Overview

Dataset statistics

Number of variables6
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory52.3 B

Variable types

Categorical3
Text2
Numeric1

Dataset

Description업종별관광숙박업등록현황20146
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202319

Alerts

비 고 is highly imbalanced (78.8%)Imbalance
상 호 명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 02:39:34.722182
Analysis finished2024-03-14 02:39:35.260810
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종별
Categorical

Distinct4
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
관광호텔
28 
휴양콘도
6 
가족호텔
5 
호스텔
 
1

Length

Max length4
Median length4
Mean length3.975
Min length3

Unique

Unique1 ?
Unique (%)2.5%

Sample

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

Common Values

ValueCountFrequency (%)
관광호텔 28
70.0%
휴양콘도 6
 
15.0%
가족호텔 5
 
12.5%
호스텔 1
 
2.5%

Length

2024-03-14T11:39:35.309247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:39:35.392158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광호텔 28
70.0%
휴양콘도 6
 
15.0%
가족호텔 5
 
12.5%
호스텔 1
 
2.5%

상 호 명
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2024-03-14T11:39:35.566272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.7
Min length4

Characters and Unicode

Total characters308
Distinct characters102
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row풍남관광호텔
2nd row전주코아호텔
3rd row㈜호텔르윈
4th row전주호텔한성
5th row째즈어라운드호텔
ValueCountFrequency (%)
풍남관광호텔 1
 
2.4%
호텔마음 1
 
2.4%
무주덕유산리조트가족호텔 1
 
2.4%
지리산구룡관광호텔 1
 
2.4%
스위트관광호텔 1
 
2.4%
대둔산관광호텔 1
 
2.4%
호텔티롤 1
 
2.4%
선운산관광호텔 1
 
2.4%
채석강스타힐스 1
 
2.4%
호텔 1
 
2.4%
Other values (32) 32
76.2%
2024-03-14T11:39:35.872905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
호 32
 
10.4%
텔 32
 
10.4%
관 18
 
5.8%
광 18
 
5.8%
산 15
 
4.9%
리 14
 
4.5%
스 13
 
4.2%
주 8
 
2.6%
트 8
 
2.6%
조 5
 
1.6%
Other values (92) 145
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 305
99.0%
Space Separator 2
 
0.6%
Other Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 32
 
10.5%
텔 32
 
10.5%
관 18
 
5.9%
광 18
 
5.9%
산 15
 
4.9%
리 14
 
4.6%
스 13
 
4.3%
주 8
 
2.6%
트 8
 
2.6%
조 5
 
1.6%
Other values (90) 142
46.6%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 306
99.4%
Common 2
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 32
 
10.5%
텔 32
 
10.5%
관 18
 
5.9%
광 18
 
5.9%
산 15
 
4.9%
리 14
 
4.6%
스 13
 
4.2%
주 8
 
2.6%
트 8
 
2.6%
조 5
 
1.6%
Other values (91) 143
46.7%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 305
99.0%
ASCII 2
 
0.6%
None 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
호 32
 
10.5%
텔 32
 
10.5%
관 18
 
5.9%
광 18
 
5.9%
산 15
 
4.9%
리 14
 
4.6%
스 13
 
4.3%
주 8
 
2.6%
트 8
 
2.6%
조 5
 
1.6%
Other values (90) 142
46.6%
ASCII
ValueCountFrequency (%)
2
100.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지
Text

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2024-03-14T11:39:36.122971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length14.3
Min length10

Characters and Unicode

Total characters572
Distinct characters91
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

Unique37 ?
Unique (%)92.5%

Sample

1st row전주시 완산구 객사2길 45-7
2nd row전주시 완산구 팔달로 262-2
3rd row전주시 완산구 기린대로 85
4th row전주시 완산구 전주객사5길 44-5
5th row전주시 덕진구 정언신로 182
ValueCountFrequency (%)
군산시 11
 
7.6%
전주시 10
 
6.9%
완산구 7
 
4.8%
남원시 7
 
4.8%
무주군 5
 
3.4%
덕진구 3
 
2.1%
만선로 3
 
2.1%
185 3
 
2.1%
설천면 3
 
2.1%
부안군 3
 
2.1%
Other values (81) 90
62.1%
2024-03-14T11:39:36.649823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
105
 
18.4%
산 31
 
5.4%
시 30
 
5.2%
5 24
 
4.2%
군 21
 
3.7%
1 21
 
3.7%
길 20
 
3.5%
로 20
 
3.5%
주 20
 
3.5%
4 17
 
3.0%
Other values (81) 263
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 346
60.5%
Decimal Number 112
 
19.6%
Space Separator 105
 
18.4%
Dash Punctuation 9
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 31
 
9.0%
시 30
 
8.7%
군 21
 
6.1%
길 20
 
5.8%
로 20
 
5.8%
주 20
 
5.8%
면 15
 
4.3%
전 13
 
3.8%
원 11
 
3.2%
구 11
 
3.2%
Other values (69) 154
44.5%
Decimal Number
ValueCountFrequency (%)
5 24
21.4%
1 21
18.8%
4 17
15.2%
2 14
12.5%
3 9
 
8.0%
7 7
 
6.2%
6 7
 
6.2%
8 6
 
5.4%
0 5
 
4.5%
9 2
 
1.8%
Space Separator
ValueCountFrequency (%)
105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 346
60.5%
Common 226
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 31
 
9.0%
시 30
 
8.7%
군 21
 
6.1%
길 20
 
5.8%
로 20
 
5.8%
주 20
 
5.8%
면 15
 
4.3%
전 13
 
3.8%
원 11
 
3.2%
구 11
 
3.2%
Other values (69) 154
44.5%
Common
ValueCountFrequency (%)
105
46.5%
5 24
 
10.6%
1 21
 
9.3%
4 17
 
7.5%
2 14
 
6.2%
- 9
 
4.0%
3 9
 
4.0%
7 7
 
3.1%
6 7
 
3.1%
8 6
 
2.7%
Other values (2) 7
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 346
60.5%
ASCII 226
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
105
46.5%
5 24
 
10.6%
1 21
 
9.3%
4 17
 
7.5%
2 14
 
6.2%
- 9
 
4.0%
3 9
 
4.0%
7 7
 
3.1%
6 7
 
3.1%
8 6
 
2.7%
Other values (2) 7
 
3.1%
Hangul
ValueCountFrequency (%)
산 31
 
9.0%
시 30
 
8.7%
군 21
 
6.1%
길 20
 
5.8%
로 20
 
5.8%
주 20
 
5.8%
면 15
 
4.3%
전 13
 
3.8%
원 11
 
3.2%
구 11
 
3.2%
Other values (69) 154
44.5%

등급
Categorical

Distinct7
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
-
12 
2
11 
1
5 
3
5 
특2
3 
Other values (2)
4 

Length

Max length2
Median length1
Mean length1.175
Min length1

Unique

Unique1 ?
Unique (%)2.5%

Sample

1st row2
2nd row특2
3rd row특2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
- 12
30.0%
2 11
27.5%
1 5
12.5%
3 5
12.5%
특2 3
 
7.5%
미정 3
 
7.5%
특1 1
 
2.5%

Length

2024-03-14T11:39:36.757302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:39:36.849446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12
30.0%
2 11
27.5%
1 5
12.5%
3 5
12.5%
특2 3
 
7.5%
미정 3
 
7.5%
특1 1
 
2.5%

객실수
Real number (ℝ)

Distinct33
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.6
Minimum17
Maximum974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2024-03-14T11:39:36.973452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile30
Q140
median59.5
Q3118.75
95-th percentile422.3
Maximum974
Range957
Interquartile range (IQR)78.75

Descriptive statistics

Standard deviation169.4033
Coefficient of variation (CV)1.4405042
Kurtosis17.425995
Mean117.6
Median Absolute Deviation (MAD)26.5
Skewness3.9038803
Sum4704
Variance28697.477
MonotonicityNot monotonic
2024-03-14T11:39:37.100954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
40 3
 
7.5%
35 3
 
7.5%
30 2
 
5.0%
68 2
 
5.0%
42 2
 
5.0%
63 1
 
2.5%
17 1
 
2.5%
36 1
 
2.5%
974 1
 
2.5%
418 1
 
2.5%
Other values (23) 23
57.5%
ValueCountFrequency (%)
17 1
 
2.5%
30 2
5.0%
31 1
 
2.5%
35 3
7.5%
36 1
 
2.5%
38 1
 
2.5%
40 3
7.5%
42 2
5.0%
45 1
 
2.5%
47 1
 
2.5%
ValueCountFrequency (%)
974 1
2.5%
504 1
2.5%
418 1
2.5%
181 1
2.5%
167 1
2.5%
166 1
2.5%
156 1
2.5%
153 1
2.5%
147 1
2.5%
121 1
2.5%

비 고
Categorical

IMBALANCE 

Distinct3
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
-
38 
(구)전주코아리베라
 
1
(2006.8.18부터～공사중단)
 
1

Length

Max length18
Median length1
Mean length1.65
Min length1

Unique

Unique2 ?
Unique (%)5.0%

Sample

1st row-
2nd row-
3rd row(구)전주코아리베라
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 38
95.0%
(구)전주코아리베라 1
 
2.5%
(2006.8.18부터～공사중단) 1
 
2.5%

Length

2024-03-14T11:39:37.259143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:39:37.395354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38
95.0%
구)전주코아리베라 1
 
2.5%
2006.8.18부터～공사중단 1
 
2.5%

Interactions

2024-03-14T11:39:34.981536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:39:37.457460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종별상 호 명소재지등급객실수비 고
업종별1.0001.0000.8020.6050.5010.000
상 호 명1.0001.0001.0001.0001.0001.000
소재지0.8021.0001.0000.0000.0001.000
등급0.6051.0000.0001.0000.0000.449
객실수0.5011.0000.0000.0001.0000.000
비 고0.0001.0001.0000.4490.0001.000
2024-03-14T11:39:37.538221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종별비 고등급
업종별1.0000.0000.441
비 고0.0001.0000.314
등급0.4410.3141.000
2024-03-14T11:39:37.610782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실수업종별등급비 고
객실수1.0000.4210.0000.000
업종별0.4211.0000.4410.000
등급0.0000.4411.0000.314
비 고0.0000.0000.3141.000

Missing values

2024-03-14T11:39:35.136172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:39:35.229005image/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

업종별상 호 명소재지등급객실수비 고
0관광호텔풍남관광호텔전주시 완산구 객사2길 45-7263-
1관광호텔전주코아호텔전주시 완산구 팔달로 262-2특2111-
2관광호텔㈜호텔르윈전주시 완산구 기린대로 85특2166(구)전주코아리베라
3관광호텔전주호텔한성전주시 완산구 전주객사5길 44-5240-
4관광호텔째즈어라운드호텔전주시 덕진구 정언신로 182135-
5관광호텔화이트관광호텔전주시 덕진구 전주천동로 501235-
6관광호텔궁관광호텔전주시 덕진구 용산1길 17-4330-
7관광호텔전주한옥태조궁관광호텔전주시 완산구 전라감영로 40330-
8관광호텔전주관광호텔전주시 완산구 객사5길 44-5331-
9관광호텔군산폭스관광호텔군산시 백릉안1길 8247-
업종별상 호 명소재지등급객실수비 고
30가족호텔무주덕유산리조트국민호텔무주군 설천면 만선로 185-418-
31가족호텔대명리조트변산가족호텔부안군 변산면 변산해변로 51-504-
32가족호텔모항해나루가족호텔부안군 변산면 모항해변길 73-112-
33호스텔소리울호스텔전주시 완산구 팔달로 144-4-17-
34휴양콘도켄싱턴리조트지리산남원남원시 소리길 66-156-
35휴양콘도중앙하이츠콘도남원시 장승안길 2-9-153-
36휴양콘도지리산토비스콘도남원시 산내면 산내원천길 4-5-60-
37휴양콘도일성지리산콘도남원시 산내면 천왕봉로 626-25-167-
38휴양콘도일성무주콘도무주군 무풍면 라제통문로 455-121-
39휴양콘도무주토비스콘도무주군 무풍면 구천동로 350-106-