Overview

Dataset statistics

Number of variables15
Number of observations88
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory123.5 B

Variable types

Numeric1
Categorical10
Text4

Alerts

자료출처 has constant value ""Constant
공개여부 has constant value ""Constant
작성일 has constant value ""Constant
갱신주기 has constant value ""Constant
시군명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
관광정보 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
순번 is highly overall correlated with 시군명High correlation
전화번호 is highly overall correlated with 결제방법High correlation
주차장유무 is highly overall correlated with 관광정보High correlation
부대시설 is highly overall correlated with 결제방법High correlation
결제방법 is highly overall correlated with 전화번호 and 1 other fieldsHigh correlation
시군명 is highly imbalanced (73.3%)Imbalance
주차장유무 is highly imbalanced (68.5%)Imbalance
관광정보 is highly imbalanced (62.9%)Imbalance
부대시설 is highly imbalanced (77.5%)Imbalance
결제방법 is highly imbalanced (51.4%)Imbalance
순번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 00:59:41.282158
Analysis finished2024-03-14 00:59:42.644807
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.5
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2024-03-14T09:59:42.701485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.35
Q122.75
median44.5
Q366.25
95-th percentile83.65
Maximum88
Range87
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation25.547342
Coefficient of variation (CV)0.57409757
Kurtosis-1.2
Mean44.5
Median Absolute Deviation (MAD)22
Skewness0
Sum3916
Variance652.66667
MonotonicityStrictly increasing
2024-03-14T09:59:42.803038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
46 1
 
1.1%
66 1
 
1.1%
65 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%
Other values (78) 78
88.6%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%
81 1
1.1%
80 1
1.1%
79 1
1.1%

시군명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
전주시
84 
군산시
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 84
95.5%
군산시 4
 
4.5%

Length

2024-03-14T09:59:42.899214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:59:42.977878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전주시 84
95.5%
군산시 4
 
4.5%

시설명
Text

UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-14T09:59:43.214293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length14
Mean length5
Min length1

Characters and Unicode

Total characters440
Distinct characters180
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

Unique88 ?
Unique (%)100.0%

Sample

1st row전주게스트하우스
2nd row해 달 별
3rd row천년마루
4th row마르타숙소
5th row60-6게스트하우스
ValueCountFrequency (%)
게스트하우스 4
 
3.4%
모 2
 
1.7%
별 2
 
1.7%
촌 2
 
1.7%
해 2
 
1.7%
집 2
 
1.7%
향 2
 
1.7%
정 2
 
1.7%
전주게스트하우스 1
 
0.8%
숲정이 1
 
0.8%
Other values (99) 99
83.2%
2024-03-14T09:59:43.580173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 32
 
7.3%
31
 
7.0%
하 20
 
4.5%
우 16
 
3.6%
트 15
 
3.4%
게 14
 
3.2%
전 7
 
1.6%
나 6
 
1.4%
리 6
 
1.4%
마 6
 
1.4%
Other values (170) 287
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 378
85.9%
Space Separator 31
 
7.0%
Lowercase Letter 16
 
3.6%
Decimal Number 7
 
1.6%
Uppercase Letter 5
 
1.1%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 32
 
8.5%
하 20
 
5.3%
우 16
 
4.2%
트 15
 
4.0%
게 14
 
3.7%
전 7
 
1.9%
나 6
 
1.6%
리 6
 
1.6%
마 6
 
1.6%
한 5
 
1.3%
Other values (147) 251
66.4%
Lowercase Letter
ValueCountFrequency (%)
e 3
18.8%
s 2
12.5%
u 2
12.5%
n 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%
H 1
20.0%
G 1
20.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 378
85.9%
Common 41
 
9.3%
Latin 21
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 32
 
8.5%
하 20
 
5.3%
우 16
 
4.2%
트 15
 
4.0%
게 14
 
3.7%
전 7
 
1.9%
나 6
 
1.6%
리 6
 
1.6%
마 6
 
1.6%
한 5
 
1.3%
Other values (147) 251
66.4%
Latin
ValueCountFrequency (%)
e 3
14.3%
s 2
 
9.5%
u 2
 
9.5%
n 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 (%)
31
75.6%
3 3
 
7.3%
6 2
 
4.9%
( 1
 
2.4%
) 1
 
2.4%
2 1
 
2.4%
- 1
 
2.4%
0 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 378
85.9%
ASCII 62
 
14.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 32
 
8.5%
하 20
 
5.3%
우 16
 
4.2%
트 15
 
4.0%
게 14
 
3.7%
전 7
 
1.9%
나 6
 
1.6%
리 6
 
1.6%
마 6
 
1.6%
한 5
 
1.3%
Other values (147) 251
66.4%
ASCII
ValueCountFrequency (%)
31
50.0%
e 3
 
4.8%
3 3
 
4.8%
s 2
 
3.2%
6 2
 
3.2%
u 2
 
3.2%
n 2
 
3.2%
P 2
 
3.2%
g 1
 
1.6%
r 1
 
1.6%
Other values (13) 13
21.0%

도로명주소
Text

Distinct87
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-14T09:59:43.842132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length16.534091
Min length11

Characters and Unicode

Total characters1455
Distinct characters79
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

Unique86 ?
Unique (%)97.7%

Sample

1st row전주시 완산구 경기전길 46
2nd row전주시 완산구 어진길 33-9
3rd row전주시 완산구 경기전길 186
4th row전주시 완산구 오목대길 52
5th row전주시 완산구 오목대길 49-1
ValueCountFrequency (%)
전주시 84
24.1%
완산구 81
23.2%
향교길 13
 
3.7%
오목대길 7
 
2.0%
어진길 7
 
2.0%
전주천동로 6
 
1.7%
경기전길 5
 
1.4%
팔달로 5
 
1.4%
전동성당길 5
 
1.4%
군산시 4
 
1.1%
Other values (114) 132
37.8%
2024-03-14T09:59:44.251584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
261
17.9%
전 106
 
7.3%
주 90
 
6.2%
시 88
 
6.0%
구 87
 
6.0%
산 86
 
5.9%
완 81
 
5.6%
길 64
 
4.4%
1 58
 
4.0%
- 55
 
3.8%
Other values (69) 479
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 852
58.6%
Decimal Number 277
 
19.0%
Space Separator 261
 
17.9%
Dash Punctuation 55
 
3.8%
Open Punctuation 5
 
0.3%
Close Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 106
12.4%
주 90
10.6%
시 88
10.3%
구 87
10.2%
산 86
10.1%
완 81
9.5%
길 64
 
7.5%
로 25
 
2.9%
동 18
 
2.1%
교 14
 
1.6%
Other values (55) 193
22.7%
Decimal Number
ValueCountFrequency (%)
1 58
20.9%
3 38
13.7%
2 34
12.3%
5 30
10.8%
6 25
9.0%
9 23
 
8.3%
4 21
 
7.6%
8 20
 
7.2%
0 17
 
6.1%
7 11
 
4.0%
Space Separator
ValueCountFrequency (%)
261
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 852
58.6%
Common 603
41.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 106
12.4%
주 90
10.6%
시 88
10.3%
구 87
10.2%
산 86
10.1%
완 81
9.5%
길 64
 
7.5%
로 25
 
2.9%
동 18
 
2.1%
교 14
 
1.6%
Other values (55) 193
22.7%
Common
ValueCountFrequency (%)
261
43.3%
1 58
 
9.6%
- 55
 
9.1%
3 38
 
6.3%
2 34
 
5.6%
5 30
 
5.0%
6 25
 
4.1%
9 23
 
3.8%
4 21
 
3.5%
8 20
 
3.3%
Other values (4) 38
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 852
58.6%
ASCII 603
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
261
43.3%
1 58
 
9.6%
- 55
 
9.1%
3 38
 
6.3%
2 34
 
5.6%
5 30
 
5.0%
6 25
 
4.1%
9 23
 
3.8%
4 21
 
3.5%
8 20
 
3.3%
Other values (4) 38
 
6.3%
Hangul
ValueCountFrequency (%)
전 106
12.4%
주 90
10.6%
시 88
10.3%
구 87
10.2%
산 86
10.1%
완 81
9.5%
길 64
 
7.5%
로 25
 
2.9%
동 18
 
2.1%
교 14
 
1.6%
Other values (55) 193
22.7%

전화번호
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Memory size836.0 B
-
53 
063-445-7585
 
2
063-286-2215
 
1
063-284-5953
 
1
063-282-6763
 
1
Other values (30)
30 

Length

Max length13
Median length1
Mean length5.3636364
Min length1

Unique

Unique33 ?
Unique (%)37.5%

Sample

1st row063-286-8886
2nd row063-288-4860
3rd row063-286-2215
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 53
60.2%
063-445-7585 2
 
2.3%
063-286-2215 1
 
1.1%
063-284-5953 1
 
1.1%
063-282-6763 1
 
1.1%
063-254-4704 1
 
1.1%
063-284-5942 1
 
1.1%
063-285-4051 1
 
1.1%
063-283-8880 1
 
1.1%
063-902-9345 1
 
1.1%
Other values (25) 25
28.4%

Length

2024-03-14T09:59:44.365953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
53
60.2%
063-445-7585 2
 
2.3%
063-286-8886 1
 
1.1%
063-288-0159 1
 
1.1%
1588-7467 1
 
1.1%
063-277-3116 1
 
1.1%
063-232-1809 1
 
1.1%
070-4606-3117 1
 
1.1%
063-232-6748 1
 
1.1%
063-228-1267 1
 
1.1%
Other values (25) 25
28.4%

객실수
Text

Distinct46
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-14T09:59:44.517338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.5909091
Min length1

Characters and Unicode

Total characters404
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)28.4%

Sample

1st row6(12)
2nd row3(6)
3rd row4(16)
4th row3(10)
5th row4(15)
ValueCountFrequency (%)
2(8 7
 
8.0%
2(10 6
 
6.8%
5(20 5
 
5.7%
7(30 4
 
4.5%
3(12 3
 
3.4%
5(15 3
 
3.4%
4(10 3
 
3.4%
4(8 3
 
3.4%
3(9 3
 
3.4%
4(12 3
 
3.4%
Other values (36) 48
54.5%
2024-03-14T09:59:44.784021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 84
20.8%
) 83
20.5%
2 43
10.6%
1 39
9.7%
0 32
 
7.9%
5 31
 
7.7%
3 23
 
5.7%
4 22
 
5.4%
6 16
 
4.0%
8 15
 
3.7%
Other values (2) 16
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 237
58.7%
Open Punctuation 84
 
20.8%
Close Punctuation 83
 
20.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 43
18.1%
1 39
16.5%
0 32
13.5%
5 31
13.1%
3 23
9.7%
4 22
9.3%
6 16
 
6.8%
8 15
 
6.3%
9 9
 
3.8%
7 7
 
3.0%
Open Punctuation
ValueCountFrequency (%)
( 84
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 404
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
( 84
20.8%
) 83
20.5%
2 43
10.6%
1 39
9.7%
0 32
 
7.9%
5 31
 
7.7%
3 23
 
5.7%
4 22
 
5.4%
6 16
 
4.0%
8 15
 
3.7%
Other values (2) 16
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 84
20.8%
) 83
20.5%
2 43
10.6%
1 39
9.7%
0 32
 
7.9%
5 31
 
7.7%
3 23
 
5.7%
4 22
 
5.4%
6 16
 
4.0%
8 15
 
3.7%
Other values (2) 16
 
4.0%

홈페이지
Text

Distinct77
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size836.0 B
2024-03-14T09:59:44.970671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length26
Mean length18.897727
Min length1

Characters and Unicode

Total characters1663
Distinct characters56
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)83.0%

Sample

1st rowhttp://cafe.daum.net/chonjukorea
2nd rowwww.jhsms.com/
3rd rowwww.maru1000y.com
4th rowhttp://www.cyworld.com/kmarta/
5th rowhttp://www.cyworld.com/kmarta/
ValueCountFrequency (%)
8
 
9.1%
blog.naver.com/lwg1987 3
 
3.4%
http://www.cyworld.com/kmarta 2
 
2.3%
cafe.daum.net/hinokijam 2
 
2.3%
www.hellojeje.com 1
 
1.1%
yooseol96.wix.com/bibimguesthouse 1
 
1.1%
www.cheongsachorong.co.kr 1
 
1.1%
blog.naver.com/soopji 1
 
1.1%
blog.naver.com/rmfladlcmd 1
 
1.1%
blog.naver.com/kimpd1987 1
 
1.1%
Other values (67) 67
76.1%
2024-03-14T09:59:45.256204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 160
 
9.6%
. 156
 
9.4%
a 99
 
6.0%
w 96
 
5.8%
e 91
 
5.5%
r 91
 
5.5%
m 86
 
5.2%
n 84
 
5.1%
c 75
 
4.5%
/ 68
 
4.1%
Other values (46) 657
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1343
80.8%
Other Punctuation 237
 
14.3%
Decimal Number 54
 
3.2%
Other Letter 17
 
1.0%
Dash Punctuation 8
 
0.5%
Uppercase Letter 2
 
0.1%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 160
 
11.9%
a 99
 
7.4%
w 96
 
7.1%
e 91
 
6.8%
r 91
 
6.8%
m 86
 
6.4%
n 84
 
6.3%
c 75
 
5.6%
g 62
 
4.6%
t 62
 
4.6%
Other values (14) 437
32.5%
Other Letter
ValueCountFrequency (%)
스 2
 
11.8%
목 1
 
5.9%
우 1
 
5.9%
하 1
 
5.9%
트 1
 
5.9%
게 1
 
5.9%
길 1
 
5.9%
꽃 1
 
5.9%
담 1
 
5.9%
주 1
 
5.9%
Other values (6) 6
35.3%
Decimal Number
ValueCountFrequency (%)
1 10
18.5%
0 10
18.5%
9 8
14.8%
2 6
11.1%
8 5
9.3%
7 4
 
7.4%
4 3
 
5.6%
5 3
 
5.6%
6 3
 
5.6%
3 2
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 156
65.8%
/ 68
28.7%
: 13
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Uppercase Letter
ValueCountFrequency (%)
H 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1345
80.9%
Common 301
 
18.1%
Hangul 17
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 160
 
11.9%
a 99
 
7.4%
w 96
 
7.1%
e 91
 
6.8%
r 91
 
6.8%
m 86
 
6.4%
n 84
 
6.2%
c 75
 
5.6%
g 62
 
4.6%
t 62
 
4.6%
Other values (15) 439
32.6%
Hangul
ValueCountFrequency (%)
스 2
 
11.8%
목 1
 
5.9%
우 1
 
5.9%
하 1
 
5.9%
트 1
 
5.9%
게 1
 
5.9%
길 1
 
5.9%
꽃 1
 
5.9%
담 1
 
5.9%
주 1
 
5.9%
Other values (6) 6
35.3%
Common
ValueCountFrequency (%)
. 156
51.8%
/ 68
22.6%
: 13
 
4.3%
1 10
 
3.3%
0 10
 
3.3%
- 8
 
2.7%
9 8
 
2.7%
2 6
 
2.0%
8 5
 
1.7%
7 4
 
1.3%
Other values (5) 13
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1646
99.0%
Hangul 17
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 160
 
9.7%
. 156
 
9.5%
a 99
 
6.0%
w 96
 
5.8%
e 91
 
5.5%
r 91
 
5.5%
m 86
 
5.2%
n 84
 
5.1%
c 75
 
4.6%
/ 68
 
4.1%
Other values (30) 640
38.9%
Hangul
ValueCountFrequency (%)
스 2
 
11.8%
목 1
 
5.9%
우 1
 
5.9%
하 1
 
5.9%
트 1
 
5.9%
게 1
 
5.9%
길 1
 
5.9%
꽃 1
 
5.9%
담 1
 
5.9%
주 1
 
5.9%
Other values (6) 6
35.3%

주차장유무
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
-
83 
유
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row유
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 83
94.3%
유 5
 
5.7%

Length

2024-03-14T09:59:45.379869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:59:45.460140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
83
94.3%
유 5
 
5.7%

관광정보
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size836.0 B
전주한옥마을
71 
군산근대역사박물관, 진포해양테마공원
 
4
한옥마을
 
3
전동성당, 한옥마을
 
2
덕진공원, 전주동물원
 
1
Other values (7)
 
7

Length

Max length19
Median length6
Mean length7.1931818
Min length4

Unique

Unique8 ?
Unique (%)9.1%

Sample

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

Common Values

ValueCountFrequency (%)
전주한옥마을 71
80.7%
군산근대역사박물관, 진포해양테마공원 4
 
4.5%
한옥마을 3
 
3.4%
전동성당, 한옥마을 2
 
2.3%
덕진공원, 전주동물원 1
 
1.1%
한옥마을, 아중저수지 1
 
1.1%
경기전, 풍남문, 한옥마을 1
 
1.1%
한옥마을, 자연생태박물관 1
 
1.1%
풍남문, 전동성당, 한옥마을 1
 
1.1%
경기전, 한옥마을 1
 
1.1%
Other values (2) 2
 
2.3%

Length

2024-03-14T09:59:45.566762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주한옥마을 72
68.6%
한옥마을 11
 
10.5%
군산근대역사박물관 4
 
3.8%
진포해양테마공원 4
 
3.8%
전동성당 4
 
3.8%
경기전 2
 
1.9%
풍남문 2
 
1.9%
덕진공원 1
 
1.0%
전주동물원 1
 
1.0%
아중저수지 1
 
1.0%
Other values (3) 3
 
2.9%

부대시설
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size836.0 B
-
80 
여성전용
 
1
애완견동반
 
1
여성전용룸 구비
 
1
반려동물 동반
 
1
Other values (4)
 
4

Length

Max length12
Median length1
Mean length1.5113636
Min length1

Unique

Unique8 ?
Unique (%)9.1%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row여성전용

Common Values

ValueCountFrequency (%)
- 80
90.9%
여성전용 1
 
1.1%
애완견동반 1
 
1.1%
여성전용룸 구비 1
 
1.1%
반려동물 동반 1
 
1.1%
1층 식당 1
 
1.1%
막걸리파티 1
 
1.1%
조식(콩나물국밥) 제공 1
 
1.1%
옥상 바비큐장 1
 
1.1%

Length

2024-03-14T09:59:45.719075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:59:45.822137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
80
86.0%
여성전용 1
 
1.1%
애완견동반 1
 
1.1%
여성전용룸 1
 
1.1%
구비 1
 
1.1%
반려동물 1
 
1.1%
동반 1
 
1.1%
1층 1
 
1.1%
식당 1
 
1.1%
막걸리파티 1
 
1.1%
Other values (4) 4
 
4.3%

결제방법
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size836.0 B
계좌이체
71 
-
16 
신용카드
 
1

Length

Max length4
Median length4
Mean length3.4545455
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
계좌이체 71
80.7%
- 16
 
18.2%
신용카드 1
 
1.1%

Length

2024-03-14T09:59:45.955121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:59:46.045808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계좌이체 71
80.7%
16
 
18.2%
신용카드 1
 
1.1%

자료출처
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size836.0 B
관광총괄과
88 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관광총괄과 88
100.0%

Length

2024-03-14T09:59:46.130511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:59:46.229172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광총괄과 88
100.0%

공개여부
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size836.0 B
공개
88 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공개 88
100.0%

Length

2024-03-14T09:59:46.352964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:59:46.449679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공개 88
100.0%

작성일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size836.0 B
2015.1
88 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015.1
2nd row2015.1
3rd row2015.1
4th row2015.1
5th row2015.1

Common Values

ValueCountFrequency (%)
2015.1 88
100.0%

Length

2024-03-14T09:59:46.531450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:59:46.609834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015.1 88
100.0%

갱신주기
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size836.0 B
1년
88 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1년
2nd row1년
3rd row1년
4th row1년
5th row1년

Common Values

ValueCountFrequency (%)
1년 88
100.0%

Length

2024-03-14T09:59:46.686815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:59:46.758105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1년 88
100.0%

Interactions

2024-03-14T09:59:42.355422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:59:46.811661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명시설명도로명주소전화번호객실수홈페이지주차장유무관광정보부대시설결제방법
순번1.0000.7541.0000.9410.4640.6510.8790.0680.5420.0310.204
시군명0.7541.0001.0001.0000.4980.8181.0000.0001.0000.0000.000
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소0.9411.0001.0001.0000.9760.9950.9981.0001.0001.0000.656
전화번호0.4640.4981.0000.9761.0000.0000.9840.0000.0000.0000.896
객실수0.6510.8181.0000.9950.0001.0000.9480.6500.0000.8760.000
홈페이지0.8791.0001.0000.9980.9840.9481.0001.0001.0000.9531.000
주차장유무0.0680.0001.0001.0000.0000.6501.0001.0000.6970.0000.000
관광정보0.5421.0001.0001.0000.0000.0001.0000.6971.0000.4700.000
부대시설0.0310.0001.0001.0000.0000.8760.9530.0000.4701.0000.933
결제방법0.2040.0001.0000.6560.8960.0001.0000.0000.0000.9331.000
2024-03-14T09:59:46.926193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전화번호주차장유무결제방법시군명부대시설관광정보
전화번호1.0000.0000.5750.3260.0000.000
주차장유무0.0001.0000.0000.0000.0000.517
결제방법0.5750.0001.0000.0000.6720.000
시군명0.3260.0000.0001.0000.0000.940
부대시설0.0000.0000.6720.0001.0000.213
관광정보0.0000.5170.0000.9400.2131.000
2024-03-14T09:59:47.015519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명전화번호주차장유무관광정보부대시설결제방법
순번1.0000.5640.1290.0350.2560.0000.113
시군명0.5641.0000.3260.0000.9400.0000.000
전화번호0.1290.3261.0000.0000.0000.0000.575
주차장유무0.0350.0000.0001.0000.5170.0000.000
관광정보0.2560.9400.0000.5171.0000.2130.000
부대시설0.0000.0000.0000.0000.2131.0000.672
결제방법0.1130.0000.5750.0000.0000.6721.000

Missing values

2024-03-14T09:59:42.443162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:59:42.588804image/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

순번시군명시설명도로명주소전화번호객실수홈페이지주차장유무관광정보부대시설결제방법자료출처공개여부작성일갱신주기
01전주시전주게스트하우스전주시 완산구 경기전길 46063-286-88866(12)http://cafe.daum.net/chonjukorea-전주한옥마을-계좌이체관광총괄과공개2015.11년
12전주시해 달 별전주시 완산구 어진길 33-9063-288-48603(6)www.jhsms.com/유전주한옥마을-계좌이체관광총괄과공개2015.11년
23전주시천년마루전주시 완산구 경기전길 186063-286-22154(16)www.maru1000y.com-전주한옥마을-계좌이체관광총괄과공개2015.11년
34전주시마르타숙소전주시 완산구 오목대길 52-3(10)http://www.cyworld.com/kmarta/-전주한옥마을-계좌이체관광총괄과공개2015.11년
45전주시60-6게스트하우스전주시 완산구 오목대길 49-1-4(15)http://www.cyworld.com/kmarta/-전주한옥마을여성전용계좌이체관광총괄과공개2015.11년
56전주시해 밀전주시 완산구 향교길 19-23-6(20)www.haemilgh.com유전주한옥마을-계좌이체관광총괄과공개2015.11년
67전주시별 빛 향전주시 완산구 오목대길 27-21-3(9)http://1.wcr.co.kr/starlv/-전주한옥마을-계좌이체관광총괄과공개2015.11년
78전주시초 정전주시 완산구 향교길 88063-284-59533(12)http://blog.naver.com/sus3043-전주한옥마을-계좌이체관광총괄과공개2015.11년
89전주시향기나무전주시 완산구 향교길 68-5(20)blog.naver.com/scentwood-전주한옥마을-계좌이체관광총괄과공개2015.11년
910전주시꽃 담전주시 완산구 어진길 38-4063-282-67633(9)http://한옥마을꽃담.com-전주한옥마을-계좌이체관광총괄과공개2015.11년
순번시군명시설명도로명주소전화번호객실수홈페이지주차장유무관광정보부대시설결제방법자료출처공개여부작성일갱신주기
7879전주시휴전주시 완산구 전주천동로 80-15-4(8)jeonjuguest.com-전주한옥마을-계좌이체관광총괄과공개2015.11년
7980전주시전주 어린왕자 게스트하우스전주시 완산구 태조로 14-1-6(12)blog.naver.com/amour_22-전주한옥마을-계좌이체관광총괄과공개2015.11년
8081군산시히노키잠군산시 구영6길 54-1063-445-75857(30)cafe.daum.net/Hinokijam-군산근대역사박물관, 진포해양테마공원-계좌이체관광총괄과공개2015.11년
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