Overview

Dataset statistics

Number of variables4
Number of observations178
Missing cells82
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory32.7 B

Variable types

Categorical1
Text3

Dataset

Description충청남도 계룡시 공중위생업소 현황(업종명, 업소명, 주소, 전화번호)에 관한 정보를 공공데이터로 제공합니다.
URLhttps://www.data.go.kr/data/15024977/fileData.do

Alerts

소재지전화 has 82 (46.1%) missing valuesMissing
업소명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:16:08.847113
Analysis finished2023-12-12 23:16:09.267410
Duration0.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct15
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
일반미용업
36 
미용업
28 
피부미용업
24 
숙박업(일반)
21 
세탁업
18 
Other values (10)
51 

Length

Max length23
Median length12
Mean length5.2977528
Min length3

Unique

Unique3 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 36
20.2%
미용업 28
15.7%
피부미용업 24
13.5%
숙박업(일반) 21
11.8%
세탁업 18
10.1%
종합미용업 15
8.4%
건물위생관리업 9
 
5.1%
이용업 8
 
4.5%
네일미용업 7
 
3.9%
목욕장업 3
 
1.7%
Other values (5) 9
 
5.1%

Length

2023-12-13T08:16:09.351861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 41
21.0%
미용업 33
16.9%
피부미용업 28
14.4%
숙박업(일반 21
10.8%
세탁업 18
9.2%
종합미용업 15
 
7.7%
네일미용업 14
 
7.2%
건물위생관리업 9
 
4.6%
이용업 8
 
4.1%
화장ㆍ분장 5
 
2.6%

업소명
Text

UNIQUE 

Distinct178
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T08:16:09.637381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length6.1797753
Min length2

Characters and Unicode

Total characters1100
Distinct characters285
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

Unique178 ?
Unique (%)100.0%

Sample

1st rowJAVA 자바모텔
2nd rowS 모텔
3rd row헤븐모텔
4th row썬파크
5th row호텔더존
ValueCountFrequency (%)
헤어 12
 
4.5%
hair 5
 
1.9%
네일 4
 
1.5%
에스테틱 4
 
1.5%
뷰티샵 3
 
1.1%
미용실 3
 
1.1%
피부관리샵 2
 
0.8%
모텔 2
 
0.8%
드 2
 
0.8%
살롱 2
 
0.8%
Other values (218) 225
85.2%
2023-12-13T08:16:10.123427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86
 
7.8%
어 45
 
4.1%
헤 42
 
3.8%
이 24
 
2.2%
리 22
 
2.0%
스 22
 
2.0%
미 18
 
1.6%
용 17
 
1.5%
아 16
 
1.5%
샵 16
 
1.5%
Other values (275) 792
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 888
80.7%
Space Separator 86
 
7.8%
Lowercase Letter 64
 
5.8%
Uppercase Letter 38
 
3.5%
Decimal Number 7
 
0.6%
Close Punctuation 6
 
0.5%
Open Punctuation 6
 
0.5%
Other Punctuation 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 45
 
5.1%
헤 42
 
4.7%
이 24
 
2.7%
리 22
 
2.5%
스 22
 
2.5%
미 18
 
2.0%
용 17
 
1.9%
아 16
 
1.8%
샵 16
 
1.8%
텔 15
 
1.7%
Other values (233) 651
73.3%
Uppercase Letter
ValueCountFrequency (%)
B 4
10.5%
S 3
 
7.9%
A 3
 
7.9%
M 3
 
7.9%
L 3
 
7.9%
I 3
 
7.9%
O 3
 
7.9%
D 3
 
7.9%
E 2
 
5.3%
C 2
 
5.3%
Other values (7) 9
23.7%
Lowercase Letter
ValueCountFrequency (%)
a 10
15.6%
r 7
10.9%
i 6
9.4%
n 6
9.4%
o 6
9.4%
s 5
7.8%
e 4
 
6.2%
h 4
 
6.2%
l 4
 
6.2%
y 3
 
4.7%
Other values (5) 9
14.1%
Other Punctuation
ValueCountFrequency (%)
. 2
40.0%
# 1
20.0%
' 1
20.0%
& 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 3
42.9%
0 3
42.9%
9 1
 
14.3%
Space Separator
ValueCountFrequency (%)
86
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 888
80.7%
Common 110
 
10.0%
Latin 102
 
9.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 45
 
5.1%
헤 42
 
4.7%
이 24
 
2.7%
리 22
 
2.5%
스 22
 
2.5%
미 18
 
2.0%
용 17
 
1.9%
아 16
 
1.8%
샵 16
 
1.8%
텔 15
 
1.7%
Other values (233) 651
73.3%
Latin
ValueCountFrequency (%)
a 10
 
9.8%
r 7
 
6.9%
i 6
 
5.9%
n 6
 
5.9%
o 6
 
5.9%
s 5
 
4.9%
e 4
 
3.9%
h 4
 
3.9%
l 4
 
3.9%
B 4
 
3.9%
Other values (22) 46
45.1%
Common
ValueCountFrequency (%)
86
78.2%
) 6
 
5.5%
( 6
 
5.5%
2 3
 
2.7%
0 3
 
2.7%
. 2
 
1.8%
# 1
 
0.9%
' 1
 
0.9%
& 1
 
0.9%
9 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 888
80.7%
ASCII 212
 
19.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
86
40.6%
a 10
 
4.7%
r 7
 
3.3%
i 6
 
2.8%
n 6
 
2.8%
o 6
 
2.8%
) 6
 
2.8%
( 6
 
2.8%
s 5
 
2.4%
e 4
 
1.9%
Other values (32) 70
33.0%
Hangul
ValueCountFrequency (%)
어 45
 
5.1%
헤 42
 
4.7%
이 24
 
2.7%
리 22
 
2.5%
스 22
 
2.5%
미 18
 
2.0%
용 17
 
1.9%
아 16
 
1.8%
샵 16
 
1.8%
텔 15
 
1.7%
Other values (233) 651
73.3%

도로명주소
Text

Distinct161
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T08:16:10.535940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length39
Mean length25.977528
Min length9

Characters and Unicode

Total characters4624
Distinct characters117
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

Unique148 ?
Unique (%)83.1%

Sample

1st row충청남도 계룡시 엄사면 번영1길 21
2nd row충청남도 계룡시 엄사면 번영1길 7
3rd row충청남도 계룡시 엄사면 번영1길 9
4th row충청남도 계룡시 엄사면 번영1길 17
5th row충청남도 계룡시 엄사면 번영1길 5-20
ValueCountFrequency (%)
충청남도 176
17.0%
계룡시 176
17.0%
엄사면 117
 
11.3%
금암동 43
 
4.2%
번영3길 28
 
2.7%
1층 23
 
2.2%
번영로 22
 
2.1%
엄사중앙로 20
 
1.9%
장안로 15
 
1.5%
두마면 13
 
1.3%
Other values (205) 400
38.7%
2023-12-13T08:16:11.076921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
855
18.5%
1 225
 
4.9%
계 193
 
4.2%
룡 187
 
4.0%
남 182
 
3.9%
도 181
 
3.9%
시 177
 
3.8%
충 176
 
3.8%
청 176
 
3.8%
사 145
 
3.1%
Other values (107) 2127
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2705
58.5%
Space Separator 855
 
18.5%
Decimal Number 757
 
16.4%
Other Punctuation 87
 
1.9%
Close Punctuation 67
 
1.4%
Open Punctuation 67
 
1.4%
Dash Punctuation 64
 
1.4%
Uppercase Letter 17
 
0.4%
Math Symbol 4
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 193
 
7.1%
룡 187
 
6.9%
남 182
 
6.7%
도 181
 
6.7%
시 177
 
6.5%
충 176
 
6.5%
청 176
 
6.5%
사 145
 
5.4%
엄 137
 
5.1%
면 133
 
4.9%
Other values (81) 1018
37.6%
Decimal Number
ValueCountFrequency (%)
1 225
29.7%
3 109
14.4%
2 90
 
11.9%
0 90
 
11.9%
6 54
 
7.1%
4 49
 
6.5%
7 42
 
5.5%
5 41
 
5.4%
9 38
 
5.0%
8 19
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
E 4
23.5%
A 4
23.5%
L 2
11.8%
C 2
11.8%
R 2
11.8%
P 2
11.8%
B 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 85
97.7%
@ 2
 
2.3%
Math Symbol
ValueCountFrequency (%)
> 2
50.0%
< 2
50.0%
Space Separator
ValueCountFrequency (%)
855
100.0%
Close Punctuation
ValueCountFrequency (%)
) 67
100.0%
Open Punctuation
ValueCountFrequency (%)
( 67
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2705
58.5%
Common 1901
41.1%
Latin 18
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 193
 
7.1%
룡 187
 
6.9%
남 182
 
6.7%
도 181
 
6.7%
시 177
 
6.5%
충 176
 
6.5%
청 176
 
6.5%
사 145
 
5.4%
엄 137
 
5.1%
면 133
 
4.9%
Other values (81) 1018
37.6%
Common
ValueCountFrequency (%)
855
45.0%
1 225
 
11.8%
3 109
 
5.7%
2 90
 
4.7%
0 90
 
4.7%
, 85
 
4.5%
) 67
 
3.5%
( 67
 
3.5%
- 64
 
3.4%
6 54
 
2.8%
Other values (8) 195
 
10.3%
Latin
ValueCountFrequency (%)
E 4
22.2%
A 4
22.2%
L 2
11.1%
C 2
11.1%
R 2
11.1%
P 2
11.1%
B 1
 
5.6%
e 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2705
58.5%
ASCII 1919
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
855
44.6%
1 225
 
11.7%
3 109
 
5.7%
2 90
 
4.7%
0 90
 
4.7%
, 85
 
4.4%
) 67
 
3.5%
( 67
 
3.5%
- 64
 
3.3%
6 54
 
2.8%
Other values (16) 213
 
11.1%
Hangul
ValueCountFrequency (%)
계 193
 
7.1%
룡 187
 
6.9%
남 182
 
6.7%
도 181
 
6.7%
시 177
 
6.5%
충 176
 
6.5%
청 176
 
6.5%
사 145
 
5.4%
엄 137
 
5.1%
면 133
 
4.9%
Other values (81) 1018
37.6%

소재지전화
Text

MISSING 

Distinct96
Distinct (%)100.0%
Missing82
Missing (%)46.1%
Memory size1.5 KiB
2023-12-13T08:16:11.396018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1152
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

Unique96 ?
Unique (%)100.0%

Sample

1st row042-551-0056
2nd row042-551-7777
3rd row042-841-9655
4th row042-841-6161
5th row042-841-7676
ValueCountFrequency (%)
042-551-1628 1
 
1.0%
042-841-9655 1
 
1.0%
0428-4168-07 1
 
1.0%
042-621-4057 1
 
1.0%
042-621-3658 1
 
1.0%
042-841-1145 1
 
1.0%
042-840-8700 1
 
1.0%
042-840-5557 1
 
1.0%
042-841-9990 1
 
1.0%
042-841-6292 1
 
1.0%
Other values (86) 86
89.6%
2023-12-13T08:16:11.812809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 205
17.8%
- 192
16.7%
0 154
13.4%
2 137
11.9%
1 121
10.5%
8 108
9.4%
5 82
 
7.1%
7 49
 
4.3%
6 48
 
4.2%
9 28
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 960
83.3%
Dash Punctuation 192
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 205
21.4%
0 154
16.0%
2 137
14.3%
1 121
12.6%
8 108
11.2%
5 82
 
8.5%
7 49
 
5.1%
6 48
 
5.0%
9 28
 
2.9%
3 28
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 205
17.8%
- 192
16.7%
0 154
13.4%
2 137
11.9%
1 121
10.5%
8 108
9.4%
5 82
 
7.1%
7 49
 
4.3%
6 48
 
4.2%
9 28
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 205
17.8%
- 192
16.7%
0 154
13.4%
2 137
11.9%
1 121
10.5%
8 108
9.4%
5 82
 
7.1%
7 49
 
4.3%
6 48
 
4.2%
9 28
 
2.4%

Correlations

2023-12-13T08:16:11.902888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명소재지전화
업종명1.0001.000
소재지전화1.0001.000

Missing values

2023-12-13T08:16:09.143631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:16:09.225648image/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

업종명업소명도로명주소소재지전화
0숙박업(일반)JAVA 자바모텔충청남도 계룡시 엄사면 번영1길 21042-551-0056
1숙박업(일반)S 모텔충청남도 계룡시 엄사면 번영1길 7042-551-7777
2숙박업(일반)헤븐모텔충청남도 계룡시 엄사면 번영1길 9042-841-9655
3숙박업(일반)썬파크충청남도 계룡시 엄사면 번영1길 17042-841-6161
4숙박업(일반)호텔더존충청남도 계룡시 엄사면 번영1길 5-20042-841-7676
5숙박업(일반)호텔비너스충청남도 계룡시 엄사면 번영1길 6-19042-841-6886
6숙박업(일반)호텔 비바체충청남도 계룡시 엄사면 번영1길 6-17042-841-0788
7숙박업(일반)브라운도트(계룡시청점)충청남도 계룡시 계룡대로 316 (금암동)042-841-6744
8숙박업(일반)몽이야충청남도 계룡시 금암로 118-7 (금암동)042-841-6789
9숙박업(일반)굿타임모텔충청남도 계룡시 엄사면 번영1길 6-11042-841-5861
업종명업소명도로명주소소재지전화
168네일미용업영블리 네일충청남도 계룡시 엄사면 번영6길 8-6, 103호<NA>
169일반미용업, 네일미용업네일하는밍 뷰티샵충청남도 계룡시 엄사면 번영로 99<NA>
170피부미용업, 네일미용업아르떼충청남도 계룡시 서금암로 10, 107호 (금암동, 한양아이클래스)042-551-1456
171피부미용업, 네일미용업아임 네일(I'm nail)충청남도 계룡시 엄사면 엄사중앙로 94, 1층 112호042-533-4151
172피부미용업, 네일미용업슈슈네일충청남도 계룡시 엄사면 번영3길 73-12 (삼진아파트)<NA>
173화장ㆍ분장 미용업뷰티샵BB충청남도 계룡시 서금암2길 20 (금암동)<NA>
174일반미용업, 피부미용업, 화장ㆍ분장 미용업알레그라도충청남도 계룡시 두마면 농소로 38, 비전타워 401호<NA>
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