Overview

Dataset statistics

Number of variables6
Number of observations1409
Missing cells1011
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.2 KiB
Average record size in memory48.1 B

Variable types

Categorical1
Text4
DateTime1

Dataset

Description파주시 이미용업에 대한 데이터로서 업종명, 업소명(운정스파사우나, 금촌이용원, 수정이용원 등), 지번주소, 도로명주소, 전화번호 등의 정보를 제공합니다.
Author경기도 파주시
URLhttps://www.data.go.kr/data/15006987/fileData.do

Alerts

데이터기준일 has constant value ""Constant
도로명주소 has 80 (5.7%) missing valuesMissing
전화번호 has 926 (65.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:36:17.754542
Analysis finished2023-12-12 22:36:18.939422
Duration1.18 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct18
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
일반미용업
429 
미용업
260 
피부미용업
175 
네일미용업
148 
종합미용업
123 
Other values (13)
274 

Length

Max length22
Median length5
Mean length5.594748
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row네일미용업+화장ㆍ분장 미용업
2nd row일반미용업
3rd row네일미용업+화장ㆍ분장 미용업
4th row미용업
5th row종합미용업

Common Values

ValueCountFrequency (%)
일반미용업 429
30.4%
미용업 260
18.5%
피부미용업 175
12.4%
네일미용업 148
 
10.5%
종합미용업 123
 
8.7%
이용업 93
 
6.6%
화장ㆍ분장 미용업 55
 
3.9%
네일미용업+화장ㆍ분장 미용업 35
 
2.5%
피부미용업+네일미용업 20
 
1.4%
일반미용업+네일미용업+화장ㆍ분장 미용업 20
 
1.4%
Other values (8) 51
 
3.6%

Length

2023-12-13T07:36:19.030526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 429
27.6%
미용업 404
26.0%
피부미용업 175
11.3%
네일미용업 148
 
9.5%
종합미용업 123
 
7.9%
이용업 93
 
6.0%
화장ㆍ분장 57
 
3.7%
네일미용업+화장ㆍ분장 35
 
2.3%
피부미용업+네일미용업 22
 
1.4%
일반미용업+네일미용업+화장ㆍ분장 20
 
1.3%
Other values (7) 49
 
3.2%
Distinct1356
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2023-12-13T07:36:19.423063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length6.4442867
Min length1

Characters and Unicode

Total characters9080
Distinct characters602
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1308 ?
Unique (%)92.8%

Sample

1st row#베네코코
2nd row101% 헤어 스튜디오
3rd row3.14브로우(3.14BROW)
4th row6000컷트
5th row6000헤어
ValueCountFrequency (%)
헤어 40
 
2.3%
네일 18
 
1.0%
hair 18
 
1.0%
nail 13
 
0.7%
에스테틱 12
 
0.7%
미용실 11
 
0.6%
beauty 6
 
0.3%
뷰티 6
 
0.3%
운정점 5
 
0.3%
by 5
 
0.3%
Other values (1484) 1622
92.4%
2023-12-13T07:36:19.988791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
530
 
5.8%
513
 
5.6%
348
 
3.8%
257
 
2.8%
208
 
2.3%
202
 
2.2%
192
 
2.1%
185
 
2.0%
173
 
1.9%
( 169
 
1.9%
Other values (592) 6303
69.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7153
78.8%
Lowercase Letter 593
 
6.5%
Uppercase Letter 494
 
5.4%
Space Separator 348
 
3.8%
Open Punctuation 169
 
1.9%
Close Punctuation 169
 
1.9%
Other Punctuation 94
 
1.0%
Decimal Number 48
 
0.5%
Dash Punctuation 8
 
0.1%
Connector Punctuation 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
530
 
7.4%
513
 
7.2%
257
 
3.6%
208
 
2.9%
202
 
2.8%
192
 
2.7%
185
 
2.6%
173
 
2.4%
155
 
2.2%
133
 
1.9%
Other values (518) 4605
64.4%
Lowercase Letter
ValueCountFrequency (%)
a 79
13.3%
e 68
11.5%
i 59
9.9%
o 47
 
7.9%
n 39
 
6.6%
l 37
 
6.2%
r 35
 
5.9%
s 31
 
5.2%
h 29
 
4.9%
t 27
 
4.6%
Other values (15) 142
23.9%
Uppercase Letter
ValueCountFrequency (%)
A 45
 
9.1%
S 41
 
8.3%
H 40
 
8.1%
N 38
 
7.7%
I 34
 
6.9%
O 33
 
6.7%
R 33
 
6.7%
E 27
 
5.5%
B 27
 
5.5%
Y 22
 
4.5%
Other values (15) 154
31.2%
Decimal Number
ValueCountFrequency (%)
1 12
25.0%
0 9
18.8%
2 8
16.7%
3 5
10.4%
4 4
 
8.3%
6 4
 
8.3%
8 4
 
8.3%
7 1
 
2.1%
9 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 23
24.5%
& 21
22.3%
. 16
17.0%
# 14
14.9%
' 10
10.6%
: 5
 
5.3%
· 4
 
4.3%
% 1
 
1.1%
Space Separator
ValueCountFrequency (%)
348
100.0%
Open Punctuation
ValueCountFrequency (%)
( 169
100.0%
Close Punctuation
ValueCountFrequency (%)
) 169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7144
78.7%
Latin 1087
 
12.0%
Common 840
 
9.3%
Han 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
530
 
7.4%
513
 
7.2%
257
 
3.6%
208
 
2.9%
202
 
2.8%
192
 
2.7%
185
 
2.6%
173
 
2.4%
155
 
2.2%
133
 
1.9%
Other values (514) 4596
64.3%
Latin
ValueCountFrequency (%)
a 79
 
7.3%
e 68
 
6.3%
i 59
 
5.4%
o 47
 
4.3%
A 45
 
4.1%
S 41
 
3.8%
H 40
 
3.7%
n 39
 
3.6%
N 38
 
3.5%
l 37
 
3.4%
Other values (40) 594
54.6%
Common
ValueCountFrequency (%)
348
41.4%
( 169
20.1%
) 169
20.1%
, 23
 
2.7%
& 21
 
2.5%
. 16
 
1.9%
# 14
 
1.7%
1 12
 
1.4%
' 10
 
1.2%
0 9
 
1.1%
Other values (14) 49
 
5.8%
Han
ValueCountFrequency (%)
6
66.7%
1
 
11.1%
1
 
11.1%
1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7143
78.7%
ASCII 1922
 
21.2%
CJK 9
 
0.1%
None 4
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
530
 
7.4%
513
 
7.2%
257
 
3.6%
208
 
2.9%
202
 
2.8%
192
 
2.7%
185
 
2.6%
173
 
2.4%
155
 
2.2%
133
 
1.9%
Other values (513) 4595
64.3%
ASCII
ValueCountFrequency (%)
348
18.1%
( 169
 
8.8%
) 169
 
8.8%
a 79
 
4.1%
e 68
 
3.5%
i 59
 
3.1%
o 47
 
2.4%
A 45
 
2.3%
S 41
 
2.1%
H 40
 
2.1%
Other values (62) 857
44.6%
CJK
ValueCountFrequency (%)
6
66.7%
1
 
11.1%
1
 
11.1%
1
 
11.1%
None
ValueCountFrequency (%)
· 4
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct1307
Distinct (%)98.3%
Missing80
Missing (%)5.7%
Memory size11.1 KiB
2023-12-13T07:36:20.340687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length52
Mean length33.102333
Min length17

Characters and Unicode

Total characters43993
Distinct characters354
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1287 ?
Unique (%)96.8%

Sample

1st row경기도 파주시 문산읍 문향로75번길 19-12, 1층 일부
2nd row경기도 파주시 경의로 1100, 206호 (야당동)
3rd row경기도 파주시 경의로1004번길 37, 305호 (야당동)
4th row경기도 파주시 파주읍 술이홀로 470 (1층 우측 일부)
5th row경기도 파주시 청석로 262 (동패동,이지타운 117호)
ValueCountFrequency (%)
경기도 1329
 
14.2%
파주시 1329
 
14.2%
1층 342
 
3.7%
일부 209
 
2.2%
야당동 172
 
1.8%
와동동 167
 
1.8%
금촌동 167
 
1.8%
문산읍 163
 
1.7%
목동동 156
 
1.7%
2층 122
 
1.3%
Other values (1458) 5198
55.6%
2023-12-13T07:36:20.886890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8036
 
18.3%
1 2176
 
4.9%
1792
 
4.1%
1513
 
3.4%
1459
 
3.3%
1411
 
3.2%
1385
 
3.1%
1375
 
3.1%
, 1360
 
3.1%
1346
 
3.1%
Other values (344) 22140
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23968
54.5%
Space Separator 8036
 
18.3%
Decimal Number 7987
 
18.2%
Other Punctuation 1366
 
3.1%
Open Punctuation 1100
 
2.5%
Close Punctuation 1099
 
2.5%
Dash Punctuation 330
 
0.8%
Uppercase Letter 99
 
0.2%
Letter Number 7
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1792
 
7.5%
1513
 
6.3%
1459
 
6.1%
1411
 
5.9%
1385
 
5.8%
1375
 
5.7%
1346
 
5.6%
1135
 
4.7%
885
 
3.7%
606
 
2.5%
Other values (304) 11061
46.1%
Uppercase Letter
ValueCountFrequency (%)
D 15
15.2%
A 14
14.1%
B 10
10.1%
F 8
8.1%
S 7
7.1%
H 7
7.1%
N 7
7.1%
M 6
 
6.1%
I 6
 
6.1%
P 4
 
4.0%
Other values (8) 15
15.2%
Decimal Number
ValueCountFrequency (%)
1 2176
27.2%
2 1339
16.8%
0 1167
14.6%
3 669
 
8.4%
5 524
 
6.6%
7 519
 
6.5%
4 510
 
6.4%
6 480
 
6.0%
8 328
 
4.1%
9 275
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 1360
99.6%
@ 3
 
0.2%
/ 2
 
0.1%
& 1
 
0.1%
Letter Number
ValueCountFrequency (%)
3
42.9%
3
42.9%
1
 
14.3%
Space Separator
ValueCountFrequency (%)
8036
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1099
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 330
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23968
54.5%
Common 19918
45.3%
Latin 107
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1792
 
7.5%
1513
 
6.3%
1459
 
6.1%
1411
 
5.9%
1385
 
5.8%
1375
 
5.7%
1346
 
5.6%
1135
 
4.7%
885
 
3.7%
606
 
2.5%
Other values (304) 11061
46.1%
Latin
ValueCountFrequency (%)
D 15
14.0%
A 14
13.1%
B 10
9.3%
F 8
 
7.5%
S 7
 
6.5%
H 7
 
6.5%
N 7
 
6.5%
M 6
 
5.6%
I 6
 
5.6%
P 4
 
3.7%
Other values (12) 23
21.5%
Common
ValueCountFrequency (%)
8036
40.3%
1 2176
 
10.9%
, 1360
 
6.8%
2 1339
 
6.7%
0 1167
 
5.9%
( 1100
 
5.5%
) 1099
 
5.5%
3 669
 
3.4%
5 524
 
2.6%
7 519
 
2.6%
Other values (8) 1929
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23968
54.5%
ASCII 20018
45.5%
Number Forms 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8036
40.1%
1 2176
 
10.9%
, 1360
 
6.8%
2 1339
 
6.7%
0 1167
 
5.8%
( 1100
 
5.5%
) 1099
 
5.5%
3 669
 
3.3%
5 524
 
2.6%
7 519
 
2.6%
Other values (27) 2029
 
10.1%
Hangul
ValueCountFrequency (%)
1792
 
7.5%
1513
 
6.3%
1459
 
6.1%
1411
 
5.9%
1385
 
5.8%
1375
 
5.7%
1346
 
5.6%
1135
 
4.7%
885
 
3.7%
606
 
2.5%
Other values (304) 11061
46.1%
Number Forms
ValueCountFrequency (%)
3
42.9%
3
42.9%
1
 
14.3%
Distinct1240
Distinct (%)88.3%
Missing5
Missing (%)0.4%
Memory size11.1 KiB
2023-12-13T07:36:21.194229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length46
Mean length25.701567
Min length14

Characters and Unicode

Total characters36085
Distinct characters345
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1157 ?
Unique (%)82.4%

Sample

1st row경기도 파주시 문산읍 문산리 73-29 1층 일부
2nd row경기도 파주시 야당동 1074-2 206호
3rd row경기도 파주시 야당동 1052
4th row경기도 파주시 파주읍 연풍리 294-9 1층 우측 일부
5th row경기도 파주시 동패동 1696-2 이지타운 117호
ValueCountFrequency (%)
경기도 1404
 
17.9%
파주시 1404
 
17.9%
금촌동 201
 
2.6%
문산읍 185
 
2.4%
야당동 172
 
2.2%
와동동 167
 
2.1%
1층 159
 
2.0%
목동동 156
 
2.0%
동패동 124
 
1.6%
문산리 108
 
1.4%
Other values (1571) 3742
47.8%
2023-12-13T07:36:21.661553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7494
20.8%
1 1963
 
5.4%
1724
 
4.8%
1608
 
4.5%
1479
 
4.1%
1463
 
4.1%
1421
 
3.9%
1414
 
3.9%
1406
 
3.9%
- 1045
 
2.9%
Other values (335) 15068
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19346
53.6%
Decimal Number 8037
22.3%
Space Separator 7494
 
20.8%
Dash Punctuation 1045
 
2.9%
Uppercase Letter 66
 
0.2%
Open Punctuation 32
 
0.1%
Close Punctuation 31
 
0.1%
Other Punctuation 31
 
0.1%
Letter Number 2
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1724
 
8.9%
1608
 
8.3%
1479
 
7.6%
1463
 
7.6%
1421
 
7.3%
1414
 
7.3%
1406
 
7.3%
544
 
2.8%
507
 
2.6%
378
 
2.0%
Other values (296) 7402
38.3%
Uppercase Letter
ValueCountFrequency (%)
A 10
15.2%
D 7
10.6%
F 6
9.1%
I 6
9.1%
N 5
7.6%
H 5
7.6%
M 4
 
6.1%
P 4
 
6.1%
L 4
 
6.1%
K 3
 
4.5%
Other values (7) 12
18.2%
Decimal Number
ValueCountFrequency (%)
1 1963
24.4%
2 1030
12.8%
0 999
12.4%
3 793
9.9%
4 678
 
8.4%
9 558
 
6.9%
5 555
 
6.9%
6 541
 
6.7%
7 469
 
5.8%
8 451
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 23
74.2%
@ 4
 
12.9%
/ 2
 
6.5%
. 1
 
3.2%
& 1
 
3.2%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
7494
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1045
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19346
53.6%
Common 16670
46.2%
Latin 69
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1724
 
8.9%
1608
 
8.3%
1479
 
7.6%
1463
 
7.6%
1421
 
7.3%
1414
 
7.3%
1406
 
7.3%
544
 
2.8%
507
 
2.6%
378
 
2.0%
Other values (296) 7402
38.3%
Latin
ValueCountFrequency (%)
A 10
14.5%
D 7
10.1%
F 6
 
8.7%
I 6
 
8.7%
N 5
 
7.2%
H 5
 
7.2%
M 4
 
5.8%
P 4
 
5.8%
L 4
 
5.8%
K 3
 
4.3%
Other values (10) 15
21.7%
Common
ValueCountFrequency (%)
7494
45.0%
1 1963
 
11.8%
- 1045
 
6.3%
2 1030
 
6.2%
0 999
 
6.0%
3 793
 
4.8%
4 678
 
4.1%
9 558
 
3.3%
5 555
 
3.3%
6 541
 
3.2%
Other values (9) 1014
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19346
53.6%
ASCII 16737
46.4%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7494
44.8%
1 1963
 
11.7%
- 1045
 
6.2%
2 1030
 
6.2%
0 999
 
6.0%
3 793
 
4.7%
4 678
 
4.1%
9 558
 
3.3%
5 555
 
3.3%
6 541
 
3.2%
Other values (27) 1081
 
6.5%
Hangul
ValueCountFrequency (%)
1724
 
8.9%
1608
 
8.3%
1479
 
7.6%
1463
 
7.6%
1421
 
7.3%
1414
 
7.3%
1406
 
7.3%
544
 
2.8%
507
 
2.6%
378
 
2.0%
Other values (296) 7402
38.3%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%

전화번호
Text

MISSING 

Distinct480
Distinct (%)99.4%
Missing926
Missing (%)65.7%
Memory size11.1 KiB
2023-12-13T07:36:21.936827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.020704
Min length12

Characters and Unicode

Total characters5806
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

Unique477 ?
Unique (%)98.8%

Sample

1st row031-943-4882
2nd row031-953-7747
3rd row031-943-6980
4th row031-942-9383
5th row031-942-7189
ValueCountFrequency (%)
031-957-5577 2
 
0.4%
031-953-0344 2
 
0.4%
031-954-1599 2
 
0.4%
031-941-8682 1
 
0.2%
031-953-7522 1
 
0.2%
031-941-6543 1
 
0.2%
031-953-5180 1
 
0.2%
031-944-8098 1
 
0.2%
031-564-2326 1
 
0.2%
031-957-9997 1
 
0.2%
Other values (470) 470
97.3%
2023-12-13T07:36:22.362858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 966
16.6%
3 779
13.4%
1 709
12.2%
0 708
12.2%
9 695
12.0%
4 492
8.5%
5 427
7.4%
8 298
 
5.1%
2 266
 
4.6%
7 255
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4840
83.4%
Dash Punctuation 966
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 779
16.1%
1 709
14.6%
0 708
14.6%
9 695
14.4%
4 492
10.2%
5 427
8.8%
8 298
 
6.2%
2 266
 
5.5%
7 255
 
5.3%
6 211
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 966
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 966
16.6%
3 779
13.4%
1 709
12.2%
0 708
12.2%
9 695
12.0%
4 492
8.5%
5 427
7.4%
8 298
 
5.1%
2 266
 
4.6%
7 255
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 966
16.6%
3 779
13.4%
1 709
12.2%
0 708
12.2%
9 695
12.0%
4 492
8.5%
5 427
7.4%
8 298
 
5.1%
2 266
 
4.6%
7 255
 
4.4%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
Minimum2023-09-14 00:00:00
Maximum2023-09-14 00:00:00
2023-12-13T07:36:22.487641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:22.573219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2023-12-13T07:36:18.642417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:36:18.782790image/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.
2023-12-13T07:36:18.887943image/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

업종명업소명도로명주소지번주소전화번호데이터기준일
0네일미용업+화장ㆍ분장 미용업#베네코코경기도 파주시 문산읍 문향로75번길 19-12, 1층 일부경기도 파주시 문산읍 문산리 73-29 1층 일부<NA>2023-09-14
1일반미용업101% 헤어 스튜디오경기도 파주시 경의로 1100, 206호 (야당동)경기도 파주시 야당동 1074-2 206호<NA>2023-09-14
2네일미용업+화장ㆍ분장 미용업3.14브로우(3.14BROW)경기도 파주시 경의로1004번길 37, 305호 (야당동)경기도 파주시 야당동 1052<NA>2023-09-14
3미용업6000컷트경기도 파주시 파주읍 술이홀로 470 (1층 우측 일부)경기도 파주시 파주읍 연풍리 294-9 1층 우측 일부<NA>2023-09-14
4종합미용업6000헤어경기도 파주시 청석로 262 (동패동,이지타운 117호)경기도 파주시 동패동 1696-2 이지타운 117호031-943-48822023-09-14
5일반미용업+네일미용업+화장ㆍ분장 미용업612헤어(612HAIR)경기도 파주시 번영로 38, 1층 일부 (금촌동)경기도 파주시 금촌동 942-6 리치프라자<NA>2023-09-14
6종합미용업97미장원경기도 파주시 파주읍 봉서산로 15, 1층 일부경기도 파주시 파주읍 봉서리 832-4 1층 일부031-953-77472023-09-14
7미용업J.K헤어경기도 파주시 금바위로 100, 상가동 1층 106호 (와동동, 동문아파트)경기도 파주시 와동동 240 동문아파트 상가동 106호<NA>2023-09-14
8일반미용업JJ헤어경기도 파주시 와석순환로 61, 상가동 1층 104호 (야당동, 한빛마을7단지)경기도 파주시 야당동 1026 한빛마을7단지주출입구 상가동 104호031-943-69802023-09-14
9피부미용업JORI 에스테틱경기도 파주시 조리읍 봉천로 1, 1층 104호경기도 파주시 조리읍 봉일천리 137-2 1층 104호<NA>2023-09-14
업종명업소명도로명주소지번주소전화번호데이터기준일
1399피부미용업휠로스인히엘경기도 파주시 소리천로 25, 유은타워7차 8층 808호 (야당동)경기도 파주시 야당동 1083 유은타워7차 808호<NA>2023-09-14
1400종합미용업휴네일경기도 파주시 소리천로 25, 유은타워7차 803호 (야당동)경기도 파주시 야당동 1083 유은타워7차<NA>2023-09-14
1401피부미용업+네일미용업, 화장ㆍ분장 미용업휴뷰티샵(Hue Beauty Shop)경기도 파주시 산내로104번길 21-9, 1층 일부 (목동동)경기도 파주시 목동동 967-2<NA>2023-09-14
1402피부미용업휴피부관리실경기도 파주시 금정24길 27 (금촌동, 외2필지34,35호)경기도 파주시 금촌동 63-1 외2필지34,35호<NA>2023-09-14
1403피부미용업흐무뭇경기도 파주시 금빛로 44-1, 힘찬프라자 2층 204호 (금촌동)경기도 파주시 금촌동 987-5 힘찬프라자<NA>2023-09-14
1404일반미용업희경미용실경기도 파주시 금정22길 29-2 (금촌동)경기도 파주시 금촌동 62<NA>2023-09-14
1405일반미용업희헤어샵경기도 파주시 문산읍 봉미로 19, 2호경기도 파주시 문산읍 선유리 907-2<NA>2023-09-14
1406피부미용업+네일미용업힐링포인트경기도 파주시 와석순환로515번길 91, 프라임타워 702호 (와동동)경기도 파주시 와동동 1469-1<NA>2023-09-14
1407미용업힐링헤어경기도 파주시 탄현면 약산로 71, 1층 일부경기도 파주시 탄현면 법흥리 1580-13 1층 일부<NA>2023-09-14
1408일반미용업힐스헤어경기도 파주시 교하로 100, 상가(939)동 108호 (목동동, 힐스테이트 운정)경기도 파주시 목동동 1149 힐스테이트 운정 상가(939)동 108호<NA>2023-09-14