Overview

Dataset statistics

Number of variables4
Number of observations264
Missing cells77
Missing cells (%)7.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory32.5 B

Variable types

Categorical1
Text3

Dataset

Description2015년 8월 칠곡군 미용업소명, 업소소재지, 소재지전화
Author경상북도 칠곡군
URLhttps://www.data.go.kr/data/15069089/fileData.do

Alerts

소재지전화 has 77 (29.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 03:50:29.169233
Analysis finished2023-12-12 03:50:29.612430
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
미용업
168 
미용업(일반)
64 
미용업(피부)
22 
미용업(종합)
 
7
미용업(일반), 미용업(손톱ㆍ발톱)
 
2

Length

Max length19
Median length3
Mean length4.5568182
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 168
63.6%
미용업(일반) 64
 
24.2%
미용업(피부) 22
 
8.3%
미용업(종합) 7
 
2.7%
미용업(일반), 미용업(손톱ㆍ발톱) 2
 
0.8%
미용업(손톱ㆍ발톱) 1
 
0.4%

Length

2023-12-12T12:50:29.691679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:50:29.827034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 168
63.2%
미용업(일반 66
 
24.8%
미용업(피부 22
 
8.3%
미용업(종합 7
 
2.6%
미용업(손톱ㆍ발톱 3
 
1.1%

업소명
Text

Distinct262
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-12T12:50:30.133405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length5.5909091
Min length1

Characters and Unicode

Total characters1476
Distinct characters308
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

Unique260 ?
Unique (%)98.5%

Sample

1st row국제미용소
2nd row여왕미용실
3rd row어진이 머리방
4th row건화미용소
5th row자유미용실
ValueCountFrequency (%)
진미용실 2
 
0.7%
머리방 2
 
0.7%
hair 2
 
0.7%
머리사랑 2
 
0.7%
헤어파트너 1
 
0.4%
헤어바 1
 
0.4%
바인헤어 1
 
0.4%
국제미용소 1
 
0.4%
더존헤어살롱 1
 
0.4%
개미미용실 1
 
0.4%
Other values (259) 259
94.9%
2023-12-12T12:50:30.559209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 157
 
10.6%
헤 149
 
10.1%
미 72
 
4.9%
용 52
 
3.5%
실 47
 
3.2%
리 30
 
2.0%
스 28
 
1.9%
샵 24
 
1.6%
라 22
 
1.5%
이 20
 
1.4%
Other values (298) 875
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1354
91.7%
Uppercase Letter 38
 
2.6%
Lowercase Letter 34
 
2.3%
Other Punctuation 25
 
1.7%
Space Separator 10
 
0.7%
Close Punctuation 6
 
0.4%
Open Punctuation 6
 
0.4%
Decimal Number 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 157
 
11.6%
헤 149
 
11.0%
미 72
 
5.3%
용 52
 
3.8%
실 47
 
3.5%
리 30
 
2.2%
스 28
 
2.1%
샵 24
 
1.8%
라 22
 
1.6%
이 20
 
1.5%
Other values (257) 753
55.6%
Uppercase Letter
ValueCountFrequency (%)
H 8
21.1%
I 4
10.5%
C 3
 
7.9%
S 3
 
7.9%
D 3
 
7.9%
K 2
 
5.3%
A 2
 
5.3%
V 2
 
5.3%
T 2
 
5.3%
O 1
 
2.6%
Other values (8) 8
21.1%
Lowercase Letter
ValueCountFrequency (%)
r 5
14.7%
e 5
14.7%
a 4
11.8%
n 4
11.8%
h 3
8.8%
i 3
8.8%
g 2
 
5.9%
m 2
 
5.9%
o 2
 
5.9%
y 1
 
2.9%
Other values (3) 3
8.8%
Other Punctuation
ValueCountFrequency (%)
? 18
72.0%
. 3
 
12.0%
& 2
 
8.0%
' 1
 
4.0%
# 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1354
91.7%
Latin 72
 
4.9%
Common 50
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 157
 
11.6%
헤 149
 
11.0%
미 72
 
5.3%
용 52
 
3.8%
실 47
 
3.5%
리 30
 
2.2%
스 28
 
2.1%
샵 24
 
1.8%
라 22
 
1.6%
이 20
 
1.5%
Other values (257) 753
55.6%
Latin
ValueCountFrequency (%)
H 8
 
11.1%
r 5
 
6.9%
e 5
 
6.9%
a 4
 
5.6%
I 4
 
5.6%
n 4
 
5.6%
C 3
 
4.2%
h 3
 
4.2%
i 3
 
4.2%
S 3
 
4.2%
Other values (21) 30
41.7%
Common
ValueCountFrequency (%)
? 18
36.0%
10
20.0%
) 6
 
12.0%
( 6
 
12.0%
. 3
 
6.0%
2 2
 
4.0%
& 2
 
4.0%
1 1
 
2.0%
' 1
 
2.0%
# 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1354
91.7%
ASCII 122
 
8.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 157
 
11.6%
헤 149
 
11.0%
미 72
 
5.3%
용 52
 
3.8%
실 47
 
3.5%
리 30
 
2.2%
스 28
 
2.1%
샵 24
 
1.8%
라 22
 
1.6%
이 20
 
1.5%
Other values (257) 753
55.6%
ASCII
ValueCountFrequency (%)
? 18
 
14.8%
10
 
8.2%
H 8
 
6.6%
) 6
 
4.9%
( 6
 
4.9%
r 5
 
4.1%
e 5
 
4.1%
a 4
 
3.3%
I 4
 
3.3%
n 4
 
3.3%
Other values (31) 52
42.6%

업소소재지(도로명)
Text

Distinct258
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-12T12:50:30.829959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length44
Mean length24.75
Min length18

Characters and Unicode

Total characters6534
Distinct characters133
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

Unique252 ?
Unique (%)95.5%

Sample

1st row경상북도 칠곡군 왜관읍 석전로11길 2
2nd row경상북도 칠곡군 왜관읍 중앙로4길 14
3rd row경상북도 칠곡군 동명면 금암중앙길 38
4th row경상북도 칠곡군 왜관읍 2번도로길 91
5th row경상북도 칠곡군 왜관읍 중앙로7길 3
ValueCountFrequency (%)
경상북도 264
17.6%
칠곡군 264
17.6%
왜관읍 91
 
6.1%
북삼읍 71
 
4.7%
석적읍 69
 
4.6%
약목면 20
 
1.3%
상가동 18
 
1.2%
중앙로 17
 
1.1%
7 15
 
1.0%
석전로 14
 
0.9%
Other values (324) 654
43.7%
2023-12-12T12:50:31.386666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1233
18.9%
북 377
 
5.8%
상 295
 
4.5%
1 283
 
4.3%
도 276
 
4.2%
군 266
 
4.1%
칠 265
 
4.1%
곡 265
 
4.1%
경 264
 
4.0%
읍 231
 
3.5%
Other values (123) 2779
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3997
61.2%
Space Separator 1233
 
18.9%
Decimal Number 1026
 
15.7%
Dash Punctuation 71
 
1.1%
Other Punctuation 60
 
0.9%
Open Punctuation 57
 
0.9%
Close Punctuation 57
 
0.9%
Uppercase Letter 33
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
북 377
 
9.4%
상 295
 
7.4%
도 276
 
6.9%
군 266
 
6.7%
칠 265
 
6.6%
곡 265
 
6.6%
경 264
 
6.6%
읍 231
 
5.8%
길 181
 
4.5%
로 157
 
3.9%
Other values (104) 1420
35.5%
Decimal Number
ValueCountFrequency (%)
1 283
27.6%
2 154
15.0%
3 100
 
9.7%
0 96
 
9.4%
5 84
 
8.2%
6 66
 
6.4%
8 65
 
6.3%
9 65
 
6.3%
7 58
 
5.7%
4 55
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 25
75.8%
B 6
 
18.2%
C 1
 
3.0%
L 1
 
3.0%
Space Separator
ValueCountFrequency (%)
1233
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%
Other Punctuation
ValueCountFrequency (%)
, 60
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3997
61.2%
Common 2504
38.3%
Latin 33
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
북 377
 
9.4%
상 295
 
7.4%
도 276
 
6.9%
군 266
 
6.7%
칠 265
 
6.6%
곡 265
 
6.6%
경 264
 
6.6%
읍 231
 
5.8%
길 181
 
4.5%
로 157
 
3.9%
Other values (104) 1420
35.5%
Common
ValueCountFrequency (%)
1233
49.2%
1 283
 
11.3%
2 154
 
6.2%
3 100
 
4.0%
0 96
 
3.8%
5 84
 
3.4%
- 71
 
2.8%
6 66
 
2.6%
8 65
 
2.6%
9 65
 
2.6%
Other values (5) 287
 
11.5%
Latin
ValueCountFrequency (%)
A 25
75.8%
B 6
 
18.2%
C 1
 
3.0%
L 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3997
61.2%
ASCII 2537
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1233
48.6%
1 283
 
11.2%
2 154
 
6.1%
3 100
 
3.9%
0 96
 
3.8%
5 84
 
3.3%
- 71
 
2.8%
6 66
 
2.6%
8 65
 
2.6%
9 65
 
2.6%
Other values (9) 320
 
12.6%
Hangul
ValueCountFrequency (%)
북 377
 
9.4%
상 295
 
7.4%
도 276
 
6.9%
군 266
 
6.7%
칠 265
 
6.6%
곡 265
 
6.6%
경 264
 
6.6%
읍 231
 
5.8%
길 181
 
4.5%
로 157
 
3.9%
Other values (104) 1420
35.5%

소재지전화
Text

MISSING 

Distinct186
Distinct (%)99.5%
Missing77
Missing (%)29.2%
Memory size2.2 KiB
2023-12-12T12:50:31.734654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2244
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

Unique185 ?
Unique (%)98.9%

Sample

1st row054-974-0455
2nd row054-971-0872
3rd row054-976-4138
4th row054-974-2456
5th row054-973-6706
ValueCountFrequency (%)
054-973-9838 2
 
1.1%
054-972-3311 1
 
0.5%
054-972-1136 1
 
0.5%
054-464-6709 1
 
0.5%
054-977-1027 1
 
0.5%
054-973-5825 1
 
0.5%
054-977-2034 1
 
0.5%
054-976-6312 1
 
0.5%
054-971-0560 1
 
0.5%
054-975-0088 1
 
0.5%
Other values (176) 176
94.1%
2023-12-12T12:50:32.267474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 374
16.7%
7 301
13.4%
0 288
12.8%
5 284
12.7%
4 274
12.2%
9 245
10.9%
3 123
 
5.5%
2 108
 
4.8%
8 86
 
3.8%
6 84
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1870
83.3%
Dash Punctuation 374
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 301
16.1%
0 288
15.4%
5 284
15.2%
4 274
14.7%
9 245
13.1%
3 123
6.6%
2 108
 
5.8%
8 86
 
4.6%
6 84
 
4.5%
1 77
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 374
16.7%
7 301
13.4%
0 288
12.8%
5 284
12.7%
4 274
12.2%
9 245
10.9%
3 123
 
5.5%
2 108
 
4.8%
8 86
 
3.8%
6 84
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 374
16.7%
7 301
13.4%
0 288
12.8%
5 284
12.7%
4 274
12.2%
9 245
10.9%
3 123
 
5.5%
2 108
 
4.8%
8 86
 
3.8%
6 84
 
3.7%

Missing values

2023-12-12T12:50:29.487743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:50:29.575857image/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
0미용업국제미용소경상북도 칠곡군 왜관읍 석전로11길 2054-974-0455
1미용업여왕미용실경상북도 칠곡군 왜관읍 중앙로4길 14054-971-0872
2미용업어진이 머리방경상북도 칠곡군 동명면 금암중앙길 38054-976-4138
3미용업건화미용소경상북도 칠곡군 왜관읍 2번도로길 91054-974-2456
4미용업자유미용실경상북도 칠곡군 왜관읍 중앙로7길 3054-973-6706
5미용업각시미용실경상북도 칠곡군 왜관읍 석전로 8054-971-0404
6미용업사랑미용실경상북도 칠곡군 왜관읍 관문로 46054-971-0132
7미용업은방울미용실경상북도 칠곡군 왜관읍 중앙로 157054-974-0615
8미용업맵시나미용소경상북도 칠곡군 약목면 약목로 98-1054-974-6439
9미용업유미용실경상북도 칠곡군 약목면 약목로 79054-974-7981
업종명업소명업소소재지(도로명)소재지전화
254미용업(종합)헤어퀸경상북도 칠곡군 왜관읍 공단로4길 10-4054-971-8552
255미용업(종합)가띠엘경상북도 칠곡군 왜관읍 전원3길 17054-971-3370
256미용업(종합)JS헤어경상북도 칠곡군 석적읍 서중리5길 66-8, 상가동 104호 (금호어울림A)054-977-2860
257미용업(종합)헤어파트너경상북도 칠곡군 왜관읍 석전로 119 (외 1필지)054-971-0787
258미용업(종합)헤어스크린경상북도 칠곡군 왜관읍 군청2길 21 (A동)054-977-4523
259미용업(종합)이린헤어경상북도 칠곡군 석적읍 북중리12길 23-1054-972-0547
260미용업(종합)블리스경상북도 칠곡군 석적읍 유학로 110-19<NA>
261미용업(손톱ㆍ발톱)네일또네일경상북도 칠곡군 석적읍 남중리2길 11<NA>
262미용업(일반), 미용업(손톱ㆍ발톱)린헤어네일경상북도 칠곡군 석적읍 유학로 28, 105호<NA>
263미용업(일반), 미용업(손톱ㆍ발톱)코코네일경상북도 칠곡군 북삼읍 금오대로6길 7<NA>