Overview

Dataset statistics

Number of variables12
Number of observations159
Missing cells477
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.5 KiB
Average record size in memory99.8 B

Variable types

Text4
Categorical5
Unsupported3

Dataset

Description메인 키,분류1,분류2,분류3,분류4,검색어,명칭,지번 주소,도로명 주소,행정 시,행정 구,행정 동
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12996/S/1/datasetView.do

Alerts

분류1 has constant value ""Constant
분류2 has constant value ""Constant
행정 시 has constant value ""Constant
분류4 has 159 (100.0%) missing valuesMissing
지번 주소 has 159 (100.0%) missing valuesMissing
도로명 주소 has 159 (100.0%) missing valuesMissing
메인 키 has unique valuesUnique
분류4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
지번 주소 is an unsupported type, check if it needs cleaning or further analysisUnsupported
도로명 주소 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-14 11:13:59.673431
Analysis finished2024-04-14 11:14:03.302974
Duration3.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

메인 키
Text

UNIQUE 

Distinct159
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-14T20:14:04.000018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters2226
Distinct characters18
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

Unique159 ?
Unique (%)100.0%

Sample

1st rowBE_LiST37-0001
2nd rowBE_LiST37-0002
3rd rowBE_LiST37-0003
4th rowBE_LiST37-0004
5th rowBE_LiST37-0005
ValueCountFrequency (%)
be_list37-0001 1
 
0.6%
be_list37-0101 1
 
0.6%
be_list37-0103 1
 
0.6%
be_list37-0104 1
 
0.6%
be_list37-0105 1
 
0.6%
be_list37-0106 1
 
0.6%
be_list37-0107 1
 
0.6%
be_list37-0108 1
 
0.6%
be_list37-0109 1
 
0.6%
be_list37-0102 1
 
0.6%
Other values (149) 149
93.7%
2024-04-14T20:14:05.280414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 292
13.1%
3 195
8.8%
7 185
8.3%
B 159
 
7.1%
T 159
 
7.1%
E 159
 
7.1%
- 159
 
7.1%
S 159
 
7.1%
i 159
 
7.1%
L 159
 
7.1%
Other values (8) 441
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 954
42.9%
Uppercase Letter 795
35.7%
Dash Punctuation 159
 
7.1%
Lowercase Letter 159
 
7.1%
Connector Punctuation 159
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 292
30.6%
3 195
20.4%
7 185
19.4%
1 96
 
10.1%
4 36
 
3.8%
5 36
 
3.8%
2 36
 
3.8%
6 26
 
2.7%
8 26
 
2.7%
9 26
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
B 159
20.0%
T 159
20.0%
E 159
20.0%
S 159
20.0%
L 159
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 159
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 159
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1272
57.1%
Latin 954
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 292
23.0%
3 195
15.3%
7 185
14.5%
- 159
12.5%
_ 159
12.5%
1 96
 
7.5%
4 36
 
2.8%
5 36
 
2.8%
2 36
 
2.8%
6 26
 
2.0%
Other values (2) 52
 
4.1%
Latin
ValueCountFrequency (%)
B 159
16.7%
T 159
16.7%
E 159
16.7%
S 159
16.7%
i 159
16.7%
L 159
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 292
13.1%
3 195
8.8%
7 185
8.3%
B 159
 
7.1%
T 159
 
7.1%
E 159
 
7.1%
- 159
 
7.1%
S 159
 
7.1%
i 159
 
7.1%
L 159
 
7.1%
Other values (8) 441
19.8%

분류1
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
관광/숙박
159 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관광/숙박
2nd row관광/숙박
3rd row관광/숙박
4th row관광/숙박
5th row관광/숙박

Common Values

ValueCountFrequency (%)
관광/숙박 159
100.0%

Length

2024-04-14T20:14:05.687365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:14:06.003819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광/숙박 159
100.0%

분류2
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
숙박
159 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
숙박 159
100.0%

Length

2024-04-14T20:14:06.325795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:14:06.633796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박 159
100.0%

분류3
Categorical

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
일반호텔
75 
특1급호텔
27 
1급호텔
23 
특2급호텔
19 
2급호텔
10 

Length

Max length5
Median length4
Mean length4.3207547
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row특1급호텔
2nd row특1급호텔
3rd row특1급호텔
4th row특1급호텔
5th row특1급호텔

Common Values

ValueCountFrequency (%)
일반호텔 75
47.2%
특1급호텔 27
 
17.0%
1급호텔 23
 
14.5%
특2급호텔 19
 
11.9%
2급호텔 10
 
6.3%
호텔 기타 5
 
3.1%

Length

2024-04-14T20:14:06.958142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:14:07.299246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반호텔 75
45.7%
특1급호텔 27
 
16.5%
1급호텔 23
 
14.0%
특2급호텔 19
 
11.6%
2급호텔 10
 
6.1%
호텔 5
 
3.0%
기타 5
 
3.0%

분류4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing159
Missing (%)100.0%
Memory size1.5 KiB

검색어
Text

Distinct155
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-14T20:14:08.332031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length6.490566
Min length2

Characters and Unicode

Total characters1032
Distinct characters205
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

Unique151 ?
Unique (%)95.0%

Sample

1st rowJW메리어트/호텔서울
2nd row메리어트 이그제큐티브 아파트먼트
3rd rowW워커힐
4th row그랜드앰배서더/호텔서울
5th row그랜드인터컨티넨탈/서울파르나스
ValueCountFrequency (%)
cf호텔 2
 
1.2%
발리호텔 2
 
1.2%
시네마호텔 2
 
1.2%
테마호텔 2
 
1.2%
바비엥스위트원 1
 
0.6%
jw메리어트/호텔서울 1
 
0.6%
신라호텔/영빈관 1
 
0.6%
가야관광호텔 1
 
0.6%
벨라지오호텔 1
 
0.6%
리츠호텔 1
 
0.6%
Other values (148) 148
91.4%
2024-04-14T20:14:09.584157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 142
 
13.8%
호 140
 
13.6%
/ 48
 
4.7%
스 31
 
3.0%
서 30
 
2.9%
울 25
 
2.4%
리 25
 
2.4%
관 24
 
2.3%
광 22
 
2.1%
아 19
 
1.8%
Other values (195) 526
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 959
92.9%
Other Punctuation 50
 
4.8%
Uppercase Letter 13
 
1.3%
Space Separator 3
 
0.3%
Decimal Number 2
 
0.2%
Lowercase Letter 2
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 142
 
14.8%
호 140
 
14.6%
스 31
 
3.2%
서 30
 
3.1%
울 25
 
2.6%
리 25
 
2.6%
관 24
 
2.5%
광 22
 
2.3%
아 19
 
2.0%
이 19
 
2.0%
Other values (177) 482
50.3%
Uppercase Letter
ValueCountFrequency (%)
J 3
23.1%
F 2
15.4%
C 2
15.4%
W 2
15.4%
G 1
 
7.7%
T 1
 
7.7%
K 1
 
7.7%
R 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
/ 48
96.0%
& 2
 
4.0%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
h 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 959
92.9%
Common 58
 
5.6%
Latin 15
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 142
 
14.8%
호 140
 
14.6%
스 31
 
3.2%
서 30
 
3.1%
울 25
 
2.6%
리 25
 
2.6%
관 24
 
2.5%
광 22
 
2.3%
아 19
 
2.0%
이 19
 
2.0%
Other values (177) 482
50.3%
Latin
ValueCountFrequency (%)
J 3
20.0%
F 2
13.3%
C 2
13.3%
W 2
13.3%
G 1
 
6.7%
T 1
 
6.7%
K 1
 
6.7%
e 1
 
6.7%
h 1
 
6.7%
R 1
 
6.7%
Common
ValueCountFrequency (%)
/ 48
82.8%
3
 
5.2%
& 2
 
3.4%
) 1
 
1.7%
( 1
 
1.7%
2 1
 
1.7%
- 1
 
1.7%
1 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 959
92.9%
ASCII 73
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 142
 
14.8%
호 140
 
14.6%
스 31
 
3.2%
서 30
 
3.1%
울 25
 
2.6%
리 25
 
2.6%
관 24
 
2.5%
광 22
 
2.3%
아 19
 
2.0%
이 19
 
2.0%
Other values (177) 482
50.3%
ASCII
ValueCountFrequency (%)
/ 48
65.8%
3
 
4.1%
J 3
 
4.1%
F 2
 
2.7%
C 2
 
2.7%
& 2
 
2.7%
W 2
 
2.7%
G 1
 
1.4%
T 1
 
1.4%
) 1
 
1.4%
Other values (8) 8
 
11.0%

명칭
Text

Distinct154
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-14T20:14:10.484245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length6.1886792
Min length2

Characters and Unicode

Total characters984
Distinct characters204
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

Unique149 ?
Unique (%)93.7%

Sample

1st rowJW메리어트호텔서울
2nd row메리어트 이그제큐티브 아파트먼트
3rd rowW워커힐
4th row그랜드앰배서더호텔서울
5th row그랜드인터컨티넨탈서울파르나스
ValueCountFrequency (%)
테마호텔 2
 
1.2%
발리호텔 2
 
1.2%
cf호텔 2
 
1.2%
크라운관광호텔 2
 
1.2%
시네마호텔 2
 
1.2%
가야관광호텔 1
 
0.6%
신라호텔영빈관 1
 
0.6%
그린그래스호텔 1
 
0.6%
벨라지오호텔 1
 
0.6%
리츠호텔 1
 
0.6%
Other values (147) 147
90.7%
2024-04-14T20:14:11.645694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 142
 
14.4%
호 140
 
14.2%
스 31
 
3.2%
서 30
 
3.0%
리 25
 
2.5%
울 25
 
2.5%
관 24
 
2.4%
광 22
 
2.2%
이 19
 
1.9%
라 19
 
1.9%
Other values (194) 507
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 959
97.5%
Uppercase Letter 13
 
1.3%
Space Separator 3
 
0.3%
Other Punctuation 2
 
0.2%
Lowercase Letter 2
 
0.2%
Decimal Number 2
 
0.2%
Dash Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 142
 
14.8%
호 140
 
14.6%
스 31
 
3.2%
서 30
 
3.1%
리 25
 
2.6%
울 25
 
2.6%
관 24
 
2.5%
광 22
 
2.3%
이 19
 
2.0%
라 19
 
2.0%
Other values (177) 482
50.3%
Uppercase Letter
ValueCountFrequency (%)
J 3
23.1%
W 2
15.4%
F 2
15.4%
C 2
15.4%
T 1
 
7.7%
K 1
 
7.7%
G 1
 
7.7%
R 1
 
7.7%
Lowercase Letter
ValueCountFrequency (%)
h 1
50.0%
e 1
50.0%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 959
97.5%
Latin 15
 
1.5%
Common 10
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 142
 
14.8%
호 140
 
14.6%
스 31
 
3.2%
서 30
 
3.1%
리 25
 
2.6%
울 25
 
2.6%
관 24
 
2.5%
광 22
 
2.3%
이 19
 
2.0%
라 19
 
2.0%
Other values (177) 482
50.3%
Latin
ValueCountFrequency (%)
J 3
20.0%
W 2
13.3%
F 2
13.3%
C 2
13.3%
h 1
 
6.7%
T 1
 
6.7%
e 1
 
6.7%
K 1
 
6.7%
G 1
 
6.7%
R 1
 
6.7%
Common
ValueCountFrequency (%)
3
30.0%
& 2
20.0%
- 1
 
10.0%
( 1
 
10.0%
1 1
 
10.0%
2 1
 
10.0%
) 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 959
97.5%
ASCII 25
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 142
 
14.8%
호 140
 
14.6%
스 31
 
3.2%
서 30
 
3.1%
리 25
 
2.6%
울 25
 
2.6%
관 24
 
2.5%
광 22
 
2.3%
이 19
 
2.0%
라 19
 
2.0%
Other values (177) 482
50.3%
ASCII
ValueCountFrequency (%)
J 3
12.0%
3
12.0%
W 2
 
8.0%
& 2
 
8.0%
F 2
 
8.0%
C 2
 
8.0%
h 1
 
4.0%
T 1
 
4.0%
- 1
 
4.0%
e 1
 
4.0%
Other values (7) 7
28.0%

지번 주소
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing159
Missing (%)100.0%
Memory size1.5 KiB

도로명 주소
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing159
Missing (%)100.0%
Memory size1.5 KiB

행정 시
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
서울특별시
159 

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 159
100.0%

Length

2024-04-14T20:14:11.866487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:14:12.033309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 159
100.0%

행정 구
Categorical

Distinct23
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
강남구
39 
중구
27 
강서구
11 
서초구
10 
송파구
10 
Other values (18)
62 

Length

Max length4
Median length3
Mean length2.9119497
Min length2

Unique

Unique5 ?
Unique (%)3.1%

Sample

1st row서초구
2nd row영등포구
3rd row광진구
4th row중구
5th row강남구

Common Values

ValueCountFrequency (%)
강남구 39
24.5%
중구 27
17.0%
강서구 11
 
6.9%
서초구 10
 
6.3%
송파구 10
 
6.3%
용산구 9
 
5.7%
영등포구 8
 
5.0%
마포구 8
 
5.0%
종로구 7
 
4.4%
광진구 4
 
2.5%
Other values (13) 26
16.4%

Length

2024-04-14T20:14:12.226978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 39
24.5%
중구 27
17.0%
강서구 11
 
6.9%
서초구 10
 
6.3%
송파구 10
 
6.3%
용산구 9
 
5.7%
영등포구 8
 
5.0%
마포구 8
 
5.0%
종로구 7
 
4.4%
광진구 4
 
2.5%
Other values (13) 26
16.4%

행정 동
Text

Distinct78
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-14T20:14:13.055653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length3.7735849
Min length2

Characters and Unicode

Total characters600
Distinct characters109
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

Unique45 ?
Unique (%)28.3%

Sample

1st row반포4동
2nd row여의동
3rd row광장동
4th row장충동
5th row삼성1동
ValueCountFrequency (%)
역삼1동 12
 
7.5%
명동 9
 
5.7%
종로1.2.3.4가동 6
 
3.8%
여의동 5
 
3.1%
논현1동 5
 
3.1%
삼성2동 5
 
3.1%
청담동 4
 
2.5%
소공동 4
 
2.5%
장충동 4
 
2.5%
방이2동 4
 
2.5%
Other values (68) 101
63.5%
2024-04-14T20:14:14.077511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 160
26.7%
1 35
 
5.8%
2 28
 
4.7%
삼 24
 
4.0%
. 20
 
3.3%
역 15
 
2.5%
4 12
 
2.0%
3 11
 
1.8%
현 10
 
1.7%
명 9
 
1.5%
Other values (99) 276
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 487
81.2%
Decimal Number 93
 
15.5%
Other Punctuation 20
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 160
32.9%
삼 24
 
4.9%
역 15
 
3.1%
현 10
 
2.1%
명 9
 
1.8%
성 9
 
1.8%
로 8
 
1.6%
가 8
 
1.6%
장 8
 
1.6%
광 7
 
1.4%
Other values (92) 229
47.0%
Decimal Number
ValueCountFrequency (%)
1 35
37.6%
2 28
30.1%
4 12
 
12.9%
3 11
 
11.8%
6 5
 
5.4%
7 2
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 487
81.2%
Common 113
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 160
32.9%
삼 24
 
4.9%
역 15
 
3.1%
현 10
 
2.1%
명 9
 
1.8%
성 9
 
1.8%
로 8
 
1.6%
가 8
 
1.6%
장 8
 
1.6%
광 7
 
1.4%
Other values (92) 229
47.0%
Common
ValueCountFrequency (%)
1 35
31.0%
2 28
24.8%
. 20
17.7%
4 12
 
10.6%
3 11
 
9.7%
6 5
 
4.4%
7 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 487
81.2%
ASCII 113
 
18.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 160
32.9%
삼 24
 
4.9%
역 15
 
3.1%
현 10
 
2.1%
명 9
 
1.8%
성 9
 
1.8%
로 8
 
1.6%
가 8
 
1.6%
장 8
 
1.6%
광 7
 
1.4%
Other values (92) 229
47.0%
ASCII
ValueCountFrequency (%)
1 35
31.0%
2 28
24.8%
. 20
17.7%
4 12
 
10.6%
3 11
 
9.7%
6 5
 
4.4%
7 2
 
1.8%

Correlations

2024-04-14T20:14:14.231967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류3행정 구행정 동
분류31.0000.0000.588
행정 구0.0001.0001.000
행정 동0.5881.0001.000
2024-04-14T20:14:14.379159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류3행정 구
분류31.0000.000
행정 구0.0001.000
2024-04-14T20:14:14.672334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류3행정 구
분류31.0000.000
행정 구0.0001.000

Missing values

2024-04-14T20:14:02.558851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T20:14:03.088418image/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

메인 키분류1분류2분류3분류4검색어명칭지번 주소도로명 주소행정 시행정 구행정 동
0BE_LiST37-0001관광/숙박숙박특1급호텔<NA>JW메리어트/호텔서울JW메리어트호텔서울<NA><NA>서울특별시서초구반포4동
1BE_LiST37-0002관광/숙박숙박특1급호텔<NA>메리어트 이그제큐티브 아파트먼트메리어트 이그제큐티브 아파트먼트<NA><NA>서울특별시영등포구여의동
2BE_LiST37-0003관광/숙박숙박특1급호텔<NA>W워커힐W워커힐<NA><NA>서울특별시광진구광장동
3BE_LiST37-0004관광/숙박숙박특1급호텔<NA>그랜드앰배서더/호텔서울그랜드앰배서더호텔서울<NA><NA>서울특별시중구장충동
4BE_LiST37-0005관광/숙박숙박특1급호텔<NA>그랜드인터컨티넨탈/서울파르나스그랜드인터컨티넨탈서울파르나스<NA><NA>서울특별시강남구삼성1동
5BE_LiST37-0006관광/숙박숙박특1급호텔<NA>그랜드하얏트/서울그랜드하얏트서울<NA><NA>서울특별시용산구한남동
6BE_LiST37-0007관광/숙박숙박특1급호텔<NA>그랜드힐튼/호텔서울그랜드힐튼호텔서울<NA><NA>서울특별시서대문구홍은2동
7BE_LiST37-0008관광/숙박숙박특1급호텔<NA>노보텔/앰배서더강남노보텔앰배서더강남<NA><NA>서울특별시강남구역삼1동
8BE_LiST37-0009관광/숙박숙박특1급호텔<NA>롯데시티호텔/김포공항롯데시티호텔김포공항<NA><NA>서울특별시강서구방화2동
9BE_LiST37-0010관광/숙박숙박특1급호텔<NA>롯데호텔롯데호텔<NA><NA>서울특별시중구명동
메인 키분류1분류2분류3분류4검색어명칭지번 주소도로명 주소행정 시행정 구행정 동
149BE_LiST37-0150관광/숙박숙박일반호텔<NA>호텔라까사호텔라까사<NA><NA>서울특별시강남구신사동
150BE_LiST37-0151관광/숙박숙박일반호텔<NA>호텔마인드호텔마인드<NA><NA>서울특별시금천구가산동
151BE_LiST37-0152관광/숙박숙박일반호텔<NA>호텔엘&에스호텔엘&에스<NA><NA>서울특별시중랑구망우본동
152BE_LiST37-0153관광/숙박숙박일반호텔<NA>호텔케이피호텔케이피<NA><NA>서울특별시동대문구휘경1동
153BE_LiST37-0154관광/숙박숙박일반호텔<NA>호텔티파니호텔티파니<NA><NA>서울특별시강남구청담동
154BE_LiST37-0155관광/숙박숙박호텔 기타<NA>CF호텔CF호텔<NA><NA>서울특별시송파구방이2동
155BE_LiST37-0156관광/숙박숙박호텔 기타<NA>갤러리호텔갤러리호텔<NA><NA>서울특별시은평구녹번동
156BE_LiST37-0157관광/숙박숙박호텔 기타<NA>서울파트너스/하우스서울파트너스하우스<NA><NA>서울특별시용산구한남동
157BE_LiST37-0158관광/숙박숙박호텔 기타<NA>세림호텔세림호텔<NA><NA>서울특별시종로구종로1.2.3.4가동
158BE_LiST37-0159관광/숙박숙박호텔 기타<NA>젤리호텔젤리호텔<NA><NA>서울특별시강남구역삼1동