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 09:40:10.659090
Analysis finished2023-12-10 09:40:13.855737
Duration3.2 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

2023-12-10T18:40:13.952204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:14.204805image/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
2023-12-10T18:40:14.516000image/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
2023-12-10T18:40:14.789183image/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
2023-12-10T18:40:15.052160image/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
2023-12-10T18:40:15.358066image/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
2023-12-10T18:40:15.653381image/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
2023-12-10T18:40:16.026049image/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

Distinct888
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T18:40:16.356236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length30
Mean length8.864
Min length2

Characters and Unicode

Total characters8864
Distinct characters256
Distinct categories11 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique812 ?
Unique (%)81.2%

Sample

1st rowタンホバクビンス(1人前)
2nd rowマッコルリセンブルルベリヌンコッビンス(1人前)
3rd rowメシルチャ(ホッと)
4th rowディナー(大人)
5th row午前(大人)
ValueCountFrequency (%)
ゾクバル(大 6
 
0.6%
ゾクバル(中 6
 
0.6%
ゴルゴンジョルラ 5
 
0.5%
タンスユク 5
 
0.5%
ヤチェゴブチャン 4
 
0.4%
チョングクチャン 4
 
0.4%
ガマドロサシミ 4
 
0.4%
アメリカノ 4
 
0.4%
トクポキ 3
 
0.3%
original 3
 
0.3%
Other values (895) 1003
95.8%
2023-12-10T18:40:16.967302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 722
 
8.1%
ル 438
 
4.9%
ク 342
 
3.9%
チ 309
 
3.5%
ム 252
 
2.8%
ス 214
 
2.4%
( 180
 
2.0%
) 180
 
2.0%
ッ 168
 
1.9%
リ 166
 
1.9%
Other values (246) 5893
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7715
87.0%
Lowercase Letter 358
 
4.0%
Open Punctuation 180
 
2.0%
Close Punctuation 180
 
2.0%
Decimal Number 112
 
1.3%
Modifier Letter 101
 
1.1%
Uppercase Letter 93
 
1.0%
Other Punctuation 55
 
0.6%
Space Separator 52
 
0.6%
Math Symbol 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 722
 
9.4%
ル 438
 
5.7%
ク 342
 
4.4%
チ 309
 
4.0%
ム 252
 
3.3%
ス 214
 
2.8%
ッ 168
 
2.2%
リ 166
 
2.2%
ジ 163
 
2.1%
ョ 159
 
2.1%
Other values (174) 4782
62.0%
Lowercase Letter
ValueCountFrequency (%)
e 53
14.8%
i 35
 
9.8%
o 34
 
9.5%
a 23
 
6.4%
n 23
 
6.4%
g 22
 
6.1%
l 20
 
5.6%
c 19
 
5.3%
r 17
 
4.7%
u 14
 
3.9%
Other values (16) 98
27.4%
Uppercase Letter
ValueCountFrequency (%)
L 15
16.1%
B 13
14.0%
S 13
14.0%
I 11
11.8%
C 8
8.6%
M 7
7.5%
A 5
 
5.4%
H 4
 
4.3%
T 4
 
4.3%
E 3
 
3.2%
Other values (6) 10
10.8%
Decimal Number
ValueCountFrequency (%)
0 25
22.3%
1 23
20.5%
7 13
11.6%
3 13
11.6%
2 10
 
8.9%
5 9
 
8.0%
２ 7
 
6.2%
３ 4
 
3.6%
４ 3
 
2.7%
6 2
 
1.8%
Other values (2) 3
 
2.7%
Other Punctuation
ValueCountFrequency (%)
・ 17
30.9%
: 12
21.8%
、 11
20.0%
' 4
 
7.3%
, 4
 
7.3%
& 3
 
5.5%
. 2
 
3.6%
＆ 1
 
1.8%
/ 1
 
1.8%
Math Symbol
ValueCountFrequency (%)
~ 7
63.6%
+ 3
27.3%
＋ 1
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
〜 6
85.7%
- 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 180
100.0%
Close Punctuation
ValueCountFrequency (%)
) 180
100.0%
Modifier Letter
ValueCountFrequency (%)
ー 101
100.0%
Space Separator
ValueCountFrequency (%)
52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 7317
82.5%
Common 698
 
7.9%
Latin 451
 
5.1%
Han 326
 
3.7%
Hiragana 72
 
0.8%

Most frequent character per script

Han
ValueCountFrequency (%)
大 39
 
12.0%
中 25
 
7.7%
人 23
 
7.1%
小 20
 
6.1%
前 19
 
5.8%
日 12
 
3.7%
肉 11
 
3.4%
牛 9
 
2.8%
平 8
 
2.5%
歳 8
 
2.5%
Other values (71) 152
46.6%
Katakana
ValueCountFrequency (%)
ン 722
 
9.9%
ル 438
 
6.0%
ク 342
 
4.7%
チ 309
 
4.2%
ム 252
 
3.4%
ス 214
 
2.9%
ッ 168
 
2.3%
リ 166
 
2.3%
ジ 163
 
2.2%
ョ 159
 
2.2%
Other values (68) 4384
59.9%
Latin
ValueCountFrequency (%)
e 53
 
11.8%
i 35
 
7.8%
o 34
 
7.5%
a 23
 
5.1%
n 23
 
5.1%
g 22
 
4.9%
l 20
 
4.4%
c 19
 
4.2%
r 17
 
3.8%
L 15
 
3.3%
Other values (32) 190
42.1%
Common
ValueCountFrequency (%)
( 180
25.8%
) 180
25.8%
ー 101
14.5%
52
 
7.4%
0 25
 
3.6%
1 23
 
3.3%
・ 17
 
2.4%
7 13
 
1.9%
3 13
 
1.9%
: 12
 
1.7%
Other values (20) 82
11.7%
Hiragana
ValueCountFrequency (%)
と 13
18.1%
の 6
 
8.3%
せ 5
 
6.9%
わ 5
 
6.9%
り 5
 
6.9%
つ 5
 
6.9%
し 4
 
5.6%
き 4
 
5.6%
ゃ 3
 
4.2%
ぶ 3
 
4.2%
Other values (15) 19
26.4%

Most occurring blocks

ValueCountFrequency (%)
Katakana 7435
83.9%
ASCII 996
 
11.2%
CJK 326
 
3.7%
Hiragana 72
 
0.8%
None 35
 
0.4%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 722
 
9.7%
ル 438
 
5.9%
ク 342
 
4.6%
チ 309
 
4.2%
ム 252
 
3.4%
ス 214
 
2.9%
ッ 168
 
2.3%
リ 166
 
2.2%
ジ 163
 
2.2%
ョ 159
 
2.1%
Other values (70) 4502
60.6%
ASCII
ValueCountFrequency (%)
( 180
18.1%
) 180
18.1%
e 53
 
5.3%
52
 
5.2%
i 35
 
3.5%
o 34
 
3.4%
0 25
 
2.5%
1 23
 
2.3%
a 23
 
2.3%
n 23
 
2.3%
Other values (51) 368
36.9%
CJK
ValueCountFrequency (%)
大 39
 
12.0%
中 25
 
7.7%
人 23
 
7.1%
小 20
 
6.1%
前 19
 
5.8%
日 12
 
3.7%
肉 11
 
3.4%
牛 9
 
2.8%
平 8
 
2.5%
歳 8
 
2.5%
Other values (71) 152
46.6%
Hiragana
ValueCountFrequency (%)
と 13
18.1%
の 6
 
8.3%
せ 5
 
6.9%
わ 5
 
6.9%
り 5
 
6.9%
つ 5
 
6.9%
し 4
 
5.6%
き 4
 
5.6%
ゃ 3
 
4.2%
ぶ 3
 
4.2%
Other values (15) 19
26.4%
None
ValueCountFrequency (%)
、 11
31.4%
２ 7
20.0%
〜 6
17.1%
３ 4
 
11.4%
４ 3
 
8.6%
＆ 1
 
2.9%
ｋ 1
 
2.9%
＋ 1
 
2.9%
ｈ 1
 
2.9%

MENU_TAG_DC
Text

MISSING 

Distinct592
Distinct (%)66.7%
Missing113
Missing (%)11.3%
Memory size7.9 KiB
2023-12-10T18:40:17.425430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length40
Mean length16.694476
Min length2

Characters and Unicode

Total characters14808
Distinct characters319
Distinct categories4 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique432 ?
Unique (%)48.7%

Sample

1st rowカボチャ, かき氷
2nd row氷, 米, ブルーベリー, マッコリ, かき氷, 冷たい
3rd row梅, 茶, 温かい
4th row夕食, 大人
5th row大人
ValueCountFrequency (%)
野菜 311
 
9.2%
小麦粉 161
 
4.8%
焼き 132
 
3.9%
辛口 128
 
3.8%
麺 83
 
2.5%
チーズ 67
 
2.0%
唐辛子 62
 
1.8%
鶏肉 58
 
1.7%
卵 57
 
1.7%
ご飯 48
 
1.4%
Other values (407) 2266
67.2%
2023-12-10T18:40:18.201695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 2486
 
16.8%
2486
 
16.8%
ー 469
 
3.2%
菜 322
 
2.2%
野 311
 
2.1%
ン 275
 
1.9%
チ 247
 
1.7%
ス 231
 
1.6%
辛 192
 
1.3%
肉 178
 
1.2%
Other values (309) 7611
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9365
63.2%
Other Punctuation 2486
 
16.8%
Space Separator 2486
 
16.8%
Modifier Letter 471
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
菜 322
 
3.4%
野 311
 
3.3%
ン 275
 
2.9%
チ 247
 
2.6%
ス 231
 
2.5%
辛 192
 
2.1%
肉 178
 
1.9%
き 172
 
1.8%
小 170
 
1.8%
粉 168
 
1.8%
Other values (305) 7099
75.8%
Modifier Letter
ValueCountFrequency (%)
ー 469
99.6%
々 2
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 2486
100.0%
Space Separator
ValueCountFrequency (%)
2486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5441
36.7%
Katakana 4076
27.5%
Han 3861
26.1%
Hiragana 1430
 
9.7%

Most frequent character per script

Han
ValueCountFrequency (%)
菜 322
 
8.3%
野 311
 
8.1%
辛 192
 
5.0%
肉 178
 
4.6%
小 170
 
4.4%
粉 168
 
4.4%
麦 165
 
4.3%
焼 141
 
3.7%
麺 136
 
3.5%
口 128
 
3.3%
Other values (174) 1950
50.5%
Katakana
ValueCountFrequency (%)
ン 275
 
6.7%
チ 247
 
6.1%
ス 231
 
5.7%
ッ 166
 
4.1%
ル 155
 
3.8%
プ 141
 
3.5%
ム 138
 
3.4%
ト 136
 
3.3%
コ 132
 
3.2%
キ 110
 
2.7%
Other values (67) 2345
57.5%
Hiragana
ValueCountFrequency (%)
き 172
 
12.0%
お 79
 
5.5%
い 71
 
5.0%
ご 66
 
4.6%
し 59
 
4.1%
の 59
 
4.1%
た 51
 
3.6%
り 51
 
3.6%
ん 46
 
3.2%
ゃ 45
 
3.1%
Other values (45) 731
51.1%
Common
ValueCountFrequency (%)
, 2486
45.7%
2486
45.7%
ー 469
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4972
33.6%
Katakana 4545
30.7%
CJK 3859
26.1%
Hiragana 1430
 
9.7%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 2486
50.0%
2486
50.0%
Katakana
ValueCountFrequency (%)
ー 469
 
10.3%
ン 275
 
6.1%
チ 247
 
5.4%
ス 231
 
5.1%
ッ 166
 
3.7%
ル 155
 
3.4%
プ 141
 
3.1%
ム 138
 
3.0%
ト 136
 
3.0%
コ 132
 
2.9%
Other values (68) 2455
54.0%
CJK
ValueCountFrequency (%)
菜 322
 
8.3%
野 311
 
8.1%
辛 192
 
5.0%
肉 178
 
4.6%
小 170
 
4.4%
粉 168
 
4.4%
麦 165
 
4.3%
焼 141
 
3.7%
麺 136
 
3.5%
口 128
 
3.3%
Other values (173) 1948
50.5%
Hiragana
ValueCountFrequency (%)
き 172
 
12.0%
お 79
 
5.5%
い 71
 
5.0%
ご 66
 
4.6%
し 59
 
4.1%
の 59
 
4.1%
た 51
 
3.6%
り 51
 
3.6%
ん 46
 
3.2%
ゃ 45
 
3.1%
Other values (45) 731
51.1%
None
ValueCountFrequency (%)
々 2
100.0%

MENU_IMAGE_URL
Text

Distinct998
Distinct (%)100.0%
Missing2
Missing (%)0.2%
Memory size7.9 KiB
2023-12-10T18:40:18.683322image/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%
2023-12-10T18:40:19.506675image/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

2023-12-10T18:40:12.580070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.461718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.997512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:12.876590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.624154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:12.177457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:13.144187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:11.807330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:12.334436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:40:19.691657image/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
2023-12-10T18:40:19.849730image/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

2023-12-10T18:40:13.406921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:40:13.621389image/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.
2023-12-10T18:40:13.781734image/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タンホバクビンス(1人前)カボチャ, かき氷http://redtable.kr/image/seoul.image/menu.mobile/16455_3.jpg
1seoul1645569500マッコルリセンブルルベリヌンコッビンス(1人前)氷, 米, ブルーベリー, マッコリ, かき氷, 冷たいhttp://redtable.kr/image/seoul.image/menu.mobile/16455_6.jpg
2seoul1645578000メシルチャ(ホッと)梅, 茶, 温かいhttp://redtable.kr/image/seoul.image/menu.mobile/16455_7.jpg
3seoul33508102000ディナー(大人)夕食, 大人http://redtable.kr/image/seoul.image/menu.mobile/3350_8.jpg
4seoul3350959300午前(大人)大人http://redtable.kr/image/seoul.image/menu.mobile/3350_9.jpg
5seoul33501059300午前(大人)大人http://redtable.kr/image/seoul.image/menu.mobile/3350_10.jpg
6seoul33501190000ランチ(大人)昼食, 大人http://redtable.kr/image/seoul.image/menu.mobile/3350_11.jpg
7seoul33501260500ディナー(子供)夕食http://redtable.kr/image/seoul.image/menu.mobile/3350_12.jpg
8seoul33501356900ランチ(子供)昼食http://redtable.kr/image/seoul.image/menu.mobile/3350_13.jpg
9seoul45014146500ミルペイユ<NA>http://redtable.kr/image/seoul.image/menu.mobile/45014_14.jpg
CTY_NMRSTRNT_IDMENU_IDMENU_PRCMENU_NMMENU_TAG_DCMENU_IMAGE_URL
990seoul14513426000キツネウドン麺, うどんhttp://redtable.kr/image/seoul.image/menu.mobile/145_1342.jpg
991seoul14513436000チャルウドン麺, うどんhttp://redtable.kr/image/seoul.image/menu.mobile/145_1343.jpg
992seoul144134414000ヤンニョムグイマクチャンマクチャン, 焼き, 味付け, 辛口http://redtable.kr/image/seoul.image/menu.mobile/144_1344.jpg
993seoul144134514000ソグムグイマクチャンマクチャン, 塩焼き, 網焼きhttp://redtable.kr/image/seoul.image/menu.mobile/144_1345.jpg
994seoul4765913486000オクススグクス野菜, とうもろこし麺, 素麺http://redtable.kr/image/seoul.image/menu.mobile/47659_1348.jpg
995seoul4765913506000ムルマンドゥ野菜, 小麦粉, 豆腐, お肉, 餃子, 茹でhttp://redtable.kr/image/seoul.image/menu.mobile/47659_1350.jpg
996seoul47659135112000ヤンコチラム肉, 串http://redtable.kr/image/seoul.image/menu.mobile/47659_1351.jpg
997seoul1573913538000ファンテビビンバ牛肉, ねぎ, 春雨, カルビ, 干しスケトウダラ, スケトウダラ, プルコギ, ビビンバ, 定食http://redtable.kr/image/seoul.image/menu.mobile/15739_1353.jpg
998seoul15739135415000ノクデュベクスク鶏肉, ニンニク, 高麗人参, もち米, 緑豆, スープhttp://redtable.kr/image/seoul.image/menu.mobile/15739_1354.jpg
999seoul15739135515000ナクジパジョン野菜, 小麦粉, 卵, ねぎ, タコ, チヂミhttp://redtable.kr/image/seoul.image/menu.mobile/15739_1355.jpg