Overview

Dataset statistics

Number of variables6
Number of observations977
Missing cells1953
Missing cells (%)33.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.9 KiB
Average record size in memory49.1 B

Variable types

Categorical2
Text3
Unsupported1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15178/S/1/datasetView.do

Alerts

Unnamed: 5 has constant value ""Constant
Unnamed: 4 has 977 (100.0%) missing valuesMissing
Unnamed: 5 has 976 (99.9%) missing valuesMissing
주소 has unique valuesUnique
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 09:56:53.404763
Analysis finished2023-12-11 09:56:53.996035
Duration0.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

브랜드명
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
GS25
313 
CU
252 
세븐일레븐
251 
미니스톱
150 
씨스페이스
 
10

Length

Max length6
Median length5
Mean length3.7533265
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
GS25 313
32.0%
CU 252
25.8%
세븐일레븐 251
25.7%
미니스톱 150
15.4%
씨스페이스 10
 
1.0%
CU(신규) 1
 
0.1%

Length

2023-12-11T18:56:54.058510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:56:54.155010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gs25 313
32.0%
cu 252
25.8%
세븐일레븐 251
25.7%
미니스톱 150
15.4%
씨스페이스 10
 
1.0%
cu(신규 1
 
0.1%

자치구
Categorical

Distinct24
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
강남구
146 
송파구
63 
서초구
 
59
동대문구
 
50
마포구
 
47
Other values (19)
612 

Length

Max length4
Median length3
Mean length3.0747185
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강남구 146
 
14.9%
송파구 63
 
6.4%
서초구 59
 
6.0%
동대문구 50
 
5.1%
마포구 47
 
4.8%
강동구 47
 
4.8%
성북구 42
 
4.3%
관악구 42
 
4.3%
영등포구 39
 
4.0%
구로구 37
 
3.8%
Other values (14) 405
41.5%

Length

2023-12-11T18:56:54.271626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 146
 
14.9%
송파구 63
 
6.4%
서초구 59
 
6.0%
동대문구 50
 
5.1%
마포구 47
 
4.8%
강동구 47
 
4.8%
성북구 42
 
4.3%
관악구 42
 
4.3%
영등포구 39
 
4.0%
구로구 37
 
3.8%
Other values (14) 405
41.5%

점포명
Text

Distinct966
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2023-12-11T18:56:54.460262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length5.6499488
Min length2

Characters and Unicode

Total characters5520
Distinct characters377
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique955 ?
Unique (%)97.7%

Sample

1st row남대문로5가점
2nd row남대문점
3rd row명동화이자점
4th row순화점
5th row중구필동점
ValueCountFrequency (%)
세븐일레븐 45
 
4.3%
b 16
 
1.5%
대방점 2
 
0.2%
동부이촌점 2
 
0.2%
문정공원점 2
 
0.2%
개포사랑 2
 
0.2%
구일역점 2
 
0.2%
장위점 2
 
0.2%
역삼타운점 2
 
0.2%
장안햇살 2
 
0.2%
Other values (961) 967
92.6%
2023-12-11T18:56:54.763875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 706
 
12.8%
동 165
 
3.0%
대 124
 
2.2%
역 98
 
1.8%
븐 91
 
1.6%
일 79
 
1.4%
신 78
 
1.4%
성 77
 
1.4%
삼 77
 
1.4%
74
 
1.3%
Other values (367) 3951
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5126
92.9%
Decimal Number 148
 
2.7%
Space Separator 74
 
1.3%
Open Punctuation 62
 
1.1%
Close Punctuation 62
 
1.1%
Uppercase Letter 47
 
0.9%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 706
 
13.8%
동 165
 
3.2%
대 124
 
2.4%
역 98
 
1.9%
븐 91
 
1.8%
일 79
 
1.5%
신 78
 
1.5%
성 77
 
1.5%
삼 77
 
1.5%
로 69
 
1.3%
Other values (336) 3562
69.5%
Uppercase Letter
ValueCountFrequency (%)
B 17
36.2%
C 4
 
8.5%
K 3
 
6.4%
I 3
 
6.4%
T 2
 
4.3%
N 2
 
4.3%
L 2
 
4.3%
G 2
 
4.3%
E 2
 
4.3%
M 2
 
4.3%
Other values (7) 8
17.0%
Decimal Number
ValueCountFrequency (%)
2 47
31.8%
1 35
23.6%
3 32
21.6%
4 13
 
8.8%
5 7
 
4.7%
8 4
 
2.7%
7 3
 
2.0%
6 3
 
2.0%
9 2
 
1.4%
0 2
 
1.4%
Space Separator
ValueCountFrequency (%)
74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5126
92.9%
Common 347
 
6.3%
Latin 47
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 706
 
13.8%
동 165
 
3.2%
대 124
 
2.4%
역 98
 
1.9%
븐 91
 
1.8%
일 79
 
1.5%
신 78
 
1.5%
성 77
 
1.5%
삼 77
 
1.5%
로 69
 
1.3%
Other values (336) 3562
69.5%
Latin
ValueCountFrequency (%)
B 17
36.2%
C 4
 
8.5%
K 3
 
6.4%
I 3
 
6.4%
T 2
 
4.3%
N 2
 
4.3%
L 2
 
4.3%
G 2
 
4.3%
E 2
 
4.3%
M 2
 
4.3%
Other values (7) 8
17.0%
Common
ValueCountFrequency (%)
74
21.3%
( 62
17.9%
) 62
17.9%
2 47
13.5%
1 35
10.1%
3 32
9.2%
4 13
 
3.7%
5 7
 
2.0%
8 4
 
1.2%
7 3
 
0.9%
Other values (4) 8
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5126
92.9%
ASCII 394
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 706
 
13.8%
동 165
 
3.2%
대 124
 
2.4%
역 98
 
1.9%
븐 91
 
1.8%
일 79
 
1.5%
신 78
 
1.5%
성 77
 
1.5%
삼 77
 
1.5%
로 69
 
1.3%
Other values (336) 3562
69.5%
ASCII
ValueCountFrequency (%)
74
18.8%
( 62
15.7%
) 62
15.7%
2 47
11.9%
1 35
8.9%
3 32
8.1%
B 17
 
4.3%
4 13
 
3.3%
5 7
 
1.8%
C 4
 
1.0%
Other values (21) 41
10.4%

주소
Text

UNIQUE 

Distinct977
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2023-12-11T18:56:55.010360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length44
Mean length22.807574
Min length6

Characters and Unicode

Total characters22283
Distinct characters369
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique977 ?
Unique (%)100.0%

Sample

1st row남대문로5가 84-6번지
2nd row남대문로3가 85번지
3rd row서울특별시 중구 회현동3가 1-9번지
4th row서울특별시 중구 순화동 1-104번지
5th row서울 중구 서애로21 1층
ValueCountFrequency (%)
서울 348
 
8.0%
서울특별시 315
 
7.3%
강남구 132
 
3.0%
1층 122
 
2.8%
서울시 114
 
2.6%
서초구 55
 
1.3%
동대문구 47
 
1.1%
송파구 45
 
1.0%
성북구 40
 
0.9%
강동구 37
 
0.9%
Other values (1992) 3080
71.0%
2023-12-11T18:56:55.394684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3450
 
15.5%
1 1400
 
6.3%
동 1154
 
5.2%
서 914
 
4.1%
구 878
 
3.9%
울 777
 
3.5%
2 742
 
3.3%
- 722
 
3.2%
3 632
 
2.8%
4 529
 
2.4%
Other values (359) 11085
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11460
51.4%
Decimal Number 5646
25.3%
Space Separator 3450
 
15.5%
Dash Punctuation 722
 
3.2%
Close Punctuation 351
 
1.6%
Open Punctuation 351
 
1.6%
Other Punctuation 215
 
1.0%
Uppercase Letter 68
 
0.3%
Lowercase Letter 17
 
0.1%
Control 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1154
 
10.1%
서 914
 
8.0%
구 878
 
7.7%
울 777
 
6.8%
시 440
 
3.8%
로 431
 
3.8%
특 315
 
2.7%
별 315
 
2.7%
길 275
 
2.4%
지 253
 
2.2%
Other values (308) 5708
49.8%
Uppercase Letter
ValueCountFrequency (%)
B 15
22.1%
A 10
14.7%
M 5
 
7.4%
C 5
 
7.4%
E 4
 
5.9%
S 4
 
5.9%
D 3
 
4.4%
I 3
 
4.4%
P 3
 
4.4%
L 3
 
4.4%
Other values (10) 13
19.1%
Lowercase Letter
ValueCountFrequency (%)
a 3
17.6%
k 2
11.8%
l 2
11.8%
o 2
11.8%
e 2
11.8%
t 1
 
5.9%
s 1
 
5.9%
r 1
 
5.9%
j 1
 
5.9%
g 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 1400
24.8%
2 742
13.1%
3 632
11.2%
4 529
 
9.4%
0 452
 
8.0%
6 428
 
7.6%
5 408
 
7.2%
7 375
 
6.6%
8 370
 
6.6%
9 310
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 206
95.8%
. 5
 
2.3%
@ 3
 
1.4%
/ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
3450
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 722
100.0%
Close Punctuation
ValueCountFrequency (%)
) 351
100.0%
Open Punctuation
ValueCountFrequency (%)
( 351
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11459
51.4%
Common 10738
48.2%
Latin 85
 
0.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1154
 
10.1%
서 914
 
8.0%
구 878
 
7.7%
울 777
 
6.8%
시 440
 
3.8%
로 431
 
3.8%
특 315
 
2.7%
별 315
 
2.7%
길 275
 
2.4%
지 253
 
2.2%
Other values (307) 5707
49.8%
Latin
ValueCountFrequency (%)
B 15
17.6%
A 10
 
11.8%
M 5
 
5.9%
C 5
 
5.9%
E 4
 
4.7%
S 4
 
4.7%
D 3
 
3.5%
I 3
 
3.5%
P 3
 
3.5%
L 3
 
3.5%
Other values (21) 30
35.3%
Common
ValueCountFrequency (%)
3450
32.1%
1 1400
13.0%
2 742
 
6.9%
- 722
 
6.7%
3 632
 
5.9%
4 529
 
4.9%
0 452
 
4.2%
6 428
 
4.0%
5 408
 
3.8%
7 375
 
3.5%
Other values (10) 1600
14.9%
Han
ValueCountFrequency (%)
現 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11459
51.4%
ASCII 10823
48.6%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3450
31.9%
1 1400
12.9%
2 742
 
6.9%
- 722
 
6.7%
3 632
 
5.8%
4 529
 
4.9%
0 452
 
4.2%
6 428
 
4.0%
5 408
 
3.8%
7 375
 
3.5%
Other values (41) 1685
15.6%
Hangul
ValueCountFrequency (%)
동 1154
 
10.1%
서 914
 
8.0%
구 878
 
7.7%
울 777
 
6.8%
시 440
 
3.8%
로 431
 
3.8%
특 315
 
2.7%
별 315
 
2.7%
길 275
 
2.4%
지 253
 
2.2%
Other values (307) 5707
49.8%
CJK
ValueCountFrequency (%)
現 1
100.0%

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing977
Missing (%)100.0%
Memory size8.7 KiB

Unnamed: 5
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing976
Missing (%)99.9%
Memory size7.8 KiB
2023-12-11T18:56:55.534216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters35
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row이촌로 303 현대아파트 21동 104호-나호, 105-가호
ValueCountFrequency (%)
이촌로 1
16.7%
303 1
16.7%
현대아파트 1
16.7%
21동 1
16.7%
104호-나호 1
16.7%
105-가호 1
16.7%
2023-12-11T18:56:55.780615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
20.0%
1 3
 
8.6%
0 3
 
8.6%
호 3
 
8.6%
3 2
 
5.7%
- 2
 
5.7%
이 1
 
2.9%
5 1
 
2.9%
, 1
 
2.9%
나 1
 
2.9%
Other values (11) 11
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14
40.0%
Decimal Number 11
31.4%
Space Separator 7
20.0%
Dash Punctuation 2
 
5.7%
Other Punctuation 1
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 3
21.4%
이 1
 
7.1%
나 1
 
7.1%
동 1
 
7.1%
트 1
 
7.1%
촌 1
 
7.1%
파 1
 
7.1%
아 1
 
7.1%
대 1
 
7.1%
현 1
 
7.1%
Other values (2) 2
14.3%
Decimal Number
ValueCountFrequency (%)
1 3
27.3%
0 3
27.3%
3 2
18.2%
5 1
 
9.1%
4 1
 
9.1%
2 1
 
9.1%
Space Separator
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
60.0%
Hangul 14
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 3
21.4%
이 1
 
7.1%
나 1
 
7.1%
동 1
 
7.1%
트 1
 
7.1%
촌 1
 
7.1%
파 1
 
7.1%
아 1
 
7.1%
대 1
 
7.1%
현 1
 
7.1%
Other values (2) 2
14.3%
Common
ValueCountFrequency (%)
7
33.3%
1 3
14.3%
0 3
14.3%
3 2
 
9.5%
- 2
 
9.5%
5 1
 
4.8%
, 1
 
4.8%
4 1
 
4.8%
2 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
60.0%
Hangul 14
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7
33.3%
1 3
14.3%
0 3
14.3%
3 2
 
9.5%
- 2
 
9.5%
5 1
 
4.8%
, 1
 
4.8%
4 1
 
4.8%
2 1
 
4.8%
Hangul
ValueCountFrequency (%)
호 3
21.4%
이 1
 
7.1%
나 1
 
7.1%
동 1
 
7.1%
트 1
 
7.1%
촌 1
 
7.1%
파 1
 
7.1%
아 1
 
7.1%
대 1
 
7.1%
현 1
 
7.1%
Other values (2) 2
14.3%

Correlations

2023-12-11T18:56:55.861426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
브랜드명자치구
브랜드명1.0000.420
자치구0.4201.000
2023-12-11T18:56:55.940230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구브랜드명
자치구1.0000.180
브랜드명0.1801.000
2023-12-11T18:56:56.013711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
브랜드명자치구
브랜드명1.0000.180
자치구0.1801.000

Missing values

2023-12-11T18:56:53.851878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:56:53.954976image/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

브랜드명자치구점포명주소Unnamed: 4Unnamed: 5
0CU중구남대문로5가점남대문로5가 84-6번지<NA><NA>
1CU중구남대문점남대문로3가 85번지<NA><NA>
2CU중구명동화이자점서울특별시 중구 회현동3가 1-9번지<NA><NA>
3CU중구순화점서울특별시 중구 순화동 1-104번지<NA><NA>
4CU중구중구필동점서울 중구 서애로21 1층<NA><NA>
5CU중구중구회현점서울특별시 중구 회현동1가 190번지<NA><NA>
6CU중구중구흥인점서울특별시 중구 흥인동 156번지<NA><NA>
7CU중구청구대로점서울특별시 중구 신당동 336-3번지<NA><NA>
8CU중구청구역점서울특별시 중구 신당동 387-3번지<NA><NA>
9GS25중구북창서울 중구 북창동 98<NA><NA>
브랜드명자치구점포명주소Unnamed: 4Unnamed: 5
967세븐일레븐강동구명일삼익점서울특별시 강동구 명일동 양재대로 128길 47<NA><NA>
968세븐일레븐강동구명일점서울 강동구 명일동 306-5<NA><NA>
969세븐일레븐강동구암사희망점암사동 469-17<NA><NA>
970세븐일레븐강동구천호쌍용점서울 강동구 천호동 432-10<NA><NA>
971세븐일레븐강동구천호역점서울 강동구 천호2동 429-2<NA><NA>
972세븐일레븐강동구세븐일레븐 성내삼성점서울특별시 강동구 성내로9길 351층 (성내동)<NA><NA>
973세븐일레븐강동구세븐일레븐 강동고덕점서울특별시 강동구 동남로75길 13-25<NA><NA>
974세븐일레븐강동구세븐일레븐 길동4호점서울특별시 강동구 명일로210 (길동)<NA><NA>
975씨스페이스강동구강동상일점서울시 강동구 상일동 437-8 1층<NA><NA>
976GS25강동구천호중앙점상암로162<NA><NA>