Overview

Dataset statistics

Number of variables4
Number of observations244
Missing cells83
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory32.5 B

Variable types

Categorical1
Text3

Dataset

Description문경시에 등록된 미용업에 관련 현황으로 일반, 피부, 네일, 종합, 화장업 등의 미용업으로 상호명, 도로명주소, 지번주소, 영업장 전화번호를 포함합니다.
Author경상북도 문경시
URLhttps://www.data.go.kr/data/15006825/fileData.do

Alerts

소재지전화 has 83 (34.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 14:39:37.808016
Analysis finished2023-12-12 14:39:38.308877
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct13
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
미용업
123 
일반미용업
53 
피부미용업
28 
네일미용업
13 
종합미용업
 
8
Other values (8)
19 

Length

Max length23
Median length3
Mean length4.7131148
Min length3

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 123
50.4%
일반미용업 53
21.7%
피부미용업 28
 
11.5%
네일미용업 13
 
5.3%
종합미용업 8
 
3.3%
일반미용업 피부미용업 5
 
2.0%
피부미용업 네일미용업 3
 
1.2%
일반미용업 네일미용업 화장ㆍ분장 미용업 3
 
1.2%
일반미용업 네일미용업 2
 
0.8%
화장ㆍ분장 미용업 2
 
0.8%
Other values (3) 4
 
1.6%

Length

2023-12-12T23:39:38.414458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 132
48.4%
일반미용업 64
23.4%
피부미용업 38
 
13.9%
네일미용업 22
 
8.1%
화장ㆍ분장 9
 
3.3%
종합미용업 8
 
2.9%

업소명
Text

Distinct243
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T23:39:38.776917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length5.7704918
Min length2

Characters and Unicode

Total characters1408
Distinct characters299
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique242 ?
Unique (%)99.2%

Sample

1st row마마미용소
2nd row윤정미용실
3rd row장미미용소
4th row보리수미용소
5th row석미용소
ValueCountFrequency (%)
헤어 7
 
2.5%
미용실 5
 
1.8%
약손 2
 
0.7%
머리방 2
 
0.7%
2
 
0.7%
제일미용소 2
 
0.7%
the 2
 
0.7%
skin 1
 
0.4%
영이 1
 
0.4%
다온헤어 1
 
0.4%
Other values (259) 259
91.2%
2023-12-12T23:39:39.280875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 92
 
6.5%
어 91
 
6.5%
헤 81
 
5.8%
용 73
 
5.2%
실 62
 
4.4%
40
 
2.8%
리 40
 
2.8%
스 31
 
2.2%
일 20
 
1.4%
머 19
 
1.3%
Other values (289) 859
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1245
88.4%
Uppercase Letter 52
 
3.7%
Space Separator 40
 
2.8%
Lowercase Letter 39
 
2.8%
Open Punctuation 12
 
0.9%
Close Punctuation 12
 
0.9%
Other Punctuation 6
 
0.4%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 92
 
7.4%
어 91
 
7.3%
헤 81
 
6.5%
용 73
 
5.9%
실 62
 
5.0%
리 40
 
3.2%
스 31
 
2.5%
일 20
 
1.6%
머 19
 
1.5%
샵 18
 
1.4%
Other values (252) 718
57.7%
Uppercase Letter
ValueCountFrequency (%)
T 6
11.5%
H 6
11.5%
N 5
9.6%
I 5
9.6%
S 5
9.6%
O 4
7.7%
A 4
7.7%
R 3
 
5.8%
P 3
 
5.8%
E 2
 
3.8%
Other values (7) 9
17.3%
Lowercase Letter
ValueCountFrequency (%)
e 6
15.4%
i 6
15.4%
h 5
12.8%
r 4
10.3%
a 4
10.3%
n 3
7.7%
o 3
7.7%
b 2
 
5.1%
l 2
 
5.1%
t 2
 
5.1%
Other values (2) 2
 
5.1%
Other Punctuation
ValueCountFrequency (%)
& 3
50.0%
. 2
33.3%
＆ 1
 
16.7%
Decimal Number
ValueCountFrequency (%)
7 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1245
88.4%
Latin 91
 
6.5%
Common 72
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 92
 
7.4%
어 91
 
7.3%
헤 81
 
6.5%
용 73
 
5.9%
실 62
 
5.0%
리 40
 
3.2%
스 31
 
2.5%
일 20
 
1.6%
머 19
 
1.5%
샵 18
 
1.4%
Other values (252) 718
57.7%
Latin
ValueCountFrequency (%)
e 6
 
6.6%
T 6
 
6.6%
i 6
 
6.6%
H 6
 
6.6%
N 5
 
5.5%
I 5
 
5.5%
S 5
 
5.5%
h 5
 
5.5%
O 4
 
4.4%
A 4
 
4.4%
Other values (19) 39
42.9%
Common
ValueCountFrequency (%)
40
55.6%
( 12
 
16.7%
) 12
 
16.7%
& 3
 
4.2%
. 2
 
2.8%
7 1
 
1.4%
＆ 1
 
1.4%
1 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1245
88.4%
ASCII 162
 
11.5%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 92
 
7.4%
어 91
 
7.3%
헤 81
 
6.5%
용 73
 
5.9%
실 62
 
5.0%
리 40
 
3.2%
스 31
 
2.5%
일 20
 
1.6%
머 19
 
1.5%
샵 18
 
1.4%
Other values (252) 718
57.7%
ASCII
ValueCountFrequency (%)
40
24.7%
( 12
 
7.4%
) 12
 
7.4%
e 6
 
3.7%
T 6
 
3.7%
i 6
 
3.7%
H 6
 
3.7%
N 5
 
3.1%
I 5
 
3.1%
S 5
 
3.1%
Other values (26) 59
36.4%
None
ValueCountFrequency (%)
＆ 1
100.0%

영업소주소
Text

Distinct234
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T23:39:39.998164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length40
Mean length24.127049
Min length16

Characters and Unicode

Total characters5887
Distinct characters130
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique224 ?
Unique (%)91.8%

Sample

1st row경상북도 문경시 흥덕동 315-5
2nd row경상북도 문경시 신기동 86
3rd row경상북도 문경시 점촌로 29-1 (점촌동)
4th row경상북도 문경시 상신로 12 (점촌동)
5th row경상북도 문경시 중앙시장길 36 (점촌동)
ValueCountFrequency (%)
경상북도 244
18.3%
문경시 244
18.3%
모전동 92
 
6.9%
점촌동 73
 
5.5%
1층 50
 
3.8%
흥덕동 41
 
3.1%
중앙로 30
 
2.3%
호서로 16
 
1.2%
문경읍 14
 
1.1%
상신로 13
 
1.0%
Other values (269) 514
38.6%
2023-12-12T23:39:40.623261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1174
19.9%
경 506
 
8.6%
문 268
 
4.6%
상 266
 
4.5%
시 257
 
4.4%
북 245
 
4.2%
도 245
 
4.2%
1 243
 
4.1%
동 230
 
3.9%
( 210
 
3.6%
Other values (120) 2243
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3437
58.4%
Space Separator 1174
 
19.9%
Decimal Number 796
 
13.5%
Open Punctuation 210
 
3.6%
Close Punctuation 210
 
3.6%
Dash Punctuation 60
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 506
14.7%
문 268
 
7.8%
상 266
 
7.7%
시 257
 
7.5%
북 245
 
7.1%
도 245
 
7.1%
동 230
 
6.7%
길 126
 
3.7%
로 118
 
3.4%
모 101
 
2.9%
Other values (106) 1075
31.3%
Decimal Number
ValueCountFrequency (%)
1 243
30.5%
2 138
17.3%
3 85
 
10.7%
6 57
 
7.2%
8 56
 
7.0%
4 56
 
7.0%
5 51
 
6.4%
7 48
 
6.0%
0 41
 
5.2%
9 21
 
2.6%
Space Separator
ValueCountFrequency (%)
1174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 210
100.0%
Close Punctuation
ValueCountFrequency (%)
) 210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3437
58.4%
Common 2450
41.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 506
14.7%
문 268
 
7.8%
상 266
 
7.7%
시 257
 
7.5%
북 245
 
7.1%
도 245
 
7.1%
동 230
 
6.7%
길 126
 
3.7%
로 118
 
3.4%
모 101
 
2.9%
Other values (106) 1075
31.3%
Common
ValueCountFrequency (%)
1174
47.9%
1 243
 
9.9%
( 210
 
8.6%
) 210
 
8.6%
2 138
 
5.6%
3 85
 
3.5%
- 60
 
2.4%
6 57
 
2.3%
8 56
 
2.3%
4 56
 
2.3%
Other values (4) 161
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3437
58.4%
ASCII 2450
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1174
47.9%
1 243
 
9.9%
( 210
 
8.6%
) 210
 
8.6%
2 138
 
5.6%
3 85
 
3.5%
- 60
 
2.4%
6 57
 
2.3%
8 56
 
2.3%
4 56
 
2.3%
Other values (4) 161
 
6.6%
Hangul
ValueCountFrequency (%)
경 506
14.7%
문 268
 
7.8%
상 266
 
7.7%
시 257
 
7.5%
북 245
 
7.1%
도 245
 
7.1%
동 230
 
6.7%
길 126
 
3.7%
로 118
 
3.4%
모 101
 
2.9%
Other values (106) 1075
31.3%

소재지전화
Text

MISSING 

Distinct160
Distinct (%)99.4%
Missing83
Missing (%)34.0%
Memory size2.0 KiB
2023-12-12T23:39:41.011951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.838509
Min length12

Characters and Unicode

Total characters2228
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

Unique159 ?
Unique (%)98.8%

Sample

1st row 054- 555-4733
2nd row 053- 553-4161
3rd row 054- 555-6671
4th row 053- 552-2895
5th row 054- 555-4514
ValueCountFrequency (%)
054 141
39.0%
552 11
 
3.0%
553 11
 
3.0%
555 9
 
2.5%
556 9
 
2.5%
554 7
 
1.9%
571 6
 
1.7%
053 4
 
1.1%
554-0158 2
 
0.6%
054-552-1696 1
 
0.3%
Other values (161) 161
44.5%
2023-12-12T23:39:41.569577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 532
23.9%
- 322
14.5%
294
13.2%
0 252
11.3%
4 234
10.5%
7 106
 
4.8%
2 103
 
4.6%
3 95
 
4.3%
1 91
 
4.1%
6 77
 
3.5%
Other values (2) 122
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1612
72.4%
Dash Punctuation 322
 
14.5%
Space Separator 294
 
13.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 532
33.0%
0 252
15.6%
4 234
14.5%
7 106
 
6.6%
2 103
 
6.4%
3 95
 
5.9%
1 91
 
5.6%
6 77
 
4.8%
9 65
 
4.0%
8 57
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 322
100.0%
Space Separator
ValueCountFrequency (%)
294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 532
23.9%
- 322
14.5%
294
13.2%
0 252
11.3%
4 234
10.5%
7 106
 
4.8%
2 103
 
4.6%
3 95
 
4.3%
1 91
 
4.1%
6 77
 
3.5%
Other values (2) 122
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 532
23.9%
- 322
14.5%
294
13.2%
0 252
11.3%
4 234
10.5%
7 106
 
4.8%
2 103
 
4.6%
3 95
 
4.3%
1 91
 
4.1%
6 77
 
3.5%
Other values (2) 122
 
5.5%

Missing values

2023-12-12T23:39:38.162750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:39:38.271637image/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

업종명업소명영업소주소소재지전화
0미용업마마미용소경상북도 문경시 흥덕동 315-5054- 555-4733
1미용업윤정미용실경상북도 문경시 신기동 86053- 553-4161
2미용업장미미용소경상북도 문경시 점촌로 29-1 (점촌동)054- 555-6671
3미용업보리수미용소경상북도 문경시 상신로 12 (점촌동)053- 552-2895
4미용업석미용소경상북도 문경시 중앙시장길 36 (점촌동)054- 555-4514
5미용업춘추미용실경상북도 문경시 신흥3길 17 (흥덕동)054- 555-7422
6미용업아모레미용실경상북도 문경시 문경읍 문희로 20054- 572-1792
7미용업백합미용소경상북도 문경시 문경읍 문희로 13054- 571-0059
8미용업서울미용실경상북도 문경시 문경읍 문희로 20 행복공인중개사053- 571-8651
9미용업성창미머리방경상북도 문경시 문경읍 문희로 36-3054- 571-9636
업종명업소명영업소주소소재지전화
234피부미용업 네일미용업네일도 꽃길경상북도 문경시 모전로 83 2층 (모전동)<NA>
235화장ㆍ분장 미용업요니뷰티경상북도 문경시 매봉3길 7-1 (모전동)<NA>
236화장ㆍ분장 미용업바비걸경상북도 문경시 상신로 54 1층 (모전동)<NA>
237일반미용업 화장ㆍ분장 미용업조혜정헤어경상북도 문경시 신흥3길 6 1층 (흥덕동)<NA>
238피부미용업 화장ㆍ분장 미용업여우스킨케어경상북도 문경시 매봉로 56 (모전동)<NA>
239피부미용업 화장ㆍ분장 미용업K뷰티케어경상북도 문경시 흥덕로 27 1층 (흥덕동)<NA>
240네일미용업 화장ㆍ분장 미용업네일두옴경상북도 문경시 매봉2길 23-18 1층 (모전동)<NA>
241일반미용업 네일미용업 화장ㆍ분장 미용업디바헤어경상북도 문경시 호서로 6 2층 (점촌동)054-554-2255
242일반미용업 네일미용업 화장ㆍ분장 미용업아리헤어 랑네일경상북도 문경시 반쟁이3길 30 2층 (모전동)054-554-7887
243일반미용업 네일미용업 화장ㆍ분장 미용업루아경상북도 문경시 돈달로 18 1층 (모전동)<NA>