Overview

Dataset statistics

Number of variables10
Number of observations1017
Missing cells1763
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-13047/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 12:33:49.582569
Analysis finished2024-04-06 12:33:53.709679
Duration4.13 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
2024-04-06T21:33:54.140746image/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_IW10-0117
2nd rowBE_IW10-0118
3rd rowBE_IW10-0119
4th rowBE_IW10-0120
5th rowBE_IW10-0121
ValueCountFrequency (%)
be_iw10-0117 1
 
0.1%
be_iw10-0550 1
 
0.1%
be_iw10-0552 1
 
0.1%
be_iw10-0539 1
 
0.1%
be_iw10-0563 1
 
0.1%
be_iw10-0540 1
 
0.1%
be_iw10-0541 1
 
0.1%
be_iw10-0542 1
 
0.1%
be_iw10-0543 1
 
0.1%
be_iw10-0544 1
 
0.1%
Other values (1007) 1007
99.0%
2024-04-06T21:33:55.024018image/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%
7 302
 
2.5%
5 302
 
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%
7 302
 
4.9%
5 302
 
4.9%
6 302
 
4.9%
2 302
 
4.9%
3 302
 
4.9%
4 302
 
4.9%
8 301
 
4.9%
9 301
 
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%
7 302
 
3.7%
5 302
 
3.7%
6 302
 
3.7%
2 302
 
3.7%
3 302
 
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%
7 302
 
2.5%
5 302
 
2.5%
Other values (6) 1810
14.8%

명칭
Text

Distinct940
Distinct (%)92.8%
Missing4
Missing (%)0.4%
Memory size8.1 KiB
2024-04-06T21:33:55.575704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length7.3514314
Min length1

Characters and Unicode

Total characters7447
Distinct characters845
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique882 ?
Unique (%)87.1%

Sample

1st row祥明大博物?
2nd row???少年修??施??
3rd row天主?大??心?廷???
4th row?路?立小???（13?）
5th row?路???
ValueCountFrequency (%)
博物 7
 
0.7%
址 5
 
0.5%
廊 4
 
0.4%
the 4
 
0.4%
4
 
0.4%
草?生活史博物 4
 
0.4%
弦?美 3
 
0.3%
首 3
 
0.3%
名人博物 3
 
0.3%
安息博物 3
 
0.3%
Other values (942) 1008
96.2%
2024-04-06T21:33:57.125443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
? 2276
30.6%
址 166
 
2.2%
大 98
 
1.3%
物 84
 
1.1%
博 81
 
1.1%
的 77
 
1.0%
洞 74
 
1.0%
1 70
 
0.9%
首 66
 
0.9%
故 60
 
0.8%
Other values (835) 4395
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4175
56.1%
Other Punctuation 2322
31.2%
Decimal Number 270
 
3.6%
Lowercase Letter 188
 
2.5%
Uppercase Letter 165
 
2.2%
Open Punctuation 105
 
1.4%
Close Punctuation 105
 
1.4%
Space Separator 45
 
0.6%
Final Punctuation 25
 
0.3%
Initial Punctuation 25
 
0.3%
Other values (2) 22
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
址 166
 
4.0%
大 98
 
2.3%
物 84
 
2.0%
博 81
 
1.9%
的 77
 
1.8%
洞 74
 
1.8%
首 66
 
1.6%
故 60
 
1.4%
廊 57
 
1.4%
居 55
 
1.3%
Other values (759) 3357
80.4%
Uppercase Letter
ValueCountFrequency (%)
S 29
17.6%
B 28
17.0%
M 22
13.3%
C 19
11.5%
T 11
 
6.7%
A 8
 
4.8%
K 8
 
4.8%
I 7
 
4.2%
P 5
 
3.0%
Y 4
 
2.4%
Other values (13) 24
14.5%
Lowercase Letter
ValueCountFrequency (%)
a 30
16.0%
n 21
11.2%
e 19
10.1%
o 15
8.0%
i 14
 
7.4%
h 12
 
6.4%
l 12
 
6.4%
t 11
 
5.9%
r 10
 
5.3%
g 8
 
4.3%
Other values (11) 36
19.1%
Decimal Number
ValueCountFrequency (%)
1 70
25.9%
2 59
21.9%
0 57
21.1%
3 23
 
8.5%
9 17
 
6.3%
8 11
 
4.1%
4 10
 
3.7%
6 8
 
3.0%
5 8
 
3.0%
7 7
 
2.6%
Other Punctuation
ValueCountFrequency (%)
? 2276
98.0%
， 35
 
1.5%
. 5
 
0.2%
& 2
 
0.1%
？ 2
 
0.1%
' 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
（ 58
55.2%
【 33
31.4%
( 10
 
9.5%
[ 2
 
1.9%
《 1
 
1.0%
{ 1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
） 55
52.4%
】 30
28.6%
) 14
 
13.3%
] 5
 
4.8%
》 1
 
1.0%
Space Separator
ValueCountFrequency (%)
45
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 25
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 4161
55.9%
Common 2919
39.2%
Latin 353
 
4.7%
Hangul 14
 
0.2%

Most frequent character per script

Han
ValueCountFrequency (%)
址 166
 
4.0%
大 98
 
2.4%
物 84
 
2.0%
博 81
 
1.9%
的 77
 
1.9%
洞 74
 
1.8%
首 66
 
1.6%
故 60
 
1.4%
廊 57
 
1.4%
居 55
 
1.3%
Other values (751) 3343
80.3%
Latin
ValueCountFrequency (%)
a 30
 
8.5%
S 29
 
8.2%
B 28
 
7.9%
M 22
 
6.2%
n 21
 
5.9%
e 19
 
5.4%
C 19
 
5.4%
o 15
 
4.2%
i 14
 
4.0%
h 12
 
3.4%
Other values (34) 144
40.8%
Common
ValueCountFrequency (%)
? 2276
78.0%
1 70
 
2.4%
2 59
 
2.0%
（ 58
 
2.0%
0 57
 
2.0%
） 55
 
1.9%
45
 
1.5%
， 35
 
1.2%
【 33
 
1.1%
】 30
 
1.0%
Other values (22) 201
 
6.9%
Hangul
ValueCountFrequency (%)
씉 4
28.6%
싲 3
21.4%
쎦 2
14.3%
정 1
 
7.1%
얉 1
 
7.1%
쎇 1
 
7.1%
소 1
 
7.1%
쎱 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 4148
55.7%
ASCII 3007
40.4%
None 215
 
2.9%
Punctuation 50
 
0.7%
Hangul 14
 
0.2%
CJK Compat Ideographs 13
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 2276
75.7%
1 70
 
2.3%
2 59
 
2.0%
0 57
 
1.9%
45
 
1.5%
a 30
 
1.0%
S 29
 
1.0%
B 28
 
0.9%
3 23
 
0.8%
M 22
 
0.7%
Other values (56) 368
 
12.2%
CJK
ValueCountFrequency (%)
址 166
 
4.0%
大 98
 
2.4%
物 84
 
2.0%
博 81
 
2.0%
的 77
 
1.9%
洞 74
 
1.8%
首 66
 
1.6%
故 60
 
1.4%
廊 57
 
1.4%
居 55
 
1.3%
Other values (745) 3330
80.3%
None
ValueCountFrequency (%)
（ 58
27.0%
） 55
25.6%
， 35
16.3%
【 33
15.3%
】 30
14.0%
？ 2
 
0.9%
》 1
 
0.5%
《 1
 
0.5%
Punctuation
ValueCountFrequency (%)
’ 25
50.0%
‘ 25
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
李 6
46.2%
金 3
23.1%
隸 1
 
7.7%
吏 1
 
7.7%
梨 1
 
7.7%
廉 1
 
7.7%
Hangul
ValueCountFrequency (%)
씉 4
28.6%
싲 3
21.4%
쎦 2
14.3%
정 1
 
7.1%
얉 1
 
7.1%
쎇 1
 
7.1%
소 1
 
7.1%
쎱 1
 
7.1%

행정 시
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
首?特?市
821 
<NA>
196 

Length

Max length5
Median length5
Mean length4.8072763
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 821
80.7%
<NA> 196
 
19.3%

Length

2024-04-06T21:33:57.476223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:33:57.705766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
首?特?市 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
?路?
811 
<NA>
196 
中?
 
9
?山?
 
1

Length

Max length4
Median length3
Mean length3.1838741
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
?路? 811
79.7%
<NA> 196
 
19.3%
中? 9
 
0.9%
?山? 1
 
0.1%

Length

2024-04-06T21:33:57.927717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:33:58.138720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
路 811
79.7%
na 196
 
19.3%
中 9
 
0.9%
山 1
 
0.1%

행정 동
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
?路1.2.3.4街洞
218 
<NA>
196 
梨花洞
113 
社稷洞
85 
嘉?洞
71 
Other values (17)
334 

Length

Max length11
Median length7
Mean length5.0943953
Min length2

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row平?洞
2nd row梨花洞
3rd row?化洞
4th row?路1.2.3.4街洞
5th row社稷洞

Common Values

ValueCountFrequency (%)
?路1.2.3.4街洞 218
21.4%
<NA> 196
19.3%
梨花洞 113
11.1%
社稷洞 85
 
8.4%
嘉?洞 71
 
7.0%
?化洞 67
 
6.6%
三?洞 65
 
6.4%
?云孝子洞 63
 
6.2%
付岩洞 41
 
4.0%
平?洞 37
 
3.6%
Other values (12) 61
 
6.0%

Length

2024-04-06T21:33:58.454383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
路1.2.3.4街洞 218
21.4%
na 196
19.3%
梨花洞 113
11.1%
社稷洞 85
 
8.4%
嘉?洞 71
 
7.0%
化洞 67
 
6.6%
三?洞 65
 
6.4%
云孝子洞 63
 
6.2%
付岩洞 41
 
4.0%
平?洞 37
 
3.6%
Other values (12) 61
 
6.0%

전화번호
Text

MISSING 

Distinct321
Distinct (%)87.7%
Missing651
Missing (%)64.0%
Memory size8.1 KiB
2024-04-06T21:33:58.858570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length11
Mean length11.931694
Min length6

Characters and Unicode

Total characters4367
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

Unique287 ?
Unique (%)78.4%

Sample

1st row02-02-781-7920
2nd row02-766-9363
3rd row02-740-9720?1
4th row02-2148-3932
5th row02-721-0711~6
ValueCountFrequency (%)
02-766-6494 4
 
1.1%
02-3675-3737 3
 
0.8%
02-395-3222 3
 
0.8%
02-2148-4171 3
 
0.8%
02-730-1610 3
 
0.8%
02-3673-2778 3
 
0.8%
02-2148-4175 3
 
0.8%
02-741-5978 3
 
0.8%
02-766-0272 3
 
0.8%
02-766-6000 3
 
0.8%
Other values (315) 343
91.7%
2024-04-06T21:33:59.778794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 759
17.4%
0 702
16.1%
2 643
14.7%
7 504
11.5%
3 359
8.2%
6 267
 
6.1%
4 267
 
6.1%
1 267
 
6.1%
5 200
 
4.6%
8 170
 
3.9%
Other values (5) 229
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3542
81.1%
Dash Punctuation 759
 
17.4%
Math Symbol 37
 
0.8%
Other Punctuation 16
 
0.4%
Space Separator 13
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 702
19.8%
2 643
18.2%
7 504
14.2%
3 359
10.1%
6 267
 
7.5%
4 267
 
7.5%
1 267
 
7.5%
5 200
 
5.6%
8 170
 
4.8%
9 163
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
? 1
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 759
100.0%
Math Symbol
ValueCountFrequency (%)
~ 37
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4367
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 759
17.4%
0 702
16.1%
2 643
14.7%
7 504
11.5%
3 359
8.2%
6 267
 
6.1%
4 267
 
6.1%
1 267
 
6.1%
5 200
 
4.6%
8 170
 
3.9%
Other values (5) 229
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 759
17.4%
0 702
16.1%
2 643
14.7%
7 504
11.5%
3 359
8.2%
6 267
 
6.1%
4 267
 
6.1%
1 267
 
6.1%
5 200
 
4.6%
8 170
 
3.9%
Other values (5) 229
 
5.2%

홈페이지주소
Text

MISSING 

Distinct268
Distinct (%)83.0%
Missing694
Missing (%)68.2%
Memory size8.1 KiB
2024-04-06T21:34:00.376928image/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://museum.smu.ac.kr
2nd rowhttp://www.youthnet.or.kr
3rd rowhttp://lib.catholic.ac.kr
4th rowhttp://lib.jongno.go.kr
5th rowhttp://jnlib.sen.go.kr
ValueCountFrequency (%)
http://www.ijongno.co.kr 5
 
1.5%
http://www.wjmuseum.com 3
 
0.9%
http://www.kokdumuseum.com 3
 
0.9%
http://www.sejongpac.or.kr 3
 
0.9%
http://www.dymuseum.com 3
 
0.9%
http://www.hahnmoosook.com 3
 
0.9%
http://www.mokkumto.com 3
 
0.9%
http://www.arkoartcenter.or.kr 3
 
0.9%
http://www.medicalmuseum.org 3
 
0.9%
http://www.hansangsoo.com 3
 
0.9%
Other values (257) 292
90.1%
2024-04-06T21:34:01.663319image/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%
B 1
 
3.2%
R 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 (%)
대 1
20.0%
학 1
20.0%
로 1
20.0%
티 1
20.0%
켓 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%
1 26
 
1.2%
_ 26
 
1.2%
17
 
0.8%
2 15
 
0.7%
& 15
 
0.7%
Other values (10) 77
 
3.5%
Hangul
ValueCountFrequency (%)
대 1
20.0%
학 1
20.0%
로 1
20.0%
티 1
20.0%
켓 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 (%)
대 1
20.0%
학 1
20.0%
로 1
20.0%
티 1
20.0%
켓 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

2024-04-06T21:34:02.508836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:34:02.737354image/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
2024-04-06T21:34:02.937835image/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
2024-04-06T21:34:03.340094image/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%
127.0037106 7
 
0.7%
126.9580981 7
 
0.7%
126.9864633 6
 
0.6%
127.0035361 5
 
0.5%
126.9769632 5
 
0.5%
126.9823368 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
2024-04-06T21:34:03.718843image/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
2024-04-06T21:34:04.111687image/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%
37.5760084 7
 
0.7%
37.5836832 7
 
0.7%
37.5843579 6
 
0.6%
37.5739012 5
 
0.5%
37.5799333 5
 
0.5%
37.5806903 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

2024-04-06T21:33:51.879776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:51.273201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:52.149253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:51.563558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T21:34:04.376027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 구행정 동X 좌표Y 좌표
행정 구1.0000.9450.0000.644
행정 동0.9451.0000.7390.603
X 좌표0.0000.7391.0000.676
Y 좌표0.6440.6030.6761.000
2024-04-06T21:34:04.580195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 구행정 시행정 동
행정 구1.0001.0000.748
행정 시1.0001.0001.000
행정 동0.7481.0001.000
2024-04-06T21:34:04.784733image/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 구0.0000.3071.0001.0000.748
행정 동0.4970.3431.0000.7481.000

Missing values

2024-04-06T21:33:52.478126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T21:33:52.981311image/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.
2024-04-06T21:33:53.401994image/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_IW10-0117祥明大博物?首?特?市?路?平?洞02-02-781-7920http://museum.smu.ac.krY126.97596637.610555
1BE_IW10-0118???少年修??施??首?特?市?路?梨花洞02-766-9363http://www.youthnet.or.krY127.0046737.576124
2BE_IW10-0119天主?大??心?廷???首?特?市?路??化洞02-740-9720?1http://lib.catholic.ac.krY127.00376637.585991
3BE_IW10-0120?路?立小???（13?）首?特?市?路??路1.2.3.4街洞02-2148-3932http://lib.jongno.go.krY126.97964237.573033
4BE_IW10-0121?路???首?特?市?路?社稷洞02-721-0711~6http://jnlib.sen.go.krY126.9672737.575944
5BE_IW10-0122?育信息?????首?特?市?路?社稷洞02-2100-6117http://library.mest.go.krY126.97542437.574848
6BE_IW10-0123精????首?特?市?路?三?洞02-2011-5799http://jdlib.sen.go.krY126.98312437.580784
7BE_IW10-0124首?市立?童???首?特?市?路?社稷洞02-731-2300http://www.childrenlib.go.krY126.9672737.575944
8BE_IW10-0125?德???廊首?特?市?路??路1.2.3.4街洞02-732-6458http://www.gallerydongduk.comY126.9831937.575032
9BE_IW10-0126PKM?廊首?特?市?路?三?洞02-734-9467~8http://www.pkmgallery.comY126.98269837.579048
키명칭행정 시행정 구행정 동전화번호홈페이지주소HTML사용여부X 좌표Y 좌표
1007BE_IW10-0389昌德?景薰?三仙?波?首?特?市?路??路1.2.3.4街洞<NA><NA>Y126.99132137.582776
1008BE_IW10-0390昌德?景薰?朝日仙??首?特?市?路??路1.2.3.4街洞<NA><NA>Y126.99132137.582776
1009BE_IW10-0391首?塔谷公?首?特?市?路??路1.2.3.4街洞<NA><NA>Y126.98834837.571206
1010BE_IW10-0392首?地藏庵木造毘盧遮那佛坐像首?特?市?路?昌信2洞<NA><NA>Y127.00878537.577655
1011BE_IW10-0393首?付岩洞白石洞川首?特?市?路?付岩洞<NA><NA>Y126.96633537.600429
1012BE_IW10-0394文化??<NA><NA><NA>010-123-1234<NA>Y<NA><NA>
1013BE_IW10-0395?安堂<NA><NA><NA><NA><NA>Y<NA><NA>
1014BE_IW10-0396玉??<NA><NA><NA><NA><NA>Y<NA><NA>
1015BE_IW10-0397首?白岳山日?首?特?市?路??云孝子洞<NA><NA>Y126.96586237.584838
1016BE_IW10-0403?溪川<NA><NA><NA><NA><NA>Y<NA><NA>