Overview

Dataset statistics

Number of variables5
Number of observations175
Missing cells44
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory40.8 B

Variable types

Categorical2
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

업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation
업태명 is highly imbalanced (55.9%)Imbalance
소재지전화 has 44 (25.1%) missing valuesMissing

Reproduction

Analysis started2023-12-11 00:20:49.879737
Analysis finished2023-12-11 00:20:50.302995
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
미용업
94 
일반미용업
43 
피부미용업
15 
네일미용업
 
8
종합미용업
 
6
Other values (6)
 
9

Length

Max length23
Median length3
Mean length4.6057143
Min length3

Unique

Unique4 ?
Unique (%)2.3%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 94
53.7%
일반미용업 43
24.6%
피부미용업 15
 
8.6%
네일미용업 8
 
4.6%
종합미용업 6
 
3.4%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 3
 
1.7%
피부미용업, 화장ㆍ분장 미용업 2
 
1.1%
일반미용업, 네일미용업 1
 
0.6%
피부미용업, 네일미용업 1
 
0.6%
네일미용업, 화장ㆍ분장 미용업 1
 
0.6%

Length

2023-12-11T09:20:50.376631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 101
51.8%
일반미용업 47
24.1%
피부미용업 19
 
9.7%
네일미용업 15
 
7.7%
화장ㆍ분장 7
 
3.6%
종합미용업 6
 
3.1%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
일반미용업
144 
피부미용업
16 
네일아트업
 
14
기타
 
1

Length

Max length5
Median length5
Mean length4.9828571
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 144
82.3%
피부미용업 16
 
9.1%
네일아트업 14
 
8.0%
기타 1
 
0.6%

Length

2023-12-11T09:20:50.505850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:50.631162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 144
82.3%
피부미용업 16
 
9.1%
네일아트업 14
 
8.0%
기타 1
 
0.6%

업소명
Text

Distinct174
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T09:20:50.931622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length5.5371429
Min length2

Characters and Unicode

Total characters969
Distinct characters251
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

Unique173 ?
Unique (%)98.9%

Sample

1st row명랑미용실
2nd row경남미용실
3rd row동백미용실
4th row낙원미용실
5th row은하미장원
ValueCountFrequency (%)
헤어샵 4
 
2.0%
진미용실 2
 
1.0%
헤어 2
 
1.0%
헤어매니아 2
 
1.0%
아름다운 2
 
1.0%
소망미용실 1
 
0.5%
지성헤어 1
 
0.5%
남영원헤어뷰 1
 
0.5%
혜선헤어 1
 
0.5%
후리지아미용실 1
 
0.5%
Other values (180) 180
91.4%
2023-12-11T09:20:51.484084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 64
 
6.6%
어 62
 
6.4%
헤 61
 
6.3%
실 54
 
5.6%
용 52
 
5.4%
리 34
 
3.5%
22
 
2.3%
머 17
 
1.8%
스 15
 
1.5%
샵 14
 
1.4%
Other values (241) 574
59.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 870
89.8%
Lowercase Letter 32
 
3.3%
Uppercase Letter 26
 
2.7%
Space Separator 22
 
2.3%
Other Punctuation 7
 
0.7%
Close Punctuation 4
 
0.4%
Open Punctuation 4
 
0.4%
Decimal Number 3
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 64
 
7.4%
어 62
 
7.1%
헤 61
 
7.0%
실 54
 
6.2%
용 52
 
6.0%
리 34
 
3.9%
머 17
 
2.0%
스 15
 
1.7%
샵 14
 
1.6%
이 12
 
1.4%
Other values (201) 485
55.7%
Uppercase Letter
ValueCountFrequency (%)
S 5
19.2%
B 3
11.5%
A 2
 
7.7%
Y 2
 
7.7%
D 2
 
7.7%
N 2
 
7.7%
R 1
 
3.8%
H 1
 
3.8%
L 1
 
3.8%
T 1
 
3.8%
Other values (6) 6
23.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
12.5%
a 4
12.5%
i 3
9.4%
y 3
9.4%
l 3
9.4%
r 3
9.4%
o 3
9.4%
n 2
6.2%
b 2
6.2%
t 2
6.2%
Other values (3) 3
9.4%
Other Punctuation
ValueCountFrequency (%)
. 3
42.9%
& 2
28.6%
' 1
 
14.3%
, 1
 
14.3%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
2 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 870
89.8%
Latin 58
 
6.0%
Common 41
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 64
 
7.4%
어 62
 
7.1%
헤 61
 
7.0%
실 54
 
6.2%
용 52
 
6.0%
리 34
 
3.9%
머 17
 
2.0%
스 15
 
1.7%
샵 14
 
1.6%
이 12
 
1.4%
Other values (201) 485
55.7%
Latin
ValueCountFrequency (%)
S 5
 
8.6%
e 4
 
6.9%
a 4
 
6.9%
i 3
 
5.2%
y 3
 
5.2%
l 3
 
5.2%
r 3
 
5.2%
B 3
 
5.2%
o 3
 
5.2%
n 2
 
3.4%
Other values (19) 25
43.1%
Common
ValueCountFrequency (%)
22
53.7%
) 4
 
9.8%
( 4
 
9.8%
. 3
 
7.3%
& 2
 
4.9%
- 1
 
2.4%
3 1
 
2.4%
2 1
 
2.4%
1 1
 
2.4%
' 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 870
89.8%
ASCII 99
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 64
 
7.4%
어 62
 
7.1%
헤 61
 
7.0%
실 54
 
6.2%
용 52
 
6.0%
리 34
 
3.9%
머 17
 
2.0%
스 15
 
1.7%
샵 14
 
1.6%
이 12
 
1.4%
Other values (201) 485
55.7%
ASCII
ValueCountFrequency (%)
22
22.2%
S 5
 
5.1%
) 4
 
4.0%
( 4
 
4.0%
e 4
 
4.0%
a 4
 
4.0%
i 3
 
3.0%
. 3
 
3.0%
y 3
 
3.0%
l 3
 
3.0%
Other values (30) 44
44.4%

영업소 주소(도로명)
Text

Distinct163
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T09:20:51.801619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length38
Mean length21.885714
Min length17

Characters and Unicode

Total characters3830
Distinct characters115
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)86.9%

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 175
18.8%
창녕군 175
18.8%
창녕읍 76
 
8.2%
남지읍 52
 
5.6%
영산면 21
 
2.3%
1층 19
 
2.0%
낙동로 17
 
1.8%
종로 12
 
1.3%
명덕로 11
 
1.2%
부곡면 10
 
1.1%
Other values (212) 364
39.1%
2023-12-11T09:20:52.269149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
767
20.0%
창 264
 
6.9%
녕 260
 
6.8%
남 242
 
6.3%
상 183
 
4.8%
도 180
 
4.7%
경 177
 
4.6%
군 177
 
4.6%
1 140
 
3.7%
읍 128
 
3.3%
Other values (105) 1312
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2457
64.2%
Space Separator 767
 
20.0%
Decimal Number 512
 
13.4%
Dash Punctuation 45
 
1.2%
Other Punctuation 29
 
0.8%
Open Punctuation 10
 
0.3%
Close Punctuation 10
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 264
 
10.7%
녕 260
 
10.6%
남 242
 
9.8%
상 183
 
7.4%
도 180
 
7.3%
경 177
 
7.2%
군 177
 
7.2%
읍 128
 
5.2%
길 85
 
3.5%
로 82
 
3.3%
Other values (90) 679
27.6%
Decimal Number
ValueCountFrequency (%)
1 140
27.3%
2 75
14.6%
4 57
11.1%
3 44
 
8.6%
0 41
 
8.0%
6 39
 
7.6%
9 33
 
6.4%
5 31
 
6.1%
7 30
 
5.9%
8 22
 
4.3%
Space Separator
ValueCountFrequency (%)
767
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Other Punctuation
ValueCountFrequency (%)
, 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2457
64.2%
Common 1373
35.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 264
 
10.7%
녕 260
 
10.6%
남 242
 
9.8%
상 183
 
7.4%
도 180
 
7.3%
경 177
 
7.2%
군 177
 
7.2%
읍 128
 
5.2%
길 85
 
3.5%
로 82
 
3.3%
Other values (90) 679
27.6%
Common
ValueCountFrequency (%)
767
55.9%
1 140
 
10.2%
2 75
 
5.5%
4 57
 
4.2%
- 45
 
3.3%
3 44
 
3.2%
0 41
 
3.0%
6 39
 
2.8%
9 33
 
2.4%
5 31
 
2.3%
Other values (5) 101
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2457
64.2%
ASCII 1373
35.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
767
55.9%
1 140
 
10.2%
2 75
 
5.5%
4 57
 
4.2%
- 45
 
3.3%
3 44
 
3.2%
0 41
 
3.0%
6 39
 
2.8%
9 33
 
2.4%
5 31
 
2.3%
Other values (5) 101
 
7.4%
Hangul
ValueCountFrequency (%)
창 264
 
10.7%
녕 260
 
10.6%
남 242
 
9.8%
상 183
 
7.4%
도 180
 
7.3%
경 177
 
7.2%
군 177
 
7.2%
읍 128
 
5.2%
길 85
 
3.5%
로 82
 
3.3%
Other values (90) 679
27.6%

소재지전화
Text

MISSING 

Distinct131
Distinct (%)100.0%
Missing44
Missing (%)25.1%
Memory size1.5 KiB
2023-12-11T09:20:52.526824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.007634
Min length12

Characters and Unicode

Total characters1573
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

Unique131 ?
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 (%)
055-533-7565 1
 
0.8%
055-536-9702 1
 
0.8%
055-533-7183 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%
055-533-3126 1
 
0.8%
055-526-5155 1
 
0.8%
Other values (121) 121
92.4%
2023-12-11T09:20:52.885057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 440
28.0%
- 262
16.7%
0 203
12.9%
3 162
 
10.3%
2 148
 
9.4%
6 89
 
5.7%
1 86
 
5.5%
7 61
 
3.9%
9 44
 
2.8%
4 39
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1311
83.3%
Dash Punctuation 262
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 440
33.6%
0 203
15.5%
3 162
 
12.4%
2 148
 
11.3%
6 89
 
6.8%
1 86
 
6.6%
7 61
 
4.7%
9 44
 
3.4%
4 39
 
3.0%
8 39
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 440
28.0%
- 262
16.7%
0 203
12.9%
3 162
 
10.3%
2 148
 
9.4%
6 89
 
5.7%
1 86
 
5.5%
7 61
 
3.9%
9 44
 
2.8%
4 39
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 440
28.0%
- 262
16.7%
0 203
12.9%
3 162
 
10.3%
2 148
 
9.4%
6 89
 
5.7%
1 86
 
5.5%
7 61
 
3.9%
9 44
 
2.8%
4 39
 
2.5%

Correlations

2023-12-11T09:20:52.982736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.888
업태명0.8881.000
2023-12-11T09:20:53.068535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.758
업태명0.7581.000
2023-12-11T09:20:53.138321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.758
업태명0.7581.000

Missing values

2023-12-11T09:20:50.165645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:20:50.267487image/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

업종명업태명업소명영업소 주소(도로명)소재지전화
0미용업일반미용업명랑미용실경상남도 창녕군 남지읍 남지리 284-2055-526-2331
1미용업일반미용업경남미용실경상남도 창녕군 창녕읍 당산1길 26-16055-532-1719
2미용업일반미용업동백미용실경상남도 창녕군 영산면 영산중앙길 9055-536-2919
3미용업일반미용업낙원미용실경상남도 창녕군 대합면 창한로 106-1055-532-1907
4미용업일반미용업은하미장원경상남도 창녕군 이방면 장천리 5055-532-6034
5미용업일반미용업서울미용실경상남도 창녕군 이방면 이방대합로 340-1055-532-5842
6미용업일반미용업소양미용실경상남도 창녕군 창녕읍 옥만길 30055-533-3013
7미용업일반미용업이방미장원경상남도 창녕군 이방면 장천리 6-3055-532-5172
8미용업일반미용업우리미용실경상남도 창녕군 창녕읍 우포2로 1213055-533-7312
9미용업일반미용업새미용실경상남도 창녕군 남지읍 낙동로 466055-526-4300
업종명업태명업소명영업소 주소(도로명)소재지전화
165네일미용업네일아트업빈스네일경상남도 창녕군 창녕읍 종로 42<NA>
166일반미용업, 네일미용업네일아트업정헤어&네일경상남도 창녕군 남지읍 남지중앙로 82, 중앙상가 106호<NA>
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168피부미용업, 화장ㆍ분장 미용업피부미용업미모스킨경상남도 창녕군 창녕읍 옥만길 31<NA>
169피부미용업, 화장ㆍ분장 미용업피부미용업모나리자 Beauty경상남도 창녕군 창녕읍 시장3길 6, 1층<NA>
170네일미용업, 화장ㆍ분장 미용업네일아트업나나살롱경상남도 창녕군 창녕읍 종로 7<NA>
171일반미용업, 네일미용업, 화장ㆍ분장 미용업일반미용업킴헤어경상남도 창녕군 남지읍 남지중앙로 8-2<NA>
172일반미용업, 네일미용업, 화장ㆍ분장 미용업일반미용업주니헤어경상남도 창녕군 창녕읍 옥만길 43055-532-8262
173일반미용업, 네일미용업, 화장ㆍ분장 미용업일반미용업모아Style경상남도 창녕군 창녕읍 화왕산1로 37, 1층<NA>
174피부미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업네일,혜경상남도 창녕군 남지읍 백암길 23<NA>