Overview

Dataset statistics

Number of variables7
Number of observations1000
Missing cells115
Missing cells (%)1.6%
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

CTY_NM has constant value ""Constant
MENU_TAG_DC has 113 (11.3%) missing valuesMissing
MENU_ID has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:00:40.155800
Analysis finished2023-12-10 10:00:43.995065
Duration3.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CTY_NM
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
seoul 1000
100.0%

Length

2023-12-10T19:00:44.128795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:00:44.290353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 1000
100.0%

RSTRNT_ID
Real number (ℝ)

Distinct255
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19376.641
Minimum128
Maximum51532
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:00:44.504169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile450
Q13173.75
median12004
Q335007.25
95-th percentile50274.05
Maximum51532
Range51404
Interquartile range (IQR)31833.5

Descriptive statistics

Standard deviation18225.791
Coefficient of variation (CV)0.94060635
Kurtosis-1.2513705
Mean19376.641
Median Absolute Deviation (MAD)11033
Skewness0.56304267
Sum19376641
Variance3.3217948 × 108
MonotonicityNot monotonic
2023-12-10T19:00:44.785429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
623 20
 
2.0%
1176 10
 
1.0%
7756 9
 
0.9%
38386 9
 
0.9%
49582 9
 
0.9%
34035 8
 
0.8%
8111 8
 
0.8%
951 7
 
0.7%
11888 7
 
0.7%
31014 7
 
0.7%
Other values (245) 906
90.6%
ValueCountFrequency (%)
128 2
 
0.2%
129 7
0.7%
136 4
0.4%
139 1
 
0.1%
140 3
0.3%
144 2
 
0.2%
145 4
0.4%
164 4
0.4%
267 4
0.4%
335 1
 
0.1%
ValueCountFrequency (%)
51532 4
0.4%
51475 3
0.3%
51405 3
0.3%
51401 6
0.6%
51307 5
0.5%
51306 1
 
0.1%
51305 3
0.3%
51304 3
0.3%
51059 3
0.3%
50460 3
0.3%

MENU_ID
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639.1
Minimum3
Maximum1355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:00:45.050232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile65.95
Q1304.75
median602.5
Q3952.25
95-th percentile1279.05
Maximum1355
Range1352
Interquartile range (IQR)647.5

Descriptive statistics

Standard deviation382.82081
Coefficient of variation (CV)0.59899986
Kurtosis-1.1335431
Mean639.1
Median Absolute Deviation (MAD)320.5
Skewness0.16878273
Sum639100
Variance146551.77
MonotonicityStrictly increasing
2023-12-10T19:00:45.336841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
0.1%
857 1
 
0.1%
841 1
 
0.1%
842 1
 
0.1%
843 1
 
0.1%
844 1
 
0.1%
847 1
 
0.1%
849 1
 
0.1%
850 1
 
0.1%
851 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
3 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
14 1
0.1%
ValueCountFrequency (%)
1355 1
0.1%
1354 1
0.1%
1353 1
0.1%
1351 1
0.1%
1350 1
0.1%
1348 1
0.1%
1345 1
0.1%
1344 1
0.1%
1343 1
0.1%
1342 1
0.1%

MENU_PRC
Real number (ℝ)

Distinct174
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16841.27
Minimum0
Maximum180000
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T19:00:45.652615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2500
Q16000
median11000
Q320000
95-th percentile46525
Maximum180000
Range180000
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation18864.202
Coefficient of variation (CV)1.1201176
Kurtosis20.680067
Mean16841.27
Median Absolute Deviation (MAD)6000
Skewness3.7451413
Sum16841270
Variance3.5585813 × 108
MonotonicityNot monotonic
2023-12-10T19:00:45.972963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7000 49
 
4.9%
8000 40
 
4.0%
6000 37
 
3.7%
15000 34
 
3.4%
10000 34
 
3.4%
9000 31
 
3.1%
12000 31
 
3.1%
5000 29
 
2.9%
3000 26
 
2.6%
18000 25
 
2.5%
Other values (164) 664
66.4%
ValueCountFrequency (%)
0 2
 
0.2%
370 1
 
0.1%
500 1
 
0.1%
1000 3
 
0.3%
1200 1
 
0.1%
1500 2
 
0.2%
1700 5
 
0.5%
1800 3
 
0.3%
1900 1
 
0.1%
2000 19
1.9%
ValueCountFrequency (%)
180000 2
0.2%
150000 1
0.1%
140000 1
0.1%
130000 1
0.1%
125000 1
0.1%
115000 1
0.1%
110000 2
0.2%
102000 1
0.1%
100000 2
0.2%
98000 1
0.1%

MENU_NM
Text

Distinct887
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T19:00:46.458840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length37
Mean length7.139
Min length1

Characters and Unicode

Total characters7139
Distinct characters595
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

Unique804 ?
Unique (%)80.4%

Sample

1st row南瓜刨冰(1人份)
2nd rowmakgeolli saeng blueberry nunkkot bingsu(1 serving(s))
3rd row青梅茶(热)
4th row晚餐(成人)
5th row午前(成人)
ValueCountFrequency (%)
酱猪蹄(大 6
 
0.5%
酱猪蹄(中 6
 
0.5%
spaghetti 6
 
0.5%
vongole 5
 
0.4%
糖醋肉 5
 
0.4%
戈根索拉芝士 5
 
0.4%
蔬菜牛小肠 4
 
0.4%
ilpum 4
 
0.4%
set 4
 
0.4%
cream 4
 
0.4%
Other values (949) 1076
95.6%
2023-12-10T19:00:47.335674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 243
 
3.4%
( 242
 
3.4%
e 170
 
2.4%
a 149
 
2.1%
132
 
1.8%
肉 130
 
1.8%
面 119
 
1.7%
i 111
 
1.6%
o 109
 
1.5%
牛 100
 
1.4%
Other values (585) 5634
78.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4712
66.0%
Lowercase Letter 1341
 
18.8%
Uppercase Letter 266
 
3.7%
Close Punctuation 243
 
3.4%
Open Punctuation 242
 
3.4%
Decimal Number 144
 
2.0%
Space Separator 133
 
1.9%
Other Punctuation 36
 
0.5%
Math Symbol 19
 
0.3%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
肉 130
 
2.8%
面 119
 
2.5%
牛 100
 
2.1%
大 93
 
2.0%
鱼 79
 
1.7%
辣 77
 
1.6%
烤 65
 
1.4%
猪 60
 
1.3%
饼 59
 
1.3%
酱 59
 
1.3%
Other values (512) 3871
82.2%
Lowercase Letter
ValueCountFrequency (%)
e 170
12.7%
a 149
 
11.1%
i 111
 
8.3%
o 109
 
8.1%
n 82
 
6.1%
u 78
 
5.8%
m 69
 
5.1%
l 62
 
4.6%
s 61
 
4.5%
r 61
 
4.5%
Other values (15) 389
29.0%
Uppercase Letter
ValueCountFrequency (%)
S 47
17.7%
C 23
 
8.6%
L 21
 
7.9%
B 20
 
7.5%
I 16
 
6.0%
P 16
 
6.0%
A 16
 
6.0%
M 14
 
5.3%
G 11
 
4.1%
R 10
 
3.8%
Other values (14) 72
27.1%
Decimal Number
ValueCountFrequency (%)
1 30
20.8%
0 30
20.8%
3 22
15.3%
2 20
13.9%
7 14
9.7%
5 12
 
8.3%
4 8
 
5.6%
6 6
 
4.2%
8 2
 
1.4%
Other Punctuation
ValueCountFrequency (%)
: 12
33.3%
， 5
13.9%
/ 5
13.9%
、 4
 
11.1%
. 4
 
11.1%
& 2
 
5.6%
, 2
 
5.6%
' 2
 
5.6%
Space Separator
ValueCountFrequency (%)
132
99.2%
  1
 
0.8%
Math Symbol
ValueCountFrequency (%)
~ 14
73.7%
+ 5
 
26.3%
Close Punctuation
ValueCountFrequency (%)
) 243
100.0%
Open Punctuation
ValueCountFrequency (%)
( 242
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 4712
66.0%
Latin 1607
 
22.5%
Common 820
 
11.5%

Most frequent character per script

Han
ValueCountFrequency (%)
肉 130
 
2.8%
面 119
 
2.5%
牛 100
 
2.1%
大 93
 
2.0%
鱼 79
 
1.7%
辣 77
 
1.6%
烤 65
 
1.4%
猪 60
 
1.3%
饼 59
 
1.3%
酱 59
 
1.3%
Other values (512) 3871
82.2%
Latin
ValueCountFrequency (%)
e 170
 
10.6%
a 149
 
9.3%
i 111
 
6.9%
o 109
 
6.8%
n 82
 
5.1%
u 78
 
4.9%
m 69
 
4.3%
l 62
 
3.9%
s 61
 
3.8%
r 61
 
3.8%
Other values (39) 655
40.8%
Common
ValueCountFrequency (%)
) 243
29.6%
( 242
29.5%
132
16.1%
1 30
 
3.7%
0 30
 
3.7%
3 22
 
2.7%
2 20
 
2.4%
7 14
 
1.7%
~ 14
 
1.7%
5 12
 
1.5%
Other values (14) 61
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
CJK 4711
66.0%
ASCII 2417
33.9%
None 10
 
0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 243
 
10.1%
( 242
 
10.0%
e 170
 
7.0%
a 149
 
6.2%
132
 
5.5%
i 111
 
4.6%
o 109
 
4.5%
n 82
 
3.4%
u 78
 
3.2%
m 69
 
2.9%
Other values (60) 1032
42.7%
CJK
ValueCountFrequency (%)
肉 130
 
2.8%
面 119
 
2.5%
牛 100
 
2.1%
大 93
 
2.0%
鱼 79
 
1.7%
辣 77
 
1.6%
烤 65
 
1.4%
猪 60
 
1.3%
饼 59
 
1.3%
酱 59
 
1.3%
Other values (511) 3870
82.1%
None
ValueCountFrequency (%)
， 5
50.0%
、 4
40.0%
  1
 
10.0%
CJK Compat Ideographs
ValueCountFrequency (%)
茶 1
100.0%

MENU_TAG_DC
Text

MISSING 

Distinct592
Distinct (%)66.7%
Missing113
Missing (%)11.3%
Memory size7.9 KiB
2023-12-10T19:00:48.008410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length36
Mean length13.959414
Min length1

Characters and Unicode

Total characters12382
Distinct characters427
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique433 ?
Unique (%)48.8%

Sample

1st row甜南瓜, 刨冰
2nd row冰块, 大米, 蓝莓, 马格利酒, 刨冰, 冷
3rd row青梅, 茶, 暖
4th row晚餐, 成人
5th row成人
ValueCountFrequency (%)
蔬菜 311
 
9.2%
面粉 161
 
4.8%
烤 132
 
3.9%
辣味 128
 
3.8%
面条 110
 
3.3%
奶酪 67
 
2.0%
辣椒粉 59
 
1.7%
鸡肉 58
 
1.7%
鸡蛋 57
 
1.7%
米饭 48
 
1.4%
Other values (402) 2242
66.5%
2023-12-10T19:00:49.002556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2486
20.1%
2486
20.1%
菜 461
 
3.7%
面 408
 
3.3%
蔬 311
 
2.5%
肉 249
 
2.0%
辣 246
 
2.0%
粉 245
 
2.0%
味 166
 
1.3%
奶 146
 
1.2%
Other values (417) 5178
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7398
59.7%
Other Punctuation 2486
 
20.1%
Space Separator 2486
 
20.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Lowercase Letter 3
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
菜 461
 
6.2%
面 408
 
5.5%
蔬 311
 
4.2%
肉 249
 
3.4%
辣 246
 
3.3%
粉 245
 
3.3%
味 166
 
2.2%
奶 146
 
2.0%
烤 143
 
1.9%
鸡 138
 
1.9%
Other values (409) 4885
66.0%
Lowercase Letter
ValueCountFrequency (%)
n 2
66.7%
a 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
Q 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 2486
100.0%
Space Separator
ValueCountFrequency (%)
2486
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 7398
59.7%
Common 4978
40.2%
Latin 6
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
菜 461
 
6.2%
面 408
 
5.5%
蔬 311
 
4.2%
肉 249
 
3.4%
辣 246
 
3.3%
粉 245
 
3.3%
味 166
 
2.2%
奶 146
 
2.0%
烤 143
 
1.9%
鸡 138
 
1.9%
Other values (409) 4885
66.0%
Common
ValueCountFrequency (%)
, 2486
49.9%
2486
49.9%
( 3
 
0.1%
) 3
 
0.1%
Latin
ValueCountFrequency (%)
n 2
33.3%
B 2
33.3%
Q 1
16.7%
a 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
CJK 7398
59.7%
ASCII 4984
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 2486
49.9%
2486
49.9%
( 3
 
0.1%
) 3
 
0.1%
n 2
 
< 0.1%
B 2
 
< 0.1%
Q 1
 
< 0.1%
a 1
 
< 0.1%
CJK
ValueCountFrequency (%)
菜 461
 
6.2%
面 408
 
5.5%
蔬 311
 
4.2%
肉 249
 
3.4%
辣 246
 
3.3%
粉 245
 
3.3%
味 166
 
2.2%
奶 146
 
2.0%
烤 143
 
1.9%
鸡 138
 
1.9%
Other values (409) 4885
66.0%

MENU_IMAGE_URL
Text

Distinct998
Distinct (%)100.0%
Missing2
Missing (%)0.2%
Memory size7.9 KiB
2023-12-10T19:00:49.557296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length62
Mean length61.537074
Min length59

Characters and Unicode

Total characters61414
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

Unique998 ?
Unique (%)100.0%

Sample

1st rowhttp://redtable.kr/image/seoul.image/menu.mobile/16455_3.jpg
2nd rowhttp://redtable.kr/image/seoul.image/menu.mobile/16455_6.jpg
3rd rowhttp://redtable.kr/image/seoul.image/menu.mobile/16455_7.jpg
4th rowhttp://redtable.kr/image/seoul.image/menu.mobile/3350_8.jpg
5th rowhttp://redtable.kr/image/seoul.image/menu.mobile/3350_9.jpg
ValueCountFrequency (%)
http://redtable.kr/image/seoul.image/menu.mobile/25983_23.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/51401_854.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/1176_871.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/51401_856.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/5731_840.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/5731_841.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/679_842.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/679_843.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/679_844.jpg 1
 
0.1%
http://redtable.kr/image/seoul.image/menu.mobile/28995_847.jpg 1
 
0.1%
Other values (988) 988
99.0%
2023-12-10T19:00:50.271895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6986
 
11.4%
/ 5988
 
9.8%
. 3992
 
6.5%
m 3992
 
6.5%
t 2994
 
4.9%
g 2994
 
4.9%
a 2994
 
4.9%
i 2994
 
4.9%
l 2994
 
4.9%
b 1996
 
3.3%
Other values (22) 23490
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41916
68.3%
Other Punctuation 10978
 
17.9%
Decimal Number 7522
 
12.2%
Connector Punctuation 998
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6986
16.7%
m 3992
 
9.5%
t 2994
 
7.1%
g 2994
 
7.1%
a 2994
 
7.1%
i 2994
 
7.1%
l 2994
 
7.1%
b 1996
 
4.8%
o 1996
 
4.8%
u 1996
 
4.8%
Other values (8) 9980
23.8%
Decimal Number
ValueCountFrequency (%)
1 1077
14.3%
4 858
11.4%
3 855
11.4%
2 814
10.8%
5 789
10.5%
8 657
8.7%
7 633
8.4%
6 624
8.3%
0 614
8.2%
9 601
8.0%
Other Punctuation
ValueCountFrequency (%)
/ 5988
54.5%
. 3992
36.4%
: 998
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41916
68.3%
Common 19498
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6986
16.7%
m 3992
 
9.5%
t 2994
 
7.1%
g 2994
 
7.1%
a 2994
 
7.1%
i 2994
 
7.1%
l 2994
 
7.1%
b 1996
 
4.8%
o 1996
 
4.8%
u 1996
 
4.8%
Other values (8) 9980
23.8%
Common
ValueCountFrequency (%)
/ 5988
30.7%
. 3992
20.5%
1 1077
 
5.5%
_ 998
 
5.1%
: 998
 
5.1%
4 858
 
4.4%
3 855
 
4.4%
2 814
 
4.2%
5 789
 
4.0%
8 657
 
3.4%
Other values (4) 2472
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6986
 
11.4%
/ 5988
 
9.8%
. 3992
 
6.5%
m 3992
 
6.5%
t 2994
 
4.9%
g 2994
 
4.9%
a 2994
 
4.9%
i 2994
 
4.9%
l 2994
 
4.9%
b 1996
 
3.3%
Other values (22) 23490
38.2%

Interactions

2023-12-10T19:00:42.728718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:41.190264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:41.866436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:42.996035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:41.429128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:42.163863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:43.193163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:41.639491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:42.449275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:00:50.447287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDMENU_IDMENU_PRC
RSTRNT_ID1.0000.5520.206
MENU_ID0.5521.0000.287
MENU_PRC0.2060.2871.000
2023-12-10T19:00:50.622703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDMENU_IDMENU_PRC
RSTRNT_ID1.000-0.086-0.053
MENU_ID-0.0861.0000.222
MENU_PRC-0.0530.2221.000

Missing values

2023-12-10T19:00:43.484799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:00:43.716108image/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.
2023-12-10T19:00:43.893666image/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

CTY_NMRSTRNT_IDMENU_IDMENU_PRCMENU_NMMENU_TAG_DCMENU_IMAGE_URL
0seoul16455313500南瓜刨冰(1人份)甜南瓜, 刨冰http://redtable.kr/image/seoul.image/menu.mobile/16455_3.jpg
1seoul1645569500makgeolli saeng blueberry nunkkot bingsu(1 serving(s))冰块, 大米, 蓝莓, 马格利酒, 刨冰, 冷http://redtable.kr/image/seoul.image/menu.mobile/16455_6.jpg
2seoul1645578000青梅茶(热)青梅, 茶, 暖http://redtable.kr/image/seoul.image/menu.mobile/16455_7.jpg
3seoul33508102000晚餐(成人)晚餐, 成人http://redtable.kr/image/seoul.image/menu.mobile/3350_8.jpg
4seoul3350959300午前(成人)成人http://redtable.kr/image/seoul.image/menu.mobile/3350_9.jpg
5seoul33501059300午前(成人)成人http://redtable.kr/image/seoul.image/menu.mobile/3350_10.jpg
6seoul33501190000午餐(成人)午餐, 成人http://redtable.kr/image/seoul.image/menu.mobile/3350_11.jpg
7seoul33501260500晚餐(儿童)晚餐http://redtable.kr/image/seoul.image/menu.mobile/3350_12.jpg
8seoul33501356900午餐(儿童)午餐http://redtable.kr/image/seoul.image/menu.mobile/3350_13.jpg
9seoul45014146500拿破仑<NA>http://redtable.kr/image/seoul.image/menu.mobile/45014_14.jpg
CTY_NMRSTRNT_IDMENU_IDMENU_PRCMENU_NMMENU_TAG_DCMENU_IMAGE_URL
990seoul14513426000油豆腐乌冬面面条, 乌冬面http://redtable.kr/image/seoul.image/menu.mobile/145_1342.jpg
991seoul14513436000鱼露汁乌冬面条, 乌冬面http://redtable.kr/image/seoul.image/menu.mobile/145_1343.jpg
992seoul144134414000烤调味牛皱胃皱胃, 烤, 调味, 辣味http://redtable.kr/image/seoul.image/menu.mobile/144_1344.jpg
993seoul144134514000盐烤牛皱胃皱胃, 盐烤肉, 铁网http://redtable.kr/image/seoul.image/menu.mobile/144_1345.jpg
994seoul4765913486000玉米面条蔬菜, 玉米面, 面条http://redtable.kr/image/seoul.image/menu.mobile/47659_1348.jpg
995seoul4765913506000水饺蔬菜, 面粉, 豆腐, 肉, 饺子, 煮http://redtable.kr/image/seoul.image/menu.mobile/47659_1350.jpg
996seoul47659135112000羊肉串羊肉, 串http://redtable.kr/image/seoul.image/menu.mobile/47659_1351.jpg
997seoul1573913538000明太鱼拌饭牛肉, 葱, 粉条, 排骨, 干明太鱼, 明太鱼, 烤牛肉, 拌饭, 套餐http://redtable.kr/image/seoul.image/menu.mobile/15739_1353.jpg
998seoul15739135415000绿豆清炖鸡鸡肉, 大蒜, 人参, 糯米, 绿豆, 汤http://redtable.kr/image/seoul.image/menu.mobile/15739_1354.jpg
999seoul15739135515000章鱼葱煎饼蔬菜, 面粉, 鸡蛋, 葱, 小章鱼, 煎饼http://redtable.kr/image/seoul.image/menu.mobile/15739_1355.jpg