Overview

Dataset statistics

Number of variables4
Number of observations87
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory33.5 B

Variable types

Categorical2
Text2

Dataset

Description진도군 관내 운영중인 미용업(미용업, 피부미용업, 네일미용업) 현황 데이터로 업종명, 업소명, 영업소 주소 항목을 제공합니다.
URLhttps://www.data.go.kr/data/3069661/fileData.do

Alerts

업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation

Reproduction

Analysis started2023-12-12 03:01:38.346453
Analysis finished2023-12-12 03:01:38.970611
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size828.0 B
일반미용업
50 
미용업
14 
피부미용업
12 
네일미용업
6 
종합미용업
 
4

Length

Max length12
Median length5
Mean length4.7586207
Min length3

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 50
57.5%
미용업 14
 
16.1%
피부미용업 12
 
13.8%
네일미용업 6
 
6.9%
종합미용업 4
 
4.6%
일반미용업, 네일미용업 1
 
1.1%

Length

2023-12-12T12:01:39.081259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:01:39.246081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 51
58.0%
미용업 14
 
15.9%
피부미용업 12
 
13.6%
네일미용업 7
 
8.0%
종합미용업 4
 
4.5%

업소명
Text

Distinct86
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size828.0 B
2023-12-12T12:01:39.622097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length5.1264368
Min length1

Characters and Unicode

Total characters446
Distinct characters156
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

Unique85 ?
Unique (%)97.7%

Sample

1st row꽃샘
2nd row금희
3rd row옥주
4th row은하
5th row화니
ValueCountFrequency (%)
미용실 7
 
5.4%
진 4
 
3.1%
헤어 4
 
3.1%
네일 3
 
2.3%
에스디 2
 
1.5%
머리 2
 
1.5%
봄 2
 
1.5%
the 2
 
1.5%
하나헤어 1
 
0.8%
새론헤어 1
 
0.8%
Other values (102) 102
78.5%
2023-12-12T12:01:40.314099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43
 
9.6%
어 24
 
5.4%
헤 22
 
4.9%
미 19
 
4.3%
용 15
 
3.4%
실 15
 
3.4%
리 13
 
2.9%
일 10
 
2.2%
네 9
 
2.0%
이 9
 
2.0%
Other values (146) 267
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 366
82.1%
Space Separator 43
 
9.6%
Uppercase Letter 15
 
3.4%
Lowercase Letter 9
 
2.0%
Other Punctuation 7
 
1.6%
Decimal Number 3
 
0.7%
Connector Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 24
 
6.6%
헤 22
 
6.0%
미 19
 
5.2%
용 15
 
4.1%
실 15
 
4.1%
리 13
 
3.6%
일 10
 
2.7%
네 9
 
2.5%
이 9
 
2.5%
피 8
 
2.2%
Other values (118) 222
60.7%
Uppercase Letter
ValueCountFrequency (%)
T 3
20.0%
E 2
13.3%
A 2
13.3%
D 1
 
6.7%
S 1
 
6.7%
O 1
 
6.7%
I 1
 
6.7%
N 1
 
6.7%
H 1
 
6.7%
M 1
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
a 2
22.2%
i 2
22.2%
l 2
22.2%
e 1
11.1%
h 1
11.1%
n 1
11.1%
Other Punctuation
ValueCountFrequency (%)
& 3
42.9%
: 2
28.6%
# 1
 
14.3%
, 1
 
14.3%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
3 1
33.3%
7 1
33.3%
Space Separator
ValueCountFrequency (%)
43
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 366
82.1%
Common 56
 
12.6%
Latin 24
 
5.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 24
 
6.6%
헤 22
 
6.0%
미 19
 
5.2%
용 15
 
4.1%
실 15
 
4.1%
리 13
 
3.6%
일 10
 
2.7%
네 9
 
2.5%
이 9
 
2.5%
피 8
 
2.2%
Other values (118) 222
60.7%
Latin
ValueCountFrequency (%)
T 3
12.5%
E 2
 
8.3%
A 2
 
8.3%
a 2
 
8.3%
i 2
 
8.3%
l 2
 
8.3%
D 1
 
4.2%
e 1
 
4.2%
S 1
 
4.2%
O 1
 
4.2%
Other values (7) 7
29.2%
Common
ValueCountFrequency (%)
43
76.8%
& 3
 
5.4%
: 2
 
3.6%
_ 1
 
1.8%
( 1
 
1.8%
) 1
 
1.8%
# 1
 
1.8%
, 1
 
1.8%
2 1
 
1.8%
3 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 366
82.1%
ASCII 80
 
17.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43
53.8%
& 3
 
3.8%
T 3
 
3.8%
E 2
 
2.5%
: 2
 
2.5%
A 2
 
2.5%
a 2
 
2.5%
i 2
 
2.5%
l 2
 
2.5%
_ 1
 
1.2%
Other values (18) 18
22.5%
Hangul
ValueCountFrequency (%)
어 24
 
6.6%
헤 22
 
6.0%
미 19
 
5.2%
용 15
 
4.1%
실 15
 
4.1%
리 13
 
3.6%
일 10
 
2.7%
네 9
 
2.5%
이 9
 
2.5%
피 8
 
2.2%
Other values (118) 222
60.7%

영업소 주소(도로명)
Text

Distinct85
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size828.0 B
2023-12-12T12:01:40.738546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length21.850575
Min length18

Characters and Unicode

Total characters1901
Distinct characters86
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)95.4%

Sample

1st row전라남도 진도군 지산면 인지독치1길 9-8
2nd row전라남도 진도군 조도면 창유2길 2-1
3rd row전라남도 진도군 진도읍 남문길 31
4th row전라남도 진도군 임회면 십일시길 23-13
5th row전라남도 진도군 고군면 오일시1길 44
ValueCountFrequency (%)
전라남도 87
18.8%
진도군 87
18.8%
진도읍 64
13.9%
남문길 21
 
4.5%
남동1길 13
 
2.8%
1층 12
 
2.6%
옥주길 10
 
2.2%
고군면 7
 
1.5%
임회면 6
 
1.3%
5 5
 
1.1%
Other values (104) 150
32.5%
2023-12-12T12:01:41.678172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
375
19.7%
도 244
12.8%
진 155
 
8.2%
남 125
 
6.6%
군 96
 
5.0%
라 88
 
4.6%
전 87
 
4.6%
길 82
 
4.3%
1 80
 
4.2%
읍 64
 
3.4%
Other values (76) 505
26.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1208
63.5%
Space Separator 375
 
19.7%
Decimal Number 259
 
13.6%
Dash Punctuation 33
 
1.7%
Other Punctuation 21
 
1.1%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 244
20.2%
진 155
12.8%
남 125
10.3%
군 96
 
7.9%
라 88
 
7.3%
전 87
 
7.2%
길 82
 
6.8%
읍 64
 
5.3%
동 27
 
2.2%
문 23
 
1.9%
Other values (60) 217
18.0%
Decimal Number
ValueCountFrequency (%)
1 80
30.9%
2 39
15.1%
3 30
 
11.6%
4 27
 
10.4%
5 26
 
10.0%
6 18
 
6.9%
0 12
 
4.6%
7 11
 
4.2%
9 8
 
3.1%
8 8
 
3.1%
Space Separator
ValueCountFrequency (%)
375
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Other Punctuation
ValueCountFrequency (%)
, 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1208
63.5%
Common 693
36.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 244
20.2%
진 155
12.8%
남 125
10.3%
군 96
 
7.9%
라 88
 
7.3%
전 87
 
7.2%
길 82
 
6.8%
읍 64
 
5.3%
동 27
 
2.2%
문 23
 
1.9%
Other values (60) 217
18.0%
Common
ValueCountFrequency (%)
375
54.1%
1 80
 
11.5%
2 39
 
5.6%
- 33
 
4.8%
3 30
 
4.3%
4 27
 
3.9%
5 26
 
3.8%
, 21
 
3.0%
6 18
 
2.6%
0 12
 
1.7%
Other values (6) 32
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1208
63.5%
ASCII 693
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
375
54.1%
1 80
 
11.5%
2 39
 
5.6%
- 33
 
4.8%
3 30
 
4.3%
4 27
 
3.9%
5 26
 
3.8%
, 21
 
3.0%
6 18
 
2.6%
0 12
 
1.7%
Other values (6) 32
 
4.6%
Hangul
ValueCountFrequency (%)
도 244
20.2%
진 155
12.8%
남 125
10.3%
군 96
 
7.9%
라 88
 
7.3%
전 87
 
7.2%
길 82
 
6.8%
읍 64
 
5.3%
동 27
 
2.2%
문 23
 
1.9%
Other values (60) 217
18.0%

업태명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size828.0 B
일반미용업
64 
피부미용업
13 
네일아트업
7 
기타
 
2
메이크업업
 
1

Length

Max length5
Median length5
Mean length4.9310345
Min length2

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 64
73.6%
피부미용업 13
 
14.9%
네일아트업 7
 
8.0%
기타 2
 
2.3%
메이크업업 1
 
1.1%

Length

2023-12-12T12:01:41.832278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:01:41.967143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 64
73.6%
피부미용업 13
 
14.9%
네일아트업 7
 
8.0%
기타 2
 
2.3%
메이크업업 1
 
1.1%

Correlations

2023-12-12T12:01:42.057852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업소명영업소 주소(도로명)업태명
업종명1.0000.0000.0000.880
업소명0.0001.0001.0000.000
영업소 주소(도로명)0.0001.0001.0000.000
업태명0.8800.0000.0001.000
2023-12-12T12:01:42.185468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.803
업태명0.8031.000
2023-12-12T12:01:42.278035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.803
업태명0.8031.000

Missing values

2023-12-12T12:01:38.795374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:01:38.916043image/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

업종명업소명영업소 주소(도로명)업태명
0미용업꽃샘전라남도 진도군 지산면 인지독치1길 9-8일반미용업
1미용업금희전라남도 진도군 조도면 창유2길 2-1일반미용업
2미용업옥주전라남도 진도군 진도읍 남문길 31일반미용업
3미용업은하전라남도 진도군 임회면 십일시길 23-13일반미용업
4미용업화니전라남도 진도군 고군면 오일시1길 44일반미용업
5미용업박전라남도 진도군 진도읍 서문길 5일반미용업
6미용업진일전라남도 진도군 진도읍 남동2길 21-2, 1층 (대영빌라2)일반미용업
7미용업수헤어전라남도 진도군 진도읍 쌍정1길 10일반미용업
8미용업시온 헤어센터전라남도 진도군 고군면 지수길 1-4일반미용업
9미용업머리 이야기전라남도 진도군 군내면 금골길 6일반미용업
업종명업소명영업소 주소(도로명)업태명
77종합미용업쉼 뷰티& 테라피전라남도 진도군 진도읍 남동1길 35, 프린스모텔피부미용업
78종합미용업해니뷰티&피부관리#전라남도 진도군 진도읍 남산로 130-40메이크업업
79종합미용업하이네일:왁싱전라남도 진도군 진도읍 남문길 59-1기타
80네일미용업에스디 네일전라남도 진도군 진도읍 동외4길 55네일아트업
81네일미용업퀸 네일아트전라남도 진도군 진도읍 옥주길 4네일아트업
82네일미용업닥터아이티엔 씨유네일전라남도 진도군 진도읍 남문길 5, 진도공용버스터미널네일아트업
83네일미용업Nail DI:TE (네일디테)전라남도 진도군 진도읍 남동1길 43, 3층네일아트업
84네일미용업다온네일전라남도 진도군 진도읍 남문길 42-5네일아트업
85네일미용업늘 봄, 네일전라남도 진도군 진도읍 교동3길 19네일아트업
86일반미용업, 네일미용업O_nail전라남도 진도군 진도읍 옥주길 9-2네일아트업