Overview

Dataset statistics

Number of variables6
Number of observations904
Missing cells298
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.5 KiB
Average record size in memory48.1 B

Variable types

Categorical2
DateTime1
Text3

Dataset

Description충청북도 제천시 공중위생업소현황 자료입니다.- 업종명(숙박, 목욕탕, 이용, 미용, 네일, 세탁, 건물청소 등)- 신고일자- 업소명- 업소소재지(도로명)- 소재지전화
Author충청북도 제천시
URLhttps://www.data.go.kr/data/15028159/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 298 (33.0%) missing valuesMissing

Reproduction

Analysis started2024-03-14 17:53:53.842775
Analysis finished2024-03-14 17:53:55.246910
Duration1.4 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct21
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
일반미용업
202 
미용업
185 
숙박업(일반)
137 
세탁업
82 
이용업
63 
Other values (16)
235 

Length

Max length23
Median length16
Mean length5.2621681
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 202
22.3%
미용업 185
20.5%
숙박업(일반) 137
15.2%
세탁업 82
9.1%
이용업 63
 
7.0%
피부미용업 60
 
6.6%
건물위생관리업 45
 
5.0%
네일미용업 28
 
3.1%
종합미용업 24
 
2.7%
목욕장업 22
 
2.4%
Other values (11) 56
 
6.2%

Length

2024-03-15T02:53:55.564929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 225
22.5%
일반미용업 220
22.0%
숙박업(일반 137
13.7%
세탁업 82
 
8.2%
피부미용업 79
 
7.9%
이용업 63
 
6.3%
네일미용업 57
 
5.7%
건물위생관리업 45
 
4.5%
화장ㆍ분장 40
 
4.0%
종합미용업 24
 
2.4%
Other values (2) 27
 
2.7%

신고일자
Date

Distinct832
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
Minimum1964-07-20 00:00:00
Maximum2023-12-28 00:00:00
2024-03-15T02:53:56.035392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:53:56.480520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업소명
Text

Distinct900
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2024-03-15T02:53:57.538548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length25
Mean length5.9911504
Min length2

Characters and Unicode

Total characters5416
Distinct characters535
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique896 ?
Unique (%)99.1%

Sample

1st row귀빈여인숙
2nd row영광 여인숙
3rd row자유 여인숙
4th row백운 여인숙
5th row성진여인숙
ValueCountFrequency (%)
헤어 12
 
1.1%
여인숙 9
 
0.8%
뷰티 7
 
0.6%
미용실 7
 
0.6%
여관 6
 
0.5%
제천점 6
 
0.5%
주식회사 5
 
0.4%
헤어샵 4
 
0.4%
모텔 3
 
0.3%
에스테틱 3
 
0.3%
Other values (1005) 1050
94.4%
2024-03-15T02:53:59.031434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 247
 
4.6%
헤 238
 
4.4%
208
 
3.8%
이 145
 
2.7%
미 132
 
2.4%
리 95
 
1.8%
용 92
 
1.7%
스 90
 
1.7%
실 82
 
1.5%
관 78
 
1.4%
Other values (525) 4009
74.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4657
86.0%
Lowercase Letter 214
 
4.0%
Space Separator 208
 
3.8%
Uppercase Letter 145
 
2.7%
Open Punctuation 63
 
1.2%
Close Punctuation 63
 
1.2%
Other Punctuation 37
 
0.7%
Decimal Number 24
 
0.4%
Dash Punctuation 3
 
0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 247
 
5.3%
헤 238
 
5.1%
이 145
 
3.1%
미 132
 
2.8%
리 95
 
2.0%
용 92
 
2.0%
스 90
 
1.9%
실 82
 
1.8%
관 78
 
1.7%
텔 74
 
1.6%
Other values (462) 3384
72.7%
Uppercase Letter
ValueCountFrequency (%)
B 14
 
9.7%
A 14
 
9.7%
N 12
 
8.3%
S 11
 
7.6%
O 11
 
7.6%
J 9
 
6.2%
H 9
 
6.2%
L 9
 
6.2%
E 8
 
5.5%
D 6
 
4.1%
Other values (12) 42
29.0%
Lowercase Letter
ValueCountFrequency (%)
a 27
12.6%
e 23
10.7%
l 20
9.3%
n 20
9.3%
i 19
8.9%
u 15
 
7.0%
o 14
 
6.5%
r 12
 
5.6%
y 11
 
5.1%
m 9
 
4.2%
Other values (11) 44
20.6%
Decimal Number
ValueCountFrequency (%)
2 7
29.2%
1 7
29.2%
5 3
12.5%
9 3
12.5%
6 1
 
4.2%
3 1
 
4.2%
4 1
 
4.2%
0 1
 
4.2%
Other Punctuation
ValueCountFrequency (%)
& 15
40.5%
. 7
18.9%
, 6
 
16.2%
: 4
 
10.8%
# 3
 
8.1%
' 1
 
2.7%
? 1
 
2.7%
Space Separator
ValueCountFrequency (%)
208
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4656
86.0%
Common 400
 
7.4%
Latin 359
 
6.6%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 247
 
5.3%
헤 238
 
5.1%
이 145
 
3.1%
미 132
 
2.8%
리 95
 
2.0%
용 92
 
2.0%
스 90
 
1.9%
실 82
 
1.8%
관 78
 
1.7%
텔 74
 
1.6%
Other values (461) 3383
72.7%
Latin
ValueCountFrequency (%)
a 27
 
7.5%
e 23
 
6.4%
l 20
 
5.6%
n 20
 
5.6%
i 19
 
5.3%
u 15
 
4.2%
B 14
 
3.9%
A 14
 
3.9%
o 14
 
3.9%
N 12
 
3.3%
Other values (33) 181
50.4%
Common
ValueCountFrequency (%)
208
52.0%
( 63
 
15.8%
) 63
 
15.8%
& 15
 
3.8%
2 7
 
1.8%
. 7
 
1.8%
1 7
 
1.8%
, 6
 
1.5%
: 4
 
1.0%
- 3
 
0.8%
Other values (10) 17
 
4.2%
Han
ValueCountFrequency (%)
美 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4656
86.0%
ASCII 759
 
14.0%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 247
 
5.3%
헤 238
 
5.1%
이 145
 
3.1%
미 132
 
2.8%
리 95
 
2.0%
용 92
 
2.0%
스 90
 
1.9%
실 82
 
1.8%
관 78
 
1.7%
텔 74
 
1.6%
Other values (461) 3383
72.7%
ASCII
ValueCountFrequency (%)
208
27.4%
( 63
 
8.3%
) 63
 
8.3%
a 27
 
3.6%
e 23
 
3.0%
l 20
 
2.6%
n 20
 
2.6%
i 19
 
2.5%
u 15
 
2.0%
& 15
 
2.0%
Other values (53) 286
37.7%
CJK
ValueCountFrequency (%)
美 1
100.0%

업소소재지(도로명)
Text

Distinct880
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2024-03-15T02:54:00.365690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length45
Mean length27.057522
Min length16

Characters and Unicode

Total characters24460
Distinct characters224
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

Unique857 ?
Unique (%)94.8%

Sample

1st row충청북도 제천시 내토로22길 7 (영천동)
2nd row충청북도 제천시 내토로 431-8 (영천동)
3rd row충청북도 제천시 내토로22길 9-1 (영천동)
4th row충청북도 제천시 내토로24길 6 (영천동)
5th row충청북도 제천시 내토로27길 4 (화산동)
ValueCountFrequency (%)
충청북도 904
 
17.7%
제천시 904
 
17.7%
1층 192
 
3.8%
청전동 121
 
2.4%
장락동 103
 
2.0%
화산동 73
 
1.4%
2층 72
 
1.4%
중앙로2가 64
 
1.3%
영천동 64
 
1.3%
하소동 53
 
1.0%
Other values (810) 2548
50.0%
2024-03-15T02:54:02.194852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4237
 
17.3%
1 1251
 
5.1%
천 1110
 
4.5%
청 1106
 
4.5%
제 982
 
4.0%
로 973
 
4.0%
시 920
 
3.8%
도 911
 
3.7%
북 908
 
3.7%
충 904
 
3.7%
Other values (214) 11158
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14047
57.4%
Space Separator 4237
 
17.3%
Decimal Number 3843
 
15.7%
Close Punctuation 832
 
3.4%
Open Punctuation 832
 
3.4%
Other Punctuation 459
 
1.9%
Dash Punctuation 187
 
0.8%
Uppercase Letter 16
 
0.1%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 1110
 
7.9%
청 1106
 
7.9%
제 982
 
7.0%
로 973
 
6.9%
시 920
 
6.5%
도 911
 
6.5%
북 908
 
6.5%
충 904
 
6.4%
동 820
 
5.8%
길 485
 
3.5%
Other values (189) 4928
35.1%
Decimal Number
ValueCountFrequency (%)
1 1251
32.6%
2 660
17.2%
3 381
 
9.9%
0 305
 
7.9%
4 277
 
7.2%
5 267
 
6.9%
6 215
 
5.6%
7 190
 
4.9%
8 152
 
4.0%
9 145
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 6
37.5%
B 6
37.5%
T 1
 
6.2%
M 1
 
6.2%
R 1
 
6.2%
E 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 454
98.9%
" 3
 
0.7%
: 1
 
0.2%
@ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
4237
100.0%
Close Punctuation
ValueCountFrequency (%)
) 832
100.0%
Open Punctuation
ValueCountFrequency (%)
( 832
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 187
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14047
57.4%
Common 10397
42.5%
Latin 16
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 1110
 
7.9%
청 1106
 
7.9%
제 982
 
7.0%
로 973
 
6.9%
시 920
 
6.5%
도 911
 
6.5%
북 908
 
6.5%
충 904
 
6.4%
동 820
 
5.8%
길 485
 
3.5%
Other values (189) 4928
35.1%
Common
ValueCountFrequency (%)
4237
40.8%
1 1251
 
12.0%
) 832
 
8.0%
( 832
 
8.0%
2 660
 
6.3%
, 454
 
4.4%
3 381
 
3.7%
0 305
 
2.9%
4 277
 
2.7%
5 267
 
2.6%
Other values (9) 901
 
8.7%
Latin
ValueCountFrequency (%)
A 6
37.5%
B 6
37.5%
T 1
 
6.2%
M 1
 
6.2%
R 1
 
6.2%
E 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14047
57.4%
ASCII 10413
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4237
40.7%
1 1251
 
12.0%
) 832
 
8.0%
( 832
 
8.0%
2 660
 
6.3%
, 454
 
4.4%
3 381
 
3.7%
0 305
 
2.9%
4 277
 
2.7%
5 267
 
2.6%
Other values (15) 917
 
8.8%
Hangul
ValueCountFrequency (%)
천 1110
 
7.9%
청 1106
 
7.9%
제 982
 
7.0%
로 973
 
6.9%
시 920
 
6.5%
도 911
 
6.5%
북 908
 
6.5%
충 904
 
6.4%
동 820
 
5.8%
길 485
 
3.5%
Other values (189) 4928
35.1%

전화번호
Text

MISSING 

Distinct595
Distinct (%)98.2%
Missing298
Missing (%)33.0%
Memory size7.2 KiB
2024-03-15T02:54:03.344629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.971947
Min length12

Characters and Unicode

Total characters8467
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique584 ?
Unique (%)96.4%

Sample

1st row 043- 642-8986
2nd row043 -643 -8344
3rd row043 -647 -5296
4th row 043- 647-3321
5th row043 -648 -6985
ValueCountFrequency (%)
043 585
41.6%
648 25
 
1.8%
652 23
 
1.6%
644 22
 
1.6%
647 20
 
1.4%
643 20
 
1.4%
642 20
 
1.4%
19
 
1.4%
645 17
 
1.2%
646 15
 
1.1%
Other values (605) 640
45.5%
2024-03-15T02:54:05.237566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 1388
16.4%
- 1212
14.3%
1187
14.0%
3 929
11.0%
0 926
10.9%
6 884
10.4%
5 419
 
4.9%
2 355
 
4.2%
7 331
 
3.9%
8 331
 
3.9%
Other values (2) 505
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6068
71.7%
Dash Punctuation 1212
 
14.3%
Space Separator 1187
 
14.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1388
22.9%
3 929
15.3%
0 926
15.3%
6 884
14.6%
5 419
 
6.9%
2 355
 
5.9%
7 331
 
5.5%
8 331
 
5.5%
1 274
 
4.5%
9 231
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 1212
100.0%
Space Separator
ValueCountFrequency (%)
1187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8467
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1388
16.4%
- 1212
14.3%
1187
14.0%
3 929
11.0%
0 926
10.9%
6 884
10.4%
5 419
 
4.9%
2 355
 
4.2%
7 331
 
3.9%
8 331
 
3.9%
Other values (2) 505
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1388
16.4%
- 1212
14.3%
1187
14.0%
3 929
11.0%
0 926
10.9%
6 884
10.4%
5 419
 
4.9%
2 355
 
4.2%
7 331
 
3.9%
8 331
 
3.9%
Other values (2) 505
 
6.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2024-01-02
904 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-02
2nd row2024-01-02
3rd row2024-01-02
4th row2024-01-02
5th row2024-01-02

Common Values

ValueCountFrequency (%)
2024-01-02 904
100.0%

Length

2024-03-15T02:54:05.514263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:54:05.792542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-02 904
100.0%

Missing values

2024-03-15T02:53:54.764534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:53:55.107832image/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

업종명신고일자업소명업소소재지(도로명)전화번호데이터기준일자
0숙박업(일반)1967-03-28귀빈여인숙충청북도 제천시 내토로22길 7 (영천동)043- 642-89862024-01-02
1숙박업(일반)1967-09-04영광 여인숙충청북도 제천시 내토로 431-8 (영천동)043 -643 -83442024-01-02
2숙박업(일반)1968-12-14자유 여인숙충청북도 제천시 내토로22길 9-1 (영천동)043 -647 -52962024-01-02
3숙박업(일반)1972-07-11백운 여인숙충청북도 제천시 내토로24길 6 (영천동)<NA>2024-01-02
4숙박업(일반)1973-08-28성진여인숙충청북도 제천시 내토로27길 4 (화산동)<NA>2024-01-02
5숙박업(일반)1974-05-25태평여인숙충청북도 제천시 내토로 427-5 (영천동)043- 647-33212024-01-02
6숙박업(일반)1974-07-19한미 여인숙충청북도 제천시 내토로27길 21 (화산동)<NA>2024-01-02
7숙박업(일반)1976-06-21왔다 파크충청북도 제천시 내토로27길 11 (화산동)043 -648 -69852024-01-02
8숙박업(일반)1977-07-02모텔카라충청북도 제천시 의림대로17길 14 (중앙로2가)043- 643-33332024-01-02
9숙박업(일반)1977-07-02한별여인숙충청북도 제천시 내토로26길 10 (영천동)043 -647 -52962024-01-02
업종명신고일자업소명업소소재지(도로명)전화번호데이터기준일자
894일반미용업, 네일미용업, 화장ㆍ분장 미용업2018-05-10코코살롱충청북도 제천시 용두대로5길 33, 지하1층 (하소동)<NA>2024-01-02
895일반미용업, 네일미용업, 화장ㆍ분장 미용업2019-07-03J 헤어충청북도 제천시 용두대로5길 30, 1층 (하소동)<NA>2024-01-02
896일반미용업, 네일미용업, 화장ㆍ분장 미용업2020-02-26헤어살롱드윰충청북도 제천시 의림대로 119-1, 2층 (중앙로2가)<NA>2024-01-02
897일반미용업, 네일미용업, 화장ㆍ분장 미용업2020-12-29라보떼헤어충청북도 제천시 풍양로15길 13, 2층 (중앙로2가)043- 644-77762024-01-02
898일반미용업, 네일미용업, 화장ㆍ분장 미용업2021-03-24원헤어충청북도 제천시 죽하로13길 9 (장락동)<NA>2024-01-02
899일반미용업, 네일미용업, 화장ㆍ분장 미용업2021-04-20경헤어살롱충청북도 제천시 내토로28길 12, 1층 (화산동)<NA>2024-01-02
900피부미용업, 네일미용업, 화장ㆍ분장 미용업2016-02-25벨라뷰티충청북도 제천시 칠성로 72 (의림동)<NA>2024-01-02
901피부미용업, 네일미용업, 화장ㆍ분장 미용업2017-06-30뷰티노리터충청북도 제천시 청풍호로10안길 23-13 (강제동)<NA>2024-01-02
902피부미용업, 네일미용업, 화장ㆍ분장 미용업2020-06-02Beauty Blassom(뷰티블라썸)충청북도 제천시 내제로 165-1, 102호 (청전동)<NA>2024-01-02
903피부미용업, 네일미용업, 화장ㆍ분장 미용업2022-05-10반함뷰티충청북도 제천시 의병대로16길 11, 1층 101호 (중앙로1가)<NA>2024-01-02