Overview

Dataset statistics

Number of variables7
Number of observations677
Missing cells722
Missing cells (%)15.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.2 KiB
Average record size in memory56.2 B

Variable types

Text4
Categorical3

Dataset

Description키,명칭,행정시,행정구,행정동,전화번호,홈페이지주소
Author중구
URLhttps://data.seoul.go.kr/dataList/OA-13023/S/1/datasetView.do

Alerts

행정동 is highly overall correlated with 행정시 and 1 other fieldsHigh correlation
행정구 is highly overall correlated with 행정시 and 1 other fieldsHigh correlation
행정시 is highly overall correlated with 행정구 and 1 other fieldsHigh correlation
행정시 is highly imbalanced (75.8%)Imbalance
행정구 is highly imbalanced (82.2%)Imbalance
전화번호 has 232 (34.3%) missing valuesMissing
홈페이지주소 has 490 (72.4%) missing valuesMissing
키 has unique valuesUnique

Reproduction

Analysis started2023-12-11 04:00:08.192459
Analysis finished2023-12-11 04:00:09.147937
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct677
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2023-12-11T13:00:09.361181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters8124
Distinct characters16
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

Unique677 ?
Unique (%)100.0%

Sample

1st rowBE_IW12-0596
2nd rowBE_IW12-0597
3rd rowBE_IW12-0598
4th rowBE_IW12-0599
5th rowBE_IW12-0600
ValueCountFrequency (%)
be_iw12-0596 1
 
0.1%
be_iw12-0201 1
 
0.1%
be_iw12-0211 1
 
0.1%
be_iw12-0194 1
 
0.1%
be_iw12-0195 1
 
0.1%
be_iw12-0196 1
 
0.1%
be_iw12-0197 1
 
0.1%
be_iw12-0198 1
 
0.1%
be_iw12-0199 1
 
0.1%
be_iw12-0200 1
 
0.1%
Other values (667) 667
98.5%
2023-12-11T13:00:10.122629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 915
11.3%
2 915
11.3%
0 912
11.2%
B 677
8.3%
E 677
8.3%
_ 677
8.3%
I 677
8.3%
W 677
8.3%
- 677
8.3%
5 238
 
2.9%
Other values (6) 1082
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4062
50.0%
Uppercase Letter 2708
33.3%
Connector Punctuation 677
 
8.3%
Dash Punctuation 677
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 915
22.5%
2 915
22.5%
0 912
22.5%
5 238
 
5.9%
3 238
 
5.9%
4 238
 
5.9%
6 216
 
5.3%
7 136
 
3.3%
9 127
 
3.1%
8 127
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 677
25.0%
E 677
25.0%
I 677
25.0%
W 677
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 677
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5416
66.7%
Latin 2708
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 915
16.9%
2 915
16.9%
0 912
16.8%
_ 677
12.5%
- 677
12.5%
5 238
 
4.4%
3 238
 
4.4%
4 238
 
4.4%
6 216
 
4.0%
7 136
 
2.5%
Other values (2) 254
 
4.7%
Latin
ValueCountFrequency (%)
B 677
25.0%
E 677
25.0%
I 677
25.0%
W 677
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 915
11.3%
2 915
11.3%
0 912
11.2%
B 677
8.3%
E 677
8.3%
_ 677
8.3%
I 677
8.3%
W 677
8.3%
- 677
8.3%
5 238
 
2.9%
Other values (6) 1082
13.3%

명칭
Text

Distinct537
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2023-12-11T13:00:10.500306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length6.2776957
Min length2

Characters and Unicode

Total characters4250
Distinct characters518
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique417 ?
Unique (%)61.6%

Sample

1st row??PLAZA
2nd rowAPM服씉城
3rd rowAPM服씉城
4th rowUUS
5th rowUUS
ValueCountFrequency (%)
址 20
 
2.5%
16
 
2.0%
明洞 10
 
1.3%
崇 5
 
0.6%
洞 5
 
0.6%
平和市 5
 
0.6%
town 5
 
0.6%
plaza 4
 
0.5%
明洞天主?堂 4
 
0.5%
大 4
 
0.5%
Other values (564) 708
90.1%
2023-12-11T13:00:11.059115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 1129
26.6%
114
 
2.7%
址 78
 
1.8%
A 72
 
1.7%
E 69
 
1.6%
家 68
 
1.6%
店 63
 
1.5%
洞 58
 
1.4%
N 48
 
1.1%
O 46
 
1.1%
Other values (508) 2505
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2188
51.5%
Other Punctuation 1143
26.9%
Uppercase Letter 615
 
14.5%
Lowercase Letter 123
 
2.9%
Space Separator 114
 
2.7%
Close Punctuation 21
 
0.5%
Open Punctuation 21
 
0.5%
Decimal Number 20
 
0.5%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
址 78
 
3.6%
家 68
 
3.1%
店 63
 
2.9%
洞 58
 
2.7%
市 40
 
1.8%
明 37
 
1.7%
首 36
 
1.6%
南 34
 
1.6%
公 33
 
1.5%
院 29
 
1.3%
Other values (442) 1712
78.2%
Uppercase Letter
ValueCountFrequency (%)
A 72
11.7%
E 69
 
11.2%
N 48
 
7.8%
O 46
 
7.5%
T 38
 
6.2%
S 36
 
5.9%
I 36
 
5.9%
L 34
 
5.5%
R 32
 
5.2%
M 28
 
4.6%
Other values (15) 176
28.6%
Lowercase Letter
ValueCountFrequency (%)
a 17
13.8%
i 13
10.6%
e 12
9.8%
o 9
 
7.3%
c 9
 
7.3%
u 9
 
7.3%
b 7
 
5.7%
r 7
 
5.7%
l 7
 
5.7%
n 5
 
4.1%
Other values (11) 28
22.8%
Other Punctuation
ValueCountFrequency (%)
? 1129
98.8%
： 5
 
0.4%
. 4
 
0.3%
& 3
 
0.3%
； 1
 
0.1%
！ 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 8
40.0%
1 4
20.0%
6 3
 
15.0%
4 2
 
10.0%
5 2
 
10.0%
3 1
 
5.0%
Close Punctuation
ValueCountFrequency (%)
） 19
90.5%
) 1
 
4.8%
】 1
 
4.8%
Open Punctuation
ValueCountFrequency (%)
（ 18
85.7%
( 2
 
9.5%
【 1
 
4.8%
Space Separator
ValueCountFrequency (%)
114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 2176
51.2%
Common 1324
31.2%
Latin 738
 
17.4%
Hangul 12
 
0.3%

Most frequent character per script

Han
ValueCountFrequency (%)
址 78
 
3.6%
家 68
 
3.1%
店 63
 
2.9%
洞 58
 
2.7%
市 40
 
1.8%
明 37
 
1.7%
首 36
 
1.7%
南 34
 
1.6%
公 33
 
1.5%
院 29
 
1.3%
Other values (437) 1700
78.1%
Latin
ValueCountFrequency (%)
A 72
 
9.8%
E 69
 
9.3%
N 48
 
6.5%
O 46
 
6.2%
T 38
 
5.1%
S 36
 
4.9%
I 36
 
4.9%
L 34
 
4.6%
R 32
 
4.3%
M 28
 
3.8%
Other values (36) 299
40.5%
Common
ValueCountFrequency (%)
? 1129
85.3%
114
 
8.6%
） 19
 
1.4%
（ 18
 
1.4%
0 8
 
0.6%
- 5
 
0.4%
： 5
 
0.4%
1 4
 
0.3%
. 4
 
0.3%
6 3
 
0.2%
Other values (10) 15
 
1.1%
Hangul
ValueCountFrequency (%)
씉 5
41.7%
쎱 4
33.3%
싪 1
 
8.3%
쒛 1
 
8.3%
싓 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 2176
51.2%
ASCII 2016
47.4%
None 46
 
1.1%
Hangul 12
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 1129
56.0%
114
 
5.7%
A 72
 
3.6%
E 69
 
3.4%
N 48
 
2.4%
O 46
 
2.3%
T 38
 
1.9%
S 36
 
1.8%
I 36
 
1.8%
L 34
 
1.7%
Other values (49) 394
 
19.5%
CJK
ValueCountFrequency (%)
址 78
 
3.6%
家 68
 
3.1%
店 63
 
2.9%
洞 58
 
2.7%
市 40
 
1.8%
明 37
 
1.7%
首 36
 
1.7%
南 34
 
1.6%
公 33
 
1.5%
院 29
 
1.3%
Other values (437) 1700
78.1%
None
ValueCountFrequency (%)
） 19
41.3%
（ 18
39.1%
： 5
 
10.9%
【 1
 
2.2%
； 1
 
2.2%
！ 1
 
2.2%
】 1
 
2.2%
Hangul
ValueCountFrequency (%)
씉 5
41.7%
쎱 4
33.3%
싪 1
 
8.3%
쒛 1
 
8.3%
싓 1
 
8.3%

행정시
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
首?特?市
650 
<NA>
 
27

Length

Max length5
Median length5
Mean length4.9601182
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row首?特?市
2nd row首?特?市
3rd row首?特?市
4th row首?特?市
5th row首?特?市

Common Values

ValueCountFrequency (%)
首?特?市 650
96.0%
<NA> 27
 
4.0%

Length

2023-12-11T13:00:11.207180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:00:11.326176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
首?特?市 650
96.0%
na 27
 
4.0%

행정구
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
中?
641 
<NA>
 
27
?山?
 
6
?路?
 
3

Length

Max length4
Median length2
Mean length2.0930576
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row中?
2nd row中?
3rd row中?
4th row中?
5th row中?

Common Values

ValueCountFrequency (%)
中? 641
94.7%
<NA> 27
 
4.0%
?山? 6
 
0.9%
?路? 3
 
0.4%

Length

2023-12-11T13:00:11.464170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:00:11.599420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
中 641
94.7%
na 27
 
4.0%
山 6
 
0.9%
路 3
 
0.4%

행정동
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
明洞
163 
小公洞
129 
光熙洞
61 
??洞
61 
?忠洞
53 
Other values (14)
210 

Length

Max length11
Median length3
Mean length2.7976366
Min length2

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row新堂洞
2nd row新堂洞
3rd row新堂洞
4th row新堂洞
5th row新堂洞

Common Values

ValueCountFrequency (%)
明洞 163
24.1%
小公洞 129
19.1%
光熙洞 61
 
9.0%
??洞 61
 
9.0%
?忠洞 53
 
7.8%
?洞 50
 
7.4%
新堂洞 47
 
6.9%
乙支路洞 33
 
4.9%
<NA> 27
 
4.0%
中林洞 16
 
2.4%
Other values (9) 37
 
5.5%

Length

2023-12-11T13:00:11.725223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
明洞 163
24.1%
小公洞 129
19.1%
洞 111
16.4%
光熙洞 61
 
9.0%
忠洞 53
 
7.8%
新堂洞 47
 
6.9%
乙支路洞 33
 
4.9%
na 27
 
4.0%
中林洞 16
 
2.4%
茶山洞 11
 
1.6%
Other values (8) 26
 
3.8%

전화번호
Text

MISSING 

Distinct383
Distinct (%)86.1%
Missing232
Missing (%)34.3%
Memory size5.4 KiB
2023-12-11T13:00:11.987535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length11.867416
Min length5

Characters and Unicode

Total characters5281
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique326 ?
Unique (%)73.3%

Sample

1st row02-2232-2000
2nd row02-2250-2027
3rd row02-2250-2027
4th row02-6270-1000
5th row02-6270-1000
ValueCountFrequency (%)
02-2232-2000 4
 
0.9%
02-2265-0220 3
 
0.7%
02-2048-5100 3
 
0.7%
02-752-3353 3
 
0.7%
02-2268-0592 3
 
0.7%
02-2290-1234 3
 
0.7%
02-3455-8341~2 3
 
0.7%
042-481-4650 3
 
0.7%
1544-1122 3
 
0.7%
02-3396-5855 3
 
0.7%
Other values (349) 420
93.1%
2023-12-11T13:00:12.426190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1091
20.7%
- 882
16.7%
0 798
15.1%
7 466
8.8%
3 373
 
7.1%
5 350
 
6.6%
1 310
 
5.9%
6 289
 
5.5%
8 223
 
4.2%
4 195
 
3.7%
Other values (4) 304
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4271
80.9%
Dash Punctuation 882
 
16.7%
Space Separator 110
 
2.1%
Math Symbol 17
 
0.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1091
25.5%
0 798
18.7%
7 466
10.9%
3 373
 
8.7%
5 350
 
8.2%
1 310
 
7.3%
6 289
 
6.8%
8 223
 
5.2%
4 195
 
4.6%
9 176
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 882
100.0%
Space Separator
ValueCountFrequency (%)
110
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5281
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1091
20.7%
- 882
16.7%
0 798
15.1%
7 466
8.8%
3 373
 
7.1%
5 350
 
6.6%
1 310
 
5.9%
6 289
 
5.5%
8 223
 
4.2%
4 195
 
3.7%
Other values (4) 304
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1091
20.7%
- 882
16.7%
0 798
15.1%
7 466
8.8%
3 373
 
7.1%
5 350
 
6.6%
1 310
 
5.9%
6 289
 
5.5%
8 223
 
4.2%
4 195
 
3.7%
Other values (4) 304
 
5.8%

홈페이지주소
Text

MISSING 

Distinct168
Distinct (%)89.8%
Missing490
Missing (%)72.4%
Memory size5.4 KiB
2023-12-11T13:00:12.704023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length121
Median length62
Mean length32.187166
Min length13

Characters and Unicode

Total characters6019
Distinct characters68
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)80.7%

Sample

1st rowhttp://art-plaza.co.kr
2nd rowhttp://apm-korea.com/
3rd rowhttp://apm-korea.com/main.asp
4th rowhttp://www.uus.co.kr/
5th rowhttp://www.uus.co.kr/
ValueCountFrequency (%)
http://art-plaza.co.kr 4
 
2.1%
http://www.outback.co.kr 3
 
1.6%
http://emart.shinsegae.com/branch/floor/floor.jsp?id=951 3
 
1.6%
http://www.veneziamegamall.net 3
 
1.6%
http://www.cmah.or.kr 2
 
1.1%
http://www.dutyfree24.com/?domain=naver3 2
 
1.1%
http://www.goodmorningcity.kr 2
 
1.1%
http://www.ktxcinema.co.kr 2
 
1.1%
http://www.myungbo.com 2
 
1.1%
http://www.sejongpac.or.kr/sngad 2
 
1.1%
Other values (134) 163
86.7%
2023-12-11T13:00:13.214218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 582
 
9.7%
t 528
 
8.8%
. 498
 
8.3%
w 430
 
7.1%
o 388
 
6.4%
h 291
 
4.8%
p 279
 
4.6%
e 278
 
4.6%
a 277
 
4.6%
r 260
 
4.3%
Other values (58) 2208
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4427
73.6%
Other Punctuation 1292
 
21.5%
Decimal Number 106
 
1.8%
Space Separator 58
 
1.0%
Uppercase Letter 55
 
0.9%
Connector Punctuation 30
 
0.5%
Math Symbol 27
 
0.4%
Dash Punctuation 13
 
0.2%
Other Letter 11
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 528
11.9%
w 430
 
9.7%
o 388
 
8.8%
h 291
 
6.6%
p 279
 
6.3%
e 278
 
6.3%
a 277
 
6.3%
r 260
 
5.9%
n 218
 
4.9%
c 202
 
4.6%
Other values (15) 1276
28.8%
Uppercase Letter
ValueCountFrequency (%)
C 14
25.5%
S 7
12.7%
D 4
 
7.3%
M 4
 
7.3%
T 4
 
7.3%
F 4
 
7.3%
N 3
 
5.5%
H 3
 
5.5%
V 3
 
5.5%
I 2
 
3.6%
Other values (5) 7
12.7%
Decimal Number
ValueCountFrequency (%)
1 31
29.2%
0 30
28.3%
2 15
14.2%
4 7
 
6.6%
6 5
 
4.7%
7 5
 
4.7%
9 5
 
4.7%
5 4
 
3.8%
3 4
 
3.8%
Other Letter
ValueCountFrequency (%)
장 2
18.2%
산 2
18.2%
충 1
9.1%
가 1
9.1%
동 1
9.1%
종 1
9.1%
시 1
9.1%
방 1
9.1%
합 1
9.1%
Other Punctuation
ValueCountFrequency (%)
/ 582
45.0%
. 498
38.5%
: 183
 
14.2%
? 19
 
1.5%
& 8
 
0.6%
, 2
 
0.2%
Space Separator
ValueCountFrequency (%)
58
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 30
100.0%
Math Symbol
ValueCountFrequency (%)
= 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4482
74.5%
Common 1526
 
25.4%
Hangul 11
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 528
 
11.8%
w 430
 
9.6%
o 388
 
8.7%
h 291
 
6.5%
p 279
 
6.2%
e 278
 
6.2%
a 277
 
6.2%
r 260
 
5.8%
n 218
 
4.9%
c 202
 
4.5%
Other values (30) 1331
29.7%
Common
ValueCountFrequency (%)
/ 582
38.1%
. 498
32.6%
: 183
 
12.0%
58
 
3.8%
1 31
 
2.0%
_ 30
 
2.0%
0 30
 
2.0%
= 27
 
1.8%
? 19
 
1.2%
2 15
 
1.0%
Other values (9) 53
 
3.5%
Hangul
ValueCountFrequency (%)
장 2
18.2%
산 2
18.2%
충 1
9.1%
가 1
9.1%
동 1
9.1%
종 1
9.1%
시 1
9.1%
방 1
9.1%
합 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6008
99.8%
Hangul 11
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 582
 
9.7%
t 528
 
8.8%
. 498
 
8.3%
w 430
 
7.2%
o 388
 
6.5%
h 291
 
4.8%
p 279
 
4.6%
e 278
 
4.6%
a 277
 
4.6%
r 260
 
4.3%
Other values (49) 2197
36.6%
Hangul
ValueCountFrequency (%)
장 2
18.2%
산 2
18.2%
충 1
9.1%
가 1
9.1%
동 1
9.1%
종 1
9.1%
시 1
9.1%
방 1
9.1%
합 1
9.1%

Correlations

2023-12-11T13:00:13.345787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구행정동
행정구1.0001.000
행정동1.0001.000
2023-12-11T13:00:13.461787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동행정구행정시
행정동1.0000.9881.000
행정구0.9881.0001.000
행정시1.0001.0001.000
2023-12-11T13:00:13.569141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정시행정구행정동
행정시1.0001.0001.000
행정구1.0001.0000.988
행정동1.0000.9881.000

Missing values

2023-12-11T13:00:08.780849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:00:08.934381image/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.
2023-12-11T13:00:09.060187image/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

키명칭행정시행정구행정동전화번호홈페이지주소
0BE_IW12-0596??PLAZA首?特?市中?新堂洞02-2232-2000http://art-plaza.co.kr
1BE_IW12-0597APM服씉城首?特?市中?新堂洞02-2250-2027http://apm-korea.com/
2BE_IW12-0598APM服씉城首?特?市中?新堂洞02-2250-2027http://apm-korea.com/main.asp
3BE_IW12-0599UUS首?特?市中?新堂洞02-6270-1000http://www.uus.co.kr/
4BE_IW12-0600UUS首?特?市中?新堂洞02-6270-1000http://www.uus.co.kr/
5BE_IW12-0601CERESTAR首?特?市中?光熙洞02-2265-3531<NA>
6BE_IW12-0602CERESTAR首?特?市中?光熙洞02-2265-3531<NA>
7BE_IW12-0603科技商家首?特?市中?新堂洞02-2232-4821<NA>
8BE_IW12-0604科技商家首?特?市中?新堂洞02-2232-4821<NA>
9BE_IW12-0605HELLO A.P.M首?特?市中?光熙洞02-6388-1114http://www.helloapm.com/
키명칭행정시행정구행정동전화번호홈페이지주소
667BE_IW12-0181五??首?特?市中??洞02-2273-3031http://osinjung.com
668BE_IW12-0182五?排骨首?特?市中?光熙洞02-2267-9328<NA>
669BE_IW12-0183五?洞河豚家首?特?市中?光熙洞02-2268-6771<NA>
670BE_IW12-0184五?洞新?面屋首?特?市中?光熙洞02-2273-4889<NA>
671BE_IW12-0185五?洞咸?冷面首?特?市中?靑丘洞02-2234-2121<NA>
672BE_IW12-0186五?洞河豚家首?特?市中?光熙洞011-739-8515<NA>
673BE_IW12-0187五?洞?南家首?特?市中?光熙洞02-2266-0735<NA>
674BE_IW12-0188玉石?首?特?市中??洞02-2266-2409<NA>
675BE_IW12-0189??海?火?首?特?市中?明洞02-777-3536<NA>
676BE_IW12-0190?水山首?特?市中?明洞02-771-5553http://www.yongsusan.co.kr