Overview

Dataset statistics

Number of variables4
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory804.0 B
Average record size in memory38.3 B

Variable types

Categorical2
Text1
DateTime1

Dataset

Description인천국제공항 터미널별 공항철도에서 체크인카운터까지의 소요시간 정보(터미널, 공항철도, 체크인카운터, 소요시간(분), 상세시간(초)) 제공 * 출발지 상세위치: 공항철도 출입구 게이트 * 도착지 상세위치: 체크인카운터별 중간지점
Author인천국제공항공사
URLhttps://www.data.go.kr/data/15102222/fileData.do

Alerts

터미널 is highly overall correlated with 공항철도High correlation
공항철도 is highly overall correlated with 터미널High correlation

Reproduction

Analysis started2023-12-12 14:25:35.386938
Analysis finished2023-12-12 14:25:35.744721
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

터미널
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
제1터미널
13 
제2터미널
8 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제1터미널
2nd row제1터미널
3rd row제1터미널
4th row제1터미널
5th row제1터미널

Common Values

ValueCountFrequency (%)
제1터미널 13
61.9%
제2터미널 8
38.1%

Length

2023-12-12T23:25:35.808489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:35.905897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제1터미널 13
61.9%
제2터미널 8
38.1%

공항철도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
인천공항1터미널 역
13 
인천공항2터미널 역
8 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천공항1터미널 역
2nd row인천공항1터미널 역
3rd row인천공항1터미널 역
4th row인천공항1터미널 역
5th row인천공항1터미널 역

Common Values

ValueCountFrequency (%)
인천공항1터미널 역 13
61.9%
인천공항2터미널 역 8
38.1%

Length

2023-12-12T23:25:36.006825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:36.108577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
역 21
50.0%
인천공항1터미널 13
31.0%
인천공항2터미널 8
 
19.0%

체크인카운터
Text

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T23:25:36.234623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)23.8%

Sample

1st rowA
2nd rowB
3rd rowC
4th rowD
5th rowE
ValueCountFrequency (%)
a 2
9.5%
b 2
9.5%
c 2
9.5%
d 2
9.5%
e 2
9.5%
f 2
9.5%
g 2
9.5%
h 2
9.5%
j 1
 
4.8%
k 1
 
4.8%
Other values (3) 3
14.3%
2023-12-12T23:25:36.790220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2
9.5%
B 2
9.5%
C 2
9.5%
D 2
9.5%
E 2
9.5%
F 2
9.5%
G 2
9.5%
H 2
9.5%
J 1
 
4.8%
K 1
 
4.8%
Other values (3) 3
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2
9.5%
B 2
9.5%
C 2
9.5%
D 2
9.5%
E 2
9.5%
F 2
9.5%
G 2
9.5%
H 2
9.5%
J 1
 
4.8%
K 1
 
4.8%
Other values (3) 3
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 21
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2
9.5%
B 2
9.5%
C 2
9.5%
D 2
9.5%
E 2
9.5%
F 2
9.5%
G 2
9.5%
H 2
9.5%
J 1
 
4.8%
K 1
 
4.8%
Other values (3) 3
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2
9.5%
B 2
9.5%
C 2
9.5%
D 2
9.5%
E 2
9.5%
F 2
9.5%
G 2
9.5%
H 2
9.5%
J 1
 
4.8%
K 1
 
4.8%
Other values (3) 3
14.3%

소요시간(분)
Date

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2023-12-12 05:45:00
Maximum2023-12-12 15:20:00
2023-12-12T23:25:36.941648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:37.051602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

Correlations

2023-12-12T23:25:37.132809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
터미널공항철도체크인카운터소요시간(분)
터미널1.0000.9860.0001.000
공항철도0.9861.0000.0001.000
체크인카운터0.0000.0001.0000.224
소요시간(분)1.0001.0000.2241.000
2023-12-12T23:25:37.235498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공항철도터미널
공항철도1.0000.893
터미널0.8931.000
2023-12-12T23:25:37.340243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
터미널공항철도
터미널1.0000.893
공항철도0.8931.000

Missing values

2023-12-12T23:25:35.553260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:25:35.703009image/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

터미널공항철도체크인카운터소요시간(분)
0제1터미널인천공항1터미널 역A15:20
1제1터미널인천공항1터미널 역B14:20
2제1터미널인천공항1터미널 역C13:20
3제1터미널인천공항1터미널 역D12:20
4제1터미널인천공항1터미널 역E11:20
5제1터미널인천공항1터미널 역F10:20
6제1터미널인천공항1터미널 역G09:20
7제1터미널인천공항1터미널 역H10:20
8제1터미널인천공항1터미널 역J11:20
9제1터미널인천공항1터미널 역K12:20
터미널공항철도체크인카운터소요시간(분)
11제1터미널인천공항1터미널 역M14:20
12제1터미널인천공항1터미널 역N15:20
13제2터미널인천공항2터미널 역A08:45
14제2터미널인천공항2터미널 역B07:45
15제2터미널인천공항2터미널 역C06:45
16제2터미널인천공항2터미널 역D05:45
17제2터미널인천공항2터미널 역E05:45
18제2터미널인천공항2터미널 역F06:45
19제2터미널인천공항2터미널 역G07:45
20제2터미널인천공항2터미널 역H08:45