Overview

Dataset statistics

Number of variables6
Number of observations500
Missing cells227
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.1 KiB
Average record size in memory49.3 B

Variable types

Text4
Boolean1
Categorical1

Dataset

Description해당 파일 데이터는 신용보증기금의 고객기본정보주소통합정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093080/fileData.do

Alerts

삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
사업장주소1 has 226 (45.2%) missing valuesMissing
주소통합아이디 has unique valuesUnique

Reproduction

Analysis started2024-04-17 19:08:11.130160
Analysis finished2024-04-17 19:08:11.515856
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주소통합아이디
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T04:08:11.677132image/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 row9dnS0LCQ5a
2nd row9dnS0KLQjF
3rd row9dnS0HFb18
4th row9dnS0Hb01K
5th row9dnS0GFDEB
ValueCountFrequency (%)
9dns0lcq5a 1
 
0.2%
9dnsxyjnac 1
 
0.2%
9dnsxq74ou 1
 
0.2%
9dnsxrv6iq 1
 
0.2%
9dnsxrw4gz 1
 
0.2%
9dnsxrrczx 1
 
0.2%
9dnsxr4gxg 1
 
0.2%
9dnsxr48pj 1
 
0.2%
9dnsxsclwh 1
 
0.2%
9dnsxsoens 1
 
0.2%
Other values (490) 490
98.0%
2024-04-18T04:08:11.978335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 549
 
11.0%
S 545
 
10.9%
9 540
 
10.8%
d 531
 
10.6%
X 145
 
2.9%
W 139
 
2.8%
Y 135
 
2.7%
Z 124
 
2.5%
0 110
 
2.2%
V 83
 
1.7%
Other values (52) 2099
42.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2045
40.9%
Uppercase Letter 1929
38.6%
Decimal Number 1026
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 549
26.8%
d 531
26.0%
e 52
 
2.5%
o 52
 
2.5%
r 47
 
2.3%
k 46
 
2.2%
c 45
 
2.2%
m 45
 
2.2%
l 45
 
2.2%
w 44
 
2.2%
Other values (16) 589
28.8%
Uppercase Letter
ValueCountFrequency (%)
S 545
28.3%
X 145
 
7.5%
W 139
 
7.2%
Y 135
 
7.0%
Z 124
 
6.4%
V 83
 
4.3%
T 48
 
2.5%
I 48
 
2.5%
F 47
 
2.4%
B 46
 
2.4%
Other values (16) 569
29.5%
Decimal Number
ValueCountFrequency (%)
9 540
52.6%
0 110
 
10.7%
8 59
 
5.8%
7 50
 
4.9%
5 48
 
4.7%
6 48
 
4.7%
2 45
 
4.4%
3 44
 
4.3%
4 42
 
4.1%
1 40
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3974
79.5%
Common 1026
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 549
 
13.8%
S 545
 
13.7%
d 531
 
13.4%
X 145
 
3.6%
W 139
 
3.5%
Y 135
 
3.4%
Z 124
 
3.1%
V 83
 
2.1%
e 52
 
1.3%
o 52
 
1.3%
Other values (42) 1619
40.7%
Common
ValueCountFrequency (%)
9 540
52.6%
0 110
 
10.7%
8 59
 
5.8%
7 50
 
4.9%
5 48
 
4.7%
6 48
 
4.7%
2 45
 
4.4%
3 44
 
4.3%
4 42
 
4.1%
1 40
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 549
 
11.0%
S 545
 
10.9%
9 540
 
10.8%
d 531
 
10.6%
X 145
 
2.9%
W 139
 
2.8%
Y 135
 
2.7%
Z 124
 
2.5%
0 110
 
2.2%
V 83
 
1.7%
Other values (52) 2099
42.0%

고객아이디
Text

Distinct423
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T04:08:12.194636image/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

Unique350 ?
Unique (%)70.0%

Sample

1st row9dnS0LB534
2nd row9dnS0KLvcH
3rd row9crGU5D5yG
4th row9dnS0HbA6q
5th row9dnS0GEeHK
ValueCountFrequency (%)
9c9ddnhvjt 5
 
1.0%
aaaaadppy7 3
 
0.6%
9dnsw4lmlm 2
 
0.4%
9dnotnmf1p 2
 
0.4%
aaaaabwwp0 2
 
0.4%
9dnswwqvfk 2
 
0.4%
9dmjbtyzvg 2
 
0.4%
9dkikqfb6g 2
 
0.4%
9dnccfje5r 2
 
0.4%
9dg2bcr5vl 2
 
0.4%
Other values (413) 476
95.2%
2024-04-18T04:08:12.521785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 513
 
10.3%
d 412
 
8.2%
n 322
 
6.4%
a 288
 
5.8%
S 273
 
5.5%
c 145
 
2.9%
W 111
 
2.2%
Y 104
 
2.1%
b 103
 
2.1%
X 88
 
1.8%
Other values (52) 2641
52.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2284
45.7%
Uppercase Letter 1707
34.1%
Decimal Number 1009
20.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 412
18.0%
n 322
14.1%
a 288
12.6%
c 145
 
6.3%
b 103
 
4.5%
f 72
 
3.2%
e 61
 
2.7%
v 56
 
2.5%
p 55
 
2.4%
m 54
 
2.4%
Other values (16) 716
31.3%
Uppercase Letter
ValueCountFrequency (%)
S 273
 
16.0%
W 111
 
6.5%
Y 104
 
6.1%
X 88
 
5.2%
Z 76
 
4.5%
P 64
 
3.7%
D 60
 
3.5%
B 57
 
3.3%
M 56
 
3.3%
E 56
 
3.3%
Other values (16) 762
44.6%
Decimal Number
ValueCountFrequency (%)
9 513
50.8%
0 86
 
8.5%
1 57
 
5.6%
5 56
 
5.6%
6 55
 
5.5%
7 52
 
5.2%
2 49
 
4.9%
3 48
 
4.8%
8 48
 
4.8%
4 45
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 3991
79.8%
Common 1009
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 412
 
10.3%
n 322
 
8.1%
a 288
 
7.2%
S 273
 
6.8%
c 145
 
3.6%
W 111
 
2.8%
Y 104
 
2.6%
b 103
 
2.6%
X 88
 
2.2%
Z 76
 
1.9%
Other values (42) 2069
51.8%
Common
ValueCountFrequency (%)
9 513
50.8%
0 86
 
8.5%
1 57
 
5.6%
5 56
 
5.6%
6 55
 
5.5%
7 52
 
5.2%
2 49
 
4.9%
3 48
 
4.8%
8 48
 
4.8%
4 45
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 513
 
10.3%
d 412
 
8.2%
n 322
 
6.4%
a 288
 
5.8%
S 273
 
5.5%
c 145
 
2.9%
W 111
 
2.2%
Y 104
 
2.1%
b 103
 
2.1%
X 88
 
1.8%
Other values (52) 2641
52.8%

우편번호
Text

Distinct337
Distinct (%)67.5%
Missing1
Missing (%)0.2%
Memory size4.0 KiB
2024-04-18T04:08:12.796425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.0240481
Min length5

Characters and Unicode

Total characters2507
Distinct characters11
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

Unique202 ?
Unique (%)40.5%

Sample

1st row10883
2nd row10551
3rd row18315
4th row10894
5th row14058
ValueCountFrequency (%)
46539 4
 
0.8%
59673 4
 
0.8%
34012 4
 
0.8%
21068 4
 
0.8%
03116 4
 
0.8%
05397 4
 
0.8%
16944 3
 
0.6%
17794 3
 
0.6%
08840 3
 
0.6%
31204 3
 
0.6%
Other values (327) 463
92.8%
2024-04-18T04:08:13.190146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 354
14.1%
0 350
14.0%
4 276
11.0%
2 272
10.8%
3 256
10.2%
5 246
9.8%
7 214
8.5%
8 208
8.3%
6 184
7.3%
9 141
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2501
99.8%
Dash Punctuation 6
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 354
14.2%
0 350
14.0%
4 276
11.0%
2 272
10.9%
3 256
10.2%
5 246
9.8%
7 214
8.6%
8 208
8.3%
6 184
7.4%
9 141
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2507
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 354
14.1%
0 350
14.0%
4 276
11.0%
2 272
10.8%
3 256
10.2%
5 246
9.8%
7 214
8.5%
8 208
8.3%
6 184
7.3%
9 141
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 354
14.1%
0 350
14.0%
4 276
11.0%
2 272
10.8%
3 256
10.2%
5 246
9.8%
7 214
8.5%
8 208
8.3%
6 184
7.3%
9 141
 
5.6%

사업장주소1
Text

MISSING 

Distinct172
Distinct (%)62.8%
Missing226
Missing (%)45.2%
Memory size4.0 KiB
2024-04-18T04:08:13.469388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15.5
Mean length12.883212
Min length6

Characters and Unicode

Total characters3530
Distinct characters172
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)37.6%

Sample

1st row경기도 화성시 봉담읍
2nd row서울특별시 종로구 숭인동
3rd row서울특별시 종로구 숭인동
4th row경기도 이천시 중리동
5th row경기도 성남시 분당구 대장동
ValueCountFrequency (%)
경기도 62
 
7.1%
서울특별시 55
 
6.3%
인천광역시 18
 
2.1%
경기 17
 
1.9%
부산광역시 16
 
1.8%
천안시 14
 
1.6%
충청남도 12
 
1.4%
서북구 10
 
1.1%
김포시 10
 
1.1%
남구 9
 
1.0%
Other values (279) 655
74.6%
2024-04-18T04:08:13.862050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
608
 
17.2%
시 260
 
7.4%
동 259
 
7.3%
구 188
 
5.3%
도 126
 
3.6%
서 99
 
2.8%
경 98
 
2.8%
기 82
 
2.3%
광 79
 
2.2%
산 77
 
2.2%
Other values (162) 1654
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2901
82.2%
Space Separator 608
 
17.2%
Decimal Number 21
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 260
 
9.0%
동 259
 
8.9%
구 188
 
6.5%
도 126
 
4.3%
서 99
 
3.4%
경 98
 
3.4%
기 82
 
2.8%
광 79
 
2.7%
산 77
 
2.7%
울 69
 
2.4%
Other values (155) 1564
53.9%
Decimal Number
ValueCountFrequency (%)
1 9
42.9%
2 5
23.8%
3 2
 
9.5%
7 2
 
9.5%
6 2
 
9.5%
4 1
 
4.8%
Space Separator
ValueCountFrequency (%)
608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2901
82.2%
Common 629
 
17.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 260
 
9.0%
동 259
 
8.9%
구 188
 
6.5%
도 126
 
4.3%
서 99
 
3.4%
경 98
 
3.4%
기 82
 
2.8%
광 79
 
2.7%
산 77
 
2.7%
울 69
 
2.4%
Other values (155) 1564
53.9%
Common
ValueCountFrequency (%)
608
96.7%
1 9
 
1.4%
2 5
 
0.8%
3 2
 
0.3%
7 2
 
0.3%
6 2
 
0.3%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2901
82.2%
ASCII 629
 
17.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
608
96.7%
1 9
 
1.4%
2 5
 
0.8%
3 2
 
0.3%
7 2
 
0.3%
6 2
 
0.3%
4 1
 
0.2%
Hangul
ValueCountFrequency (%)
시 260
 
9.0%
동 259
 
8.9%
구 188
 
6.5%
도 126
 
4.3%
서 99
 
3.4%
경 98
 
3.4%
기 82
 
2.8%
광 79
 
2.7%
산 77
 
2.7%
울 69
 
2.4%
Other values (155) 1564
53.9%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2024-04-18T04:08:13.952981image/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

2024-04-18T04:08:14.035708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:08:14.122310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

Missing values

2024-04-18T04:08:11.333607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T04:08:11.412855image/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.
2024-04-18T04:08:11.481004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

주소통합아이디고객아이디우편번호사업장주소1삭제여부최종수정수
09dnS0LCQ5a9dnS0LB53410883<NA>N1
19dnS0KLQjF9dnS0KLvcH10551<NA>N1
29dnS0HFb189crGU5D5yG18315경기도 화성시 봉담읍N1
39dnS0Hb01K9dnS0HbA6q10894<NA>N1
49dnS0GFDEB9dnS0GEeHK14058<NA>N1
59dnS0GEZk59dnS0GEeHK04595<NA>N1
69dnS0E5kTa9dnS0E4R4704595<NA>N1
79dnS0EPXic9dnS0EPfUe03116서울특별시 종로구 숭인동N1
89dnS0EO1hZ9dnS0EOLVo03116서울특별시 종로구 숭인동N1
99dnS0EiSyA9dnS0EhsX658560<NA>N1
주소통합아이디고객아이디우편번호사업장주소1삭제여부최종수정수
4909dnSVM46Di9dnOtnmF1P21068인천광역시 계양구 계산동N1
4919dnSVMYvI69dnSVMWW5904567<NA>N1
4929dnSVMXUsP9dnSVMWW5904780<NA>N1
4939dnSVLHFF89b2YPdx2GZ31204충남 천안시 동남구 신방동N1
4949dnSVLHzyQ9b2YPiry5N31204충청남도 천안시 동남구 신방동N1
4959dnSVKBIs19dnOdHKef614344경기도 광명시 일직동N1
4969dnSVIWCjI9dnSVIVhY007761<NA>N1
4979dnSVIV9jV9dnSVIVhY007761<NA>N1
4989dnSVHLEAr9cZrHN3uKj37874경상북도 포항시 남구 대송면N1
4999dnSVHDLm89dasGm8pAB15825경기도 군포시 산본동N1