Overview

Dataset statistics

Number of variables9
Number of observations903
Missing cells396
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.6 KiB
Average record size in memory72.1 B

Variable types

Text4
Categorical3
DateTime2

Dataset

Description대구광역시_동구_담배소매인지정현황_20200428
Author대구광역시 동구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15035585&dataSetDetailId=150355851a43c1b3006c4&provdMethod=FILE

Alerts

영업구분 has constant value ""Constant
데이터기준일자 has constant value ""Constant
법인구분 is highly imbalanced (59.1%)Imbalance
업소전화번호 has 396 (43.9%) missing valuesMissing

Reproduction

Analysis started2024-04-19 05:21:51.867843
Analysis finished2024-04-19 05:21:52.670268
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업소명
Text

Distinct789
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2024-04-19T14:21:52.879467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length7.0885936
Min length1

Characters and Unicode

Total characters6401
Distinct characters462
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique760 ?
Unique (%)84.2%

Sample

1st row씨유 율하엘크루점
2nd row빅씨마트
3rd row나이스마트(방촌점)
4th row씨유 뉴이시아점
5th row세븐일레븐 대구용계타운점
ValueCountFrequency (%)
53
 
4.5%
세븐일레븐 37
 
3.1%
씨유 31
 
2.6%
gs25 24
 
2.0%
주)코리아세븐 16
 
1.4%
이마트24 10
 
0.8%
주식회사 10
 
0.8%
지에스(gs)25 10
 
0.8%
나이스마트 10
 
0.8%
홈마트 9
 
0.8%
Other values (828) 967
82.2%
2024-04-19T14:21:53.287298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 308
 
4.8%
280
 
4.4%
마 248
 
3.9%
트 248
 
3.9%
대 216
 
3.4%
구 187
 
2.9%
동 126
 
2.0%
신 122
 
1.9%
븐 104
 
1.6%
스 99
 
1.5%
Other values (452) 4463
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5527
86.3%
Space Separator 280
 
4.4%
Decimal Number 176
 
2.7%
Uppercase Letter 160
 
2.5%
Open Punctuation 88
 
1.4%
Close Punctuation 88
 
1.4%
Dash Punctuation 59
 
0.9%
Lowercase Letter 14
 
0.2%
Other Punctuation 8
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 308
 
5.6%
마 248
 
4.5%
트 248
 
4.5%
대 216
 
3.9%
구 187
 
3.4%
동 126
 
2.3%
신 122
 
2.2%
븐 104
 
1.9%
스 99
 
1.8%
유 95
 
1.7%
Other values (405) 3774
68.3%
Uppercase Letter
ValueCountFrequency (%)
S 52
32.5%
G 49
30.6%
C 13
 
8.1%
K 8
 
5.0%
U 6
 
3.8%
O 5
 
3.1%
M 4
 
2.5%
E 4
 
2.5%
L 3
 
1.9%
A 3
 
1.9%
Other values (9) 13
 
8.1%
Lowercase Letter
ValueCountFrequency (%)
e 2
14.3%
r 2
14.3%
l 2
14.3%
i 1
7.1%
u 1
7.1%
o 1
7.1%
k 1
7.1%
t 1
7.1%
a 1
7.1%
h 1
7.1%
Decimal Number
ValueCountFrequency (%)
2 77
43.8%
5 60
34.1%
4 15
 
8.5%
1 7
 
4.0%
3 5
 
2.8%
6 4
 
2.3%
0 4
 
2.3%
7 3
 
1.7%
8 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 5
62.5%
? 2
 
25.0%
& 1
 
12.5%
Space Separator
ValueCountFrequency (%)
280
100.0%
Open Punctuation
ValueCountFrequency (%)
( 88
100.0%
Close Punctuation
ValueCountFrequency (%)
) 88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%
Math Symbol
ValueCountFrequency (%)
∥ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5527
86.3%
Common 700
 
10.9%
Latin 174
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 308
 
5.6%
마 248
 
4.5%
트 248
 
4.5%
대 216
 
3.9%
구 187
 
3.4%
동 126
 
2.3%
신 122
 
2.2%
븐 104
 
1.9%
스 99
 
1.8%
유 95
 
1.7%
Other values (405) 3774
68.3%
Latin
ValueCountFrequency (%)
S 52
29.9%
G 49
28.2%
C 13
 
7.5%
K 8
 
4.6%
U 6
 
3.4%
O 5
 
2.9%
M 4
 
2.3%
E 4
 
2.3%
L 3
 
1.7%
A 3
 
1.7%
Other values (20) 27
15.5%
Common
ValueCountFrequency (%)
280
40.0%
( 88
 
12.6%
) 88
 
12.6%
2 77
 
11.0%
5 60
 
8.6%
- 59
 
8.4%
4 15
 
2.1%
1 7
 
1.0%
3 5
 
0.7%
. 5
 
0.7%
Other values (7) 16
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5524
86.3%
ASCII 873
 
13.6%
Compat Jamo 3
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 308
 
5.6%
마 248
 
4.5%
트 248
 
4.5%
대 216
 
3.9%
구 187
 
3.4%
동 126
 
2.3%
신 122
 
2.2%
븐 104
 
1.9%
스 99
 
1.8%
유 95
 
1.7%
Other values (403) 3771
68.3%
ASCII
ValueCountFrequency (%)
280
32.1%
( 88
 
10.1%
) 88
 
10.1%
2 77
 
8.8%
5 60
 
6.9%
- 59
 
6.8%
S 52
 
6.0%
G 49
 
5.6%
4 15
 
1.7%
C 13
 
1.5%
Other values (36) 92
 
10.5%
Compat Jamo
ValueCountFrequency (%)
ㅇ 2
66.7%
ㅅ 1
33.3%
Math Operators
ValueCountFrequency (%)
∥ 1
100.0%

소매인구분
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
578 
일반소매인
281 
구내소매인
 
35
구분없음
 
9

Length

Max length5
Median length1
Mean length2.4296788
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
578
64.0%
일반소매인 281
31.1%
구내소매인 35
 
3.9%
구분없음 9
 
1.0%

Length

2024-04-19T14:21:53.422818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:21:53.542763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반소매인 281
86.5%
구내소매인 35
 
10.8%
구분없음 9
 
2.8%

지번주소
Text

Distinct785
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2024-04-19T14:21:53.821676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length46
Mean length21.492802
Min length1

Characters and Unicode

Total characters19408
Distinct characters262
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

Unique777 ?
Unique (%)86.0%

Sample

1st row대구광역시 동구 용계동 755번지 91호
2nd row대구광역시 동구 신천동 335번지 8호
3rd row대구광역시 동구 방촌동 1084번지 716호
4th row대구광역시 동구 봉무동 1530번지 이시아폴리스 더샵 3차
5th row대구광역시 동구 용계동 388번지 6호
ValueCountFrequency (%)
대구광역시 790
 
17.9%
동구 789
 
17.8%
호 165
 
3.7%
신암동 115
 
2.6%
신천동 107
 
2.4%
1호 80
 
1.8%
효목동 64
 
1.4%
1층 63
 
1.4%
방촌동 60
 
1.4%
2호 49
 
1.1%
Other values (1001) 2140
48.4%
2024-04-19T14:21:54.264688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3983
20.5%
동 1675
 
8.6%
구 1598
 
8.2%
1 1008
 
5.2%
지 818
 
4.2%
대 817
 
4.2%
시 807
 
4.2%
호 806
 
4.2%
역 801
 
4.1%
광 791
 
4.1%
Other values (252) 6304
32.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11344
58.5%
Decimal Number 4017
 
20.7%
Space Separator 3983
 
20.5%
Other Punctuation 29
 
0.1%
Uppercase Letter 12
 
0.1%
Lowercase Letter 6
 
< 0.1%
Dash Punctuation 5
 
< 0.1%
Math Symbol 5
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1675
14.8%
구 1598
14.1%
지 818
 
7.2%
대 817
 
7.2%
시 807
 
7.1%
호 806
 
7.1%
역 801
 
7.1%
광 791
 
7.0%
번 755
 
6.7%
신 358
 
3.2%
Other values (222) 2118
18.7%
Decimal Number
ValueCountFrequency (%)
1 1008
25.1%
2 422
10.5%
0 390
 
9.7%
3 387
 
9.6%
5 378
 
9.4%
4 340
 
8.5%
6 320
 
8.0%
9 262
 
6.5%
7 261
 
6.5%
8 249
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
A 4
33.3%
B 3
25.0%
C 1
 
8.3%
K 1
 
8.3%
H 1
 
8.3%
G 1
 
8.3%
T 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
a 2
33.3%
k 1
16.7%
e 1
16.7%
n 1
16.7%
i 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 27
93.1%
/ 2
 
6.9%
Space Separator
ValueCountFrequency (%)
3983
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11344
58.5%
Common 8046
41.5%
Latin 18
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1675
14.8%
구 1598
14.1%
지 818
 
7.2%
대 817
 
7.2%
시 807
 
7.1%
호 806
 
7.1%
역 801
 
7.1%
광 791
 
7.0%
번 755
 
6.7%
신 358
 
3.2%
Other values (222) 2118
18.7%
Common
ValueCountFrequency (%)
3983
49.5%
1 1008
 
12.5%
2 422
 
5.2%
0 390
 
4.8%
3 387
 
4.8%
5 378
 
4.7%
4 340
 
4.2%
6 320
 
4.0%
9 262
 
3.3%
7 261
 
3.2%
Other values (8) 295
 
3.7%
Latin
ValueCountFrequency (%)
A 4
22.2%
B 3
16.7%
a 2
11.1%
k 1
 
5.6%
C 1
 
5.6%
K 1
 
5.6%
e 1
 
5.6%
n 1
 
5.6%
i 1
 
5.6%
H 1
 
5.6%
Other values (2) 2
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11344
58.5%
ASCII 8063
41.5%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3983
49.4%
1 1008
 
12.5%
2 422
 
5.2%
0 390
 
4.8%
3 387
 
4.8%
5 378
 
4.7%
4 340
 
4.2%
6 320
 
4.0%
9 262
 
3.2%
7 261
 
3.2%
Other values (19) 312
 
3.9%
Hangul
ValueCountFrequency (%)
동 1675
14.8%
구 1598
14.1%
지 818
 
7.2%
대 817
 
7.2%
시 807
 
7.1%
호 806
 
7.1%
역 801
 
7.1%
광 791
 
7.0%
번 755
 
6.7%
신 358
 
3.2%
Other values (222) 2118
18.7%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

도로명주소
Text

Distinct873
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2024-04-19T14:21:54.563811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length52
Mean length27.077519
Min length1

Characters and Unicode

Total characters24451
Distinct characters284
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

Unique862 ?
Unique (%)95.5%

Sample

1st row대구광역시 동구 반야월로2길 19 (용계동)
2nd row대구광역시 동구 동부로26길 43. 1층 (신천동)
3rd row대구광역시 동구 화랑로 387. 717 (방촌동)
4th row대구광역시 동구 팔공로51길 10. 1동 104호 (봉무동. 이시아폴리스 더샵 3차)
5th row대구광역시 동구 신평로28길 17. 1층 (용계동)
ValueCountFrequency (%)
동구 882
 
17.5%
대구광역시 881
 
17.5%
신암동 147
 
2.9%
1층 141
 
2.8%
신천동 123
 
2.4%
효목동 82
 
1.6%
방촌동 58
 
1.2%
율하동 48
 
1.0%
신서동 47
 
0.9%
아양로 41
 
0.8%
Other values (1012) 2581
51.3%
2024-04-19T14:21:54.997220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4268
17.5%
동 2195
 
9.0%
구 1814
 
7.4%
1 1084
 
4.4%
대 952
 
3.9%
로 917
 
3.8%
시 903
 
3.7%
역 900
 
3.7%
( 883
 
3.6%
) 883
 
3.6%
Other values (274) 9652
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13936
57.0%
Space Separator 4268
 
17.5%
Decimal Number 3846
 
15.7%
Open Punctuation 883
 
3.6%
Close Punctuation 883
 
3.6%
Other Punctuation 498
 
2.0%
Dash Punctuation 109
 
0.4%
Uppercase Letter 15
 
0.1%
Math Symbol 7
 
< 0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2195
15.8%
구 1814
13.0%
대 952
 
6.8%
로 917
 
6.6%
시 903
 
6.5%
역 900
 
6.5%
광 882
 
6.3%
신 475
 
3.4%
길 431
 
3.1%
호 226
 
1.6%
Other values (244) 4241
30.4%
Decimal Number
ValueCountFrequency (%)
1 1084
28.2%
2 542
14.1%
0 406
 
10.6%
3 350
 
9.1%
4 326
 
8.5%
5 320
 
8.3%
6 249
 
6.5%
7 207
 
5.4%
9 182
 
4.7%
8 180
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
A 5
33.3%
B 3
20.0%
C 2
 
13.3%
H 1
 
6.7%
G 1
 
6.7%
T 1
 
6.7%
D 1
 
6.7%
K 1
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
a 2
40.0%
n 1
20.0%
i 1
20.0%
e 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 497
99.8%
/ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
4268
100.0%
Open Punctuation
ValueCountFrequency (%)
( 883
100.0%
Close Punctuation
ValueCountFrequency (%)
) 883
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13936
57.0%
Common 10495
42.9%
Latin 20
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2195
15.8%
구 1814
13.0%
대 952
 
6.8%
로 917
 
6.6%
시 903
 
6.5%
역 900
 
6.5%
광 882
 
6.3%
신 475
 
3.4%
길 431
 
3.1%
호 226
 
1.6%
Other values (244) 4241
30.4%
Common
ValueCountFrequency (%)
4268
40.7%
1 1084
 
10.3%
( 883
 
8.4%
) 883
 
8.4%
2 542
 
5.2%
. 497
 
4.7%
0 406
 
3.9%
3 350
 
3.3%
4 326
 
3.1%
5 320
 
3.0%
Other values (8) 936
 
8.9%
Latin
ValueCountFrequency (%)
A 5
25.0%
B 3
15.0%
C 2
 
10.0%
a 2
 
10.0%
n 1
 
5.0%
i 1
 
5.0%
e 1
 
5.0%
H 1
 
5.0%
G 1
 
5.0%
T 1
 
5.0%
Other values (2) 2
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13936
57.0%
ASCII 10514
43.0%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4268
40.6%
1 1084
 
10.3%
( 883
 
8.4%
) 883
 
8.4%
2 542
 
5.2%
. 497
 
4.7%
0 406
 
3.9%
3 350
 
3.3%
4 326
 
3.1%
5 320
 
3.0%
Other values (19) 955
 
9.1%
Hangul
ValueCountFrequency (%)
동 2195
15.8%
구 1814
13.0%
대 952
 
6.8%
로 917
 
6.6%
시 903
 
6.5%
역 900
 
6.5%
광 882
 
6.3%
신 475
 
3.4%
길 431
 
3.1%
호 226
 
1.6%
Other values (244) 4241
30.4%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

업소전화번호
Text

MISSING 

Distinct482
Distinct (%)95.1%
Missing396
Missing (%)43.9%
Memory size7.2 KiB
2024-04-19T14:21:55.238677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.994083
Min length9

Characters and Unicode

Total characters6081
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

Unique470 ?
Unique (%)92.7%

Sample

1st row053-965-0826
2nd row053-939-3888
3rd row053-943-0300
4th row053-744-7779
5th row053-559-2080
ValueCountFrequency (%)
053-742-2631 12
 
2.4%
053-961-0448 3
 
0.6%
053-750-4482 3
 
0.6%
053-665-1052 3
 
0.6%
053-941-0836 2
 
0.4%
053-425-0551 2
 
0.4%
053-985-1495 2
 
0.4%
053-941-0019 2
 
0.4%
053-985-0111 2
 
0.4%
053-751-4511 2
 
0.4%
Other values (472) 474
93.5%
2024-04-19T14:21:55.615833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1012
16.6%
5 930
15.3%
0 807
13.3%
3 774
12.7%
9 556
9.1%
4 379
 
6.2%
8 353
 
5.8%
2 342
 
5.6%
1 340
 
5.6%
6 303
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5069
83.4%
Dash Punctuation 1012
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 930
18.3%
0 807
15.9%
3 774
15.3%
9 556
11.0%
4 379
7.5%
8 353
 
7.0%
2 342
 
6.7%
1 340
 
6.7%
6 303
 
6.0%
7 285
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 1012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6081
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1012
16.6%
5 930
15.3%
0 807
13.3%
3 774
12.7%
9 556
9.1%
4 379
 
6.2%
8 353
 
5.8%
2 342
 
5.6%
1 340
 
5.6%
6 303
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1012
16.6%
5 930
15.3%
0 807
13.3%
3 774
12.7%
9 556
9.1%
4 379
 
6.2%
8 353
 
5.8%
2 342
 
5.6%
1 340
 
5.6%
6 303
 
5.0%

지정일자
Date

Distinct769
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
Minimum1962-01-01 00:00:00
Maximum2020-04-02 00:00:00
2024-04-19T14:21:55.755738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:21:55.893271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

법인구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
개인
829 
법인
 
74

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 829
91.8%
법인 74
 
8.2%

Length

2024-04-19T14:21:56.017785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:21:56.104403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 829
91.8%
법인 74
 
8.2%

영업구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
정상영업
903 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상영업 903
100.0%

Length

2024-04-19T14:21:56.203258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:21:56.286717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상영업 903
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
Minimum2020-04-28 00:00:00
Maximum2020-04-28 00:00:00
2024-04-19T14:21:56.379445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:21:56.478818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-04-19T14:21:56.540992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소매인구분법인구분
소매인구분1.0000.163
법인구분0.1631.000
2024-04-19T14:21:56.634988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인구분소매인구분
법인구분1.0000.108
소매인구분0.1081.000
2024-04-19T14:21:56.728589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소매인구분법인구분
소매인구분1.0000.108
법인구분0.1081.000

Missing values

2024-04-19T14:21:52.485648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:21:52.616630image/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

업소명소매인구분지번주소도로명주소업소전화번호지정일자법인구분영업구분데이터기준일자
0씨유 율하엘크루점대구광역시 동구 용계동 755번지 91호대구광역시 동구 반야월로2길 19 (용계동)053-965-08262020-04-02개인정상영업2020-04-28
1빅씨마트대구광역시 동구 신천동 335번지 8호대구광역시 동구 동부로26길 43. 1층 (신천동)<NA>2020-03-27개인정상영업2020-04-28
2나이스마트(방촌점)대구광역시 동구 방촌동 1084번지 716호대구광역시 동구 화랑로 387. 717 (방촌동)<NA>2020-03-23개인정상영업2020-04-28
3씨유 뉴이시아점대구광역시 동구 봉무동 1530번지 이시아폴리스 더샵 3차대구광역시 동구 팔공로51길 10. 1동 104호 (봉무동. 이시아폴리스 더샵 3차)<NA>2020-03-17개인정상영업2020-04-28
4세븐일레븐 대구용계타운점대구광역시 동구 용계동 388번지 6호대구광역시 동구 신평로28길 17. 1층 (용계동)<NA>2020-03-17개인정상영업2020-04-28
5가고파대구광역시 동구 신암동 214번지 12호 궁전라벤더대구광역시 동구 아양로 52. 궁전라벤더 (신암동)<NA>2020-03-13개인정상영업2020-04-28
6마트 프라임대구광역시 동구 검사동 991번지 103호대구광역시 동구 해동로 177. 1층 (검사동)<NA>2020-03-13개인정상영업2020-04-28
7지저할인마트공항점대구광역시 동구 지저동 659번지 2호대구광역시 동구 공항로49길 14. 1층 (지저동)<NA>2020-03-05개인정상영업2020-04-28
8세븐일레븐 대구용계대로점대구광역시 동구 용계동 157번지 16호대구광역시 동구 반야월로 45 (용계동)<NA>2020-03-04개인정상영업2020-04-28
9오복공인중개사사무소대구광역시 동구 신서동 548번지 5호대구광역시 동구 동호로9길 75. 104호 (신서동)<NA>2020-02-25개인정상영업2020-04-28
업소명소매인구분지번주소도로명주소업소전화번호지정일자법인구분영업구분데이터기준일자
893동대구윤업사일반소매인대구광역시 동구 아양로 164-1 (신암동)053-941-35591964-01-01개인정상영업2020-04-28
894-일반소매인대구광역시 동구 신암동 221번지 16 호대구광역시 동구 동대구로99길 18 (신암동)053-958-15011984-12-27개인정상영업2020-04-28
895거북수퍼일반소매인대구광역시 동구 신암동 153번지 116호 12통 3반대구광역시 동구 동북로 394-6 (신암동)053-941-95921985-04-18개인정상영업2020-04-28
896-일반소매인대구광역시 동구 신암동 106번지 54호대구광역시 동구 동북로 417-1 (신암동)053-942-41881980-12-22개인정상영업2020-04-28
897-일반소매인대구광역시 동구 신암동 67호 보성2차상가 113 1대구광역시 동구 아양로49길 77. 113동 1호 (신암동.보성2차상가)053-957-94241988-12-26개인정상영업2020-04-28
898코리아세븐대구신암점일반소매인대구광역시 동구 신암동 20호대구광역시 동구 아양로39길 16 (신암동)053-952-71301987-01-08법인정상영업2020-04-28
899한빛의료기대구광역시 동구 효목동 526번지 1호대구광역시 동구 화랑로 105 (효목동)053-742-29222000-06-09개인정상영업2020-04-28
900대구축산업협동조합대구광역시 동구 신암동 645번지 4 호대구광역시 동구 동북로 296 (신암동)053-950-12722000-03-30법인정상영업2020-04-28
901-일반소매인대구광역시 동구 각산동 861번지 1호대구광역시 동구 반야월북로11길 42 (각산동)053-962-14842000-03-17개인정상영업2020-04-28
902영남슈퍼일반소매인대구광역시 동구 신암동 625번지 117 호대구광역시 동구 아양로15길 90-48 (신암동)053-942-52822000-02-10개인정상영업2020-04-28