Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory59.3 B

Variable types

Categorical4
Text2
Numeric1

Alerts

ldgs_addr is highly overall correlated with ldgs_nmHigh correlation
ldgs_nm is highly overall correlated with ldgs_addrHigh correlation
rstrnt_avrg_score_value is highly overall correlated with cl_cnHigh correlation
cl_cn is highly overall correlated with rstrnt_avrg_score_valueHigh correlation
ldgs_rstrnt_btwn_dstnc_value has 8 (8.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:47:47.582563
Analysis finished2023-12-10 09:47:48.992532
Duration1.41 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ldgs_nm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
MERCURE TOKYO GINZA
47 
ibis Styles Tokyo Ginza
40 
The Millennials Shibuya
10 
hostel den
 
3

Length

Max length23
Median length21
Mean length20.73
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowibis Styles Tokyo Ginza
2nd rowhostel den
3rd rowibis Styles Tokyo Ginza
4th rowibis Styles Tokyo Ginza
5th rowibis Styles Tokyo Ginza

Common Values

ValueCountFrequency (%)
MERCURE TOKYO GINZA 47
47.0%
ibis Styles Tokyo Ginza 40
40.0%
The Millennials Shibuya 10
 
10.0%
hostel den 3
 
3.0%

Length

2023-12-10T18:47:49.115732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:47:49.311443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
tokyo 87
25.8%
ginza 87
25.8%
mercure 47
13.9%
ibis 40
11.9%
styles 40
11.9%
the 10
 
3.0%
millennials 10
 
3.0%
shibuya 10
 
3.0%
hostel 3
 
0.9%
den 3
 
0.9%

ldgs_addr
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
104-0061 Tokyo, Tokyo 2 9 4 Ginza, Chuo Ku
47 
7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061
40 
1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041
10 
4 Chome-13-8 Nihonbashihoncho, Chuo City, Tokyo
 
3

Length

Max length50
Median length47
Mean length44.15
Min length42

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061
2nd row4 Chome-13-8 Nihonbashihoncho, Chuo City, Tokyo
3rd row7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061
4th row7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061
5th row7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061

Common Values

ValueCountFrequency (%)
104-0061 Tokyo, Tokyo 2 9 4 Ginza, Chuo Ku 47
47.0%
7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061 40
40.0%
1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041 10
 
10.0%
4 Chome-13-8 Nihonbashihoncho, Chuo City, Tokyo 3
 
3.0%

Length

2023-12-10T18:47:49.576027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:47:49.815471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
tokyo 147
18.6%
chuo 90
11.4%
104-0061 87
11.0%
ginza 87
11.0%
city 53
 
6.7%
4 50
 
6.3%
2 47
 
5.9%
9 47
 
5.9%
ku 47
 
5.9%
chome-10-9 40
 
5.1%
Other values (8) 96
12.1%

rstrnt_nm
Text

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:47:50.354910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length31.5
Mean length19.59
Min length3

Characters and Unicode

Total characters1959
Distinct characters76
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

Unique22 ?
Unique (%)22.0%

Sample

1st rowYakiniku a Five Toku Ginza8chome
2nd rowBrozers' Ningyocho
3rd rowIPPUDO Ginza
4th rowAndy's Shin Hinomoto
5th rowGyuan
ValueCountFrequency (%)
ginza 35
 
12.2%
sushizanmai 10
 
3.5%
honten 10
 
3.5%
tokyo 6
 
2.1%
shibuya 5
 
1.7%
bekkan 4
 
1.4%
tsukiji 4
 
1.4%
sushi 4
 
1.4%
shabusen 2
 
0.7%
스시 2
 
0.7%
Other values (128) 204
71.3%
2023-12-10T18:47:51.207541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 197
 
10.1%
i 190
 
9.7%
184
 
9.4%
n 169
 
8.6%
o 114
 
5.8%
e 110
 
5.6%
u 88
 
4.5%
s 76
 
3.9%
r 63
 
3.2%
h 56
 
2.9%
Other values (66) 712
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1406
71.8%
Uppercase Letter 295
 
15.1%
Space Separator 186
 
9.5%
Other Letter 42
 
2.1%
Other Punctuation 14
 
0.7%
Dash Punctuation 8
 
0.4%
Decimal Number 8
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 197
14.0%
i 190
13.5%
n 169
12.0%
o 114
 
8.1%
e 110
 
7.8%
u 88
 
6.3%
s 76
 
5.4%
r 63
 
4.5%
h 56
 
4.0%
k 55
 
3.9%
Other values (15) 288
20.5%
Uppercase Letter
ValueCountFrequency (%)
G 41
13.9%
S 36
12.2%
T 36
12.2%
B 26
 
8.8%
H 23
 
7.8%
L 14
 
4.7%
A 13
 
4.4%
K 12
 
4.1%
M 12
 
4.1%
U 10
 
3.4%
Other values (12) 72
24.4%
Other Letter
ValueCountFrequency (%)
이 5
 
11.9%
도 4
 
9.5%
리 2
 
4.8%
코 2
 
4.8%
자 2
 
4.8%
카 2
 
4.8%
스 2
 
4.8%
긴 2
 
4.8%
시 2
 
4.8%
베 2
 
4.8%
Other values (9) 17
40.5%
Decimal Number
ValueCountFrequency (%)
5 2
25.0%
2 2
25.0%
1 2
25.0%
8 2
25.0%
Other Punctuation
ValueCountFrequency (%)
' 7
50.0%
, 5
35.7%
. 2
 
14.3%
Space Separator
ValueCountFrequency (%)
184
98.9%
　 2
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1701
86.8%
Common 216
 
11.0%
Hangul 42
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 197
 
11.6%
i 190
 
11.2%
n 169
 
9.9%
o 114
 
6.7%
e 110
 
6.5%
u 88
 
5.2%
s 76
 
4.5%
r 63
 
3.7%
h 56
 
3.3%
k 55
 
3.2%
Other values (37) 583
34.3%
Hangul
ValueCountFrequency (%)
이 5
 
11.9%
도 4
 
9.5%
리 2
 
4.8%
코 2
 
4.8%
자 2
 
4.8%
카 2
 
4.8%
스 2
 
4.8%
긴 2
 
4.8%
시 2
 
4.8%
베 2
 
4.8%
Other values (9) 17
40.5%
Common
ValueCountFrequency (%)
184
85.2%
- 8
 
3.7%
' 7
 
3.2%
, 5
 
2.3%
5 2
 
0.9%
　 2
 
0.9%
2 2
 
0.9%
1 2
 
0.9%
. 2
 
0.9%
8 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1915
97.8%
Hangul 42
 
2.1%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 197
 
10.3%
i 190
 
9.9%
184
 
9.6%
n 169
 
8.8%
o 114
 
6.0%
e 110
 
5.7%
u 88
 
4.6%
s 76
 
4.0%
r 63
 
3.3%
h 56
 
2.9%
Other values (46) 668
34.9%
Hangul
ValueCountFrequency (%)
이 5
 
11.9%
도 4
 
9.5%
리 2
 
4.8%
코 2
 
4.8%
자 2
 
4.8%
카 2
 
4.8%
스 2
 
4.8%
긴 2
 
4.8%
시 2
 
4.8%
베 2
 
4.8%
Other values (9) 17
40.5%
None
ValueCountFrequency (%)
　 2
100.0%

rstrnt_addr
Text

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:47:51.889055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length50
Mean length37.87
Min length19

Characters and Unicode

Total characters3787
Distinct characters79
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)21.0%

Sample

1st row8-3-1 Ginza Tokiden Bldg. 9F, Ginza 주오 도쿄도
2nd row2-28-5 Nihonbashi Ningyocho, 주오 도쿄도
3rd row4-10-3 센트럴 빌딩 1F, Ginza 주오 도쿄도
4th row2-4-4, Yurakucho 치요다 도쿄도
5th row6-13-6 B1F Kakyoshokokai Bldg., Ginza 주오 도쿄도
ValueCountFrequency (%)
도쿄도 100
 
14.9%
ginza 72
 
10.8%
주오 68
 
10.2%
bldg 32
 
4.8%
치요다 19
 
2.8%
tsukiji 16
 
2.4%
tokyo 13
 
1.9%
1f 13
 
1.9%
시부야 10
 
1.5%
b1f 9
 
1.3%
Other values (154) 317
47.4%
2023-12-10T18:47:52.777310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
569
 
15.0%
a 229
 
6.0%
i 215
 
5.7%
도 200
 
5.3%
- 192
 
5.1%
1 151
 
4.0%
n 128
 
3.4%
o 113
 
3.0%
, 106
 
2.8%
쿄 100
 
2.6%
Other values (69) 1784
47.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1533
40.5%
Space Separator 569
 
15.0%
Other Letter 542
 
14.3%
Decimal Number 438
 
11.6%
Uppercase Letter 377
 
10.0%
Dash Punctuation 192
 
5.1%
Other Punctuation 135
 
3.6%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 229
14.9%
i 215
14.0%
n 128
 
8.3%
o 113
 
7.4%
z 82
 
5.3%
h 81
 
5.3%
u 78
 
5.1%
k 68
 
4.4%
l 64
 
4.2%
e 64
 
4.2%
Other values (16) 411
26.8%
Uppercase Letter
ValueCountFrequency (%)
G 76
20.2%
B 60
15.9%
F 59
15.6%
T 36
9.5%
M 22
 
5.8%
S 21
 
5.6%
C 15
 
4.0%
P 13
 
3.4%
H 13
 
3.4%
Y 12
 
3.2%
Other values (10) 50
13.3%
Other Letter
ValueCountFrequency (%)
도 200
36.9%
쿄 100
18.5%
주 68
 
12.5%
오 68
 
12.5%
치 19
 
3.5%
요 19
 
3.5%
다 19
 
3.5%
야 10
 
1.8%
부 10
 
1.8%
시 10
 
1.8%
Other values (8) 19
 
3.5%
Decimal Number
ValueCountFrequency (%)
1 151
34.5%
2 59
 
13.5%
5 47
 
10.7%
3 34
 
7.8%
4 32
 
7.3%
6 28
 
6.4%
8 25
 
5.7%
7 24
 
5.5%
0 22
 
5.0%
9 16
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 106
78.5%
. 29
 
21.5%
Space Separator
ValueCountFrequency (%)
569
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1910
50.4%
Common 1335
35.3%
Hangul 542
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 229
 
12.0%
i 215
 
11.3%
n 128
 
6.7%
o 113
 
5.9%
z 82
 
4.3%
h 81
 
4.2%
u 78
 
4.1%
G 76
 
4.0%
k 68
 
3.6%
l 64
 
3.4%
Other values (36) 776
40.6%
Hangul
ValueCountFrequency (%)
도 200
36.9%
쿄 100
18.5%
주 68
 
12.5%
오 68
 
12.5%
치 19
 
3.5%
요 19
 
3.5%
다 19
 
3.5%
야 10
 
1.8%
부 10
 
1.8%
시 10
 
1.8%
Other values (8) 19
 
3.5%
Common
ValueCountFrequency (%)
569
42.6%
- 192
 
14.4%
1 151
 
11.3%
, 106
 
7.9%
2 59
 
4.4%
5 47
 
3.5%
3 34
 
2.5%
4 32
 
2.4%
. 29
 
2.2%
6 28
 
2.1%
Other values (5) 88
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3245
85.7%
Hangul 542
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
569
17.5%
a 229
 
7.1%
i 215
 
6.6%
- 192
 
5.9%
1 151
 
4.7%
n 128
 
3.9%
o 113
 
3.5%
, 106
 
3.3%
z 82
 
2.5%
h 81
 
2.5%
Other values (51) 1379
42.5%
Hangul
ValueCountFrequency (%)
도 200
36.9%
쿄 100
18.5%
주 68
 
12.5%
오 68
 
12.5%
치 19
 
3.5%
요 19
 
3.5%
다 19
 
3.5%
야 10
 
1.8%
부 10
 
1.8%
시 10
 
1.8%
Other values (8) 19
 
3.5%

rstrnt_avrg_score_value
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4.5
64 
4.0
28 
5.0
8 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row4.5
4th row4.5
5th row4.5

Common Values

ValueCountFrequency (%)
4.5 64
64.0%
4.0 28
28.0%
5.0 8
 
8.0%

Length

2023-12-10T18:47:53.045180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:47:53.243406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.5 64
64.0%
4.0 28
28.0%
5.0 8
 
8.0%

cl_cn
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
일본 요리 해산물 스시
12 
스시
8 
일본 요리
 
6
일본 요리 아시아 요리
 
6
일본 요리 아시아 요리 수프
 
4
Other values (37)
64 

Length

Max length21
Median length17
Mean length11.79
Min length1

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row일본 요리 스테이크하우스 바베큐
2nd row미국 요리 오스트리아 다이너
3rd row일본 요리 아시아 요리 수프
4th row일본 요리 해산물 아시아 요리
5th row일본 요리 스테이크하우스 아시아 요리

Common Values

ValueCountFrequency (%)
일본 요리 해산물 스시 12
 
12.0%
스시 8
 
8.0%
일본 요리 6
 
6.0%
일본 요리 아시아 요리 6
 
6.0%
일본 요리 아시아 요리 수프 4
 
4.0%
프랑스 요리 유럽 요리 4
 
4.0%
일본 요리 해산물 아시아 요리 3
 
3.0%
일본 요리 아시아 요리 채식주의 식단 3
 
3.0%
일본 요리 스테이크하우스 아시아 요리 3
 
3.0%
프랑스 요리 유럽 요리 채식주의 식단 2
 
2.0%
Other values (32) 49
49.0%

Length

2023-12-10T18:47:53.554592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
요리 101
27.8%
일본 51
14.0%
스시 26
 
7.2%
아시아 26
 
7.2%
해산물 25
 
6.9%
유럽 10
 
2.8%
채식주의 10
 
2.8%
식단 10
 
2.8%
스테이크하우스 10
 
2.8%
인터내셔널 8
 
2.2%
Other values (31) 86
23.7%

ldgs_rstrnt_btwn_dstnc_value
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.3
Minimum0
Maximum990
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:47:53.866810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1330
median510
Q3700
95-th percentile921.5
Maximum990
Range990
Interquartile range (IQR)370

Descriptive statistics

Standard deviation269.98487
Coefficient of variation (CV)0.53964595
Kurtosis-0.7890985
Mean500.3
Median Absolute Deviation (MAD)190
Skewness-0.24608698
Sum50030
Variance72891.828
MonotonicityNot monotonic
2023-12-10T18:47:54.248446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
8.0%
440 7
 
7.0%
790 6
 
6.0%
700 4
 
4.0%
360 3
 
3.0%
570 3
 
3.0%
380 3
 
3.0%
740 3
 
3.0%
680 3
 
3.0%
600 2
 
2.0%
Other values (44) 58
58.0%
ValueCountFrequency (%)
0 8
8.0%
60 1
 
1.0%
120 1
 
1.0%
130 1
 
1.0%
140 1
 
1.0%
160 2
 
2.0%
170 2
 
2.0%
180 1
 
1.0%
190 1
 
1.0%
200 1
 
1.0%
ValueCountFrequency (%)
990 2
 
2.0%
970 2
 
2.0%
950 1
 
1.0%
920 1
 
1.0%
900 1
 
1.0%
870 2
 
2.0%
790 6
6.0%
780 1
 
1.0%
770 1
 
1.0%
750 2
 
2.0%

Interactions

2023-12-10T18:47:48.426695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:47:54.477623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_nmldgs_addrrstrnt_nmrstrnt_addrrstrnt_avrg_score_valuecl_cnldgs_rstrnt_btwn_dstnc_value
ldgs_nm1.0001.0000.7680.7970.4360.8060.529
ldgs_addr1.0001.0000.7680.7970.4360.8060.529
rstrnt_nm0.7680.7681.0001.0001.0001.0000.645
rstrnt_addr0.7970.7971.0001.0001.0001.0000.700
rstrnt_avrg_score_value0.4360.4361.0001.0001.0000.9550.386
cl_cn0.8060.8061.0001.0000.9551.0000.638
ldgs_rstrnt_btwn_dstnc_value0.5290.5290.6450.7000.3860.6381.000
2023-12-10T18:47:54.738272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
cl_cnldgs_addrrstrnt_avrg_score_valueldgs_nm
cl_cn1.0000.4250.6120.425
ldgs_addr0.4251.0000.4271.000
rstrnt_avrg_score_value0.6120.4271.0000.427
ldgs_nm0.4251.0000.4271.000
2023-12-10T18:47:54.936027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_rstrnt_btwn_dstnc_valueldgs_nmldgs_addrrstrnt_avrg_score_valuecl_cn
ldgs_rstrnt_btwn_dstnc_value1.0000.3320.3320.2400.213
ldgs_nm0.3321.0001.0000.4270.425
ldgs_addr0.3321.0001.0000.4270.425
rstrnt_avrg_score_value0.2400.4270.4271.0000.612
cl_cn0.2130.4250.4250.6121.000

Missing values

2023-12-10T18:47:48.662587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:47:48.878949image/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

ldgs_nmldgs_addrrstrnt_nmrstrnt_addrrstrnt_avrg_score_valuecl_cnldgs_rstrnt_btwn_dstnc_value
0ibis Styles Tokyo Ginza7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061Yakiniku a Five Toku Ginza8chome8-3-1 Ginza Tokiden Bldg. 9F, Ginza 주오 도쿄도5.0일본 요리 스테이크하우스 바베큐360
1hostel den4 Chome-13-8 Nihonbashihoncho, Chuo City, TokyoBrozers' Ningyocho2-28-5 Nihonbashi Ningyocho, 주오 도쿄도4.0미국 요리 오스트리아 다이너870
2ibis Styles Tokyo Ginza7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061IPPUDO Ginza4-10-3 센트럴 빌딩 1F, Ginza 주오 도쿄도4.5일본 요리 아시아 요리 수프440
3ibis Styles Tokyo Ginza7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061Andy's Shin Hinomoto2-4-4, Yurakucho 치요다 도쿄도4.5일본 요리 해산물 아시아 요리660
4ibis Styles Tokyo Ginza7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061Gyuan6-13-6 B1F Kakyoshokokai Bldg., Ginza 주오 도쿄도4.5일본 요리 스테이크하우스 아시아 요리790
5ibis Styles Tokyo Ginza7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061큐베이8-7-6, Ginza 주오 도쿄도4.5일본 요리 해산물 스시170
6ibis Styles Tokyo Ginza7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061Ginza Ukai-Tei5-15-8 1F Jiji Press Bldg., Ginza 주오 도쿄도4.5스테이크하우스 퓨전440
7hostel den4 Chome-13-8 Nihonbashihoncho, Chuo City, TokyoMandarin Bar2-1-1, Nihonbashimuromachi 37F, Mandarin Oriental Tokyo, 주오 도쿄도4.5바550
8ibis Styles Tokyo Ginza7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061Sushizanmai Tsukijiekimae-Ten3-11-9 Tsukiji Square bldg1F, Tsukiji 주오 도쿄도4.5스시 건강식 일본식 - 기타870
9ibis Styles Tokyo Ginza7 Chome-10-9 Ginza, Chuo City, Tokyo 104-0061Umegaoka Sushino Midori Ginza7-2 Tokyo Highway Yamashita Bldg. 1F, Ginza 주오 도쿄도4.5일본 요리 해산물 스시440
ldgs_nmldgs_addrrstrnt_nmrstrnt_addrrstrnt_avrg_score_valuecl_cnldgs_rstrnt_btwn_dstnc_value
90The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041star star17-10 Sakuragaokacho Yoshino Bldg. 3F, 시부야 도쿄도5.0바 펍 채식주의 식단680
91The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041Nabezo Shibuya Center-gai31-2`Udagawacho Shibuya Beam 6F, 시부야 도쿄도5.0일본 요리 아시아 요리 채식주의 식단210
92The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041Music Bar ROCKAHOLIC Shibuya11-1 Udagawacho Ryuko Bldg. Annex B1F, 시부야 도쿄도5.0펍220
93The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041Nabezo Shibuya Koendori20-15 Udagawacho Humax Pavilion Shibuya Koen St. 8F, 시부야 도쿄도5.0일본 요리 아시아 요리 건강식140
94The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041maidreamin SHIBUYA30-1 Udagawacho B1F Horaiya Bldg., 시부야 도쿄도5.0자가 맥주 판매pub 카페 인터내셔널200
95The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041Hakushu - Kobe Teppanyaki17-10 Sakuragaokacho MCD Bldg. B1F, 시부야 도쿄도4.5일본 요리 스테이크하우스 아시아 요리680
96The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041Hanno Daidokoro Bettei2-29-8 Dogenzaka Dogenzaka Center Bldg 7F, 시부야 도쿄도4.5바베큐 한국 글루텐 프리470
97The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041Tenku no Tsuki5-18 Maruyamacho 3F Dogenzaka Square, 시부야 도쿄도5.0일본 요리 해산물 바베큐670
98The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041카이카야23-7 Maruyamacho, 시부야 도쿄도4.5일본 요리 해산물 스시780
99The Millennials Shibuya1 Chome-20-13 Jinnan, Shibuya City, Tokyo 150-0041Uobei Shibuya Dogenzaka2-29-11 Dogenzaka 1f, 시부야 도쿄도4.5일본 요리 해산물 패스트푸드340