Overview

Dataset statistics

Number of variables6
Number of observations861
Missing cells571
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.5 KiB
Average record size in memory48.1 B

Variable types

Categorical1
Text4
DateTime1

Dataset

Description남양주시 신고체육시설업 정보 데이터로 구분(체육도장,당구장,스크린골프업장,수영장,승마장,종합체육시설 등), 상호, 주소(지번), 전화번호 등의 항목을 제공합니다.
Author경기도 남양주시
URLhttps://www.data.go.kr/data/3073527/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
시설주소(도로명) has 15 (1.7%) missing valuesMissing
시설전화번호 has 555 (64.5%) missing valuesMissing

Reproduction

Analysis started2024-03-14 20:40:02.374727
Analysis finished2024-03-14 20:40:04.149147
Duration1.77 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct13
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
체육도장업
240 
체력단련장업
148 
당구장업
143 
골프연습장업
111 
가상체험 체육시설업
106 
Other values (8)
113 

Length

Max length10
Median length7
Mean length5.7154472
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row가상체험 체육시설업
2nd row가상체험 체육시설업
3rd row가상체험 체육시설업
4th row가상체험 체육시설업
5th row가상체험 체육시설업

Common Values

ValueCountFrequency (%)
체육도장업 240
27.9%
체력단련장업 148
17.2%
당구장업 143
16.6%
골프연습장업 111
12.9%
가상체험 체육시설업 106
12.3%
체육교습업 63
 
7.3%
<NA> 15
 
1.7%
수영장업 11
 
1.3%
야구장업 9
 
1.0%
승마장업 6
 
0.7%
Other values (3) 9
 
1.0%

Length

2024-03-15T05:40:04.275940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
체육도장업 240
24.8%
체력단련장업 148
15.3%
당구장업 143
14.8%
골프연습장업 111
11.5%
가상체험 106
11.0%
체육시설업 106
11.0%
체육교습업 63
 
6.5%
na 15
 
1.6%
수영장업 11
 
1.1%
야구장업 9
 
0.9%
Other values (4) 15
 
1.6%

상호
Text

Distinct840
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
2024-03-15T05:40:05.221826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length8.6329849
Min length2

Characters and Unicode

Total characters7433
Distinct characters474
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique824 ?
Unique (%)95.7%

Sample

1st row동성 스크린 골프
2nd row골프존파크 월문점
3rd row골프연습장 월문점
4th row프렌즈스크린 덕소도곡점
5th row골프존파크 와부팔당역점
ValueCountFrequency (%)
태권도장 71
 
4.4%
용인대 40
 
2.5%
태권도 31
 
1.9%
당구장 24
 
1.5%
당구클럽 23
 
1.4%
골프 23
 
1.4%
스크린 22
 
1.4%
다산 20
 
1.3%
골프존 18
 
1.1%
아카데미 15
 
0.9%
Other values (955) 1311
82.0%
2024-03-15T05:40:06.555036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
737
 
9.9%
스 242
 
3.3%
도 228
 
3.1%
장 220
 
3.0%
프 214
 
2.9%
태 180
 
2.4%
골 178
 
2.4%
권 176
 
2.4%
구 143
 
1.9%
당 124
 
1.7%
Other values (464) 4991
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5948
80.0%
Space Separator 737
 
9.9%
Uppercase Letter 483
 
6.5%
Lowercase Letter 117
 
1.6%
Decimal Number 39
 
0.5%
Open Punctuation 38
 
0.5%
Close Punctuation 38
 
0.5%
Other Punctuation 28
 
0.4%
Dash Punctuation 4
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 242
 
4.1%
도 228
 
3.8%
장 220
 
3.7%
프 214
 
3.6%
태 180
 
3.0%
골 178
 
3.0%
권 176
 
3.0%
구 143
 
2.4%
당 124
 
2.1%
클 120
 
2.0%
Other values (396) 4123
69.3%
Uppercase Letter
ValueCountFrequency (%)
M 51
 
10.6%
T 40
 
8.3%
G 37
 
7.7%
S 35
 
7.2%
A 33
 
6.8%
J 28
 
5.8%
D 25
 
5.2%
O 24
 
5.0%
K 22
 
4.6%
B 20
 
4.1%
Other values (16) 168
34.8%
Lowercase Letter
ValueCountFrequency (%)
i 20
17.1%
n 13
11.1%
l 10
 
8.5%
r 8
 
6.8%
e 8
 
6.8%
a 7
 
6.0%
g 7
 
6.0%
o 7
 
6.0%
m 6
 
5.1%
t 5
 
4.3%
Other values (10) 26
22.2%
Decimal Number
ValueCountFrequency (%)
1 10
25.6%
2 7
17.9%
3 7
17.9%
0 5
12.8%
4 3
 
7.7%
7 2
 
5.1%
5 2
 
5.1%
6 1
 
2.6%
8 1
 
2.6%
9 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
& 11
39.3%
. 11
39.3%
' 3
 
10.7%
, 2
 
7.1%
· 1
 
3.6%
Open Punctuation
ValueCountFrequency (%)
( 37
97.4%
[ 1
 
2.6%
Close Punctuation
ValueCountFrequency (%)
) 37
97.4%
] 1
 
2.6%
Space Separator
ValueCountFrequency (%)
737
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5948
80.0%
Common 884
 
11.9%
Latin 601
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 242
 
4.1%
도 228
 
3.8%
장 220
 
3.7%
프 214
 
3.6%
태 180
 
3.0%
골 178
 
3.0%
권 176
 
3.0%
구 143
 
2.4%
당 124
 
2.1%
클 120
 
2.0%
Other values (396) 4123
69.3%
Latin
ValueCountFrequency (%)
M 51
 
8.5%
T 40
 
6.7%
G 37
 
6.2%
S 35
 
5.8%
A 33
 
5.5%
J 28
 
4.7%
D 25
 
4.2%
O 24
 
4.0%
K 22
 
3.7%
i 20
 
3.3%
Other values (37) 286
47.6%
Common
ValueCountFrequency (%)
737
83.4%
( 37
 
4.2%
) 37
 
4.2%
& 11
 
1.2%
. 11
 
1.2%
1 10
 
1.1%
2 7
 
0.8%
3 7
 
0.8%
0 5
 
0.6%
- 4
 
0.5%
Other values (11) 18
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5948
80.0%
ASCII 1483
 
20.0%
None 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
737
49.7%
M 51
 
3.4%
T 40
 
2.7%
G 37
 
2.5%
( 37
 
2.5%
) 37
 
2.5%
S 35
 
2.4%
A 33
 
2.2%
J 28
 
1.9%
D 25
 
1.7%
Other values (56) 423
28.5%
Hangul
ValueCountFrequency (%)
스 242
 
4.1%
도 228
 
3.8%
장 220
 
3.7%
프 214
 
3.6%
태 180
 
3.0%
골 178
 
3.0%
권 176
 
3.0%
구 143
 
2.4%
당 124
 
2.1%
클 120
 
2.0%
Other values (396) 4123
69.3%
None
ValueCountFrequency (%)
· 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

시설주소(지번)
Text

Distinct764
Distinct (%)88.8%
Missing1
Missing (%)0.1%
Memory size6.9 KiB
2024-03-15T05:40:07.896846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length44
Mean length26.930233
Min length17

Characters and Unicode

Total characters23160
Distinct characters329
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique689 ?
Unique (%)80.1%

Sample

1st row경기도 남양주시 와부읍 도곡리 1058-3 동성프라자
2nd row경기도 남양주시 와부읍 월문리 1057-19
3rd row경기도 남양주시 와부읍 월문리 1054-2
4th row경기도 남양주시 와부읍 도곡리 1062-18 강변스포렉스
5th row경기도 남양주시 와부읍 팔당리 624-1
ValueCountFrequency (%)
경기도 860
 
17.8%
남양주시 860
 
17.8%
다산동 166
 
3.4%
진접읍 118
 
2.4%
화도읍 100
 
2.1%
별내동 90
 
1.9%
오남읍 71
 
1.5%
와부읍 66
 
1.4%
호평동 62
 
1.3%
평내동 52
 
1.1%
Other values (1154) 2381
49.3%
2024-03-15T05:40:09.721837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4742
20.5%
도 990
 
4.3%
남 989
 
4.3%
양 924
 
4.0%
주 889
 
3.8%
시 878
 
3.8%
경 863
 
3.7%
기 861
 
3.7%
1 830
 
3.6%
- 672
 
2.9%
Other values (319) 10522
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12830
55.4%
Space Separator 4742
 
20.5%
Decimal Number 4689
 
20.2%
Dash Punctuation 672
 
2.9%
Other Punctuation 92
 
0.4%
Uppercase Letter 63
 
0.3%
Math Symbol 34
 
0.1%
Close Punctuation 15
 
0.1%
Open Punctuation 15
 
0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 990
 
7.7%
남 989
 
7.7%
양 924
 
7.2%
주 889
 
6.9%
시 878
 
6.8%
경 863
 
6.7%
기 861
 
6.7%
리 476
 
3.7%
동 462
 
3.6%
읍 413
 
3.2%
Other values (273) 5085
39.6%
Uppercase Letter
ValueCountFrequency (%)
B 14
22.2%
A 8
12.7%
E 7
11.1%
K 4
 
6.3%
N 4
 
6.3%
O 3
 
4.8%
S 3
 
4.8%
Z 3
 
4.8%
W 3
 
4.8%
Y 2
 
3.2%
Other values (10) 12
19.0%
Decimal Number
ValueCountFrequency (%)
1 830
17.7%
0 640
13.6%
2 538
11.5%
3 497
10.6%
6 495
10.6%
4 471
10.0%
5 414
8.8%
7 304
 
6.5%
8 272
 
5.8%
9 228
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 83
90.2%
@ 3
 
3.3%
. 2
 
2.2%
: 2
 
2.2%
/ 2
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
r 1
25.0%
e 1
25.0%
w 1
25.0%
o 1
25.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅱ 2
50.0%
Space Separator
ValueCountFrequency (%)
4742
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 672
100.0%
Math Symbol
ValueCountFrequency (%)
~ 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12830
55.4%
Common 10259
44.3%
Latin 71
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 990
 
7.7%
남 989
 
7.7%
양 924
 
7.2%
주 889
 
6.9%
시 878
 
6.8%
경 863
 
6.7%
기 861
 
6.7%
리 476
 
3.7%
동 462
 
3.6%
읍 413
 
3.2%
Other values (273) 5085
39.6%
Latin
ValueCountFrequency (%)
B 14
19.7%
A 8
 
11.3%
E 7
 
9.9%
K 4
 
5.6%
N 4
 
5.6%
O 3
 
4.2%
S 3
 
4.2%
Z 3
 
4.2%
W 3
 
4.2%
Y 2
 
2.8%
Other values (16) 20
28.2%
Common
ValueCountFrequency (%)
4742
46.2%
1 830
 
8.1%
- 672
 
6.6%
0 640
 
6.2%
2 538
 
5.2%
3 497
 
4.8%
6 495
 
4.8%
4 471
 
4.6%
5 414
 
4.0%
7 304
 
3.0%
Other values (10) 656
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12830
55.4%
ASCII 10326
44.6%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4742
45.9%
1 830
 
8.0%
- 672
 
6.5%
0 640
 
6.2%
2 538
 
5.2%
3 497
 
4.8%
6 495
 
4.8%
4 471
 
4.6%
5 414
 
4.0%
7 304
 
2.9%
Other values (34) 723
 
7.0%
Hangul
ValueCountFrequency (%)
도 990
 
7.7%
남 989
 
7.7%
양 924
 
7.2%
주 889
 
6.9%
시 878
 
6.8%
경 863
 
6.7%
기 861
 
6.7%
리 476
 
3.7%
동 462
 
3.6%
읍 413
 
3.2%
Other values (273) 5085
39.6%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅱ 2
50.0%

시설주소(도로명)
Text

MISSING 

Distinct832
Distinct (%)98.3%
Missing15
Missing (%)1.7%
Memory size6.9 KiB
2024-03-15T05:40:11.100785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length55
Mean length33.504728
Min length18

Characters and Unicode

Total characters28345
Distinct characters351
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique820 ?
Unique (%)96.9%

Sample

1st row경기도 남양주시 와부읍 덕소로 269, 동성프라자 4층
2nd row경기도 남양주시 와부읍 석실로 200, 2층
3rd row경기도 남양주시 와부읍 월문천로 171, 1층
4th row경기도 남양주시 와부읍 덕소로 278, 강변스포렉스 701, 702, 703호
5th row경기도 남양주시 와부읍 팔당로81번길 50, 2층
ValueCountFrequency (%)
경기도 846
 
14.9%
남양주시 846
 
14.9%
다산동 153
 
2.7%
2층 147
 
2.6%
진접읍 118
 
2.1%
3층 100
 
1.8%
화도읍 98
 
1.7%
별내동 91
 
1.6%
오남읍 71
 
1.3%
와부읍 66
 
1.2%
Other values (1234) 3128
55.2%
2024-03-15T05:40:12.896593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4836
 
17.1%
1 1042
 
3.7%
남 1006
 
3.5%
도 969
 
3.4%
경 940
 
3.3%
, 912
 
3.2%
양 890
 
3.1%
2 875
 
3.1%
주 870
 
3.1%
시 866
 
3.1%
Other values (341) 15139
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15789
55.7%
Decimal Number 5426
 
19.1%
Space Separator 4836
 
17.1%
Other Punctuation 920
 
3.2%
Open Punctuation 480
 
1.7%
Close Punctuation 480
 
1.7%
Dash Punctuation 237
 
0.8%
Uppercase Letter 108
 
0.4%
Math Symbol 59
 
0.2%
Letter Number 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 1006
 
6.4%
도 969
 
6.1%
경 940
 
6.0%
양 890
 
5.6%
주 870
 
5.5%
시 866
 
5.5%
로 864
 
5.5%
기 848
 
5.4%
층 496
 
3.1%
동 480
 
3.0%
Other values (290) 7560
47.9%
Uppercase Letter
ValueCountFrequency (%)
B 23
21.3%
C 11
 
10.2%
R 7
 
6.5%
E 7
 
6.5%
D 6
 
5.6%
A 6
 
5.6%
O 5
 
4.6%
M 5
 
4.6%
I 5
 
4.6%
N 5
 
4.6%
Other values (13) 28
25.9%
Decimal Number
ValueCountFrequency (%)
1 1042
19.2%
2 875
16.1%
0 722
13.3%
3 665
12.3%
4 468
8.6%
5 464
8.6%
6 346
 
6.4%
7 312
 
5.8%
8 278
 
5.1%
9 254
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 912
99.1%
: 2
 
0.2%
· 2
 
0.2%
/ 2
 
0.2%
. 1
 
0.1%
@ 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
o 1
25.0%
r 1
25.0%
e 1
25.0%
w 1
25.0%
Math Symbol
ValueCountFrequency (%)
~ 58
98.3%
+ 1
 
1.7%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
66.7%
Ⅰ 2
33.3%
Space Separator
ValueCountFrequency (%)
4836
100.0%
Open Punctuation
ValueCountFrequency (%)
( 480
100.0%
Close Punctuation
ValueCountFrequency (%)
) 480
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15789
55.7%
Common 12438
43.9%
Latin 118
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 1006
 
6.4%
도 969
 
6.1%
경 940
 
6.0%
양 890
 
5.6%
주 870
 
5.5%
시 866
 
5.5%
로 864
 
5.5%
기 848
 
5.4%
층 496
 
3.1%
동 480
 
3.0%
Other values (290) 7560
47.9%
Latin
ValueCountFrequency (%)
B 23
19.5%
C 11
 
9.3%
R 7
 
5.9%
E 7
 
5.9%
D 6
 
5.1%
A 6
 
5.1%
O 5
 
4.2%
M 5
 
4.2%
I 5
 
4.2%
N 5
 
4.2%
Other values (19) 38
32.2%
Common
ValueCountFrequency (%)
4836
38.9%
1 1042
 
8.4%
, 912
 
7.3%
2 875
 
7.0%
0 722
 
5.8%
3 665
 
5.3%
( 480
 
3.9%
) 480
 
3.9%
4 468
 
3.8%
5 464
 
3.7%
Other values (12) 1494
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15789
55.7%
ASCII 12548
44.3%
Number Forms 6
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4836
38.5%
1 1042
 
8.3%
, 912
 
7.3%
2 875
 
7.0%
0 722
 
5.8%
3 665
 
5.3%
( 480
 
3.8%
) 480
 
3.8%
4 468
 
3.7%
5 464
 
3.7%
Other values (38) 1604
 
12.8%
Hangul
ValueCountFrequency (%)
남 1006
 
6.4%
도 969
 
6.1%
경 940
 
6.0%
양 890
 
5.6%
주 870
 
5.5%
시 866
 
5.5%
로 864
 
5.5%
기 848
 
5.4%
층 496
 
3.1%
동 480
 
3.0%
Other values (290) 7560
47.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
66.7%
Ⅰ 2
33.3%
None
ValueCountFrequency (%)
· 2
100.0%

시설전화번호
Text

MISSING 

Distinct301
Distinct (%)98.4%
Missing555
Missing (%)64.5%
Memory size6.9 KiB
2024-03-15T05:40:13.843950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.009804
Min length9

Characters and Unicode

Total characters3675
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

Unique298 ?
Unique (%)97.4%

Sample

1st row031-576-1476
2nd row031-573-7050
3rd row031-529-6643
4th row031-527-4449
5th row031-575-1918
ValueCountFrequency (%)
031-590-4511 4
 
1.3%
031-529-1600 2
 
0.7%
031-595-0536 2
 
0.7%
031-592-5209 1
 
0.3%
031-595-7854 1
 
0.3%
031-528-8353 1
 
0.3%
031-575-8181 1
 
0.3%
031-572-5227 1
 
0.3%
031-575-0994 1
 
0.3%
031-572-7515 1
 
0.3%
Other values (291) 291
95.1%
2024-03-15T05:40:15.137628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 610
16.6%
5 509
13.9%
1 502
13.7%
0 485
13.2%
3 446
12.1%
7 273
7.4%
9 224
 
6.1%
2 202
 
5.5%
8 147
 
4.0%
4 141
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3065
83.4%
Dash Punctuation 610
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 509
16.6%
1 502
16.4%
0 485
15.8%
3 446
14.6%
7 273
8.9%
9 224
7.3%
2 202
 
6.6%
8 147
 
4.8%
4 141
 
4.6%
6 136
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 610
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 610
16.6%
5 509
13.9%
1 502
13.7%
0 485
13.2%
3 446
12.1%
7 273
7.4%
9 224
 
6.1%
2 202
 
5.5%
8 147
 
4.0%
4 141
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 610
16.6%
5 509
13.9%
1 502
13.7%
0 485
13.2%
3 446
12.1%
7 273
7.4%
9 224
 
6.1%
2 202
 
5.5%
8 147
 
4.0%
4 141
 
3.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
Minimum2024-01-27 00:00:00
Maximum2024-01-27 00:00:00
2024-03-15T05:40:15.493411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:40:15.796944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2024-03-15T05:40:03.511103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:40:03.783517image/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.
2024-03-15T05:40:03.988565image/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

업종상호시설주소(지번)시설주소(도로명)시설전화번호데이터기준일자
0가상체험 체육시설업동성 스크린 골프경기도 남양주시 와부읍 도곡리 1058-3 동성프라자경기도 남양주시 와부읍 덕소로 269, 동성프라자 4층<NA>2024-01-27
1가상체험 체육시설업골프존파크 월문점경기도 남양주시 와부읍 월문리 1057-19경기도 남양주시 와부읍 석실로 200, 2층<NA>2024-01-27
2가상체험 체육시설업골프연습장 월문점경기도 남양주시 와부읍 월문리 1054-2경기도 남양주시 와부읍 월문천로 171, 1층<NA>2024-01-27
3가상체험 체육시설업프렌즈스크린 덕소도곡점경기도 남양주시 와부읍 도곡리 1062-18 강변스포렉스경기도 남양주시 와부읍 덕소로 278, 강변스포렉스 701, 702, 703호<NA>2024-01-27
4가상체험 체육시설업골프존파크 와부팔당역점경기도 남양주시 와부읍 팔당리 624-1경기도 남양주시 와부읍 팔당로81번길 50, 2층<NA>2024-01-27
5가상체험 체육시설업팔당스크린골프경기도 남양주시 와부읍 팔당리 624-1경기도 남양주시 와부읍 팔당로81번길 50, 3층<NA>2024-01-27
6가상체험 체육시설업비케이스크린경기도 남양주시 와부읍 월문리 184-3경기도 남양주시 와부읍 수레로 633, 2층031-576-14762024-01-27
7가상체험 체육시설업J(제이)골프존경기도 남양주시 진접읍 장현리 33-2경기도 남양주시 진접읍 금강로 1605, 4층<NA>2024-01-27
8가상체험 체육시설업비앤비골프존파크, 골프아카데미경기도 남양주시 진접읍 연평리 195-6 1동경기도 남양주시 진접읍 양진로 956, 1동 1층031-573-70502024-01-27
9가상체험 체육시설업장현 GTS 골프아카데미경기도 남양주시 진접읍 장현리 347-28경기도 남양주시 진접읍 장현천로 18-8<NA>2024-01-27
업종상호시설주소(지번)시설주소(도로명)시설전화번호데이터기준일자
851<NA>아센 스포츠센타경기도 남양주시 평내동 579 하나프라자 8층경기도 남양주시 경춘로 1260 (평내동)<NA>2024-01-27
852<NA>타이거 에어로빅경기도 남양주시 호평동 660-1<NA><NA>2024-01-27
853<NA>인창 체육관경기도 남양주시 호평동 674-1 호평아카데미 403,404호<NA><NA>2024-01-27
854<NA>리치 플러스 스포츠센터경기도 남양주시 평내동 140경기도 남양주시 경춘로1350번길 35 (평내동)<NA>2024-01-27
855<NA>상용볼링센타경기도 남양주시 금곡동 402-4경기도 남양주시 사릉로 20 (금곡동)031-591-00612024-01-27
856<NA>남양주체육문화센터 테니스장경기도 남양주시 이패동 산 87경기도 남양주시 강변북로 842 (이패동)031-590-45112024-01-27
857<NA>영에어로빅경기도 남양주시 금곡동 154-14<NA>031-592-52092024-01-27
858<NA>남양주체육문화센터경기도 남양주시 이패동 산 87경기도 남양주시 강변북로 842 (이패동)031-590-45112024-01-27
859<NA>청학째즈에어로빅경기도 남양주시 별내면 청학리 1-4 평화상가 3층<NA>031-848-19952024-01-27
860<NA>김지영 에어로빅경기도 남양주시 별내면 청학리 417-1 행운빌딩 302호경기도 남양주시 별내면 청학로68번길 5-25<NA>2024-01-27