Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells53
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory85.3 B

Variable types

Text4
Categorical3
Numeric2
DateTime1

Alerts

base_ymd has constant value ""Constant
city_gn_gu_cd is highly overall correlated with xpos_lo and 3 other fieldsHigh correlation
area_nm is highly overall correlated with xpos_lo and 3 other fieldsHigh correlation
city_do_cd is highly overall correlated with xpos_lo and 3 other fieldsHigh correlation
xpos_lo is highly overall correlated with ypos_la and 3 other fieldsHigh correlation
ypos_la is highly overall correlated with xpos_lo and 3 other fieldsHigh correlation
city_do_cd is highly imbalanced (87.9%)Imbalance
homepage_url has 37 (37.0%) missing valuesMissing
tel_no has 14 (14.0%) missing valuesMissing
entrp_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:58:10.791291
Analysis finished2023-12-10 09:58:14.064787
Duration3.27 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
2023-12-10T18:58:14.525023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13.5
Mean length7.82
Min length2

Characters and Unicode

Total characters782
Distinct characters227
Distinct categories6 ?
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 row롯데영플라자
2nd row현대프리미엄아울렛 남양주점
3rd row롯대백화점 에비뉴엘점
4th row구구스 명동롯데점
5th row티파니손바느질
ValueCountFrequency (%)
명동점 20
 
11.7%
가로수길점 4
 
2.3%
명동 4
 
2.3%
보이런던 3
 
1.8%
명동2호점 3
 
1.8%
명동본점 2
 
1.2%
명동대리점 2
 
1.2%
캘빈클라인언더웨어 2
 
1.2%
스토어 2
 
1.2%
현대프리미엄아울렛 2
 
1.2%
Other values (123) 127
74.3%
2023-12-10T18:58:15.625325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
9.1%
점 51
 
6.5%
명 45
 
5.8%
동 45
 
5.8%
스 27
 
3.5%
이 18
 
2.3%
로 15
 
1.9%
아 11
 
1.4%
어 10
 
1.3%
리 10
 
1.3%
Other values (217) 479
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 616
78.8%
Space Separator 71
 
9.1%
Uppercase Letter 60
 
7.7%
Lowercase Letter 20
 
2.6%
Decimal Number 13
 
1.7%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 51
 
8.3%
명 45
 
7.3%
동 45
 
7.3%
스 27
 
4.4%
이 18
 
2.9%
로 15
 
2.4%
아 11
 
1.8%
어 10
 
1.6%
리 10
 
1.6%
니 9
 
1.5%
Other values (176) 375
60.9%
Uppercase Letter
ValueCountFrequency (%)
I 6
 
10.0%
E 6
 
10.0%
M 5
 
8.3%
H 5
 
8.3%
P 4
 
6.7%
L 4
 
6.7%
Y 3
 
5.0%
F 3
 
5.0%
J 3
 
5.0%
S 2
 
3.3%
Other values (13) 19
31.7%
Lowercase Letter
ValueCountFrequency (%)
i 3
15.0%
o 3
15.0%
r 2
10.0%
a 2
10.0%
n 2
10.0%
s 2
10.0%
t 2
10.0%
e 2
10.0%
d 1
 
5.0%
y 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
2 7
53.8%
1 3
23.1%
5 1
 
7.7%
4 1
 
7.7%
0 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 616
78.8%
Common 86
 
11.0%
Latin 80
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 51
 
8.3%
명 45
 
7.3%
동 45
 
7.3%
스 27
 
4.4%
이 18
 
2.9%
로 15
 
2.4%
아 11
 
1.8%
어 10
 
1.6%
리 10
 
1.6%
니 9
 
1.5%
Other values (176) 375
60.9%
Latin
ValueCountFrequency (%)
I 6
 
7.5%
E 6
 
7.5%
M 5
 
6.2%
H 5
 
6.2%
P 4
 
5.0%
L 4
 
5.0%
Y 3
 
3.8%
i 3
 
3.8%
F 3
 
3.8%
J 3
 
3.8%
Other values (23) 38
47.5%
Common
ValueCountFrequency (%)
71
82.6%
2 7
 
8.1%
1 3
 
3.5%
5 1
 
1.2%
4 1
 
1.2%
. 1
 
1.2%
0 1
 
1.2%
& 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 616
78.8%
ASCII 166
 
21.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
42.8%
2 7
 
4.2%
I 6
 
3.6%
E 6
 
3.6%
M 5
 
3.0%
H 5
 
3.0%
P 4
 
2.4%
L 4
 
2.4%
Y 3
 
1.8%
i 3
 
1.8%
Other values (31) 52
31.3%
Hangul
ValueCountFrequency (%)
점 51
 
8.3%
명 45
 
7.3%
동 45
 
7.3%
스 27
 
4.4%
이 18
 
2.9%
로 15
 
2.4%
아 11
 
1.8%
어 10
 
1.6%
리 10
 
1.6%
니 9
 
1.5%
Other values (176) 375
60.9%

load_addr
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:58:16.327037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length29.5
Mean length21.52
Min length15

Characters and Unicode

Total characters2152
Distinct characters144
Distinct categories10 ?
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 row서울특별시 중구 남대문로 67 롯데영플라자
2nd row경기도 남양주시 다산순환로 50, 다산진건 자족시설용지 1BL 판매시설 (다산동)
3rd row서울특별시 중구 남대문로 73 롯데백화점에비뉴엘
4th row서울특별시 중구 남대문로7길 11 태양빌딩 1~2층
5th row서울특별시 중구 다동길 46
ValueCountFrequency (%)
서울특별시 97
20.5%
중구 57
 
12.0%
강남구 40
 
8.4%
1층 14
 
3.0%
명동8길 12
 
2.5%
명동4길 9
 
1.9%
명동길 8
 
1.7%
강남대로162길 6
 
1.3%
강남대로156길 6
 
1.3%
퇴계로 6
 
1.3%
Other values (157) 219
46.2%
2023-12-10T18:58:17.206993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
374
17.4%
1 110
 
5.1%
시 104
 
4.8%
서 99
 
4.6%
구 98
 
4.6%
울 98
 
4.6%
별 98
 
4.6%
특 98
 
4.6%
길 85
 
3.9%
남 68
 
3.2%
Other values (134) 920
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1301
60.5%
Decimal Number 428
 
19.9%
Space Separator 374
 
17.4%
Dash Punctuation 18
 
0.8%
Uppercase Letter 16
 
0.7%
Lowercase Letter 11
 
0.5%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 104
 
8.0%
서 99
 
7.6%
구 98
 
7.5%
울 98
 
7.5%
별 98
 
7.5%
특 98
 
7.5%
길 85
 
6.5%
남 68
 
5.2%
강 64
 
4.9%
중 57
 
4.4%
Other values (100) 432
33.2%
Decimal Number
ValueCountFrequency (%)
1 110
25.7%
5 66
15.4%
2 49
11.4%
6 45
10.5%
4 43
 
10.0%
8 36
 
8.4%
3 30
 
7.0%
0 18
 
4.2%
7 18
 
4.2%
9 13
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
o 2
18.2%
m 1
9.1%
i 1
9.1%
f 1
9.1%
n 1
9.1%
q 1
9.1%
u 1
9.1%
e 1
9.1%
r 1
9.1%
a 1
9.1%
Uppercase Letter
ValueCountFrequency (%)
F 6
37.5%
B 4
25.0%
K 1
 
6.2%
Y 1
 
6.2%
J 1
 
6.2%
L 1
 
6.2%
N 1
 
6.2%
S 1
 
6.2%
Space Separator
ValueCountFrequency (%)
374
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1301
60.5%
Common 824
38.3%
Latin 27
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 104
 
8.0%
서 99
 
7.6%
구 98
 
7.5%
울 98
 
7.5%
별 98
 
7.5%
특 98
 
7.5%
길 85
 
6.5%
남 68
 
5.2%
강 64
 
4.9%
중 57
 
4.4%
Other values (100) 432
33.2%
Latin
ValueCountFrequency (%)
F 6
22.2%
B 4
14.8%
o 2
 
7.4%
m 1
 
3.7%
i 1
 
3.7%
K 1
 
3.7%
Y 1
 
3.7%
J 1
 
3.7%
f 1
 
3.7%
L 1
 
3.7%
Other values (8) 8
29.6%
Common
ValueCountFrequency (%)
374
45.4%
1 110
 
13.3%
5 66
 
8.0%
2 49
 
5.9%
6 45
 
5.5%
4 43
 
5.2%
8 36
 
4.4%
3 30
 
3.6%
- 18
 
2.2%
0 18
 
2.2%
Other values (6) 35
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1301
60.5%
ASCII 851
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
374
43.9%
1 110
 
12.9%
5 66
 
7.8%
2 49
 
5.8%
6 45
 
5.3%
4 43
 
5.1%
8 36
 
4.2%
3 30
 
3.5%
- 18
 
2.1%
0 18
 
2.1%
Other values (24) 62
 
7.3%
Hangul
ValueCountFrequency (%)
시 104
 
8.0%
서 99
 
7.6%
구 98
 
7.5%
울 98
 
7.5%
별 98
 
7.5%
특 98
 
7.5%
길 85
 
6.5%
남 68
 
5.2%
강 64
 
4.9%
중 57
 
4.4%
Other values (100) 432
33.2%

city_do_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
97 
41
 
1
30
 
1
45
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row11
2nd row41
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 97
97.0%
41 1
 
1.0%
30 1
 
1.0%
45 1
 
1.0%

Length

2023-12-10T18:58:17.454258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:17.778064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 97
97.0%
41 1
 
1.0%
30 1
 
1.0%
45 1
 
1.0%

city_gn_gu_cd
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11140
57 
11680
40 
41360
 
1
30200
 
1
45210
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row11140
2nd row41360
3rd row11140
4th row11140
5th row11140

Common Values

ValueCountFrequency (%)
11140 57
57.0%
11680 40
40.0%
41360 1
 
1.0%
30200 1
 
1.0%
45210 1
 
1.0%

Length

2023-12-10T18:58:18.020261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:18.271222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11140 57
57.0%
11680 40
40.0%
41360 1
 
1.0%
30200 1
 
1.0%
45210 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.7%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean127.00437
Minimum126.98018
Maximum127.39869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:58:18.621920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.98018
5-th percentile126.98163
Q1126.98529
median126.98595
Q3127.02233
95-th percentile127.02423
Maximum127.39869
Range0.4185052
Interquartile range (IQR)0.0370374

Descriptive statistics

Standard deviation0.044218303
Coefficient of variation (CV)0.00034816362
Kurtosis65.546729
Mean127.00437
Median Absolute Deviation (MAD)0.0036365
Skewness7.3620713
Sum12573.433
Variance0.0019552583
MonotonicityNot monotonic
2023-12-10T18:58:19.006828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9852932 15
 
15.0%
126.9858092 5
 
5.0%
126.9823099 4
 
4.0%
126.9859464 4
 
4.0%
126.9855328 3
 
3.0%
127.0226083 2
 
2.0%
127.0239141 2
 
2.0%
127.0215216 2
 
2.0%
127.0242292 2
 
2.0%
127.0241827 2
 
2.0%
Other values (56) 58
58.0%
ValueCountFrequency (%)
126.9801848 1
 
1.0%
126.9809781 1
 
1.0%
126.9810075 1
 
1.0%
126.981247 1
 
1.0%
126.9815293 1
 
1.0%
126.9816382 1
 
1.0%
126.9817268 1
 
1.0%
126.9823099 4
4.0%
126.9828878 1
 
1.0%
126.9829357 1
 
1.0%
ValueCountFrequency (%)
127.39869 1
1.0%
127.0247698 1
1.0%
127.0244452 2
2.0%
127.0242631 1
1.0%
127.0242292 2
2.0%
127.0241827 2
2.0%
127.0239141 2
2.0%
127.0239138 1
1.0%
127.0239079 1
1.0%
127.0239017 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.7%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean37.517162
Minimum35.817183
Maximum37.568866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:58:19.313915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.817183
5-th percentile37.51864
Q137.520625
median37.561264
Q337.564037
95-th percentile37.567227
Maximum37.568866
Range1.7516828
Interquartile range (IQR)0.043411915

Descriptive statistics

Standard deviation0.20729087
Coefficient of variation (CV)0.0055252279
Kurtosis54.371076
Mean37.517162
Median Absolute Deviation (MAD)0.00366952
Skewness-7.2650267
Sum3714.1991
Variance0.042969506
MonotonicityNot monotonic
2023-12-10T18:58:19.594035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.56403715 15
 
15.0%
37.56444711 5
 
5.0%
37.56886578 4
 
4.0%
37.56300398 4
 
4.0%
37.56493383 3
 
3.0%
37.52066055 2
 
2.0%
37.52163703 2
 
2.0%
37.51927149 2
 
2.0%
37.5207922 2
 
2.0%
37.5212133 2
 
2.0%
Other values (56) 58
58.0%
ValueCountFrequency (%)
35.817183 1
1.0%
36.423914 1
1.0%
37.51815391 1
1.0%
37.51826994 1
1.0%
37.51839174 1
1.0%
37.51866773 1
1.0%
37.51868983 1
1.0%
37.51888719 1
1.0%
37.51896855 1
1.0%
37.51902299 1
1.0%
ValueCountFrequency (%)
37.56886578 4
4.0%
37.56786212 1
 
1.0%
37.56715637 1
 
1.0%
37.56534637 1
 
1.0%
37.56493383 3
3.0%
37.56467764 1
 
1.0%
37.56463398 1
 
1.0%
37.5644843 1
 
1.0%
37.56444711 5
5.0%
37.56420443 1
 
1.0%

area_nm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
명동
57 
가로수길
40 
경기도
 
1
대전
 
1
전북
 
1

Length

Max length4
Median length2
Mean length2.81
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row명동
2nd row경기도
3rd row명동
4th row명동
5th row명동

Common Values

ValueCountFrequency (%)
명동 57
57.0%
가로수길 40
40.0%
경기도 1
 
1.0%
대전 1
 
1.0%
전북 1
 
1.0%

Length

2023-12-10T18:58:19.980724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:58:20.228854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
명동 57
57.0%
가로수길 40
40.0%
경기도 1
 
1.0%
대전 1
 
1.0%
전북 1
 
1.0%

homepage_url
Text

MISSING 

Distinct63
Distinct (%)100.0%
Missing37
Missing (%)37.0%
Memory size932.0 B
2023-12-10T18:58:20.667958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length35
Mean length26.365079
Min length13

Characters and Unicode

Total characters1661
Distinct characters45
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

Unique63 ?
Unique (%)100.0%

Sample

1st rowhttps://www.lotteshopping.com/
2nd rowhttps://www.lotteshopping.com/main/main
3rd rowhttp://www.gugus.co.kr/
4th rowhttp://www.티파니손바느질.kr/default/
5th rowhttp://www.evisujeans.co.kr/
ValueCountFrequency (%)
https://www.lotteshopping.com 1
 
1.6%
www.attention-row.com 1
 
1.6%
www.heich.kr 1
 
1.6%
http://avouavou.com 1
 
1.6%
www.avouavou.com 1
 
1.6%
https://www.instagram.com/unusualangle 1
 
1.6%
http://qbbbbbbu.shop.blogpay.co.kr 1
 
1.6%
www.vibrate.co.kr 1
 
1.6%
https://www.ssfshop.com 1
 
1.6%
http://boutiqueb.co.kr 1
 
1.6%
Other values (53) 53
84.1%
2023-12-10T18:58:21.385173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 176
 
10.6%
t 152
 
9.2%
. 136
 
8.2%
w 125
 
7.5%
o 116
 
7.0%
c 75
 
4.5%
h 72
 
4.3%
p 72
 
4.3%
s 67
 
4.0%
a 66
 
4.0%
Other values (35) 604
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1262
76.0%
Other Punctuation 365
 
22.0%
Decimal Number 14
 
0.8%
Other Letter 7
 
0.4%
Dash Punctuation 6
 
0.4%
Connector Punctuation 6
 
0.4%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 152
 
12.0%
w 125
 
9.9%
o 116
 
9.2%
c 75
 
5.9%
h 72
 
5.7%
p 72
 
5.7%
s 67
 
5.3%
a 66
 
5.2%
e 65
 
5.2%
r 64
 
5.1%
Other values (16) 388
30.7%
Other Letter
ValueCountFrequency (%)
바 1
14.3%
느 1
14.3%
질 1
14.3%
손 1
14.3%
니 1
14.3%
파 1
14.3%
티 1
14.3%
Decimal Number
ValueCountFrequency (%)
2 4
28.6%
1 4
28.6%
8 3
21.4%
6 1
 
7.1%
0 1
 
7.1%
5 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/ 176
48.2%
. 136
37.3%
: 53
 
14.5%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1263
76.0%
Common 391
 
23.5%
Hangul 7
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 152
 
12.0%
w 125
 
9.9%
o 116
 
9.2%
c 75
 
5.9%
h 72
 
5.7%
p 72
 
5.7%
s 67
 
5.3%
a 66
 
5.2%
e 65
 
5.1%
r 64
 
5.1%
Other values (17) 389
30.8%
Common
ValueCountFrequency (%)
/ 176
45.0%
. 136
34.8%
: 53
 
13.6%
- 6
 
1.5%
_ 6
 
1.5%
2 4
 
1.0%
1 4
 
1.0%
8 3
 
0.8%
6 1
 
0.3%
0 1
 
0.3%
Hangul
ValueCountFrequency (%)
바 1
14.3%
느 1
14.3%
질 1
14.3%
손 1
14.3%
니 1
14.3%
파 1
14.3%
티 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1654
99.6%
Hangul 7
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 176
 
10.6%
t 152
 
9.2%
. 136
 
8.2%
w 125
 
7.6%
o 116
 
7.0%
c 75
 
4.5%
h 72
 
4.4%
p 72
 
4.4%
s 67
 
4.1%
a 66
 
4.0%
Other values (28) 597
36.1%
Hangul
ValueCountFrequency (%)
바 1
14.3%
느 1
14.3%
질 1
14.3%
손 1
14.3%
니 1
14.3%
파 1
14.3%
티 1
14.3%

tel_no
Text

MISSING 

Distinct83
Distinct (%)96.5%
Missing14
Missing (%)14.0%
Memory size932.0 B
2023-12-10T18:58:21.845808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.511628
Min length9

Characters and Unicode

Total characters990
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)93.0%

Sample

1st row02-771-2500
2nd row02-771-2500
3rd row02-310-9210
4th row02-755-3464
5th row02-318-8378
ValueCountFrequency (%)
02-771-2500 2
 
2.3%
2
 
2.3%
02-752-3572 2
 
2.3%
02-775-9314 2
 
2.3%
070-4218-0220 1
 
1.1%
02-518-3305 1
 
1.1%
02-548-0226 1
 
1.1%
070-7090-1145 1
 
1.1%
02-6408-6333 1
 
1.1%
02-514-6313 1
 
1.1%
Other values (74) 74
84.1%
2023-12-10T18:58:22.671395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 169
17.1%
0 159
16.1%
2 122
12.3%
7 111
11.2%
1 74
7.5%
5 74
7.5%
3 69
7.0%
8 63
 
6.4%
4 59
 
6.0%
9 44
 
4.4%
Other values (3) 46
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 817
82.5%
Dash Punctuation 169
 
17.1%
Space Separator 2
 
0.2%
Math Symbol 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 159
19.5%
2 122
14.9%
7 111
13.6%
1 74
9.1%
5 74
9.1%
3 69
8.4%
8 63
 
7.7%
4 59
 
7.2%
9 44
 
5.4%
6 42
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
| 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 169
17.1%
0 159
16.1%
2 122
12.3%
7 111
11.2%
1 74
7.5%
5 74
7.5%
3 69
7.0%
8 63
 
6.4%
4 59
 
6.0%
9 44
 
4.4%
Other values (3) 46
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 169
17.1%
0 159
16.1%
2 122
12.3%
7 111
11.2%
1 74
7.5%
5 74
7.5%
3 69
7.0%
8 63
 
6.4%
4 59
 
6.0%
9 44
 
4.4%
Other values (3) 46
 
4.6%

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
2023-12-10T18:58:22.895407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:23.085628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T18:58:12.697942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:12.343491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:12.947545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:58:12.496584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:58:23.246293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_no
entrp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.000
load_addr1.0001.0001.0001.0001.0001.0001.0001.0000.976
city_do_cd1.0001.0001.0001.0000.9411.0001.000NaNNaN
city_gn_gu_cd1.0001.0001.0001.0000.8261.0001.0001.0001.000
xpos_lo1.0001.0000.9410.8261.0000.9410.8261.0001.000
ypos_la1.0001.0001.0001.0000.9411.0001.000NaNNaN
area_nm1.0001.0001.0001.0000.8261.0001.0001.0001.000
homepage_url1.0001.000NaN1.0001.000NaN1.0001.0001.000
tel_no1.0000.976NaN1.0001.000NaN1.0001.0001.000
2023-12-10T18:58:23.479604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_gn_gu_cdarea_nmcity_do_cd
city_gn_gu_cd1.0001.0000.995
area_nm1.0001.0000.995
city_do_cd0.9950.9951.000
2023-12-10T18:58:23.632039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
xpos_loypos_lacity_do_cdcity_gn_gu_cdarea_nm
xpos_lo1.000-0.7640.7010.8890.889
ypos_la-0.7641.0001.0000.9950.995
city_do_cd0.7011.0001.0000.9950.995
city_gn_gu_cd0.8890.9950.9951.0001.000
area_nm0.8890.9950.9951.0001.000

Missing values

2023-12-10T18:58:13.244207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:58:13.571995image/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.
2023-12-10T18:58:13.925879image/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_nobase_ymd
0롯데영플라자서울특별시 중구 남대문로 67 롯데영플라자1111140126.98152937.563486명동https://www.lotteshopping.com/02-771-25002020-12-31
1현대프리미엄아울렛 남양주점경기도 남양주시 다산순환로 50, 다산진건 자족시설용지 1BL 판매시설 (다산동)4141360<NA><NA>경기도<NA><NA>2020-12-31
2롯대백화점 에비뉴엘점서울특별시 중구 남대문로 73 롯데백화점에비뉴엘1111140126.98163837.564204명동https://www.lotteshopping.com/main/main02-771-25002020-12-31
3구구스 명동롯데점서울특별시 중구 남대문로7길 11 태양빌딩 1~2층1111140126.98124737.563684명동http://www.gugus.co.kr/02-310-92102020-12-31
4티파니손바느질서울특별시 중구 다동길 461111140126.98172737.567862명동http://www.티파니손바느질.kr/default/02-755-34642020-12-31
5모노로그 명동2호점서울특별시 중구 명동10길 121111140126.98520337.564634명동<NA><NA>2020-12-31
6럭키팩토리 명동점서울특별시 중구 명동10길 201111140126.98834337.563655명동<NA>02-318-83782020-12-31
7현대프리미엄아울렛 대전점대전광역시 유성구 용산동 579번지3030200127.3986936.423914대전<NA><NA>2020-12-31
8에비수 명동점서울특별시 중구 명동4길 181111140126.98553337.564934명동http://www.evisujeans.co.kr/02-778-89332020-12-31
9모노로그 명동1호점서울특별시 중구 명동4길 221111140126.9823137.568866명동<NA>02-3789-20572020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmhomepage_urltel_nobase_ymd
90아이아이 쇼룸서울특별시 강남구 논현로153길 461111680127.02444537.520527가로수길http://eyeye-official.com/070-8829-28702020-12-31
91라실루엣드유제니서울특별시 강남구 논현로153길 53 성원빌딩 6층1111680127.02390837.520157가로수길http://eugenny.com/02-518-33052020-12-31
922nd street서울특별시 강남구 논현로153길 61 1f1111680127.02335537.519947가로수길https://www.instagram.com/2ndstreet_garosu/<NA>2020-12-31
93하이니크서울특별시 강남구 논현로157길 52-11111680127.02418337.521213가로수길http://heinique.com/<NA>2020-12-31
94제이와이킴서울특별시 강남구 논현로157길 52-1 1층 JYKim 쇼룸1111680127.02418337.521213가로수길http://byjykim.com/<NA>2020-12-31
95컷아웃포서울특별시 강남구 논현로157길 551111680127.02422937.520792가로수길www.cutoutfor.com/<NA>2020-12-31
96코르크서울특별시 강남구 논현로157길 55 102호1111680127.02422937.520792가로수길https://smartstore.naver.com/cork518<NA>2020-12-31
97프레이트서울특별시 강남구 논현로159길 54 1층1111680127.02390237.521918가로수길www.fr8ight.co.kr/070-4221-11212020-12-31
98리바이스 신사 직영점서울특별시 강남구 논현로159길 551111680127.02391437.521637가로수길https://www.levi.co.kr/02-511-60152020-12-31
99느와르라르메스서울특별시 강남구 논현로159길 55 지하 1층1111680127.02391437.521637가로수길www.noirlarmes.co.kr/070-7604-44122020-12-31