Overview

Dataset statistics

Number of variables4
Number of observations935
Missing cells283
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.3 KiB
Average record size in memory32.1 B

Variable types

Categorical1
Text3

Dataset

Description종로구 관내에 소재하는 공중위생업소에 대한 데이터로(피부미용업소, 세탁소, 숙박업소, 목욕업소)등의 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15005999/fileData.do

Alerts

소재지전화 has 283 (30.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 06:09:26.025789
Analysis finished2023-12-12 06:09:26.598406
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct21
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
숙박업(일반)
219 
일반미용업
158 
이용업
89 
미용업
80 
세탁업
76 
Other values (16)
313 

Length

Max length23
Median length19
Mean length5.7187166
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 (%)
숙박업(일반) 219
23.4%
일반미용업 158
16.9%
이용업 89
9.5%
미용업 80
 
8.6%
세탁업 76
 
8.1%
건물위생관리업 64
 
6.8%
피부미용업 62
 
6.6%
종합미용업 45
 
4.8%
네일미용업 42
 
4.5%
목욕장업 26
 
2.8%
Other values (11) 74
 
7.9%

Length

2023-12-12T15:09:26.681313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 219
21.1%
일반미용업 181
17.5%
미용업 122
11.8%
이용업 89
8.6%
피부미용업 79
 
7.6%
네일미용업 77
 
7.4%
세탁업 76
 
7.3%
건물위생관리업 64
 
6.2%
종합미용업 45
 
4.3%
화장ㆍ분장 42
 
4.1%
Other values (2) 43
 
4.1%

업소명
Text

Distinct909
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2023-12-12T15:09:26.967076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length28
Mean length6.5026738
Min length1

Characters and Unicode

Total characters6080
Distinct characters525
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

Unique887 ?
Unique (%)94.9%

Sample

1st row이지스테이
2nd row호스텔 바닐라 1
3rd row영모텔
4th row호텔더디자이너스 종로
5th row대진
ValueCountFrequency (%)
호텔 37
 
2.7%
주식회사 12
 
0.9%
헤어 12
 
0.9%
네일 11
 
0.8%
hair 10
 
0.7%
미용실 10
 
0.7%
인사동 8
 
0.6%
서울 8
 
0.6%
더 7
 
0.5%
호스텔 7
 
0.5%
Other values (1084) 1236
91.0%
2023-12-12T15:09:27.437403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
423
 
7.0%
스 176
 
2.9%
이 168
 
2.8%
어 152
 
2.5%
헤 134
 
2.2%
텔 131
 
2.2%
호 111
 
1.8%
) 110
 
1.8%
( 109
 
1.8%
리 97
 
1.6%
Other values (515) 4469
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4581
75.3%
Space Separator 423
 
7.0%
Uppercase Letter 400
 
6.6%
Lowercase Letter 366
 
6.0%
Close Punctuation 110
 
1.8%
Open Punctuation 109
 
1.8%
Decimal Number 68
 
1.1%
Other Punctuation 18
 
0.3%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 176
 
3.8%
이 168
 
3.7%
어 152
 
3.3%
헤 134
 
2.9%
텔 131
 
2.9%
호 111
 
2.4%
리 97
 
2.1%
미 86
 
1.9%
사 84
 
1.8%
용 63
 
1.4%
Other values (447) 3379
73.8%
Uppercase Letter
ValueCountFrequency (%)
A 41
 
10.2%
H 37
 
9.2%
T 31
 
7.8%
E 28
 
7.0%
S 26
 
6.5%
N 25
 
6.2%
O 25
 
6.2%
L 25
 
6.2%
I 23
 
5.8%
R 15
 
3.8%
Other values (15) 124
31.0%
Lowercase Letter
ValueCountFrequency (%)
e 52
14.2%
a 38
10.4%
o 34
9.3%
i 32
8.7%
r 29
7.9%
s 25
 
6.8%
l 24
 
6.6%
t 22
 
6.0%
h 21
 
5.7%
n 21
 
5.7%
Other values (14) 68
18.6%
Decimal Number
ValueCountFrequency (%)
2 17
25.0%
1 14
20.6%
0 8
11.8%
3 8
11.8%
4 7
10.3%
5 6
 
8.8%
9 4
 
5.9%
8 2
 
2.9%
7 1
 
1.5%
6 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 8
44.4%
, 5
27.8%
& 3
 
16.7%
' 1
 
5.6%
# 1
 
5.6%
Space Separator
ValueCountFrequency (%)
423
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4576
75.3%
Latin 766
 
12.6%
Common 733
 
12.1%
Han 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 176
 
3.8%
이 168
 
3.7%
어 152
 
3.3%
헤 134
 
2.9%
텔 131
 
2.9%
호 111
 
2.4%
리 97
 
2.1%
미 86
 
1.9%
사 84
 
1.8%
용 63
 
1.4%
Other values (442) 3374
73.7%
Latin
ValueCountFrequency (%)
e 52
 
6.8%
A 41
 
5.4%
a 38
 
5.0%
H 37
 
4.8%
o 34
 
4.4%
i 32
 
4.2%
T 31
 
4.0%
r 29
 
3.8%
E 28
 
3.7%
S 26
 
3.4%
Other values (39) 418
54.6%
Common
ValueCountFrequency (%)
423
57.7%
) 110
 
15.0%
( 109
 
14.9%
2 17
 
2.3%
1 14
 
1.9%
. 8
 
1.1%
0 8
 
1.1%
3 8
 
1.1%
4 7
 
1.0%
5 6
 
0.8%
Other values (9) 23
 
3.1%
Han
ValueCountFrequency (%)
妃 1
20.0%
美 1
20.0%
手 1
20.0%
綠 1
20.0%
李 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4576
75.3%
ASCII 1499
 
24.7%
CJK 3
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
423
28.2%
) 110
 
7.3%
( 109
 
7.3%
e 52
 
3.5%
A 41
 
2.7%
a 38
 
2.5%
H 37
 
2.5%
o 34
 
2.3%
i 32
 
2.1%
T 31
 
2.1%
Other values (58) 592
39.5%
Hangul
ValueCountFrequency (%)
스 176
 
3.8%
이 168
 
3.7%
어 152
 
3.3%
헤 134
 
2.9%
텔 131
 
2.9%
호 111
 
2.4%
리 97
 
2.1%
미 86
 
1.9%
사 84
 
1.8%
용 63
 
1.4%
Other values (442) 3374
73.7%
CJK
ValueCountFrequency (%)
妃 1
33.3%
美 1
33.3%
手 1
33.3%
CJK Compat Ideographs
ValueCountFrequency (%)
綠 1
50.0%
李 1
50.0%

영업소 주소(도로명)
Text

Distinct908
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2023-12-12T15:09:27.869081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length50
Mean length30.217112
Min length21

Characters and Unicode

Total characters28253
Distinct characters286
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique883 ?
Unique (%)94.4%

Sample

1st row서울특별시 종로구 창신길 9-8 (창신동)
2nd row서울특별시 종로구 창신길 28-7 (창신동)
3rd row서울특별시 종로구 보문로7길 5-1 (숭인동)
4th row서울특별시 종로구 수표로 89-8 (관수동)
5th row서울특별시 종로구 창신1길 6 (창신동)
ValueCountFrequency (%)
서울특별시 935
 
16.5%
종로구 935
 
16.5%
1층 144
 
2.5%
창신동 105
 
1.9%
숭인동 99
 
1.7%
종로 93
 
1.6%
2층 80
 
1.4%
낙원동 49
 
0.9%
지하1층 41
 
0.7%
3층 39
 
0.7%
Other values (1107) 3143
55.5%
2023-12-12T15:09:28.542610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4730
 
16.7%
로 1869
 
6.6%
종 1287
 
4.6%
1 1125
 
4.0%
구 957
 
3.4%
서 956
 
3.4%
( 944
 
3.3%
) 944
 
3.3%
시 943
 
3.3%
울 942
 
3.3%
Other values (276) 13556
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16304
57.7%
Space Separator 4730
 
16.7%
Decimal Number 4356
 
15.4%
Open Punctuation 944
 
3.3%
Close Punctuation 944
 
3.3%
Other Punctuation 677
 
2.4%
Dash Punctuation 222
 
0.8%
Uppercase Letter 50
 
0.2%
Lowercase Letter 15
 
0.1%
Math Symbol 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1869
 
11.5%
종 1287
 
7.9%
구 957
 
5.9%
서 956
 
5.9%
시 943
 
5.8%
울 942
 
5.8%
특 935
 
5.7%
별 935
 
5.7%
동 869
 
5.3%
길 577
 
3.5%
Other values (233) 6034
37.0%
Uppercase Letter
ValueCountFrequency (%)
B 21
42.0%
K 4
 
8.0%
L 4
 
8.0%
A 3
 
6.0%
M 2
 
4.0%
S 2
 
4.0%
C 2
 
4.0%
G 2
 
4.0%
E 2
 
4.0%
T 2
 
4.0%
Other values (5) 6
 
12.0%
Decimal Number
ValueCountFrequency (%)
1 1125
25.8%
2 748
17.2%
3 497
11.4%
4 393
 
9.0%
5 338
 
7.8%
0 297
 
6.8%
6 282
 
6.5%
9 237
 
5.4%
8 231
 
5.3%
7 208
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
e 2
13.3%
l 2
13.3%
t 2
13.3%
c 2
13.3%
s 2
13.3%
g 1
6.7%
u 1
6.7%
h 1
6.7%
o 1
6.7%
a 1
6.7%
Other Punctuation
ValueCountFrequency (%)
, 675
99.7%
/ 1
 
0.1%
& 1
 
0.1%
Space Separator
ValueCountFrequency (%)
4730
100.0%
Open Punctuation
ValueCountFrequency (%)
( 944
100.0%
Close Punctuation
ValueCountFrequency (%)
) 944
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 222
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16304
57.7%
Common 11884
42.1%
Latin 65
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1869
 
11.5%
종 1287
 
7.9%
구 957
 
5.9%
서 956
 
5.9%
시 943
 
5.8%
울 942
 
5.8%
특 935
 
5.7%
별 935
 
5.7%
동 869
 
5.3%
길 577
 
3.5%
Other values (233) 6034
37.0%
Latin
ValueCountFrequency (%)
B 21
32.3%
K 4
 
6.2%
L 4
 
6.2%
A 3
 
4.6%
M 2
 
3.1%
e 2
 
3.1%
S 2
 
3.1%
l 2
 
3.1%
t 2
 
3.1%
c 2
 
3.1%
Other values (15) 21
32.3%
Common
ValueCountFrequency (%)
4730
39.8%
1 1125
 
9.5%
( 944
 
7.9%
) 944
 
7.9%
2 748
 
6.3%
, 675
 
5.7%
3 497
 
4.2%
4 393
 
3.3%
5 338
 
2.8%
0 297
 
2.5%
Other values (8) 1193
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16304
57.7%
ASCII 11949
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4730
39.6%
1 1125
 
9.4%
( 944
 
7.9%
) 944
 
7.9%
2 748
 
6.3%
, 675
 
5.6%
3 497
 
4.2%
4 393
 
3.3%
5 338
 
2.8%
0 297
 
2.5%
Other values (33) 1258
 
10.5%
Hangul
ValueCountFrequency (%)
로 1869
 
11.5%
종 1287
 
7.9%
구 957
 
5.9%
서 956
 
5.9%
시 943
 
5.8%
울 942
 
5.8%
특 935
 
5.7%
별 935
 
5.7%
동 869
 
5.3%
길 577
 
3.5%
Other values (233) 6034
37.0%

소재지전화
Text

MISSING 

Distinct640
Distinct (%)98.2%
Missing283
Missing (%)30.3%
Memory size7.4 KiB
2023-12-12T15:09:28.851190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.28681
Min length11

Characters and Unicode

Total characters7359
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

Unique629 ?
Unique (%)96.5%

Sample

1st row02-764-5537
2nd row02-6459-5113
3rd row02-927-6393
4th row02-2267-7474
5th row02-743-5242
ValueCountFrequency (%)
02-744-2299 3
 
0.5%
02-6030-5000 2
 
0.3%
02-741-7811 2
 
0.3%
02-2267-1347 2
 
0.3%
02-6353-7700 2
 
0.3%
02-928-0832 2
 
0.3%
02-765-1341 2
 
0.3%
070-8838-8992 2
 
0.3%
02-739-1357 2
 
0.3%
02-6730-1131 2
 
0.3%
Other values (630) 631
96.8%
2023-12-12T15:09:29.345993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1304
17.7%
2 1224
16.6%
0 1050
14.3%
7 845
11.5%
3 635
8.6%
6 491
 
6.7%
4 421
 
5.7%
1 358
 
4.9%
5 355
 
4.8%
9 341
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6055
82.3%
Dash Punctuation 1304
 
17.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1224
20.2%
0 1050
17.3%
7 845
14.0%
3 635
10.5%
6 491
8.1%
4 421
 
7.0%
1 358
 
5.9%
5 355
 
5.9%
9 341
 
5.6%
8 335
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 1304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1304
17.7%
2 1224
16.6%
0 1050
14.3%
7 845
11.5%
3 635
8.6%
6 491
 
6.7%
4 421
 
5.7%
1 358
 
4.9%
5 355
 
4.8%
9 341
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1304
17.7%
2 1224
16.6%
0 1050
14.3%
7 845
11.5%
3 635
8.6%
6 491
 
6.7%
4 421
 
5.7%
1 358
 
4.9%
5 355
 
4.8%
9 341
 
4.6%

Missing values

2023-12-12T15:09:26.484767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:09:26.565703image/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
0숙박업(일반)이지스테이서울특별시 종로구 창신길 9-8 (창신동)02-764-5537
1숙박업(일반)호스텔 바닐라 1서울특별시 종로구 창신길 28-7 (창신동)02-6459-5113
2숙박업(일반)영모텔서울특별시 종로구 보문로7길 5-1 (숭인동)02-927-6393
3숙박업(일반)호텔더디자이너스 종로서울특별시 종로구 수표로 89-8 (관수동)02-2267-7474
4숙박업(일반)대진서울특별시 종로구 창신1길 6 (창신동)02-743-5242
5숙박업(일반)브릭스서울특별시 종로구 성균관로1길 6-2 (명륜3가)02-745-5656
6숙박업(일반)호스텔 서울서울특별시 종로구 보문로9길 9 (숭인동)070-8967-6888
7숙박업(일반)동미서울특별시 종로구 종로65길 12-28 (숭인동)02-763-3638
8숙박업(일반)삼오여관서울특별시 종로구 종로60길 9 (숭인동)02-2232-7122
9숙박업(일반)만남모텔서울특별시 종로구 보문로1길 7-20 (숭인동)02-928-5974
업종명업소명영업소 주소(도로명)소재지전화
925일반미용업, 네일미용업, 화장ㆍ분장 미용업YAAD 야드서울특별시 종로구 사직로10길 17, 인왕빌딩 1,2층 (내자동)<NA>
926일반미용업, 네일미용업, 화장ㆍ분장 미용업란 헤어서울특별시 종로구 종로 329-5, 1층 (창신동)<NA>
927일반미용업, 네일미용업, 화장ㆍ분장 미용업헤어, 그대와서울특별시 종로구 지봉로 77-4, 2층 (창신동)<NA>
928일반미용업, 네일미용업, 화장ㆍ분장 미용업르솜 살롱서울특별시 종로구 지봉로8길 1-32, 1층 (숭인동)<NA>
929일반미용업, 네일미용업, 화장ㆍ분장 미용업박승철헤어스투디오 서대문역점서울특별시 종로구 송월길 99, 상가동 1층 2152~2155호 (홍파동, 경희궁자이 2단지)<NA>
930피부미용업, 네일미용업, 화장ㆍ분장 미용업더스파앳더포시즌스서울특별시 종로구 새문안로 97, 9층 (당주동, 포시즌스호텔)02-6388-5250
931피부미용업, 네일미용업, 화장ㆍ분장 미용업와이키키서울특별시 종로구 창경궁로 248-4, 2층 (명륜2가)02-762-0727
932피부미용업, 네일미용업, 화장ㆍ분장 미용업요요살롱서울특별시 종로구 통일로 230, 상가 지하1층 113호 (무악동, 경희궁 롯데캐슬)<NA>
933피부미용업, 네일미용업, 화장ㆍ분장 미용업You are beautiful서울특별시 종로구 사직로8길 34, 경희궁의아침 3단지 163,164호 (내수동)<NA>
934피부미용업, 네일미용업, 화장ㆍ분장 미용업너 참, 예쁘다서울특별시 종로구 사직로8길 34, 경희궁의아침 3단지 165,166호 (내수동)<NA>