Overview

Dataset statistics

Number of variables10
Number of observations900
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.1 KiB
Average record size in memory83.1 B

Variable types

Numeric3
Text4
Categorical3

Alerts

CRNCY_CD has constant value ""Constant
COUNTRY_NM has constant value ""Constant
CTY_NM has constant value ""Constant
MENU_ID is highly overall correlated with RSTRNT_IDHigh correlation
RSTRNT_ID is highly overall correlated with MENU_IDHigh correlation
MENU_ID has unique valuesUnique
MENU_PRC has 105 (11.7%) zerosZeros

Reproduction

Analysis started2023-12-10 10:03:40.052537
Analysis finished2023-12-10 10:03:43.102105
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MENU_ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct900
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1145.7878
Minimum3
Maximum2277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-10T19:03:43.243821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile101.95
Q1588.5
median1173
Q31724.25
95-th percentile2152.05
Maximum2277
Range2274
Interquartile range (IQR)1135.75

Descriptive statistics

Standard deviation658.97156
Coefficient of variation (CV)0.57512532
Kurtosis-1.202878
Mean1145.7878
Median Absolute Deviation (MAD)571.5
Skewness-0.051686484
Sum1031209
Variance434243.52
MonotonicityStrictly increasing
2023-12-10T19:03:43.830077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
0.1%
1464 1
 
0.1%
1517 1
 
0.1%
1521 1
 
0.1%
1522 1
 
0.1%
1524 1
 
0.1%
1527 1
 
0.1%
1530 1
 
0.1%
1531 1
 
0.1%
1532 1
 
0.1%
Other values (890) 890
98.9%
ValueCountFrequency (%)
3 1
0.1%
4 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
11 1
0.1%
17 1
0.1%
18 1
0.1%
19 1
0.1%
ValueCountFrequency (%)
2277 1
0.1%
2275 1
0.1%
2273 1
0.1%
2272 1
0.1%
2264 1
0.1%
2263 1
0.1%
2259 1
0.1%
2257 1
0.1%
2250 1
0.1%
2243 1
0.1%

MENU_CHNLNG_NM
Text

Distinct419
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2023-12-10T19:03:44.430550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.2722222
Min length2

Characters and Unicode

Total characters2945
Distinct characters480
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)24.8%

Sample

1st row安妮PIZZA
2nd rowcheese蛋糕
3rd rowpizza
4th row提拉米苏
5th row凯撒沙拉
ValueCountFrequency (%)
烤鸭 16
 
1.8%
水煮鱼 11
 
1.2%
豌豆黄 11
 
1.2%
宫保鸡丁 11
 
1.2%
担担面 9
 
1.0%
口水鸡 9
 
1.0%
毛血旺 9
 
1.0%
榴莲酥 9
 
1.0%
奶油蘑菇汤 8
 
0.9%
盐水鸭肝 8
 
0.9%
Other values (406) 799
88.8%
2023-12-10T19:03:45.196718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
鱼 94
 
3.2%
肉 76
 
2.6%
鸭 63
 
2.1%
烤 62
 
2.1%
豆 60
 
2.0%
鸡 56
 
1.9%
牛 56
 
1.9%
面 49
 
1.7%
虾 41
 
1.4%
水 41
 
1.4%
Other values (470) 2347
79.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2897
98.4%
Lowercase Letter 25
 
0.8%
Uppercase Letter 23
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
鱼 94
 
3.2%
肉 76
 
2.6%
鸭 63
 
2.2%
烤 62
 
2.1%
豆 60
 
2.1%
鸡 56
 
1.9%
牛 56
 
1.9%
面 49
 
1.7%
虾 41
 
1.4%
水 41
 
1.4%
Other values (453) 2299
79.4%
Uppercase Letter
ValueCountFrequency (%)
Z 4
17.4%
P 3
13.0%
A 3
13.0%
I 3
13.0%
E 3
13.0%
C 2
8.7%
H 2
8.7%
S 2
8.7%
V 1
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
z 8
32.0%
i 4
16.0%
a 4
16.0%
e 3
 
12.0%
p 3
 
12.0%
h 1
 
4.0%
c 1
 
4.0%
s 1
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Han 2897
98.4%
Latin 48
 
1.6%

Most frequent character per script

Han
ValueCountFrequency (%)
鱼 94
 
3.2%
肉 76
 
2.6%
鸭 63
 
2.2%
烤 62
 
2.1%
豆 60
 
2.1%
鸡 56
 
1.9%
牛 56
 
1.9%
面 49
 
1.7%
虾 41
 
1.4%
水 41
 
1.4%
Other values (453) 2299
79.4%
Latin
ValueCountFrequency (%)
z 8
16.7%
Z 4
 
8.3%
i 4
 
8.3%
a 4
 
8.3%
e 3
 
6.2%
p 3
 
6.2%
P 3
 
6.2%
A 3
 
6.2%
I 3
 
6.2%
E 3
 
6.2%
Other values (7) 10
20.8%

Most occurring blocks

ValueCountFrequency (%)
CJK 2897
98.4%
ASCII 48
 
1.6%

Most frequent character per block

CJK
ValueCountFrequency (%)
鱼 94
 
3.2%
肉 76
 
2.6%
鸭 63
 
2.2%
烤 62
 
2.1%
豆 60
 
2.1%
鸡 56
 
1.9%
牛 56
 
1.9%
面 49
 
1.7%
虾 41
 
1.4%
水 41
 
1.4%
Other values (453) 2299
79.4%
ASCII
ValueCountFrequency (%)
z 8
16.7%
Z 4
 
8.3%
i 4
 
8.3%
a 4
 
8.3%
e 3
 
6.2%
p 3
 
6.2%
P 3
 
6.2%
A 3
 
6.2%
I 3
 
6.2%
E 3
 
6.2%
Other values (7) 10
20.8%

MENU_KLANG_NM
Text

Distinct412
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2023-12-10T19:03:45.971392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.98
Min length2

Characters and Unicode

Total characters5382
Distinct characters182
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)24.1%

Sample

1st row안니 PIZZA
2nd rowCHEESE 단가오
3rd rowPIZZA
4th row티라미스
5th row카이사 샤라
ValueCountFrequency (%)
지 66
 
3.1%
유 65
 
3.1%
바오 43
 
2.0%
야 43
 
2.0%
카오 40
 
1.9%
간 37
 
1.7%
미엔 36
 
1.7%
탕 34
 
1.6%
지아오 30
 
1.4%
로우 30
 
1.4%
Other values (399) 1697
80.0%
2023-12-10T19:03:47.104707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1221
22.7%
오 323
 
6.0%
우 316
 
5.9%
지 218
 
4.1%
이 189
 
3.5%
엔 173
 
3.2%
시 117
 
2.2%
아 109
 
2.0%
유 98
 
1.8%
바 78
 
1.4%
Other values (172) 2540
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4113
76.4%
Space Separator 1221
 
22.7%
Uppercase Letter 48
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
오 323
 
7.9%
우 316
 
7.7%
지 218
 
5.3%
이 189
 
4.6%
엔 173
 
4.2%
시 117
 
2.8%
아 109
 
2.7%
유 98
 
2.4%
바 78
 
1.9%
로 74
 
1.8%
Other values (162) 2418
58.8%
Uppercase Letter
ValueCountFrequency (%)
Z 12
25.0%
A 7
14.6%
I 7
14.6%
E 6
12.5%
P 6
12.5%
H 3
 
6.2%
C 3
 
6.2%
S 3
 
6.2%
V 1
 
2.1%
Space Separator
ValueCountFrequency (%)
1221
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4113
76.4%
Common 1221
 
22.7%
Latin 48
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
오 323
 
7.9%
우 316
 
7.7%
지 218
 
5.3%
이 189
 
4.6%
엔 173
 
4.2%
시 117
 
2.8%
아 109
 
2.7%
유 98
 
2.4%
바 78
 
1.9%
로 74
 
1.8%
Other values (162) 2418
58.8%
Latin
ValueCountFrequency (%)
Z 12
25.0%
A 7
14.6%
I 7
14.6%
E 6
12.5%
P 6
12.5%
H 3
 
6.2%
C 3
 
6.2%
S 3
 
6.2%
V 1
 
2.1%
Common
ValueCountFrequency (%)
1221
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4113
76.4%
ASCII 1269
 
23.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1221
96.2%
Z 12
 
0.9%
A 7
 
0.6%
I 7
 
0.6%
E 6
 
0.5%
P 6
 
0.5%
H 3
 
0.2%
C 3
 
0.2%
S 3
 
0.2%
V 1
 
0.1%
Hangul
ValueCountFrequency (%)
오 323
 
7.9%
우 316
 
7.7%
지 218
 
5.3%
이 189
 
4.6%
엔 173
 
4.2%
시 117
 
2.8%
아 109
 
2.7%
유 98
 
2.4%
바 78
 
1.9%
로 74
 
1.8%
Other values (162) 2418
58.8%

MENU_JLANG_NM
Text

Distinct407
Distinct (%)45.4%
Missing3
Missing (%)0.3%
Memory size7.2 KiB
2023-12-10T19:03:47.569356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.3578595
Min length2

Characters and Unicode

Total characters5703
Distinct characters78
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique211 ?
Unique (%)23.5%

Sample

1st rowアンニ PIZZA
2nd rowCHEESEダンガオ
3rd rowPIZZA
4th rowティラミス
5th rowカイサシャラ
ValueCountFrequency (%)
ゴンバオジディン 17
 
1.9%
カオヤ 16
 
1.8%
シウジュユ 11
 
1.2%
ワンドウフアン 11
 
1.2%
リウリエンス 9
 
1.0%
ダンダンミエン 9
 
1.0%
コウシュイジ 9
 
1.0%
マオシュエワン 9
 
1.0%
ジエモヤジャン 8
 
0.9%
トゥドウニ 8
 
0.9%
Other values (397) 791
88.1%
2023-12-10T19:03:48.467541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 944
16.6%
ジ 383
 
6.7%
ウ 334
 
5.9%
オ 325
 
5.7%
シ 307
 
5.4%
ャ 257
 
4.5%
エ 214
 
3.8%
チ 199
 
3.5%
ア 198
 
3.5%
イ 195
 
3.4%
Other values (68) 2347
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5654
99.1%
Uppercase Letter 48
 
0.8%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 944
16.7%
ジ 383
 
6.8%
ウ 334
 
5.9%
オ 325
 
5.7%
シ 307
 
5.4%
ャ 257
 
4.5%
エ 214
 
3.8%
チ 199
 
3.5%
ア 198
 
3.5%
イ 195
 
3.4%
Other values (58) 2298
40.6%
Uppercase Letter
ValueCountFrequency (%)
Z 12
25.0%
A 7
14.6%
I 7
14.6%
P 6
12.5%
E 6
12.5%
C 3
 
6.2%
H 3
 
6.2%
S 3
 
6.2%
V 1
 
2.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 5654
99.1%
Latin 48
 
0.8%
Common 1
 
< 0.1%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 944
16.7%
ジ 383
 
6.8%
ウ 334
 
5.9%
オ 325
 
5.7%
シ 307
 
5.4%
ャ 257
 
4.5%
エ 214
 
3.8%
チ 199
 
3.5%
ア 198
 
3.5%
イ 195
 
3.4%
Other values (58) 2298
40.6%
Latin
ValueCountFrequency (%)
Z 12
25.0%
A 7
14.6%
I 7
14.6%
P 6
12.5%
E 6
12.5%
C 3
 
6.2%
H 3
 
6.2%
S 3
 
6.2%
V 1
 
2.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Katakana 5654
99.1%
ASCII 49
 
0.9%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 944
16.7%
ジ 383
 
6.8%
ウ 334
 
5.9%
オ 325
 
5.7%
シ 307
 
5.4%
ャ 257
 
4.5%
エ 214
 
3.8%
チ 199
 
3.5%
ア 198
 
3.5%
イ 195
 
3.4%
Other values (58) 2298
40.6%
ASCII
ValueCountFrequency (%)
Z 12
24.5%
A 7
14.3%
I 7
14.3%
P 6
12.2%
E 6
12.2%
C 3
 
6.1%
H 3
 
6.1%
S 3
 
6.1%
V 1
 
2.0%
1
 
2.0%

MENU_ENGL_NM
Text

Distinct524
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2023-12-10T19:03:49.248527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length22
Mean length11.687778
Min length4

Characters and Unicode

Total characters10519
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)38.4%

Sample

1st rowAnni Pizza
2nd rowCHEESE dangao
3rd rowPIZZA
4th rowTi La Mi Su
5th rowKaisa shala
ValueCountFrequency (%)
yu 75
 
3.1%
bao 53
 
2.2%
ya 53
 
2.2%
kao 50
 
2.1%
ji 50
 
2.1%
rou 50
 
2.1%
tang 41
 
1.7%
mian 39
 
1.6%
gan 38
 
1.6%
niu 35
 
1.4%
Other values (384) 1947
80.1%
2023-12-10T19:03:50.167083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1531
14.6%
a 1394
13.3%
i 1047
10.0%
n 986
 
9.4%
u 878
 
8.3%
o 716
 
6.8%
g 472
 
4.5%
h 463
 
4.4%
e 234
 
2.2%
S 182
 
1.7%
Other values (40) 2616
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7258
69.0%
Uppercase Letter 1730
 
16.4%
Space Separator 1531
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1394
19.2%
i 1047
14.4%
n 986
13.6%
u 878
12.1%
o 716
9.9%
g 472
 
6.5%
h 463
 
6.4%
e 234
 
3.2%
s 124
 
1.7%
d 102
 
1.4%
Other values (16) 842
11.6%
Uppercase Letter
ValueCountFrequency (%)
S 182
 
10.5%
Y 153
 
8.8%
Z 134
 
7.7%
G 114
 
6.6%
J 104
 
6.0%
L 99
 
5.7%
M 98
 
5.7%
D 91
 
5.3%
C 87
 
5.0%
X 80
 
4.6%
Other values (13) 588
34.0%
Space Separator
ValueCountFrequency (%)
1531
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8988
85.4%
Common 1531
 
14.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1394
15.5%
i 1047
11.6%
n 986
11.0%
u 878
 
9.8%
o 716
 
8.0%
g 472
 
5.3%
h 463
 
5.2%
e 234
 
2.6%
S 182
 
2.0%
Y 153
 
1.7%
Other values (39) 2463
27.4%
Common
ValueCountFrequency (%)
1531
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1531
14.6%
a 1394
13.3%
i 1047
10.0%
n 986
 
9.4%
u 878
 
8.3%
o 716
 
6.8%
g 472
 
4.5%
h 463
 
4.4%
e 234
 
2.2%
S 182
 
1.7%
Other values (40) 2616
24.9%

MENU_PRC
Real number (ℝ)

ZEROS 

Distinct122
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.048889
Minimum0
Maximum998
Zeros105
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-10T19:03:50.510406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median34
Q368
95-th percentile200
Maximum998
Range998
Interquartile range (IQR)55

Descriptive statistics

Standard deviation94.54196
Coefficient of variation (CV)1.5744165
Kurtosis30.16443
Mean60.048889
Median Absolute Deviation (MAD)24
Skewness4.551602
Sum54044
Variance8938.1823
MonotonicityNot monotonic
2023-12-10T19:03:50.808768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 105
 
11.7%
48 35
 
3.9%
28 33
 
3.7%
38 30
 
3.3%
88 29
 
3.2%
15 26
 
2.9%
25 25
 
2.8%
18 25
 
2.8%
20 25
 
2.8%
36 20
 
2.2%
Other values (112) 547
60.8%
ValueCountFrequency (%)
0 105
11.7%
1 3
 
0.3%
2 17
 
1.9%
3 10
 
1.1%
4 10
 
1.1%
5 13
 
1.4%
6 12
 
1.3%
7 8
 
0.9%
8 15
 
1.7%
9 1
 
0.1%
ValueCountFrequency (%)
998 2
 
0.2%
698 1
 
0.1%
638 2
 
0.2%
588 1
 
0.1%
498 4
0.4%
412 6
0.7%
400 1
 
0.1%
378 1
 
0.1%
369 1
 
0.1%
368 2
 
0.2%

CRNCY_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
CNY
900 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
CNY 900
100.0%

Length

2023-12-10T19:03:51.035590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:03:51.253122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cny 900
100.0%

COUNTRY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
CHINA
900 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
CHINA 900
100.0%

Length

2023-12-10T19:03:51.515481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:03:51.763855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
china 900
100.0%

CTY_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
Beijing
900 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Beijing 900
100.0%

Length

2023-12-10T19:03:52.038587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:03:52.198828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
beijing 900
100.0%

RSTRNT_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.97
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 KiB
2023-12-10T19:03:52.390637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median53
Q381
95-th percentile101
Maximum106
Range105
Interquartile range (IQR)55

Descriptive statistics

Standard deviation30.864305
Coefficient of variation (CV)0.5826752
Kurtosis-1.235828
Mean52.97
Median Absolute Deviation (MAD)27
Skewness-0.0069510562
Sum47673
Variance952.60533
MonotonicityIncreasing
2023-12-10T19:03:52.661111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 16
 
1.8%
38 16
 
1.8%
78 15
 
1.7%
51 15
 
1.7%
92 15
 
1.7%
27 15
 
1.7%
8 14
 
1.6%
35 14
 
1.6%
71 14
 
1.6%
55 14
 
1.6%
Other values (83) 752
83.6%
ValueCountFrequency (%)
1 13
1.4%
2 13
1.4%
3 8
0.9%
5 10
1.1%
6 10
1.1%
7 10
1.1%
8 14
1.6%
9 8
0.9%
10 8
0.9%
11 9
1.0%
ValueCountFrequency (%)
106 5
 
0.6%
105 5
 
0.6%
104 5
 
0.6%
103 11
1.2%
102 12
1.3%
101 13
1.4%
99 10
1.1%
98 12
1.3%
97 8
0.9%
95 7
0.8%

Interactions

2023-12-10T19:03:42.090909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:40.945107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.551679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:42.278132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.106550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.755754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:42.436517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.349891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:03:41.926861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:03:52.829197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MENU_IDMENU_PRCRSTRNT_ID
MENU_ID1.0000.2080.998
MENU_PRC0.2081.0000.177
RSTRNT_ID0.9980.1771.000
2023-12-10T19:03:52.994609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MENU_IDMENU_PRCRSTRNT_ID
MENU_ID1.000-0.0171.000
MENU_PRC-0.0171.000-0.018
RSTRNT_ID1.000-0.0181.000

Missing values

2023-12-10T19:03:42.702483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:03:42.997149image/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

MENU_IDMENU_CHNLNG_NMMENU_KLANG_NMMENU_JLANG_NMMENU_ENGL_NMMENU_PRCCRNCY_CDCOUNTRY_NMCTY_NMRSTRNT_ID
03安妮PIZZA안니 PIZZAアンニ PIZZAAnni Pizza56CNYCHINABeijing1
14cheese蛋糕CHEESE 단가오CHEESEダンガオCHEESE dangao0CNYCHINABeijing1
26pizzaPIZZAPIZZAPIZZA0CNYCHINABeijing1
37提拉米苏티라미스ティラミスTi La Mi Su35CNYCHINABeijing1
48凯撒沙拉카이사 샤라カイサシャラKaisa shala35CNYCHINABeijing1
59鸡翅지치ジチJichi38CNYCHINABeijing1
611沙拉샤라シャラShala15CNYCHINABeijing1
717土豆泥투도우 니トゥドウニTudou ni20CNYCHINABeijing1
818千层面치엔 쳉미엔チエンチェンミエンQian cengmian49CNYCHINABeijing1
919意大利面이다리 미엔イダリミエンYidali mian32CNYCHINABeijing1
MENU_IDMENU_CHNLNG_NMMENU_KLANG_NMMENU_JLANG_NMMENU_ENGL_NMMENU_PRCCRNCY_CDCOUNTRY_NMCTY_NMRSTRNT_ID
8902243小米粥샤오미 조우シャオミジョウXiao Mi Zhou2CNYCHINABeijing105
8912250灌肠구앙창グアンチャンGuan Chang7CNYCHINABeijing105
8922257北冰洋베이빙양ベイビンヤンBeibingyang4CNYCHINABeijing105
8932259蒜汁수안 지スアンジSuan Zhi1CNYCHINABeijing105
8942263炸灌肠자 구엔창ジャグエンチャンZha Guan Chang7CNYCHINABeijing105
8952264羊杂汤양 자 탕ヤンジャタンYang Za Tang8CNYCHINABeijing106
8962272爆肚바오두バオドゥBao Du18CNYCHINABeijing106
8972273芝麻火烧지마 훠샤오ジマフォシャオZhima huoshao2CNYCHINABeijing106
8982275小碗牛肉샤오 완 니우로우シャオワンニウロウXiao wan niurou25CNYCHINABeijing106
8992277麻豆腐마 도후マドフMa doufu8CNYCHINABeijing106