Overview

Dataset statistics

Number of variables15
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.7 KiB
Average record size in memory124.3 B

Variable types

Text4
Categorical9
Boolean1
Numeric1

Dataset

Description해당 파일 데이터는 신용보증기금의 고객기본정보고객정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093055/fileData.do

Alerts

삭제여부 has constant value ""Constant
고객대분류코드 is highly overall correlated with 최종수정수 and 2 other fieldsHigh correlation
고객소분류코드 is highly overall correlated with 최종수정수 and 4 other fieldsHigh correlation
고객상태변동일자 is highly overall correlated with 고객상태코드High correlation
고객상태코드 is highly overall correlated with 고객대분류코드 and 3 other fieldsHigh correlation
최종수정수 is highly overall correlated with 고객대분류코드 and 1 other fieldsHigh correlation
설립형태코드 is highly overall correlated with 고객소분류코드 and 2 other fieldsHigh correlation
기업형태코드 is highly overall correlated with 고객소분류코드 and 2 other fieldsHigh correlation
기업형태앞뒤구분코드 is highly overall correlated with 기업형태코드High correlation
고객대분류코드 is highly imbalanced (96.2%)Imbalance
고객상태변동일자 is highly imbalanced (83.7%)Imbalance
고객상태코드 is highly imbalanced (59.3%)Imbalance
보안조치고객상태코드 is highly imbalanced (90.5%)Imbalance
고객ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:21:57.709200
Analysis finished2023-12-12 15:21:59.263553
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고객ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:21:59.507168image/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

Unique500 ?
Unique (%)100.0%

Sample

1st row9dnS0HbA6q
2nd row9dkL05X954
3rd row9dnS0GEeHK
4th row9dg5td9bgx
5th row9dnS0E4R47
ValueCountFrequency (%)
9dns0hba6q 1
 
0.2%
9dnsvm1htk 1
 
0.2%
9dnsuli5gi 1
 
0.2%
9dnojsmgnj 1
 
0.2%
9dnsuwtomt 1
 
0.2%
9dnsu3h9wx 1
 
0.2%
9dnsu3itrv 1
 
0.2%
9dnsu3nkb5 1
 
0.2%
9dnmmch1t5 1
 
0.2%
9czbduy6qu 1
 
0.2%
Other values (490) 490
98.0%
2023-12-13T00:22:00.003868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 532
 
10.6%
d 480
 
9.6%
n 422
 
8.4%
S 386
 
7.7%
a 120
 
2.4%
W 93
 
1.9%
X 91
 
1.8%
c 89
 
1.8%
Y 84
 
1.7%
0 79
 
1.6%
Other values (52) 2624
52.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2170
43.4%
Uppercase Letter 1814
36.3%
Decimal Number 1016
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 480
22.1%
n 422
19.4%
a 120
 
5.5%
c 89
 
4.1%
b 61
 
2.8%
k 58
 
2.7%
h 57
 
2.6%
f 56
 
2.6%
l 53
 
2.4%
i 52
 
2.4%
Other values (16) 722
33.3%
Uppercase Letter
ValueCountFrequency (%)
S 386
21.3%
W 93
 
5.1%
X 91
 
5.0%
Y 84
 
4.6%
T 73
 
4.0%
V 71
 
3.9%
Z 70
 
3.9%
U 69
 
3.8%
Q 66
 
3.6%
B 65
 
3.6%
Other values (16) 746
41.1%
Decimal Number
ValueCountFrequency (%)
9 532
52.4%
0 79
 
7.8%
5 64
 
6.3%
4 54
 
5.3%
6 50
 
4.9%
8 49
 
4.8%
7 49
 
4.8%
1 48
 
4.7%
3 47
 
4.6%
2 44
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3984
79.7%
Common 1016
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 480
 
12.0%
n 422
 
10.6%
S 386
 
9.7%
a 120
 
3.0%
W 93
 
2.3%
X 91
 
2.3%
c 89
 
2.2%
Y 84
 
2.1%
T 73
 
1.8%
V 71
 
1.8%
Other values (42) 2075
52.1%
Common
ValueCountFrequency (%)
9 532
52.4%
0 79
 
7.8%
5 64
 
6.3%
4 54
 
5.3%
6 50
 
4.9%
8 49
 
4.8%
7 49
 
4.8%
1 48
 
4.7%
3 47
 
4.6%
2 44
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 532
 
10.6%
d 480
 
9.6%
n 422
 
8.4%
S 386
 
7.7%
a 120
 
2.4%
W 93
 
1.9%
X 91
 
1.8%
c 89
 
1.8%
Y 84
 
1.7%
0 79
 
1.6%
Other values (52) 2624
52.5%

고객대분류코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
498 
5
 
2

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 (%)
1 498
99.6%
5 2
 
0.4%

Length

2023-12-13T00:22:00.163392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:00.285277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 498
99.6%
5 2
 
0.4%

고객소분류코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3
322 
1
138 
2
38 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 322
64.4%
1 138
27.6%
2 38
 
7.6%
5 2
 
0.4%

Length

2023-12-13T00:22:00.404400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:00.534274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 322
64.4%
1 138
27.6%
2 38
 
7.6%
5 2
 
0.4%

설립형태코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
318 
178 
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 318
63.6%
178
35.6%
3 4
 
0.8%

Length

2023-12-13T00:22:00.675683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:00.786863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 318
98.8%
3 4
 
1.2%

기업형태코드
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
10
241 
140 
31
106 
34
 
7
11
 
3
Other values (2)
 
3

Length

Max length2
Median length2
Mean length1.72
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row
2nd row31
3rd row10
4th row
5th row

Common Values

ValueCountFrequency (%)
10 241
48.2%
140
28.0%
31 106
21.2%
34 7
 
1.4%
11 3
 
0.6%
99 2
 
0.4%
37 1
 
0.2%

Length

2023-12-13T00:22:00.946966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:01.097412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 241
66.9%
31 106
29.4%
34 7
 
1.9%
11 3
 
0.8%
99 2
 
0.6%
37 1
 
0.3%

기업형태앞뒤구분코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
384 
1
89 
2
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
384
76.8%
1 89
 
17.8%
2 27
 
5.4%

Length

2023-12-13T00:22:01.251769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:01.386880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 89
76.7%
2 27
 
23.3%

최종업종차수
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
266 
10
233 
9
 
1

Length

Max length2
Median length1
Mean length1.466
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row10
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 266
53.2%
10 233
46.6%
9 1
 
0.2%

Length

2023-12-13T00:22:01.527039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:01.645116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 266
53.2%
10 233
46.6%
9 1
 
0.2%

업종코드
Text

Distinct145
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:22:02.046981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length3.34
Min length1

Characters and Unicode

Total characters1670
Distinct characters26
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

Unique102 ?
Unique (%)20.4%

Sample

1st row
2nd rowC25111
3rd row
4th row
5th row
ValueCountFrequency (%)
i56111 25
 
10.7%
g47912 10
 
4.3%
s96999 5
 
2.1%
i56113 4
 
1.7%
h49301 3
 
1.3%
j58221 3
 
1.3%
l68112 3
 
1.3%
s96112 3
 
1.3%
i56193 3
 
1.3%
i56221 3
 
1.3%
Other values (134) 172
73.5%
2023-12-13T00:22:02.604139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 282
16.9%
266
15.9%
2 162
9.7%
9 151
9.0%
6 135
8.1%
4 114
6.8%
5 91
 
5.4%
3 81
 
4.9%
G 66
 
4.0%
0 63
 
3.8%
Other values (16) 259
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1170
70.1%
Space Separator 266
 
15.9%
Uppercase Letter 234
 
14.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 66
28.2%
I 46
19.7%
C 38
16.2%
S 14
 
6.0%
L 10
 
4.3%
R 10
 
4.3%
J 9
 
3.8%
F 9
 
3.8%
P 8
 
3.4%
H 7
 
3.0%
Other values (5) 17
 
7.3%
Decimal Number
ValueCountFrequency (%)
1 282
24.1%
2 162
13.8%
9 151
12.9%
6 135
11.5%
4 114
9.7%
5 91
 
7.8%
3 81
 
6.9%
0 63
 
5.4%
7 53
 
4.5%
8 38
 
3.2%
Space Separator
ValueCountFrequency (%)
266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1436
86.0%
Latin 234
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 66
28.2%
I 46
19.7%
C 38
16.2%
S 14
 
6.0%
L 10
 
4.3%
R 10
 
4.3%
J 9
 
3.8%
F 9
 
3.8%
P 8
 
3.4%
H 7
 
3.0%
Other values (5) 17
 
7.3%
Common
ValueCountFrequency (%)
1 282
19.6%
266
18.5%
2 162
11.3%
9 151
10.5%
6 135
9.4%
4 114
7.9%
5 91
 
6.3%
3 81
 
5.6%
0 63
 
4.4%
7 53
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 282
16.9%
266
15.9%
2 162
9.7%
9 151
9.0%
6 135
8.1%
4 114
6.8%
5 91
 
5.4%
3 81
 
4.9%
G 66
 
4.0%
0 63
 
3.8%
Other values (16) 259
15.5%

고객상태변동일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
488 
00:00.0
 
12

Length

Max length26
Median length26
Mean length25.544
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
3rd row0001-01-01 00:00:00.000000
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 488
97.6%
00:00.0 12
 
2.4%

Length

2023-12-13T00:22:02.814757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:02.943313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 488
49.4%
00:00:00.000000 488
49.4%
00:00.0 12
 
1.2%

고객상태코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
31
312 
1
175 
34
 
6
 
3
41
 
2
Other values (2)
 
2

Length

Max length2
Median length2
Mean length1.644
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row1
2nd row31
3rd row31
4th row1
5th row1

Common Values

ValueCountFrequency (%)
31 312
62.4%
1 175
35.0%
34 6
 
1.2%
3
 
0.6%
41 2
 
0.4%
32 1
 
0.2%
45 1
 
0.2%

Length

2023-12-13T00:22:03.065803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:03.176882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31 312
62.8%
1 175
35.2%
34 6
 
1.2%
41 2
 
0.4%
32 1
 
0.2%
45 1
 
0.2%

보안조치고객상태코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
490 
1
 
9
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
490
98.0%
1 9
 
1.8%
3 1
 
0.2%

Length

2023-12-13T00:22:03.302972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:22:03.430222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9
90.0%
3 1
 
10.0%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-13T00:22:03.512056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.968
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:22:03.613204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum21
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6100187
Coefficient of variation (CV)0.81809895
Kurtosis51.112872
Mean1.968
Median Absolute Deviation (MAD)1
Skewness5.4614492
Sum984
Variance2.5921603
MonotonicityNot monotonic
2023-12-13T00:22:03.765451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 242
48.4%
2 146
29.2%
3 62
 
12.4%
4 31
 
6.2%
5 8
 
1.6%
6 4
 
0.8%
7 3
 
0.6%
9 2
 
0.4%
16 1
 
0.2%
21 1
 
0.2%
ValueCountFrequency (%)
1 242
48.4%
2 146
29.2%
3 62
 
12.4%
4 31
 
6.2%
5 8
 
1.6%
6 4
 
0.8%
7 3
 
0.6%
9 2
 
0.4%
16 1
 
0.2%
21 1
 
0.2%
ValueCountFrequency (%)
21 1
 
0.2%
16 1
 
0.2%
9 2
 
0.4%
7 3
 
0.6%
6 4
 
0.8%
5 8
 
1.6%
4 31
 
6.2%
3 62
 
12.4%
2 146
29.2%
1 242
48.4%

처리직원번호
Text

Distinct93
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:22:04.093665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.744
Min length4

Characters and Unicode

Total characters2372
Distinct characters11
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

Unique55 ?
Unique (%)11.0%

Sample

1st row99007
2nd row4147
3rd row99001
4th row99016
5th row99001
ValueCountFrequency (%)
88889 97
19.4%
99001 58
11.6%
99023 58
11.6%
99006 47
 
9.4%
99016 39
 
7.8%
99007 34
 
6.8%
99002 28
 
5.6%
88888 5
 
1.0%
3965 4
 
0.8%
5814 4
 
0.8%
Other values (83) 126
25.2%
2023-12-13T00:22:04.517602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 682
28.8%
0 487
20.5%
8 435
18.3%
6 134
 
5.6%
1 131
 
5.5%
3 128
 
5.4%
2 112
 
4.7%
7 93
 
3.9%
5 89
 
3.8%
4 79
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2370
99.9%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 682
28.8%
0 487
20.5%
8 435
18.4%
6 134
 
5.7%
1 131
 
5.5%
3 128
 
5.4%
2 112
 
4.7%
7 93
 
3.9%
5 89
 
3.8%
4 79
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2370
99.9%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
9 682
28.8%
0 487
20.5%
8 435
18.4%
6 134
 
5.7%
1 131
 
5.5%
3 128
 
5.4%
2 112
 
4.7%
7 93
 
3.9%
5 89
 
3.8%
4 79
 
3.3%
Latin
ValueCountFrequency (%)
C 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 682
28.8%
0 487
20.5%
8 435
18.3%
6 134
 
5.6%
1 131
 
5.5%
3 128
 
5.4%
2 112
 
4.7%
7 93
 
3.9%
5 89
 
3.8%
4 79
 
3.3%

최초처리직원번호
Text

Distinct108
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:22:04.789398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.738
Min length3

Characters and Unicode

Total characters2369
Distinct characters16
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

Unique71 ?
Unique (%)14.2%

Sample

1st row99007
2nd row88889
3rd row99001
4th row99016
5th row99001
ValueCountFrequency (%)
88889 101
20.3%
99001 66
13.3%
99023 47
 
9.5%
99006 42
 
8.5%
99016 37
 
7.4%
99007 35
 
7.0%
99002 25
 
5.0%
88888 5
 
1.0%
99015 4
 
0.8%
3973 4
 
0.8%
Other values (97) 131
26.4%
2023-12-13T00:22:05.232343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 669
28.2%
0 478
20.2%
8 455
19.2%
1 140
 
5.9%
3 132
 
5.6%
6 121
 
5.1%
2 112
 
4.7%
7 84
 
3.5%
5 81
 
3.4%
4 77
 
3.3%
Other values (6) 20
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2349
99.2%
Space Separator 15
 
0.6%
Uppercase Letter 5
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 669
28.5%
0 478
20.3%
8 455
19.4%
1 140
 
6.0%
3 132
 
5.6%
6 121
 
5.2%
2 112
 
4.8%
7 84
 
3.6%
5 81
 
3.4%
4 77
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
20.0%
A 1
20.0%
T 1
20.0%
C 1
20.0%
H 1
20.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
99.8%
Latin 5
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
9 669
28.3%
0 478
20.2%
8 455
19.2%
1 140
 
5.9%
3 132
 
5.6%
6 121
 
5.1%
2 112
 
4.7%
7 84
 
3.6%
5 81
 
3.4%
4 77
 
3.3%
Latin
ValueCountFrequency (%)
B 1
20.0%
A 1
20.0%
T 1
20.0%
C 1
20.0%
H 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 669
28.2%
0 478
20.2%
8 455
19.2%
1 140
 
5.9%
3 132
 
5.6%
6 121
 
5.1%
2 112
 
4.7%
7 84
 
3.5%
5 81
 
3.4%
4 77
 
3.3%
Other values (6) 20
 
0.8%

Interactions

2023-12-13T00:21:58.803426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:22:05.342876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객대분류코드고객소분류코드설립형태코드기업형태코드기업형태앞뒤구분코드최종업종차수고객상태변동일자고객상태코드보안조치고객상태코드최종수정수처리직원번호
고객대분류코드1.0001.0000.0340.0000.0000.0000.0000.7500.0001.0000.881
고객소분류코드1.0001.0000.6750.7590.4120.4900.1320.8300.1530.7250.750
설립형태코드0.0340.6751.0000.7130.3490.8190.0610.7660.3450.2850.338
기업형태코드0.0000.7590.7131.0000.7910.5370.0000.7510.1770.1280.852
기업형태앞뒤구분코드0.0000.4120.3490.7911.0000.0000.0000.1650.0000.0620.924
최종업종차수0.0000.4900.8190.5370.0001.0000.0940.5940.2230.4320.000
고객상태변동일자0.0000.1320.0610.0000.0000.0941.0000.8400.0000.1590.913
고객상태코드0.7500.8300.7660.7510.1650.5940.8401.0000.1250.7570.960
보안조치고객상태코드0.0000.1530.3450.1770.0000.2230.0000.1251.0000.2450.945
최종수정수1.0000.7250.2850.1280.0620.4320.1590.7570.2451.0000.771
처리직원번호0.8810.7500.3380.8520.9240.0000.9130.9600.9450.7711.000
2023-12-13T00:22:05.492950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업형태코드고객대분류코드기업형태앞뒤구분코드고객소분류코드설립형태코드최종업종차수고객상태변동일자보안조치고객상태코드고객상태코드
기업형태코드1.0000.0000.7360.6390.6290.4240.0000.1190.350
고객대분류코드0.0001.0000.0000.9980.0570.0000.0000.0000.809
기업형태앞뒤구분코드0.7360.0001.0000.4040.1220.0000.0000.0000.111
고객소분류코드0.6390.9980.4041.0000.7040.4880.0870.1440.740
설립형태코드0.6290.0570.1220.7041.0000.4900.1000.1200.700
최종업종차수0.4240.0000.0000.4880.4901.0000.1560.0710.485
고객상태변동일자0.0000.0000.0000.0870.1000.1561.0000.0000.906
보안조치고객상태코드0.1190.0000.0000.1440.1200.0710.0001.0000.083
고객상태코드0.3500.8090.1110.7400.7000.4850.9060.0831.000
2023-12-13T00:22:05.639584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수고객대분류코드고객소분류코드설립형태코드기업형태코드기업형태앞뒤구분코드최종업종차수고객상태변동일자고객상태코드보안조치고객상태코드
최종수정수1.0000.9960.5840.1310.0000.0000.2080.1480.3880.000
고객대분류코드0.9961.0000.9980.0570.0000.0000.0000.0000.8090.000
고객소분류코드0.5840.9981.0000.7040.6390.4040.4880.0870.7400.144
설립형태코드0.1310.0570.7041.0000.6290.1220.4900.1000.7000.120
기업형태코드0.0000.0000.6390.6291.0000.7360.4240.0000.3500.119
기업형태앞뒤구분코드0.0000.0000.4040.1220.7361.0000.0000.0000.1110.000
최종업종차수0.2080.0000.4880.4900.4240.0001.0000.1560.4850.071
고객상태변동일자0.1480.0000.0870.1000.0000.0000.1561.0000.9060.000
고객상태코드0.3880.8090.7400.7000.3500.1110.4850.9061.0000.083
보안조치고객상태코드0.0000.0000.1440.1200.1190.0000.0710.0000.0831.000

Missing values

2023-12-13T00:21:58.961747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:21:59.177528image/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

고객ID고객대분류코드고객소분류코드설립형태코드기업형태코드기업형태앞뒤구분코드최종업종차수업종코드고객상태변동일자고객상태코드보안조치고객상태코드삭제여부최종수정수처리직원번호최초처리직원번호
09dnS0HbA6q1100001-01-01 00:00:00.0000001N19900799007
19dkL05X95413131210C251110001-01-01 00:00:00.00000031N2414788889
29dnS0GEeHK1311000001-01-01 00:00:00.00000031N19900199001
39dg5td9bgx1100001-01-01 00:00:00.00000011N49901699016
49dnS0E4R471100001-01-01 00:00:00.0000001N19900199001
59dnS0EPfUe13134210K649990001-01-01 00:00:00.00000031N258145814
69dnS0EOLVo1234200001-01-01 00:00:00.0000001N158145814
79dnS0EhsX61311000001-01-01 00:00:00.00000031N19900199001
89dnSXT5WVf1311010S961290001-01-01 00:00:00.00000031N29902399023
99dg5tfMJAB1311010R912990001-01-01 00:00:00.00000031N39901699016
고객ID고객대분류코드고객소분류코드설립형태코드기업형태코드기업형태앞뒤구분코드최종업종차수업종코드고객상태변동일자고객상태코드보안조치고객상태코드삭제여부최종수정수처리직원번호최초처리직원번호
4909dnSQft1761100001-01-01 00:00:00.0000001N18888988889
4919dnN8QAgwg13131110H492120001-01-01 00:00:00.00000031N3327988889
492BBB35480065500001-01-01 00:00:00.000000N213279
4939dnSQb3oPf1100001-01-01 00:00:00.0000001N19900699006
4949dnDzBtlkh13131210C281230001-01-01 00:00:00.00000031N2405588889
4959dnN8QzlM01231100001-01-01 00:00:00.0000001N2327988889
4969dagtqOC2A1311000001-01-01 00:00:00.00000031N29901699001
4979dnSP2ktf41100001-01-01 00:00:00.0000001N146904690
4989dnozN0Fol1311010S969990001-01-01 00:00:00.00000031N29900299002
4999dbkm5JGgT1100001-01-01 00:00:00.00000011N3600399016