Overview

Dataset statistics

Number of variables7
Number of observations1020
Missing cells12
Missing cells (%)0.2%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory56.9 KiB
Average record size in memory57.1 B

Variable types

DateTime2
Text3
Numeric1
Categorical1

Dataset

Description서울특별시 성동구 휴게음식점 현황 저옵입니다. 인허가 일자, 업소명, 도로명주소, 지번주소, 영업장면적, 영업자시작일, 업태명 등의 정보를 포함하고 있습니다.
Author서울특별시 성동구
URLhttps://www.data.go.kr/data/15098757/fileData.do

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
도로명주소 has 11 (1.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 01:04:15.206521
Analysis finished2023-12-12 01:04:16.461238
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

인허가일자
Date

Distinct834
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
Minimum1976-02-05 00:00:00
Maximum2022-01-03 00:00:00
2023-12-12T10:04:16.554096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:04:16.763436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업소명
Text

Distinct1000
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
2023-12-12T10:04:17.050219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length29
Mean length8.9862745
Min length1

Characters and Unicode

Total characters9166
Distinct characters598
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique995 ?
Unique (%)97.5%

Sample

1st row왕다방
2nd row우희
3rd row학다방
4th row명커피숍
5th row피자스쿨
ValueCountFrequency (%)
씨유 32
 
1.9%
세븐일레븐 28
 
1.7%
gs25 27
 
1.6%
이마트24 24
 
1.5%
카페 23
 
1.4%
왕십리점 18
 
1.1%
coffee 17
 
1.0%
성수점 14
 
0.8%
한양대점 11
 
0.7%
빽다방 11
 
0.7%
Other values (1201) 1444
87.6%
2023-12-12T10:04:17.498634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
635
 
6.9%
점 424
 
4.6%
스 277
 
3.0%
이 229
 
2.5%
리 179
 
2.0%
( 174
 
1.9%
성 174
 
1.9%
) 173
 
1.9%
수 169
 
1.8%
피 149
 
1.6%
Other values (588) 6583
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6594
71.9%
Uppercase Letter 699
 
7.6%
Space Separator 635
 
6.9%
Lowercase Letter 577
 
6.3%
Decimal Number 271
 
3.0%
Open Punctuation 174
 
1.9%
Close Punctuation 173
 
1.9%
Other Punctuation 35
 
0.4%
Dash Punctuation 6
 
0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 424
 
6.4%
스 277
 
4.2%
이 229
 
3.5%
리 179
 
2.7%
성 174
 
2.6%
수 169
 
2.6%
피 149
 
2.3%
커 127
 
1.9%
트 117
 
1.8%
카 109
 
1.7%
Other values (513) 4640
70.4%
Uppercase Letter
ValueCountFrequency (%)
S 92
13.2%
C 76
 
10.9%
G 62
 
8.9%
E 61
 
8.7%
A 47
 
6.7%
O 45
 
6.4%
F 35
 
5.0%
P 30
 
4.3%
T 28
 
4.0%
U 27
 
3.9%
Other values (16) 196
28.0%
Lowercase Letter
ValueCountFrequency (%)
e 86
14.9%
o 62
10.7%
a 62
10.7%
f 40
 
6.9%
s 36
 
6.2%
n 34
 
5.9%
c 30
 
5.2%
i 28
 
4.9%
l 25
 
4.3%
t 25
 
4.3%
Other values (15) 149
25.8%
Decimal Number
ValueCountFrequency (%)
2 111
41.0%
5 69
25.5%
4 35
 
12.9%
1 18
 
6.6%
3 13
 
4.8%
0 11
 
4.1%
7 6
 
2.2%
8 3
 
1.1%
9 3
 
1.1%
6 2
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 10
28.6%
& 10
28.6%
' 7
20.0%
, 4
 
11.4%
? 1
 
2.9%
! 1
 
2.9%
: 1
 
2.9%
/ 1
 
2.9%
Space Separator
ValueCountFrequency (%)
635
100.0%
Open Punctuation
ValueCountFrequency (%)
( 174
100.0%
Close Punctuation
ValueCountFrequency (%)
) 173
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6593
71.9%
Common 1296
 
14.1%
Latin 1276
 
13.9%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 424
 
6.4%
스 277
 
4.2%
이 229
 
3.5%
리 179
 
2.7%
성 174
 
2.6%
수 169
 
2.6%
피 149
 
2.3%
커 127
 
1.9%
트 117
 
1.8%
카 109
 
1.7%
Other values (512) 4639
70.4%
Latin
ValueCountFrequency (%)
S 92
 
7.2%
e 86
 
6.7%
C 76
 
6.0%
G 62
 
4.9%
o 62
 
4.9%
a 62
 
4.9%
E 61
 
4.8%
A 47
 
3.7%
O 45
 
3.5%
f 40
 
3.1%
Other values (41) 643
50.4%
Common
ValueCountFrequency (%)
635
49.0%
( 174
 
13.4%
) 173
 
13.3%
2 111
 
8.6%
5 69
 
5.3%
4 35
 
2.7%
1 18
 
1.4%
3 13
 
1.0%
0 11
 
0.8%
. 10
 
0.8%
Other values (14) 47
 
3.6%
Han
ValueCountFrequency (%)
米 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6593
71.9%
ASCII 2571
 
28.0%
CJK 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
635
24.7%
( 174
 
6.8%
) 173
 
6.7%
2 111
 
4.3%
S 92
 
3.6%
e 86
 
3.3%
C 76
 
3.0%
5 69
 
2.7%
G 62
 
2.4%
o 62
 
2.4%
Other values (64) 1031
40.1%
Hangul
ValueCountFrequency (%)
점 424
 
6.4%
스 277
 
4.2%
이 229
 
3.5%
리 179
 
2.7%
성 174
 
2.6%
수 169
 
2.6%
피 149
 
2.3%
커 127
 
1.9%
트 117
 
1.8%
카 109
 
1.7%
Other values (512) 4639
70.4%
CJK
ValueCountFrequency (%)
米 1
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

도로명주소
Text

MISSING 

Distinct970
Distinct (%)96.1%
Missing11
Missing (%)1.1%
Memory size8.1 KiB
2023-12-12T10:04:17.874365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length46
Mean length29.535183
Min length16

Characters and Unicode

Total characters29801
Distinct characters307
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique945 ?
Unique (%)93.7%

Sample

1st row성동구 광나루로 296 (성수동2가)
2nd row성동구 독서당로 295-1 (금호동3가)
3rd row성동구 왕십리로 296-1 (행당동,,197-1)
4th row성동구 뚝섬로 389 (성수동2가)
5th row성동구 성덕정길 78 (성수동2가)
ValueCountFrequency (%)
성동구 1009
 
17.0%
1층 563
 
9.5%
성수동2가 225
 
3.8%
행당동 171
 
2.9%
성수동1가 170
 
2.9%
왕십리로 129
 
2.2%
2층 70
 
1.2%
하왕십리동 66
 
1.1%
지하1층 63
 
1.1%
17 57
 
1.0%
Other values (1124) 3395
57.4%
2023-12-12T10:04:18.452660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4912
 
16.5%
1 2275
 
7.6%
동 2204
 
7.4%
성 1607
 
5.4%
, 1271
 
4.3%
2 1061
 
3.6%
) 1046
 
3.5%
( 1046
 
3.5%
구 1017
 
3.4%
층 925
 
3.1%
Other values (297) 12437
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15198
51.0%
Decimal Number 5941
 
19.9%
Space Separator 4912
 
16.5%
Other Punctuation 1272
 
4.3%
Close Punctuation 1046
 
3.5%
Open Punctuation 1046
 
3.5%
Dash Punctuation 215
 
0.7%
Uppercase Letter 141
 
0.5%
Lowercase Letter 23
 
0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2204
 
14.5%
성 1607
 
10.6%
구 1017
 
6.7%
층 925
 
6.1%
로 747
 
4.9%
가 618
 
4.1%
수 610
 
4.0%
호 529
 
3.5%
길 529
 
3.5%
리 367
 
2.4%
Other values (251) 6045
39.8%
Uppercase Letter
ValueCountFrequency (%)
B 29
20.6%
A 17
12.1%
L 13
9.2%
T 10
 
7.1%
C 10
 
7.1%
R 10
 
7.1%
S 9
 
6.4%
E 7
 
5.0%
I 6
 
4.3%
K 5
 
3.5%
Other values (10) 25
17.7%
Decimal Number
ValueCountFrequency (%)
1 2275
38.3%
2 1061
17.9%
0 536
 
9.0%
3 489
 
8.2%
4 392
 
6.6%
7 296
 
5.0%
5 262
 
4.4%
6 225
 
3.8%
8 220
 
3.7%
9 185
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
e 7
30.4%
r 5
21.7%
o 4
17.4%
w 4
17.4%
t 1
 
4.3%
n 1
 
4.3%
m 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 1271
99.9%
· 1
 
0.1%
Letter Number
ValueCountFrequency (%)
Ⅲ 3
75.0%
Ⅱ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
4912
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1046
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1046
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 215
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15198
51.0%
Common 14435
48.4%
Latin 168
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2204
 
14.5%
성 1607
 
10.6%
구 1017
 
6.7%
층 925
 
6.1%
로 747
 
4.9%
가 618
 
4.1%
수 610
 
4.0%
호 529
 
3.5%
길 529
 
3.5%
리 367
 
2.4%
Other values (251) 6045
39.8%
Latin
ValueCountFrequency (%)
B 29
17.3%
A 17
 
10.1%
L 13
 
7.7%
T 10
 
6.0%
C 10
 
6.0%
R 10
 
6.0%
S 9
 
5.4%
e 7
 
4.2%
E 7
 
4.2%
I 6
 
3.6%
Other values (19) 50
29.8%
Common
ValueCountFrequency (%)
4912
34.0%
1 2275
15.8%
, 1271
 
8.8%
2 1061
 
7.4%
) 1046
 
7.2%
( 1046
 
7.2%
0 536
 
3.7%
3 489
 
3.4%
4 392
 
2.7%
7 296
 
2.1%
Other values (7) 1111
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15198
51.0%
ASCII 14598
49.0%
Number Forms 4
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4912
33.6%
1 2275
15.6%
, 1271
 
8.7%
2 1061
 
7.3%
) 1046
 
7.2%
( 1046
 
7.2%
0 536
 
3.7%
3 489
 
3.3%
4 392
 
2.7%
7 296
 
2.0%
Other values (33) 1274
 
8.7%
Hangul
ValueCountFrequency (%)
동 2204
 
14.5%
성 1607
 
10.6%
구 1017
 
6.7%
층 925
 
6.1%
로 747
 
4.9%
가 618
 
4.1%
수 610
 
4.0%
호 529
 
3.5%
길 529
 
3.5%
리 367
 
2.4%
Other values (251) 6045
39.8%
Number Forms
ValueCountFrequency (%)
Ⅲ 3
75.0%
Ⅱ 1
 
25.0%
None
ValueCountFrequency (%)
· 1
100.0%

지번주소
Text

Distinct850
Distinct (%)83.4%
Missing1
Missing (%)0.1%
Memory size8.1 KiB
2023-12-12T10:04:18.832487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length42
Mean length25.435721
Min length17

Characters and Unicode

Total characters25919
Distinct characters310
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

Unique778 ?
Unique (%)76.3%

Sample

1st row서울특별시 성동구 성수동2가 45
2nd row서울특별시 성동구 금호동3가 413
3rd row서울특별시 성동구 행당동 196-27 ,197-1
4th row서울특별시 성동구 성수동2가 339-1
5th row서울특별시 성동구 성수동2가 346-2
ValueCountFrequency (%)
서울특별시 1021
21.2%
성동구 1019
21.1%
성수동2가 250
 
5.2%
행당동 197
 
4.1%
성수동1가 175
 
3.6%
지상1층 75
 
1.6%
하왕십리동 68
 
1.4%
옥수동 48
 
1.0%
용답동 41
 
0.8%
1층 37
 
0.8%
Other values (1104) 1895
39.3%
2023-12-12T10:04:19.394155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4808
18.6%
동 2096
 
8.1%
성 1500
 
5.8%
1 1284
 
5.0%
서 1063
 
4.1%
울 1060
 
4.1%
시 1026
 
4.0%
별 1022
 
3.9%
구 1022
 
3.9%
특 1021
 
3.9%
Other values (300) 10017
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14775
57.0%
Decimal Number 5295
 
20.4%
Space Separator 4808
 
18.6%
Dash Punctuation 765
 
3.0%
Uppercase Letter 119
 
0.5%
Open Punctuation 57
 
0.2%
Close Punctuation 57
 
0.2%
Lowercase Letter 22
 
0.1%
Other Punctuation 16
 
0.1%
Letter Number 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2096
14.2%
성 1500
 
10.2%
서 1063
 
7.2%
울 1060
 
7.2%
시 1026
 
6.9%
별 1022
 
6.9%
구 1022
 
6.9%
특 1021
 
6.9%
가 551
 
3.7%
수 515
 
3.5%
Other values (254) 3899
26.4%
Uppercase Letter
ValueCountFrequency (%)
T 20
16.8%
A 14
11.8%
E 12
10.1%
C 9
 
7.6%
K 9
 
7.6%
I 8
 
6.7%
R 6
 
5.0%
O 5
 
4.2%
S 5
 
4.2%
L 4
 
3.4%
Other values (13) 27
22.7%
Decimal Number
ValueCountFrequency (%)
1 1284
24.2%
2 867
16.4%
3 561
10.6%
6 499
 
9.4%
0 415
 
7.8%
5 380
 
7.2%
7 351
 
6.6%
8 347
 
6.6%
4 317
 
6.0%
9 274
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
e 7
31.8%
r 5
22.7%
w 4
18.2%
o 4
18.2%
n 1
 
4.5%
t 1
 
4.5%
Letter Number
ValueCountFrequency (%)
Ⅲ 3
60.0%
Ⅱ 2
40.0%
Space Separator
ValueCountFrequency (%)
4808
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 765
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14775
57.0%
Common 10998
42.4%
Latin 146
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2096
14.2%
성 1500
 
10.2%
서 1063
 
7.2%
울 1060
 
7.2%
시 1026
 
6.9%
별 1022
 
6.9%
구 1022
 
6.9%
특 1021
 
6.9%
가 551
 
3.7%
수 515
 
3.5%
Other values (254) 3899
26.4%
Latin
ValueCountFrequency (%)
T 20
 
13.7%
A 14
 
9.6%
E 12
 
8.2%
C 9
 
6.2%
K 9
 
6.2%
I 8
 
5.5%
e 7
 
4.8%
R 6
 
4.1%
r 5
 
3.4%
O 5
 
3.4%
Other values (21) 51
34.9%
Common
ValueCountFrequency (%)
4808
43.7%
1 1284
 
11.7%
2 867
 
7.9%
- 765
 
7.0%
3 561
 
5.1%
6 499
 
4.5%
0 415
 
3.8%
5 380
 
3.5%
7 351
 
3.2%
8 347
 
3.2%
Other values (5) 721
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14774
57.0%
ASCII 11139
43.0%
Number Forms 5
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4808
43.2%
1 1284
 
11.5%
2 867
 
7.8%
- 765
 
6.9%
3 561
 
5.0%
6 499
 
4.5%
0 415
 
3.7%
5 380
 
3.4%
7 351
 
3.2%
8 347
 
3.1%
Other values (34) 862
 
7.7%
Hangul
ValueCountFrequency (%)
동 2096
14.2%
성 1500
 
10.2%
서 1063
 
7.2%
울 1060
 
7.2%
시 1026
 
6.9%
별 1022
 
6.9%
구 1022
 
6.9%
특 1021
 
6.9%
가 551
 
3.7%
수 515
 
3.5%
Other values (253) 3898
26.4%
Number Forms
ValueCountFrequency (%)
Ⅲ 3
60.0%
Ⅱ 2
40.0%
Compat Jamo
ValueCountFrequency (%)
ㅣ 1
100.0%

영업장면적
Real number (ℝ)

Distinct579
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.010275
Minimum0
Maximum538.62
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2023-12-12T10:04:19.603042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.3
Q110
median26
Q351.58
95-th percentile137
Maximum538.62
Range538.62
Interquartile range (IQR)41.58

Descriptive statistics

Standard deviation57.709831
Coefficient of variation (CV)1.3417685
Kurtosis16.859683
Mean43.010275
Median Absolute Deviation (MAD)19
Skewness3.5597954
Sum43870.48
Variance3330.4246
MonotonicityNot monotonic
2023-12-12T10:04:19.784978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 86
 
8.4%
6.6 40
 
3.9%
10.0 32
 
3.1%
33.0 18
 
1.8%
6.0 11
 
1.1%
5.0 11
 
1.1%
9.9 10
 
1.0%
19.8 9
 
0.9%
3.0 9
 
0.9%
23.0 9
 
0.9%
Other values (569) 785
77.0%
ValueCountFrequency (%)
0.0 3
0.3%
1.0 7
0.7%
1.2 1
 
0.1%
1.5 6
0.6%
1.83 1
 
0.1%
2.0 2
 
0.2%
2.1 1
 
0.1%
2.25 1
 
0.1%
2.44 2
 
0.2%
2.57 1
 
0.1%
ValueCountFrequency (%)
538.62 1
0.1%
422.0 1
0.1%
391.77 1
0.1%
382.94 1
0.1%
380.83 1
0.1%
367.53 1
0.1%
362.77 1
0.1%
348.56 1
0.1%
343.2 1
0.1%
321.32 1
0.1%

영업자시작일
Date

Distinct761
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
Minimum1993-08-31 00:00:00
Maximum2022-01-03 00:00:00
2023-12-12T10:04:19.972282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:04:20.486762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태명
Categorical

Distinct13
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
커피숍
428 
일반조리판매
191 
기타 휴게음식점
180 
편의점
144 
패스트푸드
45 
Other values (8)
 
32

Length

Max length8
Median length3
Mean length4.554902
Min length2

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row다방
2nd row다방
3rd row다방
4th row다방
5th row일반조리판매

Common Values

ValueCountFrequency (%)
커피숍 428
42.0%
일반조리판매 191
18.7%
기타 휴게음식점 180
17.6%
편의점 144
 
14.1%
패스트푸드 45
 
4.4%
아이스크림 9
 
0.9%
다방 8
 
0.8%
철도역구내 5
 
0.5%
백화점 3
 
0.3%
푸드트럭 3
 
0.3%
Other values (3) 4
 
0.4%

Length

2023-12-12T10:04:20.660890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
커피숍 428
35.7%
일반조리판매 191
15.9%
기타 180
15.0%
휴게음식점 180
15.0%
편의점 144
 
12.0%
패스트푸드 45
 
3.8%
아이스크림 9
 
0.8%
다방 8
 
0.7%
철도역구내 5
 
0.4%
백화점 3
 
0.2%
Other values (4) 7
 
0.6%

Interactions

2023-12-12T10:04:15.981774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:04:20.752762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업장면적업태명
영업장면적1.0000.254
업태명0.2541.000
2023-12-12T10:04:20.866160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업장면적업태명
영업장면적1.0000.111
업태명0.1111.000

Missing values

2023-12-12T10:04:16.141363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:04:16.279845image/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.
2023-12-12T10:04:16.400896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

인허가일자업소명도로명주소지번주소영업장면적영업자시작일업태명
01976-02-05왕다방성동구 광나루로 296 (성수동2가)서울특별시 성동구 성수동2가 4563.242006-01-25다방
11976-02-05우희성동구 독서당로 295-1 (금호동3가)서울특별시 성동구 금호동3가 41368.951996-12-05다방
21976-02-05학다방성동구 왕십리로 296-1 (행당동,,197-1)서울특별시 성동구 행당동 196-27 ,197-177.72011-07-07다방
31982-10-30명커피숍성동구 뚝섬로 389 (성수동2가)서울특별시 성동구 성수동2가 339-195.62004-08-02다방
41982-07-15피자스쿨성동구 성덕정길 78 (성수동2가)서울특별시 성동구 성수동2가 346-231.792006-08-16일반조리판매
51987-10-22아람다방성동구 연무장길 6 (성수동1가)서울특별시 성동구 성수동1가 16-2269.522002-10-02다방
61991-11-16대지다방성동구 자동차시장1길 70, 지하1층 B호 별동 11,12호 (용답동, 234)서울특별시 성동구 용답동 234 B호 별동 11,12호 지하1층52.02003-01-14다방
71992-12-15베스킨라빈스-한양대점성동구 마조로 25 (행당동,제일빌딩)서울특별시 성동구 행당동 6-2 제일빌딩0.02020-07-01기타 휴게음식점
81993-08-31GS25금호역점성동구 매봉길 50, 126동 지하2층 201호 (옥수동, 이편한세상옥수파크힐스)서울특별시 성동구 옥수동 528 이편한세상옥수파크힐스7.551993-08-31편의점
91993-03-31광전다방성동구 자동차시장1길 41 (용답동)서울특별시 성동구 용답동 229-933.082002-08-03다방
인허가일자업소명도로명주소지번주소영업장면적영업자시작일업태명
10102021-12-10씨유 왕십리큐브스테이트점성동구 무학봉28길 30, 지1층 B101호, B102호 (행당동)서울특별시 성동구 행당동 286-253.32021-12-10편의점
10112021-12-13세븐일레븐 성수성덕정길점성동구 성덕정길 116, 성우빌딩 1층 (성수동2가)서울특별시 성동구 성수동2가 574-15 성우빌딩1.52021-12-13편의점
10122021-12-15파프카페성동구 상원12길 35 (주)대도도금 공장 1층 (성수동1가)서울특별시 성동구 성수동1가 13-18 (주)대도도금 공장124.842021-12-15커피숍
10132021-12-20하이라인(Highline)성동구 성덕정9길 10, 2층 (성수동1가)서울특별시 성동구 성수동1가 272140.02021-12-20커피숍
10142021-12-21제이제이크로플성동구 무학봉15길 28-1, 1층 (하왕십리동)서울특별시 성동구 하왕십리동 955-159.912021-12-21기타 휴게음식점
10152021-12-21GS25성수이로점성동구 성수이로 127, 1층 (성수동2가)서울특별시 성동구 성수동2가 289-83.32021-12-21편의점
10162021-12-22파스쿠찌 왕십리역사성동구 왕십리광장로 17, 왕십리민자역사 4층 (행당동)서울특별시 성동구 행당동 168-151 왕십리민자역사76.02021-12-22커피숍
10172021-12-28상왕제약성동구 왕십리로33길 8, 1층 (하왕십리동)서울특별시 성동구 하왕십리동 889-136.02021-12-28기타 휴게음식점
10182022-01-03카페 MU성동구 성수일로4길 33, 1층 (성수동2가)서울특별시 성동구 성수동2가 333-776.62022-01-03커피숍
10192022-01-03아토커피 왕십리성동구 무학로8길 21, 1층 (홍익동)서울특별시 성동구 홍익동 26092.712022-01-03커피숍

Duplicate rows

Most frequently occurring

인허가일자업소명도로명주소지번주소영업장면적영업자시작일업태명# duplicates
02013-05-02(주)이마트 왕십리점성동구 왕십리광장로 17, 지상2층 (행당동, 왕십리민자역사 이마트내)서울특별시 성동구 행당동 168-200 왕십리민자역사 이마트내19.52020-04-21일반조리판매2