Overview

Dataset statistics

Number of variables5
Number of observations5554
Missing cells1651
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory217.1 KiB
Average record size in memory40.0 B

Variable types

Text4
Categorical1

Dataset

Description전세계 공항에 대한 공항 코드 정보를 제공 (ICAO 공항코드는 국제민간항공기구에서 비행정보구역과 공항에 부여하는 4자리수 코드이며 IATA 공항코드는 지명 또는 공항명 자체를 세글자로 축약하여 코드 부여) 공항운영상태구분 항목을 추가하여 기존공항과 신규구축공항을 모두 포함한 리스트 제공
URLhttps://www.data.go.kr/data/15100965/fileData.do

Alerts

공항운영상태구분 is highly imbalanced (76.9%)Imbalance
공항코드1(IATA) has 360 (6.5%) missing valuesMissing
공항코드2(ICAO) has 1291 (23.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 20:56:24.510682
Analysis finished2023-12-12 20:56:25.529720
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영문공항명
Text

Distinct5553
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size43.5 KiB
2023-12-13T05:56:25.835929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length56
Mean length23.271336
Min length3

Characters and Unicode

Total characters129249
Distinct characters62
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5552 ?
Unique (%)> 99.9%

Sample

1st rowAalborg Airport
2nd rowAlesund Airport
3rd rowAarhus Airport
4th rowAbadan Airport
5th rowAbaiang Airport
ValueCountFrequency (%)
airport 5382
31.8%
international 759
 
4.5%
regional 190
 
1.1%
island 132
 
0.8%
new 84
 
0.5%
san 61
 
0.4%
municipal 56
 
0.3%
de 55
 
0.3%
city 52
 
0.3%
base 51
 
0.3%
Other values (7646) 10128
59.8%
2023-12-13T05:56:26.335021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 15633
12.1%
11399
 
8.8%
a 11081
 
8.6%
o 11064
 
8.6%
i 11022
 
8.5%
t 9597
 
7.4%
n 7407
 
5.7%
A 6093
 
4.7%
p 6091
 
4.7%
e 5915
 
4.6%
Other values (52) 33947
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100229
77.5%
Uppercase Letter 17188
 
13.3%
Space Separator 11399
 
8.8%
Dash Punctuation 184
 
0.1%
Other Punctuation 133
 
0.1%
Open Punctuation 57
 
< 0.1%
Close Punctuation 57
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 15633
15.6%
a 11081
11.1%
o 11064
11.0%
i 11022
11.0%
t 9597
9.6%
n 7407
7.4%
p 6091
 
6.1%
e 5915
 
5.9%
l 4217
 
4.2%
u 2882
 
2.9%
Other values (16) 15320
15.3%
Uppercase Letter
ValueCountFrequency (%)
A 6093
35.4%
I 1115
 
6.5%
S 998
 
5.8%
M 924
 
5.4%
C 875
 
5.1%
B 865
 
5.0%
P 674
 
3.9%
R 568
 
3.3%
L 558
 
3.2%
T 516
 
3.0%
Other values (16) 4002
23.3%
Other Punctuation
ValueCountFrequency (%)
/ 56
42.1%
' 51
38.3%
. 23
17.3%
, 3
 
2.3%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
7 1
50.0%
Space Separator
ValueCountFrequency (%)
11399
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 117417
90.8%
Common 11832
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 15633
13.3%
a 11081
 
9.4%
o 11064
 
9.4%
i 11022
 
9.4%
t 9597
 
8.2%
n 7407
 
6.3%
A 6093
 
5.2%
p 6091
 
5.2%
e 5915
 
5.0%
l 4217
 
3.6%
Other values (42) 29297
25.0%
Common
ValueCountFrequency (%)
11399
96.3%
- 184
 
1.6%
( 57
 
0.5%
) 57
 
0.5%
/ 56
 
0.5%
' 51
 
0.4%
. 23
 
0.2%
, 3
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 15633
12.1%
11399
 
8.8%
a 11081
 
8.6%
o 11064
 
8.6%
i 11022
 
8.5%
t 9597
 
7.4%
n 7407
 
5.7%
A 6093
 
4.7%
p 6091
 
4.7%
e 5915
 
4.6%
Other values (52) 33947
26.3%

한글공항명
Text

Distinct5518
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size43.5 KiB
2023-12-13T05:56:26.708209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length8.7738567
Min length2

Characters and Unicode

Total characters48730
Distinct characters892
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5485 ?
Unique (%)98.8%

Sample

1st row올보르 공항
2nd row바이그라 공항
3rd row터스럽 공항
4th row아바단 공항
5th row아바이앙 공항
ValueCountFrequency (%)
공항 4363
31.8%
국제공항 727
 
5.3%
지역공항 107
 
0.8%
시공항 91
 
0.7%
카운티 55
 
0.4%
아일랜드 48
 
0.3%
공군기지 38
 
0.3%
필드 34
 
0.2%
헬리포트 34
 
0.2%
레이크 34
 
0.2%
Other values (6832) 8203
59.7%
2023-12-13T05:56:27.178980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8673
 
17.8%
공 5405
 
11.1%
항 5362
 
11.0%
스 1178
 
2.4%
라 866
 
1.8%
이 866
 
1.8%
제 851
 
1.7%
리 778
 
1.6%
국 774
 
1.6%
아 664
 
1.4%
Other values (882) 23313
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39710
81.5%
Space Separator 8673
 
17.8%
Uppercase Letter 160
 
0.3%
Other Punctuation 76
 
0.2%
Lowercase Letter 36
 
0.1%
Dash Punctuation 23
 
< 0.1%
Close Punctuation 22
 
< 0.1%
Open Punctuation 22
 
< 0.1%
Decimal Number 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 5405
 
13.6%
항 5362
 
13.5%
스 1178
 
3.0%
라 866
 
2.2%
이 866
 
2.2%
제 851
 
2.1%
리 778
 
2.0%
국 774
 
1.9%
아 664
 
1.7%
르 566
 
1.4%
Other values (831) 22400
56.4%
Uppercase Letter
ValueCountFrequency (%)
A 24
15.0%
F 18
11.2%
R 17
10.6%
P 15
9.4%
S 13
 
8.1%
B 13
 
8.1%
C 7
 
4.4%
L 7
 
4.4%
I 7
 
4.4%
G 6
 
3.8%
Other values (14) 33
20.6%
Lowercase Letter
ValueCountFrequency (%)
i 4
11.1%
a 4
11.1%
n 4
11.1%
u 4
11.1%
h 3
8.3%
r 2
 
5.6%
x 2
 
5.6%
e 2
 
5.6%
y 2
 
5.6%
v 2
 
5.6%
Other values (6) 7
19.4%
Decimal Number
ValueCountFrequency (%)
2 3
37.5%
7 2
25.0%
4 2
25.0%
3 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 44
57.9%
/ 31
40.8%
, 1
 
1.3%
Space Separator
ValueCountFrequency (%)
8673
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39710
81.5%
Common 8824
 
18.1%
Latin 196
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 5405
 
13.6%
항 5362
 
13.5%
스 1178
 
3.0%
라 866
 
2.2%
이 866
 
2.2%
제 851
 
2.1%
리 778
 
2.0%
국 774
 
1.9%
아 664
 
1.7%
르 566
 
1.4%
Other values (831) 22400
56.4%
Latin
ValueCountFrequency (%)
A 24
 
12.2%
F 18
 
9.2%
R 17
 
8.7%
P 15
 
7.7%
S 13
 
6.6%
B 13
 
6.6%
C 7
 
3.6%
L 7
 
3.6%
I 7
 
3.6%
G 6
 
3.1%
Other values (30) 69
35.2%
Common
ValueCountFrequency (%)
8673
98.3%
. 44
 
0.5%
/ 31
 
0.4%
- 23
 
0.3%
) 22
 
0.2%
( 22
 
0.2%
2 3
 
< 0.1%
7 2
 
< 0.1%
4 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39710
81.5%
ASCII 9020
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8673
96.2%
. 44
 
0.5%
/ 31
 
0.3%
A 24
 
0.3%
- 23
 
0.3%
) 22
 
0.2%
( 22
 
0.2%
F 18
 
0.2%
R 17
 
0.2%
P 15
 
0.2%
Other values (41) 131
 
1.5%
Hangul
ValueCountFrequency (%)
공 5405
 
13.6%
항 5362
 
13.5%
스 1178
 
3.0%
라 866
 
2.2%
이 866
 
2.2%
제 851
 
2.1%
리 778
 
2.0%
국 774
 
1.9%
아 664
 
1.7%
르 566
 
1.4%
Other values (831) 22400
56.4%

공항코드1(IATA)
Text

MISSING 

Distinct5191
Distinct (%)99.9%
Missing360
Missing (%)6.5%
Memory size43.5 KiB
2023-12-13T05:56:27.562773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15582
Distinct characters26
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

Unique5188 ?
Unique (%)99.9%

Sample

1st rowAAL
2nd rowAES
3rd rowAAR
4th rowABD
5th rowABF
ValueCountFrequency (%)
dxj 2
 
< 0.1%
mqn 2
 
< 0.1%
pkr 2
 
< 0.1%
szi 1
 
< 0.1%
tyn 1
 
< 0.1%
tta 1
 
< 0.1%
tmw 1
 
< 0.1%
tsl 1
 
< 0.1%
tam 1
 
< 0.1%
tmp 1
 
< 0.1%
Other values (5181) 5181
99.7%
2023-12-13T05:56:28.043836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 921
 
5.9%
S 852
 
5.5%
L 788
 
5.1%
M 780
 
5.0%
B 778
 
5.0%
K 741
 
4.8%
T 740
 
4.7%
C 706
 
4.5%
R 696
 
4.5%
N 675
 
4.3%
Other values (16) 7905
50.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15582
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 921
 
5.9%
S 852
 
5.5%
L 788
 
5.1%
M 780
 
5.0%
B 778
 
5.0%
K 741
 
4.8%
T 740
 
4.7%
C 706
 
4.5%
R 696
 
4.5%
N 675
 
4.3%
Other values (16) 7905
50.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 15582
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 921
 
5.9%
S 852
 
5.5%
L 788
 
5.1%
M 780
 
5.0%
B 778
 
5.0%
K 741
 
4.8%
T 740
 
4.7%
C 706
 
4.5%
R 696
 
4.5%
N 675
 
4.3%
Other values (16) 7905
50.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 921
 
5.9%
S 852
 
5.5%
L 788
 
5.1%
M 780
 
5.0%
B 778
 
5.0%
K 741
 
4.8%
T 740
 
4.7%
C 706
 
4.5%
R 696
 
4.5%
N 675
 
4.3%
Other values (16) 7905
50.7%

공항코드2(ICAO)
Text

MISSING 

Distinct4255
Distinct (%)99.8%
Missing1291
Missing (%)23.2%
Memory size43.5 KiB
2023-12-13T05:56:28.423399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters17052
Distinct characters31
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

Unique4247 ?
Unique (%)99.6%

Sample

1st rowEKYT
2nd rowENAL
3rd rowEKAH
4th rowOIAA
5th rowNGAB
ValueCountFrequency (%)
lfsb 2
 
< 0.1%
uhss 2
 
< 0.1%
rplp 2
 
< 0.1%
uemh 2
 
< 0.1%
zgxx 2
 
< 0.1%
usuu 2
 
< 0.1%
vnpk 2
 
< 0.1%
wicm 2
 
< 0.1%
sawd 1
 
< 0.1%
pasc 1
 
< 0.1%
Other values (4245) 4245
99.6%
2023-12-13T05:56:28.911330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1186
 
7.0%
K 1160
 
6.8%
A 968
 
5.7%
L 955
 
5.6%
M 914
 
5.4%
B 820
 
4.8%
E 762
 
4.5%
C 758
 
4.4%
P 728
 
4.3%
T 717
 
4.2%
Other values (21) 8084
47.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17044
> 99.9%
Decimal Number 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 1186
 
7.0%
K 1160
 
6.8%
A 968
 
5.7%
L 955
 
5.6%
M 914
 
5.4%
B 820
 
4.8%
E 762
 
4.5%
C 758
 
4.4%
P 728
 
4.3%
T 717
 
4.2%
Other values (16) 8076
47.4%
Decimal Number
ValueCountFrequency (%)
6 3
37.5%
5 2
25.0%
9 1
 
12.5%
4 1
 
12.5%
1 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 17044
> 99.9%
Common 8
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1186
 
7.0%
K 1160
 
6.8%
A 968
 
5.7%
L 955
 
5.6%
M 914
 
5.4%
B 820
 
4.8%
E 762
 
4.5%
C 758
 
4.4%
P 728
 
4.3%
T 717
 
4.2%
Other values (16) 8076
47.4%
Common
ValueCountFrequency (%)
6 3
37.5%
5 2
25.0%
9 1
 
12.5%
4 1
 
12.5%
1 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1186
 
7.0%
K 1160
 
6.8%
A 968
 
5.7%
L 955
 
5.6%
M 914
 
5.4%
B 820
 
4.8%
E 762
 
4.5%
C 758
 
4.4%
P 728
 
4.3%
T 717
 
4.2%
Other values (21) 8084
47.4%

공항운영상태구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.5 KiB
기존공항
5345 
신규공항
 
209

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기존공항 5345
96.2%
신규공항 209
 
3.8%

Length

2023-12-13T05:56:29.035811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:56:29.128043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기존공항 5345
96.2%
신규공항 209
 
3.8%

Missing values

2023-12-13T05:56:25.154119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:56:25.272996image/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.
2023-12-13T05:56:25.457530image/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

영문공항명한글공항명공항코드1(IATA)공항코드2(ICAO)공항운영상태구분
0Aalborg Airport올보르 공항AALEKYT기존공항
1Alesund Airport바이그라 공항AESENAL기존공항
2Aarhus Airport터스럽 공항AAREKAH기존공항
3Abadan Airport아바단 공항ABDOIAA기존공항
4Abaiang Airport아바이앙 공항ABFNGAB기존공항
5Abakan Airport아바칸 공항ABAUNAA기존공항
6Abbotsford International Airport애보트포드 국제공항YXXCYXX기존공항
7Abecher Airport아베셰 공항AEHFTTC기존공항
8Abemama Airport아베마마 공항AEANGTB기존공항
9Aberdeen Regional Airport애버딘 지역공항ABRKABR기존공항
영문공항명한글공항명공항코드1(IATA)공항코드2(ICAO)공항운영상태구분
5544Weining Airport웨이닝 공항<NA><NA>신규공항
5545West Antalya Airport웨스트안탈리아 공항<NA><NA>신규공항
5546Western Sydney International (Nancy-Bird Walton) Airport웨스턴 시드니 국제공항WSI<NA>신규공항
5547Xiamen Xiangan Airport샤먼샹안 공항<NA><NA>신규공항
5548Xiangxi Biancheng Airport샹시 볜청 공항DXJZGXX신규공항
5549Xinyang Huangchuan Airport신양 황촨 공항<NA><NA>신규공항
5550Yozgat Hattusas Airport요즈가트 하투사스 공항<NA><NA>신규공항
5551Zhoukou Airport저우커우 공항<NA><NA>신규공항
5552Zhundong Qitai Airport전둥 치타이 공항<NA><NA>신규공항
5553Cukurova Airport쿠쿠로바 공항<NA><NA>신규공항