Overview

Dataset statistics

Number of variables4
Number of observations719
Missing cells350
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.6 KiB
Average record size in memory32.2 B

Variable types

Categorical1
Text3

Dataset

Description해당 자료는 부산광역시사상구에 위치한 이미용업 현황(업종명,업소명,소재지,전화번호)에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3078907/fileData.do

Alerts

소재지전화 has 350 (48.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 18:27:38.362390
Analysis finished2023-12-12 18:27:38.852672
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct15
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
일반미용업
396 
이용업
112 
네일미용업
60 
피부미용업
52 
종합미용업
 
35
Other values (10)
64 

Length

Max length23
Median length5
Mean length5.3755216
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 396
55.1%
이용업 112
 
15.6%
네일미용업 60
 
8.3%
피부미용업 52
 
7.2%
종합미용업 35
 
4.9%
미용업 14
 
1.9%
네일미용업, 화장ㆍ분장 미용업 11
 
1.5%
피부미용업, 네일미용업 7
 
1.0%
일반미용업, 네일미용업 6
 
0.8%
화장ㆍ분장 미용업 6
 
0.8%
Other values (5) 20
 
2.8%

Length

2023-12-13T03:27:38.924970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 412
50.9%
이용업 112
 
13.8%
네일미용업 94
 
11.6%
피부미용업 70
 
8.7%
미용업 50
 
6.2%
화장ㆍ분장 36
 
4.4%
종합미용업 35
 
4.3%

업소명
Text

Distinct691
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2023-12-13T03:27:39.213403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length5.4019471
Min length1

Characters and Unicode

Total characters3884
Distinct characters463
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique666 ?
Unique (%)92.6%

Sample

1st row김석남성헤어
2nd row새한
3rd row태명
4th row대원
5th row미성
ValueCountFrequency (%)
헤어 15
 
1.7%
네일 11
 
1.3%
사상점 7
 
0.8%
이용원 5
 
0.6%
hair 5
 
0.6%
더 5
 
0.6%
미용실 5
 
0.6%
퀸즈헤나 3
 
0.3%
엘샤론 3
 
0.3%
구내이용원 3
 
0.3%
Other values (769) 815
92.9%
2023-12-13T03:27:39.672523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 271
 
7.0%
헤 270
 
7.0%
158
 
4.1%
이 106
 
2.7%
일 88
 
2.3%
네 85
 
2.2%
미 83
 
2.1%
리 80
 
2.1%
용 66
 
1.7%
스 57
 
1.5%
Other values (453) 2620
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3303
85.0%
Uppercase Letter 162
 
4.2%
Space Separator 158
 
4.1%
Lowercase Letter 119
 
3.1%
Close Punctuation 44
 
1.1%
Open Punctuation 43
 
1.1%
Other Punctuation 31
 
0.8%
Decimal Number 21
 
0.5%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 271
 
8.2%
헤 270
 
8.2%
이 106
 
3.2%
일 88
 
2.7%
네 85
 
2.6%
미 83
 
2.5%
리 80
 
2.4%
용 66
 
2.0%
스 57
 
1.7%
원 49
 
1.5%
Other values (391) 2148
65.0%
Uppercase Letter
ValueCountFrequency (%)
T 13
 
8.0%
N 13
 
8.0%
J 13
 
8.0%
H 12
 
7.4%
I 12
 
7.4%
A 12
 
7.4%
S 11
 
6.8%
E 9
 
5.6%
R 9
 
5.6%
M 8
 
4.9%
Other values (13) 50
30.9%
Lowercase Letter
ValueCountFrequency (%)
a 23
19.3%
i 13
10.9%
r 12
10.1%
h 9
 
7.6%
n 7
 
5.9%
e 7
 
5.9%
o 7
 
5.9%
u 6
 
5.0%
t 5
 
4.2%
l 4
 
3.4%
Other values (12) 26
21.8%
Other Punctuation
ValueCountFrequency (%)
& 13
41.9%
. 5
 
16.1%
, 5
 
16.1%
# 5
 
16.1%
: 1
 
3.2%
' 1
 
3.2%
＆ 1
 
3.2%
Decimal Number
ValueCountFrequency (%)
1 5
23.8%
2 5
23.8%
0 5
23.8%
3 3
14.3%
6 2
 
9.5%
4 1
 
4.8%
Space Separator
ValueCountFrequency (%)
158
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3300
85.0%
Common 300
 
7.7%
Latin 281
 
7.2%
Han 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 271
 
8.2%
헤 270
 
8.2%
이 106
 
3.2%
일 88
 
2.7%
네 85
 
2.6%
미 83
 
2.5%
리 80
 
2.4%
용 66
 
2.0%
스 57
 
1.7%
원 49
 
1.5%
Other values (389) 2145
65.0%
Latin
ValueCountFrequency (%)
a 23
 
8.2%
i 13
 
4.6%
T 13
 
4.6%
N 13
 
4.6%
J 13
 
4.6%
H 12
 
4.3%
r 12
 
4.3%
I 12
 
4.3%
A 12
 
4.3%
S 11
 
3.9%
Other values (35) 147
52.3%
Common
ValueCountFrequency (%)
158
52.7%
) 44
 
14.7%
( 43
 
14.3%
& 13
 
4.3%
1 5
 
1.7%
. 5
 
1.7%
2 5
 
1.7%
0 5
 
1.7%
, 5
 
1.7%
# 5
 
1.7%
Other values (7) 12
 
4.0%
Han
ValueCountFrequency (%)
美 2
66.7%
場 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3300
85.0%
ASCII 580
 
14.9%
CJK 3
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 271
 
8.2%
헤 270
 
8.2%
이 106
 
3.2%
일 88
 
2.7%
네 85
 
2.6%
미 83
 
2.5%
리 80
 
2.4%
용 66
 
2.0%
스 57
 
1.7%
원 49
 
1.5%
Other values (389) 2145
65.0%
ASCII
ValueCountFrequency (%)
158
27.2%
) 44
 
7.6%
( 43
 
7.4%
a 23
 
4.0%
& 13
 
2.2%
i 13
 
2.2%
T 13
 
2.2%
N 13
 
2.2%
J 13
 
2.2%
H 12
 
2.1%
Other values (51) 235
40.5%
CJK
ValueCountFrequency (%)
美 2
66.7%
場 1
33.3%
None
ValueCountFrequency (%)
＆ 1
100.0%

영업소 주소(도로명)
Text

Distinct703
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2023-12-13T03:27:39.937462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length50
Mean length31.884562
Min length21

Characters and Unicode

Total characters22925
Distinct characters221
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

Unique687 ?
Unique (%)95.5%

Sample

1st row부산광역시 사상구 백양대로 372-16, 반도보라메머드타운 상가동 205호 (주례동)
2nd row부산광역시 사상구 사상로277번길 5 (덕포동)
3rd row부산광역시 사상구 진사로 12 (주례동)
4th row부산광역시 사상구 주례로28번길 14 (주례동)
5th row부산광역시 사상구 새벽로 169 (감전동)
ValueCountFrequency (%)
부산광역시 719
 
16.0%
사상구 719
 
16.0%
1층 203
 
4.5%
주례동 167
 
3.7%
모라동 128
 
2.9%
괘법동 122
 
2.7%
2층 93
 
2.1%
백양대로 89
 
2.0%
엄궁동 83
 
1.8%
덕포동 82
 
1.8%
Other values (739) 2084
46.4%
2023-12-13T03:27:40.375700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3770
 
16.4%
상 1012
 
4.4%
동 968
 
4.2%
1 881
 
3.8%
사 870
 
3.8%
광 756
 
3.3%
산 748
 
3.3%
시 746
 
3.3%
부 729
 
3.2%
구 728
 
3.2%
Other values (211) 11717
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13188
57.5%
Decimal Number 3806
 
16.6%
Space Separator 3770
 
16.4%
Open Punctuation 722
 
3.1%
Close Punctuation 722
 
3.1%
Other Punctuation 585
 
2.6%
Dash Punctuation 105
 
0.5%
Uppercase Letter 25
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1012
 
7.7%
동 968
 
7.3%
사 870
 
6.6%
광 756
 
5.7%
산 748
 
5.7%
시 746
 
5.7%
부 729
 
5.5%
구 728
 
5.5%
역 722
 
5.5%
로 721
 
5.5%
Other values (183) 5188
39.3%
Uppercase Letter
ValueCountFrequency (%)
B 10
40.0%
A 5
20.0%
E 2
 
8.0%
C 1
 
4.0%
G 1
 
4.0%
K 1
 
4.0%
O 1
 
4.0%
L 1
 
4.0%
H 1
 
4.0%
T 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
1 881
23.1%
2 649
17.1%
0 422
11.1%
3 377
9.9%
4 326
 
8.6%
6 258
 
6.8%
9 248
 
6.5%
7 226
 
5.9%
5 210
 
5.5%
8 209
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 582
99.5%
@ 3
 
0.5%
Space Separator
ValueCountFrequency (%)
3770
100.0%
Open Punctuation
ValueCountFrequency (%)
( 722
100.0%
Close Punctuation
ValueCountFrequency (%)
) 722
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 105
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13188
57.5%
Common 9712
42.4%
Latin 25
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1012
 
7.7%
동 968
 
7.3%
사 870
 
6.6%
광 756
 
5.7%
산 748
 
5.7%
시 746
 
5.7%
부 729
 
5.5%
구 728
 
5.5%
역 722
 
5.5%
로 721
 
5.5%
Other values (183) 5188
39.3%
Common
ValueCountFrequency (%)
3770
38.8%
1 881
 
9.1%
( 722
 
7.4%
) 722
 
7.4%
2 649
 
6.7%
, 582
 
6.0%
0 422
 
4.3%
3 377
 
3.9%
4 326
 
3.4%
6 258
 
2.7%
Other values (7) 1003
 
10.3%
Latin
ValueCountFrequency (%)
B 10
40.0%
A 5
20.0%
E 2
 
8.0%
C 1
 
4.0%
G 1
 
4.0%
K 1
 
4.0%
O 1
 
4.0%
L 1
 
4.0%
H 1
 
4.0%
T 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13188
57.5%
ASCII 9737
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3770
38.7%
1 881
 
9.0%
( 722
 
7.4%
) 722
 
7.4%
2 649
 
6.7%
, 582
 
6.0%
0 422
 
4.3%
3 377
 
3.9%
4 326
 
3.3%
6 258
 
2.6%
Other values (18) 1028
 
10.6%
Hangul
ValueCountFrequency (%)
상 1012
 
7.7%
동 968
 
7.3%
사 870
 
6.6%
광 756
 
5.7%
산 748
 
5.7%
시 746
 
5.7%
부 729
 
5.5%
구 728
 
5.5%
역 722
 
5.5%
로 721
 
5.5%
Other values (183) 5188
39.3%

소재지전화
Text

MISSING 

Distinct367
Distinct (%)99.5%
Missing350
Missing (%)48.7%
Memory size5.7 KiB
2023-12-13T03:27:40.638638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters4428
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

Unique365 ?
Unique (%)98.9%

Sample

1st row051-802-9427
2nd row051-301-4755
3rd row0513-1428-25
4th row051-322-8664
5th row051-304-2979
ValueCountFrequency (%)
051-314-1123 2
 
0.5%
051-304-9479 2
 
0.5%
051-322-7266 1
 
0.3%
051-802-9427 1
 
0.3%
051-322-4775 1
 
0.3%
051-325-1996 1
 
0.3%
051-302-1707 1
 
0.3%
051-324-9916 1
 
0.3%
051-647-5333 1
 
0.3%
051-315-4319 1
 
0.3%
Other values (357) 357
96.7%
2023-12-13T03:27:41.030488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 738
16.7%
1 705
15.9%
0 617
13.9%
3 564
12.7%
5 550
12.4%
2 358
8.1%
8 190
 
4.3%
4 189
 
4.3%
7 179
 
4.0%
6 174
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3690
83.3%
Dash Punctuation 738
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 705
19.1%
0 617
16.7%
3 564
15.3%
5 550
14.9%
2 358
9.7%
8 190
 
5.1%
4 189
 
5.1%
7 179
 
4.9%
6 174
 
4.7%
9 164
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 738
16.7%
1 705
15.9%
0 617
13.9%
3 564
12.7%
5 550
12.4%
2 358
8.1%
8 190
 
4.3%
4 189
 
4.3%
7 179
 
4.0%
6 174
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 738
16.7%
1 705
15.9%
0 617
13.9%
3 564
12.7%
5 550
12.4%
2 358
8.1%
8 190
 
4.3%
4 189
 
4.3%
7 179
 
4.0%
6 174
 
3.9%

Missing values

2023-12-13T03:27:38.739997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:27:38.822747image/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
0이용업김석남성헤어부산광역시 사상구 백양대로 372-16, 반도보라메머드타운 상가동 205호 (주례동)051-802-9427
1이용업새한부산광역시 사상구 사상로277번길 5 (덕포동)051-301-4755
2이용업태명부산광역시 사상구 진사로 12 (주례동)<NA>
3이용업대원부산광역시 사상구 주례로28번길 14 (주례동)0513-1428-25
4이용업미성부산광역시 사상구 새벽로 169 (감전동)051-322-8664
5이용업행복부산광역시 사상구 모라로 61 (모라동)051-304-2979
6이용업명성부산광역시 사상구 주감로 6-1 (감전동)<NA>
7이용업명천부산광역시 사상구 새벽로137번길 40 (감전동)<NA>
8이용업엄궁부산광역시 사상구 낙동대로 761-12 (엄궁동)<NA>
9이용업대동부산광역시 사상구 대동로 112 (학장동)<NA>
업종명업소명영업소 주소(도로명)소재지전화
709일반미용업, 네일미용업, 화장ㆍ분장 미용업어썸헤어 모라점부산광역시 사상구 모라로110번길 47, 3층 301호 (모라동)051-901-9222
710일반미용업, 네일미용업, 화장ㆍ분장 미용업뷰티살롱 이루다부산광역시 사상구 모라로51번길 16, 1층 (모라동, 하동오리불고기)<NA>
711일반미용업, 네일미용업, 화장ㆍ분장 미용업미르뷰티아카데미부산광역시 사상구 냉정로 110, 2층 (주례동)<NA>
712일반미용업, 네일미용업, 화장ㆍ분장 미용업퀸네일부산광역시 사상구 대동로 94, 학장반도보라타운 상가동 110호 (학장동)<NA>
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