Overview

Dataset statistics

Number of variables4
Number of observations69
Missing cells3
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory34.9 B

Variable types

Numeric1
Text2
Categorical1

Dataset

Description전라북도 우수숙박시설 현황 (한국관광품질인증)
Author전라북도
URLhttps://www.data.go.kr/data/3081264/fileData.do

Alerts

연번 has 1 (1.4%) missing valuesMissing
시설명 has 1 (1.4%) missing valuesMissing
소재지 has 1 (1.4%) missing valuesMissing

Reproduction

Analysis started2023-12-13 00:29:45.651335
Analysis finished2023-12-13 00:29:46.161399
Duration0.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

MISSING 

Distinct68
Distinct (%)100.0%
Missing1
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean34.5
Minimum1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2023-12-13T09:29:46.214789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.35
Q117.75
median34.5
Q351.25
95-th percentile64.65
Maximum68
Range67
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation19.77372
Coefficient of variation (CV)0.5731513
Kurtosis-1.2
Mean34.5
Median Absolute Deviation (MAD)17
Skewness0
Sum2346
Variance391
MonotonicityStrictly increasing
2023-12-13T09:29:46.317410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
45 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
46 1
 
1.4%
44 1
 
1.4%
36 1
 
1.4%
Other values (58) 58
84.1%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%
61 1
1.4%
60 1
1.4%
59 1
1.4%

시설명
Text

MISSING 

Distinct68
Distinct (%)100.0%
Missing1
Missing (%)1.4%
Memory size684.0 B
2023-12-13T09:29:46.542773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length5.1470588
Min length1

Characters and Unicode

Total characters350
Distinct characters163
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

Unique68 ?
Unique (%)100.0%

Sample

1st row라임호텔
2nd row아리랑호텔
3rd row전주호텔
4th row가은채
5th row가은채2
ValueCountFrequency (%)
호텔 2
 
2.7%
라임호텔 1
 
1.3%
해뜨는언덕 1
 
1.3%
그린피아모텔 1
 
1.3%
메이드모텔 1
 
1.3%
발리모텔 1
 
1.3%
정읍교동안진사고택 1
 
1.3%
오뉴월 1
 
1.3%
달 1
 
1.3%
h(에이치)모텔 1
 
1.3%
Other values (64) 64
85.3%
2023-12-13T09:29:46.848587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 18
 
5.1%
한 12
 
3.4%
옥 10
 
2.9%
모 10
 
2.9%
호 9
 
2.6%
이 7
 
2.0%
7
 
2.0%
리 6
 
1.7%
주 6
 
1.7%
스 6
 
1.7%
Other values (153) 259
74.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 323
92.3%
Lowercase Letter 12
 
3.4%
Space Separator 7
 
2.0%
Decimal Number 3
 
0.9%
Close Punctuation 2
 
0.6%
Open Punctuation 2
 
0.6%
Uppercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 18
 
5.6%
한 12
 
3.7%
옥 10
 
3.1%
모 10
 
3.1%
호 9
 
2.8%
이 7
 
2.2%
리 6
 
1.9%
주 6
 
1.9%
스 6
 
1.9%
당 5
 
1.5%
Other values (139) 234
72.4%
Lowercase Letter
ValueCountFrequency (%)
u 2
16.7%
e 2
16.7%
r 2
16.7%
j 1
8.3%
s 1
8.3%
m 1
8.3%
o 1
8.3%
t 1
8.3%
l 1
8.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Decimal Number
ValueCountFrequency (%)
2 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
H 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 323
92.3%
Common 14
 
4.0%
Latin 13
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 18
 
5.6%
한 12
 
3.7%
옥 10
 
3.1%
모 10
 
3.1%
호 9
 
2.8%
이 7
 
2.2%
리 6
 
1.9%
주 6
 
1.9%
스 6
 
1.9%
당 5
 
1.5%
Other values (139) 234
72.4%
Latin
ValueCountFrequency (%)
u 2
15.4%
e 2
15.4%
r 2
15.4%
j 1
7.7%
s 1
7.7%
m 1
7.7%
o 1
7.7%
t 1
7.7%
l 1
7.7%
H 1
7.7%
Common
ValueCountFrequency (%)
7
50.0%
2 3
21.4%
) 2
 
14.3%
( 2
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 323
92.3%
ASCII 27
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 18
 
5.6%
한 12
 
3.7%
옥 10
 
3.1%
모 10
 
3.1%
호 9
 
2.8%
이 7
 
2.2%
리 6
 
1.9%
주 6
 
1.9%
스 6
 
1.9%
당 5
 
1.5%
Other values (139) 234
72.4%
ASCII
ValueCountFrequency (%)
7
25.9%
2 3
11.1%
) 2
 
7.4%
( 2
 
7.4%
u 2
 
7.4%
e 2
 
7.4%
r 2
 
7.4%
j 1
 
3.7%
s 1
 
3.7%
m 1
 
3.7%
Other values (4) 4
14.8%

소재지
Text

MISSING 

Distinct68
Distinct (%)100.0%
Missing1
Missing (%)1.4%
Memory size684.0 B
2023-12-13T09:29:47.094174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length20.323529
Min length14

Characters and Unicode

Total characters1382
Distinct characters111
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

Unique68 ?
Unique (%)100.0%

Sample

1st row전주시 덕진구 아중4길 13-3 (우아동2가)
2nd row전주시 덕진구 용산2길 17-6 (금암동)
3rd row전주시 덕진구 산정2길 31 (산정동)
4th row전주시 완산구 한지길 68-13 (풍남동3가)
5th row전주시 완산구 한지길 100-20 (풍남동3가)
ValueCountFrequency (%)
전주시 37
 
11.9%
완산구 33
 
10.6%
교동 15
 
4.8%
풍남동3가 10
 
3.2%
은행로 9
 
2.9%
무주군 8
 
2.6%
군산시 7
 
2.3%
설천면 6
 
1.9%
남원시 5
 
1.6%
향교길 5
 
1.6%
Other values (145) 175
56.5%
2023-12-13T09:29:47.431360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
246
 
17.8%
동 62
 
4.5%
주 54
 
3.9%
시 51
 
3.7%
( 49
 
3.5%
) 49
 
3.5%
1 49
 
3.5%
구 48
 
3.5%
산 46
 
3.3%
전 44
 
3.2%
Other values (101) 684
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 758
54.8%
Space Separator 246
 
17.8%
Decimal Number 242
 
17.5%
Open Punctuation 49
 
3.5%
Close Punctuation 49
 
3.5%
Dash Punctuation 38
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 62
 
8.2%
주 54
 
7.1%
시 51
 
6.7%
구 48
 
6.3%
산 46
 
6.1%
전 44
 
5.8%
완 37
 
4.9%
길 37
 
4.9%
로 32
 
4.2%
군 24
 
3.2%
Other values (87) 323
42.6%
Decimal Number
ValueCountFrequency (%)
1 49
20.2%
3 40
16.5%
2 33
13.6%
4 24
9.9%
5 23
9.5%
8 18
 
7.4%
6 16
 
6.6%
7 15
 
6.2%
9 14
 
5.8%
0 10
 
4.1%
Space Separator
ValueCountFrequency (%)
246
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 758
54.8%
Common 624
45.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 62
 
8.2%
주 54
 
7.1%
시 51
 
6.7%
구 48
 
6.3%
산 46
 
6.1%
전 44
 
5.8%
완 37
 
4.9%
길 37
 
4.9%
로 32
 
4.2%
군 24
 
3.2%
Other values (87) 323
42.6%
Common
ValueCountFrequency (%)
246
39.4%
( 49
 
7.9%
) 49
 
7.9%
1 49
 
7.9%
3 40
 
6.4%
- 38
 
6.1%
2 33
 
5.3%
4 24
 
3.8%
5 23
 
3.7%
8 18
 
2.9%
Other values (4) 55
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 758
54.8%
ASCII 624
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246
39.4%
( 49
 
7.9%
) 49
 
7.9%
1 49
 
7.9%
3 40
 
6.4%
- 38
 
6.1%
2 33
 
5.3%
4 24
 
3.8%
5 23
 
3.7%
8 18
 
2.9%
Other values (4) 55
 
8.8%
Hangul
ValueCountFrequency (%)
동 62
 
8.2%
주 54
 
7.1%
시 51
 
6.7%
구 48
 
6.3%
산 46
 
6.1%
전 44
 
5.8%
완 37
 
4.9%
길 37
 
4.9%
로 32
 
4.2%
군 24
 
3.2%
Other values (87) 323
42.6%

비고
Categorical

Distinct4
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size684.0 B
한옥체험업
37 
숙박업
27 
외국인관광 도시민박업
4 
<NA>
 
1

Length

Max length11
Median length5
Mean length4.5507246
Min length3

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row숙박업
2nd row숙박업
3rd row숙박업
4th row한옥체험업
5th row한옥체험업

Common Values

ValueCountFrequency (%)
한옥체험업 37
53.6%
숙박업 27
39.1%
외국인관광 도시민박업 4
 
5.8%
<NA> 1
 
1.4%

Length

2023-12-13T09:29:47.547052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:29:47.636466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한옥체험업 37
50.7%
숙박업 27
37.0%
외국인관광 4
 
5.5%
도시민박업 4
 
5.5%
na 1
 
1.4%

Interactions

2023-12-13T09:29:45.880056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:29:47.692248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설명소재지비고
연번1.0001.0001.0000.644
시설명1.0001.0001.0001.000
소재지1.0001.0001.0001.000
비고0.6441.0001.0001.000
2023-12-13T09:29:47.759063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번비고
연번1.0000.463
비고0.4631.000

Missing values

2023-12-13T09:29:45.980983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:29:46.048942image/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.
2023-12-13T09:29:46.120411image/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

연번시설명소재지비고
01라임호텔전주시 덕진구 아중4길 13-3 (우아동2가)숙박업
12아리랑호텔전주시 덕진구 용산2길 17-6 (금암동)숙박업
23전주호텔전주시 덕진구 산정2길 31 (산정동)숙박업
34가은채전주시 완산구 한지길 68-13 (풍남동3가)한옥체험업
45가은채2전주시 완산구 한지길 100-20 (풍남동3가)한옥체험업
56교동가온전주시 완산구 은행로 73-1 (교동)한옥체험업
67교동살래전주시 완산구 전주천동로 66-3 (교동)한옥체험업
78단경전주시 완산구 한지길 99 (풍남동3가)한옥체험업
89달빛향전주시 완산구 전주천동로 64-16 (교동)한옥체험업
910더 한옥전주시 완산구 은행로 68-15 (교동)한옥체험업
연번시설명소재지비고
5960문리버무주군 무풍면 구천동로 289-4숙박업
6061mujuresortel무주군 설천면 구천동로 878숙박업
6162설국무주군 설천면 원삼공2길 9-7숙박업
6263기린모텔무주군 무주읍 단천로 74숙박업
6364다숲펜션무주군 설천면 구천동1로 149숙박업
6465석정레져주식회사고창군 고창읍 석정2로 192숙박업
6566고창읍성한옥마을고창군 고창읍 동리로 128한옥체험업
6667보그호텔고창군 흥덕면 부안로 27숙박업
6768샤니모텔부안군 부안읍 동중3길 18숙박업
68<NA><NA><NA><NA>