Overview

Dataset statistics

Number of variables4
Number of observations380
Missing cells303
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory32.3 B

Variable types

Text3
Categorical1

Dataset

Description서울특별시 관악구 편의점 등 안전상비의약품으로 지정된 의약품을 판매하는 업소정보 현황(안전상비의약품판매업소명, 소재지 도로명 주소, 업소 전화번호)
URLhttps://www.data.go.kr/data/15048010/fileData.do

Alerts

데이터기준일 has constant value ""Constant
전화번호 has 303 (79.7%) missing valuesMissing
업소명 has unique valuesUnique
소재지도로명주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:24:17.875953
Analysis finished2023-12-12 03:24:18.380381
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업소명
Text

UNIQUE 

Distinct380
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T12:24:18.610528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length10.607895
Min length4

Characters and Unicode

Total characters4031
Distinct characters256
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)100.0%

Sample

1st row세븐일레븐 남현르메이에르점
2nd row씨유 관악남현점
3rd row세븐일레븐 사당고운점
4th row씨유 서울대학교신양점
5th rowGS25 서울대관악사점
ValueCountFrequency (%)
gs25 92
 
12.4%
씨유 75
 
10.1%
세븐일레븐 55
 
7.4%
cu 40
 
5.4%
지에스25 34
 
4.6%
주)코리아세븐 33
 
4.4%
이마트24 5
 
0.7%
지에스25(gs25 5
 
0.7%
지에스(gs)25 4
 
0.5%
봉천역점 3
 
0.4%
Other values (357) 396
53.4%
2023-12-12T12:24:19.456278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
363
 
9.0%
점 350
 
8.7%
관 176
 
4.4%
악 175
 
4.3%
2 164
 
4.1%
5 158
 
3.9%
븐 145
 
3.6%
신 111
 
2.8%
S 111
 
2.8%
G 109
 
2.7%
Other values (246) 2169
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2899
71.9%
Space Separator 363
 
9.0%
Decimal Number 335
 
8.3%
Uppercase Letter 319
 
7.9%
Open Punctuation 57
 
1.4%
Close Punctuation 57
 
1.4%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 350
 
12.1%
관 176
 
6.1%
악 175
 
6.0%
븐 145
 
5.0%
신 111
 
3.8%
림 106
 
3.7%
세 93
 
3.2%
유 85
 
2.9%
씨 83
 
2.9%
스 80
 
2.8%
Other values (226) 1495
51.6%
Uppercase Letter
ValueCountFrequency (%)
S 111
34.8%
G 109
34.2%
C 47
14.7%
U 45
14.1%
R 2
 
0.6%
T 1
 
0.3%
K 1
 
0.3%
F 1
 
0.3%
M 1
 
0.3%
V 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
2 164
49.0%
5 158
47.2%
4 9
 
2.7%
3 2
 
0.6%
1 1
 
0.3%
6 1
 
0.3%
Space Separator
ValueCountFrequency (%)
363
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2900
71.9%
Common 812
 
20.1%
Latin 319
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 350
 
12.1%
관 176
 
6.1%
악 175
 
6.0%
븐 145
 
5.0%
신 111
 
3.8%
림 106
 
3.7%
세 93
 
3.2%
유 85
 
2.9%
씨 83
 
2.9%
스 80
 
2.8%
Other values (227) 1496
51.6%
Latin
ValueCountFrequency (%)
S 111
34.8%
G 109
34.2%
C 47
14.7%
U 45
14.1%
R 2
 
0.6%
T 1
 
0.3%
K 1
 
0.3%
F 1
 
0.3%
M 1
 
0.3%
V 1
 
0.3%
Common
ValueCountFrequency (%)
363
44.7%
2 164
20.2%
5 158
19.5%
( 57
 
7.0%
) 57
 
7.0%
4 9
 
1.1%
3 2
 
0.2%
1 1
 
0.1%
6 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2899
71.9%
ASCII 1131
 
28.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
363
32.1%
2 164
14.5%
5 158
14.0%
S 111
 
9.8%
G 109
 
9.6%
( 57
 
5.0%
) 57
 
5.0%
C 47
 
4.2%
U 45
 
4.0%
4 9
 
0.8%
Other values (9) 11
 
1.0%
Hangul
ValueCountFrequency (%)
점 350
 
12.1%
관 176
 
6.1%
악 175
 
6.0%
븐 145
 
5.0%
신 111
 
3.8%
림 106
 
3.7%
세 93
 
3.2%
유 85
 
2.9%
씨 83
 
2.9%
스 80
 
2.8%
Other values (226) 1495
51.6%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지도로명주소
Text

UNIQUE 

Distinct380
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T12:24:19.844981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length47
Mean length29.994737
Min length21

Characters and Unicode

Total characters11398
Distinct characters220
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

Unique380 ?
Unique (%)100.0%

Sample

1st row서울특별시 관악구 과천대로 939 (남현동+ 르메이에르강남타운2)
2nd row서울특별시 관악구 과천대로 945+ 1층 (남현동)
3rd row서울특별시 관악구 과천대로 951 (남현동)
4th row서울특별시 관악구 관악로 1+ 서울대학교 16-1동 1층 (신림동)
5th row서울특별시 관악구 관악로 1+ 서울대학교 대학원기숙사 900동 지하2층 (신림동)
ValueCountFrequency (%)
서울특별시 380
16.5%
관악구 380
16.5%
신림동 196
 
8.5%
1층 193
 
8.4%
봉천동 159
 
6.9%
남부순환로 35
 
1.5%
101호 27
 
1.2%
신림로 18
 
0.8%
남현동 17
 
0.7%
지하1층 15
 
0.6%
Other values (553) 888
38.5%
2023-12-12T12:24:20.465577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1928
 
16.9%
1 609
 
5.3%
관 427
 
3.7%
악 414
 
3.6%
동 413
 
3.6%
서 400
 
3.5%
울 386
 
3.4%
시 385
 
3.4%
구 383
 
3.4%
별 380
 
3.3%
Other values (210) 5673
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6622
58.1%
Space Separator 1928
 
16.9%
Decimal Number 1693
 
14.9%
Close Punctuation 380
 
3.3%
Open Punctuation 380
 
3.3%
Math Symbol 354
 
3.1%
Uppercase Letter 22
 
0.2%
Dash Punctuation 18
 
0.2%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 427
 
6.4%
악 414
 
6.3%
동 413
 
6.2%
서 400
 
6.0%
울 386
 
5.8%
시 385
 
5.8%
구 383
 
5.8%
별 380
 
5.7%
특 380
 
5.7%
로 284
 
4.3%
Other values (189) 2770
41.8%
Decimal Number
ValueCountFrequency (%)
1 609
36.0%
2 212
 
12.5%
3 155
 
9.2%
0 144
 
8.5%
4 131
 
7.7%
6 117
 
6.9%
5 99
 
5.8%
7 91
 
5.4%
8 70
 
4.1%
9 65
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 16
72.7%
A 3
 
13.6%
C 2
 
9.1%
G 1
 
4.5%
Math Symbol
ValueCountFrequency (%)
+ 353
99.7%
~ 1
 
0.3%
Space Separator
ValueCountFrequency (%)
1928
100.0%
Close Punctuation
ValueCountFrequency (%)
) 380
100.0%
Open Punctuation
ValueCountFrequency (%)
( 380
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6622
58.1%
Common 4753
41.7%
Latin 23
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 427
 
6.4%
악 414
 
6.3%
동 413
 
6.2%
서 400
 
6.0%
울 386
 
5.8%
시 385
 
5.8%
구 383
 
5.8%
별 380
 
5.7%
특 380
 
5.7%
로 284
 
4.3%
Other values (189) 2770
41.8%
Common
ValueCountFrequency (%)
1928
40.6%
1 609
 
12.8%
) 380
 
8.0%
( 380
 
8.0%
+ 353
 
7.4%
2 212
 
4.5%
3 155
 
3.3%
0 144
 
3.0%
4 131
 
2.8%
6 117
 
2.5%
Other values (6) 344
 
7.2%
Latin
ValueCountFrequency (%)
B 16
69.6%
A 3
 
13.0%
C 2
 
8.7%
e 1
 
4.3%
G 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6622
58.1%
ASCII 4776
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1928
40.4%
1 609
 
12.8%
) 380
 
8.0%
( 380
 
8.0%
+ 353
 
7.4%
2 212
 
4.4%
3 155
 
3.2%
0 144
 
3.0%
4 131
 
2.7%
6 117
 
2.4%
Other values (11) 367
 
7.7%
Hangul
ValueCountFrequency (%)
관 427
 
6.4%
악 414
 
6.3%
동 413
 
6.2%
서 400
 
6.0%
울 386
 
5.8%
시 385
 
5.8%
구 383
 
5.8%
별 380
 
5.7%
특 380
 
5.7%
로 284
 
4.3%
Other values (189) 2770
41.8%

전화번호
Text

MISSING 

Distinct76
Distinct (%)98.7%
Missing303
Missing (%)79.7%
Memory size3.1 KiB
2023-12-12T12:24:20.772486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11
Min length9

Characters and Unicode

Total characters847
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

Unique75 ?
Unique (%)97.4%

Sample

1st row02-552-0755
2nd row02-881-9215
3rd row02-872-3337
4th row02-883-2009
5th row02-878-6702
ValueCountFrequency (%)
02-817-0286 2
 
2.6%
02-888-8038 1
 
1.3%
02-883-5859 1
 
1.3%
02-883-9600 1
 
1.3%
02-877-5780 1
 
1.3%
02-872-7658 1
 
1.3%
02-883-4991 1
 
1.3%
02-882-9828 1
 
1.3%
02-888-0295 1
 
1.3%
02-445-5896 1
 
1.3%
Other values (66) 66
85.7%
2023-12-12T12:24:21.355770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 153
18.1%
8 135
15.9%
2 125
14.8%
0 118
13.9%
7 68
8.0%
5 52
 
6.1%
6 47
 
5.5%
3 45
 
5.3%
1 41
 
4.8%
9 35
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 694
81.9%
Dash Punctuation 153
 
18.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 135
19.5%
2 125
18.0%
0 118
17.0%
7 68
9.8%
5 52
 
7.5%
6 47
 
6.8%
3 45
 
6.5%
1 41
 
5.9%
9 35
 
5.0%
4 28
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 847
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 153
18.1%
8 135
15.9%
2 125
14.8%
0 118
13.9%
7 68
8.0%
5 52
 
6.1%
6 47
 
5.5%
3 45
 
5.3%
1 41
 
4.8%
9 35
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 153
18.1%
8 135
15.9%
2 125
14.8%
0 118
13.9%
7 68
8.0%
5 52
 
6.1%
6 47
 
5.5%
3 45
 
5.3%
1 41
 
4.8%
9 35
 
4.1%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-07-31
380 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-31
2nd row2023-07-31
3rd row2023-07-31
4th row2023-07-31
5th row2023-07-31

Common Values

ValueCountFrequency (%)
2023-07-31 380
100.0%

Length

2023-12-12T12:24:21.561175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:24:21.706653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-31 380
100.0%

Missing values

2023-12-12T12:24:18.231596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:24:18.332162image/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

업소명소재지도로명주소전화번호데이터기준일
0세븐일레븐 남현르메이에르점서울특별시 관악구 과천대로 939 (남현동+ 르메이에르강남타운2)02-552-07552023-07-31
1씨유 관악남현점서울특별시 관악구 과천대로 945+ 1층 (남현동)<NA>2023-07-31
2세븐일레븐 사당고운점서울특별시 관악구 과천대로 951 (남현동)<NA>2023-07-31
3씨유 서울대학교신양점서울특별시 관악구 관악로 1+ 서울대학교 16-1동 1층 (신림동)<NA>2023-07-31
4GS25 서울대관악사점서울특별시 관악구 관악로 1+ 서울대학교 대학원기숙사 900동 지하2층 (신림동)02-881-92152023-07-31
5GS25 서울대제일점서울특별시 관악구 관악로 104+ 지층 (봉천동)<NA>2023-07-31
6GS25 관악세봉점서울특별시 관악구 관악로 125+ 101호 (봉천동+ 서울대입구삼성아파트)<NA>2023-07-31
7씨유 서울대서연점서울특별시 관악구 관악로 132+ 서울법학원 1층 (봉천동)<NA>2023-07-31
8지에스25(GS25) 서울대역점서울특별시 관악구 관악로 144 (봉천동)<NA>2023-07-31
9(주)코리아세븐 관악구청점서울특별시 관악구 관악로 154 (봉천동)<NA>2023-07-31
업소명소재지도로명주소전화번호데이터기준일
370GS25 신림미림점서울특별시 관악구 호암로 538+ 1층 (신림동)<NA>2023-07-31
371CU 관악미림점서울특별시 관악구 호암로20길 1+ 1층 (신림동)<NA>2023-07-31
372씨유 관악으뜸점서울특별시 관악구 호암로20길 43+ 부형원룸 (신림동)<NA>2023-07-31
373(주)코리아세븐 관악미림점서울특별시 관악구 호암로20길 67 (신림동)<NA>2023-07-31
374(주)코리아세븐 관악호암로점서울특별시 관악구 호암로22길 21 (신림동)<NA>2023-07-31
375(주)코리아세븐 신림대학스타점서울특별시 관악구 호암로24길 6 (신림동)<NA>2023-07-31
376GS25 관악고시촌점서울특별시 관악구 호암로24길 74+ 1층 (신림동+ 유림재고시연구원)<NA>2023-07-31
377CU 관악동원점서울특별시 관악구 호암로26가길 43 (신림동+ 관리형독서실)02-830-06092023-07-31
378씨유 신림테라스점서울특별시 관악구 호암로26길 47+ 1층 (신림동)<NA>2023-07-31
379지에스25 관악녹두서울특별시 관악구 호암로26길 9+ 1층 (신림동)<NA>2023-07-31