Overview

Dataset statistics

Number of variables13
Number of observations134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory107.0 B

Variable types

Text7
Categorical4
Numeric2

Dataset

Description키,검색 키워드,alias,최종 표기명,지번 주소,법정 시,법정 구,법정 동,행정 시,행정 구,행정 동,중심 좌표 X,중심 좌표 Y
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12929/S/1/datasetView.do

Alerts

법정 시 has constant value ""Constant
행정 시 has constant value ""Constant
법정 구 is highly overall correlated with 중심 좌표 X and 2 other fieldsHigh correlation
행정 구 is highly overall correlated with 중심 좌표 X and 2 other fieldsHigh correlation
중심 좌표 X is highly overall correlated with 법정 구 and 1 other fieldsHigh correlation
중심 좌표 Y is highly overall correlated with 법정 구 and 1 other fieldsHigh correlation
키 has unique valuesUnique

Reproduction

Analysis started2023-12-11 06:03:54.310959
Analysis finished2023-12-11 06:03:56.818976
Duration2.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:03:57.064796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1608
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

Unique134 ?
Unique (%)100.0%

Sample

1st rowBE_IW01-0042
2nd rowBE_IW01-0043
3rd rowBE_IW01-0044
4th rowBE_IW01-0045
5th rowBE_IW01-0046
ValueCountFrequency (%)
be_iw01-0042 1
 
0.7%
be_iw01-0020 1
 
0.7%
be_iw01-0034 1
 
0.7%
be_iw01-0033 1
 
0.7%
be_iw01-0032 1
 
0.7%
be_iw01-0031 1
 
0.7%
be_iw01-0030 1
 
0.7%
be_iw01-0029 1
 
0.7%
be_iw01-0028 1
 
0.7%
be_iw01-0027 1
 
0.7%
Other values (124) 124
92.5%
2023-12-11T15:03:57.565337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 399
24.8%
1 203
12.6%
B 134
 
8.3%
E 134
 
8.3%
_ 134
 
8.3%
I 134
 
8.3%
W 134
 
8.3%
- 134
 
8.3%
2 34
 
2.1%
3 29
 
1.8%
Other values (6) 139
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 804
50.0%
Uppercase Letter 536
33.3%
Connector Punctuation 134
 
8.3%
Dash Punctuation 134
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 399
49.6%
1 203
25.2%
2 34
 
4.2%
3 29
 
3.6%
4 24
 
3.0%
5 23
 
2.9%
8 23
 
2.9%
6 23
 
2.9%
7 23
 
2.9%
9 23
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 134
25.0%
E 134
25.0%
I 134
25.0%
W 134
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1072
66.7%
Latin 536
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 399
37.2%
1 203
18.9%
_ 134
 
12.5%
- 134
 
12.5%
2 34
 
3.2%
3 29
 
2.7%
4 24
 
2.2%
5 23
 
2.1%
8 23
 
2.1%
6 23
 
2.1%
Other values (2) 46
 
4.3%
Latin
ValueCountFrequency (%)
B 134
25.0%
E 134
25.0%
I 134
25.0%
W 134
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 399
24.8%
1 203
12.6%
B 134
 
8.3%
E 134
 
8.3%
_ 134
 
8.3%
I 134
 
8.3%
W 134
 
8.3%
- 134
 
8.3%
2 34
 
2.1%
3 29
 
1.8%
Other values (6) 139
 
8.6%

검색 키워드
Text

Distinct129
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:03:57.856891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length7.0820896
Min length3

Characters and Unicode

Total characters949
Distinct characters227
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)94.0%

Sample

1st row서울신림동민속순대타운
2nd row이대/구제거리
3rd row피맛골
4th row병천토속순대
5th row능동로
ValueCountFrequency (%)
북악산길/산책로 4
 
3.0%
둘레길3구간 2
 
1.5%
둘레길1구간 2
 
1.5%
빛과통신의/거리 1
 
0.7%
메타세콰이어길 1
 
0.7%
한강자전거도로(광나루지구 1
 
0.7%
청담동/명품거리 1
 
0.7%
을지한빛거리 1
 
0.7%
경리단길 1
 
0.7%
신의주찹쌀순대 1
 
0.7%
Other values (119) 119
88.8%
2023-12-11T15:03:58.330326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 62
 
6.5%
/ 58
 
6.1%
거 55
 
5.8%
동 42
 
4.4%
길 35
 
3.7%
로 27
 
2.8%
대 21
 
2.2%
골 20
 
2.1%
목 19
 
2.0%
구 19
 
2.0%
Other values (217) 591
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 880
92.7%
Other Punctuation 58
 
6.1%
Decimal Number 7
 
0.7%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 62
 
7.0%
거 55
 
6.2%
동 42
 
4.8%
길 35
 
4.0%
로 27
 
3.1%
대 21
 
2.4%
골 20
 
2.3%
목 19
 
2.2%
구 19
 
2.2%
이 18
 
2.0%
Other values (210) 562
63.9%
Decimal Number
ValueCountFrequency (%)
3 3
42.9%
1 2
28.6%
2 1
 
14.3%
0 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
/ 58
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 880
92.7%
Common 69
 
7.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 62
 
7.0%
거 55
 
6.2%
동 42
 
4.8%
길 35
 
4.0%
로 27
 
3.1%
대 21
 
2.4%
골 20
 
2.3%
목 19
 
2.2%
구 19
 
2.2%
이 18
 
2.0%
Other values (210) 562
63.9%
Common
ValueCountFrequency (%)
/ 58
84.1%
3 3
 
4.3%
) 2
 
2.9%
1 2
 
2.9%
( 2
 
2.9%
2 1
 
1.4%
0 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 880
92.7%
ASCII 69
 
7.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 62
 
7.0%
거 55
 
6.2%
동 42
 
4.8%
길 35
 
4.0%
로 27
 
3.1%
대 21
 
2.4%
골 20
 
2.3%
목 19
 
2.2%
구 19
 
2.2%
이 18
 
2.0%
Other values (210) 562
63.9%
ASCII
ValueCountFrequency (%)
/ 58
84.1%
3 3
 
4.3%
) 2
 
2.9%
1 2
 
2.9%
( 2
 
2.9%
2 1
 
1.4%
0 1
 
1.4%

alias
Text

Distinct120
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:03:58.691758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length5.7164179
Min length3

Characters and Unicode

Total characters766
Distinct characters218
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)85.1%

Sample

1st row서울신림동민속순대타운
2nd row이대구제거리
3rd row피맛골
4th row병천토속순대
5th row능동로
ValueCountFrequency (%)
로데오거리 5
 
3.7%
차이나타운 5
 
3.7%
북악산길 4
 
3.0%
둘레길1구간 2
 
1.5%
신의주찹쌀순대 2
 
1.5%
둘레길3구간 2
 
1.5%
카페골목 1
 
0.7%
용산감자탕거리 1
 
0.7%
도배전문거리 1
 
0.7%
석촌호수길 1
 
0.7%
Other values (110) 110
82.1%
2023-12-11T15:03:59.250517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 59
 
7.7%
거 54
 
7.0%
길 32
 
4.2%
동 28
 
3.7%
골 20
 
2.6%
목 19
 
2.5%
로 19
 
2.5%
이 16
 
2.1%
구 16
 
2.1%
대 15
 
2.0%
Other values (208) 488
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 756
98.7%
Decimal Number 5
 
0.7%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 59
 
7.8%
거 54
 
7.1%
길 32
 
4.2%
동 28
 
3.7%
골 20
 
2.6%
목 19
 
2.5%
로 19
 
2.5%
이 16
 
2.1%
구 16
 
2.1%
대 15
 
2.0%
Other values (202) 478
63.2%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
3 2
40.0%
2 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 756
98.7%
Common 10
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 59
 
7.8%
거 54
 
7.1%
길 32
 
4.2%
동 28
 
3.7%
골 20
 
2.6%
목 19
 
2.5%
로 19
 
2.5%
이 16
 
2.1%
구 16
 
2.1%
대 15
 
2.0%
Other values (202) 478
63.2%
Common
ValueCountFrequency (%)
( 2
20.0%
) 2
20.0%
1 2
20.0%
3 2
20.0%
2 1
10.0%
. 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 756
98.7%
ASCII 10
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 59
 
7.8%
거 54
 
7.1%
길 32
 
4.2%
동 28
 
3.7%
골 20
 
2.6%
목 19
 
2.5%
로 19
 
2.5%
이 16
 
2.1%
구 16
 
2.1%
대 15
 
2.0%
Other values (202) 478
63.2%
ASCII
ValueCountFrequency (%)
( 2
20.0%
) 2
20.0%
1 2
20.0%
3 2
20.0%
2 1
10.0%
. 1
10.0%

최종 표기명
Text

Distinct129
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:03:59.576627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.6492537
Min length3

Characters and Unicode

Total characters891
Distinct characters226
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)94.0%

Sample

1st row서울신림동민속순대타운
2nd row이대구제거리
3rd row피맛골
4th row병천토속순대
5th row능동로
ValueCountFrequency (%)
북악산길산책로 4
 
3.0%
둘레길3구간 2
 
1.5%
둘레길1구간 2
 
1.5%
빛과통신의거리 1
 
0.7%
메타세콰이어길 1
 
0.7%
한강자전거도로(광나루지구 1
 
0.7%
청담동명품거리 1
 
0.7%
을지한빛거리 1
 
0.7%
경리단길 1
 
0.7%
신의주찹쌀순대 1
 
0.7%
Other values (119) 119
88.8%
2023-12-11T15:04:00.059266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 62
 
7.0%
거 55
 
6.2%
동 42
 
4.7%
길 35
 
3.9%
로 27
 
3.0%
대 21
 
2.4%
골 20
 
2.2%
구 19
 
2.1%
목 19
 
2.1%
이 18
 
2.0%
Other values (216) 573
64.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 880
98.8%
Decimal Number 7
 
0.8%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 62
 
7.0%
거 55
 
6.2%
동 42
 
4.8%
길 35
 
4.0%
로 27
 
3.1%
대 21
 
2.4%
골 20
 
2.3%
구 19
 
2.2%
목 19
 
2.2%
이 18
 
2.0%
Other values (210) 562
63.9%
Decimal Number
ValueCountFrequency (%)
3 3
42.9%
1 2
28.6%
2 1
 
14.3%
0 1
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 880
98.8%
Common 11
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 62
 
7.0%
거 55
 
6.2%
동 42
 
4.8%
길 35
 
4.0%
로 27
 
3.1%
대 21
 
2.4%
골 20
 
2.3%
구 19
 
2.2%
목 19
 
2.2%
이 18
 
2.0%
Other values (210) 562
63.9%
Common
ValueCountFrequency (%)
3 3
27.3%
) 2
18.2%
1 2
18.2%
( 2
18.2%
2 1
 
9.1%
0 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 880
98.8%
ASCII 11
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 62
 
7.0%
거 55
 
6.2%
동 42
 
4.8%
길 35
 
4.0%
로 27
 
3.1%
대 21
 
2.4%
골 20
 
2.3%
구 19
 
2.2%
목 19
 
2.2%
이 18
 
2.0%
Other values (210) 562
63.9%
ASCII
ValueCountFrequency (%)
3 3
27.3%
) 2
18.2%
1 2
18.2%
( 2
18.2%
2 1
 
9.1%
0 1
 
9.1%

지번 주소
Text

Distinct87
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:04:00.394421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length14.559701
Min length12

Characters and Unicode

Total characters1951
Distinct characters117
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)43.3%

Sample

1st row서울시 관악구 서원동 일대
2nd row서울시 서대문구 신촌동 일대
3rd row서울시 종로구 종로1.2.3.4가동 일대
4th row서울시 강동구 길동 일대
5th row서울시 광진구 능동 일대
ValueCountFrequency (%)
서울시 134
25.0%
일대 134
25.0%
종로구 22
 
4.1%
중구 20
 
3.7%
양천구 11
 
2.1%
강남구 7
 
1.3%
서초구 7
 
1.3%
마포구 7
 
1.3%
서대문구 6
 
1.1%
용산구 6
 
1.1%
Other values (102) 182
34.0%
2023-12-11T15:04:00.868964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
402
20.6%
서 151
 
7.7%
동 147
 
7.5%
대 144
 
7.4%
구 137
 
7.0%
일 135
 
6.9%
시 134
 
6.9%
울 134
 
6.9%
로 31
 
1.6%
종 27
 
1.4%
Other values (107) 509
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1476
75.7%
Space Separator 402
 
20.6%
Decimal Number 60
 
3.1%
Other Punctuation 13
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 151
 
10.2%
동 147
 
10.0%
대 144
 
9.8%
구 137
 
9.3%
일 135
 
9.1%
시 134
 
9.1%
울 134
 
9.1%
로 31
 
2.1%
종 27
 
1.8%
중 20
 
1.4%
Other values (98) 416
28.2%
Decimal Number
ValueCountFrequency (%)
2 18
30.0%
1 16
26.7%
4 10
16.7%
3 7
 
11.7%
5 5
 
8.3%
6 3
 
5.0%
7 1
 
1.7%
Space Separator
ValueCountFrequency (%)
402
100.0%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1476
75.7%
Common 475
 
24.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 151
 
10.2%
동 147
 
10.0%
대 144
 
9.8%
구 137
 
9.3%
일 135
 
9.1%
시 134
 
9.1%
울 134
 
9.1%
로 31
 
2.1%
종 27
 
1.8%
중 20
 
1.4%
Other values (98) 416
28.2%
Common
ValueCountFrequency (%)
402
84.6%
2 18
 
3.8%
1 16
 
3.4%
. 13
 
2.7%
4 10
 
2.1%
3 7
 
1.5%
5 5
 
1.1%
6 3
 
0.6%
7 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1476
75.7%
ASCII 475
 
24.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
402
84.6%
2 18
 
3.8%
1 16
 
3.4%
. 13
 
2.7%
4 10
 
2.1%
3 7
 
1.5%
5 5
 
1.1%
6 3
 
0.6%
7 1
 
0.2%
Hangul
ValueCountFrequency (%)
서 151
 
10.2%
동 147
 
10.0%
대 144
 
9.8%
구 137
 
9.3%
일 135
 
9.1%
시 134
 
9.1%
울 134
 
9.1%
로 31
 
2.1%
종 27
 
1.8%
중 20
 
1.4%
Other values (98) 416
28.2%

법정 시
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
서울특별시
134 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 134
100.0%

Length

2023-12-11T15:04:01.035351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:04:01.144205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 134
100.0%

법정 구
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
종로구
22 
중구
20 
양천구
11 
서초구
7 
강남구
7 
Other values (18)
67 

Length

Max length4
Median length3
Mean length2.9477612
Min length2

Unique

Unique4 ?
Unique (%)3.0%

Sample

1st row관악구
2nd row서대문구
3rd row종로구
4th row강동구
5th row광진구

Common Values

ValueCountFrequency (%)
종로구 22
16.4%
중구 20
14.9%
양천구 11
 
8.2%
서초구 7
 
5.2%
강남구 7
 
5.2%
마포구 7
 
5.2%
용산구 6
 
4.5%
강동구 6
 
4.5%
서대문구 6
 
4.5%
성북구 5
 
3.7%
Other values (13) 37
27.6%

Length

2023-12-11T15:04:01.293559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 22
16.4%
중구 20
14.9%
양천구 11
 
8.2%
서초구 7
 
5.2%
강남구 7
 
5.2%
마포구 7
 
5.2%
용산구 6
 
4.5%
강동구 6
 
4.5%
서대문구 6
 
4.5%
영등포구 5
 
3.7%
Other values (13) 37
27.6%

법정 동
Text

Distinct95
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:04:01.636256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1716418
Min length2

Characters and Unicode

Total characters425
Distinct characters108
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)52.2%

Sample

1st row신림동
2nd row대현동
3rd row청진동
4th row길동
5th row능동
ValueCountFrequency (%)
목동 7
 
5.2%
신정동 4
 
3.0%
평창동 4
 
3.0%
한남동 3
 
2.2%
신사동 3
 
2.2%
상암동 3
 
2.2%
황학동 3
 
2.2%
봉천동 3
 
2.2%
천호동 2
 
1.5%
자양동 2
 
1.5%
Other values (85) 100
74.6%
2023-12-11T15:04:02.175045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 133
31.3%
가 18
 
4.2%
신 12
 
2.8%
현 9
 
2.1%
2 8
 
1.9%
정 8
 
1.9%
천 7
 
1.6%
목 7
 
1.6%
남 7
 
1.6%
창 7
 
1.6%
Other values (98) 209
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 410
96.5%
Decimal Number 15
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 133
32.4%
가 18
 
4.4%
신 12
 
2.9%
현 9
 
2.2%
정 8
 
2.0%
천 7
 
1.7%
목 7
 
1.7%
남 7
 
1.7%
창 7
 
1.7%
암 6
 
1.5%
Other values (93) 196
47.8%
Decimal Number
ValueCountFrequency (%)
2 8
53.3%
1 2
 
13.3%
3 2
 
13.3%
5 2
 
13.3%
6 1
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 410
96.5%
Common 15
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 133
32.4%
가 18
 
4.4%
신 12
 
2.9%
현 9
 
2.2%
정 8
 
2.0%
천 7
 
1.7%
목 7
 
1.7%
남 7
 
1.7%
창 7
 
1.7%
암 6
 
1.5%
Other values (93) 196
47.8%
Common
ValueCountFrequency (%)
2 8
53.3%
1 2
 
13.3%
3 2
 
13.3%
5 2
 
13.3%
6 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 410
96.5%
ASCII 15
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 133
32.4%
가 18
 
4.4%
신 12
 
2.9%
현 9
 
2.2%
정 8
 
2.0%
천 7
 
1.7%
목 7
 
1.7%
남 7
 
1.7%
창 7
 
1.7%
암 6
 
1.5%
Other values (93) 196
47.8%
ASCII
ValueCountFrequency (%)
2 8
53.3%
1 2
 
13.3%
3 2
 
13.3%
5 2
 
13.3%
6 1
 
6.7%

행정 시
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
서울특별시
134 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 134
100.0%

Length

2023-12-11T15:04:02.394500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:04:02.531784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 134
100.0%

행정 구
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
종로구
22 
중구
20 
양천구
11 
서초구
7 
강남구
7 
Other values (18)
67 

Length

Max length4
Median length3
Mean length2.9477612
Min length2

Unique

Unique4 ?
Unique (%)3.0%

Sample

1st row관악구
2nd row서대문구
3rd row종로구
4th row강동구
5th row광진구

Common Values

ValueCountFrequency (%)
종로구 22
16.4%
중구 20
14.9%
양천구 11
 
8.2%
서초구 7
 
5.2%
강남구 7
 
5.2%
마포구 7
 
5.2%
용산구 6
 
4.5%
강동구 6
 
4.5%
서대문구 6
 
4.5%
성북구 5
 
3.7%
Other values (13) 37
27.6%

Length

2023-12-11T15:04:02.671496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 22
16.4%
중구 20
14.9%
양천구 11
 
8.2%
서초구 7
 
5.2%
강남구 7
 
5.2%
마포구 7
 
5.2%
용산구 6
 
4.5%
강동구 6
 
4.5%
서대문구 6
 
4.5%
영등포구 5
 
3.7%
Other values (13) 37
27.6%

행정 동
Text

Distinct87
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T15:04:02.984955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.6119403
Min length2

Characters and Unicode

Total characters484
Distinct characters104
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)43.3%

Sample

1st row서원동
2nd row신촌동
3rd row종로1.2.3.4가동
4th row길동
5th row능동
ValueCountFrequency (%)
평창동 4
 
3.0%
삼청동 4
 
3.0%
명동 4
 
3.0%
목5동 4
 
3.0%
신촌동 4
 
3.0%
종로1.2.3.4가동 4
 
3.0%
황학동 3
 
2.2%
상암동 3
 
2.2%
한남동 3
 
2.2%
목1동 3
 
2.2%
Other values (77) 98
73.1%
2023-12-11T15:04:03.472641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 134
27.7%
2 18
 
3.7%
1 16
 
3.3%
. 13
 
2.7%
신 12
 
2.5%
4 10
 
2.1%
가 10
 
2.1%
현 9
 
1.9%
청 9
 
1.9%
정 8
 
1.7%
Other values (94) 245
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 411
84.9%
Decimal Number 60
 
12.4%
Other Punctuation 13
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 134
32.6%
신 12
 
2.9%
가 10
 
2.4%
현 9
 
2.2%
청 9
 
2.2%
정 8
 
1.9%
이 8
 
1.9%
로 8
 
1.9%
삼 7
 
1.7%
목 7
 
1.7%
Other values (86) 199
48.4%
Decimal Number
ValueCountFrequency (%)
2 18
30.0%
1 16
26.7%
4 10
16.7%
3 7
 
11.7%
5 5
 
8.3%
6 3
 
5.0%
7 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 411
84.9%
Common 73
 
15.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 134
32.6%
신 12
 
2.9%
가 10
 
2.4%
현 9
 
2.2%
청 9
 
2.2%
정 8
 
1.9%
이 8
 
1.9%
로 8
 
1.9%
삼 7
 
1.7%
목 7
 
1.7%
Other values (86) 199
48.4%
Common
ValueCountFrequency (%)
2 18
24.7%
1 16
21.9%
. 13
17.8%
4 10
13.7%
3 7
 
9.6%
5 5
 
6.8%
6 3
 
4.1%
7 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 411
84.9%
ASCII 73
 
15.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 134
32.6%
신 12
 
2.9%
가 10
 
2.4%
현 9
 
2.2%
청 9
 
2.2%
정 8
 
1.9%
이 8
 
1.9%
로 8
 
1.9%
삼 7
 
1.7%
목 7
 
1.7%
Other values (86) 199
48.4%
ASCII
ValueCountFrequency (%)
2 18
24.7%
1 16
21.9%
. 13
17.8%
4 10
13.7%
3 7
 
9.6%
5 5
 
6.8%
6 3
 
4.1%
7 1
 
1.4%

중심 좌표 X
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98233
Minimum126.81332
Maximum127.14969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T15:04:03.630614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.81332
5-th percentile126.87259
Q1126.94655
median126.98609
Q3127.01214
95-th percentile127.12363
Maximum127.14969
Range0.33636955
Interquartile range (IQR)0.065589285

Descriptive statistics

Standard deviation0.06781327
Coefficient of variation (CV)0.00053403704
Kurtosis0.13723182
Mean126.98233
Median Absolute Deviation (MAD)0.034513979
Skewness0.16968752
Sum17015.633
Variance0.0045986396
MonotonicityNot monotonic
2023-12-11T15:04:04.123230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8758936853 7
 
5.2%
126.9959254377 5
 
3.7%
126.9873802368 4
 
3.0%
126.957402192 4
 
3.0%
126.8629865421 4
 
3.0%
126.9788194313 4
 
3.0%
126.9465464425 4
 
3.0%
126.9803912024 4
 
3.0%
127.0204336493 3
 
2.2%
126.948985568 3
 
2.2%
Other values (64) 92
68.7%
ValueCountFrequency (%)
126.8133225984 1
 
0.7%
126.8629865421 4
3.0%
126.8725938165 3
2.2%
126.8758936853 7
5.2%
126.8842347738 2
 
1.5%
126.8873071257 1
 
0.7%
126.8886175026 1
 
0.7%
126.8984840296 1
 
0.7%
126.9012770519 1
 
0.7%
126.9115342551 1
 
0.7%
ValueCountFrequency (%)
127.1496921479 1
0.7%
127.1470468799 1
0.7%
127.1308327064 1
0.7%
127.128897657 1
0.7%
127.1266790476 2
1.5%
127.126461696 1
0.7%
127.1221039568 1
0.7%
127.1040253187 1
0.7%
127.087693357 1
0.7%
127.0824623102 1
0.7%

중심 좌표 Y
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.553347
Minimum37.463202
Maximum37.664471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T15:04:04.328568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.463202
5-th percentile37.482903
Q137.526133
median37.55958
Q337.576449
95-th percentile37.61982
Maximum37.664471
Range0.2012695
Interquartile range (IQR)0.050316455

Descriptive statistics

Standard deviation0.040914786
Coefficient of variation (CV)0.001089511
Kurtosis0.082631934
Mean37.553347
Median Absolute Deviation (MAD)0.023229553
Skewness0.096974288
Sum5032.1484
Variance0.0016740197
MonotonicityNot monotonic
2023-12-11T15:04:04.549479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5365973016 7
 
5.2%
37.5613498068 5
 
3.7%
37.5744028289 4
 
3.0%
37.6178956805 4
 
3.0%
37.516577197 4
 
3.0%
37.568058597 4
 
3.0%
37.5611107256 4
 
3.0%
37.5828099517 4
 
3.0%
37.568610138 3
 
2.2%
37.4829408135 3
 
2.2%
Other values (64) 92
68.7%
ValueCountFrequency (%)
37.4632016047 2
1.5%
37.475038316 2
1.5%
37.4787998297 2
1.5%
37.482833211 1
 
0.7%
37.4829408135 3
2.2%
37.4854043468 1
 
0.7%
37.4885117081 1
 
0.7%
37.4967500548 1
 
0.7%
37.4998663454 1
 
0.7%
37.5006879112 1
 
0.7%
ValueCountFrequency (%)
37.6644711 2
1.5%
37.6386797307 1
 
0.7%
37.6351725 2
1.5%
37.6305927195 1
 
0.7%
37.6209706338 1
 
0.7%
37.6192011241 1
 
0.7%
37.6178956805 4
3.0%
37.6168942335 1
 
0.7%
37.5971607572 2
1.5%
37.59692086 2
1.5%

Interactions

2023-12-11T15:03:56.173696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:03:55.876572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:03:56.308095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:03:56.035286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:04:04.731688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지번 주소법정 구법정 동행정 구행정 동중심 좌표 X중심 좌표 Y
지번 주소1.0001.0000.9971.0001.0001.0001.000
법정 구1.0001.0001.0001.0001.0000.9530.907
법정 동0.9971.0001.0001.0000.9971.0001.000
행정 구1.0001.0001.0001.0001.0000.9530.907
행정 동1.0001.0000.9971.0001.0001.0001.000
중심 좌표 X1.0000.9531.0000.9531.0001.0000.580
중심 좌표 Y1.0000.9071.0000.9071.0000.5801.000
2023-12-11T15:04:04.865792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정 구행정 구
법정 구1.0001.000
행정 구1.0001.000
2023-12-11T15:04:04.984425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중심 좌표 X중심 좌표 Y법정 구행정 구
중심 좌표 X1.0000.0080.7290.729
중심 좌표 Y0.0081.0000.6030.603
법정 구0.7290.6031.0001.000
행정 구0.7290.6031.0001.000

Missing values

2023-12-11T15:03:56.510734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:03:56.719909image/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.

Sample

키검색 키워드alias최종 표기명지번 주소법정 시법정 구법정 동행정 시행정 구행정 동중심 좌표 X중심 좌표 Y
0BE_IW01-0042서울신림동민속순대타운서울신림동민속순대타운서울신림동민속순대타운서울시 관악구 서원동 일대서울특별시관악구신림동서울특별시관악구서원동126.93459837.463202
1BE_IW01-0043이대/구제거리이대구제거리이대구제거리서울시 서대문구 신촌동 일대서울특별시서대문구대현동서울특별시서대문구신촌동126.94654637.561111
2BE_IW01-0044피맛골피맛골피맛골서울시 종로구 종로1.2.3.4가동 일대서울특별시종로구청진동서울특별시종로구종로1.2.3.4가동126.9873837.574403
3BE_IW01-0045병천토속순대병천토속순대병천토속순대서울시 강동구 길동 일대서울특별시강동구길동서울특별시강동구길동127.14704737.53856
4BE_IW01-0046능동로능동로능동로서울시 광진구 능동 일대서울특별시광진구능동서울특별시광진구능동127.08246237.55105
5BE_IW01-0047축제의거리축제의거리축제의거리서울시 양천구 목1동 일대서울특별시양천구목동서울특별시양천구목1동126.87589437.536597
6BE_IW01-0048방배동/카페골목방배동카페골목방배동카페골목서울시 서초구 방배본동 일대서울특별시서초구방배동서울특별시서초구방배본동126.99409837.4788
7BE_IW01-0049명동거리명동거리명동거리서울시 중구 명동 일대서울특별시중구명동2가서울특별시중구명동126.97881937.568059
8BE_IW01-0050응암동/감자탕골목응암감자탕골목응암동감자탕골목서울시 은평구 응암3동 일대서울특별시은평구응암동서울특별시은평구응암3동126.92170437.592642
9BE_IW01-0051장안평자동차/용품거리자동차용품거리장안평자동차용품거리서울시 성동구 용답동 일대서울특별시성동구용답동서울특별시성동구용답동127.06129337.553694
키검색 키워드alias최종 표기명지번 주소법정 시법정 구법정 동행정 시행정 구행정 동중심 좌표 X중심 좌표 Y
124BE_IW01-0130당리순대국당리순대국당리순대국서울시 강동구 명일2동 일대서울특별시강동구명일동서울특별시강동구명일2동127.14969237.548684
125BE_IW01-0131신의주찹쌀순대/영등포구청역점신의주찹쌀순대신의주찹쌀순대영등포구청역점서울시 영등포구 당산1동 일대서울특별시영등포구당산동3가서울특별시영등포구당산1동126.89848437.52168
126BE_IW01-0132김가네순대국김가네순대국김가네순대국서울시 성동구 성수2가1동 일대서울특별시성동구성수동2가서울특별시성동구성수2가1동127.05815937.540368
127BE_IW01-0133평창동화랑가평창동화랑가평창동화랑가서울시 종로구 평창동 일대서울특별시종로구평창동서울특별시종로구평창동126.95740237.617896
128BE_IW01-0134아현동/웨딩거리아현동웨딩거리아현동웨딩거리서울시 서대문구 북아현동 일대서울특별시서대문구북아현동서울특별시서대문구북아현동126.95455437.563799
129BE_IW01-0037장수길장수길장수길서울시 성북구 삼선동 일대서울특별시성북구삼선동1가서울특별시성북구삼선동127.02078137.597161
130BE_IW01-0038둘레길2구간둘레길2구간둘레길2구간서울시 성북구 성북동 일대서울특별시성북구성북동서울특별시성북구성북동126.9921537.596921
131BE_IW01-0039둘레길/순례길구간순례길구간둘레길순례길구간서울시 강북구 우이동 일대서울특별시강북구우이동서울특별시강북구우이동126.98326637.664471
132BE_IW01-0040고향의거리고향의거리고향의거리서울시 양천구 신정6동 일대서울특별시양천구신정동서울특별시양천구신정6동126.86298737.516577
133BE_IW01-0041왕십리곱창골목왕십리곱창골목왕십리곱창골목서울시 중구 황학동 일대서울특별시중구황학동서울특별시중구황학동127.02043437.56861