Overview

Dataset statistics

Number of variables5
Number of observations1265
Missing cells488
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.5 KiB
Average record size in memory40.1 B

Variable types

Categorical1
Text4

Dataset

Description부산광역시 금정구 관내 공중위생업소(미용업,이용업,세탁업,숙박업,목욕장업,건물위생관리업) 현황 정보입니다. 참고 바랍니다.
Author부산광역시 금정구
URLhttps://www.data.go.kr/data/15006972/fileData.do

Alerts

소재지전화 has 488 (38.6%) missing valuesMissing

Reproduction

Analysis started2024-03-14 23:36:29.401944
Analysis finished2024-03-14 23:36:30.716221
Duration1.31 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct21
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
일반미용업
335 
미용업
234 
세탁업
129 
이용업
112 
피부미용업
107 
Other values (16)
348 

Length

Max length23
Median length16
Mean length5.1296443
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
일반미용업 335
26.5%
미용업 234
18.5%
세탁업 129
 
10.2%
이용업 112
 
8.9%
피부미용업 107
 
8.5%
네일미용업 69
 
5.5%
숙박업(일반) 62
 
4.9%
목욕장업 46
 
3.6%
건물위생관리업 44
 
3.5%
종합미용업 22
 
1.7%
Other values (11) 105
 
8.3%

Length

2024-03-15T08:36:30.975920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 361
25.3%
미용업 305
21.4%
피부미용업 146
10.2%
세탁업 129
 
9.0%
네일미용업 120
 
8.4%
이용업 112
 
7.8%
화장ㆍ분장 71
 
5.0%
숙박업(일반 62
 
4.3%
목욕장업 46
 
3.2%
건물위생관리업 44
 
3.1%
Other values (2) 32
 
2.2%

업소명
Text

Distinct1213
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2024-03-15T08:36:32.027381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length5.6221344
Min length1

Characters and Unicode

Total characters7112
Distinct characters575
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

Unique1176 ?
Unique (%)93.0%

Sample

1st row삼성여인숙
2nd row마산여인숙
3rd row천우장
4th row모텔 델루나
5th row향림장여관
ValueCountFrequency (%)
헤어 19
 
1.2%
hair 9
 
0.6%
부산대점 9
 
0.6%
에스테틱 9
 
0.6%
nail 7
 
0.5%
네일 7
 
0.5%
태후사랑 6
 
0.4%
salon 5
 
0.3%
컷트클럽 5
 
0.3%
주식회사 5
 
0.3%
Other values (1340) 1443
94.7%
2024-03-15T08:36:33.544570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 340
 
4.8%
헤 336
 
4.7%
266
 
3.7%
이 182
 
2.6%
스 131
 
1.8%
일 121
 
1.7%
) 118
 
1.7%
( 117
 
1.6%
미 105
 
1.5%
네 103
 
1.4%
Other values (565) 5293
74.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5774
81.2%
Lowercase Letter 430
 
6.0%
Uppercase Letter 313
 
4.4%
Space Separator 266
 
3.7%
Close Punctuation 118
 
1.7%
Open Punctuation 117
 
1.6%
Other Punctuation 49
 
0.7%
Decimal Number 42
 
0.6%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 340
 
5.9%
헤 336
 
5.8%
이 182
 
3.2%
스 131
 
2.3%
일 121
 
2.1%
미 105
 
1.8%
네 103
 
1.8%
리 102
 
1.8%
아 81
 
1.4%
티 65
 
1.1%
Other values (496) 4208
72.9%
Lowercase Letter
ValueCountFrequency (%)
a 53
12.3%
i 50
11.6%
o 47
10.9%
e 42
9.8%
n 38
8.8%
r 28
 
6.5%
l 26
 
6.0%
h 21
 
4.9%
s 20
 
4.7%
y 17
 
4.0%
Other values (14) 88
20.5%
Uppercase Letter
ValueCountFrequency (%)
A 36
 
11.5%
N 30
 
9.6%
O 25
 
8.0%
H 22
 
7.0%
M 21
 
6.7%
L 20
 
6.4%
E 19
 
6.1%
I 18
 
5.8%
B 16
 
5.1%
R 16
 
5.1%
Other values (14) 90
28.8%
Decimal Number
ValueCountFrequency (%)
2 11
26.2%
1 10
23.8%
5 6
14.3%
3 5
11.9%
4 3
 
7.1%
6 2
 
4.8%
0 2
 
4.8%
8 2
 
4.8%
9 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 14
28.6%
& 13
26.5%
, 11
22.4%
' 3
 
6.1%
: 3
 
6.1%
# 3
 
6.1%
; 1
 
2.0%
＃ 1
 
2.0%
Space Separator
ValueCountFrequency (%)
266
100.0%
Close Punctuation
ValueCountFrequency (%)
) 118
100.0%
Open Punctuation
ValueCountFrequency (%)
( 117
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5772
81.2%
Latin 743
 
10.4%
Common 595
 
8.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 340
 
5.9%
헤 336
 
5.8%
이 182
 
3.2%
스 131
 
2.3%
일 121
 
2.1%
미 105
 
1.8%
네 103
 
1.8%
리 102
 
1.8%
아 81
 
1.4%
티 65
 
1.1%
Other values (494) 4206
72.9%
Latin
ValueCountFrequency (%)
a 53
 
7.1%
i 50
 
6.7%
o 47
 
6.3%
e 42
 
5.7%
n 38
 
5.1%
A 36
 
4.8%
N 30
 
4.0%
r 28
 
3.8%
l 26
 
3.5%
O 25
 
3.4%
Other values (38) 368
49.5%
Common
ValueCountFrequency (%)
266
44.7%
) 118
19.8%
( 117
19.7%
. 14
 
2.4%
& 13
 
2.2%
2 11
 
1.8%
, 11
 
1.8%
1 10
 
1.7%
5 6
 
1.0%
3 5
 
0.8%
Other values (11) 24
 
4.0%
Han
ValueCountFrequency (%)
水 1
50.0%
休 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5772
81.2%
ASCII 1337
 
18.8%
CJK 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 340
 
5.9%
헤 336
 
5.8%
이 182
 
3.2%
스 131
 
2.3%
일 121
 
2.1%
미 105
 
1.8%
네 103
 
1.8%
리 102
 
1.8%
아 81
 
1.4%
티 65
 
1.1%
Other values (494) 4206
72.9%
ASCII
ValueCountFrequency (%)
266
19.9%
) 118
 
8.8%
( 117
 
8.8%
a 53
 
4.0%
i 50
 
3.7%
o 47
 
3.5%
e 42
 
3.1%
n 38
 
2.8%
A 36
 
2.7%
N 30
 
2.2%
Other values (58) 540
40.4%
CJK
ValueCountFrequency (%)
水 1
50.0%
休 1
50.0%
None
ValueCountFrequency (%)
＃ 1
100.0%

영업소 주소(지번)
Text

Distinct1108
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2024-03-15T08:36:34.368754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length38
Mean length21.562846
Min length17

Characters and Unicode

Total characters27277
Distinct characters211
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

Unique992 ?
Unique (%)78.4%

Sample

1st row부산광역시 금정구 서동 340-21
2nd row부산광역시 금정구 서동 302-523
3rd row부산광역시 금정구 금사동 68-32
4th row부산광역시 금정구 장전동 617-12
5th row부산광역시 금정구 구서동 463-5
ValueCountFrequency (%)
부산광역시 1265
23.7%
금정구 1265
23.7%
장전동 317
 
5.9%
부곡동 260
 
4.9%
구서동 236
 
4.4%
서동 229
 
4.3%
남산동 168
 
3.1%
금사동 26
 
0.5%
청룡동 18
 
0.3%
1층 16
 
0.3%
Other values (1232) 1540
28.8%
2024-03-15T08:36:35.880603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5316
19.5%
부 1538
 
5.6%
구 1515
 
5.6%
산 1447
 
5.3%
금 1304
 
4.8%
동 1301
 
4.8%
시 1278
 
4.7%
정 1278
 
4.7%
역 1271
 
4.7%
광 1266
 
4.6%
Other values (201) 9763
35.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14824
54.3%
Decimal Number 5902
 
21.6%
Space Separator 5316
 
19.5%
Dash Punctuation 1218
 
4.5%
Uppercase Letter 6
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 1538
10.4%
구 1515
10.2%
산 1447
9.8%
금 1304
8.8%
동 1301
8.8%
시 1278
8.6%
정 1278
8.6%
역 1271
8.6%
광 1266
8.5%
서 475
 
3.2%
Other values (182) 2151
14.5%
Decimal Number
ValueCountFrequency (%)
1 1135
19.2%
2 988
16.7%
3 741
12.6%
4 513
8.7%
6 494
8.4%
5 439
 
7.4%
0 433
 
7.3%
9 413
 
7.0%
7 398
 
6.7%
8 348
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
F 2
33.3%
A 2
33.3%
K 1
16.7%
W 1
16.7%
Space Separator
ValueCountFrequency (%)
5316
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1218
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14824
54.3%
Common 12447
45.6%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 1538
10.4%
구 1515
10.2%
산 1447
9.8%
금 1304
8.8%
동 1301
8.8%
시 1278
8.6%
정 1278
8.6%
역 1271
8.6%
광 1266
8.5%
서 475
 
3.2%
Other values (182) 2151
14.5%
Common
ValueCountFrequency (%)
5316
42.7%
- 1218
 
9.8%
1 1135
 
9.1%
2 988
 
7.9%
3 741
 
6.0%
4 513
 
4.1%
6 494
 
4.0%
5 439
 
3.5%
0 433
 
3.5%
9 413
 
3.3%
Other values (5) 757
 
6.1%
Latin
ValueCountFrequency (%)
F 2
33.3%
A 2
33.3%
K 1
16.7%
W 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14824
54.3%
ASCII 12453
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5316
42.7%
- 1218
 
9.8%
1 1135
 
9.1%
2 988
 
7.9%
3 741
 
6.0%
4 513
 
4.1%
6 494
 
4.0%
5 439
 
3.5%
0 433
 
3.5%
9 413
 
3.3%
Other values (9) 763
 
6.1%
Hangul
ValueCountFrequency (%)
부 1538
10.4%
구 1515
10.2%
산 1447
9.8%
금 1304
8.8%
동 1301
8.8%
시 1278
8.6%
정 1278
8.6%
역 1271
8.6%
광 1266
8.5%
서 475
 
3.2%
Other values (182) 2151
14.5%

우편번호(지번)
Text

Distinct68
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
2024-03-15T08:36:36.906246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters8855
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

Unique6 ?
Unique (%)0.5%

Sample

1st row609-830
2nd row609-832
3rd row609-809
4th row609-842
5th row609-806
ValueCountFrequency (%)
609-839 132
 
10.4%
609-837 53
 
4.2%
609-814 46
 
3.6%
609-832 45
 
3.6%
609-842 45
 
3.6%
609-824 43
 
3.4%
609-813 41
 
3.2%
609-848 40
 
3.2%
609-805 38
 
3.0%
609-811 38
 
3.0%
Other values (58) 744
58.8%
2024-03-15T08:36:38.271690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1565
17.7%
9 1467
16.6%
6 1382
15.6%
8 1339
15.1%
- 1265
14.3%
3 495
 
5.6%
1 390
 
4.4%
2 342
 
3.9%
4 321
 
3.6%
5 160
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7590
85.7%
Dash Punctuation 1265
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1565
20.6%
9 1467
19.3%
6 1382
18.2%
8 1339
17.6%
3 495
 
6.5%
1 390
 
5.1%
2 342
 
4.5%
4 321
 
4.2%
5 160
 
2.1%
7 129
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 1265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8855
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1565
17.7%
9 1467
16.6%
6 1382
15.6%
8 1339
15.1%
- 1265
14.3%
3 495
 
5.6%
1 390
 
4.4%
2 342
 
3.9%
4 321
 
3.6%
5 160
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1565
17.7%
9 1467
16.6%
6 1382
15.6%
8 1339
15.1%
- 1265
14.3%
3 495
 
5.6%
1 390
 
4.4%
2 342
 
3.9%
4 321
 
3.6%
5 160
 
1.8%

소재지전화
Text

MISSING 

Distinct770
Distinct (%)99.1%
Missing488
Missing (%)38.6%
Memory size10.0 KiB
2024-03-15T08:36:39.544592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.985843
Min length12

Characters and Unicode

Total characters10867
Distinct characters21
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique763 ?
Unique (%)98.2%

Sample

1st row 051- 523-6331
2nd row051 -529 -8230
3rd row 051- 523-3826
4th row 051- 512-4718
5th row 051- 514-7235
ValueCountFrequency (%)
051 748
40.2%
515 36
 
1.9%
583 26
 
1.4%
582 26
 
1.4%
516 22
 
1.2%
512 22
 
1.2%
513 20
 
1.1%
070 17
 
0.9%
518 17
 
0.9%
514 17
 
0.9%
Other values (829) 909
48.9%
2024-03-15T08:36:41.035916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1925
17.7%
- 1554
14.3%
1528
14.1%
1 1509
13.9%
0 1165
10.7%
2 687
 
6.3%
8 585
 
5.4%
3 446
 
4.1%
7 416
 
3.8%
4 360
 
3.3%
Other values (11) 692
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7775
71.5%
Dash Punctuation 1554
 
14.3%
Space Separator 1528
 
14.1%
Uppercase Letter 7
 
0.1%
Math Symbol 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1925
24.8%
1 1509
19.4%
0 1165
15.0%
2 687
 
8.8%
8 585
 
7.5%
3 446
 
5.7%
7 416
 
5.4%
4 360
 
4.6%
6 354
 
4.6%
9 328
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
E 2
28.6%
R 1
14.3%
P 1
14.3%
L 1
14.3%
A 1
14.3%
C 1
14.3%
Math Symbol
ValueCountFrequency (%)
< 1
50.0%
> 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1554
100.0%
Space Separator
ValueCountFrequency (%)
1528
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10860
99.9%
Latin 7
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1925
17.7%
- 1554
14.3%
1528
14.1%
1 1509
13.9%
0 1165
10.7%
2 687
 
6.3%
8 585
 
5.4%
3 446
 
4.1%
7 416
 
3.8%
4 360
 
3.3%
Other values (5) 685
 
6.3%
Latin
ValueCountFrequency (%)
E 2
28.6%
R 1
14.3%
P 1
14.3%
L 1
14.3%
A 1
14.3%
C 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1925
17.7%
- 1554
14.3%
1528
14.1%
1 1509
13.9%
0 1165
10.7%
2 687
 
6.3%
8 585
 
5.4%
3 446
 
4.1%
7 416
 
3.8%
4 360
 
3.3%
Other values (11) 692
 
6.4%

Correlations

2024-03-15T08:36:41.219322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(지번)
업종명1.0000.499
우편번호(지번)0.4991.000

Missing values

2024-03-15T08:36:30.265891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:36:30.588938image/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(지번)우편번호(지번)소재지전화
0숙박업(일반)삼성여인숙부산광역시 금정구 서동 340-21609-830051- 523-6331
1숙박업(일반)마산여인숙부산광역시 금정구 서동 302-523609-832051 -529 -8230
2숙박업(일반)천우장부산광역시 금정구 금사동 68-32609-809051- 523-3826
3숙박업(일반)모텔 델루나부산광역시 금정구 장전동 617-12609-842051- 512-4718
4숙박업(일반)향림장여관부산광역시 금정구 구서동 463-5609-806051- 514-7235
5숙박업(일반)청룡여관부산광역시 금정구 청룡동 31-1609-843051- 508-6316
6숙박업(일반)유성장모텔부산광역시 금정구 청룡동 44-3609-843051- 508-5890
7숙박업(일반)에스모텔부산광역시 금정구 청룡동 39-6609-843051- 508-1172
8숙박업(일반)송원장여관부산광역시 금정구 장전동 594-8609-841051- 512-4646
9숙박업(일반)백수장여관부산광역시 금정구 구서동 470-1609-806051- 512-9144
업종명업소명영업소 주소(지번)우편번호(지번)소재지전화
1255피부미용업, 네일미용업, 화장ㆍ분장 미용업인생네일부산광역시 금정구 장전동 203-6609-837051 -583 -2977
1256피부미용업, 네일미용업, 화장ㆍ분장 미용업살롱드현지부산광역시 금정구 장전동 643-83609-839<NA>
1257피부미용업, 네일미용업, 화장ㆍ분장 미용업엠에스뷰티부산광역시 금정구 구서동 252-17609-847<NA>
1258피부미용업, 네일미용업, 화장ㆍ분장 미용업다정다감부산광역시 금정구 장전동 385-53 에뜨와르609-837<NA>
1259피부미용업, 네일미용업, 화장ㆍ분장 미용업아소시에 네일(Associe nail)부산광역시 금정구 부곡동 386-1 신동아아파트609-824<NA>
1260피부미용업, 네일미용업, 화장ㆍ분장 미용업뷰티싸롱미미부산광역시 금정구 장전동 412-10609-838051 -518 -8815
1261피부미용업, 네일미용업, 화장ㆍ분장 미용업폭스뷰티부산광역시 금정구 구서동 203-5 금정산온천 레포츠609-804<NA>
1262피부미용업, 네일미용업, 화장ㆍ분장 미용업까모르살롱 네일왁싱부산광역시 금정구 장전동 414-2609-839<NA>
1263피부미용업, 네일미용업, 화장ㆍ분장 미용업마셀왁싱앤뷰티부산광역시 금정구 장전동 293-175609-839<NA>
1264피부미용업, 네일미용업, 화장ㆍ분장 미용업레이뷰티(Lei beauty)부산광역시 금정구 서동 199-2609-849051 -522 -5655