Overview

Dataset statistics

Number of variables4
Number of observations167
Missing cells45
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory32.8 B

Variable types

Categorical1
Text3

Dataset

Description경상남도 창녕군 공중위생업 미용업에 대한 현황 데이터를 포함하고 있습니다.(업종명, 업소명, 영업소 주소, 소재지전화번호 제공)
Author경상남도 창녕군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15006909

Alerts

소재지전화 has 45 (26.9%) missing valuesMissing

Reproduction

Analysis started2023-12-11 00:20:46.276204
Analysis finished2023-12-11 00:20:46.614006
Duration0.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct10
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
미용업
83 
일반미용업
46 
피부미용업
15 
네일미용업
10 
종합미용업
 
6
Other values (5)
 
7

Length

Max length23
Median length16
Mean length4.5868263
Min length3

Unique

Unique3 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 83
49.7%
일반미용업 46
27.5%
피부미용업 15
 
9.0%
네일미용업 10
 
6.0%
종합미용업 6
 
3.6%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 2
 
1.2%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 2
 
1.2%
일반미용업, 피부미용업 1
 
0.6%
일반미용업, 네일미용업 1
 
0.6%
피부미용업, 화장ㆍ분장 미용업 1
 
0.6%

Length

2023-12-11T09:20:46.687233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:46.814403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 88
48.1%
일반미용업 50
27.3%
피부미용업 19
 
10.4%
네일미용업 15
 
8.2%
종합미용업 6
 
3.3%
화장ㆍ분장 5
 
2.7%

업소명
Text

Distinct166
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T09:20:47.094657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length5.4610778
Min length2

Characters and Unicode

Total characters912
Distinct characters246
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

Unique165 ?
Unique (%)98.8%

Sample

1st row명랑미용실
2nd row경남미용실
3rd row동백미용실
4th row낙원미용실
5th row은하미장원
ValueCountFrequency (%)
헤어샵 4
 
2.1%
헤어 3
 
1.6%
진미용실 2
 
1.1%
아름다운 2
 
1.1%
남영원헤어뷰 1
 
0.5%
가위소리미용실 1
 
0.5%
안서현헤어샵 1
 
0.5%
소망미용실 1
 
0.5%
h헤어샾 1
 
0.5%
또바기 1
 
0.5%
Other values (170) 170
90.9%
2023-12-11T09:20:47.486830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 59
 
6.5%
헤 58
 
6.4%
미 57
 
6.2%
실 48
 
5.3%
용 47
 
5.2%
리 31
 
3.4%
20
 
2.2%
머 17
 
1.9%
스 17
 
1.9%
샵 14
 
1.5%
Other values (236) 544
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 818
89.7%
Uppercase Letter 29
 
3.2%
Lowercase Letter 27
 
3.0%
Space Separator 20
 
2.2%
Other Punctuation 6
 
0.7%
Close Punctuation 5
 
0.5%
Open Punctuation 5
 
0.5%
Dash Punctuation 1
 
0.1%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 59
 
7.2%
헤 58
 
7.1%
미 57
 
7.0%
실 48
 
5.9%
용 47
 
5.7%
리 31
 
3.8%
머 17
 
2.1%
스 17
 
2.1%
샵 14
 
1.7%
이 12
 
1.5%
Other values (197) 458
56.0%
Uppercase Letter
ValueCountFrequency (%)
S 5
17.2%
O 3
 
10.3%
N 2
 
6.9%
Y 2
 
6.9%
B 2
 
6.9%
A 2
 
6.9%
D 2
 
6.9%
R 1
 
3.4%
M 1
 
3.4%
Z 1
 
3.4%
Other values (8) 8
27.6%
Lowercase Letter
ValueCountFrequency (%)
o 3
11.1%
r 3
11.1%
i 3
11.1%
a 3
11.1%
e 3
11.1%
l 3
11.1%
y 2
7.4%
b 2
7.4%
n 2
7.4%
t 1
 
3.7%
Other values (2) 2
7.4%
Other Punctuation
ValueCountFrequency (%)
, 2
33.3%
& 2
33.3%
. 1
16.7%
' 1
16.7%
Space Separator
ValueCountFrequency (%)
20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Decimal Number
ValueCountFrequency (%)
7 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 818
89.7%
Latin 56
 
6.1%
Common 38
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 59
 
7.2%
헤 58
 
7.1%
미 57
 
7.0%
실 48
 
5.9%
용 47
 
5.7%
리 31
 
3.8%
머 17
 
2.1%
스 17
 
2.1%
샵 14
 
1.7%
이 12
 
1.5%
Other values (197) 458
56.0%
Latin
ValueCountFrequency (%)
S 5
 
8.9%
o 3
 
5.4%
O 3
 
5.4%
r 3
 
5.4%
i 3
 
5.4%
a 3
 
5.4%
e 3
 
5.4%
l 3
 
5.4%
y 2
 
3.6%
N 2
 
3.6%
Other values (20) 26
46.4%
Common
ValueCountFrequency (%)
20
52.6%
) 5
 
13.2%
( 5
 
13.2%
, 2
 
5.3%
& 2
 
5.3%
- 1
 
2.6%
7 1
 
2.6%
. 1
 
2.6%
' 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 818
89.7%
ASCII 94
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 59
 
7.2%
헤 58
 
7.1%
미 57
 
7.0%
실 48
 
5.9%
용 47
 
5.7%
리 31
 
3.8%
머 17
 
2.1%
스 17
 
2.1%
샵 14
 
1.7%
이 12
 
1.5%
Other values (197) 458
56.0%
ASCII
ValueCountFrequency (%)
20
21.3%
S 5
 
5.3%
) 5
 
5.3%
( 5
 
5.3%
o 3
 
3.2%
O 3
 
3.2%
r 3
 
3.2%
i 3
 
3.2%
a 3
 
3.2%
e 3
 
3.2%
Other values (29) 41
43.6%

영업소 주소(도로명)
Text

Distinct156
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T09:20:47.790672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length40
Mean length21.886228
Min length17

Characters and Unicode

Total characters3655
Distinct characters118
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

Unique146 ?
Unique (%)87.4%

Sample

1st row경상남도 창녕군 남지읍 남지리 284-2
2nd row경상남도 창녕군 창녕읍 당산1길 26-16
3rd row경상남도 창녕군 영산면 영산중앙길 9
4th row경상남도 창녕군 대합면 창한로 106-1
5th row경상남도 창녕군 이방면 장천리 5
ValueCountFrequency (%)
경상남도 167
18.8%
창녕군 167
18.8%
창녕읍 74
 
8.3%
남지읍 52
 
5.8%
영산면 18
 
2.0%
낙동로 15
 
1.7%
1층 14
 
1.6%
종로 12
 
1.3%
명덕로 11
 
1.2%
화왕산1로 9
 
1.0%
Other values (207) 351
39.4%
2023-12-11T09:20:48.234569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
729
19.9%
창 253
 
6.9%
녕 250
 
6.8%
남 238
 
6.5%
상 173
 
4.7%
도 171
 
4.7%
경 169
 
4.6%
군 168
 
4.6%
1 134
 
3.7%
읍 126
 
3.4%
Other values (108) 1244
34.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2350
64.3%
Space Separator 729
 
19.9%
Decimal Number 485
 
13.3%
Dash Punctuation 40
 
1.1%
Other Punctuation 32
 
0.9%
Close Punctuation 9
 
0.2%
Open Punctuation 9
 
0.2%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 253
 
10.8%
녕 250
 
10.6%
남 238
 
10.1%
상 173
 
7.4%
도 171
 
7.3%
경 169
 
7.2%
군 168
 
7.1%
읍 126
 
5.4%
로 83
 
3.5%
길 80
 
3.4%
Other values (92) 639
27.2%
Decimal Number
ValueCountFrequency (%)
1 134
27.6%
2 76
15.7%
4 51
 
10.5%
0 39
 
8.0%
3 38
 
7.8%
6 37
 
7.6%
9 34
 
7.0%
5 32
 
6.6%
7 23
 
4.7%
8 21
 
4.3%
Space Separator
ValueCountFrequency (%)
729
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Other Punctuation
ValueCountFrequency (%)
, 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2350
64.3%
Common 1304
35.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 253
 
10.8%
녕 250
 
10.6%
남 238
 
10.1%
상 173
 
7.4%
도 171
 
7.3%
경 169
 
7.2%
군 168
 
7.1%
읍 126
 
5.4%
로 83
 
3.5%
길 80
 
3.4%
Other values (92) 639
27.2%
Common
ValueCountFrequency (%)
729
55.9%
1 134
 
10.3%
2 76
 
5.8%
4 51
 
3.9%
- 40
 
3.1%
0 39
 
3.0%
3 38
 
2.9%
6 37
 
2.8%
9 34
 
2.6%
5 32
 
2.5%
Other values (5) 94
 
7.2%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2350
64.3%
ASCII 1305
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
729
55.9%
1 134
 
10.3%
2 76
 
5.8%
4 51
 
3.9%
- 40
 
3.1%
0 39
 
3.0%
3 38
 
2.9%
6 37
 
2.8%
9 34
 
2.6%
5 32
 
2.5%
Other values (6) 95
 
7.3%
Hangul
ValueCountFrequency (%)
창 253
 
10.8%
녕 250
 
10.6%
남 238
 
10.1%
상 173
 
7.4%
도 171
 
7.3%
경 169
 
7.2%
군 168
 
7.1%
읍 126
 
5.4%
로 83
 
3.5%
길 80
 
3.4%
Other values (92) 639
27.2%

소재지전화
Text

MISSING 

Distinct122
Distinct (%)100.0%
Missing45
Missing (%)26.9%
Memory size1.4 KiB
2023-12-11T09:20:48.495953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.016393
Min length12

Characters and Unicode

Total characters1466
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

Unique122 ?
Unique (%)100.0%

Sample

1st row055-526-2331
2nd row055-532-1719
3rd row055-536-2919
4th row055-532-1907
5th row055-532-6034
ValueCountFrequency (%)
055-533-0777 1
 
0.8%
055-526-1192 1
 
0.8%
055-532-1025 1
 
0.8%
055-536-9702 1
 
0.8%
055-532-0688 1
 
0.8%
055-526-2790 1
 
0.8%
055-532-9100 1
 
0.8%
055-521-3318 1
 
0.8%
055-521-6282 1
 
0.8%
055-532-5505 1
 
0.8%
Other values (112) 112
91.8%
2023-12-11T09:20:48.889003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 405
27.6%
- 244
16.6%
0 194
13.2%
3 154
 
10.5%
2 136
 
9.3%
6 83
 
5.7%
1 79
 
5.4%
7 58
 
4.0%
9 40
 
2.7%
4 38
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1222
83.4%
Dash Punctuation 244
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 405
33.1%
0 194
15.9%
3 154
 
12.6%
2 136
 
11.1%
6 83
 
6.8%
1 79
 
6.5%
7 58
 
4.7%
9 40
 
3.3%
4 38
 
3.1%
8 35
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1466
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 405
27.6%
- 244
16.6%
0 194
13.2%
3 154
 
10.5%
2 136
 
9.3%
6 83
 
5.7%
1 79
 
5.4%
7 58
 
4.0%
9 40
 
2.7%
4 38
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 405
27.6%
- 244
16.6%
0 194
13.2%
3 154
 
10.5%
2 136
 
9.3%
6 83
 
5.7%
1 79
 
5.4%
7 58
 
4.0%
9 40
 
2.7%
4 38
 
2.6%

Missing values

2023-12-11T09:20:46.491991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:20:46.575144image/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

업종명업소명영업소 주소(도로명)소재지전화
0미용업명랑미용실경상남도 창녕군 남지읍 남지리 284-2055-526-2331
1미용업경남미용실경상남도 창녕군 창녕읍 당산1길 26-16055-532-1719
2미용업동백미용실경상남도 창녕군 영산면 영산중앙길 9055-536-2919
3미용업낙원미용실경상남도 창녕군 대합면 창한로 106-1055-532-1907
4미용업은하미장원경상남도 창녕군 이방면 장천리 5055-532-6034
5미용업서울미용실경상남도 창녕군 이방면 이방대합로 340-1055-532-5842
6미용업소양미용실경상남도 창녕군 창녕읍 옥만길 30055-533-3013
7미용업이방미장원경상남도 창녕군 이방면 장천리 6-3055-532-5172
8미용업우리미용실경상남도 창녕군 창녕읍 우포2로 1213055-533-7312
9미용업새미용실경상남도 창녕군 남지읍 낙동로 466055-526-4300
업종명업소명영업소 주소(도로명)소재지전화
157네일미용업줌(ZOOM)네일경상남도 창녕군 남지읍 남포길 30-1<NA>
158네일미용업지안네일샵경상남도 창녕군 창녕읍 종로 52-1<NA>
159네일미용업오늘,네일경상남도 창녕군 창녕읍 옥만길 31<NA>
160일반미용업, 피부미용업힐스킨7경상남도 창녕군 창녕읍 당산길 39, 2층055-533-2840
161일반미용업, 네일미용업정헤어&네일경상남도 창녕군 남지읍 남지중앙로 82, 중앙상가 106호<NA>
162피부미용업, 화장ㆍ분장 미용업미모스킨경상남도 창녕군 창녕읍 옥만길 31<NA>
163일반미용업, 네일미용업, 화장ㆍ분장 미용업킴헤어경상남도 창녕군 남지읍 남지중앙로 8-2<NA>
164일반미용업, 네일미용업, 화장ㆍ분장 미용업모아Style경상남도 창녕군 창녕읍 화왕산1로 37, 1층<NA>
165피부미용업, 네일미용업, 화장ㆍ분장 미용업나나살롱경상남도 창녕군 창녕읍 종로 7<NA>
166피부미용업, 네일미용업, 화장ㆍ분장 미용업네일,혜경상남도 창녕군 남지읍 백암길 23<NA>