Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells71
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory101.3 B

Variable types

Text5
Categorical4
Numeric2
DateTime1

Alerts

base_ymd has constant value ""Constant
city_gn_gu_cd is highly overall correlated with xpos_lo and 2 other fieldsHigh correlation
menu_pc is highly overall correlated with xpos_lo and 2 other fieldsHigh correlation
city_do_cd is highly overall correlated with xpos_lo and 4 other fieldsHigh correlation
area_nm is highly overall correlated with xpos_lo and 4 other fieldsHigh correlation
xpos_lo is highly overall correlated with ypos_la and 4 other fieldsHigh correlation
ypos_la is highly overall correlated with xpos_lo and 2 other fieldsHigh correlation
city_do_cd is highly imbalanced (80.6%)Imbalance
city_gn_gu_cd is highly imbalanced (82.3%)Imbalance
area_nm is highly imbalanced (80.6%)Imbalance
ypos_la has 3 (3.0%) missing valuesMissing
homepage_url has 65 (65.0%) missing valuesMissing
tel_no has 3 (3.0%) missing valuesMissing
entrp_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:41:07.119914
Analysis finished2023-12-10 09:41:10.940698
Duration3.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:41:11.361594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length4.92
Min length2

Characters and Unicode

Total characters492
Distinct characters217
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row고성막국수
2nd row제주한라국수
3rd row덕원
4th row길풍식당
5th row대관원
ValueCountFrequency (%)
리치몬드과자점 2
 
1.7%
홍대점 2
 
1.7%
쿄베이커리 2
 
1.7%
남도마루 2
 
1.7%
본점 2
 
1.7%
고성막국수 1
 
0.9%
baratie 1
 
0.9%
다미 1
 
0.9%
송가 1
 
0.9%
창고43 1
 
0.9%
Other values (101) 101
87.1%
2023-12-10T18:41:12.190792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 17
 
3.5%
16
 
3.3%
점 14
 
2.8%
리 12
 
2.4%
원 8
 
1.6%
마 8
 
1.6%
동 7
 
1.4%
집 7
 
1.4%
산 7
 
1.4%
당 7
 
1.4%
Other values (207) 389
79.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 458
93.1%
Space Separator 16
 
3.3%
Decimal Number 8
 
1.6%
Lowercase Letter 8
 
1.6%
Uppercase Letter 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 17
 
3.7%
점 14
 
3.1%
리 12
 
2.6%
원 8
 
1.7%
마 8
 
1.7%
동 7
 
1.5%
집 7
 
1.5%
산 7
 
1.5%
당 7
 
1.5%
치 6
 
1.3%
Other values (191) 365
79.7%
Lowercase Letter
ValueCountFrequency (%)
a 2
25.0%
t 1
12.5%
i 1
12.5%
h 1
12.5%
r 1
12.5%
n 1
12.5%
e 1
12.5%
Decimal Number
ValueCountFrequency (%)
1 2
25.0%
8 2
25.0%
3 1
12.5%
4 1
12.5%
7 1
12.5%
9 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 458
93.1%
Common 24
 
4.9%
Latin 10
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 17
 
3.7%
점 14
 
3.1%
리 12
 
2.6%
원 8
 
1.7%
마 8
 
1.7%
동 7
 
1.5%
집 7
 
1.5%
산 7
 
1.5%
당 7
 
1.5%
치 6
 
1.3%
Other values (191) 365
79.7%
Latin
ValueCountFrequency (%)
a 2
20.0%
B 1
10.0%
t 1
10.0%
i 1
10.0%
h 1
10.0%
r 1
10.0%
n 1
10.0%
A 1
10.0%
e 1
10.0%
Common
ValueCountFrequency (%)
16
66.7%
1 2
 
8.3%
8 2
 
8.3%
3 1
 
4.2%
4 1
 
4.2%
7 1
 
4.2%
9 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 458
93.1%
ASCII 34
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 17
 
3.7%
점 14
 
3.1%
리 12
 
2.6%
원 8
 
1.7%
마 8
 
1.7%
동 7
 
1.5%
집 7
 
1.5%
산 7
 
1.5%
당 7
 
1.5%
치 6
 
1.3%
Other values (191) 365
79.7%
ASCII
ValueCountFrequency (%)
16
47.1%
1 2
 
5.9%
a 2
 
5.9%
8 2
 
5.9%
3 1
 
2.9%
4 1
 
2.9%
B 1
 
2.9%
t 1
 
2.9%
i 1
 
2.9%
h 1
 
2.9%
Other values (6) 6
 
17.6%

load_addr
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:41:12.713015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length21.16
Min length16

Characters and Unicode

Total characters2116
Distinct characters133
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row서울특별시 강서구 방화대로49길 6-7
2nd row제주특별자치도 서귀포시 중문동 2048-1
3rd row서울특별시 영등포구 버드나루로길 6
4th row서울특별시특별시 영등포구 양평로 85
5th row서울특별시 영등포구 당산로37길 1
ValueCountFrequency (%)
마포구 62
 
14.7%
서울특별시 50
 
11.8%
서울특별시특별시 48
 
11.4%
영등포구 19
 
4.5%
서대문구 11
 
2.6%
독막로 5
 
1.2%
3 5
 
1.2%
동교로 5
 
1.2%
성미산로 4
 
0.9%
1층 4
 
0.9%
Other values (161) 209
49.5%
2023-12-10T18:41:13.718581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
323
15.3%
시 151
 
7.1%
특 149
 
7.0%
별 149
 
7.0%
서 111
 
5.2%
울 99
 
4.7%
구 98
 
4.6%
로 92
 
4.3%
포 86
 
4.1%
1 67
 
3.2%
Other values (123) 791
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1456
68.8%
Space Separator 323
 
15.3%
Decimal Number 317
 
15.0%
Dash Punctuation 19
 
0.9%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 151
 
10.4%
특 149
 
10.2%
별 149
 
10.2%
서 111
 
7.6%
울 99
 
6.8%
구 98
 
6.7%
로 92
 
6.3%
포 86
 
5.9%
마 65
 
4.5%
길 54
 
3.7%
Other values (110) 402
27.6%
Decimal Number
ValueCountFrequency (%)
1 67
21.1%
3 45
14.2%
2 44
13.9%
7 33
10.4%
6 28
8.8%
8 22
 
6.9%
4 21
 
6.6%
0 20
 
6.3%
9 20
 
6.3%
5 17
 
5.4%
Space Separator
ValueCountFrequency (%)
323
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1456
68.8%
Common 659
31.1%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 151
 
10.4%
특 149
 
10.2%
별 149
 
10.2%
서 111
 
7.6%
울 99
 
6.8%
구 98
 
6.7%
로 92
 
6.3%
포 86
 
5.9%
마 65
 
4.5%
길 54
 
3.7%
Other values (110) 402
27.6%
Common
ValueCountFrequency (%)
323
49.0%
1 67
 
10.2%
3 45
 
6.8%
2 44
 
6.7%
7 33
 
5.0%
6 28
 
4.2%
8 22
 
3.3%
4 21
 
3.2%
0 20
 
3.0%
9 20
 
3.0%
Other values (2) 36
 
5.5%
Latin
ValueCountFrequency (%)
F 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1456
68.8%
ASCII 660
31.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
323
48.9%
1 67
 
10.2%
3 45
 
6.8%
2 44
 
6.7%
7 33
 
5.0%
6 28
 
4.2%
8 22
 
3.3%
4 21
 
3.2%
0 20
 
3.0%
9 20
 
3.0%
Other values (3) 37
 
5.6%
Hangul
ValueCountFrequency (%)
시 151
 
10.4%
특 149
 
10.2%
별 149
 
10.2%
서 111
 
7.6%
울 99
 
6.8%
구 98
 
6.7%
로 92
 
6.3%
포 86
 
5.9%
마 65
 
4.5%
길 54
 
3.7%
Other values (110) 402
27.6%

city_do_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
97 
<NA>
 
3

Length

Max length4
Median length2
Mean length2.06
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row<NA>
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 97
97.0%
<NA> 3
 
3.0%

Length

2023-12-10T18:41:14.112136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:14.437331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 97
97.0%
na 3
 
3.0%

city_gn_gu_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
126.9
95 
126.8
 
3
126.4
 
1
126.5
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row126.8
2nd row126.4
3rd row126.8
4th row126.9
5th row126.8

Common Values

ValueCountFrequency (%)
126.9 95
95.0%
126.8 3
 
3.0%
126.4 1
 
1.0%
126.5 1
 
1.0%

Length

2023-12-10T18:41:14.701220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:14.898932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
126.9 95
95.0%
126.8 3
 
3.0%
126.4 1
 
1.0%
126.5 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.430981
Minimum33.250304
Maximum37.623491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:15.168256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.250304
5-th percentile37.49121
Q137.548149
median37.566415
Q337.566415
95-th percentile37.566537
Maximum37.623491
Range4.373187
Interquartile range (IQR)0.018266

Descriptive statistics

Standard deviation0.71159549
Coefficient of variation (CV)0.01901087
Kurtosis29.947246
Mean37.430981
Median Absolute Deviation (MAD)0.0018985
Skewness-5.593904
Sum3743.0981
Variance0.50636814
MonotonicityNot monotonic
2023-12-10T18:41:15.388893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.566415 48
48.0%
37.577455 1
 
1.0%
37.568857 1
 
1.0%
37.550874 1
 
1.0%
37.548923 1
 
1.0%
37.560936 1
 
1.0%
37.565198 1
 
1.0%
37.523886 1
 
1.0%
37.561899 1
 
1.0%
37.560082 1
 
1.0%
Other values (43) 43
43.0%
ValueCountFrequency (%)
33.250304 1
1.0%
33.481967 1
1.0%
33.492212 1
1.0%
37.480517 1
1.0%
37.481229 1
1.0%
37.491735 1
1.0%
37.50306 1
1.0%
37.507114 1
1.0%
37.518617 1
1.0%
37.520238 1
1.0%
ValueCountFrequency (%)
37.623491 1
 
1.0%
37.577455 1
 
1.0%
37.572506 1
 
1.0%
37.570619 1
 
1.0%
37.568857 1
 
1.0%
37.566415 48
48.0%
37.565198 1
 
1.0%
37.56506 1
 
1.0%
37.562384 1
 
1.0%
37.561899 1
 
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)89.7%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean37.546992
Minimum37.480517
Maximum37.623491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:41:15.722481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.480517
5-th percentile37.516316
Q137.539819
median37.549468
Q337.558122
95-th percentile37.569944
Maximum37.623491
Range0.142974
Interquartile range (IQR)0.018303

Descriptive statistics

Standard deviation0.020119045
Coefficient of variation (CV)0.00053583639
Kurtosis3.420242
Mean37.546992
Median Absolute Deviation (MAD)0.009649
Skewness-0.60368551
Sum3642.0582
Variance0.00040477596
MonotonicityNot monotonic
2023-12-10T18:41:16.007338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.549468 2
 
2.0%
37.560082 2
 
2.0%
37.562384 2
 
2.0%
37.548022 2
 
2.0%
37.520776 2
 
2.0%
37.527602 2
 
2.0%
37.526238 2
 
2.0%
37.561426 2
 
2.0%
37.548923 2
 
2.0%
37.539819 2
 
2.0%
Other values (77) 77
77.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
37.480517 1
1.0%
37.481229 1
1.0%
37.491735 1
1.0%
37.50306 1
1.0%
37.507114 1
1.0%
37.518617 1
1.0%
37.519993 1
1.0%
37.520238 1
1.0%
37.520769 1
1.0%
37.520776 2
2.0%
ValueCountFrequency (%)
37.623491 1
1.0%
37.577455 1
1.0%
37.572506 1
1.0%
37.572489 1
1.0%
37.570619 1
1.0%
37.569775 1
1.0%
37.569449 1
1.0%
37.568857 1
1.0%
37.565198 1
1.0%
37.56506 1
1.0%

area_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
97 
제주
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row제주
3rd row서울
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 97
97.0%
제주 3
 
3.0%

Length

2023-12-10T18:41:16.271331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:41:16.441386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 97
97.0%
제주 3
 
3.0%

homepage_url
Text

MISSING 

Distinct32
Distinct (%)91.4%
Missing65
Missing (%)65.0%
Memory size932.0 B
2023-12-10T18:41:16.774697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length35
Mean length32.171429
Min length14

Characters and Unicode

Total characters1126
Distinct characters43
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

Unique29 ?
Unique (%)82.9%

Sample

1st rowhttp://itvplus.co.kr/home6/sam/
2nd rowhttp://cityfood.co.kr/h9/sundaeilbeonji
3rd rowhttp://www.instagram.com/tteurak_jk
4th rowhttp://jinjinseoul.modoo.at/
5th rowhttp://www.richemont.co.kr/
ValueCountFrequency (%)
http://www.richemont.co.kr 2
 
5.7%
http://instagram.com/osteriabaratie 2
 
5.7%
http://www.instagram.com/hakatabunko_official 2
 
5.7%
http://instagram.com/nanohana.yeonhui 1
 
2.9%
https://www.instagram.com/tuktuknoodle 1
 
2.9%
https://limpasse81.modoo.at 1
 
2.9%
http://coffeelibre.kr 1
 
2.9%
http://www.changgo43.co.kr 1
 
2.9%
http://www.facebook.com/eeddle/?ref=bookmarks 1
 
2.9%
http://blog.naver.com/ksbeom 1
 
2.9%
Other values (22) 22
62.9%
2023-12-10T18:41:17.401965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 113
 
10.0%
t 104
 
9.2%
o 85
 
7.5%
a 74
 
6.6%
. 70
 
6.2%
h 51
 
4.5%
m 50
 
4.4%
w 49
 
4.4%
c 47
 
4.2%
i 45
 
4.0%
Other values (33) 438
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 871
77.4%
Other Punctuation 219
 
19.4%
Decimal Number 26
 
2.3%
Connector Punctuation 5
 
0.4%
Other Letter 3
 
0.3%
Dash Punctuation 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 104
 
11.9%
o 85
 
9.8%
a 74
 
8.5%
h 51
 
5.9%
m 50
 
5.7%
w 49
 
5.6%
c 47
 
5.4%
i 45
 
5.2%
e 44
 
5.1%
r 44
 
5.1%
Other values (13) 278
31.9%
Decimal Number
ValueCountFrequency (%)
2 6
23.1%
1 6
23.1%
9 3
11.5%
8 3
11.5%
0 2
 
7.7%
3 2
 
7.7%
5 1
 
3.8%
7 1
 
3.8%
6 1
 
3.8%
4 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 113
51.6%
. 70
32.0%
: 35
 
16.0%
? 1
 
0.5%
Other Letter
ValueCountFrequency (%)
현 1
33.3%
래 1
33.3%
장 1
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 871
77.4%
Common 252
 
22.4%
Hangul 3
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 104
 
11.9%
o 85
 
9.8%
a 74
 
8.5%
h 51
 
5.9%
m 50
 
5.7%
w 49
 
5.6%
c 47
 
5.4%
i 45
 
5.2%
e 44
 
5.1%
r 44
 
5.1%
Other values (13) 278
31.9%
Common
ValueCountFrequency (%)
/ 113
44.8%
. 70
27.8%
: 35
 
13.9%
2 6
 
2.4%
1 6
 
2.4%
_ 5
 
2.0%
9 3
 
1.2%
8 3
 
1.2%
0 2
 
0.8%
3 2
 
0.8%
Other values (7) 7
 
2.8%
Hangul
ValueCountFrequency (%)
현 1
33.3%
래 1
33.3%
장 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1123
99.7%
Hangul 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 113
 
10.1%
t 104
 
9.3%
o 85
 
7.6%
a 74
 
6.6%
. 70
 
6.2%
h 51
 
4.5%
m 50
 
4.5%
w 49
 
4.4%
c 47
 
4.2%
i 45
 
4.0%
Other values (30) 435
38.7%
Hangul
ValueCountFrequency (%)
현 1
33.3%
래 1
33.3%
장 1
33.3%

tel_no
Text

MISSING 

Distinct90
Distinct (%)92.8%
Missing3
Missing (%)3.0%
Memory size932.0 B
2023-12-10T18:41:17.940685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.484536
Min length11

Characters and Unicode

Total characters1114
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)85.6%

Sample

1st row02-2665-1205
2nd row02-2634-8663
3rd row02-2634-1359
4th row02-2068-8791
5th row02-6083-1393
ValueCountFrequency (%)
02-338-5536 2
 
2.1%
02-712-7462 2
 
2.1%
02-325-0221 2
 
2.1%
010-6490-2352 2
 
2.1%
02-794-5090 2
 
2.1%
02-335-4764 2
 
2.1%
02-761-9937 2
 
2.1%
02-334-9245 1
 
1.0%
02-336-7656 1
 
1.0%
02-363-5887 1
 
1.0%
Other values (80) 80
82.5%
2023-12-10T18:41:18.683592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 194
17.4%
0 168
15.1%
2 158
14.2%
3 134
12.0%
6 80
7.2%
7 80
7.2%
4 71
 
6.4%
1 63
 
5.7%
8 62
 
5.6%
5 60
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 920
82.6%
Dash Punctuation 194
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168
18.3%
2 158
17.2%
3 134
14.6%
6 80
8.7%
7 80
8.7%
4 71
7.7%
1 63
 
6.8%
8 62
 
6.7%
5 60
 
6.5%
9 44
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 194
17.4%
0 168
15.1%
2 158
14.2%
3 134
12.0%
6 80
7.2%
7 80
7.2%
4 71
 
6.4%
1 63
 
5.7%
8 62
 
5.6%
5 60
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 194
17.4%
0 168
15.1%
2 158
14.2%
3 134
12.0%
6 80
7.2%
7 80
7.2%
4 71
 
6.4%
1 63
 
5.7%
8 62
 
5.6%
5 60
 
5.4%

reprsnt_menu_nm
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:41:19.150892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length13
Mean length5.72
Min length2

Characters and Unicode

Total characters572
Distinct characters218
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)91.0%

Sample

1st row물막국수
2nd row고기국수
3rd row꼬리곰탕
4th row꼬리탕
5th row삼선간짜장
ValueCountFrequency (%)
쌀국수 4
 
3.1%
삼겹살 3
 
2.3%
양지 2
 
1.5%
아이스크림 2
 
1.5%
돼지갈비 2
 
1.5%
샌드위치 2
 
1.5%
인라멘 2
 
1.5%
아메리카노 2
 
1.5%
유린기 1
 
0.8%
스페셜 1
 
0.8%
Other values (110) 110
84.0%
2023-12-10T18:41:19.867249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
5.4%
수 12
 
2.1%
국 12
 
2.1%
탕 12
 
2.1%
이 11
 
1.9%
+ 9
 
1.6%
살 9
 
1.6%
비 8
 
1.4%
고 8
 
1.4%
삼 8
 
1.4%
Other values (208) 452
79.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 504
88.1%
Space Separator 31
 
5.4%
Decimal Number 13
 
2.3%
Math Symbol 10
 
1.7%
Close Punctuation 5
 
0.9%
Open Punctuation 5
 
0.9%
Lowercase Letter 2
 
0.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 12
 
2.4%
국 12
 
2.4%
탕 12
 
2.4%
이 11
 
2.2%
살 9
 
1.8%
비 8
 
1.6%
고 8
 
1.6%
삼 8
 
1.6%
리 8
 
1.6%
트 8
 
1.6%
Other values (194) 408
81.0%
Decimal Number
ValueCountFrequency (%)
1 5
38.5%
4 2
 
15.4%
0 2
 
15.4%
2 2
 
15.4%
3 1
 
7.7%
5 1
 
7.7%
Math Symbol
ValueCountFrequency (%)
+ 9
90.0%
~ 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
O 1
50.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 504
88.1%
Common 64
 
11.2%
Latin 4
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 12
 
2.4%
국 12
 
2.4%
탕 12
 
2.4%
이 11
 
2.2%
살 9
 
1.8%
비 8
 
1.6%
고 8
 
1.6%
삼 8
 
1.6%
리 8
 
1.6%
트 8
 
1.6%
Other values (194) 408
81.0%
Common
ValueCountFrequency (%)
31
48.4%
+ 9
 
14.1%
1 5
 
7.8%
) 5
 
7.8%
( 5
 
7.8%
4 2
 
3.1%
0 2
 
3.1%
2 2
 
3.1%
3 1
 
1.6%
~ 1
 
1.6%
Latin
ValueCountFrequency (%)
g 2
50.0%
K 1
25.0%
O 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 504
88.1%
ASCII 68
 
11.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31
45.6%
+ 9
 
13.2%
1 5
 
7.4%
) 5
 
7.4%
( 5
 
7.4%
4 2
 
2.9%
g 2
 
2.9%
0 2
 
2.9%
2 2
 
2.9%
3 1
 
1.5%
Other values (4) 4
 
5.9%
Hangul
ValueCountFrequency (%)
수 12
 
2.4%
국 12
 
2.4%
탕 12
 
2.4%
이 11
 
2.2%
살 9
 
1.8%
비 8
 
1.6%
고 8
 
1.6%
삼 8
 
1.6%
리 8
 
1.6%
트 8
 
1.6%
Other values (194) 408
81.0%

menu_pc
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
8 000
9 
9 000
8 
13 000
 
6
5 000
 
6
7 000
 
4
Other values (44)
67 

Length

Max length12
Median length6
Mean length5.53
Min length2

Unique

Unique29 ?
Unique (%)29.0%

Sample

1st row7 000
2nd row<NA>
3rd row9 000
4th row20 000
5th row8 000

Common Values

ValueCountFrequency (%)
8 000 9
 
9.0%
9 000 8
 
8.0%
13 000 6
 
6.0%
5 000 6
 
6.0%
7 000 4
 
4.0%
15 000 4
 
4.0%
4 000 4
 
4.0%
14 000 3
 
3.0%
12 000 3
 
3.0%
<NA> 3
 
3.0%
Other values (39) 50
50.0%

Length

2023-12-10T18:41:20.087961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
000 80
40.6%
8 12
 
6.1%
9 10
 
5.1%
500 10
 
5.1%
5 8
 
4.1%
13 6
 
3.0%
7 6
 
3.0%
15 4
 
2.0%
4 4
 
2.0%
30 3
 
1.5%
Other values (35) 54
27.4%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-31 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T18:41:20.269202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:20.467131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T18:41:09.681404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:09.309570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:09.832221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:41:09.455132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:41:20.638735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_noreprsnt_menu_nmmenu_pc
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0000.9960.9970.978
city_gn_gu_cd1.0001.0001.0000.9540.3660.954NaN1.0001.0000.000
xpos_lo1.0001.0000.9541.000NaN0.963NaNNaN1.000NaN
ypos_la1.0001.0000.366NaN1.000NaN1.0001.0000.5230.000
area_nm1.0001.0000.9540.963NaN1.000NaNNaN1.000NaN
homepage_url1.0001.000NaNNaN1.000NaN1.0001.0000.9890.741
tel_no1.0000.9961.000NaN1.000NaN1.0001.0000.9890.000
reprsnt_menu_nm1.0000.9971.0001.0000.5231.0000.9890.9891.0000.993
menu_pc1.0000.9780.000NaN0.000NaN0.7410.0000.9931.000
2023-12-10T18:41:20.955092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_gn_gu_cdmenu_pccity_do_cdarea_nm
city_gn_gu_cd1.0000.0001.0000.798
menu_pc0.0001.0001.0001.000
city_do_cd1.0001.0001.0001.000
area_nm0.7981.0001.0001.000
2023-12-10T18:41:21.164011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
xpos_loypos_lacity_do_cdcity_gn_gu_cdarea_nmmenu_pc
xpos_lo1.0000.5161.0000.7980.8261.000
ypos_la0.5161.0001.0000.2651.0000.000
city_do_cd1.0001.0001.0001.0001.0001.000
city_gn_gu_cd0.7980.2651.0001.0000.7980.000
area_nm0.8261.0001.0000.7981.0001.000
menu_pc1.0000.0001.0000.0001.0001.000

Missing values

2023-12-10T18:41:10.088291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:41:10.393086image/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.
2023-12-10T18:41:10.697669image/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

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_noreprsnt_menu_nmmenu_pcbase_ymd
0고성막국수서울특별시 강서구 방화대로49길 6-711126.837.57745537.577455서울<NA>02-2665-1205물막국수7 0002020-12-31
1제주한라국수제주특별자치도 서귀포시 중문동 2048-1<NA>126.433.250304<NA>제주<NA><NA>고기국수<NA>2020-12-31
2덕원서울특별시 영등포구 버드나루로길 611126.837.52641337.526413서울<NA>02-2634-8663꼬리곰탕9 0002020-12-31
3길풍식당서울특별시특별시 영등포구 양평로 8511126.937.56641537.53592서울<NA>02-2634-1359꼬리탕20 0002020-12-31
4대관원서울특별시 영등포구 당산로37길 111126.837.5294237.52942서울<NA>02-2068-8791삼선간짜장8 0002020-12-31
5당산마루 능이버섯삼계탕서울특별시특별시 영등포구 선유로54길 911126.937.56641537.536159서울<NA>02-6083-1393능이버섯삼계탕14 0002020-12-31
6원조호수삼계탕서울특별시 영등포구 도림로 28211126.937.5030637.50306서울<NA>02-833-8948삼계탕13 0002020-12-31
7해월정제주특별자치도 제주시 구좌읍 종달리 608<NA>126.933.492212<NA>제주<NA><NA>보말칼국수<NA>2020-12-31
8동일루서울특별시 마포구 포은로 7511126.937.55382837.553828서울<NA>02-3144-2221찹쌀탕수육 소15 0002020-12-31
9프롬하노이서울특별시특별시 마포구 포은로8길 2011126.937.56641537.556426서울<NA>02-337-0301퍼보10 0002020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_noreprsnt_menu_nmmenu_pcbase_ymd
90마포원조주물럭서울특별시특별시 마포구 토정로 29411126.937.56641537.540833서울<NA>02-716-3001주물럭45 0002020-12-31
91마포옥서울특별시특별시 마포구 토정로 31211126.937.56641537.539882서울<NA>02-716-6661양지 설렁탕14 0002020-12-31
92참식당서울특별시 마포구 용강동 43-211126.937.54017237.540172서울<NA>02-706-2432생대구탕 1인20 0002020-12-31
93원조조박집서울특별시 마포구 토정로37길 311126.937.53981937.539819서울<NA>02-712-7462돼지갈비14 0002020-12-31
94조박집 본관서울특별시특별시 마포구 토정로37길 311126.937.56641537.539819서울<NA>02-712-7462국내산 돼지갈비15 0002020-12-31
95현래장서울특별시 마포구 마포대로 2011126.937.53842837.538428서울http://현래장.com02-712-0730손옛날짜장5 0002020-12-31
96남도포장마차서울특별시 관악구 청룡2길 311126.937.48122937.481229서울<NA>02-871-9121꽃게+수제비탕시가2020-12-31
97논밭골 왕갈비탕서울특별시 관악구 청룡길 3011126.937.48051737.480517서울<NA>02-875-6493왕갈비탕9 0002020-12-31
98도하정서울특별시특별시 마포구 마포대로4길 38 1층 도하정11126.937.56641537.538037서울https://www.instagram.com/dohajung2018/010-9440-6639소고기 듬뿍 곰탕9 0002020-12-31
99락희옥서울특별시 마포구 백범로 17011126.937.54489737.544897서울https://lakhee1.blog.me/02-719-9797보쌈30 0002020-12-31