Overview

Dataset statistics

Number of variables7
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory57.9 B

Variable types

Text2
DateTime1
Categorical4

Dataset

Description해당 데이터는 신용보증기금의 신용 조사서(조사서id, 기준일, 신규증액여부 등)에 관련한 자료입니다(2022년)..업종코드는 별개의 코드북 없이 통계청의 한국표준산업분류코드를 따르며, 아래 링크를 참조 바랍니다.https://kssc.kostat.go.kr:8443/ksscNew_web/kssc/main/main.do?gubun=1
Author신용보증기금
URLhttps://www.data.go.kr/data/15106340/fileData.do

Alerts

최종업종코드차수 has constant value ""Constant
조사서아이디(ID) has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:23:41.427299
Analysis finished2023-12-12 21:23:41.849886
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조사서아이디(ID)
Text

UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T06:23:42.097041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1500
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)100.0%

Sample

1st row9b0eAWxnF2
2nd row9b0I8JJvEz
3rd row9b0JcvcvPF
4th row9b0KEGSuer
5th row9b0l4PefLy
ValueCountFrequency (%)
9b0eawxnf2 1
 
0.7%
9b9jio0kzl 1
 
0.7%
9b9poikcjt 1
 
0.7%
9b9pxatxuc 1
 
0.7%
9b9s80ho7r 1
 
0.7%
9b9sqfkcps 1
 
0.7%
9b9svmvdsl 1
 
0.7%
9b9tvmgkef 1
 
0.7%
9b9ylvdfs8 1
 
0.7%
9b9ysd05jl 1
 
0.7%
Other values (140) 140
93.3%
2023-12-13T06:23:42.539117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 182
 
12.1%
b 163
 
10.9%
8 40
 
2.7%
7 36
 
2.4%
1 33
 
2.2%
0 32
 
2.1%
G 28
 
1.9%
4 28
 
1.9%
Q 28
 
1.9%
E 27
 
1.8%
Other values (52) 903
60.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 606
40.4%
Uppercase Letter 453
30.2%
Decimal Number 441
29.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 163
26.9%
s 26
 
4.3%
e 25
 
4.1%
f 22
 
3.6%
o 22
 
3.6%
v 21
 
3.5%
t 20
 
3.3%
y 19
 
3.1%
l 19
 
3.1%
i 19
 
3.1%
Other values (16) 250
41.3%
Uppercase Letter
ValueCountFrequency (%)
G 28
 
6.2%
Q 28
 
6.2%
E 27
 
6.0%
C 23
 
5.1%
A 22
 
4.9%
W 21
 
4.6%
B 20
 
4.4%
R 19
 
4.2%
M 18
 
4.0%
D 17
 
3.8%
Other values (16) 230
50.8%
Decimal Number
ValueCountFrequency (%)
9 182
41.3%
8 40
 
9.1%
7 36
 
8.2%
1 33
 
7.5%
0 32
 
7.3%
4 28
 
6.3%
5 26
 
5.9%
6 26
 
5.9%
2 20
 
4.5%
3 18
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1059
70.6%
Common 441
29.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 163
 
15.4%
G 28
 
2.6%
Q 28
 
2.6%
E 27
 
2.5%
s 26
 
2.5%
e 25
 
2.4%
C 23
 
2.2%
f 22
 
2.1%
o 22
 
2.1%
A 22
 
2.1%
Other values (42) 673
63.6%
Common
ValueCountFrequency (%)
9 182
41.3%
8 40
 
9.1%
7 36
 
8.2%
1 33
 
7.5%
0 32
 
7.3%
4 28
 
6.3%
5 26
 
5.9%
6 26
 
5.9%
2 20
 
4.5%
3 18
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 182
 
12.1%
b 163
 
10.9%
8 40
 
2.7%
7 36
 
2.4%
1 33
 
2.2%
0 32
 
2.1%
G 28
 
1.9%
4 28
 
1.9%
Q 28
 
1.9%
E 27
 
1.8%
Other values (52) 903
60.2%

조사기준일
Date

Distinct108
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2022-02-07 00:00:00
Maximum2022-12-29 00:00:00
2023-12-13T06:23:42.734975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:42.870731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

신규증액여부
Categorical

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
77 
Y
73 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
77
51.3%
Y 73
48.7%

Length

2023-12-13T06:23:42.988491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:23:43.086864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 73
100.0%

최종업종코드
Text

Distinct114
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T06:23:43.346628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters900
Distinct characters20
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

Unique91 ?
Unique (%)60.7%

Sample

1st rowG46721
2nd rowC10122
3rd rowG45110
4th rowG46595
5th rowG46595
ValueCountFrequency (%)
g46721 8
 
5.3%
c31114 4
 
2.7%
g46699 4
 
2.7%
g46791 4
 
2.7%
f42412 3
 
2.0%
g46539 2
 
1.3%
g46611 2
 
1.3%
f42201 2
 
1.3%
g46733 2
 
1.3%
c25122 2
 
1.3%
Other values (104) 117
78.0%
2023-12-13T06:23:43.750090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 171
19.0%
1 152
16.9%
4 106
11.8%
9 88
9.8%
C 65
 
7.2%
6 57
 
6.3%
G 52
 
5.8%
3 51
 
5.7%
7 43
 
4.8%
0 40
 
4.4%
Other values (10) 75
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
83.3%
Uppercase Letter 150
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 171
22.8%
1 152
20.3%
4 106
14.1%
9 88
11.7%
6 57
 
7.6%
3 51
 
6.8%
7 43
 
5.7%
0 40
 
5.3%
5 36
 
4.8%
8 6
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
C 65
43.3%
G 52
34.7%
F 18
 
12.0%
H 5
 
3.3%
J 3
 
2.0%
S 2
 
1.3%
D 2
 
1.3%
N 1
 
0.7%
E 1
 
0.7%
A 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 750
83.3%
Latin 150
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 171
22.8%
1 152
20.3%
4 106
14.1%
9 88
11.7%
6 57
 
7.6%
3 51
 
6.8%
7 43
 
5.7%
0 40
 
5.3%
5 36
 
4.8%
8 6
 
0.8%
Latin
ValueCountFrequency (%)
C 65
43.3%
G 52
34.7%
F 18
 
12.0%
H 5
 
3.3%
J 3
 
2.0%
S 2
 
1.3%
D 2
 
1.3%
N 1
 
0.7%
E 1
 
0.7%
A 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 171
19.0%
1 152
16.9%
4 106
11.8%
9 88
9.8%
C 65
 
7.2%
6 57
 
6.3%
G 52
 
5.8%
3 51
 
5.7%
7 43
 
4.8%
0 40
 
4.4%
Other values (10) 75
8.3%

최종업종코드차수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
10
150 

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 (%)
10 150
100.0%

Length

2023-12-13T06:23:43.872287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:23:43.954570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 150
100.0%

개인법인구분
Categorical

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
법인사업자
113 
개인사업자
37 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row법인사업자
2nd row법인사업자
3rd row개인사업자
4th row법인사업자
5th row법인사업자

Common Values

ValueCountFrequency (%)
법인사업자 113
75.3%
개인사업자 37
 
24.7%

Length

2023-12-13T06:23:44.045849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:23:44.152600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인사업자 113
75.3%
개인사업자 37
 
24.7%

주사업장 지역구분명
Categorical

Distinct16
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
경기도
42 
서울특별시
25 
인천광역시
12 
경상남도
12 
경상북도
11 
Other values (11)
48 

Length

Max length7
Median length5
Mean length4.16
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row경기도
2nd row경상북도
3rd row인천광역시
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 42
28.0%
서울특별시 25
16.7%
인천광역시 12
 
8.0%
경상남도 12
 
8.0%
경상북도 11
 
7.3%
부산광역시 10
 
6.7%
전라남도 7
 
4.7%
울산광역시 6
 
4.0%
대구광역시 5
 
3.3%
충청남도 5
 
3.3%
Other values (6) 15
 
10.0%

Length

2023-12-13T06:23:44.251861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 42
28.0%
서울특별시 25
16.7%
인천광역시 12
 
8.0%
경상남도 12
 
8.0%
경상북도 11
 
7.3%
부산광역시 10
 
6.7%
전라남도 7
 
4.7%
울산광역시 6
 
4.0%
대구광역시 5
 
3.3%
충청남도 5
 
3.3%
Other values (6) 15
 
10.0%

Correlations

2023-12-13T06:23:44.325811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신규증액여부개인법인구분주사업장 지역구분명
신규증액여부1.0000.6400.000
개인법인구분0.6401.0000.000
주사업장 지역구분명0.0000.0001.000
2023-12-13T06:23:44.415500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개인법인구분신규증액여부주사업장 지역구분명
개인법인구분1.0000.4420.000
신규증액여부0.4421.0000.000
주사업장 지역구분명0.0000.0001.000
2023-12-13T06:23:44.505881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신규증액여부개인법인구분주사업장 지역구분명
신규증액여부1.0000.4420.000
개인법인구분0.4421.0000.000
주사업장 지역구분명0.0000.0001.000

Missing values

2023-12-13T06:23:41.658726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:23:41.791291image/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

조사서아이디(ID)조사기준일신규증액여부최종업종코드최종업종코드차수개인법인구분주사업장 지역구분명
09b0eAWxnF22022-03-14G4672110법인사업자경기도
19b0I8JJvEz2022-03-30YC1012210법인사업자경상북도
29b0JcvcvPF2022-04-06YG4511010개인사업자인천광역시
39b0KEGSuer2022-05-23G4659510법인사업자경기도
49b0l4PefLy2022-02-07YG4659510법인사업자경기도
59b0mEkKm1E2022-04-04YC1071110개인사업자경상북도
69b0mpDQMeV2022-03-18H5299210법인사업자서울특별시
79b0qFILWys2022-04-06C1079910법인사업자부산광역시
89b0R9sSyAC2022-08-16YC1790210개인사업자경상북도
99b0W1E3Q6z2022-05-06YF4231110법인사업자경기도
조사서아이디(ID)조사기준일신규증액여부최종업종코드최종업종코드차수개인법인구분주사업장 지역구분명
1409bCVUwTlxS2022-04-13YG4672110법인사업자대전광역시
1419bD71kxqtM2022-04-15C3033110법인사업자경기도
1429bDutIp7Rx2022-09-06YC2913310개인사업자경기도
1439bEIJKmJT62022-08-04YG4649910개인사업자서울특별시
1449bFeVQhZJO2022-03-15YF4220410법인사업자울산광역시
1459bGMNXfQUW2022-04-27YG4661110법인사업자인천광역시
1469bGW7Sjfad2022-07-01C1721110개인사업자경기도
1479bGXmG4AsY2022-10-20YF4212210법인사업자서울특별시
1489bGxxaY8RE2022-08-26YG4662210개인사업자전라남도
1499bH6GiedlA2022-12-29C1922110법인사업자부산광역시