Overview

Dataset statistics

Number of variables7
Number of observations1000
Missing cells924
Missing cells (%)13.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.7 KiB
Average record size in memory59.1 B

Variable types

Categorical1
Numeric3
Text3

Alerts

CITY_NM has constant value ""Constant
RSTRNT_ID is highly overall correlated with MENU_IDHigh correlation
MENU_ID is highly overall correlated with RSTRNT_IDHigh correlation
MENU_TAG has 63 (6.3%) missing valuesMissing
MENU_IMAGE has 861 (86.1%) missing valuesMissing
MENU_ID has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:49:08.460939
Analysis finished2023-12-10 09:49:11.041616
Duration2.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CITY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
busan
1000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
busan 1000
100.0%

Length

2023-12-10T18:49:11.184891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:11.432751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
busan 1000
100.0%

RSTRNT_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.564
Minimum21
Maximum467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:49:11.704739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile25
Q183
median178
Q3375
95-th percentile467
Maximum467
Range446
Interquartile range (IQR)292

Descriptive statistics

Standard deviation160.08641
Coefficient of variation (CV)0.74959456
Kurtosis-1.3724043
Mean213.564
Median Absolute Deviation (MAD)151
Skewness0.40118266
Sum213564
Variance25627.66
MonotonicityNot monotonic
2023-12-10T18:49:12.444239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
466 89
 
8.9%
83 70
 
7.0%
25 67
 
6.7%
178 58
 
5.8%
467 52
 
5.2%
27 51
 
5.1%
94 51
 
5.1%
141 50
 
5.0%
423 46
 
4.6%
209 46
 
4.6%
Other values (20) 420
42.0%
ValueCountFrequency (%)
21 21
 
2.1%
22 25
 
2.5%
25 67
6.7%
27 51
5.1%
33 12
 
1.2%
34 20
 
2.0%
83 70
7.0%
84 41
4.1%
86 9
 
0.9%
94 51
5.1%
ValueCountFrequency (%)
467 52
5.2%
466 89
8.9%
423 46
4.6%
399 20
 
2.0%
391 7
 
0.7%
375 40
4.0%
372 30
 
3.0%
371 21
 
2.1%
369 33
 
3.3%
254 1
 
0.1%

MENU_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean508.445
Minimum1
Maximum1009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:49:12.728358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile58.95
Q1258.75
median508.5
Q3758.25
95-th percentile959.05
Maximum1009
Range1008
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation289.61718
Coefficient of variation (CV)0.56961358
Kurtosis-1.1926665
Mean508.445
Median Absolute Deviation (MAD)250
Skewness-0.0034610767
Sum508445
Variance83878.109
MonotonicityStrictly increasing
2023-12-10T18:49:13.026558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
681 1
 
0.1%
668 1
 
0.1%
669 1
 
0.1%
670 1
 
0.1%
671 1
 
0.1%
672 1
 
0.1%
673 1
 
0.1%
674 1
 
0.1%
675 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1009 1
0.1%
1008 1
0.1%
1007 1
0.1%
1006 1
0.1%
1005 1
0.1%
1004 1
0.1%
1003 1
0.1%
1002 1
0.1%
1001 1
0.1%
1000 1
0.1%

MENU_PC
Real number (ℝ)

Distinct123
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13458.801
Minimum0
Maximum250000
Zeros6
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:49:13.377203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2000
Q15000
median7500
Q316000
95-th percentile40050
Maximum250000
Range250000
Interquartile range (IQR)11000

Descriptive statistics

Standard deviation18059.544
Coefficient of variation (CV)1.341839
Kurtosis55.407802
Mean13458.801
Median Absolute Deviation (MAD)3500
Skewness5.7604428
Sum13458801
Variance3.2614714 × 108
MonotonicityNot monotonic
2023-12-10T18:49:13.691593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 62
 
6.2%
2000 57
 
5.7%
6000 51
 
5.1%
4000 48
 
4.8%
9000 44
 
4.4%
10000 43
 
4.3%
7000 39
 
3.9%
3000 36
 
3.6%
5500 36
 
3.6%
8000 31
 
3.1%
Other values (113) 553
55.3%
ValueCountFrequency (%)
0 6
 
0.6%
1 1
 
0.1%
500 4
 
0.4%
800 1
 
0.1%
1000 19
 
1.9%
1100 1
 
0.1%
1500 16
 
1.6%
2000 57
5.7%
2500 1
 
0.1%
2900 1
 
0.1%
ValueCountFrequency (%)
250000 1
0.1%
220000 1
0.1%
170000 1
0.1%
139000 1
0.1%
100000 1
0.1%
99000 1
0.1%
90000 2
0.2%
86000 1
0.1%
80000 1
0.1%
72000 2
0.2%

MENU_NM
Text

Distinct914
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T18:49:14.262571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length94
Median length41
Mean length18.284
Min length1

Characters and Unicode

Total characters18284
Distinct characters76
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique873 ?
Unique (%)87.3%

Sample

1st row1
2nd rowIberico Moksal, Black Angus Chadolbagi
3rd rowWagu Deunsim, Iberico Moksal, Black Angus Chadolbagi
4th rowRobster, Muneo, Jeonbok, Kijogae, Saeu, Jaecheol Jogae
5th rowDdaksaeu, Muneo, Jeonbok, Kijogae, Saeu, Jaecheol Jogae
ValueCountFrequency (%)
l 34
 
1.5%
chuga 29
 
1.3%
palmian 27
 
1.2%
jjamppong 26
 
1.1%
jjigae 26
 
1.1%
cheese 25
 
1.1%
r 24
 
1.0%
steak 19
 
0.8%
kimchi 19
 
0.8%
bokkeumbap 16
 
0.7%
Other values (938) 2067
89.4%
2023-12-10T18:49:15.123827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1894
 
10.4%
e 1392
 
7.6%
1326
 
7.3%
o 1209
 
6.6%
n 1080
 
5.9%
i 870
 
4.8%
g 811
 
4.4%
u 804
 
4.4%
l 600
 
3.3%
k 574
 
3.1%
Other values (66) 7724
42.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13421
73.4%
Uppercase Letter 2614
 
14.3%
Space Separator 1327
 
7.3%
Decimal Number 273
 
1.5%
Open Punctuation 224
 
1.2%
Close Punctuation 224
 
1.2%
Other Punctuation 134
 
0.7%
Math Symbol 49
 
0.3%
Dash Punctuation 17
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1894
14.1%
e 1392
 
10.4%
o 1209
 
9.0%
n 1080
 
8.0%
i 870
 
6.5%
g 811
 
6.0%
u 804
 
6.0%
l 600
 
4.5%
k 574
 
4.3%
m 571
 
4.3%
Other values (16) 3616
26.9%
Uppercase Letter
ValueCountFrequency (%)
S 328
12.5%
C 304
11.6%
M 232
 
8.9%
B 232
 
8.9%
G 175
 
6.7%
J 174
 
6.7%
T 164
 
6.3%
P 132
 
5.0%
H 126
 
4.8%
D 121
 
4.6%
Other values (14) 626
23.9%
Decimal Number
ValueCountFrequency (%)
0 55
20.1%
2 55
20.1%
1 47
17.2%
3 46
16.8%
5 33
12.1%
4 18
 
6.6%
6 9
 
3.3%
7 8
 
2.9%
8 1
 
0.4%
9 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/ 81
60.4%
, 26
 
19.4%
. 19
 
14.2%
& 6
 
4.5%
# 1
 
0.7%
% 1
 
0.7%
Space Separator
ValueCountFrequency (%)
1326
99.9%
  1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 222
99.1%
（ 2
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 222
99.1%
） 2
 
0.9%
Math Symbol
ValueCountFrequency (%)
+ 39
79.6%
~ 10
 
20.4%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16035
87.7%
Common 2249
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1894
 
11.8%
e 1392
 
8.7%
o 1209
 
7.5%
n 1080
 
6.7%
i 870
 
5.4%
g 811
 
5.1%
u 804
 
5.0%
l 600
 
3.7%
k 574
 
3.6%
m 571
 
3.6%
Other values (40) 6230
38.9%
Common
ValueCountFrequency (%)
1326
59.0%
( 222
 
9.9%
) 222
 
9.9%
/ 81
 
3.6%
0 55
 
2.4%
2 55
 
2.4%
1 47
 
2.1%
3 46
 
2.0%
+ 39
 
1.7%
5 33
 
1.5%
Other values (16) 123
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18277
> 99.9%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1894
 
10.4%
e 1392
 
7.6%
1326
 
7.3%
o 1209
 
6.6%
n 1080
 
5.9%
i 870
 
4.8%
g 811
 
4.4%
u 804
 
4.4%
l 600
 
3.3%
k 574
 
3.1%
Other values (61) 7717
42.2%
None
ValueCountFrequency (%)
（ 2
28.6%
） 2
28.6%
é 1
14.3%
  1
14.3%
° 1
14.3%

MENU_TAG
Text

MISSING 

Distinct641
Distinct (%)68.4%
Missing63
Missing (%)6.3%
Memory size7.9 KiB
2023-12-10T18:49:15.717648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length137
Median length89
Mean length37.613661
Min length3

Characters and Unicode

Total characters35244
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique504 ?
Unique (%)53.8%

Sample

1st rowBeef Brisket, Boston Butt, Grilled, Iberico
2nd rowBeef Brisket, Boston Butt, Sirloin, Grilled, Wagyu, Iberico
3rd rowVegetables, Shrimp, Abalone, Octopus, Clam, Pen Shell, Lobster
4th rowVegetables, Shrimp, Abalone, Octopus, Clam, Pen Shell, Red-banded Lobster
5th rowVegetables, Abalone, Clam, Red-banded Lobster
ValueCountFrequency (%)
vegetables 325
 
6.5%
spicy 207
 
4.1%
rice 164
 
3.3%
noodles 134
 
2.7%
flour 105
 
2.1%
beef 91
 
1.8%
steamed 87
 
1.7%
meat 86
 
1.7%
cold 79
 
1.6%
grilled 76
 
1.5%
Other values (405) 3635
72.9%
2023-12-10T18:49:16.639913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4338
 
12.3%
4052
 
11.5%
, 2583
 
7.3%
a 2028
 
5.8%
i 1744
 
4.9%
o 1711
 
4.9%
r 1578
 
4.5%
l 1541
 
4.4%
t 1537
 
4.4%
s 1239
 
3.5%
Other values (44) 12893
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23354
66.3%
Uppercase Letter 5114
 
14.5%
Space Separator 4052
 
11.5%
Other Punctuation 2583
 
7.3%
Dash Punctuation 135
 
0.4%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4338
18.6%
a 2028
 
8.7%
i 1744
 
7.5%
o 1711
 
7.3%
r 1578
 
6.8%
l 1541
 
6.6%
t 1537
 
6.6%
s 1239
 
5.3%
d 1026
 
4.4%
c 838
 
3.6%
Other values (16) 5774
24.7%
Uppercase Letter
ValueCountFrequency (%)
S 1079
21.1%
C 523
10.2%
B 481
9.4%
P 440
8.6%
V 332
 
6.5%
R 331
 
6.5%
F 292
 
5.7%
M 267
 
5.2%
N 169
 
3.3%
G 163
 
3.2%
Other values (13) 1037
20.3%
Space Separator
ValueCountFrequency (%)
4052
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2583
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 135
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28468
80.8%
Common 6776
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4338
15.2%
a 2028
 
7.1%
i 1744
 
6.1%
o 1711
 
6.0%
r 1578
 
5.5%
l 1541
 
5.4%
t 1537
 
5.4%
s 1239
 
4.4%
S 1079
 
3.8%
d 1026
 
3.6%
Other values (39) 10647
37.4%
Common
ValueCountFrequency (%)
4052
59.8%
, 2583
38.1%
- 135
 
2.0%
( 3
 
< 0.1%
) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4338
 
12.3%
4052
 
11.5%
, 2583
 
7.3%
a 2028
 
5.8%
i 1744
 
4.9%
o 1711
 
4.9%
r 1578
 
4.5%
l 1541
 
4.4%
t 1537
 
4.4%
s 1239
 
3.5%
Other values (44) 12893
36.6%

MENU_IMAGE
Text

MISSING 

Distinct139
Distinct (%)100.0%
Missing861
Missing (%)86.1%
Memory size7.9 KiB
2023-12-10T18:49:17.129524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length59
Mean length59.316547
Min length58

Characters and Unicode

Total characters8245
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)100.0%

Sample

1st rowhttp://redtable.kr/image/busan.image/menu.mobile/84_62.jpg
2nd rowhttp://redtable.kr/image/busan.image/menu.mobile/121_63.jpg
3rd rowhttp://redtable.kr/image/busan.image/menu.mobile/121_64.jpg
4th rowhttp://redtable.kr/image/busan.image/menu.mobile/121_65.jpg
5th rowhttp://redtable.kr/image/busan.image/menu.mobile/121_71.jpg
ValueCountFrequency (%)
http://redtable.kr/image/busan.image/menu.mobile/121_78.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_476.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_538.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_536.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_534.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_532.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_531.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_529.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_527.jpg 1
 
0.7%
http://redtable.kr/image/busan.image/menu.mobile/83_543.jpg 1
 
0.7%
Other values (129) 129
92.8%
2023-12-10T18:49:17.799784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 834
 
10.1%
e 834
 
10.1%
a 556
 
6.7%
m 556
 
6.7%
. 556
 
6.7%
b 417
 
5.1%
g 417
 
5.1%
i 417
 
5.1%
t 417
 
5.1%
n 278
 
3.4%
Other values (22) 2963
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5838
70.8%
Other Punctuation 1529
 
18.5%
Decimal Number 739
 
9.0%
Connector Punctuation 139
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 834
14.3%
a 556
 
9.5%
m 556
 
9.5%
b 417
 
7.1%
g 417
 
7.1%
i 417
 
7.1%
t 417
 
7.1%
n 278
 
4.8%
l 278
 
4.8%
r 278
 
4.8%
Other values (8) 1390
23.8%
Decimal Number
ValueCountFrequency (%)
3 122
16.5%
2 121
16.4%
1 115
15.6%
7 69
9.3%
8 69
9.3%
5 66
8.9%
4 64
8.7%
9 46
 
6.2%
6 37
 
5.0%
0 30
 
4.1%
Other Punctuation
ValueCountFrequency (%)
/ 834
54.5%
. 556
36.4%
: 139
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5838
70.8%
Common 2407
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 834
14.3%
a 556
 
9.5%
m 556
 
9.5%
b 417
 
7.1%
g 417
 
7.1%
i 417
 
7.1%
t 417
 
7.1%
n 278
 
4.8%
l 278
 
4.8%
r 278
 
4.8%
Other values (8) 1390
23.8%
Common
ValueCountFrequency (%)
/ 834
34.6%
. 556
23.1%
_ 139
 
5.8%
: 139
 
5.8%
3 122
 
5.1%
2 121
 
5.0%
1 115
 
4.8%
7 69
 
2.9%
8 69
 
2.9%
5 66
 
2.7%
Other values (4) 177
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 834
 
10.1%
e 834
 
10.1%
a 556
 
6.7%
m 556
 
6.7%
. 556
 
6.7%
b 417
 
5.1%
g 417
 
5.1%
i 417
 
5.1%
t 417
 
5.1%
n 278
 
3.4%
Other values (22) 2963
35.9%

Interactions

2023-12-10T18:49:09.925722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:08.849603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:09.385633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:10.121815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:08.992306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:09.559319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:10.296133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:09.213474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:09.723174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:49:17.975816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDMENU_IDMENU_PC
RSTRNT_ID1.0000.7710.203
MENU_ID0.7711.0000.189
MENU_PC0.2030.1891.000
2023-12-10T18:49:18.129901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDMENU_IDMENU_PC
RSTRNT_ID1.0000.614-0.103
MENU_ID0.6141.0000.061
MENU_PC-0.1030.0611.000

Missing values

2023-12-10T18:49:10.528871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:49:10.732150image/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.
2023-12-10T18:49:10.938497image/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

CITY_NMRSTRNT_IDMENU_IDMENU_PCMENU_NMMENU_TAGMENU_IMAGE
0busan254111<NA><NA>
1busan84237000Iberico Moksal, Black Angus ChadolbagiBeef Brisket, Boston Butt, Grilled, Iberico<NA>
2busan84357000Wagu Deunsim, Iberico Moksal, Black Angus ChadolbagiBeef Brisket, Boston Butt, Sirloin, Grilled, Wagyu, Iberico<NA>
3busan84472000Robster, Muneo, Jeonbok, Kijogae, Saeu, Jaecheol JogaeVegetables, Shrimp, Abalone, Octopus, Clam, Pen Shell, Lobster<NA>
4busan84572000Ddaksaeu, Muneo, Jeonbok, Kijogae, Saeu, Jaecheol JogaeVegetables, Shrimp, Abalone, Octopus, Clam, Pen Shell, Red-banded Lobster<NA>
5busan84643000Ddaksaeu, Jeonbok, Jacheol JogaeVegetables, Abalone, Clam, Red-banded Lobster<NA>
6busan84747000Wagu Seiromusi Hanpan ChugaWagyu, Extra Serving<NA>
7busan84862000Robster Haesanmul Hanpan ChugaVegetables, Seafood, Lobster, Extra Serving<NA>
8busan84962000Ddaksaeu Haesanmul Hanpan ChugaVegetables, Seafood, Red-banded Lobster, Extra Serving<NA>
9busan841015000Yachae Hanpan ChugaVegetables, Extra Serving<NA>
CITY_NMRSTRNT_IDMENU_IDMENU_PCMENU_NMMENU_TAGMENU_IMAGE
990busan39910003000Chamchi Kimchi JumeokbapSteamed Rice, Kimchi, Laver, Tuna, Riceballs<NA>
991busan39910013000Spam Danmuji JumeokbapVegetables, Steamed Rice, Egg, Laver, Pickled Radish, Spam, Riceballs, Sesame Oil<NA>
992busan39910022000Kimchi Mari GuksuVegetables, Kimchi, Chili, Broth, Thin Noodles, Noodles, Roll, Spicy<NA>
993busan39910034000Seonji HaejanggukVegetables, Green Onion, Chili, Bean Sprouts, Bone Broth, Dried Radish Greens, Beef Blood, Soup, Spicy, Hot Pepper Powder<NA>
994busan39910048000Hondonju<NA><NA>
995busan39910054000SojuSojuhttp://redtable.kr/image/busan.image/menu.mobile/399_1005.jpg
996busan39910064000MaekjuBarley, Beerhttp://redtable.kr/image/busan.image/menu.mobile/399_1006.jpg
997busan39910073000MakgeolliRice, Unrefined Rice Winehttp://redtable.kr/image/busan.image/menu.mobile/399_1007.jpg
998busan39910082000Eumryosu(Cola/Cider/Mirinda)Soft Drinks, Choose<NA>
999busan18410095000Son KalguksuVegetables, Noodles, Egg, Potatoes, Noodles, Hand-Made<NA>