Overview

Dataset statistics

Number of variables9
Number of observations581
Missing cells999
Missing cells (%)19.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.1 KiB
Average record size in memory74.2 B

Variable types

Text5
Categorical2
Numeric2

Dataset

Description키,시장명,행정시,행정구,행정동,웹주소,대표전화,중심좌표 X,중심좌표 Y
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13036/S/1/datasetView.do

Alerts

행정시 is highly overall correlated with 중심좌표 X and 2 other fieldsHigh correlation
행정구 is highly overall correlated with 중심좌표 X and 2 other fieldsHigh correlation
중심좌표 X is highly overall correlated with 행정시 and 1 other fieldsHigh correlation
중심좌표 Y is highly overall correlated with 행정시 and 1 other fieldsHigh correlation
행정시 is highly imbalanced (98.2%)Imbalance
웹주소 has 535 (92.1%) missing valuesMissing
대표전화 has 461 (79.3%) missing valuesMissing
키 has unique valuesUnique

Reproduction

Analysis started2023-12-11 05:34:32.922972
Analysis finished2023-12-11 05:34:34.177308
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct581
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2023-12-11T14:34:34.382101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6972
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

Unique581 ?
Unique (%)100.0%

Sample

1st rowBE_IW15-0275
2nd rowBE_IW15-0285
3rd rowBE_IW15-0436
4th rowBE_IW15-0217
5th rowBE_IW15-0040
ValueCountFrequency (%)
be_iw15-0275 1
 
0.2%
be_iw15-0349 1
 
0.2%
be_iw15-0192 1
 
0.2%
be_iw15-0576 1
 
0.2%
be_iw15-0501 1
 
0.2%
be_iw15-0510 1
 
0.2%
be_iw15-0220 1
 
0.2%
be_iw15-0522 1
 
0.2%
be_iw15-0564 1
 
0.2%
be_iw15-0053 1
 
0.2%
Other values (571) 571
98.3%
2023-12-11T14:34:34.800736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 800
11.5%
0 797
11.4%
5 781
11.2%
B 581
8.3%
E 581
8.3%
_ 581
8.3%
I 581
8.3%
W 581
8.3%
- 581
8.3%
2 218
 
3.1%
Other values (6) 890
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3486
50.0%
Uppercase Letter 2324
33.3%
Connector Punctuation 581
 
8.3%
Dash Punctuation 581
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 800
22.9%
0 797
22.9%
5 781
22.4%
2 218
 
6.3%
3 218
 
6.3%
4 218
 
6.3%
7 118
 
3.4%
6 118
 
3.4%
8 110
 
3.2%
9 108
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 581
25.0%
E 581
25.0%
I 581
25.0%
W 581
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 581
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 581
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4648
66.7%
Latin 2324
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 800
17.2%
0 797
17.1%
5 781
16.8%
_ 581
12.5%
- 581
12.5%
2 218
 
4.7%
3 218
 
4.7%
4 218
 
4.7%
7 118
 
2.5%
6 118
 
2.5%
Other values (2) 218
 
4.7%
Latin
ValueCountFrequency (%)
B 581
25.0%
E 581
25.0%
I 581
25.0%
W 581
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 800
11.5%
0 797
11.4%
5 781
11.2%
B 581
8.3%
E 581
8.3%
_ 581
8.3%
I 581
8.3%
W 581
8.3%
- 581
8.3%
2 218
 
3.1%
Other values (6) 890
12.8%

시장명
Text

Distinct488
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2023-12-11T14:34:35.162105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length46
Mean length21.003442
Min length6

Characters and Unicode

Total characters12203
Distinct characters63
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique399 ?
Unique (%)68.7%

Sample

1st row Inhyeon Market
2nd row Namdaemun-ro Shopping Centor
3rd rowAhyeon Market
4th rowAhyeonUsa Market
5th rowAmsa Composite Market
ValueCountFrequency (%)
market 471
29.0%
shopping 74
 
4.6%
golmok 72
 
4.4%
center 55
 
3.4%
district 20
 
1.2%
composite 20
 
1.2%
traditional 18
 
1.1%
jungang 18
 
1.1%
jeil 17
 
1.0%
dongdaemun 15
 
0.9%
Other values (429) 842
51.9%
2023-12-11T14:34:35.657269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1108
 
9.1%
n 1083
 
8.9%
1059
 
8.7%
a 1058
 
8.7%
o 929
 
7.6%
r 751
 
6.2%
t 705
 
5.8%
g 669
 
5.5%
k 665
 
5.4%
M 522
 
4.3%
Other values (53) 3654
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9412
77.1%
Uppercase Letter 1607
 
13.2%
Space Separator 1059
 
8.7%
Dash Punctuation 72
 
0.6%
Decimal Number 31
 
0.3%
Other Punctuation 8
 
0.1%
Open Punctuation 7
 
0.1%
Close Punctuation 7
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1108
11.8%
n 1083
11.5%
a 1058
11.2%
o 929
9.9%
r 751
8.0%
t 705
7.5%
g 669
 
7.1%
k 665
 
7.1%
i 447
 
4.7%
u 281
 
3.0%
Other values (16) 1716
18.2%
Uppercase Letter
ValueCountFrequency (%)
M 522
32.5%
S 240
14.9%
G 149
 
9.3%
C 117
 
7.3%
D 107
 
6.7%
J 75
 
4.7%
Y 56
 
3.5%
T 50
 
3.1%
B 44
 
2.7%
N 41
 
2.6%
Other values (13) 206
 
12.8%
Decimal Number
ValueCountFrequency (%)
2 6
19.4%
1 6
19.4%
3 6
19.4%
6 5
16.1%
4 4
12.9%
7 3
9.7%
5 1
 
3.2%
Other Punctuation
ValueCountFrequency (%)
? 4
50.0%
& 3
37.5%
, 1
 
12.5%
Space Separator
ValueCountFrequency (%)
1059
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11019
90.3%
Common 1184
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1108
 
10.1%
n 1083
 
9.8%
a 1058
 
9.6%
o 929
 
8.4%
r 751
 
6.8%
t 705
 
6.4%
g 669
 
6.1%
k 665
 
6.0%
M 522
 
4.7%
i 447
 
4.1%
Other values (39) 3082
28.0%
Common
ValueCountFrequency (%)
1059
89.4%
- 72
 
6.1%
( 7
 
0.6%
) 7
 
0.6%
2 6
 
0.5%
1 6
 
0.5%
3 6
 
0.5%
6 5
 
0.4%
? 4
 
0.3%
4 4
 
0.3%
Other values (4) 8
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1108
 
9.1%
n 1083
 
8.9%
1059
 
8.7%
a 1058
 
8.7%
o 929
 
7.6%
r 751
 
6.2%
t 705
 
5.8%
g 669
 
5.5%
k 665
 
5.4%
M 522
 
4.3%
Other values (53) 3654
29.9%

행정시
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Seoul
580 
<NA>
 
1

Length

Max length5
Median length5
Mean length4.9982788
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
Seoul 580
99.8%
<NA> 1
 
0.2%

Length

2023-12-11T14:34:35.801100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:34:35.902543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 580
99.8%
na 1
 
0.2%

행정구
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Jung-gu
56 
Jongno-gu
41 
Dongdaemun-gu
38 
Yeongdeungpo-gu
 
35
Gwanak-gu
 
35
Other values (21)
376 

Length

Max length15
Median length12
Mean length10.287435
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowJung-gu
2nd rowJung-gu
3rd rowMapo-gu
4th rowMapo-gu
5th rowGangdong-gu

Common Values

ValueCountFrequency (%)
Jung-gu 56
 
9.6%
Jongno-gu 41
 
7.1%
Dongdaemun-gu 38
 
6.5%
Yeongdeungpo-gu 35
 
6.0%
Gwanak-gu 35
 
6.0%
Gangbuk-gu 32
 
5.5%
Yangcheon-gu 28
 
4.8%
Gwangjin-gu 27
 
4.6%
Jungnang-gu 26
 
4.5%
Seongbuk-gu 25
 
4.3%
Other values (16) 238
41.0%

Length

2023-12-11T14:34:36.012391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jung-gu 56
 
9.6%
jongno-gu 41
 
7.1%
dongdaemun-gu 38
 
6.5%
yeongdeungpo-gu 35
 
6.0%
gwanak-gu 35
 
6.0%
gangbuk-gu 32
 
5.5%
yangcheon-gu 28
 
4.8%
gwangjin-gu 27
 
4.6%
jungnang-gu 26
 
4.5%
seongbuk-gu 25
 
4.3%
Other values (16) 238
41.0%

행정동
Text

Distinct217
Distinct (%)37.4%
Missing1
Missing (%)0.2%
Memory size4.7 KiB
2023-12-11T14:34:36.254590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length12.686207
Min length8

Characters and Unicode

Total characters7358
Distinct characters47
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)10.0%

Sample

1st rowJungnim-dong
2nd rowSogong-dong
3rd rowAhyeon-dong
4th rowAhyeon-dong
5th rowAmsa1-dong
ValueCountFrequency (%)
jegi-dong 16
 
2.8%
sindang-dong 15
 
2.6%
yeongdeungpo-dong 13
 
2.2%
jongno5.6ga-dong 12
 
2.1%
changsin1-dong 12
 
2.1%
gwanghui-dong 11
 
1.9%
jayang4-dong 10
 
1.7%
jongno1.2.3.4ga-dong 10
 
1.7%
euljiro-dong 8
 
1.4%
hoehyeon-dong 8
 
1.4%
Other values (207) 465
80.2%
2023-12-11T14:34:36.637434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1234
16.8%
g 1036
14.1%
o 1029
14.0%
d 640
 
8.7%
- 580
 
7.9%
a 392
 
5.3%
e 270
 
3.7%
i 194
 
2.6%
u 176
 
2.4%
S 164
 
2.2%
Other values (37) 1643
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5749
78.1%
Dash Punctuation 580
 
7.9%
Uppercase Letter 580
 
7.9%
Decimal Number 402
 
5.5%
Other Punctuation 47
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1234
21.5%
g 1036
18.0%
o 1029
17.9%
d 640
11.1%
a 392
 
6.8%
e 270
 
4.7%
i 194
 
3.4%
u 176
 
3.1%
h 129
 
2.2%
y 100
 
1.7%
Other values (11) 549
9.5%
Uppercase Letter
ValueCountFrequency (%)
S 164
28.3%
J 93
16.0%
H 45
 
7.8%
Y 43
 
7.4%
G 42
 
7.2%
M 39
 
6.7%
C 37
 
6.4%
D 29
 
5.0%
E 22
 
3.8%
B 21
 
3.6%
Other values (6) 45
 
7.8%
Decimal Number
ValueCountFrequency (%)
1 133
33.1%
2 119
29.6%
3 61
15.2%
4 41
 
10.2%
5 19
 
4.7%
6 18
 
4.5%
7 7
 
1.7%
8 4
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 580
100.0%
Other Punctuation
ValueCountFrequency (%)
. 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6329
86.0%
Common 1029
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1234
19.5%
g 1036
16.4%
o 1029
16.3%
d 640
10.1%
a 392
 
6.2%
e 270
 
4.3%
i 194
 
3.1%
u 176
 
2.8%
S 164
 
2.6%
h 129
 
2.0%
Other values (27) 1065
16.8%
Common
ValueCountFrequency (%)
- 580
56.4%
1 133
 
12.9%
2 119
 
11.6%
3 61
 
5.9%
. 47
 
4.6%
4 41
 
4.0%
5 19
 
1.8%
6 18
 
1.7%
7 7
 
0.7%
8 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1234
16.8%
g 1036
14.1%
o 1029
14.0%
d 640
 
8.7%
- 580
 
7.9%
a 392
 
5.3%
e 270
 
3.7%
i 194
 
2.6%
u 176
 
2.4%
S 164
 
2.2%
Other values (37) 1643
22.3%

웹주소
Text

MISSING 

Distinct45
Distinct (%)97.8%
Missing535
Missing (%)92.1%
Memory size4.7 KiB
2023-12-11T14:34:36.881338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length30
Mean length20.521739
Min length10

Characters and Unicode

Total characters944
Distinct characters62
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)95.7%

Sample

1st rowwww.아현전통시장.co.kr
2nd rowwww.belpost.co.kr
3rd rowwww.bangsantmk.co.kr
4th rowbangsin.co.kr
5th rowcookingm.com/bukbooplaza
ValueCountFrequency (%)
sungangsijang.net 2
 
4.3%
www.suyumarket.com 2
 
4.3%
www.visitseoul.net 1
 
2.2%
www.ydmarket.kr 1
 
2.2%
http://www.sph.co.kr 1
 
2.2%
namguromarket.com 1
 
2.2%
www.nmsj.kr 1
 
2.2%
www.pangnapm.co.kr 1
 
2.2%
http://www.pyounghwa.com 1
 
2.2%
www.tour.hunggu.seoul.kr 1
 
2.2%
Other values (34) 34
73.9%
2023-12-11T14:34:37.243359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 103
 
10.9%
. 102
 
10.8%
a 58
 
6.1%
m 56
 
5.9%
o 56
 
5.9%
n 53
 
5.6%
t 44
 
4.7%
c 44
 
4.7%
r 43
 
4.6%
k 42
 
4.4%
Other values (52) 343
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 751
79.6%
Other Punctuation 149
 
15.8%
Other Letter 16
 
1.7%
Uppercase Letter 15
 
1.6%
Decimal Number 9
 
1.0%
Dash Punctuation 2
 
0.2%
Math Symbol 1
 
0.1%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 103
13.7%
a 58
 
7.7%
m 56
 
7.5%
o 56
 
7.5%
n 53
 
7.1%
t 44
 
5.9%
c 44
 
5.9%
r 43
 
5.7%
k 42
 
5.6%
e 33
 
4.4%
Other values (15) 219
29.2%
Uppercase Letter
ValueCountFrequency (%)
U 2
13.3%
S 2
13.3%
M 2
13.3%
Y 1
6.7%
A 1
6.7%
R 1
6.7%
E 1
6.7%
K 1
6.7%
J 1
6.7%
D 1
6.7%
Other values (2) 2
13.3%
Other Letter
ValueCountFrequency (%)
장 4
25.0%
시 3
18.8%
통 1
 
6.2%
전 1
 
6.2%
현 1
 
6.2%
아 1
 
6.2%
광 1
 
6.2%
사 1
 
6.2%
신 1
 
6.2%
악 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
8 2
22.2%
3 2
22.2%
1 2
22.2%
2 1
11.1%
5 1
11.1%
0 1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 102
68.5%
/ 35
 
23.5%
: 10
 
6.7%
@ 1
 
0.7%
? 1
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 766
81.1%
Common 162
 
17.2%
Hangul 16
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 103
13.4%
a 58
 
7.6%
m 56
 
7.3%
o 56
 
7.3%
n 53
 
6.9%
t 44
 
5.7%
c 44
 
5.7%
r 43
 
5.6%
k 42
 
5.5%
e 33
 
4.3%
Other values (27) 234
30.5%
Common
ValueCountFrequency (%)
. 102
63.0%
/ 35
 
21.6%
: 10
 
6.2%
8 2
 
1.2%
3 2
 
1.2%
1 2
 
1.2%
- 2
 
1.2%
@ 1
 
0.6%
2 1
 
0.6%
= 1
 
0.6%
Other values (4) 4
 
2.5%
Hangul
ValueCountFrequency (%)
장 4
25.0%
시 3
18.8%
통 1
 
6.2%
전 1
 
6.2%
현 1
 
6.2%
아 1
 
6.2%
광 1
 
6.2%
사 1
 
6.2%
신 1
 
6.2%
악 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 928
98.3%
Hangul 16
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 103
 
11.1%
. 102
 
11.0%
a 58
 
6.2%
m 56
 
6.0%
o 56
 
6.0%
n 53
 
5.7%
t 44
 
4.7%
c 44
 
4.7%
r 43
 
4.6%
k 42
 
4.5%
Other values (41) 327
35.2%
Hangul
ValueCountFrequency (%)
장 4
25.0%
시 3
18.8%
통 1
 
6.2%
전 1
 
6.2%
현 1
 
6.2%
아 1
 
6.2%
광 1
 
6.2%
사 1
 
6.2%
신 1
 
6.2%
악 1
 
6.2%

대표전화
Text

MISSING 

Distinct119
Distinct (%)99.2%
Missing461
Missing (%)79.3%
Memory size4.7 KiB
2023-12-11T14:34:37.486297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.558333
Min length11

Characters and Unicode

Total characters1387
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

Unique118 ?
Unique (%)98.3%

Sample

1st row070-8950-7082
2nd row02-442-1040
3rd row02-2231-4678
4th row02-911-2931
5th row02-303-5514
ValueCountFrequency (%)
02-2065-7212 2
 
1.7%
02-2252-9559 1
 
0.8%
02-465-2103 1
 
0.8%
02-475-8272 1
 
0.8%
02-2214-4326 1
 
0.8%
02-962-5598 1
 
0.8%
02-414-8784 1
 
0.8%
02-3142-2503 1
 
0.8%
02-834-8900 1
 
0.8%
070-4141-1116 1
 
0.8%
Other values (109) 109
90.8%
2023-12-11T14:34:37.871862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 255
18.4%
- 240
17.3%
0 223
16.1%
4 94
 
6.8%
6 91
 
6.6%
5 91
 
6.6%
9 87
 
6.3%
1 82
 
5.9%
3 81
 
5.8%
7 76
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1147
82.7%
Dash Punctuation 240
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 255
22.2%
0 223
19.4%
4 94
 
8.2%
6 91
 
7.9%
5 91
 
7.9%
9 87
 
7.6%
1 82
 
7.1%
3 81
 
7.1%
7 76
 
6.6%
8 67
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 255
18.4%
- 240
17.3%
0 223
16.1%
4 94
 
6.8%
6 91
 
6.6%
5 91
 
6.6%
9 87
 
6.3%
1 82
 
5.9%
3 81
 
5.8%
7 76
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 255
18.4%
- 240
17.3%
0 223
16.1%
4 94
 
6.8%
6 91
 
6.6%
5 91
 
6.6%
9 87
 
6.3%
1 82
 
5.9%
3 81
 
5.8%
7 76
 
5.5%

중심좌표 X
Real number (ℝ)

HIGH CORRELATION 

Distinct503
Distinct (%)86.7%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean126.9875
Minimum126.80913
Maximum127.16897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-12-11T14:34:38.027923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80913
5-th percentile126.84416
Q1126.92001
median127.00398
Q3127.04095
95-th percentile127.12128
Maximum127.16897
Range0.35984659
Interquartile range (IQR)0.12094128

Descriptive statistics

Standard deviation0.078890012
Coefficient of variation (CV)0.00062124232
Kurtosis-0.69509529
Mean126.9875
Median Absolute Deviation (MAD)0.059445793
Skewness-0.13358987
Sum73652.753
Variance0.0062236341
MonotonicityNot monotonic
2023-12-11T14:34:38.188153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.931860986 3
 
0.5%
127.0060847401 3
 
0.5%
127.0081590655 3
 
0.5%
127.0208272836 2
 
0.3%
126.9172513232 2
 
0.3%
126.9445773732 2
 
0.3%
127.0648770285 2
 
0.3%
127.0652934376 2
 
0.3%
127.0132664094 2
 
0.3%
126.9764525181 2
 
0.3%
Other values (493) 557
95.9%
ValueCountFrequency (%)
126.8091252339 1
0.2%
126.8123548928 1
0.2%
126.8125674952 1
0.2%
126.8252645707 1
0.2%
126.825628073 1
0.2%
126.8330202003 1
0.2%
126.8334820641 1
0.2%
126.8336084819 1
0.2%
126.8346450612 1
0.2%
126.835403988 1
0.2%
ValueCountFrequency (%)
127.1689718239 1
0.2%
127.1688141667 1
0.2%
127.1514327272 1
0.2%
127.1505633958 2
0.3%
127.1498381105 1
0.2%
127.1451811952 1
0.2%
127.1451675736 1
0.2%
127.1429389865 1
0.2%
127.1428349754 1
0.2%
127.1368269248 1
0.2%

중심좌표 Y
Real number (ℝ)

HIGH CORRELATION 

Distinct503
Distinct (%)86.7%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.554421
Minimum37.440162
Maximum37.673416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2023-12-11T14:34:38.355834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.440162
5-th percentile37.481552
Q137.520631
median37.559476
Q337.579672
95-th percentile37.636392
Maximum37.673416
Range0.23325383
Interquartile range (IQR)0.059040467

Descriptive statistics

Standard deviation0.045841136
Coefficient of variation (CV)0.0012206588
Kurtosis-0.34860366
Mean37.554421
Median Absolute Deviation (MAD)0.030196894
Skewness0.10184643
Sum21781.564
Variance0.0021014098
MonotonicityNot monotonic
2023-12-11T14:34:38.503406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5002855781 3
 
0.5%
37.5063886627 3
 
0.5%
37.5694765801 3
 
0.5%
37.5187992169 2
 
0.3%
37.5111603883 2
 
0.3%
37.5117890535 2
 
0.3%
37.5363275416 2
 
0.3%
37.5363705601 2
 
0.3%
37.5695013446 2
 
0.3%
37.5502652588 2
 
0.3%
Other values (493) 557
95.9%
ValueCountFrequency (%)
37.4401621876 1
0.2%
37.4512262785 1
0.2%
37.4515988018 1
0.2%
37.4540607966 1
0.2%
37.4542769006 1
0.2%
37.4587660766 1
0.2%
37.4598292221 1
0.2%
37.4667784234 1
0.2%
37.4702173557 1
0.2%
37.470336129 1
0.2%
ValueCountFrequency (%)
37.673416016 1
0.2%
37.6702613587 1
0.2%
37.6700451994 1
0.2%
37.6663281543 1
0.2%
37.6659383095 1
0.2%
37.6648372944 1
0.2%
37.6607915102 1
0.2%
37.6599406573 1
0.2%
37.6598037216 1
0.2%
37.6555996048 1
0.2%

Interactions

2023-12-11T14:34:33.560506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:34:33.320477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:34:33.670297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:34:33.451030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:34:38.597308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구웹주소중심좌표 X중심좌표 Y
행정구1.0001.0000.9450.954
웹주소1.0001.0001.0001.000
중심좌표 X0.9451.0001.0000.732
중심좌표 Y0.9541.0000.7321.000
2023-12-11T14:34:38.703525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정시행정구
행정시1.0001.000
행정구1.0001.000
2023-12-11T14:34:39.094724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중심좌표 X중심좌표 Y행정시행정구
중심좌표 X1.0000.4101.0000.704
중심좌표 Y0.4101.0001.0000.733
행정시1.0001.0001.0001.000
행정구0.7040.7331.0001.000

Missing values

2023-12-11T14:34:33.812541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:34:33.950244image/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.
2023-12-11T14:34:34.090029image/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

키시장명행정시행정구행정동웹주소대표전화중심좌표 X중심좌표 Y
0BE_IW15-0275Inhyeon MarketSeoulJung-guJungnim-dong<NA><NA>126.96441937.554108
1BE_IW15-0285Namdaemun-ro Shopping CentorSeoulJung-guSogong-dong<NA><NA>126.97726437.560411
2BE_IW15-0436Ahyeon MarketSeoulMapo-guAhyeon-dong<NA><NA>126.9547337.556699
3BE_IW15-0217AhyeonUsa MarketSeoulMapo-guAhyeon-dongwww.아현전통시장.co.kr070-8950-7082126.95503337.556411
4BE_IW15-0040Amsa Composite MarketSeoulGangdong-guAmsa1-dong<NA>02-442-1040127.12886537.550846
5BE_IW15-0569Amsa MarketSeoulGangdong-guAmsa1-dong<NA><NA>127.12879737.550864
6BE_IW15-0267Area 6SeoulJung-guSindang-dong<NA><NA>127.01197637.567988
7BE_IW15-0182AreaSixSeoulJung-guSindang-dongwww.belpost.co.kr02-2231-4678127.01197637.567988
8BE_IW15-0091Arirang Golmok MarketSeoulSeongbuk-guJeongneung2-dong<NA>02-911-2931127.01242537.60291
9BE_IW15-0399Baegun MarketSeoulDobong-guSsangmun2-dong<NA><NA>127.03744537.660792
키시장명행정시행정구행정동웹주소대표전화중심좌표 X중심좌표 Y
571BE_IW15-0298Yongmun General MarketSeoulYongsan-guYongmun-dong<NA><NA>126.95994737.536638
572BE_IW15-0034Yongmun MarketSeoulYongsan-guYongmun-dong<NA>02-717-9882126.96001537.536032
573BE_IW15-0297Yongmun MarketSeoulYongsan-guWonhyoro2-dong<NA><NA>126.96030937.535762
574BE_IW15-0497Youngjin Market A-dongSeoulYeongdeungpo-guSingil6-dong<NA><NA>126.91577137.500131
575BE_IW15-0487Youngsin Shopping CenterSeoulYeongdeungpo-guYeongdeungpo-dong<NA><NA>126.90669937.51991
576BE_IW15-0156Yujin ArcadeSeoulSeodaemun-guHongje2-dong<NA>02-3216-9393126.94189237.591274
577BE_IW15-0417Yujin Shopping CenterSeoulSeodaemun-guYeonhui-dong<NA><NA>126.92965437.567128
578BE_IW15-0351Yuyeong MarketSeoulJungnang-guMangu3-dong<NA><NA>127.09671937.591411
579BE_IW15-0281city hall square Underground Shopping CenterSeoulJung-guEuljiro-dong<NA><NA>126.99766837.567513
580BE_IW15-0029ichon Composite MarketSeoulYongsan-guIchon1-dong<NA>02-794-1555126.97488737.520575