Overview

Dataset statistics

Number of variables11
Number of observations81
Missing cells137
Missing cells (%)15.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory89.6 B

Variable types

Text7
Categorical4

Dataset

Description키,명칭,대분류,중분류,주소,도로명 주소,행정 시,행정 구,행정 동,대표전화,홈페이지주소
Author서울관광재단
URLhttps://data.seoul.go.kr/dataList/OA-12963/S/1/datasetView.do

Alerts

대분류 has constant value ""Constant
행정 시 has constant value ""Constant
주소 has 50 (61.7%) missing valuesMissing
도로명 주소 has 31 (38.3%) missing valuesMissing
행정 동 has 50 (61.7%) missing valuesMissing
대표전화 has 5 (6.2%) missing valuesMissing
홈페이지주소 has 1 (1.2%) missing valuesMissing
키 has unique valuesUnique
명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-11 04:17:31.025771
Analysis finished2023-12-11 04:17:32.566905
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-11T13:17:32.759658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.012346
Min length12

Characters and Unicode

Total characters973
Distinct characters17
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

Unique81 ?
Unique (%)100.0%

Sample

1st row?BE_IW06-0001
2nd rowBE_IW06-0002
3rd rowBE_IW06-0003
4th rowBE_IW06-0004
5th rowBE_IW06-0005
ValueCountFrequency (%)
be_iw06-0001 1
 
1.2%
be_iw06-0042 1
 
1.2%
be_iw06-0060 1
 
1.2%
be_iw06-0059 1
 
1.2%
be_iw06-0058 1
 
1.2%
be_iw06-0057 1
 
1.2%
be_iw06-0056 1
 
1.2%
be_iw06-0055 1
 
1.2%
be_iw06-0054 1
 
1.2%
be_iw06-0053 1
 
1.2%
Other values (71) 71
87.7%
2023-12-11T13:17:33.189712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 260
26.7%
6 99
 
10.2%
- 81
 
8.3%
E 81
 
8.3%
_ 81
 
8.3%
I 81
 
8.3%
W 81
 
8.3%
B 81
 
8.3%
1 19
 
2.0%
2 18
 
1.8%
Other values (7) 91
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 486
49.9%
Uppercase Letter 324
33.3%
Dash Punctuation 81
 
8.3%
Connector Punctuation 81
 
8.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260
53.5%
6 99
 
20.4%
1 19
 
3.9%
2 18
 
3.7%
3 18
 
3.7%
4 18
 
3.7%
5 18
 
3.7%
7 18
 
3.7%
8 10
 
2.1%
9 8
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
E 81
25.0%
I 81
25.0%
W 81
25.0%
B 81
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 81
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 649
66.7%
Latin 324
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260
40.1%
6 99
 
15.3%
- 81
 
12.5%
_ 81
 
12.5%
1 19
 
2.9%
2 18
 
2.8%
3 18
 
2.8%
4 18
 
2.8%
5 18
 
2.8%
7 18
 
2.8%
Other values (3) 19
 
2.9%
Latin
ValueCountFrequency (%)
E 81
25.0%
I 81
25.0%
W 81
25.0%
B 81
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260
26.7%
6 99
 
10.2%
- 81
 
8.3%
E 81
 
8.3%
_ 81
 
8.3%
I 81
 
8.3%
W 81
 
8.3%
B 81
 
8.3%
1 19
 
2.0%
2 18
 
1.8%
Other values (7) 91
 
9.4%

명칭
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-11T13:17:33.525450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length13
Mean length8.1481481
Min length2

Characters and Unicode

Total characters660
Distinct characters187
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)100.0%

Sample

1st row63 컨벤션 센터
2nd row700 요트클럽
3rd rowAW컨벤션센터
4th rowKTNG상상마당
5th row가나아트센터
ValueCountFrequency (%)
서울 17
 
11.6%
호텔 8
 
5.4%
그랜드 5
 
3.4%
프리미어 4
 
2.7%
코엑스 4
 
2.7%
앰배서더 3
 
2.0%
센터 2
 
1.4%
메리어트 2
 
1.4%
jw 2
 
1.4%
인터컨티넨탈 2
 
1.4%
Other values (93) 98
66.7%
2023-12-11T13:17:34.011394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66
 
10.0%
서 32
 
4.8%
울 29
 
4.4%
텔 28
 
4.2%
호 25
 
3.8%
스 22
 
3.3%
리 17
 
2.6%
라 14
 
2.1%
드 13
 
2.0%
트 12
 
1.8%
Other values (177) 402
60.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 568
86.1%
Space Separator 66
 
10.0%
Uppercase Letter 12
 
1.8%
Decimal Number 8
 
1.2%
Connector Punctuation 4
 
0.6%
Lowercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 32
 
5.6%
울 29
 
5.1%
텔 28
 
4.9%
호 25
 
4.4%
스 22
 
3.9%
리 17
 
3.0%
라 14
 
2.5%
드 13
 
2.3%
트 12
 
2.1%
터 9
 
1.6%
Other values (160) 367
64.6%
Uppercase Letter
ValueCountFrequency (%)
W 3
25.0%
J 2
16.7%
T 2
16.7%
K 2
16.7%
G 1
 
8.3%
N 1
 
8.3%
A 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 2
25.0%
0 2
25.0%
6 1
12.5%
5 1
12.5%
7 1
12.5%
3 1
12.5%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
h 1
50.0%
Space Separator
ValueCountFrequency (%)
66
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 568
86.1%
Common 78
 
11.8%
Latin 14
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 32
 
5.6%
울 29
 
5.1%
텔 28
 
4.9%
호 25
 
4.4%
스 22
 
3.9%
리 17
 
3.0%
라 14
 
2.5%
드 13
 
2.3%
트 12
 
2.1%
터 9
 
1.6%
Other values (160) 367
64.6%
Latin
ValueCountFrequency (%)
W 3
21.4%
J 2
14.3%
T 2
14.3%
K 2
14.3%
e 1
 
7.1%
h 1
 
7.1%
G 1
 
7.1%
N 1
 
7.1%
A 1
 
7.1%
Common
ValueCountFrequency (%)
66
84.6%
_ 4
 
5.1%
1 2
 
2.6%
0 2
 
2.6%
6 1
 
1.3%
5 1
 
1.3%
7 1
 
1.3%
3 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 568
86.1%
ASCII 92
 
13.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66
71.7%
_ 4
 
4.3%
W 3
 
3.3%
J 2
 
2.2%
1 2
 
2.2%
T 2
 
2.2%
K 2
 
2.2%
0 2
 
2.2%
6 1
 
1.1%
e 1
 
1.1%
Other values (7) 7
 
7.6%
Hangul
ValueCountFrequency (%)
서 32
 
5.6%
울 29
 
5.1%
텔 28
 
4.9%
호 25
 
4.4%
스 22
 
3.9%
리 17
 
3.0%
라 14
 
2.5%
드 13
 
2.3%
트 12
 
2.1%
터 9
 
1.6%
Other values (160) 367
64.6%

대분류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
마이스서울
81 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마이스서울 81
100.0%

Length

2023-12-11T13:17:34.194651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:17:34.339597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마이스서울 81
100.0%

중분류
Categorical

Distinct3
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size780.0 B
컨벤션 호텔
40 
유니크베뉴
38 
컨벤션 센터
 
3

Length

Max length6
Median length6
Mean length5.5308642
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
컨벤션 호텔 40
49.4%
유니크베뉴 38
46.9%
컨벤션 센터 3
 
3.7%

Length

2023-12-11T13:17:34.476259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:17:34.608176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
컨벤션 43
34.7%
호텔 40
32.3%
유니크베뉴 38
30.6%
센터 3
 
2.4%

주소
Text

MISSING 

Distinct31
Distinct (%)100.0%
Missing50
Missing (%)61.7%
Memory size780.0 B
2023-12-11T13:17:34.919120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length21
Mean length18.258065
Min length13

Characters and Unicode

Total characters566
Distinct characters91
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row서울시 마포구 상암동 487-254
2nd row서울시 종로구 삼청동14
3rd row서울시 서초구 올림픽로 683번지 세빛섬 1섬 가빛섬
4th row서울특별시 강남구 압구정동 380-2번지 한강잠원지구
5th row서울시 서초구 잠원동 한강시민공원 잠원지구 121-9프라디아
ValueCountFrequency (%)
서울시 14
 
10.5%
강남구 13
 
9.8%
서울 9
 
6.8%
서울특별시 7
 
5.3%
중구 5
 
3.8%
서초구 5
 
3.8%
역삼동 4
 
3.0%
청담동 3
 
2.3%
삼성동 3
 
2.3%
논현동 2
 
1.5%
Other values (63) 68
51.1%
2023-12-11T13:17:35.449624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102
18.0%
서 37
 
6.5%
구 34
 
6.0%
울 32
 
5.7%
동 28
 
4.9%
시 22
 
3.9%
1 21
 
3.7%
- 17
 
3.0%
강 15
 
2.7%
2 14
 
2.5%
Other values (81) 244
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 343
60.6%
Decimal Number 103
 
18.2%
Space Separator 102
 
18.0%
Dash Punctuation 17
 
3.0%
Other Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 37
 
10.8%
구 34
 
9.9%
울 32
 
9.3%
동 28
 
8.2%
시 22
 
6.4%
강 15
 
4.4%
남 13
 
3.8%
지 10
 
2.9%
삼 8
 
2.3%
별 7
 
2.0%
Other values (68) 137
39.9%
Decimal Number
ValueCountFrequency (%)
1 21
20.4%
2 14
13.6%
3 14
13.6%
6 9
8.7%
5 9
8.7%
4 8
 
7.8%
0 8
 
7.8%
8 8
 
7.8%
9 7
 
6.8%
7 5
 
4.9%
Space Separator
ValueCountFrequency (%)
102
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 344
60.8%
Common 222
39.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 37
 
10.8%
구 34
 
9.9%
울 32
 
9.3%
동 28
 
8.1%
시 22
 
6.4%
강 15
 
4.4%
남 13
 
3.8%
지 10
 
2.9%
삼 8
 
2.3%
별 7
 
2.0%
Other values (69) 138
40.1%
Common
ValueCountFrequency (%)
102
45.9%
1 21
 
9.5%
- 17
 
7.7%
2 14
 
6.3%
3 14
 
6.3%
6 9
 
4.1%
5 9
 
4.1%
4 8
 
3.6%
0 8
 
3.6%
8 8
 
3.6%
Other values (2) 12
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 343
60.6%
ASCII 222
39.2%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
102
45.9%
1 21
 
9.5%
- 17
 
7.7%
2 14
 
6.3%
3 14
 
6.3%
6 9
 
4.1%
5 9
 
4.1%
4 8
 
3.6%
0 8
 
3.6%
8 8
 
3.6%
Other values (2) 12
 
5.4%
Hangul
ValueCountFrequency (%)
서 37
 
10.8%
구 34
 
9.9%
울 32
 
9.3%
동 28
 
8.2%
시 22
 
6.4%
강 15
 
4.4%
남 13
 
3.8%
지 10
 
2.9%
삼 8
 
2.3%
별 7
 
2.0%
Other values (68) 137
39.9%
None
ValueCountFrequency (%)
㈜ 1
100.0%

도로명 주소
Text

MISSING 

Distinct48
Distinct (%)96.0%
Missing31
Missing (%)38.3%
Memory size780.0 B
2023-12-11T13:17:35.823425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length16.6
Min length12

Characters and Unicode

Total characters830
Distinct characters101
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

Unique46 ?
Unique (%)92.0%

Sample

1st row서울특별시 영등포구 63로 50
2nd row서울 종로구 자하문로 255
3rd row서울특별시 마포구 어울마당로 65
4th row서울특별시 종로구 평창30길 28
5th row서울특별시 중구 장충단로 59
ValueCountFrequency (%)
서울시 28
 
13.9%
서울특별시 17
 
8.4%
중구 10
 
5.0%
강남구 9
 
4.5%
종로구 8
 
4.0%
서울 6
 
3.0%
영등포구 4
 
2.0%
용산구 3
 
1.5%
광진구 3
 
1.5%
동호로 3
 
1.5%
Other values (97) 111
55.0%
2023-12-11T13:17:36.306494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153
18.4%
서 58
 
7.0%
로 56
 
6.7%
구 54
 
6.5%
울 52
 
6.3%
시 47
 
5.7%
2 27
 
3.3%
5 21
 
2.5%
3 20
 
2.4%
1 18
 
2.2%
Other values (91) 324
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 521
62.8%
Decimal Number 154
 
18.6%
Space Separator 153
 
18.4%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 58
 
11.1%
로 56
 
10.7%
구 54
 
10.4%
울 52
 
10.0%
시 47
 
9.0%
별 18
 
3.5%
특 18
 
3.5%
남 13
 
2.5%
강 12
 
2.3%
종 11
 
2.1%
Other values (79) 182
34.9%
Decimal Number
ValueCountFrequency (%)
2 27
17.5%
5 21
13.6%
3 20
13.0%
1 18
11.7%
0 14
9.1%
6 13
8.4%
7 11
7.1%
4 11
7.1%
9 10
 
6.5%
8 9
 
5.8%
Space Separator
ValueCountFrequency (%)
153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 521
62.8%
Common 309
37.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 58
 
11.1%
로 56
 
10.7%
구 54
 
10.4%
울 52
 
10.0%
시 47
 
9.0%
별 18
 
3.5%
특 18
 
3.5%
남 13
 
2.5%
강 12
 
2.3%
종 11
 
2.1%
Other values (79) 182
34.9%
Common
ValueCountFrequency (%)
153
49.5%
2 27
 
8.7%
5 21
 
6.8%
3 20
 
6.5%
1 18
 
5.8%
0 14
 
4.5%
6 13
 
4.2%
7 11
 
3.6%
4 11
 
3.6%
9 10
 
3.2%
Other values (2) 11
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 521
62.8%
ASCII 309
37.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153
49.5%
2 27
 
8.7%
5 21
 
6.8%
3 20
 
6.5%
1 18
 
5.8%
0 14
 
4.5%
6 13
 
4.2%
7 11
 
3.6%
4 11
 
3.6%
9 10
 
3.2%
Other values (2) 11
 
3.6%
Hangul
ValueCountFrequency (%)
서 58
 
11.1%
로 56
 
10.7%
구 54
 
10.4%
울 52
 
10.0%
시 47
 
9.0%
별 18
 
3.5%
특 18
 
3.5%
남 13
 
2.5%
강 12
 
2.3%
종 11
 
2.1%
Other values (79) 182
34.9%

행정 시
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
서울특별시
81 

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 81
100.0%

Length

2023-12-11T13:17:36.499963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:17:36.619786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 81
100.0%

행정 구
Categorical

Distinct16
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size780.0 B
강남구
22 
중구
15 
종로구
9 
서초구
7 
영등포구
5 
Other values (11)
23 

Length

Max length4
Median length3
Mean length2.8888889
Min length2

Unique

Unique5 ?
Unique (%)6.2%

Sample

1st row영등포구
2nd row마포구
3rd row종로구
4th row마포구
5th row종로구

Common Values

ValueCountFrequency (%)
강남구 22
27.2%
중구 15
18.5%
종로구 9
11.1%
서초구 7
 
8.6%
영등포구 5
 
6.2%
마포구 4
 
4.9%
용산구 3
 
3.7%
송파구 3
 
3.7%
성북구 3
 
3.7%
광진구 3
 
3.7%
Other values (6) 7
 
8.6%

Length

2023-12-11T13:17:36.766092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 22
27.2%
중구 15
18.5%
종로구 9
11.1%
서초구 7
 
8.6%
영등포구 5
 
6.2%
마포구 4
 
4.9%
용산구 3
 
3.7%
송파구 3
 
3.7%
성북구 3
 
3.7%
광진구 3
 
3.7%
Other values (6) 7
 
8.6%

행정 동
Text

MISSING 

Distinct22
Distinct (%)71.0%
Missing50
Missing (%)61.7%
Memory size780.0 B
2023-12-11T13:17:36.961827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4516129
Min length2

Characters and Unicode

Total characters107
Distinct characters40
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

Unique17 ?
Unique (%)54.8%

Sample

1st row상암동
2nd row삼청동
3rd row천호2동
4th row신사동
5th row잠원동
ValueCountFrequency (%)
역삼1동 4
 
12.9%
소공동 3
 
9.7%
청담동 3
 
9.7%
삼성1동 2
 
6.5%
반포4동 2
 
6.5%
명동 1
 
3.2%
을지로동 1
 
3.2%
논현2동 1
 
3.2%
여의동 1
 
3.2%
장충동 1
 
3.2%
Other values (12) 12
38.7%
2023-12-11T13:17:37.410227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 31
29.0%
삼 8
 
7.5%
1 7
 
6.5%
역 4
 
3.7%
청 4
 
3.7%
2 4
 
3.7%
담 3
 
2.8%
소 3
 
2.8%
공 3
 
2.8%
성 3
 
2.8%
Other values (30) 37
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
86.9%
Decimal Number 14
 
13.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 31
33.3%
삼 8
 
8.6%
역 4
 
4.3%
청 4
 
4.3%
담 3
 
3.2%
소 3
 
3.2%
공 3
 
3.2%
성 3
 
3.2%
잠 2
 
2.2%
논 2
 
2.2%
Other values (26) 30
32.3%
Decimal Number
ValueCountFrequency (%)
1 7
50.0%
2 4
28.6%
4 2
 
14.3%
3 1
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
86.9%
Common 14
 
13.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 31
33.3%
삼 8
 
8.6%
역 4
 
4.3%
청 4
 
4.3%
담 3
 
3.2%
소 3
 
3.2%
공 3
 
3.2%
성 3
 
3.2%
잠 2
 
2.2%
논 2
 
2.2%
Other values (26) 30
32.3%
Common
ValueCountFrequency (%)
1 7
50.0%
2 4
28.6%
4 2
 
14.3%
3 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
86.9%
ASCII 14
 
13.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 31
33.3%
삼 8
 
8.6%
역 4
 
4.3%
청 4
 
4.3%
담 3
 
3.2%
소 3
 
3.2%
공 3
 
3.2%
성 3
 
3.2%
잠 2
 
2.2%
논 2
 
2.2%
Other values (26) 30
32.3%
ASCII
ValueCountFrequency (%)
1 7
50.0%
2 4
28.6%
4 2
 
14.3%
3 1
 
7.1%

대표전화
Text

MISSING 

Distinct75
Distinct (%)98.7%
Missing5
Missing (%)6.2%
Memory size780.0 B
2023-12-11T13:17:37.732889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length12
Mean length11.763158
Min length11

Characters and Unicode

Total characters894
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

Unique74 ?
Unique (%)97.4%

Sample

1st row02-789-5704
2nd row02-396-2442
3rd row02-330-6205
4th row02-3217-1093
5th row02-2280-4292
ValueCountFrequency (%)
02-3210-2100 2
 
2.6%
02-717-9441 1
 
1.3%
02-771-1000 1
 
1.3%
02-6474-2000 1
 
1.3%
02-6466-1234 1
 
1.3%
02-753-7788 1
 
1.3%
02-2660-9000 1
 
1.3%
02-3451-8000 1
 
1.3%
02-555-0501 1
 
1.3%
02-419-7000 1
 
1.3%
Other values (65) 65
85.5%
2023-12-11T13:17:38.178260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 209
23.4%
2 161
18.0%
- 155
17.3%
1 82
 
9.2%
5 57
 
6.4%
7 49
 
5.5%
3 48
 
5.4%
6 45
 
5.0%
4 43
 
4.8%
9 25
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 739
82.7%
Dash Punctuation 155
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
28.3%
2 161
21.8%
1 82
 
11.1%
5 57
 
7.7%
7 49
 
6.6%
3 48
 
6.5%
6 45
 
6.1%
4 43
 
5.8%
9 25
 
3.4%
8 20
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 894
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
23.4%
2 161
18.0%
- 155
17.3%
1 82
 
9.2%
5 57
 
6.4%
7 49
 
5.5%
3 48
 
5.4%
6 45
 
5.0%
4 43
 
4.8%
9 25
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
23.4%
2 161
18.0%
- 155
17.3%
1 82
 
9.2%
5 57
 
6.4%
7 49
 
5.5%
3 48
 
5.4%
6 45
 
5.0%
4 43
 
4.8%
9 25
 
2.8%

홈페이지주소
Text

MISSING 

Distinct80
Distinct (%)100.0%
Missing1
Missing (%)1.2%
Memory size780.0 B
2023-12-11T13:17:38.483462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length28
Mean length21.6625
Min length9

Characters and Unicode

Total characters1733
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)100.0%

Sample

1st rowhttp://www.63convention.co.kr
2nd rowhttp://www.700yachtclub.com
3rd rowhttp://www.awconventioncenter.co.kr
4th rowhttp://www.sangsangmadang.com
5th rowhttp://www.ganaart.com
ValueCountFrequency (%)
http://www.63convention.co.kr 1
 
1.2%
http://www.700yachtclub.com 1
 
1.2%
hotelkukdo.com 1
 
1.2%
hilton.co.kr 1
 
1.2%
mayfield.co.kr 1
 
1.2%
ritzcarltonseoul.com 1
 
1.2%
www.marriott.com/selrn 1
 
1.2%
www.hotellotte.co.kr 1
 
1.2%
www.lottehotel.com 1
 
1.2%
www.ramadaseoul.co.kr 1
 
1.2%
Other values (70) 70
87.5%
2023-12-11T13:17:38.903240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 181
 
10.4%
w 178
 
10.3%
o 145
 
8.4%
t 143
 
8.3%
r 104
 
6.0%
/ 90
 
5.2%
e 89
 
5.1%
c 88
 
5.1%
a 88
 
5.1%
h 69
 
4.0%
Other values (28) 558
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1407
81.2%
Other Punctuation 309
 
17.8%
Decimal Number 13
 
0.8%
Dash Punctuation 2
 
0.1%
Uppercase Letter 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 178
12.7%
o 145
 
10.3%
t 143
 
10.2%
r 104
 
7.4%
e 89
 
6.3%
c 88
 
6.3%
a 88
 
6.3%
h 69
 
4.9%
n 63
 
4.5%
k 62
 
4.4%
Other values (16) 378
26.9%
Decimal Number
ValueCountFrequency (%)
0 4
30.8%
1 3
23.1%
3 2
15.4%
6 2
15.4%
7 1
 
7.7%
5 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 181
58.6%
/ 90
29.1%
: 38
 
12.3%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1408
81.2%
Common 325
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 178
12.6%
o 145
 
10.3%
t 143
 
10.2%
r 104
 
7.4%
e 89
 
6.3%
c 88
 
6.2%
a 88
 
6.2%
h 69
 
4.9%
n 63
 
4.5%
k 62
 
4.4%
Other values (17) 379
26.9%
Common
ValueCountFrequency (%)
. 181
55.7%
/ 90
27.7%
: 38
 
11.7%
0 4
 
1.2%
1 3
 
0.9%
3 2
 
0.6%
6 2
 
0.6%
- 2
 
0.6%
7 1
 
0.3%
5 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 181
 
10.4%
w 178
 
10.3%
o 145
 
8.4%
t 143
 
8.3%
r 104
 
6.0%
/ 90
 
5.2%
e 89
 
5.1%
c 88
 
5.1%
a 88
 
5.1%
h 69
 
4.0%
Other values (28) 558
32.2%

Correlations

2023-12-11T13:17:39.047757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
키명칭중분류주소도로명 주소행정 구행정 동대표전화홈페이지주소
키1.0001.0001.0001.0001.0001.0001.0001.0001.000
명칭1.0001.0001.0001.0001.0001.0001.0001.0001.000
중분류1.0001.0001.0001.0000.9450.0000.6531.0001.000
주소1.0001.0001.0001.000NaN1.0001.0001.0001.000
도로명 주소1.0001.0000.945NaN1.0001.000NaN1.0001.000
행정 구1.0001.0000.0001.0001.0001.0001.0000.9951.000
행정 동1.0001.0000.6531.000NaN1.0001.0001.0001.000
대표전화1.0001.0001.0001.0001.0000.9951.0001.0001.000
홈페이지주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-11T13:17:39.193120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정 구중분류
행정 구1.0000.000
중분류0.0001.000
2023-12-11T13:17:39.291890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중분류행정 구
중분류1.0000.000
행정 구0.0001.000

Missing values

2023-12-11T13:17:31.856783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:17:32.036597image/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.
2023-12-11T13:17:32.457424image/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

키명칭대분류중분류주소도로명 주소행정 시행정 구행정 동대표전화홈페이지주소
0?BE_IW06-000163 컨벤션 센터마이스서울유니크베뉴<NA>서울특별시 영등포구 63로 50서울특별시영등포구<NA>02-789-5704http://www.63convention.co.kr
1BE_IW06-0002700 요트클럽마이스서울유니크베뉴서울시 마포구 상암동 487-254<NA>서울특별시마포구상암동<NA>http://www.700yachtclub.com
2BE_IW06-0003AW컨벤션센터마이스서울유니크베뉴<NA>서울 종로구 자하문로 255서울특별시종로구<NA>02-396-2442http://www.awconventioncenter.co.kr
3BE_IW06-0004KTNG상상마당마이스서울유니크베뉴<NA>서울특별시 마포구 어울마당로 65서울특별시마포구<NA>02-330-6205http://www.sangsangmadang.com
4BE_IW06-0005가나아트센터마이스서울유니크베뉴<NA>서울특별시 종로구 평창30길 28서울특별시종로구<NA>02-3217-1093http://www.ganaart.com
5BE_IW06-0006국립중앙극장마이스서울유니크베뉴<NA>서울특별시 중구 장충단로 59서울특별시중구<NA>02-2280-4292http://www.ntok.go.kr
6BE_IW06-0007국립중앙박물관마이스서울유니크베뉴<NA>서울시 용산구 서빙고로 137서울특별시용산구<NA>02-2077-9000-02-2077-9227http://museum.go.kr
7BE_IW06-0008나인트리컨벤션센터 광화문마이스서울유니크베뉴<NA>서울시 종로구 종로33서울특별시종로구<NA>02-2158-9000http://www.eninetree.com
8BE_IW06-0009동대문디자인플라자마이스서울유니크베뉴<NA>서울시 중구 을지로 281서울특별시중구<NA>02-2153-0072http://www.ddp.or.kr
9BE_IW06-0010두가헌마이스서울유니크베뉴서울시 종로구 삼청동14<NA>서울특별시종로구삼청동02-3210-2100http://www.dugahun.com
키명칭대분류중분류주소도로명 주소행정 시행정 구행정 동대표전화홈페이지주소
71BE_IW06-0072신라호텔마이스서울컨벤션 호텔<NA>서울시 중구 동호로 249서울특별시중구장충동02-2233-3131www.shilla.net
72BE_IW06-0073엘루이호텔마이스서울컨벤션 호텔서울시 강남구 청담동 129<NA>서울특별시강남구청담동02-514-3535ellui.com
73BE_IW06-0074여의도 렉싱턴호텔마이스서울컨벤션 호텔서울 영등포구 여의도동 13-3<NA>서울특별시영등포구여의동02-6670-7000thelexington.co.kr
74BE_IW06-0075오크우드 프리미어 코엑스 센터마이스서울컨벤션 호텔서울특별시 강남구 삼성동 159<NA>서울특별시강남구삼성1동02-3466-7000www.oakwoodpremier.co.kr
75BE_IW06-0076인터컨티넨탈 서울 코엑스마이스서울컨벤션 호텔<NA>서울시 강남구 봉은사로 524서울특별시강남구<NA>02-3452-2500www.iccoex.com
76BE_IW06-0077임페리얼펠리스호텔마이스서울컨벤션 호텔서울 강남구 논현동 248-7<NA>서울특별시강남구논현2동02-3440-8000imperialpalace.co.kr
77BE_IW06-0078파크 하얏트 서울마이스서울컨벤션 호텔<NA>서울시 강남구 테헤란로 606서울특별시강남구<NA>02-2016-1234parkhyattseoul.com
78BE_IW06-0079플라자 호텔마이스서울컨벤션 호텔서울시 중구 태평로 2가 23<NA>서울특별시중구소공동02-771-2200www.hoteltheplaza.com
79BE_IW06-0080호텔프라마마이스서울컨벤션 호텔서울 강남구 청담동 52-3<NA>서울특별시강남구청담동02-6006-9114prima.co.kr
80BE_IW06-0081훌리데이인 성북 서울호텔마이스서울컨벤션 호텔서울특별시 성북구 종암동 3-1343<NA>서울특별시성북구종암동02-929-2000holiday.co.kr