Overview

Dataset statistics

Number of variables5
Number of observations1337
Missing cells227
Missing cells (%)3.4%
Duplicate rows2
Duplicate rows (%)0.1%
Total size in memory52.4 KiB
Average record size in memory40.1 B

Variable types

Categorical1
Text4

Dataset

Description남양주시 미용업소에 대한 데이터로 업소명, 업소소재지(도로명), 업소소재지(지번), 소재지 전화번호 등의 항목을 제공합니다.
Author경기도 남양주시
URLhttps://www.data.go.kr/data/15050446/fileData.do

Alerts

Dataset has 2 (0.1%) duplicate rowsDuplicates
업소소재지(도로명) has 107 (8.0%) missing valuesMissing
소재지전화(031) has 120 (9.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 16:08:26.816040
Analysis finished2023-12-12 16:08:27.799232
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
미용업
557 
미용업(일반)
407 
미용업(피부)
165 
미용업(종합)
107 
미용업(손톱ㆍ발톱)
57 
Other values (9)
 
44

Length

Max length31
Median length7
Mean length5.9281975
Min length3

Unique

Unique3 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 557
41.7%
미용업(일반) 407
30.4%
미용업(피부) 165
 
12.3%
미용업(종합) 107
 
8.0%
미용업(손톱ㆍ발톱) 57
 
4.3%
미용업(피부), 미용업(손톱ㆍ발톱) 18
 
1.3%
미용업(일반), 미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장) 7
 
0.5%
미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장) 5
 
0.4%
미용업(일반), 미용업(화장ㆍ분장) 5
 
0.4%
미용업(일반), 미용업(손톱ㆍ발톱) 4
 
0.3%
Other values (4) 5
 
0.4%

Length

2023-12-13T01:08:27.879829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 557
40.1%
미용업(일반 425
30.6%
미용업(피부 187
 
13.5%
미용업(종합 107
 
7.7%
미용업(손톱ㆍ발톱 92
 
6.6%
미용업(화장ㆍ분장 20
 
1.4%

업소명
Text

Distinct1232
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
2023-12-13T01:08:28.150920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length5.4360509
Min length1

Characters and Unicode

Total characters7268
Distinct characters560
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1153 ?
Unique (%)86.2%

Sample

1st row지현미용실
2nd row미도파미용실
3rd row블루클럽(금곡점)
4th row컷팅클럽
5th row유진미용실
ValueCountFrequency (%)
헤어 25
 
1.7%
헤어샵 10
 
0.7%
오렌지헤어 6
 
0.4%
앤 6
 
0.4%
제이헤어 6
 
0.4%
미용실 5
 
0.3%
에스테틱 5
 
0.3%
헤어스토리 4
 
0.3%
hair 4
 
0.3%
더 4
 
0.3%
Other values (1294) 1428
95.0%
2023-12-13T01:08:28.598782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 702
 
9.7%
헤 679
 
9.3%
미 216
 
3.0%
스 196
 
2.7%
리 182
 
2.5%
168
 
2.3%
이 164
 
2.3%
실 128
 
1.8%
용 124
 
1.7%
일 97
 
1.3%
Other values (550) 4612
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6635
91.3%
Space Separator 168
 
2.3%
Uppercase Letter 150
 
2.1%
Lowercase Letter 147
 
2.0%
Close Punctuation 49
 
0.7%
Open Punctuation 49
 
0.7%
Decimal Number 36
 
0.5%
Other Punctuation 28
 
0.4%
Math Symbol 3
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 702
 
10.6%
헤 679
 
10.2%
미 216
 
3.3%
스 196
 
3.0%
리 182
 
2.7%
이 164
 
2.5%
실 128
 
1.9%
용 124
 
1.9%
일 97
 
1.5%
샵 97
 
1.5%
Other values (488) 4050
61.0%
Uppercase Letter
ValueCountFrequency (%)
S 15
 
10.0%
A 13
 
8.7%
N 12
 
8.0%
H 12
 
8.0%
C 11
 
7.3%
J 9
 
6.0%
L 9
 
6.0%
B 7
 
4.7%
Y 6
 
4.0%
T 6
 
4.0%
Other values (14) 50
33.3%
Lowercase Letter
ValueCountFrequency (%)
a 23
15.6%
e 20
13.6%
i 20
13.6%
l 16
10.9%
n 9
 
6.1%
h 8
 
5.4%
y 8
 
5.4%
o 7
 
4.8%
r 7
 
4.8%
t 6
 
4.1%
Other values (7) 23
15.6%
Decimal Number
ValueCountFrequency (%)
1 9
25.0%
2 6
16.7%
8 5
13.9%
0 5
13.9%
7 5
13.9%
3 4
11.1%
9 1
 
2.8%
5 1
 
2.8%
Other Punctuation
ValueCountFrequency (%)
& 18
64.3%
. 7
 
25.0%
＆ 1
 
3.6%
' 1
 
3.6%
, 1
 
3.6%
Math Symbol
ValueCountFrequency (%)
> 1
33.3%
< 1
33.3%
= 1
33.3%
Space Separator
ValueCountFrequency (%)
168
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6635
91.3%
Common 336
 
4.6%
Latin 297
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 702
 
10.6%
헤 679
 
10.2%
미 216
 
3.3%
스 196
 
3.0%
리 182
 
2.7%
이 164
 
2.5%
실 128
 
1.9%
용 124
 
1.9%
일 97
 
1.5%
샵 97
 
1.5%
Other values (488) 4050
61.0%
Latin
ValueCountFrequency (%)
a 23
 
7.7%
e 20
 
6.7%
i 20
 
6.7%
l 16
 
5.4%
S 15
 
5.1%
A 13
 
4.4%
N 12
 
4.0%
H 12
 
4.0%
C 11
 
3.7%
J 9
 
3.0%
Other values (31) 146
49.2%
Common
ValueCountFrequency (%)
168
50.0%
) 49
 
14.6%
( 49
 
14.6%
& 18
 
5.4%
1 9
 
2.7%
. 7
 
2.1%
2 6
 
1.8%
8 5
 
1.5%
0 5
 
1.5%
7 5
 
1.5%
Other values (11) 15
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6633
91.3%
ASCII 631
 
8.7%
Compat Jamo 2
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 702
 
10.6%
헤 679
 
10.2%
미 216
 
3.3%
스 196
 
3.0%
리 182
 
2.7%
이 164
 
2.5%
실 128
 
1.9%
용 124
 
1.9%
일 97
 
1.5%
샵 97
 
1.5%
Other values (487) 4048
61.0%
ASCII
ValueCountFrequency (%)
168
26.6%
) 49
 
7.8%
( 49
 
7.8%
a 23
 
3.6%
e 20
 
3.2%
i 20
 
3.2%
& 18
 
2.9%
l 16
 
2.5%
S 15
 
2.4%
A 13
 
2.1%
Other values (50) 240
38.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%
None
ValueCountFrequency (%)
´ 1
50.0%
＆ 1
50.0%

업소소재지(도로명)
Text

MISSING 

Distinct1213
Distinct (%)98.6%
Missing107
Missing (%)8.0%
Memory size10.6 KiB
2023-12-13T01:08:28.950132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length45
Mean length32.307317
Min length18

Characters and Unicode

Total characters39738
Distinct characters327
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1197 ?
Unique (%)97.3%

Sample

1st row경기도 남양주시 경춘로 464 (지금동)
2nd row경기도 남양주시 경춘로 997 (금곡동)
3rd row경기도 남양주시 경춘로 997 (금곡동)
4th row경기도 남양주시 경춘로 474-37 (지금동,지층)
5th row경기도 남양주시 퇴계원면 경춘북로 554
ValueCountFrequency (%)
경기도 1230
 
15.6%
남양주시 1230
 
15.6%
1층 271
 
3.4%
화도읍 193
 
2.4%
진접읍 174
 
2.2%
별내동 140
 
1.8%
와부읍 125
 
1.6%
호평동 103
 
1.3%
오남읍 101
 
1.3%
평내동 75
 
0.9%
Other values (1393) 4256
53.9%
2023-12-13T01:08:29.428149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6670
 
16.8%
1 1924
 
4.8%
도 1566
 
3.9%
남 1475
 
3.7%
경 1341
 
3.4%
주 1285
 
3.2%
시 1276
 
3.2%
양 1270
 
3.2%
로 1254
 
3.2%
기 1230
 
3.1%
Other values (317) 20447
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22830
57.5%
Decimal Number 7010
 
17.6%
Space Separator 6670
 
16.8%
Other Punctuation 1043
 
2.6%
Close Punctuation 881
 
2.2%
Open Punctuation 881
 
2.2%
Dash Punctuation 371
 
0.9%
Uppercase Letter 38
 
0.1%
Lowercase Letter 8
 
< 0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1566
 
6.9%
남 1475
 
6.5%
경 1341
 
5.9%
주 1285
 
5.6%
시 1276
 
5.6%
양 1270
 
5.6%
로 1254
 
5.5%
기 1230
 
5.4%
호 829
 
3.6%
동 728
 
3.2%
Other values (279) 10576
46.3%
Decimal Number
ValueCountFrequency (%)
1 1924
27.4%
2 1097
15.6%
0 821
11.7%
3 706
 
10.1%
4 503
 
7.2%
5 482
 
6.9%
6 442
 
6.3%
9 352
 
5.0%
8 346
 
4.9%
7 337
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
B 11
28.9%
A 7
18.4%
C 5
13.2%
H 5
13.2%
M 2
 
5.3%
N 2
 
5.3%
F 2
 
5.3%
I 2
 
5.3%
T 1
 
2.6%
P 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 1008
96.6%
@ 29
 
2.8%
. 4
 
0.4%
& 1
 
0.1%
/ 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 3
37.5%
o 2
25.0%
l 1
 
12.5%
w 1
 
12.5%
b 1
 
12.5%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅲ 2
50.0%
Math Symbol
ValueCountFrequency (%)
∏ 1
50.0%
∥ 1
50.0%
Space Separator
ValueCountFrequency (%)
6670
100.0%
Close Punctuation
ValueCountFrequency (%)
) 881
100.0%
Open Punctuation
ValueCountFrequency (%)
( 881
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22830
57.5%
Common 16858
42.4%
Latin 50
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1566
 
6.9%
남 1475
 
6.5%
경 1341
 
5.9%
주 1285
 
5.6%
시 1276
 
5.6%
양 1270
 
5.6%
로 1254
 
5.5%
기 1230
 
5.4%
호 829
 
3.6%
동 728
 
3.2%
Other values (279) 10576
46.3%
Common
ValueCountFrequency (%)
6670
39.6%
1 1924
 
11.4%
2 1097
 
6.5%
, 1008
 
6.0%
) 881
 
5.2%
( 881
 
5.2%
0 821
 
4.9%
3 706
 
4.2%
4 503
 
3.0%
5 482
 
2.9%
Other values (11) 1885
 
11.2%
Latin
ValueCountFrequency (%)
B 11
22.0%
A 7
14.0%
C 5
10.0%
H 5
10.0%
e 3
 
6.0%
o 2
 
4.0%
Ⅰ 2
 
4.0%
Ⅲ 2
 
4.0%
M 2
 
4.0%
N 2
 
4.0%
Other values (7) 9
18.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22830
57.5%
ASCII 16902
42.5%
Number Forms 4
 
< 0.1%
Math Operators 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6670
39.5%
1 1924
 
11.4%
2 1097
 
6.5%
, 1008
 
6.0%
) 881
 
5.2%
( 881
 
5.2%
0 821
 
4.9%
3 706
 
4.2%
4 503
 
3.0%
5 482
 
2.9%
Other values (24) 1929
 
11.4%
Hangul
ValueCountFrequency (%)
도 1566
 
6.9%
남 1475
 
6.5%
경 1341
 
5.9%
주 1285
 
5.6%
시 1276
 
5.6%
양 1270
 
5.6%
로 1254
 
5.5%
기 1230
 
5.4%
호 829
 
3.6%
동 728
 
3.2%
Other values (279) 10576
46.3%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅲ 2
50.0%
Math Operators
ValueCountFrequency (%)
∏ 1
50.0%
∥ 1
50.0%

업소소재지(지번)
Text

Distinct1293
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
2023-12-13T01:08:29.697131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length48
Mean length31.658938
Min length5

Characters and Unicode

Total characters42328
Distinct characters315
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1251 ?
Unique (%)93.6%

Sample

1st row경기도 남양주시 지금동 135번지 1호
2nd row경기도 남양주시 화도읍 마석우리 294번지
3rd row경기도 남양주시 금곡동 158번지 14호
4th row경기도 남양주시 금곡동 158번지 14호
5th row경기도 남양주시 도농동 45번지 40호
ValueCountFrequency (%)
경기도 1336
 
15.5%
남양주시 1336
 
15.5%
화도읍 218
 
2.5%
진접읍 194
 
2.2%
1호 176
 
2.0%
별내동 140
 
1.6%
와부읍 132
 
1.5%
호평동 117
 
1.4%
오남읍 114
 
1.3%
1층 107
 
1.2%
Other values (1297) 4753
55.1%
2023-12-13T01:08:30.124515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10240
24.2%
1 1776
 
4.2%
도 1680
 
4.0%
호 1634
 
3.9%
남 1558
 
3.7%
지 1552
 
3.7%
양 1384
 
3.3%
시 1379
 
3.3%
주 1374
 
3.2%
경 1341
 
3.2%
Other values (305) 18410
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24365
57.6%
Space Separator 10240
24.2%
Decimal Number 7469
 
17.6%
Other Punctuation 80
 
0.2%
Dash Punctuation 63
 
0.1%
Uppercase Letter 40
 
0.1%
Open Punctuation 32
 
0.1%
Close Punctuation 32
 
0.1%
Letter Number 3
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1680
 
6.9%
호 1634
 
6.7%
남 1558
 
6.4%
지 1552
 
6.4%
양 1384
 
5.7%
시 1379
 
5.7%
주 1374
 
5.6%
경 1341
 
5.5%
번 1337
 
5.5%
기 1336
 
5.5%
Other values (272) 9790
40.2%
Decimal Number
ValueCountFrequency (%)
1 1776
23.8%
0 954
12.8%
2 924
12.4%
4 645
 
8.6%
6 642
 
8.6%
3 630
 
8.4%
7 529
 
7.1%
5 517
 
6.9%
8 456
 
6.1%
9 396
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
B 14
35.0%
A 8
20.0%
C 6
15.0%
H 6
15.0%
F 3
 
7.5%
M 1
 
2.5%
L 1
 
2.5%
G 1
 
2.5%
Other Punctuation
ValueCountFrequency (%)
@ 39
48.8%
, 33
41.2%
/ 4
 
5.0%
. 3
 
3.8%
& 1
 
1.2%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅲ 1
33.3%
Math Symbol
ValueCountFrequency (%)
∏ 1
50.0%
∥ 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
b 1
50.0%
Space Separator
ValueCountFrequency (%)
10240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24365
57.6%
Common 17918
42.3%
Latin 45
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1680
 
6.9%
호 1634
 
6.7%
남 1558
 
6.4%
지 1552
 
6.4%
양 1384
 
5.7%
시 1379
 
5.7%
주 1374
 
5.6%
경 1341
 
5.5%
번 1337
 
5.5%
기 1336
 
5.5%
Other values (272) 9790
40.2%
Common
ValueCountFrequency (%)
10240
57.1%
1 1776
 
9.9%
0 954
 
5.3%
2 924
 
5.2%
4 645
 
3.6%
6 642
 
3.6%
3 630
 
3.5%
7 529
 
3.0%
5 517
 
2.9%
8 456
 
2.5%
Other values (11) 605
 
3.4%
Latin
ValueCountFrequency (%)
B 14
31.1%
A 8
17.8%
C 6
13.3%
H 6
13.3%
F 3
 
6.7%
Ⅰ 2
 
4.4%
Ⅲ 1
 
2.2%
M 1
 
2.2%
e 1
 
2.2%
L 1
 
2.2%
Other values (2) 2
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24365
57.6%
ASCII 17958
42.4%
Number Forms 3
 
< 0.1%
Math Operators 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10240
57.0%
1 1776
 
9.9%
0 954
 
5.3%
2 924
 
5.1%
4 645
 
3.6%
6 642
 
3.6%
3 630
 
3.5%
7 529
 
2.9%
5 517
 
2.9%
8 456
 
2.5%
Other values (19) 645
 
3.6%
Hangul
ValueCountFrequency (%)
도 1680
 
6.9%
호 1634
 
6.7%
남 1558
 
6.4%
지 1552
 
6.4%
양 1384
 
5.7%
시 1379
 
5.7%
주 1374
 
5.6%
경 1341
 
5.5%
번 1337
 
5.5%
기 1336
 
5.5%
Other values (272) 9790
40.2%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅲ 1
33.3%
Math Operators
ValueCountFrequency (%)
∏ 1
50.0%
∥ 1
50.0%

소재지전화(031)
Text

MISSING 

Distinct757
Distinct (%)62.2%
Missing120
Missing (%)9.0%
Memory size10.6 KiB
2023-12-13T01:08:30.381716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.9137223
Min length1

Characters and Unicode

Total characters9631
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique749 ?
Unique (%)61.5%

Sample

1st row031-567-4541
2nd row031-594-0130
3rd row031-592-3372
4th row031-592-3372
5th row031-563-7457
ValueCountFrequency (%)
031-572-3039 2
 
0.3%
031-577-0803 2
 
0.3%
031-528-1003 2
 
0.3%
031-592-3372 2
 
0.3%
031-595-2778 2
 
0.3%
031-856-2000 2
 
0.3%
031-573-9161 2
 
0.3%
031-529-8088 1
 
0.1%
031-510-4165 1
 
0.1%
031-574-6231 1
 
0.1%
Other values (746) 746
97.8%
2023-12-13T01:08:30.761015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1526
15.8%
1 1304
13.5%
5 1171
12.2%
0 1169
12.1%
3 1145
11.9%
7 665
6.9%
2 567
 
5.9%
9 510
 
5.3%
454
 
4.7%
4 387
 
4.0%
Other values (2) 733
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7651
79.4%
Dash Punctuation 1526
 
15.8%
Space Separator 454
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1304
17.0%
5 1171
15.3%
0 1169
15.3%
3 1145
15.0%
7 665
8.7%
2 567
7.4%
9 510
 
6.7%
4 387
 
5.1%
8 386
 
5.0%
6 347
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 1526
100.0%
Space Separator
ValueCountFrequency (%)
454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9631
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1526
15.8%
1 1304
13.5%
5 1171
12.2%
0 1169
12.1%
3 1145
11.9%
7 665
6.9%
2 567
 
5.9%
9 510
 
5.3%
454
 
4.7%
4 387
 
4.0%
Other values (2) 733
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1526
15.8%
1 1304
13.5%
5 1171
12.2%
0 1169
12.1%
3 1145
11.9%
7 665
6.9%
2 567
 
5.9%
9 510
 
5.3%
454
 
4.7%
4 387
 
4.0%
Other values (2) 733
7.6%

Missing values

2023-12-13T01:08:27.524483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:08:27.628794image/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.
2023-12-13T01:08:27.743288image/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

업종명업소명업소소재지(도로명)업소소재지(지번)소재지전화(031)
0미용업지현미용실경기도 남양주시 경춘로 464 (지금동)경기도 남양주시 지금동 135번지 1호031-567-4541
1미용업미도파미용실<NA>경기도 남양주시 화도읍 마석우리 294번지031-594-0130
2미용업블루클럽(금곡점)경기도 남양주시 경춘로 997 (금곡동)경기도 남양주시 금곡동 158번지 14호031-592-3372
3미용업컷팅클럽경기도 남양주시 경춘로 997 (금곡동)경기도 남양주시 금곡동 158번지 14호031-592-3372
4미용업유진미용실<NA>경기도 남양주시 도농동 45번지 40호031-563-7457
5미용업나현미용실경기도 남양주시 경춘로 474-37 (지금동,지층)경기도 남양주시 지금동 96번지 6호 지층031-563-5441
6미용업귀분미용실경기도 남양주시 퇴계원면 경춘북로 554경기도 남양주시 퇴계원면 퇴계원리 211번지 10호031-572-9831
7미용업서라벌미장원<NA>경기도 남양주시 화도읍 창현리 488번지 4호031-593-1050
8미용업은혜헤어경기도 남양주시 진접읍 장현로 63 (2층)경기도 남양주시 진접읍 장현리 644번지 4호 2층031-572-2908
9미용업박지연헤어스탁<NA>경기도 남양주시 금곡동 419번지 10호031-592-7333
업종명업소명업소소재지(도로명)업소소재지(지번)소재지전화(031)
1327미용업(피부), 미용업(손톱ㆍ발톱)칼라발라경기도 남양주시 진접읍 경복대로 420 (2층일부)경기도 남양주시 진접읍 금곡리 379번지 2층일부
1328미용업(피부), 미용업(손톱ㆍ발톱)썬바디앤아이경기도 남양주시 의안로 121-10, 1층 (평내동, 정환빌딩)경기도 남양주시 평내동 604번지 9호 정환빌딩
1329미용업(피부), 미용업(손톱ㆍ발톱)아샤뷰티경기도 남양주시 호평로 76-4, 1층 (호평동)경기도 남양주시 호평동 666번지 3호031-593-5855
1330미용업(피부), 미용업(손톱ㆍ발톱)엄지공주경기도 남양주시 진접읍 장현로 115경기도 남양주시 진접읍 장현리 351번지 25호031-529-4224
1331미용업(피부), 미용업(손톱ㆍ발톱)블링블링경기도 남양주시 진건읍 진건오남로86번길 3 (1층일부)경기도 남양주시 진건읍 용정리 753번지 2호070-8160-9557
1332미용업(피부), 미용업(손톱ㆍ발톱)르네셀앤네일경기도 남양주시 호평로46번안길 4-18, 1층 (호평동)경기도 남양주시 호평동 652번지 7호
1333미용업(피부), 미용업(손톱ㆍ발톱)순수스킨케어경기도 남양주시 경춘로1256번길 6, 306호 (평내동, 다모아프라자)경기도 남양주시 평내동 578번지 1호 다모아프라자 306호031-510-7959
1334미용업(피부), 미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장)네일라떼경기도 남양주시 두물로39번길 10, 1층 (별내동)경기도 남양주시 별내동 965번지 2호031-575-8891
1335미용업(피부), 미용업(화장ㆍ분장)러블리경기도 남양주시 별내중앙로 36-19, 1층 109일부호 (별내동, 스마트리치안오피스텔)경기도 남양주시 별내동 1007번지
1336미용업(화장ㆍ분장)아이러브뷰티경기도 남양주시 호평로68번길 23, 106호 (호평동, 호평마을금강아파트 주상가동)경기도 남양주시 호평동 668번지 호평마을금강아파트 주상가동 106호

Duplicate rows

Most frequently occurring

업종명업소명업소소재지(도로명)업소소재지(지번)소재지전화(031)# duplicates
0미용업아르떼헤어경기도 남양주시 평내로29번길 51-6 (평내동)경기도 남양주시 평내동 582번지 9호2
1미용업(일반)오렌지헤어경기도 남양주시 진접읍 장현로 125 (1층 일부)경기도 남양주시 진접읍 장현리 356번지 15호 1층 일부031-572-30392