Overview

Dataset statistics

Number of variables12
Number of observations90
Missing cells171
Missing cells (%)15.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory97.5 B

Variable types

Text6
Categorical6

Dataset

Description외국인관광도시민박업20157현재
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202427

Alerts

Unnamed: 4 is highly overall correlated with Unnamed: 1 and 3 other fieldsHigh correlation
Unnamed: 8 is highly overall correlated with Unnamed: 1 and 3 other fieldsHigh correlation
Unnamed: 9 is highly overall correlated with Unnamed: 1 and 3 other fieldsHigh correlation
Unnamed: 11 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 1 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 7 is highly overall correlated with Unnamed: 4 and 1 other fieldsHigh correlation
Unnamed: 1 is highly imbalanced (88.9%)Imbalance
Unnamed: 7 is highly imbalanced (75.0%)Imbalance
Unnamed: 8 is highly imbalanced (56.6%)Imbalance
Unnamed: 9 is highly imbalanced (61.2%)Imbalance
Unnamed: 11 is highly imbalanced (88.9%)Imbalance
외국인관광도시민박업 현황 has 1 (1.1%) missing valuesMissing
Unnamed: 2 has 1 (1.1%) missing valuesMissing
Unnamed: 3 has 1 (1.1%) missing valuesMissing
Unnamed: 5 has 72 (80.0%) missing valuesMissing
Unnamed: 6 has 15 (16.7%) missing valuesMissing
Unnamed: 10 has 81 (90.0%) missing valuesMissing

Reproduction

Analysis started2024-03-14 01:35:08.894722
Analysis finished2024-03-14 01:35:09.915094
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

외국인관광도시민박업 현황
Text

MISSING 

Distinct89
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Memory size852.0 B
2024-03-14T10:35:10.096972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.8988764
Min length1

Characters and Unicode

Total characters169
Distinct characters12
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

Unique89 ?
Unique (%)100.0%

Sample

1st row연번
2nd row1
3rd row2
4th row3
5th row4
ValueCountFrequency (%)
20 1
 
1.1%
45 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
59 1
 
1.1%
58 1
 
1.1%
57 1
 
1.1%
Other values (79) 79
88.8%
2024-03-14T10:35:10.407922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 19
11.2%
3 19
11.2%
4 19
11.2%
5 19
11.2%
6 19
11.2%
7 19
11.2%
1 19
11.2%
8 18
10.7%
0 8
4.7%
9 8
4.7%
Other values (2) 2
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 167
98.8%
Other Letter 2
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19
11.4%
3 19
11.4%
4 19
11.4%
5 19
11.4%
6 19
11.4%
7 19
11.4%
1 19
11.4%
8 18
10.8%
0 8
4.8%
9 8
4.8%
Other Letter
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 167
98.8%
Hangul 2
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19
11.4%
3 19
11.4%
4 19
11.4%
5 19
11.4%
6 19
11.4%
7 19
11.4%
1 19
11.4%
8 18
10.8%
0 8
4.8%
9 8
4.8%
Hangul
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167
98.8%
Hangul 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 19
11.4%
3 19
11.4%
4 19
11.4%
5 19
11.4%
6 19
11.4%
7 19
11.4%
1 19
11.4%
8 18
10.8%
0 8
4.8%
9 8
4.8%
Hangul
ValueCountFrequency (%)
연 1
50.0%
번 1
50.0%

Unnamed: 1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
외국인관광도시민박업
88 
<NA>
 
1
업종명
 
1

Length

Max length10
Median length10
Mean length9.8555556
Min length3

Unique

Unique2 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row업종명
3rd row외국인관광도시민박업
4th row외국인관광도시민박업
5th row외국인관광도시민박업

Common Values

ValueCountFrequency (%)
외국인관광도시민박업 88
97.8%
<NA> 1
 
1.1%
업종명 1
 
1.1%

Length

2024-03-14T10:35:10.515296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:35:10.602102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
외국인관광도시민박업 88
97.8%
na 1
 
1.1%
업종명 1
 
1.1%

Unnamed: 2
Text

MISSING 

Distinct89
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Memory size852.0 B
2024-03-14T10:35:10.898589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length14
Mean length5
Min length1

Characters and Unicode

Total characters445
Distinct characters183
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

Unique89 ?
Unique (%)100.0%

Sample

1st row시 설 명
2nd row전주게스트하우스
3rd row해 달 별
4th row천년마루
5th row마르타숙소
ValueCountFrequency (%)
게스트하우스 4
 
3.3%
집 2
 
1.6%
정 2
 
1.6%
모 2
 
1.6%
향 2
 
1.6%
해 2
 
1.6%
별 2
 
1.6%
촌 2
 
1.6%
설 1
 
0.8%
시 1
 
0.8%
Other values (102) 102
83.6%
2024-03-14T10:35:11.402955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33
 
7.4%
스 32
 
7.2%
하 20
 
4.5%
우 16
 
3.6%
트 15
 
3.4%
게 14
 
3.1%
전 7
 
1.6%
나 6
 
1.3%
리 6
 
1.3%
마 6
 
1.3%
Other values (173) 290
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 381
85.6%
Space Separator 33
 
7.4%
Lowercase Letter 16
 
3.6%
Decimal Number 7
 
1.6%
Uppercase Letter 5
 
1.1%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 32
 
8.4%
하 20
 
5.2%
우 16
 
4.2%
트 15
 
3.9%
게 14
 
3.7%
전 7
 
1.8%
나 6
 
1.6%
리 6
 
1.6%
마 6
 
1.6%
정 5
 
1.3%
Other values (150) 254
66.7%
Lowercase Letter
ValueCountFrequency (%)
e 3
18.8%
u 2
12.5%
n 2
12.5%
s 2
12.5%
g 1
 
6.2%
r 1
 
6.2%
a 1
 
6.2%
d 1
 
6.2%
i 1
 
6.2%
o 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
3 3
42.9%
6 2
28.6%
2 1
 
14.3%
0 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
P 2
40.0%
D 1
20.0%
G 1
20.0%
H 1
20.0%
Space Separator
ValueCountFrequency (%)
33
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 381
85.6%
Common 43
 
9.7%
Latin 21
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 32
 
8.4%
하 20
 
5.2%
우 16
 
4.2%
트 15
 
3.9%
게 14
 
3.7%
전 7
 
1.8%
나 6
 
1.6%
리 6
 
1.6%
마 6
 
1.6%
정 5
 
1.3%
Other values (150) 254
66.7%
Latin
ValueCountFrequency (%)
e 3
14.3%
u 2
 
9.5%
n 2
 
9.5%
s 2
 
9.5%
P 2
 
9.5%
g 1
 
4.8%
r 1
 
4.8%
a 1
 
4.8%
d 1
 
4.8%
D 1
 
4.8%
Other values (5) 5
23.8%
Common
ValueCountFrequency (%)
33
76.7%
3 3
 
7.0%
6 2
 
4.7%
) 1
 
2.3%
2 1
 
2.3%
( 1
 
2.3%
- 1
 
2.3%
0 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 381
85.6%
ASCII 64
 
14.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33
51.6%
3 3
 
4.7%
e 3
 
4.7%
u 2
 
3.1%
n 2
 
3.1%
s 2
 
3.1%
P 2
 
3.1%
6 2
 
3.1%
g 1
 
1.6%
r 1
 
1.6%
Other values (13) 13
 
20.3%
Hangul
ValueCountFrequency (%)
스 32
 
8.4%
하 20
 
5.2%
우 16
 
4.2%
트 15
 
3.9%
게 14
 
3.7%
전 7
 
1.8%
나 6
 
1.6%
리 6
 
1.6%
마 6
 
1.6%
정 5
 
1.3%
Other values (150) 254
66.7%

Unnamed: 3
Text

MISSING 

Distinct89
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Memory size852.0 B
2024-03-14T10:35:11.701617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length18.483146
Min length3

Characters and Unicode

Total characters1645
Distinct characters85
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)100.0%

Sample

1st row소대지
2nd row전주시 완산구 경원동 2가62
3rd row전주시 완산구 풍남동 3가34-5
4th row전주시 완산구 교동 222-6
5th row전주시 완산구 교동 59-5
ValueCountFrequency (%)
전주시 84
23.3%
완산구 81
22.5%
교동 24
 
6.7%
풍남동3가 5
 
1.4%
전동성당길 5
 
1.4%
전동 4
 
1.1%
군산시 4
 
1.1%
팔달로 4
 
1.1%
향교길 4
 
1.1%
기린대로 3
 
0.8%
Other values (126) 142
39.4%
2024-03-14T10:35:12.067445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
271
16.5%
전 102
 
6.2%
시 88
 
5.3%
구 87
 
5.3%
산 86
 
5.2%
주 86
 
5.2%
완 81
 
4.9%
동 78
 
4.7%
1 73
 
4.4%
- 63
 
3.8%
Other values (75) 630
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 927
56.4%
Decimal Number 314
 
19.1%
Space Separator 271
 
16.5%
Dash Punctuation 63
 
3.8%
Close Punctuation 35
 
2.1%
Open Punctuation 35
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 102
11.0%
시 88
 
9.5%
구 87
 
9.4%
산 86
 
9.3%
주 86
 
9.3%
완 81
 
8.7%
동 78
 
8.4%
길 37
 
4.0%
교 30
 
3.2%
가 29
 
3.1%
Other values (61) 223
24.1%
Decimal Number
ValueCountFrequency (%)
1 73
23.2%
3 48
15.3%
2 40
12.7%
6 36
11.5%
5 28
 
8.9%
4 24
 
7.6%
9 21
 
6.7%
0 16
 
5.1%
8 14
 
4.5%
7 14
 
4.5%
Space Separator
ValueCountFrequency (%)
271
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 927
56.4%
Common 718
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 102
11.0%
시 88
 
9.5%
구 87
 
9.4%
산 86
 
9.3%
주 86
 
9.3%
완 81
 
8.7%
동 78
 
8.4%
길 37
 
4.0%
교 30
 
3.2%
가 29
 
3.1%
Other values (61) 223
24.1%
Common
ValueCountFrequency (%)
271
37.7%
1 73
 
10.2%
- 63
 
8.8%
3 48
 
6.7%
2 40
 
5.6%
6 36
 
5.0%
) 35
 
4.9%
( 35
 
4.9%
5 28
 
3.9%
4 24
 
3.3%
Other values (4) 65
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 927
56.4%
ASCII 718
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
271
37.7%
1 73
 
10.2%
- 63
 
8.8%
3 48
 
6.7%
2 40
 
5.6%
6 36
 
5.0%
) 35
 
4.9%
( 35
 
4.9%
5 28
 
3.9%
4 24
 
3.3%
Other values (4) 65
 
9.1%
Hangul
ValueCountFrequency (%)
전 102
11.0%
시 88
 
9.5%
구 87
 
9.4%
산 86
 
9.3%
주 86
 
9.3%
완 81
 
8.7%
동 78
 
8.4%
길 37
 
4.0%
교 30
 
3.2%
가 29
 
3.1%
Other values (61) 223
24.1%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size852.0 B
4
18 
5
17 
2
15 
3
14 
6
11 
Other values (6)
15 

Length

Max length4
Median length1
Mean length1.0555556
Min length1

Unique

Unique3 ?
Unique (%)3.3%

Sample

1st row<NA>
2nd row객실수
3rd row6
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 18
20.0%
5 17
18.9%
2 15
16.7%
3 14
15.6%
6 11
12.2%
7 7
 
7.8%
9 3
 
3.3%
8 2
 
2.2%
<NA> 1
 
1.1%
객실수 1
 
1.1%

Length

2024-03-14T10:35:12.187019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 18
20.0%
5 17
18.9%
2 15
16.7%
3 14
15.6%
6 11
12.2%
7 7
 
7.8%
9 3
 
3.3%
8 2
 
2.2%
na 1
 
1.1%
객실수 1
 
1.1%

Unnamed: 5
Text

MISSING 

Distinct18
Distinct (%)100.0%
Missing72
Missing (%)80.0%
Memory size852.0 B
2024-03-14T10:35:12.339447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.388889
Min length3

Characters and Unicode

Total characters205
Distinct characters14
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

Unique18 ?
Unique (%)100.0%

Sample

1st row연락처
2nd row063-286-8886
3rd row063-288-4860
4th row063-286-2215
5th row063-282-9366
ValueCountFrequency (%)
연락처 1
 
5.6%
063-286-8886 1
 
5.6%
063-231-5116 1
 
5.6%
063-285-3454 1
 
5.6%
063-231-6106 1
 
5.6%
063-231-3630 1
 
5.6%
1588-7467 1
 
5.6%
063-247-5552 1
 
5.6%
063-287-4880 1
 
5.6%
070-4234-2534 1
 
5.6%
Other values (8) 8
44.4%
2024-03-14T10:35:12.618384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 33
16.1%
6 26
12.7%
3 25
12.2%
0 23
11.2%
8 23
11.2%
2 21
10.2%
5 17
8.3%
4 14
6.8%
1 11
 
5.4%
7 7
 
3.4%
Other values (4) 5
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 169
82.4%
Dash Punctuation 33
 
16.1%
Other Letter 3
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 26
15.4%
3 25
14.8%
0 23
13.6%
8 23
13.6%
2 21
12.4%
5 17
10.1%
4 14
8.3%
1 11
6.5%
7 7
 
4.1%
9 2
 
1.2%
Other Letter
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 202
98.5%
Hangul 3
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 33
16.3%
6 26
12.9%
3 25
12.4%
0 23
11.4%
8 23
11.4%
2 21
10.4%
5 17
8.4%
4 14
6.9%
1 11
 
5.4%
7 7
 
3.5%
Hangul
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 202
98.5%
Hangul 3
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 33
16.3%
6 26
12.9%
3 25
12.4%
0 23
11.4%
8 23
11.4%
2 21
10.4%
5 17
8.4%
4 14
6.9%
1 11
 
5.4%
7 7
 
3.5%
Hangul
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%

Unnamed: 6
Text

MISSING 

Distinct71
Distinct (%)94.7%
Missing15
Missing (%)16.7%
Memory size852.0 B
2024-03-14T10:35:12.831779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length24
Mean length19.72
Min length4

Characters and Unicode

Total characters1479
Distinct characters58
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

Unique68 ?
Unique (%)90.7%

Sample

1st row홈페이지
2nd rowhttp://cafe.daum.net/chonjukorea
3rd rowwww.jhsms.com/
4th rowwww.maru1000y.com
5th rowhttp://www.cyworld.com/kmarta/
ValueCountFrequency (%)
blog.naver.com/lwg1987 3
 
4.0%
http://www.cyworld.com/kmarta 2
 
2.7%
cafe.daum.net/hinokijam 2
 
2.7%
morninggarden.fortour.kr 1
 
1.3%
www.kkumdarak.com 1
 
1.3%
www.hellojeje.com 1
 
1.3%
bellaluna.fortour.kr 1
 
1.3%
www.seosunya.kr 1
 
1.3%
blog.naver.com/flowerwon09 1
 
1.3%
gglim.co.kr 1
 
1.3%
Other values (61) 61
81.3%
2024-03-14T10:35:13.193350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 148
 
10.0%
. 145
 
9.8%
w 92
 
6.2%
a 91
 
6.2%
e 86
 
5.8%
r 84
 
5.7%
m 82
 
5.5%
n 80
 
5.4%
c 74
 
5.0%
g 58
 
3.9%
Other values (48) 539
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1221
82.6%
Other Punctuation 194
 
13.1%
Decimal Number 39
 
2.6%
Other Letter 21
 
1.4%
Uppercase Letter 2
 
0.1%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 148
 
12.1%
w 92
 
7.5%
a 91
 
7.5%
e 86
 
7.0%
r 84
 
6.9%
m 82
 
6.7%
n 80
 
6.6%
c 74
 
6.1%
g 58
 
4.8%
u 46
 
3.8%
Other values (14) 380
31.1%
Other Letter
ValueCountFrequency (%)
스 2
 
9.5%
우 1
 
4.8%
하 1
 
4.8%
트 1
 
4.8%
주 1
 
4.8%
게 1
 
4.8%
길 1
 
4.8%
목 1
 
4.8%
골 1
 
4.8%
페 1
 
4.8%
Other values (10) 10
47.6%
Decimal Number
ValueCountFrequency (%)
1 9
23.1%
9 7
17.9%
0 7
17.9%
8 5
12.8%
2 4
10.3%
7 4
10.3%
6 1
 
2.6%
4 1
 
2.6%
5 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 145
74.7%
/ 44
 
22.7%
: 5
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
H 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1223
82.7%
Common 235
 
15.9%
Hangul 21
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 148
 
12.1%
w 92
 
7.5%
a 91
 
7.4%
e 86
 
7.0%
r 84
 
6.9%
m 82
 
6.7%
n 80
 
6.5%
c 74
 
6.1%
g 58
 
4.7%
u 46
 
3.8%
Other values (15) 382
31.2%
Hangul
ValueCountFrequency (%)
스 2
 
9.5%
우 1
 
4.8%
하 1
 
4.8%
트 1
 
4.8%
주 1
 
4.8%
게 1
 
4.8%
길 1
 
4.8%
목 1
 
4.8%
골 1
 
4.8%
페 1
 
4.8%
Other values (10) 10
47.6%
Common
ValueCountFrequency (%)
. 145
61.7%
/ 44
 
18.7%
1 9
 
3.8%
9 7
 
3.0%
0 7
 
3.0%
8 5
 
2.1%
: 5
 
2.1%
2 4
 
1.7%
7 4
 
1.7%
_ 2
 
0.9%
Other values (3) 3
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1458
98.6%
Hangul 21
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 148
 
10.2%
. 145
 
9.9%
w 92
 
6.3%
a 91
 
6.2%
e 86
 
5.9%
r 84
 
5.8%
m 82
 
5.6%
n 80
 
5.5%
c 74
 
5.1%
g 58
 
4.0%
Other values (28) 518
35.5%
Hangul
ValueCountFrequency (%)
스 2
 
9.5%
우 1
 
4.8%
하 1
 
4.8%
트 1
 
4.8%
주 1
 
4.8%
게 1
 
4.8%
길 1
 
4.8%
목 1
 
4.8%
골 1
 
4.8%
페 1
 
4.8%
Other values (10) 10
47.6%

Unnamed: 7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
<NA>
84 
유
 
5
주차장유무
 
1

Length

Max length5
Median length4
Mean length3.8444444
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row<NA>
2nd row주차장유무
3rd row<NA>
4th row유
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 84
93.3%
유 5
 
5.6%
주차장유무 1
 
1.1%

Length

2024-03-14T10:35:13.399185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:35:13.488166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 84
93.3%
유 5
 
5.6%
주차장유무 1
 
1.1%

Unnamed: 8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size852.0 B
계좌이체
71 
<NA>
17 
결제방법
 
1
신용카드, 계좌이체
 
1

Length

Max length10
Median length4
Mean length4.0666667
Min length4

Unique

Unique2 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row결제방법
3rd row계좌이체
4th row계좌이체
5th row계좌이체

Common Values

ValueCountFrequency (%)
계좌이체 71
78.9%
<NA> 17
 
18.9%
결제방법 1
 
1.1%
신용카드, 계좌이체 1
 
1.1%

Length

2024-03-14T10:35:13.572933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:35:13.660990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계좌이체 72
79.1%
na 17
 
18.7%
결제방법 1
 
1.1%
신용카드 1
 
1.1%

Unnamed: 9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size852.0 B
전주한옥마을
71 
군산근대역사박물관, 진포해양테마공원
 
4
한옥마을
 
3
전동성당, 한옥마을
 
2
<NA>
 
1
Other values (9)
9 

Length

Max length19
Median length6
Mean length7.1444444
Min length4

Unique

Unique10 ?
Unique (%)11.1%

Sample

1st row<NA>
2nd row주변관광정보
3rd row전주한옥마을
4th row전주한옥마을
5th row전주한옥마을

Common Values

ValueCountFrequency (%)
전주한옥마을 71
78.9%
군산근대역사박물관, 진포해양테마공원 4
 
4.4%
한옥마을 3
 
3.3%
전동성당, 한옥마을 2
 
2.2%
<NA> 1
 
1.1%
주변관광정보 1
 
1.1%
덕진공원, 전주동물원 1
 
1.1%
한옥마을, 아중저수지 1
 
1.1%
경기전, 풍남문, 한옥마을 1
 
1.1%
한옥마을, 자연생태박물관 1
 
1.1%
Other values (4) 4
 
4.4%

Length

2024-03-14T10:35:13.786556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주한옥마을 72
67.3%
한옥마을 11
 
10.3%
군산근대역사박물관 4
 
3.7%
진포해양테마공원 4
 
3.7%
전동성당 4
 
3.7%
경기전 2
 
1.9%
풍남문 2
 
1.9%
na 1
 
0.9%
주변관광정보 1
 
0.9%
덕진공원 1
 
0.9%
Other values (5) 5
 
4.7%

Unnamed: 10
Text

MISSING 

Distinct9
Distinct (%)100.0%
Missing81
Missing (%)90.0%
Memory size852.0 B
2024-03-14T10:35:13.946953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length6.3333333
Min length4

Characters and Unicode

Total characters57
Distinct characters42
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

Unique9 ?
Unique (%)100.0%

Sample

1st row부대시설
2nd row여성전용
3rd row애완견동반
4th row여성전용룸 구비
5th row반려동물 동반
ValueCountFrequency (%)
부대시설 1
 
7.1%
여성전용 1
 
7.1%
애완견동반 1
 
7.1%
여성전용룸 1
 
7.1%
구비 1
 
7.1%
반려동물 1
 
7.1%
동반 1
 
7.1%
1층 1
 
7.1%
식당 1
 
7.1%
막걸리파티 1
 
7.1%
Other values (4) 4
28.6%
2024-03-14T10:35:14.207241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
8.8%
반 3
 
5.3%
동 3
 
5.3%
용 2
 
3.5%
물 2
 
3.5%
비 2
 
3.5%
식 2
 
3.5%
전 2
 
3.5%
성 2
 
3.5%
여 2
 
3.5%
Other values (32) 32
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49
86.0%
Space Separator 5
 
8.8%
Open Punctuation 1
 
1.8%
Close Punctuation 1
 
1.8%
Decimal Number 1
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 3
 
6.1%
동 3
 
6.1%
용 2
 
4.1%
물 2
 
4.1%
비 2
 
4.1%
식 2
 
4.1%
전 2
 
4.1%
성 2
 
4.1%
여 2
 
4.1%
밥 1
 
2.0%
Other values (28) 28
57.1%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49
86.0%
Common 8
 
14.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 3
 
6.1%
동 3
 
6.1%
용 2
 
4.1%
물 2
 
4.1%
비 2
 
4.1%
식 2
 
4.1%
전 2
 
4.1%
성 2
 
4.1%
여 2
 
4.1%
밥 1
 
2.0%
Other values (28) 28
57.1%
Common
ValueCountFrequency (%)
5
62.5%
( 1
 
12.5%
) 1
 
12.5%
1 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49
86.0%
ASCII 8
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5
62.5%
( 1
 
12.5%
) 1
 
12.5%
1 1
 
12.5%
Hangul
ValueCountFrequency (%)
반 3
 
6.1%
동 3
 
6.1%
용 2
 
4.1%
물 2
 
4.1%
비 2
 
4.1%
식 2
 
4.1%
전 2
 
4.1%
성 2
 
4.1%
여 2
 
4.1%
밥 1
 
2.0%
Other values (28) 28
57.1%

Unnamed: 11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
2015.07.30
88 
<NA>
 
1
데이터기준일자
 
1

Length

Max length10
Median length10
Mean length9.9
Min length4

Unique

Unique2 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row데이터기준일자
3rd row2015.07.30
4th row2015.07.30
5th row2015.07.30

Common Values

ValueCountFrequency (%)
2015.07.30 88
97.8%
<NA> 1
 
1.1%
데이터기준일자 1
 
1.1%

Length

2024-03-14T10:35:14.322087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:35:14.414390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015.07.30 88
97.8%
na 1
 
1.1%
데이터기준일자 1
 
1.1%

Correlations

2024-03-14T10:35:14.480221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인관광도시민박업 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
외국인관광도시민박업 현황1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0000.691
Unnamed: 21.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 41.0001.0001.0001.0001.0001.0000.8271.0000.9300.6911.0001.000
Unnamed: 51.0001.0001.0001.0001.0001.0001.0000.0001.0001.000NaN1.000
Unnamed: 61.0001.0001.0001.0000.8271.0001.0001.0001.0001.0001.0001.000
Unnamed: 71.0000.0001.0001.0001.0000.0001.0001.0000.0001.000NaN0.000
Unnamed: 81.0001.0001.0001.0000.9301.0001.0000.0001.0000.7851.0001.000
Unnamed: 91.0001.0001.0001.0000.6911.0001.0001.0000.7851.0001.0001.000
Unnamed: 101.0001.0001.0001.0001.000NaN1.000NaN1.0001.0001.0001.000
Unnamed: 111.0000.6911.0001.0001.0001.0001.0000.0001.0001.0001.0001.000
2024-03-14T10:35:14.597370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 11Unnamed: 1
Unnamed: 41.0000.5000.6600.3640.9530.953
Unnamed: 70.5001.0000.0000.7070.0000.000
Unnamed: 80.6600.0001.0000.5870.9930.993
Unnamed: 90.3640.7070.5871.0000.9350.935
Unnamed: 110.9530.0000.9930.9351.0000.485
Unnamed: 10.9530.0000.9930.9350.4851.000
2024-03-14T10:35:14.681859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 4Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 11
Unnamed: 11.0000.9530.0000.9930.9350.485
Unnamed: 40.9531.0000.5000.6600.3640.953
Unnamed: 70.0000.5001.0000.0000.7070.000
Unnamed: 80.9930.6600.0001.0000.5870.993
Unnamed: 90.9350.3640.7070.5871.0000.935
Unnamed: 110.4850.9530.0000.9930.9351.000

Missing values

2024-03-14T10:35:09.510647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:35:09.646658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-14T10:35:09.810662image/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

외국인관광도시민박업 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1연번업종명시 설 명소대지객실수연락처홈페이지주차장유무결제방법주변관광정보부대시설데이터기준일자
21외국인관광도시민박업전주게스트하우스전주시 완산구 경원동 2가626063-286-8886http://cafe.daum.net/chonjukorea<NA>계좌이체전주한옥마을<NA>2015.07.30
32외국인관광도시민박업해 달 별전주시 완산구 풍남동 3가34-53063-288-4860www.jhsms.com/유계좌이체전주한옥마을<NA>2015.07.30
43외국인관광도시민박업천년마루전주시 완산구 교동 222-64063-286-2215www.maru1000y.com<NA>계좌이체전주한옥마을<NA>2015.07.30
54외국인관광도시민박업마르타숙소전주시 완산구 교동 59-53<NA>http://www.cyworld.com/kmarta/<NA>계좌이체전주한옥마을<NA>2015.07.30
65외국인관광도시민박업60-6게스트하우스전주시 완산구 교동 126-144<NA>http://www.cyworld.com/kmarta/<NA>계좌이체전주한옥마을여성전용2015.07.30
76외국인관광도시민박업해 밀전주시 완산구 전동 208-56<NA>www.haemilgh.com유계좌이체전주한옥마을<NA>2015.07.30
87외국인관광도시민박업별 빛 향전주시 완산구 교동 47-123<NA>http://1.wcr.co.kr/starlv/<NA>계좌이체전주한옥마을<NA>2015.07.30
98외국인관광도시민박업초 정전주시 완산구 교동 1393<NA><NA><NA>계좌이체전주한옥마을<NA>2015.07.30
외국인관광도시민박업 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
8079외국인관광도시민박업휴전주시 완산구 전주천동로 80-154<NA>jeonjuguest.com<NA>계좌이체전주한옥마을<NA>2015.07.30
8180외국인관광도시민박업전주 어린왕자 게스트하우스전주시 완산구 태조로 14-16<NA>blog.naver.com/amour_22<NA>계좌이체전주한옥마을<NA>2015.07.30
8281외국인관광도시민박업아침정원전주시 완산구 학전길 15-1(원당동)4<NA>morninggarden.fortour.kr유계좌이체전주한옥마을, 모악산<NA>2015.07.30
8382외국인관광도시민박업마르코폴로전주시 완산구 팔달로 150-5(전동)4063-231-5116<NA><NA><NA>전주한옥마을<NA>2015.07.30
8483외국인관광도시민박업게스트하우스 바닐라전주시 완산구 서학1길 23-1(서서학동)4<NA><NA><NA><NA>전주한옥마을<NA>2015.07.30
8584외국인관광도시민박업소담소담전주시 완산구 대성동 4371<NA><NA><NA><NA>전주한옥마을<NA>2015.07.30
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