Overview

Dataset statistics

Number of variables4
Number of observations1656
Missing cells487
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.9 KiB
Average record size in memory32.1 B

Variable types

Categorical1
Text3

Dataset

Description수성구 미용업 현황(2018. 08. 01. 기준)
Author대구광역시 수성구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15054504&dataSetDetailId=150545042a343ac07a7f9&provdMethod=FILE

Alerts

업종명 is highly imbalanced (50.1%)Imbalance
소재지전화 has 487 (29.4%) missing valuesMissing

Reproduction

Analysis started2024-04-21 14:44:59.332924
Analysis finished2024-04-21 14:45:00.534591
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

IMBALANCE 

Distinct17
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
미용업(일반)
1010 
미용업(피부)
225 
이용업
163 
미용업(손톱ㆍ발톱)
 
88
미용업(종합)
 
54
Other values (12)
116 

Length

Max length31
Median length7
Mean length7.65157
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업(일반) 1010
61.0%
미용업(피부) 225
 
13.6%
이용업 163
 
9.8%
미용업(손톱ㆍ발톱) 88
 
5.3%
미용업(종합) 54
 
3.3%
미용업(피부) 미용업(손톱ㆍ발톱) 20
 
1.2%
미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장) 16
 
1.0%
미용업(일반) 미용업(손톱ㆍ발톱) 15
 
0.9%
미용업(피부) 미용업(화장ㆍ분장) 14
 
0.8%
미용업(일반) 미용업(화장ㆍ분장) 11
 
0.7%
Other values (7) 40
 
2.4%

Length

2024-04-21T23:45:00.764699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업(일반 1050
59.1%
미용업(피부 286
 
16.1%
이용업 163
 
9.2%
미용업(손톱ㆍ발톱 154
 
8.7%
미용업(화장ㆍ분장 65
 
3.7%
미용업(종합 54
 
3.0%
미용업 6
 
0.3%

업소명
Text

Distinct1560
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
2024-04-21T23:45:01.797758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length25
Mean length6.1678744
Min length1

Characters and Unicode

Total characters10214
Distinct characters591
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1484 ?
Unique (%)89.6%

Sample

1st row성하이용소
2nd row범물복지관커트실
3rd row신매이용소
4th row금릉이용소
5th row삼성이용소
ValueCountFrequency (%)
헤어 15
 
0.8%
hair 10
 
0.6%
미용실 9
 
0.5%
nail 7
 
0.4%
태후사랑 5
 
0.3%
by 5
 
0.3%
석미용실 4
 
0.2%
j 4
 
0.2%
도도헤어 4
 
0.2%
윤헤어 3
 
0.2%
Other values (1609) 1720
96.3%
2024-04-21T23:45:03.298557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 617
 
6.0%
헤 596
 
5.8%
용 412
 
4.0%
미 377
 
3.7%
이 313
 
3.1%
소 246
 
2.4%
스 239
 
2.3%
( 207
 
2.0%
) 207
 
2.0%
실 202
 
2.0%
Other values (581) 6798
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8434
82.6%
Lowercase Letter 648
 
6.3%
Uppercase Letter 446
 
4.4%
Open Punctuation 207
 
2.0%
Close Punctuation 207
 
2.0%
Space Separator 158
 
1.5%
Other Punctuation 66
 
0.6%
Decimal Number 44
 
0.4%
Math Symbol 1
 
< 0.1%
Other Symbol 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 617
 
7.3%
헤 596
 
7.1%
용 412
 
4.9%
미 377
 
4.5%
이 313
 
3.7%
소 246
 
2.9%
스 239
 
2.8%
실 202
 
2.4%
리 151
 
1.8%
일 133
 
1.6%
Other values (510) 5148
61.0%
Lowercase Letter
ValueCountFrequency (%)
a 91
14.0%
i 84
13.0%
e 64
9.9%
l 52
8.0%
n 50
7.7%
h 42
 
6.5%
o 40
 
6.2%
r 39
 
6.0%
s 31
 
4.8%
y 31
 
4.8%
Other values (15) 124
19.1%
Uppercase Letter
ValueCountFrequency (%)
A 49
 
11.0%
J 36
 
8.1%
N 34
 
7.6%
H 34
 
7.6%
S 32
 
7.2%
T 29
 
6.5%
L 27
 
6.1%
I 26
 
5.8%
B 22
 
4.9%
O 20
 
4.5%
Other values (14) 137
30.7%
Decimal Number
ValueCountFrequency (%)
2 13
29.5%
1 10
22.7%
9 5
 
11.4%
3 5
 
11.4%
5 4
 
9.1%
6 3
 
6.8%
8 2
 
4.5%
7 1
 
2.3%
0 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
& 26
39.4%
# 15
22.7%
. 13
19.7%
' 9
 
13.6%
: 2
 
3.0%
% 1
 
1.5%
Open Punctuation
ValueCountFrequency (%)
( 207
100.0%
Close Punctuation
ValueCountFrequency (%)
) 207
100.0%
Space Separator
ValueCountFrequency (%)
158
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8426
82.5%
Latin 1094
 
10.7%
Common 686
 
6.7%
Han 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 617
 
7.3%
헤 596
 
7.1%
용 412
 
4.9%
미 377
 
4.5%
이 313
 
3.7%
소 246
 
2.9%
스 239
 
2.8%
실 202
 
2.4%
리 151
 
1.8%
일 133
 
1.6%
Other values (507) 5140
61.0%
Latin
ValueCountFrequency (%)
a 91
 
8.3%
i 84
 
7.7%
e 64
 
5.9%
l 52
 
4.8%
n 50
 
4.6%
A 49
 
4.5%
h 42
 
3.8%
o 40
 
3.7%
r 39
 
3.6%
J 36
 
3.3%
Other values (39) 547
50.0%
Common
ValueCountFrequency (%)
( 207
30.2%
) 207
30.2%
158
23.0%
& 26
 
3.8%
# 15
 
2.2%
2 13
 
1.9%
. 13
 
1.9%
1 10
 
1.5%
' 9
 
1.3%
9 5
 
0.7%
Other values (12) 23
 
3.4%
Han
ValueCountFrequency (%)
美 6
75.0%
挺 1
 
12.5%
花 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8426
82.5%
ASCII 1779
 
17.4%
CJK 8
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 617
 
7.3%
헤 596
 
7.1%
용 412
 
4.9%
미 377
 
4.5%
이 313
 
3.7%
소 246
 
2.9%
스 239
 
2.8%
실 202
 
2.4%
리 151
 
1.8%
일 133
 
1.6%
Other values (507) 5140
61.0%
ASCII
ValueCountFrequency (%)
( 207
 
11.6%
) 207
 
11.6%
158
 
8.9%
a 91
 
5.1%
i 84
 
4.7%
e 64
 
3.6%
l 52
 
2.9%
n 50
 
2.8%
A 49
 
2.8%
h 42
 
2.4%
Other values (60) 775
43.6%
CJK
ValueCountFrequency (%)
美 6
75.0%
挺 1
 
12.5%
花 1
 
12.5%
None
ValueCountFrequency (%)
° 1
100.0%

업소소재지(도로명)
Text

Distinct1590
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
2024-04-21T23:45:04.374967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length50
Mean length29.423913
Min length1

Characters and Unicode

Total characters48726
Distinct characters252
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

Unique1529 ?
Unique (%)92.3%

Sample

1st row대구광역시 수성구 수성로68길 7 (수성동2가)
2nd row대구광역시 수성구 범안로 79 206동 2층 202호 (범물동 범물용지아파트)
3rd row대구광역시 수성구 달구벌대로652길 2 (신매동)
4th row대구광역시 수성구 청호로96길 34 (만촌동)
5th row대구광역시 수성구 교학로 45-1 (만촌동)
ValueCountFrequency (%)
대구광역시 1654
 
17.2%
수성구 1654
 
17.2%
범어동 262
 
2.7%
1층 217
 
2.3%
지산동 211
 
2.2%
만촌동 188
 
2.0%
황금동 121
 
1.3%
수성동1가 114
 
1.2%
신매동 102
 
1.1%
두산동 102
 
1.1%
Other values (1255) 4998
51.9%
2024-04-21T23:45:05.898855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8709
17.9%
구 3636
 
7.5%
수 2248
 
4.6%
성 2189
 
4.5%
동 2137
 
4.4%
대 1984
 
4.1%
1 1915
 
3.9%
시 1755
 
3.6%
) 1731
 
3.6%
( 1731
 
3.6%
Other values (242) 20691
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28194
57.9%
Space Separator 8709
 
17.9%
Decimal Number 8011
 
16.4%
Close Punctuation 1731
 
3.6%
Open Punctuation 1731
 
3.6%
Dash Punctuation 266
 
0.5%
Uppercase Letter 80
 
0.2%
Other Punctuation 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3636
12.9%
수 2248
 
8.0%
성 2189
 
7.8%
동 2137
 
7.6%
대 1984
 
7.0%
시 1755
 
6.2%
로 1663
 
5.9%
역 1655
 
5.9%
광 1655
 
5.9%
길 927
 
3.3%
Other values (213) 8345
29.6%
Uppercase Letter
ValueCountFrequency (%)
A 23
28.7%
S 14
17.5%
B 13
16.2%
K 9
 
11.2%
T 4
 
5.0%
C 4
 
5.0%
N 3
 
3.8%
X 3
 
3.8%
D 3
 
3.8%
L 2
 
2.5%
Other values (2) 2
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 1915
23.9%
2 1208
15.1%
3 915
11.4%
4 742
 
9.3%
0 728
 
9.1%
5 650
 
8.1%
6 592
 
7.4%
7 430
 
5.4%
8 416
 
5.2%
9 415
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
a 1
50.0%
Space Separator
ValueCountFrequency (%)
8709
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1731
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1731
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 266
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28194
57.9%
Common 20450
42.0%
Latin 82
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3636
12.9%
수 2248
 
8.0%
성 2189
 
7.8%
동 2137
 
7.6%
대 1984
 
7.0%
시 1755
 
6.2%
로 1663
 
5.9%
역 1655
 
5.9%
광 1655
 
5.9%
길 927
 
3.3%
Other values (213) 8345
29.6%
Common
ValueCountFrequency (%)
8709
42.6%
1 1915
 
9.4%
) 1731
 
8.5%
( 1731
 
8.5%
2 1208
 
5.9%
3 915
 
4.5%
4 742
 
3.6%
0 728
 
3.6%
5 650
 
3.2%
6 592
 
2.9%
Other values (5) 1529
 
7.5%
Latin
ValueCountFrequency (%)
A 23
28.0%
S 14
17.1%
B 13
15.9%
K 9
 
11.0%
T 4
 
4.9%
C 4
 
4.9%
N 3
 
3.7%
X 3
 
3.7%
D 3
 
3.7%
L 2
 
2.4%
Other values (4) 4
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28194
57.9%
ASCII 20532
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8709
42.4%
1 1915
 
9.3%
) 1731
 
8.4%
( 1731
 
8.4%
2 1208
 
5.9%
3 915
 
4.5%
4 742
 
3.6%
0 728
 
3.5%
5 650
 
3.2%
6 592
 
2.9%
Other values (19) 1611
 
7.8%
Hangul
ValueCountFrequency (%)
구 3636
12.9%
수 2248
 
8.0%
성 2189
 
7.8%
동 2137
 
7.6%
대 1984
 
7.0%
시 1755
 
6.2%
로 1663
 
5.9%
역 1655
 
5.9%
광 1655
 
5.9%
길 927
 
3.3%
Other values (213) 8345
29.6%

소재지전화
Text

MISSING 

Distinct1158
Distinct (%)99.1%
Missing487
Missing (%)29.4%
Memory size13.1 KiB
2024-04-21T23:45:06.905573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.030796
Min length12

Characters and Unicode

Total characters14064
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

Unique1147 ?
Unique (%)98.1%

Sample

1st row053-961-2222
2nd row053-791-4821
3rd row053-756-5393
4th row053-754-8528
5th row053-764-2320
ValueCountFrequency (%)
053-754-6262 2
 
0.2%
053-764-5757 2
 
0.2%
053-741-0587 2
 
0.2%
053-765-0360 2
 
0.2%
053-791-1734 2
 
0.2%
053-782-2455 2
 
0.2%
053-784-7711 2
 
0.2%
053-761-1521 2
 
0.2%
053-745-0497 2
 
0.2%
053-784-5117 2
 
0.2%
Other values (1148) 1149
98.3%
2024-04-21T23:45:08.286738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2338
16.6%
5 2006
14.3%
3 1806
12.8%
0 1706
12.1%
7 1649
11.7%
6 882
 
6.3%
2 810
 
5.8%
4 807
 
5.7%
1 752
 
5.3%
8 683
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11726
83.4%
Dash Punctuation 2338
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2006
17.1%
3 1806
15.4%
0 1706
14.5%
7 1649
14.1%
6 882
7.5%
2 810
6.9%
4 807
6.9%
1 752
 
6.4%
8 683
 
5.8%
9 625
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 2338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2338
16.6%
5 2006
14.3%
3 1806
12.8%
0 1706
12.1%
7 1649
11.7%
6 882
 
6.3%
2 810
 
5.8%
4 807
 
5.7%
1 752
 
5.3%
8 683
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2338
16.6%
5 2006
14.3%
3 1806
12.8%
0 1706
12.1%
7 1649
11.7%
6 882
 
6.3%
2 810
 
5.8%
4 807
 
5.7%
1 752
 
5.3%
8 683
 
4.9%

Missing values

2024-04-21T23:45:00.140816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T23:45:00.421181image/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
0이용업성하이용소대구광역시 수성구 수성로68길 7 (수성동2가)<NA>
1이용업범물복지관커트실대구광역시 수성구 범안로 79 206동 2층 202호 (범물동 범물용지아파트)053-961-2222
2이용업신매이용소대구광역시 수성구 달구벌대로652길 2 (신매동)053-791-4821
3이용업금릉이용소대구광역시 수성구 청호로96길 34 (만촌동)053-756-5393
4이용업삼성이용소대구광역시 수성구 교학로 45-1 (만촌동)053-754-8528
5이용업화신이용소대구광역시 수성구 수성로41길 51-2 (중동)053-764-2320
6이용업미소이용소대구광역시 수성구 들안로78길 3 (범어동)053-753-0279
7이용업동아이용소대구광역시 수성구 지범로45길 10 (범물동)053-782-1198
8이용업득성이용소대구광역시 수성구 수성로57길 75 (수성동1가)<NA>
9이용업신세계이용소대구광역시 수성구 신천동로 350 (수성동1가)053-783-6164
업종명업소명업소소재지(도로명)소재지전화
1646미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)나야네일(NAYA NAIL)대구광역시 수성구 달구벌대로456길 37-16 1층 (수성동1가)<NA>
1647미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)콩네일대구광역시 수성구 명덕로75길 20 (수성동1가)<NA>
1648미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)라벨르네일대구광역시 수성구 청수로40길 29 105동 101호 (황금동 화성고려파크뷰)<NA>
1649미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)제이엠(JM)네일대구광역시 수성구 수성로52길 48 (중동)<NA>
1650미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)뷰티온대구광역시 수성구 동대구로8길 31 (지산동)<NA>
1651미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)러브썸뷰티대구광역시 수성구 지범로25길 15 1층 (지산동)<NA>
1652미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)지네일(Ji nail)대구광역시 수성구 동대구로6길 9 1층 (지산동)<NA>
1653미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)라보떼(LaBeaute)대구광역시 수성구 달구벌대로669길 40 C동 (사월동)053-793-8838
1654미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)인애네일대구광역시 수성구 명덕로73길 46 (수성동1가)053-745-8848
1655미용업(피부) 미용업(손톱ㆍ발톱) 미용업(화장ㆍ분장)비스팟대구광역시 수성구 동대구로20길 24 1층 (지산동)<NA>