Overview

Dataset statistics

Number of variables7
Number of observations186
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory56.7 B

Variable types

Categorical2
Text4
DateTime1

Dataset

Description광주광역시 서구 관내 네일아트업의 업종명, 업소명, 도로명주소, 지번주소, 영업시작일자, 업태명에 대한 현황입니다.
Author광주광역시 서구
URLhttps://www.data.go.kr/data/15067781/fileData.do

Alerts

업태명 has constant value ""Constant
영업소주소(도로명) has unique valuesUnique
영업소주소(지번) has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:23:40.916144
Analysis finished2023-12-12 12:23:41.663102
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct9
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
네일미용업
112 
네일미용업 화장ㆍ분장 미용업
23 
피부미용업 네일미용업
18 
피부미용업 네일미용업 화장ㆍ분장 미용업
14 
종합미용업
 
10
Other values (4)
 
9

Length

Max length23
Median length5
Mean length8.7150538
Min length5

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st row종합미용업
2nd row일반미용업
3rd row일반미용업
4th row일반미용업
5th row피부미용업 네일미용업 화장ㆍ분장 미용업

Common Values

ValueCountFrequency (%)
네일미용업 112
60.2%
네일미용업 화장ㆍ분장 미용업 23
 
12.4%
피부미용업 네일미용업 18
 
9.7%
피부미용업 네일미용업 화장ㆍ분장 미용업 14
 
7.5%
종합미용업 10
 
5.4%
일반미용업 네일미용업 4
 
2.2%
일반미용업 3
 
1.6%
일반미용업 피부미용업 네일미용업 1
 
0.5%
일반미용업 네일미용업 화장ㆍ분장 미용업 1
 
0.5%

Length

2023-12-12T21:23:41.748550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:23:41.892458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
네일미용업 173
57.5%
화장ㆍ분장 38
 
12.6%
미용업 38
 
12.6%
피부미용업 33
 
11.0%
종합미용업 10
 
3.3%
일반미용업 9
 
3.0%

업소명
Text

Distinct184
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T21:23:42.253533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length6.8548387
Min length2

Characters and Unicode

Total characters1275
Distinct characters253
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)97.8%

Sample

1st row제이에스래쉬(JS.LASH)
2nd row순수미인
3rd row디바네일
4th row바바 네일
5th row네일티크
ValueCountFrequency (%)
nail 20
 
8.0%
네일 10
 
4.0%
salon 3
 
1.2%
the 3
 
1.2%
유니네일 2
 
0.8%
beauty 2
 
0.8%
손 2
 
0.8%
오늘네일 2
 
0.8%
눈썹놀이터(네일 1
 
0.4%
네일싸롱 1
 
0.4%
Other values (203) 203
81.5%
2023-12-12T21:23:42.752463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 158
 
12.4%
네 156
 
12.2%
63
 
4.9%
) 39
 
3.1%
( 39
 
3.1%
이 27
 
2.1%
i 26
 
2.0%
l 25
 
2.0%
N 24
 
1.9%
a 23
 
1.8%
Other values (243) 695
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 806
63.2%
Lowercase Letter 162
 
12.7%
Uppercase Letter 138
 
10.8%
Space Separator 63
 
4.9%
Close Punctuation 39
 
3.1%
Open Punctuation 39
 
3.1%
Decimal Number 18
 
1.4%
Other Punctuation 9
 
0.7%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 158
19.6%
네 156
19.4%
이 27
 
3.3%
스 15
 
1.9%
니 15
 
1.9%
미 12
 
1.5%
드 11
 
1.4%
비 10
 
1.2%
바 9
 
1.1%
리 9
 
1.1%
Other values (186) 384
47.6%
Lowercase Letter
ValueCountFrequency (%)
i 26
16.0%
l 25
15.4%
a 23
14.2%
n 17
10.5%
o 13
8.0%
e 12
7.4%
y 7
 
4.3%
u 6
 
3.7%
r 4
 
2.5%
t 4
 
2.5%
Other values (11) 25
15.4%
Uppercase Letter
ValueCountFrequency (%)
N 24
17.4%
A 22
15.9%
L 19
13.8%
I 16
11.6%
S 10
7.2%
O 8
 
5.8%
M 6
 
4.3%
D 5
 
3.6%
J 5
 
3.6%
Y 4
 
2.9%
Other values (11) 19
13.8%
Decimal Number
ValueCountFrequency (%)
0 6
33.3%
3 4
22.2%
7 4
22.2%
4 2
 
11.1%
2 1
 
5.6%
1 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 3
33.3%
' 2
22.2%
# 2
22.2%
& 1
 
11.1%
: 1
 
11.1%
Space Separator
ValueCountFrequency (%)
63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 806
63.2%
Latin 300
 
23.5%
Common 169
 
13.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 158
19.6%
네 156
19.4%
이 27
 
3.3%
스 15
 
1.9%
니 15
 
1.9%
미 12
 
1.5%
드 11
 
1.4%
비 10
 
1.2%
바 9
 
1.1%
리 9
 
1.1%
Other values (186) 384
47.6%
Latin
ValueCountFrequency (%)
i 26
 
8.7%
l 25
 
8.3%
N 24
 
8.0%
a 23
 
7.7%
A 22
 
7.3%
L 19
 
6.3%
n 17
 
5.7%
I 16
 
5.3%
o 13
 
4.3%
e 12
 
4.0%
Other values (32) 103
34.3%
Common
ValueCountFrequency (%)
63
37.3%
) 39
23.1%
( 39
23.1%
0 6
 
3.6%
3 4
 
2.4%
7 4
 
2.4%
. 3
 
1.8%
' 2
 
1.2%
# 2
 
1.2%
4 2
 
1.2%
Other values (5) 5
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 806
63.2%
ASCII 469
36.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 158
19.6%
네 156
19.4%
이 27
 
3.3%
스 15
 
1.9%
니 15
 
1.9%
미 12
 
1.5%
드 11
 
1.4%
비 10
 
1.2%
바 9
 
1.1%
리 9
 
1.1%
Other values (186) 384
47.6%
ASCII
ValueCountFrequency (%)
63
 
13.4%
) 39
 
8.3%
( 39
 
8.3%
i 26
 
5.5%
l 25
 
5.3%
N 24
 
5.1%
a 23
 
4.9%
A 22
 
4.7%
L 19
 
4.1%
n 17
 
3.6%
Other values (47) 172
36.7%

영업자
Text

Distinct184
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T21:23:43.119869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length3
Mean length3.0698925
Min length2

Characters and Unicode

Total characters571
Distinct characters122
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)97.8%

Sample

1st row최진실
2nd row강정임
3rd row정현주
4th row이옥순
5th row박다원
ValueCountFrequency (%)
정현주 2
 
1.1%
김은진 2
 
1.1%
이화 1
 
0.5%
최진실 1
 
0.5%
신윤미 1
 
0.5%
박지연 1
 
0.5%
조아연 1
 
0.5%
서희원 1
 
0.5%
김하은 1
 
0.5%
김민지 1
 
0.5%
Other values (176) 176
93.6%
2023-12-12T21:23:43.699925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 49
 
8.6%
정 27
 
4.7%
미 23
 
4.0%
영 22
 
3.9%
지 21
 
3.7%
이 21
 
3.7%
현 18
 
3.2%
진 18
 
3.2%
박 17
 
3.0%
연 15
 
2.6%
Other values (112) 340
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 554
97.0%
Uppercase Letter 12
 
2.1%
Space Separator 2
 
0.4%
Open Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 49
 
8.8%
정 27
 
4.9%
미 23
 
4.2%
영 22
 
4.0%
지 21
 
3.8%
이 21
 
3.8%
현 18
 
3.2%
진 18
 
3.2%
박 17
 
3.1%
연 15
 
2.7%
Other values (99) 323
58.3%
Uppercase Letter
ValueCountFrequency (%)
E 4
33.3%
R 1
 
8.3%
D 1
 
8.3%
N 1
 
8.3%
C 1
 
8.3%
H 1
 
8.3%
I 1
 
8.3%
G 1
 
8.3%
M 1
 
8.3%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 554
97.0%
Latin 12
 
2.1%
Common 5
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 49
 
8.8%
정 27
 
4.9%
미 23
 
4.2%
영 22
 
4.0%
지 21
 
3.8%
이 21
 
3.8%
현 18
 
3.2%
진 18
 
3.2%
박 17
 
3.1%
연 15
 
2.7%
Other values (99) 323
58.3%
Latin
ValueCountFrequency (%)
E 4
33.3%
R 1
 
8.3%
D 1
 
8.3%
N 1
 
8.3%
C 1
 
8.3%
H 1
 
8.3%
I 1
 
8.3%
G 1
 
8.3%
M 1
 
8.3%
Common
ValueCountFrequency (%)
2
40.0%
( 1
20.0%
1 1
20.0%
) 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 554
97.0%
ASCII 17
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 49
 
8.8%
정 27
 
4.9%
미 23
 
4.2%
영 22
 
4.0%
지 21
 
3.8%
이 21
 
3.8%
현 18
 
3.2%
진 18
 
3.2%
박 17
 
3.1%
연 15
 
2.7%
Other values (99) 323
58.3%
ASCII
ValueCountFrequency (%)
E 4
23.5%
2
11.8%
R 1
 
5.9%
D 1
 
5.9%
( 1
 
5.9%
1 1
 
5.9%
N 1
 
5.9%
C 1
 
5.9%
H 1
 
5.9%
) 1
 
5.9%
Other values (3) 3
17.6%

영업소주소(도로명)
Text

UNIQUE 

Distinct186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T21:23:44.072588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length47
Mean length32.672043
Min length22

Characters and Unicode

Total characters6077
Distinct characters165
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique186 ?
Unique (%)100.0%

Sample

1st row광주광역시 서구 월드컵4강로 191 2층 (쌍촌동)
2nd row광주광역시 서구 상일로24번길 10 (쌍촌동)
3rd row광주광역시 서구 월드컵4강로28번길 44 (화정동 한양아파트상가106호)
4th row광주광역시 서구 유림로98번길 45 1층 104호 (동천동)
5th row광주광역시 서구 상무대로 773 306-2호 (치평동 세정아울렛)
ValueCountFrequency (%)
광주광역시 186
 
15.2%
서구 186
 
15.2%
1층 124
 
10.1%
쌍촌동 51
 
4.2%
화정동 35
 
2.9%
치평동 30
 
2.4%
풍암동 20
 
1.6%
2층 19
 
1.5%
금호동 18
 
1.5%
상가동 12
 
1.0%
Other values (330) 545
44.5%
2023-12-12T21:23:44.669114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1247
20.5%
광 384
 
6.3%
1 349
 
5.7%
동 216
 
3.6%
시 194
 
3.2%
( 193
 
3.2%
) 193
 
3.2%
주 192
 
3.2%
서 188
 
3.1%
구 186
 
3.1%
Other values (155) 2735
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3363
55.3%
Space Separator 1247
 
20.5%
Decimal Number 1028
 
16.9%
Open Punctuation 193
 
3.2%
Close Punctuation 193
 
3.2%
Dash Punctuation 44
 
0.7%
Uppercase Letter 8
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 384
 
11.4%
동 216
 
6.4%
시 194
 
5.8%
주 192
 
5.7%
서 188
 
5.6%
구 186
 
5.5%
역 186
 
5.5%
로 184
 
5.5%
층 159
 
4.7%
길 91
 
2.7%
Other values (134) 1383
41.1%
Decimal Number
ValueCountFrequency (%)
1 349
33.9%
2 146
14.2%
4 92
 
8.9%
0 87
 
8.5%
3 79
 
7.7%
7 63
 
6.1%
9 58
 
5.6%
8 56
 
5.4%
5 51
 
5.0%
6 47
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
B 3
37.5%
A 1
 
12.5%
D 1
 
12.5%
C 1
 
12.5%
Y 1
 
12.5%
S 1
 
12.5%
Space Separator
ValueCountFrequency (%)
1247
100.0%
Open Punctuation
ValueCountFrequency (%)
( 193
100.0%
Close Punctuation
ValueCountFrequency (%)
) 193
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3363
55.3%
Common 2706
44.5%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 384
 
11.4%
동 216
 
6.4%
시 194
 
5.8%
주 192
 
5.7%
서 188
 
5.6%
구 186
 
5.5%
역 186
 
5.5%
로 184
 
5.5%
층 159
 
4.7%
길 91
 
2.7%
Other values (134) 1383
41.1%
Common
ValueCountFrequency (%)
1247
46.1%
1 349
 
12.9%
( 193
 
7.1%
) 193
 
7.1%
2 146
 
5.4%
4 92
 
3.4%
0 87
 
3.2%
3 79
 
2.9%
7 63
 
2.3%
9 58
 
2.1%
Other values (5) 199
 
7.4%
Latin
ValueCountFrequency (%)
B 3
37.5%
A 1
 
12.5%
D 1
 
12.5%
C 1
 
12.5%
Y 1
 
12.5%
S 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3363
55.3%
ASCII 2714
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1247
45.9%
1 349
 
12.9%
( 193
 
7.1%
) 193
 
7.1%
2 146
 
5.4%
4 92
 
3.4%
0 87
 
3.2%
3 79
 
2.9%
7 63
 
2.3%
9 58
 
2.1%
Other values (11) 207
 
7.6%
Hangul
ValueCountFrequency (%)
광 384
 
11.4%
동 216
 
6.4%
시 194
 
5.8%
주 192
 
5.7%
서 188
 
5.6%
구 186
 
5.5%
역 186
 
5.5%
로 184
 
5.5%
층 159
 
4.7%
길 91
 
2.7%
Other values (134) 1383
41.1%

영업소주소(지번)
Text

UNIQUE 

Distinct186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T21:23:45.183434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length37
Mean length23.064516
Min length16

Characters and Unicode

Total characters4290
Distinct characters151
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique186 ?
Unique (%)100.0%

Sample

1st row광주광역시 서구 쌍촌동 947-31
2nd row광주광역시 서구 쌍촌동 1359
3rd row광주광역시 서구 화정동 1023 한양아파트상가106호
4th row광주광역시 서구 동천동 639 1층 104호
5th row광주광역시 서구 치평동 1326 세정아울렛 306-2호
ValueCountFrequency (%)
광주광역시 186
20.2%
서구 186
20.2%
쌍촌동 51
 
5.5%
1층 49
 
5.3%
화정동 35
 
3.8%
치평동 30
 
3.3%
풍암동 20
 
2.2%
금호동 19
 
2.1%
상가동 10
 
1.1%
2층 8
 
0.9%
Other values (276) 328
35.6%
2023-12-12T21:23:45.838287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
736
17.2%
광 383
 
8.9%
1 290
 
6.8%
동 211
 
4.9%
주 189
 
4.4%
시 188
 
4.4%
역 186
 
4.3%
서 186
 
4.3%
구 186
 
4.3%
- 152
 
3.5%
Other values (141) 1583
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2335
54.4%
Decimal Number 1030
24.0%
Space Separator 736
 
17.2%
Dash Punctuation 152
 
3.5%
Open Punctuation 14
 
0.3%
Close Punctuation 14
 
0.3%
Uppercase Letter 8
 
0.2%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 383
16.4%
동 211
 
9.0%
주 189
 
8.1%
시 188
 
8.1%
역 186
 
8.0%
서 186
 
8.0%
구 186
 
8.0%
층 67
 
2.9%
호 60
 
2.6%
촌 53
 
2.3%
Other values (120) 626
26.8%
Decimal Number
ValueCountFrequency (%)
1 290
28.2%
2 138
13.4%
3 100
 
9.7%
0 85
 
8.3%
6 75
 
7.3%
7 73
 
7.1%
4 70
 
6.8%
9 70
 
6.8%
8 67
 
6.5%
5 62
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
37.5%
D 1
 
12.5%
A 1
 
12.5%
C 1
 
12.5%
S 1
 
12.5%
Y 1
 
12.5%
Space Separator
ValueCountFrequency (%)
736
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2335
54.4%
Common 1947
45.4%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 383
16.4%
동 211
 
9.0%
주 189
 
8.1%
시 188
 
8.1%
역 186
 
8.0%
서 186
 
8.0%
구 186
 
8.0%
층 67
 
2.9%
호 60
 
2.6%
촌 53
 
2.3%
Other values (120) 626
26.8%
Common
ValueCountFrequency (%)
736
37.8%
1 290
 
14.9%
- 152
 
7.8%
2 138
 
7.1%
3 100
 
5.1%
0 85
 
4.4%
6 75
 
3.9%
7 73
 
3.7%
4 70
 
3.6%
9 70
 
3.6%
Other values (5) 158
 
8.1%
Latin
ValueCountFrequency (%)
B 3
37.5%
D 1
 
12.5%
A 1
 
12.5%
C 1
 
12.5%
S 1
 
12.5%
Y 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2335
54.4%
ASCII 1955
45.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
736
37.6%
1 290
 
14.8%
- 152
 
7.8%
2 138
 
7.1%
3 100
 
5.1%
0 85
 
4.3%
6 75
 
3.8%
7 73
 
3.7%
4 70
 
3.6%
9 70
 
3.6%
Other values (11) 166
 
8.5%
Hangul
ValueCountFrequency (%)
광 383
16.4%
동 211
 
9.0%
주 189
 
8.1%
시 188
 
8.1%
역 186
 
8.0%
서 186
 
8.0%
구 186
 
8.0%
층 67
 
2.9%
호 60
 
2.6%
촌 53
 
2.3%
Other values (120) 626
26.8%

영업자시작일
Date

Distinct171
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2010-06-14 00:00:00
Maximum2020-09-23 00:00:00
2023-12-12T21:23:45.984118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:23:46.122947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
네일아트업
186 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
네일아트업 186
100.0%

Length

2023-12-12T21:23:46.271312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:23:46.365730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
네일아트업 186
100.0%

Missing values

2023-12-12T21:23:41.463664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:23:41.610339image/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

업종명업소명영업자영업소주소(도로명)영업소주소(지번)영업자시작일업태명
0종합미용업제이에스래쉬(JS.LASH)최진실광주광역시 서구 월드컵4강로 191 2층 (쌍촌동)광주광역시 서구 쌍촌동 947-312010-06-14네일아트업
1일반미용업순수미인강정임광주광역시 서구 상일로24번길 10 (쌍촌동)광주광역시 서구 쌍촌동 13592012-07-02네일아트업
2일반미용업디바네일정현주광주광역시 서구 월드컵4강로28번길 44 (화정동 한양아파트상가106호)광주광역시 서구 화정동 1023 한양아파트상가106호2012-07-10네일아트업
3일반미용업바바 네일이옥순광주광역시 서구 유림로98번길 45 1층 104호 (동천동)광주광역시 서구 동천동 639 1층 104호2013-09-16네일아트업
4피부미용업 네일미용업 화장ㆍ분장 미용업네일티크박다원광주광역시 서구 상무대로 773 306-2호 (치평동 세정아울렛)광주광역시 서구 치평동 1326 세정아울렛 306-2호2013-11-18네일아트업
5네일미용업류자매네일류진광주광역시 서구 원마륵1길 32 201호 (마륵동)광주광역시 서구 마륵동 579-2012014-09-15네일아트업
6종합미용업오늘네일윤지혜광주광역시 서구 염화로134번길 23-1 (화정동 (1층))광주광역시 서구 화정동 849-17 (1층)2014-12-15네일아트업
7일반미용업 네일미용업뚜루네일박진실광주광역시 서구 풍암신흥로62번길 3-21 지하1층 (풍암동)광주광역시 서구 풍암동 1032-3 (지하1층)2014-12-15네일아트업
8피부미용업 네일미용업모드니네일문상아광주광역시 서구 상무오월로19번길 24 1층 (쌍촌동)광주광역시 서구 쌍촌동 1279-5 (1층)2015-03-10네일아트업
9네일미용업에이네일살롱(A NAIL SALON)김도아광주광역시 서구 풍암2로57번길 9 1층 (풍암동)광주광역시 서구 풍암동 1080-7 (1층)2015-05-29네일아트업
업종명업소명영업자영업소주소(도로명)영업소주소(지번)영업자시작일업태명
176네일미용업루미가넷 네일죤김홍주광주광역시 서구 죽봉대로 61 광주 이마트 2층 (화정동)광주광역시 서구 화정동 12-13 광주 이마트 2층2020-07-14네일아트업
177네일미용업이쁘다 손김은진광주광역시 서구 상무오월로19번길 14-1 1층 (쌍촌동)광주광역시 서구 쌍촌동 1292-132020-07-15네일아트업
178네일미용업다미네일정다미광주광역시 서구 화개1로 59-2 상가 128동 104호 (금호동 종원팰리스빌아파트)광주광역시 서구 금호동 240-2 종원팰리스빌아파트2020-07-23네일아트업
179네일미용업 화장ㆍ분장 미용업러브네일신사랑광주광역시 서구 금부로 75 1층 (금호동)광주광역시 서구 금호동 723-15 1층2020-08-20네일아트업
180네일미용업 화장ㆍ분장 미용업눈썹하고네일만나박세현광주광역시 서구 화정로 16 1층 일부 (쌍촌동)광주광역시 서구 쌍촌동 1244-8 1층 일부2020-08-24네일아트업
181네일미용업네일멜로김현아광주광역시 서구 상무오월로15번길 22 1층 (쌍촌동)광주광역시 서구 쌍촌동 1278-5 1층2020-08-27네일아트업
182피부미용업 네일미용업 화장ㆍ분장 미용업손 예쁘다김혜연광주광역시 서구 상무누리로 16 1층 (치평동)광주광역시 서구 치평동 1278-4 1층2020-09-02네일아트업
183피부미용업 네일미용업윤네일(Yun NAil)윤영길광주광역시 서구 내방로 337 1층 101호 (화정동 해광샹그릴라 센트럴337)광주광역시 서구 화정동 23-20 해광샹그릴라 센트럴337 101호2020-09-03네일아트업
184네일미용업살랑이진숙광주광역시 서구 계수로59번길 4-22 1층 (쌍촌동)광주광역시 서구 쌍촌동 1325-7 1층2020-09-10네일아트업
185피부미용업 네일미용업 화장ㆍ분장 미용업이슬비네일이슬비광주광역시 서구 화운로 102 1층 (화정동)광주광역시 서구 화정동 759-15 1층2020-09-23네일아트업