Overview

Dataset statistics

Number of variables7
Number of observations1000
Missing cells1801
Missing cells (%)25.7%
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
RSTRNT_ID is highly overall correlated with MENU_IDHigh correlation
MENU_ID is highly overall correlated with RSTRNT_IDHigh correlation
MENU_TAG_DC has 940 (94.0%) missing valuesMissing
MENU_IMAGE_URL has 861 (86.1%) missing valuesMissing
MENU_ID has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:13:56.907686
Analysis finished2023-12-10 10:13:59.944612
Duration3.04 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
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

2023-12-10T19:14:00.081164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:14:00.274230image/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
2023-12-10T19:14:00.498747image/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
2023-12-10T19:14:00.731501image/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
2023-12-10T19:14:00.980028image/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
2023-12-10T19:14:01.295841image/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_PRC
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
2023-12-10T19:14:01.618868image/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
2023-12-10T19:14:01.886835image/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

Distinct915
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T19:14:02.308370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length24
Mean length7.345
Min length1

Characters and Unicode

Total characters7345
Distinct characters535
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique877 ?
Unique (%)87.7%

Sample

1st row1
2nd row이베리코목살,블랙앵거스차돌박이
3rd row와규등심,이베리코목살,블랙앵거스차돌박이
4th row랍스터,문어,전복,키조개,새우,제철조개
5th row딱새우,문어,전복,키조개,새우,제철조개
ValueCountFrequency (%)
소주 12
 
1.2%
공기밥 12
 
1.2%
맥주 12
 
1.2%
된장찌개 7
 
0.7%
막걸리 6
 
0.6%
콜라 5
 
0.5%
청하 4
 
0.4%
사이다 4
 
0.4%
해물파전 3
 
0.3%
김치찌개 3
 
0.3%
Other values (910) 944
93.3%
2023-12-10T19:14:03.037823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 240
 
3.3%
( 240
 
3.3%
이 183
 
2.5%
스 155
 
2.1%
라 120
 
1.6%
리 114
 
1.6%
치 95
 
1.3%
밥 93
 
1.3%
비 85
 
1.2%
/ 85
 
1.2%
Other values (525) 5935
80.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6043
82.3%
Decimal Number 295
 
4.0%
Close Punctuation 241
 
3.3%
Open Punctuation 241
 
3.3%
Uppercase Letter 195
 
2.7%
Lowercase Letter 137
 
1.9%
Other Punctuation 126
 
1.7%
Math Symbol 49
 
0.7%
Space Separator 12
 
0.2%
Dash Punctuation 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 183
 
3.0%
스 155
 
2.6%
라 120
 
2.0%
리 114
 
1.9%
치 95
 
1.6%
밥 93
 
1.5%
비 85
 
1.4%
우 83
 
1.4%
갈 79
 
1.3%
소 72
 
1.2%
Other values (465) 4964
82.1%
Uppercase Letter
ValueCountFrequency (%)
L 28
14.4%
R 28
14.4%
E 23
11.8%
S 22
11.3%
T 20
10.3%
I 16
8.2%
B 13
6.7%
M 12
6.2%
N 9
 
4.6%
A 6
 
3.1%
Other values (10) 18
9.2%
Lowercase Letter
ValueCountFrequency (%)
g 37
27.0%
m 16
11.7%
l 15
10.9%
e 13
 
9.5%
s 11
 
8.0%
c 7
 
5.1%
a 6
 
4.4%
t 6
 
4.4%
i 6
 
4.4%
k 5
 
3.6%
Other values (7) 15
10.9%
Decimal Number
ValueCountFrequency (%)
2 60
20.3%
0 58
19.7%
3 51
17.3%
1 48
16.3%
5 38
12.9%
4 19
 
6.4%
6 9
 
3.1%
7 8
 
2.7%
9 3
 
1.0%
8 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 85
67.5%
, 28
 
22.2%
& 6
 
4.8%
. 4
 
3.2%
% 3
 
2.4%
Close Punctuation
ValueCountFrequency (%)
) 240
99.6%
] 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 240
99.6%
[ 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 39
79.6%
~ 10
 
20.4%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5978
81.4%
Common 970
 
13.2%
Latin 334
 
4.5%
Han 63
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 183
 
3.1%
스 155
 
2.6%
라 120
 
2.0%
리 114
 
1.9%
치 95
 
1.6%
밥 93
 
1.6%
비 85
 
1.4%
우 83
 
1.4%
갈 79
 
1.3%
소 72
 
1.2%
Other values (459) 4899
82.0%
Latin
ValueCountFrequency (%)
g 37
 
11.1%
L 28
 
8.4%
R 28
 
8.4%
E 23
 
6.9%
S 22
 
6.6%
T 20
 
6.0%
m 16
 
4.8%
I 16
 
4.8%
l 15
 
4.5%
e 13
 
3.9%
Other values (28) 116
34.7%
Common
ValueCountFrequency (%)
) 240
24.7%
( 240
24.7%
/ 85
 
8.8%
2 60
 
6.2%
0 58
 
6.0%
3 51
 
5.3%
1 48
 
4.9%
+ 39
 
4.0%
5 38
 
3.9%
, 28
 
2.9%
Other values (13) 83
 
8.6%
Han
ValueCountFrequency (%)
人 34
54.0%
大 12
 
19.0%
中 10
 
15.9%
小 6
 
9.5%
王 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5977
81.4%
ASCII 1302
 
17.7%
CJK 63
 
0.9%
None 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 240
18.4%
( 240
18.4%
/ 85
 
6.5%
2 60
 
4.6%
0 58
 
4.5%
3 51
 
3.9%
1 48
 
3.7%
+ 39
 
3.0%
5 38
 
2.9%
g 37
 
2.8%
Other values (50) 406
31.2%
Hangul
ValueCountFrequency (%)
이 183
 
3.1%
스 155
 
2.6%
라 120
 
2.0%
리 114
 
1.9%
치 95
 
1.6%
밥 93
 
1.6%
비 85
 
1.4%
우 83
 
1.4%
갈 79
 
1.3%
소 72
 
1.2%
Other values (458) 4898
81.9%
CJK
ValueCountFrequency (%)
人 34
54.0%
大 12
 
19.0%
中 10
 
15.9%
小 6
 
9.5%
王 1
 
1.6%
None
ValueCountFrequency (%)
º 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅒ 1
100.0%

MENU_TAG_DC
Text

MISSING 

Distinct31
Distinct (%)51.7%
Missing940
Missing (%)94.0%
Memory size7.9 KiB
2023-12-10T19:14:03.370930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length9
Mean length4.3166667
Min length1

Characters and Unicode

Total characters259
Distinct characters68
Distinct categories2 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)30.0%

Sample

1st row모듬
2nd row小
3rd row대
4th row돼지고기,생갈비,생삼겹살,목살
5th row고기구이
ValueCountFrequency (%)
한국식 8
 
13.3%
고기구이 5
 
8.3%
추가 4
 
6.7%
랍스타 3
 
5.0%
꿔바로우,튀김 3
 
5.0%
돼지고기,찹쌀가루 3
 
5.0%
딱새우 3
 
5.0%
찜 3
 
5.0%
小 2
 
3.3%
대 2
 
3.3%
Other values (21) 24
40.0%
2023-12-10T19:14:03.953485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 24
 
9.3%
기 15
 
5.8%
고 14
 
5.4%
가 10
 
3.9%
국 9
 
3.5%
지 9
 
3.5%
돼 9
 
3.5%
한 8
 
3.1%
살 8
 
3.1%
식 8
 
3.1%
Other values (58) 145
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 235
90.7%
Other Punctuation 24
 
9.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 15
 
6.4%
고 14
 
6.0%
가 10
 
4.3%
국 9
 
3.8%
지 9
 
3.8%
돼 9
 
3.8%
한 8
 
3.4%
살 8
 
3.4%
식 8
 
3.4%
우 7
 
3.0%
Other values (57) 138
58.7%
Other Punctuation
ValueCountFrequency (%)
, 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 233
90.0%
Common 24
 
9.3%
Han 2
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 15
 
6.4%
고 14
 
6.0%
가 10
 
4.3%
국 9
 
3.9%
지 9
 
3.9%
돼 9
 
3.9%
한 8
 
3.4%
살 8
 
3.4%
식 8
 
3.4%
우 7
 
3.0%
Other values (56) 136
58.4%
Common
ValueCountFrequency (%)
, 24
100.0%
Han
ValueCountFrequency (%)
小 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 233
90.0%
ASCII 24
 
9.3%
CJK 2
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 24
100.0%
Hangul
ValueCountFrequency (%)
기 15
 
6.4%
고 14
 
6.0%
가 10
 
4.3%
국 9
 
3.9%
지 9
 
3.9%
돼 9
 
3.9%
한 8
 
3.4%
살 8
 
3.4%
식 8
 
3.4%
우 7
 
3.0%
Other values (56) 136
58.4%
CJK
ValueCountFrequency (%)
小 2
100.0%

MENU_IMAGE_URL
Text

MISSING 

Distinct139
Distinct (%)100.0%
Missing861
Missing (%)86.1%
Memory size7.9 KiB
2023-12-10T19:14:04.425437image/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%
2023-12-10T19:14:05.117735image/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

2023-12-10T19:13:58.694993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:57.634533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:58.139898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:58.897407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:57.798070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:58.310247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:59.172825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:57.973148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:13:58.499098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:14:05.395058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDMENU_IDMENU_PRCMENU_TAG_DC
RSTRNT_ID1.0000.7710.2030.929
MENU_ID0.7711.0000.1890.791
MENU_PRC0.2030.1891.0000.000
MENU_TAG_DC0.9290.7910.0001.000
2023-12-10T19:14:05.548874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDMENU_IDMENU_PRC
RSTRNT_ID1.0000.614-0.103
MENU_ID0.6141.0000.061
MENU_PRC-0.1030.0611.000

Missing values

2023-12-10T19:13:59.433821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:13:59.675324image/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.
2023-12-10T19:13:59.857437image/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
0busan254111<NA><NA>
1busan84237000이베리코목살,블랙앵거스차돌박이<NA><NA>
2busan84357000와규등심,이베리코목살,블랙앵거스차돌박이<NA><NA>
3busan84472000랍스터,문어,전복,키조개,새우,제철조개<NA><NA>
4busan84572000딱새우,문어,전복,키조개,새우,제철조개<NA><NA>
5busan84643000딱새우,전복,제철조개<NA><NA>
6busan84747000와규세이로무시한판추가<NA><NA>
7busan84862000랍스터해산물한판추가<NA><NA>
8busan84962000딱새우해산물한판추가<NA><NA>
9busan841015000야채한판추가<NA><NA>
CTY_NMRSTRNT_IDMENU_IDMENU_PRCMENU_NMMENU_TAG_DCMENU_IMAGE_URL
990busan39910003000참치김치주먹밥<NA><NA>
991busan39910013000스팸단무지주먹밥<NA><NA>
992busan39910022000김치말이국수<NA><NA>
993busan39910034000선지해장국<NA><NA>
994busan39910048000혼돈주<NA><NA>
995busan39910054000소주<NA>http://redtable.kr/image/busan.image/menu.mobile/399_1005.jpg
996busan39910064000맥주<NA>http://redtable.kr/image/busan.image/menu.mobile/399_1006.jpg
997busan39910073000막걸리<NA>http://redtable.kr/image/busan.image/menu.mobile/399_1007.jpg
998busan39910082000음료수(콜라/사이다/미린다)<NA><NA>
999busan18410095000손칼국수<NA><NA>