Overview

Dataset statistics

Number of variables9
Number of observations31
Missing cells273
Missing cells (%)97.8%
Duplicate rows1
Duplicate rows (%)3.2%
Total size in memory2.5 KiB
Average record size in memory82.3 B

Variable types

Text3
Unsupported6

Dataset

Description매년 제주를 방문하는 외국인 관광객 입도통계와 방한 외국인 현황 자료를 비교한 집계결과를 권역별(중국, 구미, 일본 등)로 제공합니다
Author제주관광공사
URLhttps://www.data.go.kr/data/15007317/fileData.do

Alerts

Unnamed: 6 has constant value ""Constant
Dataset has 1 (3.2%) duplicate rowsDuplicates
Unnamed: 0 has 28 (90.3%) missing valuesMissing
Unnamed: 1 has 31 (100.0%) missing valuesMissing
Unnamed: 2 has 31 (100.0%) missing valuesMissing
Unnamed: 3 has 31 (100.0%) missing valuesMissing
Unnamed: 4 has 31 (100.0%) missing valuesMissing
Unnamed: 5 has 31 (100.0%) missing valuesMissing
Unnamed: 6 has 30 (96.8%) missing valuesMissing
Unnamed: 7 has 31 (100.0%) missing valuesMissing
Unnamed: 8 has 29 (93.5%) missing valuesMissing
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-06 08:54:05.241330
Analysis finished2024-04-06 08:54:06.249657
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing28
Missing (%)90.3%
Memory size380.0 B
2024-04-06T17:54:06.466805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length40
Mean length36
Min length26

Characters and Unicode

Total characters108
Distinct characters51
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row2023년 제주특별자치도 외국인관광객 입도통계
2nd row* 제주특별자치도관광협회 입도통계 및 한국관광공사 한국관광통계에서 발췌∙수정
3rd row* 제주특별자치도 입도통계는 확정치가 발표되지 않은 경우 잠정치를 이용함
ValueCountFrequency (%)
입도통계 2
 
10.0%
2
 
10.0%
제주특별자치도 2
 
10.0%
2023년 1
 
5.0%
입도통계는 1
 
5.0%
잠정치를 1
 
5.0%
경우 1
 
5.0%
않은 1
 
5.0%
발표되지 1
 
5.0%
확정치가 1
 
5.0%
Other values (7) 7
35.0%
2024-04-06T17:54:07.171993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
 
15.7%
도 6
 
5.6%
치 5
 
4.6%
광 4
 
3.7%
계 4
 
3.7%
통 4
 
3.7%
관 4
 
3.7%
입 3
 
2.8%
국 3
 
2.8%
정 3
 
2.8%
Other values (41) 55
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
76.9%
Space Separator 17
 
15.7%
Decimal Number 4
 
3.7%
Other Punctuation 2
 
1.9%
Math Symbol 1
 
0.9%
Control 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 6
 
7.2%
치 5
 
6.0%
광 4
 
4.8%
계 4
 
4.8%
통 4
 
4.8%
관 4
 
4.8%
입 3
 
3.6%
국 3
 
3.6%
정 3
 
3.6%
자 3
 
3.6%
Other values (34) 44
53.0%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
0 1
25.0%
3 1
25.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
* 2
100.0%
Math Symbol
ValueCountFrequency (%)
∙ 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
76.9%
Common 25
 
23.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 6
 
7.2%
치 5
 
6.0%
광 4
 
4.8%
계 4
 
4.8%
통 4
 
4.8%
관 4
 
4.8%
입 3
 
3.6%
국 3
 
3.6%
정 3
 
3.6%
자 3
 
3.6%
Other values (34) 44
53.0%
Common
ValueCountFrequency (%)
17
68.0%
* 2
 
8.0%
2 2
 
8.0%
∙ 1
 
4.0%
0 1
 
4.0%
1
 
4.0%
3 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
76.9%
ASCII 24
 
22.2%
Math Operators 1
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17
70.8%
* 2
 
8.3%
2 2
 
8.3%
0 1
 
4.2%
1
 
4.2%
3 1
 
4.2%
Hangul
ValueCountFrequency (%)
도 6
 
7.2%
치 5
 
6.0%
광 4
 
4.8%
계 4
 
4.8%
통 4
 
4.8%
관 4
 
4.8%
입 3
 
3.6%
국 3
 
3.6%
정 3
 
3.6%
자 3
 
3.6%
Other values (34) 44
53.0%
Math Operators
ValueCountFrequency (%)
∙ 1
100.0%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

Unnamed: 6
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing30
Missing (%)96.8%
Memory size380.0 B
2024-04-06T17:54:07.464662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
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

Unique1 ?
Unique (%)100.0%

Sample

1st row제주관광공사
ValueCountFrequency (%)
제주관광공사 1
100.0%
2024-04-06T17:54:07.930138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 1
14.3%
주 1
14.3%
관 1
14.3%
광 1
14.3%
공 1
14.3%
사 1
14.3%
1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
85.7%
Space Separator 1
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 1
16.7%
주 1
16.7%
관 1
16.7%
광 1
16.7%
공 1
16.7%
사 1
16.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
85.7%
Common 1
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 1
16.7%
주 1
16.7%
관 1
16.7%
광 1
16.7%
공 1
16.7%
사 1
16.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
85.7%
ASCII 1
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 1
16.7%
주 1
16.7%
관 1
16.7%
광 1
16.7%
공 1
16.7%
사 1
16.7%
ASCII
ValueCountFrequency (%)
1
100.0%

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

Unnamed: 8
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing29
Missing (%)93.5%
Memory size380.0 B
2024-04-06T17:54:08.176916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length9
Min length5

Characters and Unicode

Total characters18
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row연구조사팀
2nd row064) 740-6075
ValueCountFrequency (%)
연구조사팀 1
33.3%
064 1
33.3%
740-6075 1
33.3%
2024-04-06T17:54:08.696919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3
16.7%
6 2
11.1%
4 2
11.1%
7 2
11.1%
연 1
 
5.6%
구 1
 
5.6%
조 1
 
5.6%
사 1
 
5.6%
팀 1
 
5.6%
) 1
 
5.6%
Other values (3) 3
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
55.6%
Other Letter 5
27.8%
Close Punctuation 1
 
5.6%
Space Separator 1
 
5.6%
Dash Punctuation 1
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3
30.0%
6 2
20.0%
4 2
20.0%
7 2
20.0%
5 1
 
10.0%
Other Letter
ValueCountFrequency (%)
연 1
20.0%
구 1
20.0%
조 1
20.0%
사 1
20.0%
팀 1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13
72.2%
Hangul 5
 
27.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3
23.1%
6 2
15.4%
4 2
15.4%
7 2
15.4%
) 1
 
7.7%
1
 
7.7%
- 1
 
7.7%
5 1
 
7.7%
Hangul
ValueCountFrequency (%)
연 1
20.0%
구 1
20.0%
조 1
20.0%
사 1
20.0%
팀 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
72.2%
Hangul 5
 
27.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3
23.1%
6 2
15.4%
4 2
15.4%
7 2
15.4%
) 1
 
7.7%
1
 
7.7%
- 1
 
7.7%
5 1
 
7.7%
Hangul
ValueCountFrequency (%)
연 1
20.0%
구 1
20.0%
조 1
20.0%
사 1
20.0%
팀 1
20.0%

Correlations

2024-04-06T17:54:08.843174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0Unnamed: 8
Unnamed: 01.000NaN
Unnamed: 8NaN1.000

Missing values

2024-04-06T17:54:05.599047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:54:05.922806image/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.
2024-04-06T17:54:06.133711image/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

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1<NA><NA><NA><NA><NA><NA><NA><NA><NA>
2<NA><NA><NA><NA><NA><NA><NA><NA><NA>
3<NA><NA><NA><NA><NA><NA><NA><NA><NA>
4<NA><NA><NA><NA><NA><NA><NA><NA><NA>
52023년 제주특별자치도 외국인관광객 입도통계<NA><NA><NA><NA><NA><NA><NA><NA>
6<NA><NA><NA><NA><NA><NA><NA><NA><NA>
7<NA><NA><NA><NA><NA><NA><NA><NA><NA>
8<NA><NA><NA><NA><NA><NA><NA><NA><NA>
9<NA><NA><NA><NA><NA><NA><NA><NA><NA>
Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
21<NA><NA><NA><NA><NA><NA><NA><NA><NA>
22<NA><NA><NA><NA><NA><NA><NA><NA><NA>
23<NA><NA><NA><NA><NA><NA><NA><NA><NA>
24<NA><NA><NA><NA><NA><NA><NA><NA><NA>
25<NA><NA><NA><NA><NA><NA><NA><NA><NA>
26<NA><NA><NA><NA><NA><NA><NA><NA><NA>
27<NA><NA><NA><NA><NA><NA><NA><NA><NA>
28<NA><NA><NA><NA><NA><NA><NA><NA><NA>
29* 제주특별자치도관광협회 입도통계 및 한국관광공사 한국관광통계에서 발췌∙수정<NA><NA><NA><NA><NA><NA><NA><NA>
30* 제주특별자치도 입도통계는 확정치가 발표되지 않은 경우 잠정치를 이용함<NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

Unnamed: 0Unnamed: 6Unnamed: 8# duplicates
0<NA><NA><NA>26