Overview

Dataset statistics

Number of variables5
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory42.9 B

Variable types

Categorical3
Text1
Numeric1

Dataset

Description1) 장흥군 농어촌민박 및 펜션 소속 지역2) 장흥군 업종(펜션o농어촌민박)2) 장흥군 농어촌민박 및 펜션 업체명3)장흥군 농어촌민박 및 펜션 객실 수
Author전라남도 장흥군
URLhttps://www.data.go.kr/data/15074473/fileData.do

Alerts

시군 has constant value ""Constant
업종 has constant value ""Constant

Reproduction

Analysis started2023-12-12 18:49:41.072665
Analysis finished2023-12-12 18:49:41.810995
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
전라남도 장흥군
68 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도 장흥군
2nd row전라남도 장흥군
3rd row전라남도 장흥군
4th row전라남도 장흥군
5th row전라남도 장흥군

Common Values

ValueCountFrequency (%)
전라남도 장흥군 68
100.0%

Length

2023-12-13T03:49:41.940013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:49:42.089339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 68
50.0%
장흥군 68
50.0%

읍면
Categorical

Distinct10
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size676.0 B
안양면
19 
장흥읍
16 
관산읍
8 
회진면
6 
유치면
6 
Other values (5)
13 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)2.9%

Sample

1st row안양면
2nd row관산읍
3rd row안양면
4th row용산면
5th row용산면

Common Values

ValueCountFrequency (%)
안양면 19
27.9%
장흥읍 16
23.5%
관산읍 8
11.8%
회진면 6
 
8.8%
유치면 6
 
8.8%
용산면 5
 
7.4%
대덕읍 3
 
4.4%
장평면 3
 
4.4%
부산면 1
 
1.5%
장동면 1
 
1.5%

Length

2023-12-13T03:49:42.287849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:49:42.521181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안양면 19
27.9%
장흥읍 16
23.5%
관산읍 8
11.8%
회진면 6
 
8.8%
유치면 6
 
8.8%
용산면 5
 
7.4%
대덕읍 3
 
4.4%
장평면 3
 
4.4%
부산면 1
 
1.5%
장동면 1
 
1.5%

업종
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
민박
68 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
민박 68
100.0%

Length

2023-12-13T03:49:42.733503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:49:42.862446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민박 68
100.0%

업체명
Text

Distinct66
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-13T03:49:43.130668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length5.25
Min length2

Characters and Unicode

Total characters357
Distinct characters152
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

Unique64 ?
Unique (%)94.1%

Sample

1st row해오름
2nd row천관마루
3rd row해오름팬션
4th row길벗민박
5th row소등섬민박
ValueCountFrequency (%)
민박 5
 
6.2%
해오름 2
 
2.5%
정남진 2
 
2.5%
해오름팬션 2
 
2.5%
세상 1
 
1.2%
황토집 1
 
1.2%
온하 1
 
1.2%
옥촌한옥민박 1
 
1.2%
정남진한옥민박 1
 
1.2%
선학동민박 1
 
1.2%
Other values (64) 64
79.0%
2023-12-13T03:49:43.748704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
박 29
 
8.1%
민 28
 
7.8%
13
 
3.6%
집 8
 
2.2%
션 7
 
2.0%
다 6
 
1.7%
스 6
 
1.7%
름 5
 
1.4%
이 5
 
1.4%
우 5
 
1.4%
Other values (142) 245
68.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 312
87.4%
Lowercase Letter 23
 
6.4%
Space Separator 13
 
3.6%
Decimal Number 6
 
1.7%
Uppercase Letter 1
 
0.3%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
박 29
 
9.3%
민 28
 
9.0%
집 8
 
2.6%
션 7
 
2.2%
다 6
 
1.9%
스 6
 
1.9%
름 5
 
1.6%
이 5
 
1.6%
우 5
 
1.6%
해 4
 
1.3%
Other values (122) 209
67.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
17.4%
u 3
13.0%
o 3
13.0%
l 3
13.0%
a 2
8.7%
s 2
8.7%
h 2
8.7%
r 1
 
4.3%
w 1
 
4.3%
y 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
9 1
16.7%
2 1
16.7%
8 1
16.7%
7 1
16.7%
Space Separator
ValueCountFrequency (%)
13
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 312
87.4%
Latin 24
 
6.7%
Common 21
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
박 29
 
9.3%
민 28
 
9.0%
집 8
 
2.6%
션 7
 
2.2%
다 6
 
1.9%
스 6
 
1.9%
름 5
 
1.6%
이 5
 
1.6%
우 5
 
1.6%
해 4
 
1.3%
Other values (122) 209
67.0%
Latin
ValueCountFrequency (%)
e 4
16.7%
u 3
12.5%
o 3
12.5%
l 3
12.5%
a 2
8.3%
s 2
8.3%
h 2
8.3%
r 1
 
4.2%
D 1
 
4.2%
w 1
 
4.2%
Other values (2) 2
8.3%
Common
ValueCountFrequency (%)
13
61.9%
1 2
 
9.5%
( 1
 
4.8%
9 1
 
4.8%
2 1
 
4.8%
8 1
 
4.8%
7 1
 
4.8%
) 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 312
87.4%
ASCII 45
 
12.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
박 29
 
9.3%
민 28
 
9.0%
집 8
 
2.6%
션 7
 
2.2%
다 6
 
1.9%
스 6
 
1.9%
름 5
 
1.6%
이 5
 
1.6%
우 5
 
1.6%
해 4
 
1.3%
Other values (122) 209
67.0%
ASCII
ValueCountFrequency (%)
13
28.9%
e 4
 
8.9%
u 3
 
6.7%
o 3
 
6.7%
l 3
 
6.7%
a 2
 
4.4%
s 2
 
4.4%
h 2
 
4.4%
1 2
 
4.4%
r 1
 
2.2%
Other values (10) 10
22.2%

객실수
Real number (ℝ)

Distinct6
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5147059
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T03:49:43.918965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1780663
Coefficient of variation (CV)0.46847081
Kurtosis1.0924532
Mean2.5147059
Median Absolute Deviation (MAD)1
Skewness1.0924229
Sum171
Variance1.3878402
MonotonicityNot monotonic
2023-12-13T03:49:44.068215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 32
47.1%
3 14
20.6%
1 10
 
14.7%
4 7
 
10.3%
5 3
 
4.4%
6 2
 
2.9%
ValueCountFrequency (%)
1 10
 
14.7%
2 32
47.1%
3 14
20.6%
4 7
 
10.3%
5 3
 
4.4%
6 2
 
2.9%
ValueCountFrequency (%)
6 2
 
2.9%
5 3
 
4.4%
4 7
 
10.3%
3 14
20.6%
2 32
47.1%
1 10
 
14.7%

Interactions

2023-12-13T03:49:41.376509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:49:44.208830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면업체명객실수
읍면1.0001.0000.000
업체명1.0001.0000.895
객실수0.0000.8951.000
2023-12-13T03:49:44.385247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실수읍면
객실수1.0000.000
읍면0.0001.000

Missing values

2023-12-13T03:49:41.583776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:49:41.750134image/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

시군읍면업종업체명객실수
0전라남도 장흥군안양면민박해오름3
1전라남도 장흥군관산읍민박천관마루3
2전라남도 장흥군안양면민박해오름팬션2
3전라남도 장흥군용산면민박길벗민박2
4전라남도 장흥군용산면민박소등섬민박4
5전라남도 장흥군대덕읍민박어부손씨민박5
6전라남도 장흥군안양면민박선화민박1
7전라남도 장흥군안양면민박수문민박3
8전라남도 장흥군안양면민박안양민박6
9전라남도 장흥군용산면민박송전산방3
시군읍면업종업체명객실수
58전라남도 장흥군안양면민박다재헌1
59전라남도 장흥군안양면민박blue yellow house1
60전라남도 장흥군안양면민박해오름팬션4
61전라남도 장흥군유치면민박솔향기2
62전라남도 장흥군장동면민박스테이우디2
63전라남도 장흥군대덕읍민박참 편안한 세상2
64전라남도 장흥군장흥읍민박온하2
65전라남도 장흥군장흥읍민박익투스2
66전라남도 장흥군장흥읍민박지현이네집2
67전라남도 장흥군안양면민박다라(Dara)house2