Overview

Dataset statistics

Number of variables4
Number of observations876
Missing cells315
Missing cells (%)9.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.5 KiB
Average record size in memory32.2 B

Variable types

Categorical1
Text3

Dataset

Description서산시에 영업허가된 공중 위생업소(이발소. 미용실, 세탁소, 목욕탕, 사우나, 네일아트, 피부관리샵)정보로 업종명, 업소명, 업소소재지, 소재지에 대한 정보를 제공합니다.
Author충청남도 서산시
URLhttps://www.data.go.kr/data/15000677/fileData.do

Alerts

소재지전화 has 315 (36.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 04:14:10.637858
Analysis finished2023-12-12 04:14:11.369630
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct19
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
일반미용업
414 
세탁업
96 
피부미용업
88 
이용업
77 
건물위생관리업
55 
Other values (14)
146 

Length

Max length23
Median length5
Mean length5.2203196
Min length3

Unique

Unique3 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 414
47.3%
세탁업 96
 
11.0%
피부미용업 88
 
10.0%
이용업 77
 
8.8%
건물위생관리업 55
 
6.3%
네일미용업 43
 
4.9%
미용업 18
 
2.1%
목욕장업 15
 
1.7%
종합미용업 14
 
1.6%
피부미용업, 네일미용업 12
 
1.4%
Other values (9) 44
 
5.0%

Length

2023-12-12T13:14:11.456723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 428
44.6%
피부미용업 115
 
12.0%
세탁업 96
 
10.0%
이용업 77
 
8.0%
네일미용업 70
 
7.3%
건물위생관리업 55
 
5.7%
미용업 54
 
5.6%
화장ㆍ분장 36
 
3.8%
목욕장업 15
 
1.6%
종합미용업 14
 
1.5%

업소명
Text

Distinct859
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2023-12-12T13:14:11.781449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length22
Mean length5.9851598
Min length1

Characters and Unicode

Total characters5243
Distinct characters511
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

Unique846 ?
Unique (%)96.6%

Sample

1st row로얄목욕탕
2nd row제일목욕탕
3rd row동문목욕탕
4th row한마음목욕탕
5th row학돌사우나
ValueCountFrequency (%)
서산점 8
 
0.8%
헤어샵 7
 
0.7%
미용실 7
 
0.7%
주식회사 6
 
0.6%
중앙이용원 4
 
0.4%
스무살에머물다 4
 
0.4%
헤어 4
 
0.4%
헤어살롱 4
 
0.4%
현대이용원 4
 
0.4%
이용원 3
 
0.3%
Other values (920) 957
94.9%
2023-12-12T13:14:12.340733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 291
 
5.6%
헤 271
 
5.2%
이 176
 
3.4%
용 161
 
3.1%
미 141
 
2.7%
132
 
2.5%
실 100
 
1.9%
리 94
 
1.8%
스 93
 
1.8%
세 85
 
1.6%
Other values (501) 3699
70.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4677
89.2%
Lowercase Letter 144
 
2.7%
Space Separator 132
 
2.5%
Uppercase Letter 98
 
1.9%
Close Punctuation 74
 
1.4%
Open Punctuation 74
 
1.4%
Other Punctuation 22
 
0.4%
Decimal Number 21
 
0.4%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 291
 
6.2%
헤 271
 
5.8%
이 176
 
3.8%
용 161
 
3.4%
미 141
 
3.0%
실 100
 
2.1%
리 94
 
2.0%
스 93
 
2.0%
세 85
 
1.8%
원 83
 
1.8%
Other values (442) 3182
68.0%
Lowercase Letter
ValueCountFrequency (%)
a 18
12.5%
e 16
11.1%
o 13
 
9.0%
i 13
 
9.0%
n 10
 
6.9%
r 10
 
6.9%
l 9
 
6.2%
h 7
 
4.9%
m 6
 
4.2%
d 6
 
4.2%
Other values (14) 36
25.0%
Uppercase Letter
ValueCountFrequency (%)
O 12
12.2%
A 11
11.2%
L 7
 
7.1%
D 7
 
7.1%
R 7
 
7.1%
S 7
 
7.1%
E 6
 
6.1%
U 6
 
6.1%
N 6
 
6.1%
H 5
 
5.1%
Other values (10) 24
24.5%
Other Punctuation
ValueCountFrequency (%)
, 8
36.4%
& 4
18.2%
# 4
18.2%
. 3
 
13.6%
: 2
 
9.1%
' 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
2 8
38.1%
1 7
33.3%
4 2
 
9.5%
3 2
 
9.5%
9 2
 
9.5%
Space Separator
ValueCountFrequency (%)
132
100.0%
Close Punctuation
ValueCountFrequency (%)
) 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 74
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4677
89.2%
Common 324
 
6.2%
Latin 242
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 291
 
6.2%
헤 271
 
5.8%
이 176
 
3.8%
용 161
 
3.4%
미 141
 
3.0%
실 100
 
2.1%
리 94
 
2.0%
스 93
 
2.0%
세 85
 
1.8%
원 83
 
1.8%
Other values (442) 3182
68.0%
Latin
ValueCountFrequency (%)
a 18
 
7.4%
e 16
 
6.6%
o 13
 
5.4%
i 13
 
5.4%
O 12
 
5.0%
A 11
 
4.5%
n 10
 
4.1%
r 10
 
4.1%
l 9
 
3.7%
L 7
 
2.9%
Other values (34) 123
50.8%
Common
ValueCountFrequency (%)
132
40.7%
) 74
22.8%
( 74
22.8%
2 8
 
2.5%
, 8
 
2.5%
1 7
 
2.2%
& 4
 
1.2%
# 4
 
1.2%
. 3
 
0.9%
4 2
 
0.6%
Other values (5) 8
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4677
89.2%
ASCII 566
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 291
 
6.2%
헤 271
 
5.8%
이 176
 
3.8%
용 161
 
3.4%
미 141
 
3.0%
실 100
 
2.1%
리 94
 
2.0%
스 93
 
2.0%
세 85
 
1.8%
원 83
 
1.8%
Other values (442) 3182
68.0%
ASCII
ValueCountFrequency (%)
132
23.3%
) 74
 
13.1%
( 74
 
13.1%
a 18
 
3.2%
e 16
 
2.8%
o 13
 
2.3%
i 13
 
2.3%
O 12
 
2.1%
A 11
 
1.9%
n 10
 
1.8%
Other values (49) 193
34.1%

업소소재지(도로명)
Text

Distinct837
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2023-12-12T13:14:12.745771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length53
Mean length28.388128
Min length18

Characters and Unicode

Total characters24868
Distinct characters264
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

Unique801 ?
Unique (%)91.4%

Sample

1st row충청남도 서산시 안견로 217 (동문동)
2nd row충청남도 서산시 시장4길 255 (동문동)
3rd row충청남도 서산시 대사동14길 2 (동문동)
4th row충청남도 서산시 한마음13로 36 (석림동)
5th row충청남도 서산시 부춘2로 24 (읍내동)
ValueCountFrequency (%)
충청남도 876
 
16.0%
서산시 876
 
16.0%
1층 401
 
7.3%
동문동 288
 
5.3%
읍내동 123
 
2.2%
2층 108
 
2.0%
예천동 94
 
1.7%
석림동 86
 
1.6%
상가동 65
 
1.2%
석남동 54
 
1.0%
Other values (776) 2513
45.8%
2023-12-12T13:14:13.347108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4622
18.6%
1 1283
 
5.2%
동 1207
 
4.9%
서 987
 
4.0%
남 980
 
3.9%
산 980
 
3.9%
시 946
 
3.8%
충 902
 
3.6%
도 884
 
3.6%
청 880
 
3.5%
Other values (254) 11197
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13891
55.9%
Space Separator 4622
 
18.6%
Decimal Number 4073
 
16.4%
Close Punctuation 754
 
3.0%
Open Punctuation 754
 
3.0%
Other Punctuation 739
 
3.0%
Uppercase Letter 22
 
0.1%
Math Symbol 8
 
< 0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1207
 
8.7%
서 987
 
7.1%
남 980
 
7.1%
산 980
 
7.1%
시 946
 
6.8%
충 902
 
6.5%
도 884
 
6.4%
청 880
 
6.3%
로 757
 
5.4%
층 580
 
4.2%
Other values (228) 4788
34.5%
Decimal Number
ValueCountFrequency (%)
1 1283
31.5%
2 672
16.5%
3 445
 
10.9%
0 332
 
8.2%
4 331
 
8.1%
5 237
 
5.8%
6 234
 
5.7%
7 212
 
5.2%
8 173
 
4.2%
9 154
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 7
31.8%
B 5
22.7%
S 4
18.2%
C 3
13.6%
D 1
 
4.5%
T 1
 
4.5%
F 1
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
e 3
60.0%
s 1
 
20.0%
h 1
 
20.0%
Other Punctuation
ValueCountFrequency (%)
, 737
99.7%
@ 2
 
0.3%
Space Separator
ValueCountFrequency (%)
4622
100.0%
Close Punctuation
ValueCountFrequency (%)
) 754
100.0%
Open Punctuation
ValueCountFrequency (%)
( 754
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13891
55.9%
Common 10950
44.0%
Latin 27
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1207
 
8.7%
서 987
 
7.1%
남 980
 
7.1%
산 980
 
7.1%
시 946
 
6.8%
충 902
 
6.5%
도 884
 
6.4%
청 880
 
6.3%
로 757
 
5.4%
층 580
 
4.2%
Other values (228) 4788
34.5%
Common
ValueCountFrequency (%)
4622
42.2%
1 1283
 
11.7%
) 754
 
6.9%
( 754
 
6.9%
, 737
 
6.7%
2 672
 
6.1%
3 445
 
4.1%
0 332
 
3.0%
4 331
 
3.0%
5 237
 
2.2%
Other values (6) 783
 
7.2%
Latin
ValueCountFrequency (%)
A 7
25.9%
B 5
18.5%
S 4
14.8%
e 3
11.1%
C 3
11.1%
D 1
 
3.7%
s 1
 
3.7%
T 1
 
3.7%
h 1
 
3.7%
F 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13891
55.9%
ASCII 10977
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4622
42.1%
1 1283
 
11.7%
) 754
 
6.9%
( 754
 
6.9%
, 737
 
6.7%
2 672
 
6.1%
3 445
 
4.1%
0 332
 
3.0%
4 331
 
3.0%
5 237
 
2.2%
Other values (16) 810
 
7.4%
Hangul
ValueCountFrequency (%)
동 1207
 
8.7%
서 987
 
7.1%
남 980
 
7.1%
산 980
 
7.1%
시 946
 
6.8%
충 902
 
6.5%
도 884
 
6.4%
청 880
 
6.3%
로 757
 
5.4%
층 580
 
4.2%
Other values (228) 4788
34.5%

소재지전화
Text

MISSING 

Distinct540
Distinct (%)96.3%
Missing315
Missing (%)36.0%
Memory size7.0 KiB
2023-12-12T13:14:13.641620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.044563
Min length1

Characters and Unicode

Total characters5635
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

Unique529 ?
Unique (%)94.3%

Sample

1st row0416647715
2nd row0416621919
3rd row0416670020
4th row0416660233
5th row0416694490
ValueCountFrequency (%)
0416682309 2
 
0.4%
0416650431 2
 
0.4%
0416815333 2
 
0.4%
0416813922 2
 
0.4%
0416641562 2
 
0.4%
0416686200 2
 
0.4%
0416658474 2
 
0.4%
0416631253 2
 
0.4%
0416656949 2
 
0.4%
0416666400 2
 
0.4%
Other values (528) 528
96.4%
2023-12-12T13:14:14.185713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 1222
21.7%
0 854
15.2%
4 812
14.4%
1 806
14.3%
5 340
 
6.0%
8 337
 
6.0%
7 308
 
5.5%
3 286
 
5.1%
2 272
 
4.8%
9 253
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5490
97.4%
Space Separator 145
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1222
22.3%
0 854
15.6%
4 812
14.8%
1 806
14.7%
5 340
 
6.2%
8 337
 
6.1%
7 308
 
5.6%
3 286
 
5.2%
2 272
 
5.0%
9 253
 
4.6%
Space Separator
ValueCountFrequency (%)
145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5635
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1222
21.7%
0 854
15.2%
4 812
14.4%
1 806
14.3%
5 340
 
6.0%
8 337
 
6.0%
7 308
 
5.5%
3 286
 
5.1%
2 272
 
4.8%
9 253
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5635
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1222
21.7%
0 854
15.2%
4 812
14.4%
1 806
14.3%
5 340
 
6.0%
8 337
 
6.0%
7 308
 
5.5%
3 286
 
5.1%
2 272
 
4.8%
9 253
 
4.5%

Missing values

2023-12-12T13:14:11.212078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:14:11.326075image/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
0목욕장업로얄목욕탕충청남도 서산시 안견로 217 (동문동)0416647715
1목욕장업제일목욕탕충청남도 서산시 시장4길 255 (동문동)0416621919
2목욕장업동문목욕탕충청남도 서산시 대사동14길 2 (동문동)0416670020
3목욕장업한마음목욕탕충청남도 서산시 한마음13로 36 (석림동)0416660233
4목욕장업학돌사우나충청남도 서산시 부춘2로 24 (읍내동)0416694490
5목욕장업용천휘트니스센터충청남도 서산시 율지7로 17 (동문동)0416699220
6목욕장업하와이여성전용사우나충청남도 서산시 안견로 173, 지하층 (동문동)0416678188
7목욕장업궁전목욕탕충청남도 서산시 학동11로 4 (동문동)0416674715
8목욕장업금강산보석사우나충청남도 서산시 서해로 3370, 2,4층 (석남동, 동서1로 15224, 1층)0416686200
9목욕장업가야황토참숯가마충청남도 서산시 운산면 용장2길 1310 (가동1~2층,나동1층)0416648889
업종명업소명업소소재지(도로명)소재지전화
866네일미용업, 화장ㆍ분장 미용업씨블링뷰티살롱충청남도 서산시 쌍연남2로 84, 2층 201호 (동문동)<NA>
867네일미용업, 화장ㆍ분장 미용업네일은,설렘충청남도 서산시 고운로 377, 1층 (읍내동)<NA>
868네일미용업, 화장ㆍ분장 미용업네일빈충청남도 서산시 동헌로 18, 1층 (석남동)<NA>
869네일미용업, 화장ㆍ분장 미용업베베네일충청남도 서산시 율지15로 6, 1층 (동문동)<NA>
870네일미용업, 화장ㆍ분장 미용업무드엘(MOOD L)충청남도 서산시 안견로 403, 나눌샘 1층 (읍내동)<NA>
871네일미용업, 화장ㆍ분장 미용업또블리충청남도 서산시 서령로 36, 상가동 2층 204호 (동문동, 동문동 한성필하우스)<NA>
872네일미용업, 화장ㆍ분장 미용업헤이리즈(Hei reeze)충청남도 서산시 석남3로 7, 1층 (석남동)<NA>
873일반미용업, 피부미용업, 화장ㆍ분장 미용업뷰티헤어랑충청남도 서산시 율지5로 18, 1층 (동문동)<NA>
874일반미용업, 네일미용업, 화장ㆍ분장 미용업헤어더하다충청남도 서산시 쌍연남2로 8, 1층 (동문동)<NA>
875피부미용업, 네일미용업, 화장ㆍ분장 미용업네일도맑음충청남도 서산시 남부순환로 1028, 1층 (예천동)<NA>