Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells55
Missing cells (%)5.0%
Duplicate rows1
Duplicate rows (%)1.0%
Total size in memory9.1 KiB
Average record size in memory93.3 B

Variable types

Categorical3
Text4
Numeric3
DateTime1

Alerts

se_nm has constant value ""Constant
base_ymd has constant value ""Constant
Dataset has 1 (1.0%) duplicate rowsDuplicates
city_do_cd is highly overall correlated with city_gn_gu_cd and 2 other fieldsHigh correlation
area_nm is highly overall correlated with city_gn_gu_cd and 2 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with xpos_lo and 2 other fieldsHigh correlation
xpos_lo is highly overall correlated with city_gn_gu_cdHigh correlation
ypos_la is highly overall correlated with city_do_cd and 1 other fieldsHigh correlation
city_do_cd is highly imbalanced (80.6%)Imbalance
area_nm is highly imbalanced (74.3%)Imbalance
homepage_url has 48 (48.0%) missing valuesMissing
tel_no has 7 (7.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 09:40:03.835197
Analysis finished2023-12-10 09:40:08.711850
Duration4.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

se_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
와인바
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
와인바 100
100.0%

Length

2023-12-10T18:40:08.806744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:09.021069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
와인바 100
100.0%

entrp_nm
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:40:09.499968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length5.54
Min length1

Characters and Unicode

Total characters554
Distinct characters250
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

Unique90 ?
Unique (%)90.0%

Sample

1st row서프테이블
2nd row꼬메아모르
3rd row민트
4th row라몬타냐
5th row한우작
ValueCountFrequency (%)
안드로메다 2
 
1.7%
블루앤블루스 2
 
1.7%
포션 2
 
1.7%
베니니 2
 
1.7%
제이바 2
 
1.7%
마드레 1
 
0.9%
세계주류마켓 1
 
0.9%
세계주류 1
 
0.9%
펠리체0412 1
 
0.9%
화자다이닝펍 1
 
0.9%
Other values (102) 102
87.2%
2023-12-10T18:40:10.146059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
와 19
 
3.4%
17
 
3.1%
스 16
 
2.9%
인 15
 
2.7%
리 11
 
2.0%
루 10
 
1.8%
니 9
 
1.6%
드 9
 
1.6%
이 9
 
1.6%
아 9
 
1.6%
Other values (240) 430
77.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 492
88.8%
Uppercase Letter 23
 
4.2%
Space Separator 17
 
3.1%
Decimal Number 9
 
1.6%
Lowercase Letter 9
 
1.6%
Other Punctuation 2
 
0.4%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
와 19
 
3.9%
스 16
 
3.3%
인 15
 
3.0%
리 11
 
2.2%
루 10
 
2.0%
니 9
 
1.8%
드 9
 
1.8%
이 9
 
1.8%
아 9
 
1.8%
우 7
 
1.4%
Other values (209) 378
76.8%
Uppercase Letter
ValueCountFrequency (%)
A 4
17.4%
R 3
13.0%
N 3
13.0%
O 2
8.7%
Z 2
8.7%
E 1
 
4.3%
U 1
 
4.3%
V 1
 
4.3%
I 1
 
4.3%
C 1
 
4.3%
Other values (4) 4
17.4%
Lowercase Letter
ValueCountFrequency (%)
e 2
22.2%
f 2
22.2%
k 1
11.1%
r 1
11.1%
a 1
11.1%
p 1
11.1%
o 1
11.1%
Decimal Number
ValueCountFrequency (%)
1 3
33.3%
0 2
22.2%
5 1
 
11.1%
2 1
 
11.1%
4 1
 
11.1%
9 1
 
11.1%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 492
88.8%
Latin 32
 
5.8%
Common 30
 
5.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
와 19
 
3.9%
스 16
 
3.3%
인 15
 
3.0%
리 11
 
2.2%
루 10
 
2.0%
니 9
 
1.8%
드 9
 
1.8%
이 9
 
1.8%
아 9
 
1.8%
우 7
 
1.4%
Other values (209) 378
76.8%
Latin
ValueCountFrequency (%)
A 4
 
12.5%
R 3
 
9.4%
N 3
 
9.4%
O 2
 
6.2%
Z 2
 
6.2%
e 2
 
6.2%
f 2
 
6.2%
k 1
 
3.1%
E 1
 
3.1%
U 1
 
3.1%
Other values (11) 11
34.4%
Common
ValueCountFrequency (%)
17
56.7%
1 3
 
10.0%
0 2
 
6.7%
& 2
 
6.7%
5 1
 
3.3%
2 1
 
3.3%
4 1
 
3.3%
9 1
 
3.3%
( 1
 
3.3%
) 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 492
88.8%
ASCII 62
 
11.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
와 19
 
3.9%
스 16
 
3.3%
인 15
 
3.0%
리 11
 
2.2%
루 10
 
2.0%
니 9
 
1.8%
드 9
 
1.8%
이 9
 
1.8%
아 9
 
1.8%
우 7
 
1.4%
Other values (209) 378
76.8%
ASCII
ValueCountFrequency (%)
17
27.4%
A 4
 
6.5%
R 3
 
4.8%
N 3
 
4.8%
1 3
 
4.8%
O 2
 
3.2%
0 2
 
3.2%
Z 2
 
3.2%
& 2
 
3.2%
e 2
 
3.2%
Other values (21) 22
35.5%

load_addr
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:40:10.686501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length32
Mean length21.37
Min length14

Characters and Unicode

Total characters2137
Distinct characters214
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

Unique86 ?
Unique (%)86.0%

Sample

1st row강원도 강릉시 경강로 2107
2nd row충북 청주시 흥덕구 가로수로 1164번길 41-34
3rd row강원도 강릉시 경강로2018번길 25
4th row강원도 강릉시 관솔길12번길 27-7
5th row강원도 강릉시 교동광장로100번길 18-4 한우작
ValueCountFrequency (%)
강원도 97
 
19.5%
춘천시 24
 
4.8%
원주시 21
 
4.2%
강릉시 18
 
3.6%
동내면 7
 
1.4%
속초시 6
 
1.2%
외솔길19번길 5
 
1.0%
평창군 5
 
1.0%
1층 4
 
0.8%
동해시 4
 
0.8%
Other values (239) 306
61.6%
2023-12-10T18:40:11.485337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
398
 
18.6%
원 129
 
6.0%
강 118
 
5.5%
도 103
 
4.8%
1 100
 
4.7%
시 81
 
3.8%
길 63
 
2.9%
로 60
 
2.8%
2 46
 
2.2%
- 41
 
1.9%
Other values (204) 998
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1277
59.8%
Decimal Number 399
 
18.7%
Space Separator 398
 
18.6%
Dash Punctuation 41
 
1.9%
Lowercase Letter 10
 
0.5%
Uppercase Letter 10
 
0.5%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 129
 
10.1%
강 118
 
9.2%
도 103
 
8.1%
시 81
 
6.3%
길 63
 
4.9%
로 60
 
4.7%
천 36
 
2.8%
면 32
 
2.5%
동 26
 
2.0%
주 25
 
2.0%
Other values (178) 604
47.3%
Decimal Number
ValueCountFrequency (%)
1 100
25.1%
2 46
11.5%
5 40
 
10.0%
3 38
 
9.5%
9 33
 
8.3%
8 31
 
7.8%
6 30
 
7.5%
4 29
 
7.3%
0 28
 
7.0%
7 24
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
P 2
20.0%
I 2
20.0%
V 1
10.0%
E 1
10.0%
W 1
10.0%
L 1
10.0%
N 1
10.0%
K 1
10.0%
Lowercase Letter
ValueCountFrequency (%)
o 4
40.0%
n 2
20.0%
i 2
20.0%
t 2
20.0%
Space Separator
ValueCountFrequency (%)
398
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1277
59.8%
Common 840
39.3%
Latin 20
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 129
 
10.1%
강 118
 
9.2%
도 103
 
8.1%
시 81
 
6.3%
길 63
 
4.9%
로 60
 
4.7%
천 36
 
2.8%
면 32
 
2.5%
동 26
 
2.0%
주 25
 
2.0%
Other values (178) 604
47.3%
Common
ValueCountFrequency (%)
398
47.4%
1 100
 
11.9%
2 46
 
5.5%
- 41
 
4.9%
5 40
 
4.8%
3 38
 
4.5%
9 33
 
3.9%
8 31
 
3.7%
6 30
 
3.6%
4 29
 
3.5%
Other values (4) 54
 
6.4%
Latin
ValueCountFrequency (%)
o 4
20.0%
n 2
10.0%
i 2
10.0%
t 2
10.0%
P 2
10.0%
I 2
10.0%
V 1
 
5.0%
E 1
 
5.0%
W 1
 
5.0%
L 1
 
5.0%
Other values (2) 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1277
59.8%
ASCII 860
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
398
46.3%
1 100
 
11.6%
2 46
 
5.3%
- 41
 
4.8%
5 40
 
4.7%
3 38
 
4.4%
9 33
 
3.8%
8 31
 
3.6%
6 30
 
3.5%
4 29
 
3.4%
Other values (16) 74
 
8.6%
Hangul
ValueCountFrequency (%)
원 129
 
10.1%
강 118
 
9.2%
도 103
 
8.1%
시 81
 
6.3%
길 63
 
4.9%
로 60
 
4.7%
천 36
 
2.8%
면 32
 
2.5%
동 26
 
2.0%
주 25
 
2.0%
Other values (178) 604
47.3%

city_do_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
42
97 
43
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42
2nd row43
3rd row42
4th row42
5th row42

Common Values

ValueCountFrequency (%)
42 97
97.0%
43 3
 
3.0%

Length

2023-12-10T18:40:11.776025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:12.020333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42 97
97.0%
43 3
 
3.0%

city_gn_gu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42294.93
Minimum42110
Maximum43130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:12.204303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42110
5-th percentile42110
Q142130
median42150
Q342230
95-th percentile42811
Maximum43130
Range1020
Interquartile range (IQR)100

Descriptive statistics

Standard deviation290.23847
Coefficient of variation (CV)0.006862252
Kurtosis0.61450945
Mean42294.93
Median Absolute Deviation (MAD)40
Skewness1.4591132
Sum4229493
Variance84238.369
MonotonicityNot monotonic
2023-12-10T18:40:12.399524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
42110 24
24.0%
42130 21
21.0%
42150 18
18.0%
42210 6
 
6.0%
42720 5
 
5.0%
42760 5
 
5.0%
42170 4
 
4.0%
42770 3
 
3.0%
42750 3
 
3.0%
42230 3
 
3.0%
Other values (6) 8
 
8.0%
ValueCountFrequency (%)
42110 24
24.0%
42130 21
21.0%
42150 18
18.0%
42170 4
 
4.0%
42190 1
 
1.0%
42210 6
 
6.0%
42230 3
 
3.0%
42720 5
 
5.0%
42750 3
 
3.0%
42760 5
 
5.0%
ValueCountFrequency (%)
43130 2
 
2.0%
43113 1
 
1.0%
42830 2
 
2.0%
42810 1
 
1.0%
42800 1
 
1.0%
42770 3
3.0%
42760 5
5.0%
42750 3
3.0%
42720 5
5.0%
42230 3
3.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.27638
Minimum127.42865
Maximum129.13237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:12.716612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.42865
5-th percentile127.68875
Q1127.77766
median127.98305
Q3128.8346
95-th percentile129.10378
Maximum129.13237
Range1.703727
Interquartile range (IQR)1.0569355

Descriptive statistics

Standard deviation0.51663352
Coefficient of variation (CV)0.004027503
Kurtosis-1.5766409
Mean128.27638
Median Absolute Deviation (MAD)0.297184
Skewness0.26004855
Sum12827.638
Variance0.26691019
MonotonicityNot monotonic
2023-12-10T18:40:12.992568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.879824 2
 
2.0%
127.751256 2
 
2.0%
127.978648 2
 
2.0%
127.98746 2
 
2.0%
128.89791 2
 
2.0%
128.710491 2
 
2.0%
128.759538 2
 
2.0%
128.664895 2
 
2.0%
127.777793 1
 
1.0%
127.758198 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
127.428645 1
1.0%
127.545369 1
1.0%
127.576348 1
1.0%
127.677081 1
1.0%
127.682672 1
1.0%
127.689068 1
1.0%
127.717962 1
1.0%
127.723583 1
1.0%
127.724576 1
1.0%
127.727548 1
1.0%
ValueCountFrequency (%)
129.132372 1
1.0%
129.130922 1
1.0%
129.130291 1
1.0%
129.11731 1
1.0%
129.11723 1
1.0%
129.103077 1
1.0%
129.097534 1
1.0%
128.99075 1
1.0%
128.918747 1
1.0%
128.89791 2
2.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.640971
Minimum36.625691
Maximum38.221619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:40:13.236849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.625691
5-th percentile37.169911
Q137.348441
median37.743673
Q337.860337
95-th percentile38.173199
Maximum38.221619
Range1.595928
Interquartile range (IQR)0.5118965

Descriptive statistics

Standard deviation0.31568544
Coefficient of variation (CV)0.0083867507
Kurtosis-0.090194022
Mean37.640971
Median Absolute Deviation (MAD)0.189367
Skewness-0.44667564
Sum3764.0971
Variance0.099657298
MonotonicityNot monotonic
2023-12-10T18:40:13.515158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.755832 2
 
2.0%
37.86395 2
 
2.0%
37.324002 2
 
2.0%
37.327292 2
 
2.0%
37.75838 2
 
2.0%
37.667745 2
 
2.0%
37.971478 2
 
2.0%
37.38022 2
 
2.0%
37.875509 1
 
1.0%
37.857549 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
36.625691 1
1.0%
36.965738 1
1.0%
36.97688 1
1.0%
37.142504 1
1.0%
37.16349 1
1.0%
37.170249 1
1.0%
37.18287 1
1.0%
37.183633 1
1.0%
37.191011 1
1.0%
37.198 1
1.0%
ValueCountFrequency (%)
38.221619 1
1.0%
38.195769 1
1.0%
38.185999 1
1.0%
38.185865 1
1.0%
38.180623 1
1.0%
38.172808 1
1.0%
38.117744 1
1.0%
38.089898 1
1.0%
38.039571 1
1.0%
37.971478 2
2.0%

area_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원
92 
강원도
 
4
충청북도
 
3
서울
 
1

Length

Max length4
Median length2
Mean length2.1
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row강원
2nd row충청북도
3rd row강원
4th row강원
5th row강원

Common Values

ValueCountFrequency (%)
강원 92
92.0%
강원도 4
 
4.0%
충청북도 3
 
3.0%
서울 1
 
1.0%

Length

2023-12-10T18:40:13.948353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:40:14.163077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원 92
92.0%
강원도 4
 
4.0%
충청북도 3
 
3.0%
서울 1
 
1.0%

homepage_url
Text

MISSING 

Distinct50
Distinct (%)96.2%
Missing48
Missing (%)48.0%
Memory size932.0 B
2023-12-10T18:40:14.690572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length35.5
Mean length26.384615
Min length10

Characters and Unicode

Total characters1372
Distinct characters53
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

Unique49 ?
Unique (%)94.2%

Sample

1st rowhttps://www.instagram.com/surf_table/
2nd rowinstagram.com/la_montagna_pizza?igshid=1sdy2lrz7y9av
3rd rowinstagram.com/wawarwa13
4th rowblog.naver.com/ojh8583
5th rowhttps://store.naver.com/restaurants/detail?id=1813820599
ValueCountFrequency (%)
instagram.com/caffeandromeda_official 3
 
5.8%
www.worldliquormarket.co.kr 1
 
1.9%
wowhealthyfood.com 1
 
1.9%
pesto.modoo.at 1
 
1.9%
www.instagram.com/wine_11 1
 
1.9%
blog.naver.com/petit_bistro 1
 
1.9%
www.토종다래.com 1
 
1.9%
www.instagram.com/duemani_trattoria 1
 
1.9%
blog.naver.com/aigguy 1
 
1.9%
www.sasdb.com 1
 
1.9%
Other values (40) 40
76.9%
2023-12-10T18:40:15.638231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 126
 
9.2%
a 107
 
7.8%
. 97
 
7.1%
w 87
 
6.3%
m 79
 
5.8%
c 70
 
5.1%
n 67
 
4.9%
/ 64
 
4.7%
t 60
 
4.4%
e 60
 
4.4%
Other values (43) 555
40.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1081
78.8%
Other Punctuation 169
 
12.3%
Decimal Number 89
 
6.5%
Connector Punctuation 14
 
1.0%
Other Letter 10
 
0.7%
Dash Punctuation 5
 
0.4%
Math Symbol 3
 
0.2%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 126
11.7%
a 107
 
9.9%
w 87
 
8.0%
m 79
 
7.3%
c 70
 
6.5%
n 67
 
6.2%
t 60
 
5.6%
e 60
 
5.6%
r 57
 
5.3%
i 56
 
5.2%
Other values (16) 312
28.9%
Decimal Number
ValueCountFrequency (%)
1 15
16.9%
2 14
15.7%
3 12
13.5%
8 10
11.2%
9 10
11.2%
4 8
9.0%
5 6
 
6.7%
0 5
 
5.6%
6 5
 
5.6%
7 4
 
4.5%
Other Letter
ValueCountFrequency (%)
토 2
20.0%
종 1
10.0%
다 1
10.0%
래 1
10.0%
춘 1
10.0%
천 1
10.0%
산 1
10.0%
리 1
10.0%
니 1
10.0%
Other Punctuation
ValueCountFrequency (%)
. 97
57.4%
/ 64
37.9%
: 5
 
3.0%
? 3
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_ 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
= 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1082
78.9%
Common 280
 
20.4%
Hangul 10
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 126
11.6%
a 107
 
9.9%
w 87
 
8.0%
m 79
 
7.3%
c 70
 
6.5%
n 67
 
6.2%
t 60
 
5.5%
e 60
 
5.5%
r 57
 
5.3%
i 56
 
5.2%
Other values (17) 313
28.9%
Common
ValueCountFrequency (%)
. 97
34.6%
/ 64
22.9%
1 15
 
5.4%
_ 14
 
5.0%
2 14
 
5.0%
3 12
 
4.3%
8 10
 
3.6%
9 10
 
3.6%
4 8
 
2.9%
5 6
 
2.1%
Other values (7) 30
 
10.7%
Hangul
ValueCountFrequency (%)
토 2
20.0%
종 1
10.0%
다 1
10.0%
래 1
10.0%
춘 1
10.0%
천 1
10.0%
산 1
10.0%
리 1
10.0%
니 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1362
99.3%
Hangul 10
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 126
 
9.3%
a 107
 
7.9%
. 97
 
7.1%
w 87
 
6.4%
m 79
 
5.8%
c 70
 
5.1%
n 67
 
4.9%
/ 64
 
4.7%
t 60
 
4.4%
e 60
 
4.4%
Other values (34) 545
40.0%
Hangul
ValueCountFrequency (%)
토 2
20.0%
종 1
10.0%
다 1
10.0%
래 1
10.0%
춘 1
10.0%
천 1
10.0%
산 1
10.0%
리 1
10.0%
니 1
10.0%

tel_no
Text

MISSING 

Distinct87
Distinct (%)93.5%
Missing7
Missing (%)7.0%
Memory size932.0 B
2023-12-10T18:40:16.080372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.204301
Min length12

Characters and Unicode

Total characters1135
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

Unique82 ?
Unique (%)88.2%

Sample

1st row033-646-3642
2nd row043-232-0224
3rd row010-2563-6446
4th row033-646-7077
5th row033-920-7333
ValueCountFrequency (%)
033-655-0999 3
 
3.2%
033-734-9611 2
 
2.2%
033-563-3322 2
 
2.2%
033-262-3580 2
 
2.2%
033-742-5452 2
 
2.2%
033-264-3012 1
 
1.1%
033-646-3642 1
 
1.1%
033-252-0999 1
 
1.1%
033-254-8191 1
 
1.1%
033-264-4772 1
 
1.1%
Other values (77) 77
82.8%
2023-12-10T18:40:16.603222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 246
21.7%
- 186
16.4%
0 165
14.5%
2 92
 
8.1%
1 80
 
7.0%
4 75
 
6.6%
5 69
 
6.1%
7 67
 
5.9%
6 65
 
5.7%
9 54
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 949
83.6%
Dash Punctuation 186
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 246
25.9%
0 165
17.4%
2 92
 
9.7%
1 80
 
8.4%
4 75
 
7.9%
5 69
 
7.3%
7 67
 
7.1%
6 65
 
6.8%
9 54
 
5.7%
8 36
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1135
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 246
21.7%
- 186
16.4%
0 165
14.5%
2 92
 
8.1%
1 80
 
7.0%
4 75
 
6.6%
5 69
 
6.1%
7 67
 
5.9%
6 65
 
5.7%
9 54
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 246
21.7%
- 186
16.4%
0 165
14.5%
2 92
 
8.1%
1 80
 
7.0%
4 75
 
6.6%
5 69
 
6.1%
7 67
 
5.9%
6 65
 
5.7%
9 54
 
4.8%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2019-12-09 00:00:00
Maximum2019-12-09 00:00:00
2023-12-10T18:40:16.815564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:16.980153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T18:40:06.936338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:05.847757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:06.417861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:07.113084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:06.057729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:06.598225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:07.299115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:06.246911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:40:06.755972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:40:17.105837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_no
entrp_nm1.0001.0001.0001.0001.0001.0000.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0000.0001.0001.000
city_do_cd1.0001.0001.0001.0000.2301.0001.0001.0001.000
city_gn_gu_cd1.0001.0001.0001.0000.8030.8860.7510.0000.766
xpos_lo1.0001.0000.2300.8031.0000.9130.2790.9100.989
ypos_la1.0001.0001.0000.8860.9131.0000.7370.9390.985
area_nm0.0000.0001.0000.7510.2790.7371.0000.0000.000
homepage_url1.0001.0001.0000.0000.9100.9390.0001.0001.000
tel_no1.0001.0001.0000.7660.9890.9850.0001.0001.000
2023-12-10T18:40:17.336595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_do_cdarea_nm
city_do_cd1.0000.990
area_nm0.9901.000
2023-12-10T18:40:17.465622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_gn_gu_cdxpos_loypos_lacity_do_cdarea_nm
city_gn_gu_cd1.0000.573-0.2620.9790.578
xpos_lo0.5731.000-0.2290.2190.174
ypos_la-0.262-0.2291.0000.9640.563
city_do_cd0.9790.2190.9641.0000.990
area_nm0.5780.1740.5630.9901.000

Missing values

2023-12-10T18:40:07.910367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:40:08.444749image/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.
2023-12-10T18:40:08.632072image/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

se_nmentrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd
0와인바서프테이블강원도 강릉시 경강로 21074242150128.89656237.755733강원https://www.instagram.com/surf_table/033-646-36422019-12-09
1와인바꼬메아모르충북 청주시 흥덕구 가로수로 1164번길 41-344343113127.42864536.625691충청북도<NA>043-232-02242019-12-09
2와인바민트강원도 강릉시 경강로2018번길 254242150128.89273437.75077강원<NA>010-2563-64462019-12-09
3와인바라몬타냐강원도 강릉시 관솔길12번길 27-74242150128.86794637.738665강원instagram.com/la_montagna_pizza?igshid=1sdy2lrz7y9av033-646-70772019-12-09
4와인바한우작강원도 강릉시 교동광장로100번길 18-4 한우작4242150128.87681437.766198강원instagram.com/wawarwa13033-920-73332019-12-09
5와인바테루아르강원도 강릉시 구정면 수목원길 76-354242150128.86530737.701022강원<NA>010-3353-35752019-12-09
6와인바강릉한우백화점강원도 강릉시 구정면 회산로 924242150128.84851437.723075강원blog.naver.com/ojh8583033-641-11222019-12-09
7와인바제이스카페충북 충주시 봉현로 348-44343130127.94242136.97688충청북도https://store.naver.com/restaurants/detail?id=1813820599010-9845-36352019-12-09
8와인바상호상사(와인&차마켓)강원도 강릉시 성덕로 328-154242150128.91874737.753805강원<NA>033-653-07802019-12-09
9와인바짠강원도 강릉시 옥천로65번길 184242150128.8979137.75838강원<NA>033-647-22072019-12-09
se_nmentrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd
90와인바스위스램강원도 평창군 대관령면 대관령마루길 365-124242760128.74245237.685524강원blog.naver.com/kangkang0429/221601198276033-333-92722019-12-09
91와인바park&느리게강원도 평창군 대관령면 올림픽로 1714242760128.71049137.667745강원<NA>033-335-73542019-12-09
92와인바EUROVINO강원도 평창군 대관령면 올림픽로 1714242760128.71049137.667745강원<NA>033-336-73542019-12-09
93와인바스노우블러섬강원도 평창군 대관령면 차항길 159-234242760128.6916737.700213강원<NA>033-332-23342019-12-09
94와인바와우대관령한우 진부점강원도 평창군 진부면 까치골길 74242760128.56345337.646869강원wowhealthyfood.com/033-334-13002019-12-09
95와인바와인펜션강원도 홍천군 북방면 노일리4242720127.73435337.677362강원<NA><NA>2019-12-09
96와인바샤또나드리강원도 홍천군 서면 팔봉산로 811-284242720127.68267237.692516강원www.neobeunaewine.com010-5047-29082019-12-09
97와인바뷰링크강원도 홍천군 홍천읍 닭바위1길 65 VIEWLINK 뷰링크4242720127.90266937.695669강원<NA>033-433-83002019-12-09
98와인바돼지가와인에빠진날강원도 홍천군 홍천읍 홍천로6길 184242720127.8846737.690571강원<NA>033-433-22082019-12-09
99와인바채향원강원도 화천군 간동면 모현동로 185-194242720127.79603938.039571강원www.blueberrysuite.com033-441-13022019-12-09

Duplicate rows

Most frequently occurring

se_nmentrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd# duplicates
0와인바베니니강원도 원주시 혁신로 37-1 반곡빌딩 3층4242130127.97864837.324002강원<NA>033-742-54522019-12-092