Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells10050
Missing cells (%)16.8%
Duplicate rows14
Duplicate rows (%)0.1%
Total size in memory556.6 KiB
Average record size in memory57.0 B

Variable types

Unsupported1
Text4
Categorical1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15812/F/1/datasetView.do

Alerts

Dataset has 14 (0.1%) duplicate rowsDuplicates
Unnamed: 0 has 10000 (100.0%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-29 18:04:17.927141
Analysis finished2024-04-29 18:04:19.387875
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Unnamed: 1
Text

Distinct9159
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-30T03:04:19.553153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length26
Mean length9.5895
Min length1

Characters and Unicode

Total characters95895
Distinct characters946
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8800 ?
Unique (%)88.0%

Sample

1st rowGS25 중화제일점
2nd row고망고 경희대점
3rd row강동해물찜해천탕
4th row고메브레드
5th row”미 ”갈매기살전문
ValueCountFrequency (%)
gs25 1636
 
10.1%
주)코리아세븐 629
 
3.9%
cu 478
 
3.0%
주 222
 
1.4%
가마치통닭 58
 
0.4%
코리아세븐 51
 
0.3%
주)아워홈 46
 
0.3%
가마로강정 41
 
0.3%
주)케이에프씨코리아 37
 
0.2%
주)현대그린푸드 37
 
0.2%
Other values (9672) 12895
79.9%
2024-04-30T03:04:19.909546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6130
 
6.4%
점 5260
 
5.5%
( 2995
 
3.1%
) 2981
 
3.1%
주 2705
 
2.8%
2 2490
 
2.6%
S 2478
 
2.6%
5 2364
 
2.5%
G 2315
 
2.4%
리 1705
 
1.8%
Other values (936) 64472
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66900
69.8%
Uppercase Letter 8612
 
9.0%
Decimal Number 6615
 
6.9%
Space Separator 6130
 
6.4%
Open Punctuation 3016
 
3.1%
Close Punctuation 3002
 
3.1%
Lowercase Letter 1369
 
1.4%
Other Punctuation 192
 
0.2%
Dash Punctuation 31
 
< 0.1%
Connector Punctuation 11
 
< 0.1%
Other values (4) 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 5260
 
7.9%
주 2705
 
4.0%
리 1705
 
2.5%
고 1410
 
2.1%
아 1405
 
2.1%
이 1185
 
1.8%
스 1157
 
1.7%
코 1046
 
1.6%
가 956
 
1.4%
대 954
 
1.4%
Other values (855) 49117
73.4%
Uppercase Letter
ValueCountFrequency (%)
S 2478
28.8%
G 2315
26.9%
C 841
 
9.8%
U 618
 
7.2%
B 221
 
2.6%
K 217
 
2.5%
A 181
 
2.1%
E 174
 
2.0%
F 163
 
1.9%
O 158
 
1.8%
Other values (16) 1246
14.5%
Lowercase Letter
ValueCountFrequency (%)
e 187
13.7%
a 146
 
10.7%
o 110
 
8.0%
r 99
 
7.2%
i 96
 
7.0%
n 92
 
6.7%
t 87
 
6.4%
s 74
 
5.4%
g 58
 
4.2%
c 53
 
3.9%
Other values (16) 367
26.8%
Decimal Number
ValueCountFrequency (%)
2 2490
37.6%
5 2364
35.7%
1 353
 
5.3%
0 242
 
3.7%
8 235
 
3.6%
3 232
 
3.5%
9 222
 
3.4%
6 175
 
2.6%
4 173
 
2.6%
7 129
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 69
35.9%
& 63
32.8%
? 45
23.4%
! 7
 
3.6%
, 6
 
3.1%
: 1
 
0.5%
/ 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 2995
99.3%
（ 21
 
0.7%
Close Punctuation
ValueCountFrequency (%)
) 2981
99.3%
） 21
 
0.7%
Modifier Symbol
ValueCountFrequency (%)
´ 9
90.0%
` 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6130
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 11
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%
Final Punctuation
ValueCountFrequency (%)
” 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66890
69.8%
Common 19011
 
19.8%
Latin 9981
 
10.4%
Han 13
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 5260
 
7.9%
주 2705
 
4.0%
리 1705
 
2.5%
고 1410
 
2.1%
아 1405
 
2.1%
이 1185
 
1.8%
스 1157
 
1.7%
코 1046
 
1.6%
가 956
 
1.4%
대 954
 
1.4%
Other values (845) 49107
73.4%
Latin
ValueCountFrequency (%)
S 2478
24.8%
G 2315
23.2%
C 841
 
8.4%
U 618
 
6.2%
B 221
 
2.2%
K 217
 
2.2%
e 187
 
1.9%
A 181
 
1.8%
E 174
 
1.7%
F 163
 
1.6%
Other values (42) 2586
25.9%
Common
ValueCountFrequency (%)
6130
32.2%
( 2995
15.8%
) 2981
15.7%
2 2490
13.1%
5 2364
 
12.4%
1 353
 
1.9%
0 242
 
1.3%
8 235
 
1.2%
3 232
 
1.2%
9 222
 
1.2%
Other values (18) 767
 
4.0%
Han
ValueCountFrequency (%)
店 2
15.4%
愛 2
15.4%
唐 1
7.7%
間 1
7.7%
容 1
7.7%
辛 1
7.7%
鷄 1
7.7%
直 1
7.7%
京 1
7.7%
林 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66883
69.7%
ASCII 28939
30.2%
None 54
 
0.1%
CJK 13
 
< 0.1%
Compat Jamo 4
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6130
21.2%
( 2995
10.3%
) 2981
10.3%
2 2490
8.6%
S 2478
8.6%
5 2364
 
8.2%
G 2315
 
8.0%
C 841
 
2.9%
U 618
 
2.1%
1 353
 
1.2%
Other values (66) 5374
18.6%
Hangul
ValueCountFrequency (%)
점 5260
 
7.9%
주 2705
 
4.0%
리 1705
 
2.5%
고 1410
 
2.1%
아 1405
 
2.1%
이 1185
 
1.8%
스 1157
 
1.7%
코 1046
 
1.6%
가 956
 
1.4%
대 954
 
1.4%
Other values (840) 49100
73.4%
None
ValueCountFrequency (%)
（ 21
38.9%
） 21
38.9%
´ 9
16.7%
㈜ 3
 
5.6%
Punctuation
ValueCountFrequency (%)
” 2
100.0%
CJK
ValueCountFrequency (%)
店 2
15.4%
愛 2
15.4%
唐 1
7.7%
間 1
7.7%
容 1
7.7%
辛 1
7.7%
鷄 1
7.7%
直 1
7.7%
京 1
7.7%
林 1
7.7%
Compat Jamo
ValueCountFrequency (%)
ㅇ 1
25.0%
ㅌ 1
25.0%
ㅁ 1
25.0%
ㅢ 1
25.0%

Unnamed: 2
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
편의점
3618 
한식
3166 
일반대중음식
1782 
양식
 
359
일식
 
319
Other values (10)
756 

Length

Max length8
Median length6
Mean length3.2069
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row편의점
2nd row일반대중음식
3rd row일반대중음식
4th row제과점
5th row한식

Common Values

ValueCountFrequency (%)
편의점 3618
36.2%
한식 3166
31.7%
일반대중음식 1782
17.8%
양식 359
 
3.6%
일식 319
 
3.2%
패스트푸드 317
 
3.2%
중식 204
 
2.0%
제과점 197
 
2.0%
할인점/슈퍼마켓 24
 
0.2%
식품잡화 4
 
< 0.1%
Other values (5) 10
 
0.1%

Length

2024-04-30T03:04:20.042420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
편의점 3618
36.2%
한식 3166
31.7%
일반대중음식 1782
17.8%
양식 359
 
3.6%
일식 319
 
3.2%
패스트푸드 317
 
3.2%
중식 204
 
2.0%
제과점 197
 
2.0%
할인점/슈퍼마켓 24
 
0.2%
식품잡화 4
 
< 0.1%
Other values (5) 10
 
0.1%

Unnamed: 3
Text

Distinct5239
Distinct (%)52.5%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2024-04-30T03:04:20.236851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.6821868
Min length2

Characters and Unicode

Total characters96696
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5049 ?
Unique (%)50.6%

Sample

1st row0255555555
2nd row0211111111
3rd row024837345
4th row07041125789
5th row0236720081
ValueCountFrequency (%)
0200000000 1440
 
14.4%
0211111111 974
 
9.8%
0215770711 778
 
7.8%
0220062485 200
 
2.0%
0800803663 125
 
1.3%
0220062118 117
 
1.2%
0805552525 116
 
1.2%
07000000000 107
 
1.1%
0215778007 71
 
0.7%
020000000 69
 
0.7%
Other values (5229) 5990
60.0%
2024-04-30T03:04:20.590771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29598
30.6%
1 16293
16.8%
2 15391
15.9%
7 7005
 
7.2%
5 6004
 
6.2%
3 4903
 
5.1%
8 4781
 
4.9%
6 4570
 
4.7%
4 4217
 
4.4%
9 3932
 
4.1%
Other values (2) 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96694
> 99.9%
Other Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29598
30.6%
1 16293
16.9%
2 15391
15.9%
7 7005
 
7.2%
5 6004
 
6.2%
3 4903
 
5.1%
8 4781
 
4.9%
6 4570
 
4.7%
4 4217
 
4.4%
9 3932
 
4.1%
Other Letter
ValueCountFrequency (%)
전 1
50.0%
화 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 96694
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29598
30.6%
1 16293
16.9%
2 15391
15.9%
7 7005
 
7.2%
5 6004
 
6.2%
3 4903
 
5.1%
8 4781
 
4.9%
6 4570
 
4.7%
4 4217
 
4.4%
9 3932
 
4.1%
Hangul
ValueCountFrequency (%)
전 1
50.0%
화 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96694
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29598
30.6%
1 16293
16.9%
2 15391
15.9%
7 7005
 
7.2%
5 6004
 
6.2%
3 4903
 
5.1%
8 4781
 
4.9%
6 4570
 
4.7%
4 4217
 
4.4%
9 3932
 
4.1%
Hangul
ValueCountFrequency (%)
전 1
50.0%
화 1
50.0%

Unnamed: 4
Text

Distinct3699
Distinct (%)37.1%
Missing37
Missing (%)0.4%
Memory size156.2 KiB
2024-04-30T03:04:20.872405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0031115
Min length4

Characters and Unicode

Total characters49846
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1502 ?
Unique (%)15.1%

Sample

1st row02102
2nd row02453
3rd row05363
4th row07336
5th row03133
ValueCountFrequency (%)
06164 51
 
0.5%
07803 32
 
0.3%
02453 28
 
0.3%
07305 23
 
0.2%
04534 21
 
0.2%
03932 18
 
0.2%
07505 18
 
0.2%
05554 18
 
0.2%
04782 18
 
0.2%
06108 18
 
0.2%
Other values (3689) 9718
97.5%
2024-04-30T03:04:21.253243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13425
26.9%
6 4836
 
9.7%
7 4571
 
9.2%
5 4561
 
9.2%
3 4472
 
9.0%
4 4148
 
8.3%
2 4107
 
8.2%
1 3846
 
7.7%
8 3419
 
6.9%
9 2457
 
4.9%
Other values (4) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49842
> 99.9%
Other Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13425
26.9%
6 4836
 
9.7%
7 4571
 
9.2%
5 4561
 
9.2%
3 4472
 
9.0%
4 4148
 
8.3%
2 4107
 
8.2%
1 3846
 
7.7%
8 3419
 
6.9%
9 2457
 
4.9%
Other Letter
ValueCountFrequency (%)
우 1
25.0%
편 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49842
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13425
26.9%
6 4836
 
9.7%
7 4571
 
9.2%
5 4561
 
9.2%
3 4472
 
9.0%
4 4148
 
8.3%
2 4107
 
8.2%
1 3846
 
7.7%
8 3419
 
6.9%
9 2457
 
4.9%
Hangul
ValueCountFrequency (%)
우 1
25.0%
편 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49842
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13425
26.9%
6 4836
 
9.7%
7 4571
 
9.2%
5 4561
 
9.2%
3 4472
 
9.0%
4 4148
 
8.3%
2 4107
 
8.2%
1 3846
 
7.7%
8 3419
 
6.9%
9 2457
 
4.9%
Hangul
ValueCountFrequency (%)
우 1
25.0%
편 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 5
Text

Distinct9876
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-30T03:04:21.574921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length59
Mean length28.3598
Min length5

Characters and Unicode

Total characters283598
Distinct characters650
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

Unique9796 ?
Unique (%)98.0%

Sample

1st row서울 중랑구 동일로129길 16 (중화동)
2nd row서울특별시 동대문구 경희대로 8 , 1층(좌측점포)(회기동)
3rd row서울특별시 강동구 양재대로 1416 1층
4th row서울 영등포구 여의나루로 113, 103 (여의도동)
5th row서울 종로구 돈화문로11가길 7 (돈의동)
ValueCountFrequency (%)
서울 5111
 
8.5%
서울특별시 4888
 
8.1%
1층 4274
 
7.1%
강남구 1145
 
1.9%
마포구 652
 
1.1%
송파구 595
 
1.0%
서초구 565
 
0.9%
강서구 539
 
0.9%
영등포구 525
 
0.9%
517
 
0.9%
Other values (10396) 41170
68.6%
2024-04-30T03:04:22.018758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50054
 
17.6%
1 18296
 
6.5%
서 12199
 
4.3%
구 10749
 
3.8%
로 10332
 
3.6%
울 10094
 
3.6%
동 9355
 
3.3%
2 7526
 
2.7%
층 6938
 
2.4%
, 6271
 
2.2%
Other values (640) 141784
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157496
55.5%
Decimal Number 54235
 
19.1%
Space Separator 50054
 
17.6%
Other Punctuation 6473
 
2.3%
Open Punctuation 6183
 
2.2%
Close Punctuation 6177
 
2.2%
Dash Punctuation 1717
 
0.6%
Uppercase Letter 1134
 
0.4%
Lowercase Letter 69
 
< 0.1%
Math Symbol 58
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 12199
 
7.7%
구 10749
 
6.8%
로 10332
 
6.6%
울 10094
 
6.4%
동 9355
 
5.9%
층 6938
 
4.4%
길 5331
 
3.4%
시 5179
 
3.3%
별 4895
 
3.1%
특 4889
 
3.1%
Other values (575) 77535
49.2%
Uppercase Letter
ValueCountFrequency (%)
B 431
38.0%
A 114
 
10.1%
C 74
 
6.5%
S 71
 
6.3%
X 47
 
4.1%
K 42
 
3.7%
G 38
 
3.4%
D 36
 
3.2%
E 33
 
2.9%
M 32
 
2.8%
Other values (16) 216
19.0%
Lowercase Letter
ValueCountFrequency (%)
e 17
24.6%
b 12
17.4%
c 9
13.0%
t 6
 
8.7%
r 5
 
7.2%
n 4
 
5.8%
s 3
 
4.3%
l 3
 
4.3%
a 3
 
4.3%
g 1
 
1.4%
Other values (6) 6
 
8.7%
Decimal Number
ValueCountFrequency (%)
1 18296
33.7%
2 7526
13.9%
0 5307
 
9.8%
3 4968
 
9.2%
4 3969
 
7.3%
5 3518
 
6.5%
6 3125
 
5.8%
7 2787
 
5.1%
8 2479
 
4.6%
9 2260
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 6271
96.9%
. 194
 
3.0%
& 4
 
0.1%
/ 3
 
< 0.1%
? 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 6170
99.8%
（ 13
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 6164
99.8%
） 13
 
0.2%
Space Separator
ValueCountFrequency (%)
50054
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1717
100.0%
Math Symbol
ValueCountFrequency (%)
~ 58
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 157496
55.5%
Common 124899
44.0%
Latin 1203
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 12199
 
7.7%
구 10749
 
6.8%
로 10332
 
6.6%
울 10094
 
6.4%
동 9355
 
5.9%
층 6938
 
4.4%
길 5331
 
3.4%
시 5179
 
3.3%
별 4895
 
3.1%
특 4889
 
3.1%
Other values (575) 77535
49.2%
Latin
ValueCountFrequency (%)
B 431
35.8%
A 114
 
9.5%
C 74
 
6.2%
S 71
 
5.9%
X 47
 
3.9%
K 42
 
3.5%
G 38
 
3.2%
D 36
 
3.0%
E 33
 
2.7%
M 32
 
2.7%
Other values (32) 285
23.7%
Common
ValueCountFrequency (%)
50054
40.1%
1 18296
 
14.6%
2 7526
 
6.0%
, 6271
 
5.0%
( 6170
 
4.9%
) 6164
 
4.9%
0 5307
 
4.2%
3 4968
 
4.0%
4 3969
 
3.2%
5 3518
 
2.8%
Other values (13) 12656
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157488
55.5%
ASCII 126074
44.5%
None 28
 
< 0.1%
Compat Jamo 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50054
39.7%
1 18296
 
14.5%
2 7526
 
6.0%
, 6271
 
5.0%
( 6170
 
4.9%
) 6164
 
4.9%
0 5307
 
4.2%
3 4968
 
3.9%
4 3969
 
3.1%
5 3518
 
2.8%
Other values (52) 13831
 
11.0%
Hangul
ValueCountFrequency (%)
서 12199
 
7.7%
구 10749
 
6.8%
로 10332
 
6.6%
울 10094
 
6.4%
동 9355
 
5.9%
층 6938
 
4.4%
길 5331
 
3.4%
시 5179
 
3.3%
별 4895
 
3.1%
특 4889
 
3.1%
Other values (570) 77527
49.2%
None
ValueCountFrequency (%)
） 13
46.4%
（ 13
46.4%
´ 2
 
7.1%
Compat Jamo
ValueCountFrequency (%)
ㄷ 2
25.0%
ㅡ 2
25.0%
ㅊ 2
25.0%
ㅏ 1
12.5%
ㅎ 1
12.5%

Missing values

2024-04-30T03:04:19.170848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T03:04:19.261572image/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.
2024-04-30T03:04:19.343547image/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

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
5609<NA>GS25 중화제일점편의점025555555502102서울 중랑구 동일로129길 16 (중화동)
9299<NA>고망고 경희대점일반대중음식021111111102453서울특별시 동대문구 경희대로 8 , 1층(좌측점포)(회기동)
7759<NA>강동해물찜해천탕일반대중음식02483734505363서울특별시 강동구 양재대로 1416 1층
9315<NA>고메브레드제과점0704112578907336서울 영등포구 여의나루로 113, 103 (여의도동)
6629<NA>”미 ”갈매기살전문한식023672008103133서울 종로구 돈화문로11가길 7 (돈의동)
9965<NA>곤지암한우소머리국밥(하월곡점)한식02913628702738서울 성북구 오패산로13길 19-16, 1층 (하월곡동)
8024<NA>같이가치 정릉덮밥한식0708954778802709서울특별시 성북구 솔샘로6길 62 3층
7360<NA>감꽃당일반대중음식020000000003133서울특별시 종로구 돈화문로11다길 38 1층
9240<NA>고래한입피자&비츠샐러드청량리점패스트푸드02968898002561서울특별시 동대문구 고산자로32길 78 B층 164호 (용두동,청량리역한양수자인그라시엘)
8370<NA>게뜨망하우스양식02332723304014서울특별시 마포구 월드컵로13길 55-32 .
Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
2620<NA>(주)후니드 SBS프리즘타한식022039906503926서울 마포구 상암산로 82 (상암동,SBS프리즘타워17층)
7983<NA>강촌숯불닭갈비한식02592929306554서울특별시 서초구 방배중앙로27길 32 1층101호
432<NA>(주)놀부부대찌개 앤 철판구이한식021577687707305서울 영등포구 영중로 15, B143호 지하1 (영등포동4가, 타임스퀘어)
4076<NA>DFS대륭테크노타운한식022029626008592서울 금천구 가산디지털2로 14 (가산동, 대륭테크노타운12차)
3866<NA>CU 월곡대로점편의점02909311702739서울 성북구 종암로 182-1, 1층 (하월곡동)
7210<NA>가츠몽 양재점한식02574202006775서울특별시 서초구 논현로19길 9 101호
8844<NA>고공일반대중음식026368505004778서울특별시 성동구 왕십리로8길 10 1층
7716<NA>강남철판요리 투일식020000000006030서울특별시 강남구 압구정로28길 9-2 지하1층
7718<NA>강남치킨 상일동역본점일반대중음식02442222805274서울특별시 강동구 고덕로 380 1층112호
4805<NA>GS25 미아보람점편의점02222111101157서울 강북구 도봉로46길 9 (미아동)

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5# duplicates
4(주)아워홈일반대중음식021111111107792서울특별시 강서구 마곡중앙10로 91 (마곡동)3
5(주)이마트24편의점020000000004799서울특별시 성동구 광나루로 310 1층3
0(유) 한국맥도날드 이태원점패스트푸드0707017030504390서울특별시 용산구 이태원로 142-1 이태원동2
1(유) 한국맥도날드 전농점패스트푸드0707017054802506서울특별시 동대문구 전농로16길 8 전농동2
2(유) 한국맥도날드구의역점패스트푸드0707017043905051서울특별시 광진구 아차산로 376 자양동2
3(주)아모제일반대중음식022185777706143서울 강남구 역삼동 680-22
6(주)코리아세븐편의점021577071105548서울 송파구 오금로 95, 1층 102호 (방이동, 국제빌딩)2
7(주)코리아세븐편의점021577071106248서울 강남구 역삼로20길 9, 1층 전면 101호 (역삼동)2
8(주)코리아세븐편의점021577071108582서울 금천구 두산로 20, 2호 (독산동)2
9(주)한일관한식021577996306023서울 강남구 신사동 619-4번지2