Overview

Dataset statistics

Number of variables7
Number of observations81
Missing cells56
Missing cells (%)9.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory57.6 B

Variable types

Text5
Categorical2

Dataset

Description키,명칭,행정 시,행정 구,행정 동,대표전화,홈페이지주소
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13032/S/1/datasetView.do

Alerts

행정 시 is highly overall correlated with 행정 구High correlation
행정 구 is highly overall correlated with 행정 시High correlation
행정 동 has 50 (61.7%) missing valuesMissing
대표전화 has 5 (6.2%) missing valuesMissing
홈페이지주소 has 1 (1.2%) missing valuesMissing
키 has unique valuesUnique

Reproduction

Analysis started2024-04-17 21:01:50.882026
Analysis finished2024-04-17 21:01:51.859379
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2024-04-18T06:01:51.996866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters972
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

Unique81 ?
Unique (%)100.0%

Sample

1st rowBE_IW06-0001
2nd rowBE_IW06-0033
3rd rowBE_IW06-0034
4th rowBE_IW06-0035
5th rowBE_IW06-0036
ValueCountFrequency (%)
be_iw06-0001 1
 
1.2%
be_iw06-0074 1
 
1.2%
be_iw06-0012 1
 
1.2%
be_iw06-0011 1
 
1.2%
be_iw06-0010 1
 
1.2%
be_iw06-0009 1
 
1.2%
be_iw06-0008 1
 
1.2%
be_iw06-0007 1
 
1.2%
be_iw06-0006 1
 
1.2%
be_iw06-0005 1
 
1.2%
Other values (71) 71
87.7%
2024-04-18T06:01:52.277572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 260
26.7%
6 99
 
10.2%
B 81
 
8.3%
E 81
 
8.3%
_ 81
 
8.3%
I 81
 
8.3%
W 81
 
8.3%
- 81
 
8.3%
1 19
 
2.0%
3 18
 
1.9%
Other values (6) 90
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 486
50.0%
Uppercase Letter 324
33.3%
Connector Punctuation 81
 
8.3%
Dash Punctuation 81
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260
53.5%
6 99
 
20.4%
1 19
 
3.9%
3 18
 
3.7%
4 18
 
3.7%
5 18
 
3.7%
7 18
 
3.7%
2 18
 
3.7%
8 10
 
2.1%
9 8
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
B 81
25.0%
E 81
25.0%
I 81
25.0%
W 81
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 648
66.7%
Latin 324
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260
40.1%
6 99
 
15.3%
_ 81
 
12.5%
- 81
 
12.5%
1 19
 
2.9%
3 18
 
2.8%
4 18
 
2.8%
5 18
 
2.8%
7 18
 
2.8%
2 18
 
2.8%
Other values (2) 18
 
2.8%
Latin
ValueCountFrequency (%)
B 81
25.0%
E 81
25.0%
I 81
25.0%
W 81
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260
26.7%
6 99
 
10.2%
B 81
 
8.3%
E 81
 
8.3%
_ 81
 
8.3%
I 81
 
8.3%
W 81
 
8.3%
- 81
 
8.3%
1 19
 
2.0%
3 18
 
1.9%
Other values (6) 90
 
9.3%

명칭
Text

Distinct80
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size780.0 B
2024-04-18T06:01:52.468644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length24
Mean length8.4691358
Min length2

Characters and Unicode

Total characters686
Distinct characters170
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

Unique79 ?
Unique (%)97.5%

Sample

1st row63?展中心
2nd rowCOEX水族?
3rd row云塔
4th row普拉?
5th row??家具博物?
ValueCountFrequency (%)
天世界 2
 
1.9%
展中心 2
 
1.9%
on 2
 
1.9%
coex 2
 
1.9%
jw 2
 
1.9%
aw?展中心 1
 
1.0%
가나??中心 1
 
1.0%
家茶 1
 
1.0%
（5月?店） 1
 
1.0%
dining 1
 
1.0%
Other values (88) 88
85.4%
2024-04-18T06:01:52.760563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 147
21.4%
店 36
 
5.2%
首 29
 
4.2%
酒 29
 
4.2%
23
 
3.4%
e 20
 
2.9%
n 18
 
2.6%
a 15
 
2.2%
o 12
 
1.7%
中 12
 
1.7%
Other values (160) 345
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 297
43.3%
Other Punctuation 148
21.6%
Lowercase Letter 140
20.4%
Uppercase Letter 64
 
9.3%
Space Separator 23
 
3.4%
Decimal Number 9
 
1.3%
Dash Punctuation 3
 
0.4%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
店 36
 
12.1%
首 29
 
9.8%
酒 29
 
9.8%
中 12
 
4.0%
心 8
 
2.7%
斯 8
 
2.7%
展 5
 
1.7%
世 5
 
1.7%
家 5
 
1.7%
特 4
 
1.3%
Other values (106) 156
52.5%
Lowercase Letter
ValueCountFrequency (%)
e 20
14.3%
n 18
12.9%
a 15
10.7%
o 12
8.6%
i 11
 
7.9%
l 10
 
7.1%
t 10
 
7.1%
r 6
 
4.3%
u 5
 
3.6%
c 5
 
3.6%
Other values (12) 28
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 6
 
9.4%
C 6
 
9.4%
T 6
 
9.4%
O 5
 
7.8%
E 5
 
7.8%
S 4
 
6.2%
W 4
 
6.2%
K 4
 
6.2%
R 3
 
4.7%
J 3
 
4.7%
Other values (10) 18
28.1%
Decimal Number
ValueCountFrequency (%)
1 2
22.2%
5 2
22.2%
0 2
22.2%
7 1
11.1%
3 1
11.1%
6 1
11.1%
Other Punctuation
ValueCountFrequency (%)
? 147
99.3%
& 1
 
0.7%
Space Separator
ValueCountFrequency (%)
23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
（ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
） 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 292
42.6%
Latin 204
29.7%
Common 185
27.0%
Hangul 5
 
0.7%

Most frequent character per script

Han
ValueCountFrequency (%)
店 36
 
12.3%
首 29
 
9.9%
酒 29
 
9.9%
中 12
 
4.1%
心 8
 
2.7%
斯 8
 
2.7%
展 5
 
1.7%
世 5
 
1.7%
家 5
 
1.7%
特 4
 
1.4%
Other values (101) 151
51.7%
Latin
ValueCountFrequency (%)
e 20
 
9.8%
n 18
 
8.8%
a 15
 
7.4%
o 12
 
5.9%
i 11
 
5.4%
l 10
 
4.9%
t 10
 
4.9%
A 6
 
2.9%
C 6
 
2.9%
r 6
 
2.9%
Other values (32) 90
44.1%
Common
ValueCountFrequency (%)
? 147
79.5%
23
 
12.4%
- 3
 
1.6%
1 2
 
1.1%
5 2
 
1.1%
0 2
 
1.1%
（ 1
 
0.5%
） 1
 
0.5%
7 1
 
0.5%
3 1
 
0.5%
Other values (2) 2
 
1.1%
Hangul
ValueCountFrequency (%)
나 1
20.0%
무 1
20.0%
게 1
20.0%
원 1
20.0%
가 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
56.4%
CJK 292
42.6%
Hangul 5
 
0.7%
None 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 147
38.0%
23
 
5.9%
e 20
 
5.2%
n 18
 
4.7%
a 15
 
3.9%
o 12
 
3.1%
i 11
 
2.8%
l 10
 
2.6%
t 10
 
2.6%
A 6
 
1.6%
Other values (42) 115
29.7%
CJK
ValueCountFrequency (%)
店 36
 
12.3%
首 29
 
9.9%
酒 29
 
9.9%
中 12
 
4.1%
心 8
 
2.7%
斯 8
 
2.7%
展 5
 
1.7%
世 5
 
1.7%
家 5
 
1.7%
特 4
 
1.4%
Other values (101) 151
51.7%
Hangul
ValueCountFrequency (%)
나 1
20.0%
무 1
20.0%
게 1
20.0%
원 1
20.0%
가 1
20.0%
None
ValueCountFrequency (%)
（ 1
50.0%
） 1
50.0%

행정 시
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size780.0 B
<NA>
50 
首?特?市
31 

Length

Max length5
Median length4
Mean length4.382716
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 50
61.7%
首?特?市 31
38.3%

Length

2024-04-18T06:01:52.863118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:01:52.944391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 50
61.7%
首?特?市 31
38.3%

행정 구
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size780.0 B
<NA>
50 
江南?
13 
中?
6 
瑞草?
 
4
松坡?
 
2
Other values (5)
6 

Length

Max length4
Median length4
Mean length3.5555556
Min length2

Unique

Unique4 ?
Unique (%)4.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 50
61.7%
江南? 13
 
16.0%
中? 6
 
7.4%
瑞草? 4
 
4.9%
松坡? 2
 
2.5%
麻浦? 2
 
2.5%
永登浦? 1
 
1.2%
城北? 1
 
1.2%
?路? 1
 
1.2%
江?? 1
 
1.2%

Length

2024-04-18T06:01:53.032563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T06:01:53.133045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 50
61.7%
江南 13
 
16.0%
中 6
 
7.4%
瑞草 4
 
4.9%
松坡 2
 
2.5%
麻浦 2
 
2.5%
永登浦 1
 
1.2%
城北 1
 
1.2%
路 1
 
1.2%
江 1
 
1.2%

행정 동
Text

MISSING 

Distinct22
Distinct (%)71.0%
Missing50
Missing (%)61.7%
Memory size780.0 B
2024-04-18T06:01:53.271348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.483871
Min length2

Characters and Unicode

Total characters108
Distinct characters33
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)54.8%

Sample

1st row쒧院洞
2nd row三成1洞
3rd row淸潭洞
4th row?浦4洞
5th row良才2洞
ValueCountFrequency (%)
三1洞 4
 
12.9%
小公洞 3
 
9.7%
淸潭洞 3
 
9.7%
三成1洞 2
 
6.5%
浦4洞 2
 
6.5%
2洞 1
 
3.2%
五?洞 1
 
3.2%
千?2洞 1
 
3.2%
三?洞 1
 
3.2%
上岩洞 1
 
3.2%
Other values (12) 12
38.7%
2024-04-18T06:01:53.526828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
洞 31
28.7%
? 16
14.8%
三 8
 
7.4%
1 7
 
6.5%
2 4
 
3.7%
淸 3
 
2.8%
潭 3
 
2.8%
小 3
 
2.8%
公 3
 
2.8%
成 3
 
2.8%
Other values (23) 27
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
72.2%
Other Punctuation 16
 
14.8%
Decimal Number 14
 
13.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
洞 31
39.7%
三 8
 
10.3%
淸 3
 
3.8%
潭 3
 
3.8%
小 3
 
3.8%
公 3
 
3.8%
成 3
 
3.8%
浦 2
 
2.6%
쒧 2
 
2.6%
岩 2
 
2.6%
Other values (18) 18
23.1%
Decimal Number
ValueCountFrequency (%)
1 7
50.0%
2 4
28.6%
4 2
 
14.3%
3 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
? 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 76
70.4%
Common 30
 
27.8%
Hangul 2
 
1.9%

Most frequent character per script

Han
ValueCountFrequency (%)
洞 31
40.8%
三 8
 
10.5%
淸 3
 
3.9%
潭 3
 
3.9%
小 3
 
3.9%
公 3
 
3.9%
成 3
 
3.9%
浦 2
 
2.6%
岩 2
 
2.6%
上 1
 
1.3%
Other values (17) 17
22.4%
Common
ValueCountFrequency (%)
? 16
53.3%
1 7
23.3%
2 4
 
13.3%
4 2
 
6.7%
3 1
 
3.3%
Hangul
ValueCountFrequency (%)
쒧 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 76
70.4%
ASCII 30
 
27.8%
Hangul 2
 
1.9%

Most frequent character per block

CJK
ValueCountFrequency (%)
洞 31
40.8%
三 8
 
10.5%
淸 3
 
3.9%
潭 3
 
3.9%
小 3
 
3.9%
公 3
 
3.9%
成 3
 
3.9%
浦 2
 
2.6%
岩 2
 
2.6%
上 1
 
1.3%
Other values (17) 17
22.4%
ASCII
ValueCountFrequency (%)
? 16
53.3%
1 7
23.3%
2 4
 
13.3%
4 2
 
6.7%
3 1
 
3.3%
Hangul
ValueCountFrequency (%)
쒧 2
100.0%

대표전화
Text

MISSING 

Distinct75
Distinct (%)98.7%
Missing5
Missing (%)6.2%
Memory size780.0 B
2024-04-18T06:01:53.717479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length12
Mean length11.763158
Min length11

Characters and Unicode

Total characters894
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

Unique74 ?
Unique (%)97.4%

Sample

1st row02-789-5704
2nd row02-6002-6200
3rd row02-2198-3300
4th row02-3477-0033
5th row02-745-0181
ValueCountFrequency (%)
02-3210-2100 2
 
2.6%
02-789-5704 1
 
1.3%
02-929-2000 1
 
1.3%
02-2158-9000 1
 
1.3%
02-2077-9000-02-2077-9227 1
 
1.3%
02-2280-4292 1
 
1.3%
02-3217-1093 1
 
1.3%
02-330-6205 1
 
1.3%
02-396-2442 1
 
1.3%
02-6006-9114 1
 
1.3%
Other values (65) 65
85.5%
2024-04-18T06:01:54.014997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 209
23.4%
2 161
18.0%
- 155
17.3%
1 82
 
9.2%
5 57
 
6.4%
7 49
 
5.5%
3 48
 
5.4%
6 45
 
5.0%
4 43
 
4.8%
9 25
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 739
82.7%
Dash Punctuation 155
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
28.3%
2 161
21.8%
1 82
 
11.1%
5 57
 
7.7%
7 49
 
6.6%
3 48
 
6.5%
6 45
 
6.1%
4 43
 
5.8%
9 25
 
3.4%
8 20
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 894
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
23.4%
2 161
18.0%
- 155
17.3%
1 82
 
9.2%
5 57
 
6.4%
7 49
 
5.5%
3 48
 
5.4%
6 45
 
5.0%
4 43
 
4.8%
9 25
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
23.4%
2 161
18.0%
- 155
17.3%
1 82
 
9.2%
5 57
 
6.4%
7 49
 
5.5%
3 48
 
5.4%
6 45
 
5.0%
4 43
 
4.8%
9 25
 
2.8%

홈페이지주소
Text

MISSING 

Distinct80
Distinct (%)100.0%
Missing1
Missing (%)1.2%
Memory size780.0 B
2024-04-18T06:01:54.214540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length28
Mean length21.6625
Min length9

Characters and Unicode

Total characters1733
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st rowhttp://www.63convention.co.kr
2nd rowhttp://www.coexaqua.com
3rd rowhttp://www.topcloud.co.kr/
4th rowhttp://www.fradia.co.kr
5th rowhttp://www.kofum.com
ValueCountFrequency (%)
http://www.63convention.co.kr 1
 
1.2%
http://www.coexaqua.com 1
 
1.2%
http://www.lotteworld.com 1
 
1.2%
http://www.theraum.co.kr 1
 
1.2%
http://www.dugahun.com 1
 
1.2%
http://www.ddp.or.kr 1
 
1.2%
http://www.eninetree.com 1
 
1.2%
http://museum.go.kr 1
 
1.2%
http://www.ntok.go.kr 1
 
1.2%
http://www.ganaart.com 1
 
1.2%
Other values (70) 70
87.5%
2024-04-18T06:01:54.520056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 181
 
10.4%
w 178
 
10.3%
o 145
 
8.4%
t 143
 
8.3%
r 104
 
6.0%
/ 90
 
5.2%
e 89
 
5.1%
c 88
 
5.1%
a 88
 
5.1%
h 69
 
4.0%
Other values (28) 558
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1407
81.2%
Other Punctuation 309
 
17.8%
Decimal Number 13
 
0.8%
Dash Punctuation 2
 
0.1%
Space Separator 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 178
12.7%
o 145
 
10.3%
t 143
 
10.2%
r 104
 
7.4%
e 89
 
6.3%
c 88
 
6.3%
a 88
 
6.3%
h 69
 
4.9%
n 63
 
4.5%
k 62
 
4.4%
Other values (16) 378
26.9%
Decimal Number
ValueCountFrequency (%)
0 4
30.8%
1 3
23.1%
3 2
15.4%
6 2
15.4%
5 1
 
7.7%
7 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 181
58.6%
/ 90
29.1%
: 38
 
12.3%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1408
81.2%
Common 325
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 178
12.6%
o 145
 
10.3%
t 143
 
10.2%
r 104
 
7.4%
e 89
 
6.3%
c 88
 
6.2%
a 88
 
6.2%
h 69
 
4.9%
n 63
 
4.5%
k 62
 
4.4%
Other values (17) 379
26.9%
Common
ValueCountFrequency (%)
. 181
55.7%
/ 90
27.7%
: 38
 
11.7%
0 4
 
1.2%
1 3
 
0.9%
- 2
 
0.6%
3 2
 
0.6%
6 2
 
0.6%
5 1
 
0.3%
7 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 181
 
10.4%
w 178
 
10.3%
o 145
 
8.4%
t 143
 
8.3%
r 104
 
6.0%
/ 90
 
5.2%
e 89
 
5.1%
c 88
 
5.1%
a 88
 
5.1%
h 69
 
4.0%
Other values (28) 558
32.2%

Correlations

2024-04-18T06:01:54.609948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
키명칭행정 구행정 동대표전화홈페이지주소
키1.0001.0001.0001.0001.0001.000
명칭1.0001.0001.0001.0000.9981.000
행정 구1.0001.0001.0001.0001.0001.000
행정 동1.0001.0001.0001.0001.0001.000
대표전화1.0000.9981.0001.0001.0001.000
홈페이지주소1.0001.0001.0001.0001.0001.000
2024-04-18T06:01:54.685431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 시행정 구
행정 시1.0001.000
행정 구1.0001.000
2024-04-18T06:01:54.748949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 시행정 구
행정 시1.0001.000
행정 구1.0001.000

Missing values

2024-04-18T06:01:51.727386image/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.
2024-04-18T06:01:51.808837image/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

키명칭행정 시행정 구행정 동대표전화홈페이지주소
0BE_IW06-000163?展中心<NA><NA><NA>02-789-5704http://www.63convention.co.kr
1BE_IW06-0033COEX水族?<NA><NA><NA>02-6002-6200http://www.coexaqua.com
2BE_IW06-0034云塔<NA><NA><NA>02-2198-3300http://www.topcloud.co.kr/
3BE_IW06-0035普拉?首?特?市瑞草?쒧院洞02-3477-0033http://www.fradia.co.kr
4BE_IW06-0036??家具博物?<NA><NA><NA>02-745-0181http://www.kofum.com
5BE_IW06-0037??之家<NA><NA><NA>02-2270-1123http://www.koreahouse.or.kr
6BE_IW06-0038水木火<NA><NA><NA>070-7760-5392<NA>
7BE_IW06-0039aT 中心<NA><NA><NA>02-6300-1114atcenter.at.or.kr
8BE_IW06-0040SETEC ?展中心<NA><NA><NA>02-2222-3811setec.or.kr
9BE_IW06-0041Coex ?展中心首?特?市江南?三成1洞02-6000-1125www.coex.co.kr
키명칭행정 시행정 구행정 동대표전화홈페이지주소
71BE_IW06-0024AX-KOREA<NA><NA><NA>02-457-5114http://www.axhallkorea.com
72BE_IW06-0025阿斯?house<NA><NA><NA>02-450-6594http://www.sheratonwalkerhill.co.kr/
73BE_IW06-0026迎??<NA><NA><NA>02-2230-3552http://www.Shilla.net
74BE_IW06-0027??殿堂<NA><NA><NA>02-580-1305http://www.sac.or.kr
75BE_IW06-0028On River Station首?特?市江南?新沙洞02-2299-5577http://www.onriver.co.kr/
76BE_IW06-0029Walking On The Cloud云中漫步<NA><NA><NA>02-789-5904http://www.63restaurant.co.kr
77BE_IW06-0030yido Artce 陶?<NA><NA><NA>02-741-2411http://www.yido.kr
78BE_IW06-0031一民美??<NA><NA><NA>02-2020-2050http://www.ilmin.org
79BE_IW06-0032津津?漏<NA><NA><NA>02-3454-0633http://www.jinjinbara.com
80BE_IW06-0057首??思???酒店首?特?市江南??三1洞02-3451-8000ritzcarltonseoul.com