Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.5 KiB
Average record size in memory93.3 B

Variable types

Text3
Categorical4
DateTime2
Numeric2

Dataset

Description해당 파일 데이터는 신용보증기금의 보험청약접수고객관계에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092992/fileData.do

Alerts

이력일련번호 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
고객원장역할관계코드 is highly overall correlated with 업무구분코드High correlation
업무구분코드 is highly overall correlated with 고객원장역할관계코드High correlation
업무구분코드 is highly imbalanced (93.3%)Imbalance
최종수정수 is highly imbalanced (79.6%)Imbalance

Reproduction

Analysis started2023-12-12 14:06:34.215189
Analysis finished2023-12-12 14:06:35.639258
Duration1.42 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고객ID
Text

Distinct413
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:06:35.839485image/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

Unique370 ?
Unique (%)74.0%

Sample

1st row9dgIFHy6g1
2nd row9dgIFHzQfj
3rd row9dgIFHy6g1
4th row9dgIFHzQfj
5th row9dmUffYYxf
ValueCountFrequency (%)
9bbnkrsvx6 8
 
1.6%
9bbnkrsdil 8
 
1.6%
9dihqbtpoi 5
 
1.0%
9dihqbu7y1 5
 
1.0%
9bmefnmbwa 4
 
0.8%
9cozbdrejn 4
 
0.8%
9cxjf47puf 4
 
0.8%
9bmefnmxkk 4
 
0.8%
9cxjf47dao 4
 
0.8%
9cv6su0f05 4
 
0.8%
Other values (403) 450
90.0%
2023-12-12T23:06:36.294677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 469
 
9.4%
a 464
 
9.3%
c 285
 
5.7%
d 208
 
4.2%
b 182
 
3.6%
S 94
 
1.9%
k 83
 
1.7%
n 79
 
1.6%
g 78
 
1.6%
B 76
 
1.5%
Other values (52) 2982
59.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2435
48.7%
Uppercase Letter 1573
31.5%
Decimal Number 992
19.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 464
19.1%
c 285
 
11.7%
d 208
 
8.5%
b 182
 
7.5%
k 83
 
3.4%
n 79
 
3.2%
g 78
 
3.2%
j 72
 
3.0%
f 70
 
2.9%
o 69
 
2.8%
Other values (16) 845
34.7%
Uppercase Letter
ValueCountFrequency (%)
S 94
 
6.0%
B 76
 
4.8%
A 70
 
4.5%
R 69
 
4.4%
H 66
 
4.2%
M 66
 
4.2%
I 65
 
4.1%
Y 65
 
4.1%
U 63
 
4.0%
D 63
 
4.0%
Other values (16) 876
55.7%
Decimal Number
ValueCountFrequency (%)
9 469
47.3%
6 65
 
6.6%
4 62
 
6.2%
7 61
 
6.1%
3 61
 
6.1%
2 60
 
6.0%
5 60
 
6.0%
1 57
 
5.7%
0 56
 
5.6%
8 41
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4008
80.2%
Common 992
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 464
 
11.6%
c 285
 
7.1%
d 208
 
5.2%
b 182
 
4.5%
S 94
 
2.3%
k 83
 
2.1%
n 79
 
2.0%
g 78
 
1.9%
B 76
 
1.9%
j 72
 
1.8%
Other values (42) 2387
59.6%
Common
ValueCountFrequency (%)
9 469
47.3%
6 65
 
6.6%
4 62
 
6.2%
7 61
 
6.1%
3 61
 
6.1%
2 60
 
6.0%
5 60
 
6.0%
1 57
 
5.7%
0 56
 
5.6%
8 41
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 469
 
9.4%
a 464
 
9.3%
c 285
 
5.7%
d 208
 
4.2%
b 182
 
3.6%
S 94
 
1.9%
k 83
 
1.7%
n 79
 
1.6%
g 78
 
1.6%
B 76
 
1.5%
Other values (52) 2982
59.6%

고객원장역할관계코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
249 
3
249 
17
 
1
16
 
1

Length

Max length2
Median length1
Mean length1.004
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 249
49.8%
3 249
49.8%
17 1
 
0.2%
16 1
 
0.2%

Length

2023-12-12T23:06:36.468436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:06:36.592179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 249
49.8%
3 249
49.8%
17 1
 
0.2%
16 1
 
0.2%

업무구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
496 
N
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 496
99.2%
N 4
 
0.8%

Length

2023-12-12T23:06:36.728846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:06:36.844624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 496
99.2%
n 4
 
0.8%

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

2023-12-12T23:06:36.985824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:06:37.129571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T23:06:37.217846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:06:37.330086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효종료일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T23:06:37.438114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:06:37.531370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
484 
2
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 484
96.8%
2 16
 
3.2%

Length

2023-12-12T23:06:37.635949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:06:37.722502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 484
96.8%
2 16
 
3.2%

처리시각
Text

Distinct248
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:06:38.049930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34:32.4
2nd row34:32.4
3rd row33:56.9
4th row33:56.9
5th row25:01.9
ValueCountFrequency (%)
35:13.2 4
 
0.8%
54:54.0 4
 
0.8%
55:23.8 2
 
0.4%
17:27.7 2
 
0.4%
34:32.4 2
 
0.4%
50:16.5 2
 
0.4%
42:12.1 2
 
0.4%
39:57.6 2
 
0.4%
34:33.0 2
 
0.4%
33:49.4 2
 
0.4%
Other values (238) 476
95.2%
2023-12-12T23:06:38.485865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 352
10.1%
2 332
9.5%
1 330
9.4%
5 318
9.1%
4 290
8.3%
0 288
8.2%
6 160
 
4.6%
8 146
 
4.2%
Other values (2) 284
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 352
14.1%
2 332
13.3%
1 330
13.2%
5 318
12.7%
4 290
11.6%
0 288
11.5%
6 160
6.4%
8 146
5.8%
7 144
5.8%
9 140
 
5.6%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 352
10.1%
2 332
9.5%
1 330
9.4%
5 318
9.1%
4 290
8.3%
0 288
8.2%
6 160
 
4.6%
8 146
 
4.2%
Other values (2) 284
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 352
10.1%
2 332
9.5%
1 330
9.4%
5 318
9.1%
4 290
8.3%
0 288
8.2%
6 160
 
4.6%
8 146
 
4.2%
Other values (2) 284
8.1%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12554.104
Minimum3290
Maximum88889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:06:38.623691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3290
5-th percentile3620
Q14913
median5449
Q35851
95-th percentile88889
Maximum88889
Range85599
Interquartile range (IQR)938

Descriptive statistics

Standard deviation23745.883
Coefficient of variation (CV)1.8914837
Kurtosis6.5214217
Mean12554.104
Median Absolute Deviation (MAD)448.5
Skewness2.9128066
Sum6277052
Variance5.6386697 × 108
MonotonicityNot monotonic
2023-12-12T23:06:38.762281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88889 44
 
8.8%
3620 28
 
5.6%
5173 28
 
5.6%
5495 20
 
4.0%
4774 8
 
1.6%
5621 8
 
1.6%
5608 8
 
1.6%
6147 8
 
1.6%
4258 8
 
1.6%
5406 8
 
1.6%
Other values (117) 332
66.4%
ValueCountFrequency (%)
3290 2
 
0.4%
3447 2
 
0.4%
3548 2
 
0.4%
3555 2
 
0.4%
3590 6
 
1.2%
3611 2
 
0.4%
3620 28
5.6%
3773 2
 
0.4%
3860 2
 
0.4%
3977 2
 
0.4%
ValueCountFrequency (%)
88889 44
8.8%
6175 6
 
1.2%
6147 8
 
1.6%
6139 2
 
0.4%
6129 4
 
0.8%
6121 2
 
0.4%
6115 2
 
0.4%
6110 2
 
0.4%
6098 2
 
0.4%
6094 2
 
0.4%

최초처리시각
Text

Distinct248
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:06:39.064908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34:32.4
2nd row34:32.4
3rd row45:02.1
4th row45:02.1
5th row25:01.9
ValueCountFrequency (%)
35:13.2 4
 
0.8%
54:54.0 4
 
0.8%
55:23.8 2
 
0.4%
40:54.9 2
 
0.4%
34:32.4 2
 
0.4%
50:16.5 2
 
0.4%
42:12.1 2
 
0.4%
39:57.6 2
 
0.4%
34:33.0 2
 
0.4%
33:49.4 2
 
0.4%
Other values (238) 476
95.2%
2023-12-12T23:06:39.567592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 350
10.0%
2 338
9.7%
1 322
9.2%
5 310
8.9%
4 304
8.7%
0 290
8.3%
6 162
 
4.6%
9 150
 
4.3%
Other values (2) 274
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 350
14.0%
2 338
13.5%
1 322
12.9%
5 310
12.4%
4 304
12.2%
0 290
11.6%
6 162
6.5%
9 150
6.0%
8 138
 
5.5%
7 136
 
5.4%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 350
10.0%
2 338
9.7%
1 322
9.2%
5 310
8.9%
4 304
8.7%
0 290
8.3%
6 162
 
4.6%
9 150
 
4.3%
Other values (2) 274
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 350
10.0%
2 338
9.7%
1 322
9.2%
5 310
8.9%
4 304
8.7%
0 290
8.3%
6 162
 
4.6%
9 150
 
4.3%
Other values (2) 274
7.8%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12554.104
Minimum3290
Maximum88889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:06:39.750553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3290
5-th percentile3620
Q14913
median5449
Q35851
95-th percentile88889
Maximum88889
Range85599
Interquartile range (IQR)938

Descriptive statistics

Standard deviation23745.883
Coefficient of variation (CV)1.8914837
Kurtosis6.5214217
Mean12554.104
Median Absolute Deviation (MAD)448.5
Skewness2.9128066
Sum6277052
Variance5.6386697 × 108
MonotonicityNot monotonic
2023-12-12T23:06:39.921246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88889 44
 
8.8%
3620 28
 
5.6%
5173 28
 
5.6%
5495 20
 
4.0%
4774 8
 
1.6%
5621 8
 
1.6%
5608 8
 
1.6%
6147 8
 
1.6%
4258 8
 
1.6%
5406 8
 
1.6%
Other values (117) 332
66.4%
ValueCountFrequency (%)
3290 2
 
0.4%
3447 2
 
0.4%
3548 2
 
0.4%
3555 2
 
0.4%
3590 6
 
1.2%
3611 2
 
0.4%
3620 28
5.6%
3773 2
 
0.4%
3860 2
 
0.4%
3977 2
 
0.4%
ValueCountFrequency (%)
88889 44
8.8%
6175 6
 
1.2%
6147 8
 
1.6%
6139 2
 
0.4%
6129 4
 
0.8%
6121 2
 
0.4%
6115 2
 
0.4%
6110 2
 
0.4%
6098 2
 
0.4%
6094 2
 
0.4%

Interactions

2023-12-12T23:06:35.143984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:06:34.963444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:06:35.230943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:06:35.058358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:06:40.038031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객원장역할관계코드업무구분코드최종수정수처리직원번호최초처리직원번호
고객원장역할관계코드1.0000.8930.0000.0000.000
업무구분코드0.8931.0000.0000.0000.000
최종수정수0.0000.0001.0000.0000.000
처리직원번호0.0000.0000.0001.0001.000
최초처리직원번호0.0000.0000.0001.0001.000
2023-12-12T23:06:40.143967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분코드최종수정수고객원장역할관계코드
업무구분코드1.0000.0000.702
최종수정수0.0001.0000.000
고객원장역할관계코드0.7020.0001.000
2023-12-12T23:06:40.242493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리직원번호최초처리직원번호고객원장역할관계코드업무구분코드최종수정수
처리직원번호1.0001.0000.0000.0000.000
최초처리직원번호1.0001.0000.0000.0000.000
고객원장역할관계코드0.0000.0001.0000.7020.000
업무구분코드0.0000.0000.7021.0000.000
최종수정수0.0000.0000.0000.0001.000

Missing values

2023-12-12T23:06:35.368330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:06:35.558889image/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

고객ID고객원장역할관계코드업무구분코드이력일련번호유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dgIFHy6g11A100:00.000:00.0134:32.4499234:32.44992
19dgIFHzQfj3A100:00.000:00.0134:32.4499234:32.44992
29dgIFHy6g11A100:00.000:00.0233:56.9499245:02.14992
39dgIFHzQfj3A100:00.000:00.0233:56.9499245:02.14992
49dmUffYYxf1A100:00.000:00.0125:01.9576325:01.95763
59dmUffZf7u3A100:00.000:00.0125:01.9576325:01.95763
69dlZByAXhp1A100:00.000:00.0122:22.0507622:22.05076
79dlZByBuRc3A100:00.000:00.0122:22.0507622:22.05076
89cDUgAtH1I1A100:00.000:00.0118:44.2607218:44.26072
99cDUgAt7xX3A100:00.000:00.0118:44.2607218:44.26072
고객ID고객원장역할관계코드업무구분코드이력일련번호유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909cZBdUYmuA1A100:00.000:00.0133:43.4456533:43.44565
4919cZBdUY6qu3A100:00.000:00.0133:43.4456533:43.44565
492aaaaadDW6q1A100:00.000:00.0131:48.7604531:48.76045
493aaaaaaB8q53A100:00.000:00.0131:48.7604531:48.76045
4949bGGninGMQ1A100:00.000:00.0128:42.0484428:42.04844
4959bGGninYcV3A100:00.000:00.0128:42.0484428:42.04844
496aaaaadAptI1A100:00.000:00.0127:53.3588927:53.35889
497aaaaabkau93A100:00.000:00.0127:53.3588927:53.35889
4989dgWFhmwNz1A100:00.000:00.0127:04.1573927:04.15739
4999dgWFhJS9x3A100:00.000:00.0127:04.1573927:04.15739