02 8
 
1.6%
aad28 8
 
1.6%
aad31 7
 
1.4%
aad34 7
 
1.4%
aad29 7
 
1.4%
aad27 7
 
1.4%
aad33 7
 
1.4%
Other values (79) 425
85.0%
2023-12-12T08:23:14.086873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 793
31.7%
D 500
20.0%
N 207
 
8.3%
2 181
 
7.2%
1 171
 
6.8%
3 158
 
6.3%
0 142
 
5.7%
4 106
 
4.2%
5 58
 
2.3%
8 47
 
1.9%
Other values (3) 137
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
60.0%
Decimal Number 1000
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 181
18.1%
1 171
17.1%
3 158
15.8%
0 142
14.2%
4 106
10.6%
5 58
 
5.8%
8 47
 
4.7%
6 46
 
4.6%
7 46
 
4.6%
9 45
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
A 793
52.9%
D 500
33.3%
N 207
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
60.0%
Common 1000
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 181
18.1%
1 171
17.1%
3 158
15.8%
0 142
14.2%
4 106
10.6%
5 58
 
5.8%
8 47
 
4.7%
6 46
 
4.6%
7 46
 
4.6%
9 45
 
4.5%
Latin
ValueCountFrequency (%)
A 793
52.9%
D 500
33.3%
N 207
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 793
31.7%
D 500
20.0%
N 207
 
8.3%
2 181
 
7.2%
1 171
 
6.8%
3 158
 
6.3%
0 142
 
5.7%
4 106
 
4.2%
5 58
 
2.3%
8 47
 
1.9%
Other values (3) 137
 
5.5%

Correlations

2023-12-12T08:23:14.195122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계구분코드결산업무구분코드처리직원번호최초처리직원번호
회계구분코드1.0000.1940.0000.000
결산업무구분코드0.1941.0001.0001.000
처리직원번호0.0001.0001.0001.000
최초처리직원번호0.0001.0001.0001.000
2023-12-12T08:23:14.302676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결산업무구분코드회계구분코드
결산업무구분코드1.0000.237
회계구분코드0.2371.000
2023-12-12T08:23:14.385812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계구분코드결산업무구분코드
회계구분코드1.0000.237
결산업무구분코드0.2371.000

Missing values

2023-12-12T08:23:10.023376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:23:10.208535image/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

회계구분코드결산업무구분코드배치프로그램작업내용수행여부배치오류코드오류메시지상세내용삭제여부최종수정수처리직원번호최초처리직원번호
0G3현금및현금성자산명세서Y0** NOMAL END **N1AND35AND35
1G3내부거래분개전표생성Y0** NOMAL END **N1AND34AND34
2G3단기차입금명세서Y0** NOMAL END **N1AND33AND33
3G3자산부채조정명세서Y0** NOMAL END **N1AND32AND32
4G3건설중인자산명세서Y0** NOMAL END **N1AND31AND31
5G3직접년도증감명세서Y0** NOMAL END **N1AND30AND30
6G3당해년도증감명세서Y0** NOMAL END **N1AND29AND29
7G3장기대여금명세서Y0** NOMAL END **N1AND28AND28
8G3단기대여금명세서Y0** NOMAL END **N1AND27AND27
9G3단기미수채권잔액명세서Y0** NOMAL END **N1AND26AND26
회계구분코드결산업무구분코드배치프로그램작업내용수행여부배치오류코드오류메시지상세내용삭제여부최종수정수처리직원번호최초처리직원번호
490G1제충당금명세서Y0** NOMAL END **N1AAD33AAD33
491G1선수보증료명세서Y0** NOMAL END **N1AAD32AAD32
492G1선수수익명세서Y0** NOMAL END **N1AAD31AAD31
493G1기타유동부채명세서Y0** NOMAL END **N1AAD30AAD30
494G1예수금명세서Y0** NOMAL END **N1AAD29AAD29
495G1제수입보증금명세서Y0** NOMAL END **N1AAD28AAD28
496G1미지급금명세서Y0** NOMAL END **N1AAD27AAD27
497G1선수금명세서Y0** NOMAL END **N1AAD26AAD26
498G1기타포괄손익누계액명세서(주식)Y0** NOMAL END **N1AAD25AAD25
499G1유형자산명세서Y0** NOMAL END **N1AAD24AAD24

Duplicate rows

Most frequently occurring

회계구분코드결산업무구분코드배치프로그램작업내용수행여부배치오류코드오류메시지상세내용삭제여부최종수정수처리직원번호최초처리직원번호# duplicates
3G1관리업무비명세서Y0** NOMAL END **N1AAD43AAD435
6G1기타영업외비용명세서Y0** NOMAL END **N1AAD47AAD475
7G1기타영업외이익명세서Y0** NOMAL END **N1AAD45AAD455
8G1기타유동부채명세서Y0** NOMAL END **N1AAD30AAD305
9G1기타포괄손익누계액명세서(주식)Y0** NOMAL END **N1AAD25AAD255
18G1미지급금명세서Y0** NOMAL END **N1AAD27AAD275
20G1보증료명세서Y0** NOMAL END **N1AAD37AAD375
21G1상각채권관리비명세서Y0** NOMAL END **N1AAD42AAD425
22G1상각채권기타회수금명세서Y0** NOMAL END **N1AAD40AAD405
25G1선수금명세서Y0** NOMAL END **N1AAD26AAD265