Overview

Dataset statistics

Number of variables4
Number of observations314
Missing cells82
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory32.4 B

Variable types

Categorical1
Text3

Dataset

Description경상남도 밀양시에서 관리하는 미용업체 현황을 확인할 수 있습니다.(업종명,업소명,업소소재지(도로명),소재지 전화 정보 제공)
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15045145

Alerts

업종명 is highly imbalanced (59.0%)Imbalance
소재지전화 has 82 (26.1%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:57:26.469007
Analysis finished2023-12-10 22:57:26.819133
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
미용업(일반)
244 
미용업(피부)
49 
미용업(손톱ㆍ발톱)
 
10
미용업(종합)
 
9
미용업(피부), 미용업(손톱ㆍ발톱)
 
1

Length

Max length19
Median length7
Mean length7.1433121
Min length7

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row미용업(손톱ㆍ발톱)
2nd row미용업(손톱ㆍ발톱)
3rd row미용업(손톱ㆍ발톱)
4th row미용업(손톱ㆍ발톱)
5th row미용업(손톱ㆍ발톱)

Common Values

ValueCountFrequency (%)
미용업(일반) 244
77.7%
미용업(피부) 49
 
15.6%
미용업(손톱ㆍ발톱) 10
 
3.2%
미용업(종합) 9
 
2.9%
미용업(피부), 미용업(손톱ㆍ발톱) 1
 
0.3%
미용업(화장ㆍ분장) 1
 
0.3%

Length

2023-12-11T07:57:26.876373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:57:26.971323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업(일반 244
77.5%
미용업(피부 50
 
15.9%
미용업(손톱ㆍ발톱 11
 
3.5%
미용업(종합 9
 
2.9%
미용업(화장ㆍ분장 1
 
0.3%

업소명
Text

Distinct309
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T07:57:27.217793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length4.8598726
Min length1

Characters and Unicode

Total characters1526
Distinct characters311
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

Unique304 ?
Unique (%)96.8%

Sample

1st row마메종네일
2nd row네일바다
3rd row현주네일아트
4th row오늘네일아트
5th row네일박스
ValueCountFrequency (%)
미용실 10
 
2.7%
헤어 9
 
2.4%
네일 5
 
1.3%
피부샵 3
 
0.8%
헤어샵 3
 
0.8%
현대 2
 
0.5%
에스테틱 2
 
0.5%
hair 2
 
0.5%
뷰티샵 2
 
0.5%
현 2
 
0.5%
Other values (327) 333
89.3%
2023-12-11T07:57:27.613874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
헤 111
 
7.3%
어 106
 
6.9%
미 66
 
4.3%
60
 
3.9%
용 44
 
2.9%
실 44
 
2.9%
리 36
 
2.4%
샵 30
 
2.0%
나 27
 
1.8%
스 24
 
1.6%
Other values (301) 978
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1390
91.1%
Space Separator 60
 
3.9%
Lowercase Letter 32
 
2.1%
Other Punctuation 25
 
1.6%
Decimal Number 8
 
0.5%
Uppercase Letter 7
 
0.5%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
헤 111
 
8.0%
어 106
 
7.6%
미 66
 
4.7%
용 44
 
3.2%
실 44
 
3.2%
리 36
 
2.6%
샵 30
 
2.2%
나 27
 
1.9%
스 24
 
1.7%
이 24
 
1.7%
Other values (267) 878
63.2%
Lowercase Letter
ValueCountFrequency (%)
h 5
15.6%
i 4
12.5%
o 3
9.4%
s 3
9.4%
a 3
9.4%
r 3
9.4%
t 2
 
6.2%
y 2
 
6.2%
e 2
 
6.2%
u 1
 
3.1%
Other values (4) 4
12.5%
Uppercase Letter
ValueCountFrequency (%)
J 2
28.6%
H 1
14.3%
T 1
14.3%
K 1
14.3%
M 1
14.3%
S 1
14.3%
Decimal Number
ValueCountFrequency (%)
1 2
25.0%
2 2
25.0%
9 1
12.5%
3 1
12.5%
8 1
12.5%
6 1
12.5%
Other Punctuation
ValueCountFrequency (%)
? 16
64.0%
. 3
 
12.0%
& 3
 
12.0%
# 2
 
8.0%
: 1
 
4.0%
Space Separator
ValueCountFrequency (%)
60
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1390
91.1%
Common 97
 
6.4%
Latin 39
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
헤 111
 
8.0%
어 106
 
7.6%
미 66
 
4.7%
용 44
 
3.2%
실 44
 
3.2%
리 36
 
2.6%
샵 30
 
2.2%
나 27
 
1.9%
스 24
 
1.7%
이 24
 
1.7%
Other values (267) 878
63.2%
Latin
ValueCountFrequency (%)
h 5
12.8%
i 4
 
10.3%
o 3
 
7.7%
s 3
 
7.7%
a 3
 
7.7%
r 3
 
7.7%
J 2
 
5.1%
t 2
 
5.1%
y 2
 
5.1%
e 2
 
5.1%
Other values (10) 10
25.6%
Common
ValueCountFrequency (%)
60
61.9%
? 16
 
16.5%
. 3
 
3.1%
& 3
 
3.1%
1 2
 
2.1%
2 2
 
2.1%
# 2
 
2.1%
( 2
 
2.1%
) 2
 
2.1%
9 1
 
1.0%
Other values (4) 4
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1390
91.1%
ASCII 136
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
헤 111
 
8.0%
어 106
 
7.6%
미 66
 
4.7%
용 44
 
3.2%
실 44
 
3.2%
리 36
 
2.6%
샵 30
 
2.2%
나 27
 
1.9%
스 24
 
1.7%
이 24
 
1.7%
Other values (267) 878
63.2%
ASCII
ValueCountFrequency (%)
60
44.1%
? 16
 
11.8%
h 5
 
3.7%
i 4
 
2.9%
o 3
 
2.2%
. 3
 
2.2%
s 3
 
2.2%
& 3
 
2.2%
a 3
 
2.2%
r 3
 
2.2%
Other values (24) 33
24.3%

업소소재지(도로명)
Text

Distinct298
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T07:57:27.823729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length39
Mean length23.347134
Min length18

Characters and Unicode

Total characters7331
Distinct characters123
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

Unique282 ?
Unique (%)89.8%

Sample

1st row경상남도 밀양시 미리벌중앙로1길 21 (삼문동)
2nd row경상남도 밀양시 남천강변로3길 21, 2층 (내이동)
3rd row경상남도 밀양시 시청로1길 21 (내이동)
4th row경상남도 밀양시 백민로 16 (내이동)
5th row경상남도 밀양시 하남읍 시동중앙길 7-1
ValueCountFrequency (%)
경상남도 314
19.7%
밀양시 314
19.7%
내이동 90
 
5.6%
삼문동 64
 
4.0%
내일동 44
 
2.8%
중앙로 44
 
2.8%
가곡동 23
 
1.4%
하남읍 23
 
1.4%
석정로 22
 
1.4%
밀양대로 13
 
0.8%
Other values (332) 645
40.4%
2023-12-11T07:57:28.150430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1283
17.5%
상 369
 
5.0%
남 362
 
4.9%
시 346
 
4.7%
밀 331
 
4.5%
양 327
 
4.5%
도 316
 
4.3%
경 314
 
4.3%
동 266
 
3.6%
( 256
 
3.5%
Other values (113) 3161
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4438
60.5%
Space Separator 1283
 
17.5%
Decimal Number 959
 
13.1%
Open Punctuation 256
 
3.5%
Close Punctuation 256
 
3.5%
Dash Punctuation 105
 
1.4%
Other Punctuation 34
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 369
 
8.3%
남 362
 
8.2%
시 346
 
7.8%
밀 331
 
7.5%
양 327
 
7.4%
도 316
 
7.1%
경 314
 
7.1%
동 266
 
6.0%
로 215
 
4.8%
내 160
 
3.6%
Other values (98) 1432
32.3%
Decimal Number
ValueCountFrequency (%)
1 231
24.1%
2 157
16.4%
3 154
16.1%
4 96
10.0%
7 61
 
6.4%
5 54
 
5.6%
6 53
 
5.5%
9 52
 
5.4%
8 51
 
5.3%
0 50
 
5.2%
Space Separator
ValueCountFrequency (%)
1283
100.0%
Open Punctuation
ValueCountFrequency (%)
( 256
100.0%
Close Punctuation
ValueCountFrequency (%)
) 256
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 105
100.0%
Other Punctuation
ValueCountFrequency (%)
, 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4438
60.5%
Common 2893
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 369
 
8.3%
남 362
 
8.2%
시 346
 
7.8%
밀 331
 
7.5%
양 327
 
7.4%
도 316
 
7.1%
경 314
 
7.1%
동 266
 
6.0%
로 215
 
4.8%
내 160
 
3.6%
Other values (98) 1432
32.3%
Common
ValueCountFrequency (%)
1283
44.3%
( 256
 
8.8%
) 256
 
8.8%
1 231
 
8.0%
2 157
 
5.4%
3 154
 
5.3%
- 105
 
3.6%
4 96
 
3.3%
7 61
 
2.1%
5 54
 
1.9%
Other values (5) 240
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4438
60.5%
ASCII 2893
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1283
44.3%
( 256
 
8.8%
) 256
 
8.8%
1 231
 
8.0%
2 157
 
5.4%
3 154
 
5.3%
- 105
 
3.6%
4 96
 
3.3%
7 61
 
2.1%
5 54
 
1.9%
Other values (5) 240
 
8.3%
Hangul
ValueCountFrequency (%)
상 369
 
8.3%
남 362
 
8.2%
시 346
 
7.8%
밀 331
 
7.5%
양 327
 
7.4%
도 316
 
7.1%
경 314
 
7.1%
동 266
 
6.0%
로 215
 
4.8%
내 160
 
3.6%
Other values (98) 1432
32.3%

소재지전화
Text

MISSING 

Distinct229
Distinct (%)98.7%
Missing82
Missing (%)26.1%
Memory size2.6 KiB
2023-12-11T07:57:28.384158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.030172
Min length12

Characters and Unicode

Total characters2791
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

Unique226 ?
Unique (%)97.4%

Sample

1st row055-355-4989
2nd row055-356-8078
3rd row055-351-0028
4th row055-351-0092
5th row055-351-0517
ValueCountFrequency (%)
055-354-2667 2
 
0.9%
055-391-6107 2
 
0.9%
055-356-8078 2
 
0.9%
055-391-1618 1
 
0.4%
055-391-0875 1
 
0.4%
055-353-5299 1
 
0.4%
055-355-2719 1
 
0.4%
055-355-2877 1
 
0.4%
055-353-8707 1
 
0.4%
055-353-9541 1
 
0.4%
Other values (219) 219
94.4%
2023-12-11T07:57:28.776802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 806
28.9%
- 464
16.6%
3 356
12.8%
0 353
12.6%
1 127
 
4.6%
2 118
 
4.2%
4 116
 
4.2%
8 116
 
4.2%
6 115
 
4.1%
7 111
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2327
83.4%
Dash Punctuation 464
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 806
34.6%
3 356
15.3%
0 353
15.2%
1 127
 
5.5%
2 118
 
5.1%
4 116
 
5.0%
8 116
 
5.0%
6 115
 
4.9%
7 111
 
4.8%
9 109
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 806
28.9%
- 464
16.6%
3 356
12.8%
0 353
12.6%
1 127
 
4.6%
2 118
 
4.2%
4 116
 
4.2%
8 116
 
4.2%
6 115
 
4.1%
7 111
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 806
28.9%
- 464
16.6%
3 356
12.8%
0 353
12.6%
1 127
 
4.6%
2 118
 
4.2%
4 116
 
4.2%
8 116
 
4.2%
6 115
 
4.1%
7 111
 
4.0%

Missing values

2023-12-11T07:57:26.714168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:57:26.786593image/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

업종명업소명업소소재지(도로명)소재지전화
0미용업(손톱ㆍ발톱)마메종네일경상남도 밀양시 미리벌중앙로1길 21 (삼문동)<NA>
1미용업(손톱ㆍ발톱)네일바다경상남도 밀양시 남천강변로3길 21, 2층 (내이동)<NA>
2미용업(손톱ㆍ발톱)현주네일아트경상남도 밀양시 시청로1길 21 (내이동)<NA>
3미용업(손톱ㆍ발톱)오늘네일아트경상남도 밀양시 백민로 16 (내이동)<NA>
4미용업(손톱ㆍ발톱)네일박스경상남도 밀양시 하남읍 시동중앙길 7-1<NA>
5미용업(손톱ㆍ발톱)The 예쁜손경상남도 밀양시 석정로 41 (내일동)<NA>
6미용업(손톱ㆍ발톱)비치나네일경상남도 밀양시 내일상가3길 8 (내일동)<NA>
7미용업(손톱ㆍ발톱)네일아이경상남도 밀양시 삼랑진읍 만어로 4<NA>
8미용업(손톱ㆍ발톱)포인트뷰티경상남도 밀양시 백민로8길 20, 2층 (내이동)055-355-4989
9미용업(손톱ㆍ발톱)미시안 네일경상남도 밀양시 점필재로 45, 1층 (내이동)055-356-8078
업종명업소명업소소재지(도로명)소재지전화
304미용업(피부)피부미인경상남도 밀양시 북성로 71 (내이동)055-356-6258
305미용업(피부)예윤미 피부샵경상남도 밀양시 백민로2길 16 (내이동,(2층))055-356-6644
306미용업(피부)페이스라인경상남도 밀양시 미리벌중앙로3길 24-10, 2층 (삼문동)055-356-8678
307미용업(피부)서미경 웰빙 케어경상남도 밀양시 하남읍 수산로 33055-391-0875
308미용업(피부)아이비 스킨케어경상남도 밀양시 하남읍 시동중앙길 9-6055-391-1618
309미용업(피부)아르떼 피부관리실경상남도 밀양시 노상하4길 39 (내이동)070-7325-5840
310미용업(피부)휴앤미에스테틱경상남도 밀양시 미리벌중앙로3길 22 (삼문동)070-7383-2211
311미용업(피부)나드리에스테틱경상남도 밀양시 진장3길 16 (삼문동,성원플러스 3차 나동)070-7674-2347
312미용업(피부), 미용업(손톱ㆍ발톱)달콤한 네일경상남도 밀양시 북성로 72 (내이동)<NA>
313미용업(화장ㆍ분장)공주야경상남도 밀양시 해천안길 7-1 (내이동)<NA>