Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.3 KiB
Average record size in memory68.3 B

Variable types

Text3
Categorical3
Numeric2

Dataset

Description해당 파일 데이터는 신용보증기금의 보증접수고객관계이력에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093229/fileData.do

Alerts

업무구분코드 has constant value ""Constant
고객원장역할관계코드 is highly overall correlated with 조회순서일련번호High correlation
조회순서일련번호 is highly overall correlated with 고객원장역할관계코드High correlation

Reproduction

Analysis started2023-12-12 13:19:45.928225
Analysis finished2023-12-12 13:19:47.053402
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고객아이디
Text

Distinct338
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:19:47.246310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
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

Unique212 ?
Unique (%)42.4%

Sample

1st rowaaaaaanhDl
2nd row9dmpvbXRCS
3rd rowBBB0813116
4th rowBBB0813116
5th row9dnSLOTfgj
ValueCountFrequency (%)
bbb0886583 5
 
1.0%
9dnayghc58 5
 
1.0%
9dnayggkzm 5
 
1.0%
bbb0023100 4
 
0.8%
9dnaxcjbdq 4
 
0.8%
9ddamlz7r9 4
 
0.8%
9ddamlakfl 4
 
0.8%
9dnaxcjuld 4
 
0.8%
bbb0067496 4
 
0.8%
9bs2vb17wo 3
 
0.6%
Other values (328) 458
91.6%
2023-12-12T22:19:47.629507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 518
 
10.4%
9 413
 
8.3%
0 358
 
7.2%
d 249
 
5.0%
a 218
 
4.4%
2 173
 
3.5%
3 162
 
3.2%
6 152
 
3.0%
n 151
 
3.0%
1 144
 
2.9%
Other values (52) 2462
49.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1832
36.6%
Lowercase Letter 1634
32.7%
Uppercase Letter 1534
30.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 518
33.8%
S 84
 
5.5%
A 80
 
5.2%
Y 60
 
3.9%
X 59
 
3.8%
G 57
 
3.7%
J 45
 
2.9%
L 45
 
2.9%
R 44
 
2.9%
D 43
 
2.8%
Other values (16) 499
32.5%
Lowercase Letter
ValueCountFrequency (%)
d 249
15.2%
a 218
 
13.3%
n 151
 
9.2%
b 110
 
6.7%
c 99
 
6.1%
m 53
 
3.2%
s 52
 
3.2%
z 51
 
3.1%
i 49
 
3.0%
r 46
 
2.8%
Other values (16) 556
34.0%
Decimal Number
ValueCountFrequency (%)
9 413
22.5%
0 358
19.5%
2 173
9.4%
3 162
 
8.8%
6 152
 
8.3%
1 144
 
7.9%
8 128
 
7.0%
4 103
 
5.6%
5 102
 
5.6%
7 97
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3168
63.4%
Common 1832
36.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 518
 
16.4%
d 249
 
7.9%
a 218
 
6.9%
n 151
 
4.8%
b 110
 
3.5%
c 99
 
3.1%
S 84
 
2.7%
A 80
 
2.5%
Y 60
 
1.9%
X 59
 
1.9%
Other values (42) 1540
48.6%
Common
ValueCountFrequency (%)
9 413
22.5%
0 358
19.5%
2 173
9.4%
3 162
 
8.8%
6 152
 
8.3%
1 144
 
7.9%
8 128
 
7.0%
4 103
 
5.6%
5 102
 
5.6%
7 97
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 518
 
10.4%
9 413
 
8.3%
0 358
 
7.2%
d 249
 
5.0%
a 218
 
4.4%
2 173
 
3.5%
3 162
 
3.2%
6 152
 
3.0%
n 151
 
3.0%
1 144
 
2.9%
Other values (52) 2462
49.2%

고객원장역할관계코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
168 
3
166 
13
143 
14
19 
2
 
4

Length

Max length2
Median length1
Mean length1.324
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row13
4th row13
5th row3

Common Values

ValueCountFrequency (%)
1 168
33.6%
3 166
33.2%
13 143
28.6%
14 19
 
3.8%
2 4
 
0.8%

Length

2023-12-12T22:19:47.804777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:19:47.915633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 168
33.6%
3 166
33.2%
13 143
28.6%
14 19
 
3.8%
2 4
 
0.8%

업무구분코드
Categorical

CONSTANT 

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

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 500
100.0%

Length

2023-12-12T22:19:48.035238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:19:48.115861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

원장번호
Text

Distinct132
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:19:48.373511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6000
Distinct characters35
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

Unique20 ?
Unique (%)4.0%

Sample

1st rowTBC198300002
2nd rowTBH202103523
3rd rowTBH202103523
4th rowTBH202103523
5th rowTBH202103523
ValueCountFrequency (%)
tma202104647 12
 
2.4%
tce202101516 11
 
2.2%
thd202102025 9
 
1.8%
tah202104889 9
 
1.8%
taq202102333 9
 
1.8%
toe202102895 9
 
1.8%
tql202101630 9
 
1.8%
thj202105859 8
 
1.6%
tbf202110051 7
 
1.4%
tie202101976 6
 
1.2%
Other values (122) 411
82.2%
2023-12-12T22:19:48.807038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1248
20.8%
0 1237
20.6%
1 696
11.6%
T 508
8.5%
3 252
 
4.2%
5 244
 
4.1%
4 191
 
3.2%
6 188
 
3.1%
A 177
 
2.9%
9 174
 
2.9%
Other values (25) 1085
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4500
75.0%
Uppercase Letter 1500
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 508
33.9%
A 177
 
11.8%
H 119
 
7.9%
B 79
 
5.3%
I 77
 
5.1%
O 57
 
3.8%
Q 50
 
3.3%
E 49
 
3.3%
M 49
 
3.3%
J 43
 
2.9%
Other values (15) 292
19.5%
Decimal Number
ValueCountFrequency (%)
2 1248
27.7%
0 1237
27.5%
1 696
15.5%
3 252
 
5.6%
5 244
 
5.4%
4 191
 
4.2%
6 188
 
4.2%
9 174
 
3.9%
8 158
 
3.5%
7 112
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4500
75.0%
Latin 1500
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 508
33.9%
A 177
 
11.8%
H 119
 
7.9%
B 79
 
5.3%
I 77
 
5.1%
O 57
 
3.8%
Q 50
 
3.3%
E 49
 
3.3%
M 49
 
3.3%
J 43
 
2.9%
Other values (15) 292
19.5%
Common
ValueCountFrequency (%)
2 1248
27.7%
0 1237
27.5%
1 696
15.5%
3 252
 
5.6%
5 244
 
5.4%
4 191
 
4.2%
6 188
 
4.2%
9 174
 
3.9%
8 158
 
3.5%
7 112
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1248
20.8%
0 1237
20.6%
1 696
11.6%
T 508
8.5%
3 252
 
4.2%
5 244
 
4.1%
4 191
 
3.2%
6 188
 
3.1%
A 177
 
2.9%
9 174
 
2.9%
Other values (25) 1085
18.1%

이력일련번호
Real number (ℝ)

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.476
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T22:19:48.928603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile9
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8603824
Coefficient of variation (CV)1.1552433
Kurtosis8.2681291
Mean2.476
Median Absolute Deviation (MAD)0
Skewness2.5312472
Sum1238
Variance8.1817876
MonotonicityNot monotonic
2023-12-12T22:19:49.049939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 350
70.0%
3 51
 
10.2%
5 40
 
8.0%
7 28
 
5.6%
11 14
 
2.8%
9 13
 
2.6%
13 2
 
0.4%
21 2
 
0.4%
ValueCountFrequency (%)
1 350
70.0%
3 51
 
10.2%
5 40
 
8.0%
7 28
 
5.6%
9 13
 
2.6%
11 14
 
2.8%
13 2
 
0.4%
21 2
 
0.4%
ValueCountFrequency (%)
21 2
 
0.4%
13 2
 
0.4%
11 14
 
2.8%
9 13
 
2.6%
7 28
 
5.6%
5 40
 
8.0%
3 51
 
10.2%
1 350
70.0%

조회순서일련번호
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
419 
1
81 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 419
83.8%
1 81
 
16.2%

Length

2023-12-12T22:19:49.203302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:19:49.312601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 419
83.8%
1 81
 
16.2%

최종수정수
Real number (ℝ)

Distinct15
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.504
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T22:19:49.449187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile10
Maximum21
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3154137
Coefficient of variation (CV)0.94617971
Kurtosis4.6377753
Mean3.504
Median Absolute Deviation (MAD)1
Skewness1.8186934
Sum1752
Variance10.991968
MonotonicityNot monotonic
2023-12-12T22:19:49.590126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 217
43.4%
2 56
 
11.2%
4 42
 
8.4%
3 39
 
7.8%
5 30
 
6.0%
6 29
 
5.8%
7 26
 
5.2%
8 15
 
3.0%
10 14
 
2.8%
9 12
 
2.4%
Other values (5) 20
 
4.0%
ValueCountFrequency (%)
1 217
43.4%
2 56
 
11.2%
3 39
 
7.8%
4 42
 
8.4%
5 30
 
6.0%
6 29
 
5.8%
7 26
 
5.2%
8 15
 
3.0%
9 12
 
2.4%
10 14
 
2.8%
ValueCountFrequency (%)
21 2
 
0.4%
20 2
 
0.4%
13 2
 
0.4%
12 2
 
0.4%
11 12
2.4%
10 14
2.8%
9 12
2.4%
8 15
3.0%
7 26
5.2%
6 29
5.8%

처리직원번호
Text

Distinct91
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:19:49.891606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.29
Min length4

Characters and Unicode

Total characters2145
Distinct characters15
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

Unique15 ?
Unique (%)3.0%

Sample

1st rowBATCH
2nd row5214
3rd row5214
4th row5214
5th row5214
ValueCountFrequency (%)
99023 33
 
6.6%
99016 22
 
4.4%
99002 18
 
3.6%
99001 18
 
3.6%
5048 15
 
3.0%
99007 15
 
3.0%
99006 13
 
2.6%
4721 12
 
2.4%
4595 11
 
2.2%
5360 9
 
1.8%
Other values (81) 334
66.8%
2023-12-12T22:19:50.358848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 359
16.7%
0 347
16.2%
4 251
11.7%
6 234
10.9%
5 223
10.4%
1 204
9.5%
3 178
8.3%
2 146
6.8%
7 100
 
4.7%
8 73
 
3.4%
Other values (5) 30
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2115
98.6%
Uppercase Letter 30
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 359
17.0%
0 347
16.4%
4 251
11.9%
6 234
11.1%
5 223
10.5%
1 204
9.6%
3 178
8.4%
2 146
6.9%
7 100
 
4.7%
8 73
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
C 26
86.7%
B 1
 
3.3%
A 1
 
3.3%
T 1
 
3.3%
H 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2115
98.6%
Latin 30
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
9 359
17.0%
0 347
16.4%
4 251
11.9%
6 234
11.1%
5 223
10.5%
1 204
9.6%
3 178
8.4%
2 146
6.9%
7 100
 
4.7%
8 73
 
3.5%
Latin
ValueCountFrequency (%)
C 26
86.7%
B 1
 
3.3%
A 1
 
3.3%
T 1
 
3.3%
H 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 359
16.7%
0 347
16.2%
4 251
11.7%
6 234
10.9%
5 223
10.4%
1 204
9.5%
3 178
8.3%
2 146
6.8%
7 100
 
4.7%
8 73
 
3.4%
Other values (5) 30
 
1.4%

Interactions

2023-12-12T22:19:46.522674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:19:46.278793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:19:46.664816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:19:46.409119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:19:50.486264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객원장역할관계코드이력일련번호조회순서일련번호최종수정수처리직원번호
고객원장역할관계코드1.0000.0000.5640.0440.753
이력일련번호0.0001.0000.0000.9240.725
조회순서일련번호0.5640.0001.0000.0000.609
최종수정수0.0440.9240.0001.0000.831
처리직원번호0.7530.7250.6090.8311.000
2023-12-12T22:19:50.628366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회순서일련번호고객원장역할관계코드
조회순서일련번호1.0000.680
고객원장역할관계코드0.6801.000
2023-12-12T22:19:50.740411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호최종수정수고객원장역할관계코드조회순서일련번호
이력일련번호1.0000.4950.0000.000
최종수정수0.4951.0000.0360.000
고객원장역할관계코드0.0000.0361.0000.680
조회순서일련번호0.0000.0000.6801.000

Missing values

2023-12-12T22:19:46.821107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:19:46.996319image/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

고객아이디고객원장역할관계코드업무구분코드원장번호이력일련번호조회순서일련번호최종수정수처리직원번호
0aaaaaanhDl1GTBC198300002111BATCH
19dmpvbXRCS1GTBH2021035231055214
2BBB081311613GTBH2021035231135214
3BBB081311613GTBH2021035233125214
49dnSLOTfgj3GTBH2021035235045214
59dmpvbXRCS1GTBH2021035235045214
69dnSLOTfgj3GTBH2021035231055214
79c4cJ1UuUS1GTHD2021020675045888
8BBB021419713GTHD2021020671055888
99c4cJ1Vu063GTHD2021020671055888
고객아이디고객원장역할관계코드업무구분코드원장번호이력일련번호조회순서일련번호최종수정수처리직원번호
4909ctYq6n7Mi3GTIS2021017891039C743
4919cOmyLGvmb1GTIA2021038021053613
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