Overview

Dataset statistics

Number of variables4
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)1.9%
Total size in memory1.8 KiB
Average record size in memory34.4 B

Variable types

Text3
Categorical1

Dataset

Description외국인관광도시민박업현황20146
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202313

Alerts

Dataset has 1 (1.9%) duplicate rowsDuplicates

Reproduction

Analysis started2024-03-14 01:33:06.513374
Analysis finished2024-03-14 01:33:06.873364
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Text

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size564.0 B
2024-03-14T10:33:06.989943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.8148148
Min length1

Characters and Unicode

Total characters98
Distinct characters11
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

Unique52 ?
Unique (%)96.3%

Sample

1st row계
2nd row1
3rd row2
4th row3
5th row4
ValueCountFrequency (%)
29 2
 
3.7%
계 1
 
1.9%
27 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
Other values (43) 43
79.6%
2024-03-14T10:33:07.242506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 17
17.3%
1 16
16.3%
3 15
15.3%
4 15
15.3%
5 8
8.2%
9 6
 
6.1%
6 5
 
5.1%
7 5
 
5.1%
8 5
 
5.1%
0 5
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97
99.0%
Other Letter 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17
17.5%
1 16
16.5%
3 15
15.5%
4 15
15.5%
5 8
8.2%
9 6
 
6.2%
6 5
 
5.2%
7 5
 
5.2%
8 5
 
5.2%
0 5
 
5.2%
Other Letter
ValueCountFrequency (%)
계 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97
99.0%
Hangul 1
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 17
17.5%
1 16
16.5%
3 15
15.5%
4 15
15.5%
5 8
8.2%
9 6
 
6.2%
6 5
 
5.2%
7 5
 
5.2%
8 5
 
5.2%
0 5
 
5.2%
Hangul
ValueCountFrequency (%)
계 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97
99.0%
Hangul 1
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 17
17.5%
1 16
16.5%
3 15
15.5%
4 15
15.5%
5 8
8.2%
9 6
 
6.2%
6 5
 
5.2%
7 5
 
5.2%
8 5
 
5.2%
0 5
 
5.2%
Hangul
ValueCountFrequency (%)
계 1
100.0%

시 설 명
Text

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size564.0 B
2024-03-14T10:33:07.460274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.7407407
Min length1

Characters and Unicode

Total characters256
Distinct characters110
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

Unique52 ?
Unique (%)96.3%

Sample

1st row52개소
2nd row전주게스트하우스
3rd row해 달 별
4th row천년마루
5th row그린게스트하우스
ValueCountFrequency (%)
해 4
 
4.1%
가 3
 
3.1%
집 3
 
3.1%
향 2
 
2.0%
소 2
 
2.0%
랑 2
 
2.0%
모 2
 
2.0%
촌 2
 
2.0%
담 2
 
2.0%
정 2
 
2.0%
Other values (72) 74
75.5%
2024-03-14T10:33:07.761862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
 
17.2%
스 18
 
7.0%
하 10
 
3.9%
트 8
 
3.1%
우 8
 
3.1%
게 7
 
2.7%
리 6
 
2.3%
전 5
 
2.0%
주 4
 
1.6%
향 4
 
1.6%
Other values (100) 142
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 206
80.5%
Space Separator 44
 
17.2%
Decimal Number 5
 
2.0%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 18
 
8.7%
하 10
 
4.9%
트 8
 
3.9%
우 8
 
3.9%
게 7
 
3.4%
리 6
 
2.9%
전 5
 
2.4%
주 4
 
1.9%
향 4
 
1.9%
가 4
 
1.9%
Other values (94) 132
64.1%
Decimal Number
ValueCountFrequency (%)
6 2
40.0%
0 1
20.0%
2 1
20.0%
5 1
20.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 206
80.5%
Common 50
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 18
 
8.7%
하 10
 
4.9%
트 8
 
3.9%
우 8
 
3.9%
게 7
 
3.4%
리 6
 
2.9%
전 5
 
2.4%
주 4
 
1.9%
향 4
 
1.9%
가 4
 
1.9%
Other values (94) 132
64.1%
Common
ValueCountFrequency (%)
44
88.0%
6 2
 
4.0%
- 1
 
2.0%
0 1
 
2.0%
2 1
 
2.0%
5 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 206
80.5%
ASCII 50
 
19.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
88.0%
6 2
 
4.0%
- 1
 
2.0%
0 1
 
2.0%
2 1
 
2.0%
5 1
 
2.0%
Hangul
ValueCountFrequency (%)
스 18
 
8.7%
하 10
 
4.9%
트 8
 
3.9%
우 8
 
3.9%
게 7
 
3.4%
리 6
 
2.9%
전 5
 
2.4%
주 4
 
1.9%
향 4
 
1.9%
가 4
 
1.9%
Other values (94) 132
64.1%

소 재 지
Text

Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size564.0 B
2024-03-14T10:33:07.948000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length15.87037
Min length1

Characters and Unicode

Total characters857
Distinct characters55
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

Unique50 ?
Unique (%)92.6%

Sample

1st row-
2nd row전주시 완산구 경기전길 46
3rd row전주시 완산구 어진길 33-9
4th row전주시 완산구 경기전길 186
5th row전주시 완산구 어진길 123
ValueCountFrequency (%)
전주시 53
25.9%
완산구 53
25.9%
향교길 11
 
5.4%
어진길 7
 
3.4%
오목대길 5
 
2.4%
전주천동로 4
 
2.0%
경기전길 2
 
1.0%
은행로 2
 
1.0%
기린대로 2
 
1.0%
팔달로 2
 
1.0%
Other values (61) 64
31.2%
2024-03-14T10:33:08.282531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
151
17.6%
전 65
 
7.6%
주 58
 
6.8%
시 53
 
6.2%
완 53
 
6.2%
산 53
 
6.2%
구 53
 
6.2%
길 39
 
4.6%
- 33
 
3.9%
1 31
 
3.6%
Other values (45) 268
31.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 511
59.6%
Decimal Number 162
 
18.9%
Space Separator 151
 
17.6%
Dash Punctuation 33
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 65
12.7%
주 58
11.4%
시 53
10.4%
완 53
10.4%
산 53
10.4%
구 53
10.4%
길 39
 
7.6%
로 14
 
2.7%
교 12
 
2.3%
향 12
 
2.3%
Other values (33) 99
19.4%
Decimal Number
ValueCountFrequency (%)
1 31
19.1%
2 22
13.6%
3 20
12.3%
5 16
9.9%
8 16
9.9%
9 14
8.6%
4 12
 
7.4%
0 11
 
6.8%
6 11
 
6.8%
7 9
 
5.6%
Space Separator
ValueCountFrequency (%)
151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 511
59.6%
Common 346
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 65
12.7%
주 58
11.4%
시 53
10.4%
완 53
10.4%
산 53
10.4%
구 53
10.4%
길 39
 
7.6%
로 14
 
2.7%
교 12
 
2.3%
향 12
 
2.3%
Other values (33) 99
19.4%
Common
ValueCountFrequency (%)
151
43.6%
- 33
 
9.5%
1 31
 
9.0%
2 22
 
6.4%
3 20
 
5.8%
5 16
 
4.6%
8 16
 
4.6%
9 14
 
4.0%
4 12
 
3.5%
0 11
 
3.2%
Other values (2) 20
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 511
59.6%
ASCII 346
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
151
43.6%
- 33
 
9.5%
1 31
 
9.0%
2 22
 
6.4%
3 20
 
5.8%
5 16
 
4.6%
8 16
 
4.6%
9 14
 
4.0%
4 12
 
3.5%
0 11
 
3.2%
Other values (2) 20
 
5.8%
Hangul
ValueCountFrequency (%)
전 65
12.7%
주 58
11.4%
시 53
10.4%
완 53
10.4%
산 53
10.4%
구 53
10.4%
길 39
 
7.6%
로 14
 
2.7%
교 12
 
2.3%
향 12
 
2.3%
Other values (33) 99
19.4%

객 실 수
Categorical

Distinct10
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size564.0 B
3
13 
2
12 
4
10 
6
8 
5
6 
Other values (5)
5 

Length

Max length4
Median length1
Mean length1.0555556
Min length1

Unique

Unique5 ?
Unique (%)9.3%

Sample

1st row201실
2nd row6
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 13
24.1%
2 12
22.2%
4 10
18.5%
6 8
14.8%
5 6
11.1%
201실 1
 
1.9%
1 1
 
1.9%
8 1
 
1.9%
9 1
 
1.9%
7 1
 
1.9%

Length

2024-03-14T10:33:08.407562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:33:08.509716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 13
24.1%
2 12
22.2%
4 10
18.5%
6 8
14.8%
5 6
11.1%
201실 1
 
1.9%
1 1
 
1.9%
8 1
 
1.9%
9 1
 
1.9%
7 1
 
1.9%

Correlations

2024-03-14T10:33:08.603532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시 설 명소 재 지객 실 수
연번1.0001.0001.0001.000
시 설 명1.0001.0001.0001.000
소 재 지1.0001.0001.0000.990
객 실 수1.0001.0000.9901.000

Missing values

2024-03-14T10:33:06.703645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:33:06.829681image/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

연번시 설 명소 재 지객 실 수
0계52개소-201실
11전주게스트하우스전주시 완산구 경기전길 466
22해 달 별전주시 완산구 어진길 33-93
33천년마루전주시 완산구 경기전길 1864
44그린게스트하우스전주시 완산구 어진길 1233
55하늘정원게스트하우스전주시 완산구 팔달로 12-12
66베가게스트하우스전주시 완산구 전동성당길 33-63
77오목대펜션전주시 완산구 기린대로 781
88마르타숙소전주시 완산구 오목대길 49-13
9960-6게스트하우스전주시 완산구 오목대길 49-14
연번시 설 명소 재 지객 실 수
4443향교길숙박전주시 완산구 향교길983
4544산 아 래전주시 완산구 오목대길564
4645세인게스트하우스전주시 완산구 전라감영2길466
4746소 소전주시 완산구 향교길 1063
4847다나하루전주시 완산구 향교길 114
4948우 리 집전주시 완산구 기린대로68-257
5049전라감영게스트하우스전주시 완산구 전라감영로42-25
5150유 정전주시 완산구 어진길 90-96
5251양 지전주시 완산구 어진길 94-34
5352엘 피 스전주시 완산구 대명길 32

Duplicate rows

Most frequently occurring

연번시 설 명소 재 지객 실 수# duplicates
029전주스토리전주시 완산구 전라감영3길 12-1952