Overview

Dataset statistics

Number of variables10
Number of observations1017
Missing cells1759
Missing cells (%)17.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.6 KiB
Average record size in memory82.1 B

Variable types

Text4
Categorical4
Numeric2

Dataset

Description키,명칭,행정 시,행정 구,행정 동,전화번호,홈페이지주소,HTML사용여부,X 좌표,Y 좌표
Author종로구
URLhttps://data.seoul.go.kr/dataList/OA-13046/S/1/datasetView.do

Alerts

HTML사용여부 has constant value ""Constant
행정 구 is highly overall correlated with 행정 시 and 1 other fieldsHigh correlation
행정 시 is highly overall correlated with X 좌표 and 3 other fieldsHigh correlation
행정 동 is highly overall correlated with 행정 시 and 1 other fieldsHigh correlation
X 좌표 is highly overall correlated with 행정 시High correlation
Y 좌표 is highly overall correlated with 행정 시High correlation
행정 구 is highly imbalanced (60.6%)Imbalance
전화번호 has 651 (64.0%) missing valuesMissing
홈페이지주소 has 694 (68.2%) missing valuesMissing
X 좌표 has 207 (20.4%) missing valuesMissing
Y 좌표 has 207 (20.4%) missing valuesMissing
Y 좌표 is highly skewed (γ1 = -20.10587948)Skewed
키 has unique valuesUnique

Reproduction

Analysis started2024-04-06 11:30:35.093218
Analysis finished2024-04-06 11:30:37.603759
Duration2.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct1017
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
2024-04-06T20:30:37.895632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12204
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

Unique1017 ?
Unique (%)100.0%

Sample

1st rowBE_IW10-0963
2nd rowBE_IW10-0964
3rd rowBE_IW10-0965
4th rowBE_IW10-0966
5th rowBE_IW10-0967
ValueCountFrequency (%)
be_iw10-0963 1
 
0.1%
be_iw10-0673 1
 
0.1%
be_iw10-0675 1
 
0.1%
be_iw10-0661 1
 
0.1%
be_iw10-0662 1
 
0.1%
be_iw10-0663 1
 
0.1%
be_iw10-0664 1
 
0.1%
be_iw10-0665 1
 
0.1%
be_iw10-0666 1
 
0.1%
be_iw10-0667 1
 
0.1%
Other values (1007) 1007
99.0%
2024-04-06T20:30:38.594181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2343
19.2%
1 1345
11.0%
B 1017
8.3%
E 1017
8.3%
_ 1017
8.3%
I 1017
8.3%
W 1017
8.3%
- 1017
8.3%
6 302
 
2.5%
3 302
 
2.5%
Other values (6) 1810
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6102
50.0%
Uppercase Letter 4068
33.3%
Connector Punctuation 1017
 
8.3%
Dash Punctuation 1017
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2343
38.4%
1 1345
22.0%
6 302
 
4.9%
3 302
 
4.9%
7 302
 
4.9%
5 302
 
4.9%
2 302
 
4.9%
4 302
 
4.9%
9 301
 
4.9%
8 301
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
B 1017
25.0%
E 1017
25.0%
I 1017
25.0%
W 1017
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1017
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1017
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8136
66.7%
Latin 4068
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2343
28.8%
1 1345
16.5%
_ 1017
12.5%
- 1017
12.5%
6 302
 
3.7%
3 302
 
3.7%
7 302
 
3.7%
5 302
 
3.7%
2 302
 
3.7%
4 302
 
3.7%
Other values (2) 602
 
7.4%
Latin
ValueCountFrequency (%)
B 1017
25.0%
E 1017
25.0%
I 1017
25.0%
W 1017
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2343
19.2%
1 1345
11.0%
B 1017
8.3%
E 1017
8.3%
_ 1017
8.3%
I 1017
8.3%
W 1017
8.3%
- 1017
8.3%
6 302
 
2.5%
3 302
 
2.5%
Other values (6) 1810
14.8%

명칭
Text

Distinct966
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
2024-04-06T20:30:39.162448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length114
Median length74
Mean length28.351032
Min length3

Characters and Unicode

Total characters28833
Distinct characters79
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique924 ?
Unique (%)90.9%

Sample

1st rowArario Museum In Space
2nd rowgahoemuseum
3rd rowDonglim Knot Workshop
4th rowMyeongin Museum
5th rowWhanki Museum
ValueCountFrequency (%)
of 396
 
9.4%
site 189
 
4.5%
the 128
 
3.0%
museum 108
 
2.6%
house 75
 
1.8%
art 65
 
1.5%
gallery 58
 
1.4%
theater 54
 
1.3%
seoul 48
 
1.1%
in 47
 
1.1%
Other values (1615) 3032
72.2%
2024-04-06T20:30:40.225318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3215
 
11.2%
e 2616
 
9.1%
o 2280
 
7.9%
n 2178
 
7.6%
a 1937
 
6.7%
i 1312
 
4.6%
g 1287
 
4.5%
t 1198
 
4.2%
u 1166
 
4.0%
r 1050
 
3.6%
Other values (69) 10594
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21406
74.2%
Uppercase Letter 3323
 
11.5%
Space Separator 3215
 
11.2%
Decimal Number 260
 
0.9%
Other Punctuation 243
 
0.8%
Dash Punctuation 141
 
0.5%
Close Punctuation 115
 
0.4%
Open Punctuation 115
 
0.4%
Math Symbol 8
 
< 0.1%
Connector Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2616
12.2%
o 2280
10.7%
n 2178
 
10.2%
a 1937
 
9.0%
i 1312
 
6.1%
g 1287
 
6.0%
t 1198
 
5.6%
u 1166
 
5.4%
r 1050
 
4.9%
s 898
 
4.2%
Other values (16) 5484
25.6%
Uppercase Letter
ValueCountFrequency (%)
S 595
17.9%
M 283
 
8.5%
C 264
 
7.9%
G 257
 
7.7%
H 226
 
6.8%
T 203
 
6.1%
P 184
 
5.5%
B 160
 
4.8%
D 160
 
4.8%
A 148
 
4.5%
Other values (15) 843
25.4%
Decimal Number
ValueCountFrequency (%)
1 67
25.8%
2 59
22.7%
0 54
20.8%
9 18
 
6.9%
3 17
 
6.5%
4 11
 
4.2%
5 9
 
3.5%
7 9
 
3.5%
6 8
 
3.1%
8 8
 
3.1%
Other Punctuation
ValueCountFrequency (%)
' 144
59.3%
, 66
27.2%
. 10
 
4.1%
? 10
 
4.1%
& 9
 
3.7%
/ 2
 
0.8%
! 2
 
0.8%
Math Symbol
ValueCountFrequency (%)
< 3
37.5%
> 3
37.5%
~ 2
25.0%
Close Punctuation
ValueCountFrequency (%)
) 77
67.0%
] 38
33.0%
Open Punctuation
ValueCountFrequency (%)
( 77
67.0%
[ 38
33.0%
Space Separator
ValueCountFrequency (%)
3215
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 141
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24729
85.8%
Common 4104
 
14.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2616
 
10.6%
o 2280
 
9.2%
n 2178
 
8.8%
a 1937
 
7.8%
i 1312
 
5.3%
g 1287
 
5.2%
t 1198
 
4.8%
u 1166
 
4.7%
r 1050
 
4.2%
s 898
 
3.6%
Other values (41) 8807
35.6%
Common
ValueCountFrequency (%)
3215
78.3%
' 144
 
3.5%
- 141
 
3.4%
) 77
 
1.9%
( 77
 
1.9%
1 67
 
1.6%
, 66
 
1.6%
2 59
 
1.4%
0 54
 
1.3%
] 38
 
0.9%
Other values (18) 166
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3215
 
11.2%
e 2616
 
9.1%
o 2280
 
7.9%
n 2178
 
7.6%
a 1937
 
6.7%
i 1312
 
4.6%
g 1287
 
4.5%
t 1198
 
4.2%
u 1166
 
4.0%
r 1050
 
3.6%
Other values (69) 10594
36.7%

행정 시
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
Seoul
821 
<NA>
196 

Length

Max length5
Median length5
Mean length4.8072763
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Seoul 821
80.7%
<NA> 196
 
19.3%

Length

2024-04-06T20:30:40.483660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:30:40.683017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 821
80.7%
na 196
 
19.3%

행정 구
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
Jongno-gu
811 
<NA>
196 
Jung-gu
 
9
Yongsan-gu
 
1

Length

Max length10
Median length9
Mean length8.0196657
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowJongno-gu
2nd rowJongno-gu
3rd rowJongno-gu
4th rowJongno-gu
5th rowJongno-gu

Common Values

ValueCountFrequency (%)
Jongno-gu 811
79.7%
<NA> 196
 
19.3%
Jung-gu 9
 
0.9%
Yongsan-gu 1
 
0.1%

Length

2024-04-06T20:30:40.859947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:30:41.107147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jongno-gu 811
79.7%
na 196
 
19.3%
jung-gu 9
 
0.9%
yongsan-gu 1
 
0.1%

행정 동
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
Jongno1.2.3.4ga-dong
218 
<NA>
196 
Ihwa-dong
113 
Sajik-dong
85 
Gahoe-dong
71 
Other values (18)
334 

Length

Max length20
Median length16
Mean length12.019666
Min length4

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowGahoe-dong
2nd rowGahoe-dong
3rd rowGahoe-dong
4th rowGahoe-dong
5th rowBuam-dong

Common Values

ValueCountFrequency (%)
Jongno1.2.3.4ga-dong 218
21.4%
<NA> 196
19.3%
Ihwa-dong 113
11.1%
Sajik-dong 85
 
8.4%
Gahoe-dong 71
 
7.0%
Hyehwa-dong 67
 
6.6%
Samcheong-dong 65
 
6.4%
Cheongunhyoja-dong 63
 
6.2%
Buam-dong 41
 
4.0%
Pyeongchang-dong 37
 
3.6%
Other values (13) 61
 
6.0%

Length

2024-04-06T20:30:41.334228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jongno1.2.3.4ga-dong 218
21.4%
na 196
19.3%
ihwa-dong 113
11.1%
sajik-dong 85
 
8.4%
gahoe-dong 71
 
7.0%
hyehwa-dong 67
 
6.6%
samcheong-dong 65
 
6.4%
cheongunhyoja-dong 63
 
6.2%
buam-dong 41
 
4.0%
pyeongchang-dong 37
 
3.6%
Other values (13) 61
 
6.0%

전화번호
Text

MISSING 

Distinct313
Distinct (%)85.5%
Missing651
Missing (%)64.0%
Memory size8.1 KiB
2024-04-06T20:30:41.817519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length11
Mean length11.734973
Min length6

Characters and Unicode

Total characters4295
Distinct characters15
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

Unique273 ?
Unique (%)74.6%

Sample

1st row02-736-5700
2nd row02-3673-2778
3rd row02-766-0272
4th row02-391-7701
5th row02-395-3222
ValueCountFrequency (%)
02-766-6494 4
 
1.1%
02-395-3222 3
 
0.8%
02-396-9277 3
 
0.8%
02-766-3315 3
 
0.8%
02-730-1610 3
 
0.8%
02-2148-4175 3
 
0.8%
02-733-8945 3
 
0.8%
02-766-6000 3
 
0.8%
02-3675-3737 3
 
0.8%
02-766-0272 3
 
0.8%
Other values (307) 343
91.7%
2024-04-06T20:30:42.474413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 735
17.1%
0 678
15.8%
2 619
14.4%
7 504
11.7%
3 359
8.4%
6 267
 
6.2%
4 267
 
6.2%
1 267
 
6.2%
5 200
 
4.7%
8 170
 
4.0%
Other values (5) 229
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3494
81.4%
Dash Punctuation 735
 
17.1%
Math Symbol 37
 
0.9%
Other Punctuation 16
 
0.4%
Space Separator 13
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 678
19.4%
2 619
17.7%
7 504
14.4%
3 359
10.3%
6 267
 
7.6%
4 267
 
7.6%
1 267
 
7.6%
5 200
 
5.7%
8 170
 
4.9%
9 163
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
? 1
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 735
100.0%
Math Symbol
ValueCountFrequency (%)
~ 37
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4295
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 735
17.1%
0 678
15.8%
2 619
14.4%
7 504
11.7%
3 359
8.4%
6 267
 
6.2%
4 267
 
6.2%
1 267
 
6.2%
5 200
 
4.7%
8 170
 
4.0%
Other values (5) 229
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 735
17.1%
0 678
15.8%
2 619
14.4%
7 504
11.7%
3 359
8.4%
6 267
 
6.2%
4 267
 
6.2%
1 267
 
6.2%
5 200
 
4.7%
8 170
 
4.0%
Other values (5) 229
 
5.3%

홈페이지주소
Text

MISSING 

Distinct268
Distinct (%)83.0%
Missing694
Missing (%)68.2%
Memory size8.1 KiB
2024-04-06T20:30:42.894848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length121
Median length64
Mean length29.495356
Min length11

Characters and Unicode

Total characters9527
Distinct characters64
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

Unique225 ?
Unique (%)69.7%

Sample

1st rowhttp://www.arariomuseum.org/main.php
2nd rowhtp://www.whankimuseum.org
3rd rowhttp://www.zahamuseum.com
4th rowhttp://www.yoogeum.org
5th rowhttp://www.shuim.org
ValueCountFrequency (%)
http://www.ijongno.co.kr 5
 
1.5%
http://www.mokkumto.com 3
 
0.9%
http://www.sejongpac.or.kr 3
 
0.9%
http://bukchon.seoul.go.kr 3
 
0.9%
http://www.wjmuseum.com 3
 
0.9%
http://www.kokdumuseum.com 3
 
0.9%
http://www.medicalmuseum.org 3
 
0.9%
http://www.hansangsoo.com 3
 
0.9%
http://www.zipul.co.kr 3
 
0.9%
http://www.dymuseum.com 3
 
0.9%
Other values (257) 292
90.1%
2024-04-06T20:30:43.512595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 838
 
8.8%
/ 831
 
8.7%
w 795
 
8.3%
t 791
 
8.3%
o 598
 
6.3%
r 460
 
4.8%
m 441
 
4.6%
h 420
 
4.4%
a 409
 
4.3%
e 387
 
4.1%
Other values (54) 3557
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7271
76.3%
Other Punctuation 1988
 
20.9%
Decimal Number 151
 
1.6%
Uppercase Letter 31
 
0.3%
Math Symbol 28
 
0.3%
Connector Punctuation 26
 
0.3%
Space Separator 17
 
0.2%
Dash Punctuation 10
 
0.1%
Other Letter 5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 795
 
10.9%
t 791
 
10.9%
o 598
 
8.2%
r 460
 
6.3%
m 441
 
6.1%
h 420
 
5.8%
a 409
 
5.6%
e 387
 
5.3%
p 381
 
5.2%
c 345
 
4.7%
Other values (16) 2244
30.9%
Uppercase Letter
ValueCountFrequency (%)
C 8
25.8%
L 5
16.1%
S 3
 
9.7%
H 3
 
9.7%
M 2
 
6.5%
G 2
 
6.5%
N 2
 
6.5%
T 1
 
3.2%
I 1
 
3.2%
F 1
 
3.2%
Other values (3) 3
 
9.7%
Decimal Number
ValueCountFrequency (%)
0 56
37.1%
1 26
17.2%
2 15
 
9.9%
6 12
 
7.9%
5 10
 
6.6%
4 10
 
6.6%
7 8
 
5.3%
9 7
 
4.6%
3 6
 
4.0%
8 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 838
42.2%
/ 831
41.8%
: 292
 
14.7%
& 15
 
0.8%
? 12
 
0.6%
Other Letter
ValueCountFrequency (%)
켓 1
20.0%
티 1
20.0%
로 1
20.0%
학 1
20.0%
대 1
20.0%
Math Symbol
ValueCountFrequency (%)
= 27
96.4%
~ 1
 
3.6%
Connector Punctuation
ValueCountFrequency (%)
_ 26
100.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7302
76.6%
Common 2220
 
23.3%
Hangul 5
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 795
 
10.9%
t 791
 
10.8%
o 598
 
8.2%
r 460
 
6.3%
m 441
 
6.0%
h 420
 
5.8%
a 409
 
5.6%
e 387
 
5.3%
p 381
 
5.2%
c 345
 
4.7%
Other values (29) 2275
31.2%
Common
ValueCountFrequency (%)
. 838
37.7%
/ 831
37.4%
: 292
 
13.2%
0 56
 
2.5%
= 27
 
1.2%
_ 26
 
1.2%
1 26
 
1.2%
17
 
0.8%
2 15
 
0.7%
& 15
 
0.7%
Other values (10) 77
 
3.5%
Hangul
ValueCountFrequency (%)
켓 1
20.0%
티 1
20.0%
로 1
20.0%
학 1
20.0%
대 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9522
99.9%
Hangul 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 838
 
8.8%
/ 831
 
8.7%
w 795
 
8.3%
t 791
 
8.3%
o 598
 
6.3%
r 460
 
4.8%
m 441
 
4.6%
h 420
 
4.4%
a 409
 
4.3%
e 387
 
4.1%
Other values (49) 3552
37.3%
Hangul
ValueCountFrequency (%)
켓 1
20.0%
티 1
20.0%
로 1
20.0%
학 1
20.0%
대 1
20.0%

HTML사용여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
Y
1017 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Y 1017
100.0%

Length

2024-04-06T20:30:43.768352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:30:43.920062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 1017
100.0%

X 좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct560
Distinct (%)69.1%
Missing207
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean126.98635
Minimum126.95502
Maximum127.27336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2024-04-06T20:30:44.105964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.95502
5-th percentile126.96292
Q1126.97677
median126.98524
Q3126.99891
95-th percentile127.00493
Maximum127.27336
Range0.3183397
Interquartile range (IQR)0.0221404

Descriptive statistics

Standard deviation0.017113246
Coefficient of variation (CV)0.00013476446
Kurtosis96.530878
Mean126.98635
Median Absolute Deviation (MAD)0.0101675
Skewness5.8053225
Sum102858.94
Variance0.00029286319
MonotonicityNot monotonic
2024-04-06T20:30:44.674855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.979642 14
 
1.4%
126.9913206 14
 
1.4%
126.9767743 11
 
1.1%
126.9950431 10
 
1.0%
126.9580981 7
 
0.7%
127.0037106 7
 
0.7%
126.9864633 6
 
0.6%
126.9823368 5
 
0.5%
126.9831241 5
 
0.5%
127.002897 5
 
0.5%
Other values (550) 726
71.4%
(Missing) 207
 
20.4%
ValueCountFrequency (%)
126.9550229 1
 
0.1%
126.955532 1
 
0.1%
126.9556779 1
 
0.1%
126.9556804 2
 
0.2%
126.9560406 2
 
0.2%
126.9562643 1
 
0.1%
126.956449 1
 
0.1%
126.9570008 1
 
0.1%
126.9574 1
 
0.1%
126.9580981 7
0.7%
ValueCountFrequency (%)
127.2733626 1
 
0.1%
127.0187664 1
 
0.1%
127.0183336 4
0.4%
127.0176267 1
 
0.1%
127.0173078 1
 
0.1%
127.0167666 1
 
0.1%
127.0162555 1
 
0.1%
127.0154862 1
 
0.1%
127.014448 1
 
0.1%
127.0141832 1
 
0.1%

Y 좌표
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct560
Distinct (%)69.1%
Missing207
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean37.579837
Minimum37.015224
Maximum37.631481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2024-04-06T20:30:44.916794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.015224
5-th percentile37.569884
Q137.573901
median37.579719
Q337.583683
95-th percentile37.601344
Maximum37.631481
Range0.6162566
Interquartile range (IQR)0.009782

Descriptive statistics

Standard deviation0.022238075
Coefficient of variation (CV)0.00059175548
Kurtosis514.82002
Mean37.579837
Median Absolute Deviation (MAD)0.0050142
Skewness-20.105879
Sum30439.668
Variance0.00049453197
MonotonicityNot monotonic
2024-04-06T20:30:45.189327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.573033 14
 
1.4%
37.5827758 14
 
1.4%
37.5866061 11
 
1.1%
37.5805909 10
 
1.0%
37.5836832 7
 
0.7%
37.5760084 7
 
0.7%
37.5843579 6
 
0.6%
37.5739012 5
 
0.5%
37.5807844 5
 
0.5%
37.5799333 5
 
0.5%
Other values (550) 726
71.4%
(Missing) 207
 
20.4%
ValueCountFrequency (%)
37.0152241 1
0.1%
37.5343949 1
0.1%
37.5599933 1
0.1%
37.562758 1
0.1%
37.5627724 1
0.1%
37.564438 1
0.1%
37.56661 1
0.1%
37.5673135 1
0.1%
37.5675045 2
0.2%
37.567967 1
0.1%
ValueCountFrequency (%)
37.6314807 1
0.1%
37.6312731 1
0.1%
37.6269949 2
0.2%
37.6238013 1
0.1%
37.6227107 1
0.1%
37.6179 1
0.1%
37.6143517 1
0.1%
37.6143202 1
0.1%
37.6134542 1
0.1%
37.6130429 2
0.2%

Interactions

2024-04-06T20:30:36.579588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:30:36.219079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:30:36.748047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:30:36.414054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T20:30:45.407958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 구행정 동X 좌표Y 좌표
행정 구1.0001.0000.0000.644
행정 동1.0001.0000.7450.554
X 좌표0.0000.7451.0000.676
Y 좌표0.6440.5540.6761.000
2024-04-06T20:30:45.570797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 구행정 시행정 동
행정 구1.0001.0000.988
행정 시1.0001.0001.000
행정 동0.9881.0001.000
2024-04-06T20:30:45.734906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
X 좌표Y 좌표행정 시행정 구행정 동
X 좌표1.000-0.0411.0000.0000.497
Y 좌표-0.0411.0001.0000.3070.343
행정 시1.0001.0001.0001.0001.000
행정 구0.0000.3071.0001.0000.988
행정 동0.4970.3431.0000.9881.000

Missing values

2024-04-06T20:30:36.990731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T20:30:37.248515image/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.
2024-04-06T20:30:37.459269image/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 동전화번호홈페이지주소HTML사용여부X 좌표Y 좌표
0BE_IW10-0963Arario Museum In SpaceSeoulJongno-guGahoe-dong02-736-5700http://www.arariomuseum.org/main.phpY126.98835437.577859
1BE_IW10-0964gahoemuseumSeoulJongno-guGahoe-dong<NA><NA>Y126.98566237.582137
2BE_IW10-0965Donglim Knot WorkshopSeoulJongno-guGahoe-dong02-3673-2778<NA>Y126.98562137.581792
3BE_IW10-0966Myeongin MuseumSeoulJongno-guGahoe-dong02-766-0272<NA>Y126.98600637.577523
4BE_IW10-0967Whanki MuseumSeoulJongno-guBuam-dong02-391-7701htp://www.whankimuseum.orgY126.96643637.594407
5BE_IW10-0968Zaha MuseumSeoulJongno-guBuam-dong02-395-3222http://www.zahamuseum.comY126.96033537.589389
6BE_IW10-0969YooGeum MuseumSeoulJongno-guBuam-dong02-394-3451http://www.yoogeum.orgY126.96265337.593671
7BE_IW10-0970Seoul MuseumSeoulJongno-guBuam-dong02-395-0100<NA>Y126.96235737.595034
8BE_IW10-0971shuim MuseumSeoulJongno-guBuam-dong02-396-9277http://www.shuim.orgY126.9556837.599502
9BE_IW10-0972Kokdu MuseumSeoulJongno-guIhwa-dong02-766-3315http://www.kokdumuseum.comY127.00371137.583683
키명칭행정 시행정 구행정 동전화번호홈페이지주소HTML사용여부X 좌표Y 좌표
1007BE_IW10-0410Changgyeonggung PalaceSeoulJongno-guJongno1.2.3.4ga-dong<NA>http://cgg.cha.go.krY126.99504337.580591
1008BE_IW10-0411Deoksugung PalaceSeoulJung-guSogong-dong<NA><NA>Y126.97964237.573033
1009BE_IW10-0412Donhwamun GateSeoulJongno-guJongno1.2.3.4ga-dong<NA><NA>Y126.99132137.582776
1010BE_IW10-0413Changdeokgung Palace Dae Jo JunSeoulJongno-guJongno1.2.3.4ga-dong<NA><NA>Y126.99132137.582776
1011BE_IW10-0414Unhyeongung PalaceSeoulJongno-guJongno1.2.3.4ga-dong<NA>http://www.unhyeongung.or.krY126.98712537.576126
1012BE_IW10-0415Changdeokgung Palace Dae Jo JunSeoulJongno-guJongno1.2.3.4ga-dong<NA><NA>Y126.99132137.582776
1013BE_IW10-0416Changdeokgung HuijeongdangSeoulJongno-guJongno1.2.3.4ga-dong<NA><NA>Y126.99132137.582776
1014BE_IW10-0417Changdeokgung SeonjeongjeonSeoulJongno-guJongno1.2.3.4ga-dong<NA><NA>Y126.99132137.582776
1015BE_IW10-0418Changdeokgung InjeongjeonSeoulJongno-guJongno1.2.3.4ga-dong<NA><NA>Y126.99132137.582776
1016BE_IW10-0456YMCA Center<NA><NA><NA><NA><NA>Y<NA><NA>