Overview

Dataset statistics

Number of variables8
Number of observations5345
Missing cells1245
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory334.2 KiB
Average record size in memory64.0 B

Variable types

Text7
Categorical1

Dataset

Description전세계 공항의 영문 공항명, 한글 공항명, IATA 코드, ICAO 코드, 국가명, 지역명에 대한 정보를 제공 합니다
URLhttps://www.data.go.kr/data/3051587/fileData.do

Alerts

공항코드1(IATA) has 159 (3.0%) missing valuesMissing
공항코드2(ICAO) has 1086 (20.3%) missing valuesMissing
영문공항명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:27:16.456645
Analysis finished2023-12-12 20:27:17.962643
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영문공항명
Text

UNIQUE 

Distinct5345
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
2023-12-13T05:27:18.325002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length56
Mean length23.217961
Min length3

Characters and Unicode

Total characters124100
Distinct characters63
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

Unique5345 ?
Unique (%)100.0%

Sample

1st rowAalborg Airport
2nd rowAlesund Airport
3rd rowAarhus Airport
4th rowAbadan Airport
5th rowAbaiang Airport
ValueCountFrequency (%)
airport 5172
31.8%
international 709
 
4.4%
regional 189
 
1.2%
island 129
 
0.8%
san 60
 
0.4%
municipal 56
 
0.3%
de 55
 
0.3%
city 52
 
0.3%
lake 50
 
0.3%
base 50
 
0.3%
Other values (7446) 9760
59.9%
2023-12-13T05:27:19.055792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 15045
12.1%
10940
 
8.8%
o 10661
 
8.6%
a 10622
 
8.6%
i 10600
 
8.5%
t 9212
 
7.4%
n 7086
 
5.7%
p 5862
 
4.7%
A 5860
 
4.7%
e 5654
 
4.6%
Other values (53) 32558
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96256
77.6%
Uppercase Letter 16507
 
13.3%
Space Separator 10940
 
8.8%
Dash Punctuation 174
 
0.1%
Other Punctuation 127
 
0.1%
Open Punctuation 47
 
< 0.1%
Close Punctuation 47
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 15045
15.6%
o 10661
11.1%
a 10622
11.0%
i 10600
11.0%
t 9212
9.6%
n 7086
7.4%
p 5862
 
6.1%
e 5654
 
5.9%
l 4085
 
4.2%
u 2765
 
2.9%
Other values (16) 14664
15.2%
Uppercase Letter
ValueCountFrequency (%)
A 5860
35.5%
I 1057
 
6.4%
S 967
 
5.9%
M 893
 
5.4%
C 845
 
5.1%
B 832
 
5.0%
P 656
 
4.0%
R 557
 
3.4%
L 539
 
3.3%
T 502
 
3.0%
Other values (16) 3799
23.0%
Other Punctuation
ValueCountFrequency (%)
' 51
40.2%
/ 49
38.6%
. 23
18.1%
, 3
 
2.4%
? 1
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
7 1
50.0%
Space Separator
ValueCountFrequency (%)
10940
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 47
100.0%
Close Punctuation
ValueCountFrequency (%)
) 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112763
90.9%
Common 11337
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 15045
13.3%
o 10661
 
9.5%
a 10622
 
9.4%
i 10600
 
9.4%
t 9212
 
8.2%
n 7086
 
6.3%
p 5862
 
5.2%
A 5860
 
5.2%
e 5654
 
5.0%
l 4085
 
3.6%
Other values (42) 28076
24.9%
Common
ValueCountFrequency (%)
10940
96.5%
- 174
 
1.5%
' 51
 
0.4%
/ 49
 
0.4%
( 47
 
0.4%
) 47
 
0.4%
. 23
 
0.2%
, 3
 
< 0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 15045
12.1%
10940
 
8.8%
o 10661
 
8.6%
a 10622
 
8.6%
i 10600
 
8.5%
t 9212
 
7.4%
n 7086
 
5.7%
p 5862
 
4.7%
A 5860
 
4.7%
e 5654
 
4.6%
Other values (53) 32558
26.2%

한글공항명
Text

Distinct5311
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
2023-12-13T05:27:19.490926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length8.754537
Min length2

Characters and Unicode

Total characters46793
Distinct characters883
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

Unique5279 ?
Unique (%)98.8%

Sample

1st row올보르 공항
2nd row바이그라 공항
3rd row터스럽 공항
4th row아바단 공항
5th row아바이앙 공항
ValueCountFrequency (%)
공항 4212
31.9%
국제공항 679
 
5.1%
지역공항 107
 
0.8%
시공항 91
 
0.7%
카운티 55
 
0.4%
아일랜드 48
 
0.4%
공군기지 37
 
0.3%
필드 34
 
0.3%
헬리포트 34
 
0.3%
레이크 34
 
0.3%
Other values (6567) 7888
59.7%
2023-12-13T05:27:20.053009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8367
 
17.9%
공 5192
 
11.1%
항 5150
 
11.0%
스 1154
 
2.5%
이 833
 
1.8%
라 832
 
1.8%
제 797
 
1.7%
리 752
 
1.6%
국 724
 
1.5%
아 650
 
1.4%
Other values (873) 22342
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38099
81.4%
Space Separator 8367
 
17.9%
Uppercase Letter 160
 
0.3%
Other Punctuation 71
 
0.2%
Lowercase Letter 36
 
0.1%
Dash Punctuation 22
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Close Punctuation 15
 
< 0.1%
Decimal Number 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 5192
 
13.6%
항 5150
 
13.5%
스 1154
 
3.0%
이 833
 
2.2%
라 832
 
2.2%
제 797
 
2.1%
리 752
 
2.0%
국 724
 
1.9%
아 650
 
1.7%
르 532
 
1.4%
Other values (822) 21483
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%
I 7
 
4.4%
L 7
 
4.4%
G 6
 
3.8%
Other values (14) 33
20.6%
Lowercase Letter
ValueCountFrequency (%)
a 4
11.1%
i 4
11.1%
u 4
11.1%
n 4
11.1%
h 3
8.3%
y 2
 
5.6%
s 2
 
5.6%
r 2
 
5.6%
v 2
 
5.6%
x 2
 
5.6%
Other values (6) 7
19.4%
Decimal Number
ValueCountFrequency (%)
2 3
37.5%
4 2
25.0%
7 2
25.0%
3 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 43
60.6%
/ 27
38.0%
, 1
 
1.4%
Space Separator
ValueCountFrequency (%)
8367
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38099
81.4%
Common 8498
 
18.2%
Latin 196
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 5192
 
13.6%
항 5150
 
13.5%
스 1154
 
3.0%
이 833
 
2.2%
라 832
 
2.2%
제 797
 
2.1%
리 752
 
2.0%
국 724
 
1.9%
아 650
 
1.7%
르 532
 
1.4%
Other values (822) 21483
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%
I 7
 
3.6%
L 7
 
3.6%
G 6
 
3.1%
Other values (30) 69
35.2%
Common
ValueCountFrequency (%)
8367
98.5%
. 43
 
0.5%
/ 27
 
0.3%
- 22
 
0.3%
( 15
 
0.2%
) 15
 
0.2%
2 3
 
< 0.1%
4 2
 
< 0.1%
7 2
 
< 0.1%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38099
81.4%
ASCII 8694
 
18.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8367
96.2%
. 43
 
0.5%
/ 27
 
0.3%
A 24
 
0.3%
- 22
 
0.3%
F 18
 
0.2%
R 17
 
0.2%
P 15
 
0.2%
( 15
 
0.2%
) 15
 
0.2%
Other values (41) 131
 
1.5%
Hangul
ValueCountFrequency (%)
공 5192
 
13.6%
항 5150
 
13.5%
스 1154
 
3.0%
이 833
 
2.2%
라 832
 
2.2%
제 797
 
2.1%
리 752
 
2.0%
국 724
 
1.9%
아 650
 
1.7%
르 532
 
1.4%
Other values (822) 21483
56.4%

공항코드1(IATA)
Text

MISSING 

Distinct5185
Distinct (%)> 99.9%
Missing159
Missing (%)3.0%
Memory size41.9 KiB
2023-12-13T05:27:20.458434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15558
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

Unique5184 ?
Unique (%)> 99.9%

Sample

1st rowAAL
2nd rowAES
3rd rowAAR
4th rowABD
5th rowABF
ValueCountFrequency (%)
pkr 2
 
< 0.1%
plo 1
 
< 0.1%
aal 1
 
< 0.1%
kpc 1
 
< 0.1%
yzt 1
 
< 0.1%
phg 1
 
< 0.1%
phc 1
 
< 0.1%
pgm 1
 
< 0.1%
pog 1
 
< 0.1%
por 1
 
< 0.1%
Other values (5175) 5175
99.8%
2023-12-13T05:27:21.082601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 921
 
5.9%
S 848
 
5.5%
L 788
 
5.1%
M 779
 
5.0%
B 777
 
5.0%
K 741
 
4.8%
T 738
 
4.7%
C 706
 
4.5%
R 695
 
4.5%
N 674
 
4.3%
Other values (16) 7891
50.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 15558
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 921
 
5.9%
S 848
 
5.5%
L 788
 
5.1%
M 779
 
5.0%
B 777
 
5.0%
K 741
 
4.8%
T 738
 
4.7%
C 706
 
4.5%
R 695
 
4.5%
N 674
 
4.3%
Other values (16) 7891
50.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 15558
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 921
 
5.9%
S 848
 
5.5%
L 788
 
5.1%
M 779
 
5.0%
B 777
 
5.0%
K 741
 
4.8%
T 738
 
4.7%
C 706
 
4.5%
R 695
 
4.5%
N 674
 
4.3%
Other values (16) 7891
50.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 921
 
5.9%
S 848
 
5.5%
L 788
 
5.1%
M 779
 
5.0%
B 777
 
5.0%
K 741
 
4.8%
T 738
 
4.7%
C 706
 
4.5%
R 695
 
4.5%
N 674
 
4.3%
Other values (16) 7891
50.7%

공항코드2(ICAO)
Text

MISSING 

Distinct4252
Distinct (%)99.8%
Missing1086
Missing (%)20.3%
Memory size41.9 KiB
2023-12-13T05:27:21.550528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters17036
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

Unique4245 ?
Unique (%)99.7%

Sample

1st rowEKYT
2nd rowENAL
3rd rowEKAH
4th rowOIAA
5th rowNGAB
ValueCountFrequency (%)
uhss 2
 
< 0.1%
wicm 2
 
< 0.1%
usuu 2
 
< 0.1%
rplp 2
 
< 0.1%
vnpk 2
 
< 0.1%
lfsb 2
 
< 0.1%
uemh 2
 
< 0.1%
cypg 1
 
< 0.1%
lppr 1
 
< 0.1%
cypn 1
 
< 0.1%
Other values (4242) 4242
99.6%
2023-12-13T05:27:22.214341image/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 951
 
5.6%
M 913
 
5.4%
B 820
 
4.8%
E 762
 
4.5%
C 758
 
4.4%
P 728
 
4.3%
T 716
 
4.2%
Other values (21) 8074
47.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17028
> 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 951
 
5.6%
M 913
 
5.4%
B 820
 
4.8%
E 762
 
4.5%
C 758
 
4.5%
P 728
 
4.3%
T 716
 
4.2%
Other values (16) 8066
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 17028
> 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 951
 
5.6%
M 913
 
5.4%
B 820
 
4.8%
E 762
 
4.5%
C 758
 
4.5%
P 728
 
4.3%
T 716
 
4.2%
Other values (16) 8066
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 17036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1186
 
7.0%
K 1160
 
6.8%
A 968
 
5.7%
L 951
 
5.6%
M 913
 
5.4%
B 820
 
4.8%
E 762
 
4.5%
C 758
 
4.4%
P 728
 
4.3%
T 716
 
4.2%
Other values (21) 8074
47.4%

지역
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
북미
1194 
아시아
1104 
유럽
1052 
중남미
750 
아프리카
558 
Other values (2)
687 

Length

Max length4
Median length3
Mean length2.6512629
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유럽
2nd row유럽
3rd row유럽
4th row중동
5th row대양주

Common Values

ValueCountFrequency (%)
북미 1194
22.3%
아시아 1104
20.7%
유럽 1052
19.7%
중남미 750
14.0%
아프리카 558
10.4%
대양주 511
9.6%
중동 176
 
3.3%

Length

2023-12-13T05:27:22.420988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:27:22.598535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북미 1194
22.3%
아시아 1104
20.7%
유럽 1052
19.7%
중남미 750
14.0%
아프리카 558
10.4%
대양주 511
9.6%
중동 176
 
3.3%

영문국가명
Text

Distinct234
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
2023-12-13T05:27:23.031223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length25
Mean length10.987091
Min length4

Characters and Unicode

Total characters58726
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)0.8%

Sample

1st rowDenmark
2nd rowNorway
3rd rowDenmark
4th rowIran
5th rowKiribati
ValueCountFrequency (%)
united 975
 
10.8%
of 928
 
10.3%
states 885
 
9.8%
america 884
 
9.8%
canada 297
 
3.3%
china 261
 
2.9%
australia 217
 
2.4%
russian 203
 
2.3%
federation 203
 
2.3%
brazil 196
 
2.2%
Other values (265) 3964
44.0%
2023-12-13T05:27:23.663025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7929
13.5%
e 5256
 
9.0%
i 5215
 
8.9%
n 4514
 
7.7%
t 3851
 
6.6%
3668
 
6.2%
r 2811
 
4.8%
o 2505
 
4.3%
d 2443
 
4.2%
s 2373
 
4.0%
Other values (46) 18161
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47007
80.0%
Uppercase Letter 8030
 
13.7%
Space Separator 3668
 
6.2%
Other Punctuation 8
 
< 0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7929
16.9%
e 5256
11.2%
i 5215
11.1%
n 4514
9.6%
t 3851
8.2%
r 2811
 
6.0%
o 2505
 
5.3%
d 2443
 
5.2%
s 2373
 
5.0%
c 1504
 
3.2%
Other values (16) 8606
18.3%
Uppercase Letter
ValueCountFrequency (%)
A 1345
16.7%
S 1214
15.1%
U 1032
12.9%
C 819
10.2%
I 564
 
7.0%
F 379
 
4.7%
M 321
 
4.0%
R 320
 
4.0%
B 316
 
3.9%
P 310
 
3.9%
Other values (15) 1410
17.6%
Space Separator
ValueCountFrequency (%)
3668
100.0%
Other Punctuation
ValueCountFrequency (%)
' 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55037
93.7%
Common 3689
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7929
14.4%
e 5256
 
9.5%
i 5215
 
9.5%
n 4514
 
8.2%
t 3851
 
7.0%
r 2811
 
5.1%
o 2505
 
4.6%
d 2443
 
4.4%
s 2373
 
4.3%
c 1504
 
2.7%
Other values (41) 16636
30.2%
Common
ValueCountFrequency (%)
3668
99.4%
' 8
 
0.2%
( 5
 
0.1%
) 5
 
0.1%
- 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7929
13.5%
e 5256
 
9.0%
i 5215
 
8.9%
n 4514
 
7.7%
t 3851
 
6.6%
3668
 
6.2%
r 2811
 
4.8%
o 2505
 
4.3%
d 2443
 
4.2%
s 2373
 
4.0%
Other values (46) 18161
30.9%

한글국가명
Text

Distinct233
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
2023-12-13T05:27:24.021572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.414406
Min length1

Characters and Unicode

Total characters18250
Distinct characters206
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)0.8%

Sample

1st row덴마크
2nd row노르웨이
3rd row덴마크
4th row이란
5th row키리바시
ValueCountFrequency (%)
미국 884
 
16.3%
캐나다 297
 
5.5%
중국 261
 
4.8%
오스트레일리아 217
 
4.0%
러시아 203
 
3.7%
브라질 196
 
3.6%
인도 157
 
2.9%
인도네시아 153
 
2.8%
일본 88
 
1.6%
프랑스 79
 
1.5%
Other values (232) 2895
53.3%
2023-12-13T05:27:24.853290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 1389
 
7.6%
국 1352
 
7.4%
미 915
 
5.0%
스 762
 
4.2%
리 705
 
3.9%
시 562
 
3.1%
나 495
 
2.7%
도 440
 
2.4%
라 402
 
2.2%
이 394
 
2.2%
Other values (196) 10834
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18118
99.3%
Space Separator 85
 
0.5%
Uppercase Letter 39
 
0.2%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 1389
 
7.7%
국 1352
 
7.5%
미 915
 
5.1%
스 762
 
4.2%
리 705
 
3.9%
시 562
 
3.1%
나 495
 
2.7%
도 440
 
2.4%
라 402
 
2.2%
이 394
 
2.2%
Other values (190) 10702
59.1%
Uppercase Letter
ValueCountFrequency (%)
E 13
33.3%
A 13
33.3%
U 13
33.3%
Space Separator
ValueCountFrequency (%)
85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18118
99.3%
Common 93
 
0.5%
Latin 39
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 1389
 
7.7%
국 1352
 
7.5%
미 915
 
5.1%
스 762
 
4.2%
리 705
 
3.9%
시 562
 
3.1%
나 495
 
2.7%
도 440
 
2.4%
라 402
 
2.2%
이 394
 
2.2%
Other values (190) 10702
59.1%
Common
ValueCountFrequency (%)
85
91.4%
( 4
 
4.3%
) 4
 
4.3%
Latin
ValueCountFrequency (%)
E 13
33.3%
A 13
33.3%
U 13
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18118
99.3%
ASCII 132
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 1389
 
7.7%
국 1352
 
7.5%
미 915
 
5.1%
스 762
 
4.2%
리 705
 
3.9%
시 562
 
3.1%
나 495
 
2.7%
도 440
 
2.4%
라 402
 
2.2%
이 394
 
2.2%
Other values (190) 10702
59.1%
ASCII
ValueCountFrequency (%)
85
64.4%
E 13
 
9.8%
A 13
 
9.8%
U 13
 
9.8%
( 4
 
3.0%
) 4
 
3.0%

영문도시명
Text

Distinct5026
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
2023-12-13T05:27:25.264762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length33
Mean length8.8877456
Min length2

Characters and Unicode

Total characters47505
Distinct characters64
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4780 ?
Unique (%)89.4%

Sample

1st rowAalborg
2nd rowAalesund
3rd rowAarhus
4th rowAbadan
5th rowAbaiang
ValueCountFrequency (%)
179
 
2.5%
island 149
 
2.1%
san 60
 
0.8%
lake 46
 
0.6%
bay 43
 
0.6%
city 43
 
0.6%
fort 35
 
0.5%
de 33
 
0.5%
st 30
 
0.4%
la 30
 
0.4%
Other values (5346) 6524
91.0%
2023-12-13T05:27:25.831105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6438
 
13.6%
n 3287
 
6.9%
o 3161
 
6.7%
e 3003
 
6.3%
i 2938
 
6.2%
r 2681
 
5.6%
l 2151
 
4.5%
u 1906
 
4.0%
1880
 
4.0%
s 1828
 
3.8%
Other values (54) 18232
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38109
80.2%
Uppercase Letter 7131
 
15.0%
Space Separator 1880
 
4.0%
Other Punctuation 160
 
0.3%
Dash Punctuation 100
 
0.2%
Open Punctuation 63
 
0.1%
Close Punctuation 62
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6438
16.9%
n 3287
 
8.6%
o 3161
 
8.3%
e 3003
 
7.9%
i 2938
 
7.7%
r 2681
 
7.0%
l 2151
 
5.6%
u 1906
 
5.0%
s 1828
 
4.8%
t 1822
 
4.8%
Other values (18) 8894
23.3%
Uppercase Letter
ValueCountFrequency (%)
S 667
 
9.4%
M 564
 
7.9%
C 554
 
7.8%
B 539
 
7.6%
P 437
 
6.1%
A 429
 
6.0%
L 413
 
5.8%
K 390
 
5.5%
T 387
 
5.4%
I 302
 
4.2%
Other values (16) 2449
34.3%
Other Punctuation
ValueCountFrequency (%)
/ 95
59.4%
' 26
 
16.2%
, 23
 
14.4%
. 11
 
6.9%
? 4
 
2.5%
& 1
 
0.6%
Space Separator
ValueCountFrequency (%)
1880
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45240
95.2%
Common 2265
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6438
14.2%
n 3287
 
7.3%
o 3161
 
7.0%
e 3003
 
6.6%
i 2938
 
6.5%
r 2681
 
5.9%
l 2151
 
4.8%
u 1906
 
4.2%
s 1828
 
4.0%
t 1822
 
4.0%
Other values (44) 16025
35.4%
Common
ValueCountFrequency (%)
1880
83.0%
- 100
 
4.4%
/ 95
 
4.2%
( 63
 
2.8%
) 62
 
2.7%
' 26
 
1.1%
, 23
 
1.0%
. 11
 
0.5%
? 4
 
0.2%
& 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47501
> 99.9%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6438
 
13.6%
n 3287
 
6.9%
o 3161
 
6.7%
e 3003
 
6.3%
i 2938
 
6.2%
r 2681
 
5.6%
l 2151
 
4.5%
u 1906
 
4.0%
1880
 
4.0%
s 1828
 
3.8%
Other values (52) 18228
38.4%
None
ValueCountFrequency (%)
ı 3
75.0%
ð 1
 
25.0%

Missing values

2023-12-13T05:27:17.621712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:27:17.769486image/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.
2023-12-13T05:27:17.895277image/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)지역영문국가명한글국가명영문도시명
0Aalborg Airport올보르 공항AALEKYT유럽Denmark덴마크Aalborg
1Alesund Airport바이그라 공항AESENAL유럽Norway노르웨이Aalesund
2Aarhus Airport터스럽 공항AAREKAH유럽Denmark덴마크Aarhus
3Abadan Airport아바단 공항ABDOIAA중동Iran이란Abadan
4Abaiang Airport아바이앙 공항ABFNGAB대양주Kiribati키리바시Abaiang
5Abakan Airport아바칸 공항ABAUNAA유럽Russian Federation러시아Abakan
6Abbotsford International Airport애보트포드 국제공항YXXCYXX북미Canada캐나다Abbotsford
7Abecher Airport아베셰 공항AEHFTTC아프리카Chad차드Abeche
8Abemama Airport아베마마 공항AEANGTB대양주Kiribati키리바시Abemama
9Aberdeen Regional Airport애버딘 지역공항ABRKABR북미United States of America미국Aberdeen
영문공항명한글공항명공항코드1(IATA)공항코드2(ICAO)지역영문국가명한글국가명영문도시명
5335Louisville Clark Regional Airport루이빌 클라크 리저널 공항<NA>KJVY북미United States of America미국Louisville
5336Nashville John C. Tune Airport내슈빌 존 C. 튠 공항<NA>KJWN북미United States of America미국Nashville
5337Sioux County Regional Airport쑤 군 공항<NA>KSXK북미United States of America미국Orange City
5338Bridgewater Air Park브리지워터 에어 파크<NA>KVBW북미United States of America미국Bridgewater
5339Montevideo Melilla Airport몬테비데오 멜리야 공항<NA>SUAA중남미Uruguay우루과이Montevideo
5340Zagarzazu International Airport자가르자즈 국제공항<NA>SUCM중남미Uruguay우루과이Carmelo
5341San Carlos Airport (Venezuela)산 카를로스 공항(베네수엘라)<NA>SVCJ중남미Venezuela베네수엘라San Carlos
5342Caracas Oscar Machado Zuloaga Airport카라카스 오스카 마차도 줄로아가 공항<NA>SVCS중남미Venezuela베네수엘라Caracas
5343La Tortuga Island Airport라 토르투가 섬 공항<NA>SVDA중남미Venezuela베네수엘라La Tortuga Island
5344Higuerote Airport이과로테 공항<NA>SVGH중남미Venezuela베네수엘라Higuerote