Overview

Dataset statistics

Number of variables11
Number of observations752
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.8 KiB
Average record size in memory88.2 B

Variable types

Categorical4
Text2
DateTime5

Dataset

Description제주국제공항 국내선 운항과 관련한 데이터로 구분, 항공사, 편명, 출발지, 도착지, 운항요일, 운항시작기간, 운항종료기간 등 정보를 제공합니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15047535/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
구분 is highly overall correlated with 출발지 and 1 other fieldsHigh correlation
도착지 is highly overall correlated with 구분High correlation
출발지 is highly overall correlated with 구분High correlation

Reproduction

Analysis started2023-12-12 21:00:17.887032
Analysis finished2023-12-12 21:00:18.968771
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
출발
383 
도착
369 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
출발 383
50.9%
도착 369
49.1%

Length

2023-12-13T06:00:19.063125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:00:19.168622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
출발 383
50.9%
도착 369
49.1%

항공사
Categorical

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
티웨이항공
176 
아시아나항공
135 
대한항공
102 
제주항공
80 
진에어
72 
Other values (4)
187 

Length

Max length7
Median length6
Mean length4.6343085
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대한항공
2nd row아시아나항공
3rd row아시아나항공
4th row진에어
5th row대한항공

Common Values

ValueCountFrequency (%)
티웨이항공 176
23.4%
아시아나항공 135
18.0%
대한항공 102
13.6%
제주항공 80
10.6%
진에어 72
9.6%
에어부산 62
 
8.2%
이스타항공 61
 
8.1%
에어서울 50
 
6.6%
에어로케이항공 14
 
1.9%

Length

2023-12-13T06:00:19.283875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:00:19.413227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
티웨이항공 176
23.4%
아시아나항공 135
18.0%
대한항공 102
13.6%
제주항공 80
10.6%
진에어 72
9.6%
에어부산 62
 
8.2%
이스타항공 61
 
8.1%
에어서울 50
 
6.6%
에어로케이항공 14
 
1.9%

편명
Text

Distinct470
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2023-12-13T06:00:19.733511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.4335106
Min length5

Characters and Unicode

Total characters4086
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)50.4%

Sample

1st rowKE1118
2nd rowOZ8902
3rd rowOZ8902
4th rowLJ422
5th rowKE1126
ValueCountFrequency (%)
tw813 23
 
3.1%
tw816 21
 
2.8%
tw809 17
 
2.3%
tw810 17
 
2.3%
rs904 13
 
1.7%
rs903 13
 
1.7%
tw9871 6
 
0.8%
rs906 6
 
0.8%
tw9872 6
 
0.8%
oz8974 6
 
0.8%
Other values (460) 624
83.0%
2023-12-13T06:00:20.217528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 469
 
11.5%
8 419
 
10.3%
0 293
 
7.2%
2 278
 
6.8%
7 245
 
6.0%
9 241
 
5.9%
3 196
 
4.8%
Z 196
 
4.8%
5 187
 
4.6%
4 178
 
4.4%
Other values (14) 1384
33.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2662
65.1%
Uppercase Letter 1424
34.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 196
13.8%
T 176
12.4%
W 176
12.4%
E 163
11.4%
O 135
9.5%
K 102
7.2%
C 80
5.6%
L 72
 
5.1%
J 72
 
5.1%
R 64
 
4.5%
Other values (4) 188
13.2%
Decimal Number
ValueCountFrequency (%)
1 469
17.6%
8 419
15.7%
0 293
11.0%
2 278
10.4%
7 245
9.2%
9 241
9.1%
3 196
7.4%
5 187
 
7.0%
4 178
 
6.7%
6 156
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2662
65.1%
Latin 1424
34.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 196
13.8%
T 176
12.4%
W 176
12.4%
E 163
11.4%
O 135
9.5%
K 102
7.2%
C 80
5.6%
L 72
 
5.1%
J 72
 
5.1%
R 64
 
4.5%
Other values (4) 188
13.2%
Common
ValueCountFrequency (%)
1 469
17.6%
8 419
15.7%
0 293
11.0%
2 278
10.4%
7 245
9.2%
9 241
9.1%
3 196
7.4%
5 187
 
7.0%
4 178
 
6.7%
6 156
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 469
 
11.5%
8 419
 
10.3%
0 293
 
7.2%
2 278
 
6.8%
7 245
 
6.0%
9 241
 
5.9%
3 196
 
4.8%
Z 196
 
4.8%
5 187
 
4.6%
4 178
 
4.4%
Other values (14) 1384
33.9%

출발지
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
제주
383 
서울/김포
178 
대구
69 
청주
45 
부산/김해
 
33
Other values (7)
44 

Length

Max length7
Median length2
Mean length2.8710106
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
제주 383
50.9%
서울/김포 178
23.7%
대구 69
 
9.2%
청주 45
 
6.0%
부산/김해 33
 
4.4%
광주 24
 
3.2%
여수 8
 
1.1%
진주/사천 4
 
0.5%
군산 3
 
0.4%
포항/포항경주 2
 
0.3%
Other values (2) 3
 
0.4%

Length

2023-12-13T06:00:20.360278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제주 383
50.9%
서울/김포 178
23.7%
대구 69
 
9.2%
청주 45
 
6.0%
부산/김해 33
 
4.4%
광주 24
 
3.2%
여수 8
 
1.1%
진주/사천 4
 
0.5%
군산 3
 
0.4%
포항/포항경주 2
 
0.3%
Other values (2) 3
 
0.4%

도착지
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
제주
369 
서울/김포
186 
대구
64 
청주
50 
부산/김해
40 
Other values (7)
43 

Length

Max length7
Median length2
Mean length2.9308511
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row서울/김포
2nd row서울/김포
3rd row서울/김포
4th row대구
5th row서울/김포

Common Values

ValueCountFrequency (%)
제주 369
49.1%
서울/김포 186
24.7%
대구 64
 
8.5%
청주 50
 
6.6%
부산/김해 40
 
5.3%
광주 23
 
3.1%
여수 8
 
1.1%
진주/사천 4
 
0.5%
군산 3
 
0.4%
원주 2
 
0.3%
Other values (2) 3
 
0.4%

Length

2023-12-13T06:00:20.485607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제주 369
49.1%
서울/김포 186
24.7%
대구 64
 
8.5%
청주 50
 
6.6%
부산/김해 40
 
5.3%
광주 23
 
3.1%
여수 8
 
1.1%
진주/사천 4
 
0.5%
군산 3
 
0.4%
원주 2
 
0.3%
Other values (2) 3
 
0.4%

출발시간
Date

Distinct178
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum2023-12-13 06:00:00
Maximum2023-12-13 21:55:00
2023-12-13T06:00:20.648153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:00:20.778125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

도착시간
Date

Distinct178
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum2023-12-13 07:10:00
Maximum2023-12-13 23:00:00
2023-12-13T06:00:20.911167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:00:21.037791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

운항요일
Text

Distinct54
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2023-12-13T06:00:21.229697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length4.7619681
Min length1

Characters and Unicode

Total characters3581
Distinct characters7
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

Unique11 ?
Unique (%)1.5%

Sample

1st row월화수목금토일
2nd row월화수목금토일
3rd row토
4th row월화수목금토일
5th row목
ValueCountFrequency (%)
월화수목금토일 400
53.2%
토 33
 
4.4%
수 32
 
4.3%
화 32
 
4.3%
금 30
 
4.0%
목 27
 
3.6%
일 22
 
2.9%
월화수목토일 18
 
2.4%
월 16
 
2.1%
월수금일 12
 
1.6%
Other values (44) 130
 
17.3%
2023-12-13T06:00:21.560801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
토 522
14.6%
수 516
14.4%
화 513
14.3%
금 512
14.3%
일 512
14.3%
목 506
14.1%
월 500
14.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3581
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
토 522
14.6%
수 516
14.4%
화 513
14.3%
금 512
14.3%
일 512
14.3%
목 506
14.1%
월 500
14.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3581
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
토 522
14.6%
수 516
14.4%
화 513
14.3%
금 512
14.3%
일 512
14.3%
목 506
14.1%
월 500
14.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3581
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
토 522
14.6%
수 516
14.4%
화 513
14.3%
금 512
14.3%
일 512
14.3%
목 506
14.1%
월 500
14.0%

운항시작기간
Date

Distinct66
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum2023-10-29 00:00:00
Maximum2024-03-25 00:00:00
2023-12-13T06:00:21.716623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:00:21.880744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

운항종료기간
Date

Distinct56
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum2023-11-15 00:00:00
Maximum2024-03-30 00:00:00
2023-12-13T06:00:22.011902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:00:22.138329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
Minimum2023-11-13 00:00:00
Maximum2023-11-13 00:00:00
2023-12-13T06:00:22.248135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:00:22.333173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2023-12-13T06:00:22.402508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분항공사출발지도착지운항요일운항시작기간운항종료기간
구분1.0000.0001.0001.0000.0000.0000.000
항공사0.0001.0000.4870.4840.8360.8020.737
출발지1.0000.4871.0000.7180.3010.0000.000
도착지1.0000.4840.7181.0000.3510.0000.000
운항요일0.0000.8360.3010.3511.0000.9090.902
운항시작기간0.0000.8020.0000.0000.9091.0000.990
운항종료기간0.0000.7370.0000.0000.9020.9901.000
2023-12-13T06:00:22.509215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분도착지항공사출발지
구분1.0000.9930.0000.993
도착지0.9931.0000.2280.278
항공사0.0000.2281.0000.230
출발지0.9930.2780.2301.000
2023-12-13T06:00:22.592266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분항공사출발지도착지
구분1.0000.0000.9930.993
항공사0.0001.0000.2300.228
출발지0.9930.2301.0000.278
도착지0.9930.2280.2781.000

Missing values

2023-12-13T06:00:18.785039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:00:18.918286image/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

구분항공사편명출발지도착지출발시간도착시간운항요일운항시작기간운항종료기간데이터기준일자
0출발대한항공KE1118제주서울/김포07:0008:10월화수목금토일2023-10-292024-03-302023-11-13
1출발아시아나항공OZ8902제주서울/김포07:0008:15월화수목금토일2023-10-292024-01-012023-11-13
2출발아시아나항공OZ8902제주서울/김포07:0008:15토2024-01-022024-03-302023-11-13
3출발진에어LJ422제주대구07:0508:10월화수목금토일2023-10-292024-03-302023-11-13
4출발대한항공KE1126제주서울/김포07:3008:40목2023-10-292024-03-302023-11-13
5출발대한항공KE1704제주청주07:3008:40월화수목금토일2023-10-292024-03-302023-11-13
6출발제주항공7C140제주서울/김포07:4008:50월수금일2023-10-292024-03-302023-11-13
7출발대한항공KE1604제주광주07:4008:35월화수목금토일2023-10-292024-03-302023-11-13
8출발대한항공KE1126제주서울/김포07:4508:55월화수금토일2023-10-292024-03-302023-11-13
9출발아시아나항공OZ8122제주대구07:4508:45월화수목금토일2023-10-292023-12-172023-11-13
구분항공사편명출발지도착지출발시간도착시간운항요일운항시작기간운항종료기간데이터기준일자
742도착진에어LJ533서울/김포제주20:2521:35월화수목금토일2023-10-292024-03-302023-11-13
743도착제주항공7C147서울/김포제주20:3021:40월화수목금토일2023-10-292024-03-302023-11-13
744도착이스타항공ZE231서울/김포제주20:3021:40월화수목금토일2023-10-292024-03-302023-11-13
745도착아시아나항공OZ8995서울/김포제주20:3521:45금일2023-10-292023-12-312023-11-13
746도착아시아나항공OZ8995서울/김포제주20:3521:45금2024-01-012024-03-302023-11-13
747도착대한항공KE1233서울/김포제주20:4521:55월화수목금토일2023-10-292023-11-152023-11-13
748도착대한항공KE1233서울/김포제주20:4521:55월화목토일2023-11-162023-11-302023-11-13
749도착대한항공KE1233서울/김포제주20:4521:55월화수목금토일2023-12-012024-03-302023-11-13
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