Overview

Dataset statistics

Number of variables4
Number of observations1928
Missing cells922
Missing cells (%)12.0%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory60.4 KiB
Average record size in memory32.1 B

Variable types

Categorical1
Text3

Dataset

Description관내 미용업 정보 현황 데이터로 업종명과 업소명 영업소주소(도로명) 전화번호등 관내 미용업 현황 데이터 정보입니다.
URLhttps://www.data.go.kr/data/3079424/fileData.do

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
전화번호 has 922 (47.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 04:34:49.414627
Analysis finished2023-12-12 04:34:50.064951
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
일반미용업
1134 
피부미용업
311 
네일미용업
154 
이용업
 
71
화장ㆍ분장 미용업
 
55
Other values (12)
203 

Length

Max length23
Median length5
Mean length5.9253112
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 1134
58.8%
피부미용업 311
 
16.1%
네일미용업 154
 
8.0%
이용업 71
 
3.7%
화장ㆍ분장 미용업 55
 
2.9%
피부미용업, 네일미용업 43
 
2.2%
종합미용업 41
 
2.1%
네일미용업, 화장ㆍ분장 미용업 30
 
1.6%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 25
 
1.3%
피부미용업, 화장ㆍ분장 미용업 24
 
1.2%
Other values (7) 40
 
2.1%

Length

2023-12-12T13:34:50.148067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 1169
51.5%
피부미용업 412
 
18.2%
네일미용업 274
 
12.1%
미용업 153
 
6.7%
화장ㆍ분장 148
 
6.5%
이용업 71
 
3.1%
종합미용업 41
 
1.8%

업소명
Text

Distinct1828
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
2023-12-12T13:34:50.540269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length6.0798755
Min length1

Characters and Unicode

Total characters11722
Distinct characters629
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1754 ?
Unique (%)91.0%

Sample

1st row뉴금강
2nd row대성
3rd row대중
4th row상주이발
5th row안양공고구내
ValueCountFrequency (%)
hair 22
 
1.0%
범계점 14
 
0.6%
헤어 10
 
0.4%
머리사랑 9
 
0.4%
by 9
 
0.4%
평촌점 9
 
0.4%
나이스가이 8
 
0.4%
헤어살롱 7
 
0.3%
nail 7
 
0.3%
뷰티 7
 
0.3%
Other values (1954) 2150
95.5%
2023-12-12T13:34:51.154653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 802
 
6.8%
헤 748
 
6.4%
324
 
2.8%
이 310
 
2.6%
스 283
 
2.4%
미 269
 
2.3%
리 254
 
2.2%
일 203
 
1.7%
네 200
 
1.7%
) 152
 
1.3%
Other values (619) 8177
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9957
84.9%
Lowercase Letter 563
 
4.8%
Uppercase Letter 428
 
3.7%
Space Separator 324
 
2.8%
Close Punctuation 152
 
1.3%
Open Punctuation 152
 
1.3%
Other Punctuation 81
 
0.7%
Decimal Number 60
 
0.5%
Dash Punctuation 3
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 802
 
8.1%
헤 748
 
7.5%
이 310
 
3.1%
스 283
 
2.8%
미 269
 
2.7%
리 254
 
2.6%
일 203
 
2.0%
네 200
 
2.0%
라 150
 
1.5%
용 146
 
1.5%
Other values (544) 6592
66.2%
Uppercase Letter
ValueCountFrequency (%)
A 50
 
11.7%
N 38
 
8.9%
S 29
 
6.8%
I 28
 
6.5%
L 28
 
6.5%
O 27
 
6.3%
H 25
 
5.8%
E 24
 
5.6%
B 23
 
5.4%
U 20
 
4.7%
Other values (16) 136
31.8%
Lowercase Letter
ValueCountFrequency (%)
a 78
13.9%
i 66
11.7%
e 51
9.1%
o 44
 
7.8%
n 43
 
7.6%
r 41
 
7.3%
l 36
 
6.4%
h 31
 
5.5%
y 24
 
4.3%
s 23
 
4.1%
Other values (15) 126
22.4%
Other Punctuation
ValueCountFrequency (%)
& 29
35.8%
# 16
19.8%
, 14
17.3%
. 12
14.8%
' 4
 
4.9%
: 3
 
3.7%
· 1
 
1.2%
＆ 1
 
1.2%
? 1
 
1.2%
Decimal Number
ValueCountFrequency (%)
2 17
28.3%
1 10
16.7%
0 8
13.3%
9 7
11.7%
8 6
 
10.0%
5 5
 
8.3%
7 3
 
5.0%
4 2
 
3.3%
3 2
 
3.3%
Space Separator
ValueCountFrequency (%)
324
100.0%
Close Punctuation
ValueCountFrequency (%)
) 152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 152
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9950
84.9%
Latin 992
 
8.5%
Common 773
 
6.6%
Han 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 802
 
8.1%
헤 748
 
7.5%
이 310
 
3.1%
스 283
 
2.8%
미 269
 
2.7%
리 254
 
2.6%
일 203
 
2.0%
네 200
 
2.0%
라 150
 
1.5%
용 146
 
1.5%
Other values (541) 6585
66.2%
Latin
ValueCountFrequency (%)
a 78
 
7.9%
i 66
 
6.7%
e 51
 
5.1%
A 50
 
5.0%
o 44
 
4.4%
n 43
 
4.3%
r 41
 
4.1%
N 38
 
3.8%
l 36
 
3.6%
h 31
 
3.1%
Other values (42) 514
51.8%
Common
ValueCountFrequency (%)
324
41.9%
) 152
19.7%
( 152
19.7%
& 29
 
3.8%
2 17
 
2.2%
# 16
 
2.1%
, 14
 
1.8%
. 12
 
1.6%
1 10
 
1.3%
0 8
 
1.0%
Other values (13) 39
 
5.0%
Han
ValueCountFrequency (%)
美 4
57.1%
秀 2
28.6%
人 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9947
84.9%
ASCII 1762
 
15.0%
CJK 7
 
0.1%
Compat Jamo 3
 
< 0.1%
None 2
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 802
 
8.1%
헤 748
 
7.5%
이 310
 
3.1%
스 283
 
2.8%
미 269
 
2.7%
리 254
 
2.6%
일 203
 
2.0%
네 200
 
2.0%
라 150
 
1.5%
용 146
 
1.5%
Other values (539) 6582
66.2%
ASCII
ValueCountFrequency (%)
324
18.4%
) 152
 
8.6%
( 152
 
8.6%
a 78
 
4.4%
i 66
 
3.7%
e 51
 
2.9%
A 50
 
2.8%
o 44
 
2.5%
n 43
 
2.4%
r 41
 
2.3%
Other values (62) 761
43.2%
CJK
ValueCountFrequency (%)
美 4
57.1%
秀 2
28.6%
人 1
 
14.3%
Compat Jamo
ValueCountFrequency (%)
ㅇ 2
66.7%
ㅛ 1
33.3%
None
ValueCountFrequency (%)
· 1
50.0%
＆ 1
50.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

영업소주소(도로명)
Text

Distinct1895
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
2023-12-12T13:34:51.549515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length52
Mean length38.479253
Min length9

Characters and Unicode

Total characters74188
Distinct characters342
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1866 ?
Unique (%)96.8%

Sample

1st row경기도 안양시 만안구 양화로135번길 25 (박달동)
2nd row경기도 안양시 만안구 병목안로130번길 178 (안양동,,30)
3rd row경기도 안양시 만안구 수리산로 25 (안양동,지상1층)
4th row경기도 안양시 만안구 석천로 166-1 (석수동,1층)
5th row경기도 안양시 만안구 양화로28번길 69 (안양동)
ValueCountFrequency (%)
안양시 1928
 
12.7%
경기도 1927
 
12.6%
동안구 1130
 
7.4%
만안구 797
 
5.2%
안양동 488
 
3.2%
1층 393
 
2.6%
호계동 372
 
2.4%
관양동 348
 
2.3%
지상1층 214
 
1.4%
2층 210
 
1.4%
Other values (1690) 7429
48.8%
2023-12-12T13:34:52.193842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13308
 
17.9%
안 5023
 
6.8%
동 3496
 
4.7%
양 3290
 
4.4%
1 3114
 
4.2%
2 2145
 
2.9%
경 2090
 
2.8%
시 2086
 
2.8%
, 2086
 
2.8%
) 1988
 
2.7%
Other values (332) 35562
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42376
57.1%
Space Separator 13308
 
17.9%
Decimal Number 11949
 
16.1%
Other Punctuation 2094
 
2.8%
Close Punctuation 1988
 
2.7%
Open Punctuation 1988
 
2.7%
Uppercase Letter 291
 
0.4%
Dash Punctuation 179
 
0.2%
Lowercase Letter 10
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 5023
 
11.9%
동 3496
 
8.2%
양 3290
 
7.8%
경 2090
 
4.9%
시 2086
 
4.9%
로 1964
 
4.6%
구 1938
 
4.6%
도 1934
 
4.6%
기 1927
 
4.5%
호 1623
 
3.8%
Other values (287) 17005
40.1%
Uppercase Letter
ValueCountFrequency (%)
B 52
17.9%
A 45
15.5%
W 25
8.6%
C 22
 
7.6%
N 21
 
7.2%
O 19
 
6.5%
P 13
 
4.5%
L 12
 
4.1%
K 12
 
4.1%
T 11
 
3.8%
Other values (11) 59
20.3%
Decimal Number
ValueCountFrequency (%)
1 3114
26.1%
2 2145
18.0%
3 1412
11.8%
0 1341
11.2%
4 852
 
7.1%
5 770
 
6.4%
6 660
 
5.5%
7 634
 
5.3%
9 524
 
4.4%
8 497
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
e 7
70.0%
a 1
 
10.0%
w 1
 
10.0%
c 1
 
10.0%
Math Symbol
ValueCountFrequency (%)
~ 1
33.3%
> 1
33.3%
< 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 2086
99.6%
. 8
 
0.4%
Space Separator
ValueCountFrequency (%)
13308
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1988
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1988
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 179
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42376
57.1%
Common 31509
42.5%
Latin 303
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 5023
 
11.9%
동 3496
 
8.2%
양 3290
 
7.8%
경 2090
 
4.9%
시 2086
 
4.9%
로 1964
 
4.6%
구 1938
 
4.6%
도 1934
 
4.6%
기 1927
 
4.5%
호 1623
 
3.8%
Other values (287) 17005
40.1%
Latin
ValueCountFrequency (%)
B 52
17.2%
A 45
14.9%
W 25
 
8.3%
C 22
 
7.3%
N 21
 
6.9%
O 19
 
6.3%
P 13
 
4.3%
L 12
 
4.0%
K 12
 
4.0%
T 11
 
3.6%
Other values (16) 71
23.4%
Common
ValueCountFrequency (%)
13308
42.2%
1 3114
 
9.9%
2 2145
 
6.8%
, 2086
 
6.6%
) 1988
 
6.3%
( 1988
 
6.3%
3 1412
 
4.5%
0 1341
 
4.3%
4 852
 
2.7%
5 770
 
2.4%
Other values (9) 2505
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42376
57.1%
ASCII 31810
42.9%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13308
41.8%
1 3114
 
9.8%
2 2145
 
6.7%
, 2086
 
6.6%
) 1988
 
6.2%
( 1988
 
6.2%
3 1412
 
4.4%
0 1341
 
4.2%
4 852
 
2.7%
5 770
 
2.4%
Other values (34) 2806
 
8.8%
Hangul
ValueCountFrequency (%)
안 5023
 
11.9%
동 3496
 
8.2%
양 3290
 
7.8%
경 2090
 
4.9%
시 2086
 
4.9%
로 1964
 
4.6%
구 1938
 
4.6%
도 1934
 
4.6%
기 1927
 
4.5%
호 1623
 
3.8%
Other values (287) 17005
40.1%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
100.0%

전화번호
Text

MISSING 

Distinct996
Distinct (%)99.0%
Missing922
Missing (%)47.8%
Memory size15.2 KiB
2023-12-12T13:34:52.546197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.068588
Min length11

Characters and Unicode

Total characters12141
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

Unique986 ?
Unique (%)98.0%

Sample

1st row031-443-1620
2nd row031-449-4938
3rd row031-446-2873
4th row031-444-1257
5th row031-441-6952
ValueCountFrequency (%)
031-382-1671 2
 
0.2%
031-455-3464 2
 
0.2%
031-465-7172 2
 
0.2%
031-340-7797 2
 
0.2%
031-472-7573 2
 
0.2%
031-421-8118 2
 
0.2%
031-464-0012 2
 
0.2%
031-447-9104 2
 
0.2%
031-465-6505 2
 
0.2%
031-429-7444 2
 
0.2%
Other values (986) 986
98.0%
2023-12-12T13:34:53.071767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2012
16.6%
3 1786
14.7%
0 1556
12.8%
1 1492
12.3%
4 1413
11.6%
8 804
 
6.6%
7 710
 
5.8%
2 689
 
5.7%
5 621
 
5.1%
6 617
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10129
83.4%
Dash Punctuation 2012
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1786
17.6%
0 1556
15.4%
1 1492
14.7%
4 1413
14.0%
8 804
7.9%
7 710
 
7.0%
2 689
 
6.8%
5 621
 
6.1%
6 617
 
6.1%
9 441
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 2012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2012
16.6%
3 1786
14.7%
0 1556
12.8%
1 1492
12.3%
4 1413
11.6%
8 804
 
6.6%
7 710
 
5.8%
2 689
 
5.7%
5 621
 
5.1%
6 617
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2012
16.6%
3 1786
14.7%
0 1556
12.8%
1 1492
12.3%
4 1413
11.6%
8 804
 
6.6%
7 710
 
5.8%
2 689
 
5.7%
5 621
 
5.1%
6 617
 
5.1%

Missing values

2023-12-12T13:34:49.940950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:34:50.028047image/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

업종명업소명영업소주소(도로명)전화번호
0이용업뉴금강경기도 안양시 만안구 양화로135번길 25 (박달동)<NA>
1이용업대성경기도 안양시 만안구 병목안로130번길 178 (안양동,,30)031-443-1620
2이용업대중경기도 안양시 만안구 수리산로 25 (안양동,지상1층)031-449-4938
3이용업상주이발경기도 안양시 만안구 석천로 166-1 (석수동,1층)<NA>
4이용업안양공고구내경기도 안양시 만안구 양화로28번길 69 (안양동)031-446-2873
5이용업대전경기도 안양시 만안구 안양로139번길 19 (안양동)<NA>
6이용업이화경기도 안양시 만안구 안양로369번길 8 (안양동)031-444-1257
7이용업대운경기도 안양시 만안구 덕천로 112, 지상1층 (안양동)031-441-6952
8이용업원경기도 안양시 만안구 소곡로 10 (안양동)<NA>
9이용업성심이용원경기도 안양시 만안구 양화로128번길 18 (박달동,지상1층)031-466-2668
업종명업소명영업소주소(도로명)전화번호
1918피부미용업, 네일미용업, 화장ㆍ분장 미용업혀니네일경기도 안양시 동안구 관평로313번길 23, 1층 (관양동)<NA>
1919피부미용업, 네일미용업, 화장ㆍ분장 미용업네일리브레경기도 안양시 동안구 평촌대로227번길 26, 세종상가 301호 (호계동)031-383-7874
1920피부미용업, 네일미용업, 화장ㆍ분장 미용업프리티네일경기도 안양시 동안구 평촌대로211번길 16, 삼희월드프라자 3층 313호 (호계동)031-385-0994
1921피부미용업, 네일미용업, 화장ㆍ분장 미용업라쎄나경기도 안양시 동안구 동편로183번길 86, 1층 (관양동)<NA>
1922피부미용업, 네일미용업, 화장ㆍ분장 미용업노브살롱 범계점경기도 안양시 동안구 평촌대로217번길 27, 백두프라자 302호 (호계동)<NA>
1923피부미용업, 네일미용업, 화장ㆍ분장 미용업미앤드경기도 안양시 동안구 경수대로 539, 루미에르 205층 (호계동)<NA>
1924피부미용업, 네일미용업, 화장ㆍ분장 미용업코코네일경기도 안양시 동안구 관악대로434번길 19, 지상1층 (관양동)<NA>
1925피부미용업, 네일미용업, 화장ㆍ분장 미용업포쉬네일 롯데평촌점경기도 안양시 동안구 시민대로160번길 20, G.SQURE 주차동 2층 (호계동)031-388-4179
1926피부미용업, 네일미용업, 화장ㆍ분장 미용업네일제이스경기도 안양시 동안구 경수대로584번길 8, 3층 일부호 (호계동)<NA>
1927피부미용업, 네일미용업, 화장ㆍ분장 미용업뷰티숩 네일 왁싱 속눈썹 스킨경기도 안양시 동안구 평촌대로217번길 19, 백산프라자 403호 (호계동)<NA>

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업종명업소명영업소주소(도로명)전화번호# duplicates
0일반미용업머리사랑경기도 안양시 만안구 냉천로 126 (안양동)031-465-65052