Overview

Dataset statistics

Number of variables9
Number of observations292
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.2 KiB
Average record size in memory74.5 B

Variable types

Text3
Categorical5
Numeric1

Dataset

Description항공로에 대한 정보로 국내 항공로 명칭과 항공로 구간 길이와 단위, 항공로 폭 넓이와 단위 등에 대한 내용을 수록한 정보입니다.
Author국토교통부 항공교통본부
URLhttps://www.data.go.kr/data/15122908/fileData.do

Alerts

상위제한고도참조표면 is highly overall correlated with 하위제한고도 and 4 other fieldsHigh correlation
하위제한고도참조표면 is highly overall correlated with 하위제한고도 and 4 other fieldsHigh correlation
하위제한고도단위 is highly overall correlated with 하위제한고도 and 4 other fieldsHigh correlation
상위제한고도단위 is highly overall correlated with 하위제한고도 and 4 other fieldsHigh correlation
상위제한고도 is highly overall correlated with 상위제한고도단위 and 3 other fieldsHigh correlation
하위제한고도 is highly overall correlated with 상위제한고도단위 and 3 other fieldsHigh correlation
상위제한고도 is highly imbalanced (70.5%)Imbalance
상위제한고도단위 is highly imbalanced (59.0%)Imbalance
상위제한고도참조표면 is highly imbalanced (62.7%)Imbalance

Reproduction

Analysis started2023-12-12 16:26:49.885232
Analysis finished2023-12-12 16:26:50.804118
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

항로명
Text

Distinct54
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T01:26:50.981068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.859589
Min length3

Characters and Unicode

Total characters1127
Distinct characters18
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

Unique10 ?
Unique (%)3.4%

Sample

1st rowA582
2nd rowA582
3rd rowA582
4th rowA582
5th rowA582
ValueCountFrequency (%)
y711 16
 
5.5%
g597 15
 
5.1%
y722 15
 
5.1%
y697 15
 
5.1%
b576 14
 
4.8%
a586 14
 
4.8%
y579 13
 
4.5%
y685 11
 
3.8%
g585 11
 
3.8%
a582 10
 
3.4%
Other values (44) 158
54.1%
2023-12-13T01:26:51.367887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 200
17.7%
Y 153
13.6%
7 143
12.7%
6 98
8.7%
2 80
 
7.1%
9 71
 
6.3%
8 70
 
6.2%
1 61
 
5.4%
4 61
 
5.4%
3 46
 
4.1%
Other values (8) 144
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 835
74.1%
Uppercase Letter 292
 
25.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 200
24.0%
7 143
17.1%
6 98
11.7%
2 80
 
9.6%
9 71
 
8.5%
8 70
 
8.4%
1 61
 
7.3%
4 61
 
7.3%
3 46
 
5.5%
0 5
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
Y 153
52.4%
A 30
 
10.3%
G 27
 
9.2%
Z 27
 
9.2%
B 22
 
7.5%
V 22
 
7.5%
W 9
 
3.1%
L 2
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 835
74.1%
Latin 292
 
25.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 200
24.0%
7 143
17.1%
6 98
11.7%
2 80
 
9.6%
9 71
 
8.5%
8 70
 
8.4%
1 61
 
7.3%
4 61
 
7.3%
3 46
 
5.5%
0 5
 
0.6%
Latin
ValueCountFrequency (%)
Y 153
52.4%
A 30
 
10.3%
G 27
 
9.2%
Z 27
 
9.2%
B 22
 
7.5%
V 22
 
7.5%
W 9
 
3.1%
L 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 200
17.7%
Y 153
13.6%
7 143
12.7%
6 98
8.7%
2 80
 
7.1%
9 71
 
6.3%
8 70
 
6.2%
1 61
 
5.4%
4 61
 
5.4%
3 46
 
4.1%
Other values (8) 144
12.8%

시작FIX
Text

Distinct139
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T01:26:51.748319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6164384
Min length3

Characters and Unicode

Total characters1348
Distinct characters22
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

Unique55 ?
Unique (%)18.8%

Sample

1st rowPOLEG
2nd rowTGU
3rd rowOSPOT
4th rowBITUX
5th rowVASLI
ValueCountFrequency (%)
sot 8
 
2.7%
psn 8
 
2.7%
cju 8
 
2.7%
tgu 7
 
2.4%
sel 7
 
2.4%
kwa 5
 
1.7%
tenas 5
 
1.7%
pilit 5
 
1.7%
bulga 4
 
1.4%
kae 4
 
1.4%
Other values (129) 231
79.1%
2023-12-13T01:26:52.256310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 166
12.3%
O 150
11.1%
S 124
 
9.2%
N 87
 
6.5%
L 83
 
6.2%
I 82
 
6.1%
T 78
 
5.8%
E 77
 
5.7%
U 72
 
5.3%
K 67
 
5.0%
Other values (12) 362
26.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1348
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 166
12.3%
O 150
11.1%
S 124
 
9.2%
N 87
 
6.5%
L 83
 
6.2%
I 82
 
6.1%
T 78
 
5.8%
E 77
 
5.7%
U 72
 
5.3%
K 67
 
5.0%
Other values (12) 362
26.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1348
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 166
12.3%
O 150
11.1%
S 124
 
9.2%
N 87
 
6.5%
L 83
 
6.2%
I 82
 
6.1%
T 78
 
5.8%
E 77
 
5.7%
U 72
 
5.3%
K 67
 
5.0%
Other values (12) 362
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 166
12.3%
O 150
11.1%
S 124
 
9.2%
N 87
 
6.5%
L 83
 
6.2%
I 82
 
6.1%
T 78
 
5.8%
E 77
 
5.7%
U 72
 
5.3%
K 67
 
5.0%
Other values (12) 362
26.9%

종료FIX
Text

Distinct146
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T01:26:52.629971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.7123288
Min length3

Characters and Unicode

Total characters1376
Distinct characters21
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

Unique54 ?
Unique (%)18.5%

Sample

1st rowSOT
2nd rowKALOD
3rd rowVASLI
4th rowTGU
5th rowMAKDU
ValueCountFrequency (%)
psn 10
 
3.4%
cju 7
 
2.4%
kpo 6
 
2.1%
tgu 6
 
2.1%
poleg 4
 
1.4%
sot 4
 
1.4%
goget 4
 
1.4%
egoba 4
 
1.4%
agsus 4
 
1.4%
bulga 4
 
1.4%
Other values (136) 239
81.8%
2023-12-13T01:26:53.353707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 182
13.2%
O 150
10.9%
S 121
 
8.8%
N 94
 
6.8%
I 80
 
5.8%
L 80
 
5.8%
T 77
 
5.6%
E 77
 
5.6%
P 72
 
5.2%
M 71
 
5.2%
Other values (11) 372
27.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1376
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 182
13.2%
O 150
10.9%
S 121
 
8.8%
N 94
 
6.8%
I 80
 
5.8%
L 80
 
5.8%
T 77
 
5.6%
E 77
 
5.6%
P 72
 
5.2%
M 71
 
5.2%
Other values (11) 372
27.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1376
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 182
13.2%
O 150
10.9%
S 121
 
8.8%
N 94
 
6.8%
I 80
 
5.8%
L 80
 
5.8%
T 77
 
5.6%
E 77
 
5.6%
P 72
 
5.2%
M 71
 
5.2%
Other values (11) 372
27.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 182
13.2%
O 150
10.9%
S 121
 
8.8%
N 94
 
6.8%
I 80
 
5.8%
L 80
 
5.8%
T 77
 
5.6%
E 77
 
5.6%
P 72
 
5.2%
M 71
 
5.2%
Other values (11) 372
27.0%

상위제한고도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
269 
10000
 
16
13000
 
7

Length

Max length5
Median length4
Mean length4.0787671
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 269
92.1%
10000 16
 
5.5%
13000 7
 
2.4%

Length

2023-12-13T01:26:53.479229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:26:53.596689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 269
92.1%
10000 16
 
5.5%
13000 7
 
2.4%

상위제한고도단위
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
268 
FT
 
24

Length

Max length4
Median length4
Mean length3.8356164
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 268
91.8%
FT 24
 
8.2%

Length

2023-12-13T01:26:53.701300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:26:53.815865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 268
91.8%
ft 24
 
8.2%

상위제한고도참조표면
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
271 
MSL
 
21

Length

Max length4
Median length4
Mean length3.9280822
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 271
92.8%
MSL 21
 
7.2%

Length

2023-12-13T01:26:53.946836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:26:54.046872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 271
92.8%
msl 21
 
7.2%

하위제한고도
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5331.6438
Minimum140
Maximum13000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T01:26:54.131657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum140
5-th percentile140
Q1200
median7500
Q38000
95-th percentile11000
Maximum13000
Range12860
Interquartile range (IQR)7800

Descriptive statistics

Standard deviation3932.0124
Coefficient of variation (CV)0.73748595
Kurtosis-1.5093069
Mean5331.6438
Median Absolute Deviation (MAD)1500
Skewness-0.35848725
Sum1556840
Variance15460722
MonotonicityNot monotonic
2023-12-13T01:26:54.260741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
8000 87
29.8%
140 49
16.8%
9000 34
 
11.6%
7000 19
 
6.5%
150 18
 
6.2%
11000 17
 
5.8%
200 13
 
4.5%
7500 8
 
2.7%
5000 8
 
2.7%
240 7
 
2.4%
Other values (11) 32
 
11.0%
ValueCountFrequency (%)
140 49
16.8%
150 18
 
6.2%
160 2
 
0.7%
170 2
 
0.7%
200 13
 
4.5%
240 7
 
2.4%
250 1
 
0.3%
270 2
 
0.7%
310 5
 
1.7%
3000 2
 
0.7%
ValueCountFrequency (%)
13000 1
 
0.3%
11000 17
 
5.8%
10000 3
 
1.0%
9000 34
 
11.6%
8000 87
29.8%
7500 8
 
2.7%
7000 19
 
6.5%
6000 5
 
1.7%
5000 8
 
2.7%
4500 6
 
2.1%

하위제한고도단위
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
FT
192 
FL
99 
 
1

Length

Max length2
Median length2
Mean length1.9965753
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
FT 192
65.8%
FL 99
33.9%
1
 
0.3%

Length

2023-12-13T01:26:54.401107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:26:54.504881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ft 192
66.0%
fl 99
34.0%

하위제한고도참조표면
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
167 
MSL
107 
STD
18 

Length

Max length4
Median length4
Mean length3.5719178
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 167
57.2%
MSL 107
36.6%
STD 18
 
6.2%

Length

2023-12-13T01:26:54.609660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:26:54.722928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 167
57.2%
msl 107
36.6%
std 18
 
6.2%

Interactions

2023-12-13T01:26:50.406312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:26:54.812163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항로명상위제한고도하위제한고도하위제한고도단위하위제한고도참조표면
항로명1.0001.0000.8970.8220.956
상위제한고도1.0001.0000.145NaNNaN
하위제한고도0.8970.1451.0000.9321.000
하위제한고도단위0.822NaN0.9321.0001.000
하위제한고도참조표면0.956NaN1.0001.0001.000
2023-12-13T01:26:54.933827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상위제한고도참조표면하위제한고도참조표면하위제한고도단위상위제한고도단위상위제한고도
상위제한고도참조표면1.0001.0001.0001.0001.000
하위제한고도참조표면1.0001.0000.9961.0001.000
하위제한고도단위1.0000.9961.0001.0001.000
상위제한고도단위1.0001.0001.0001.0001.000
상위제한고도1.0001.0001.0001.0001.000
2023-12-13T01:26:55.043074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하위제한고도상위제한고도상위제한고도단위상위제한고도참조표면하위제한고도단위하위제한고도참조표면
하위제한고도1.0000.2261.0001.0000.6910.979
상위제한고도0.2261.0001.0001.0001.0001.000
상위제한고도단위1.0001.0001.0001.0001.0001.000
상위제한고도참조표면1.0001.0001.0001.0001.0001.000
하위제한고도단위0.6911.0001.0001.0001.0000.996
하위제한고도참조표면0.9791.0001.0001.0000.9961.000

Missing values

2023-12-13T01:26:50.552402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:26:50.737887image/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

항로명시작FIX종료FIX상위제한고도상위제한고도단위상위제한고도참조표면하위제한고도하위제한고도단위하위제한고도참조표면
0A582POLEGSOT<NA><NA><NA>4500FTMSL
1A582TGUKALOD<NA><NA><NA>6000FTMSL
2A582OSPOTVASLI<NA><NA><NA>8000FTMSL
3A582BITUXTGU<NA><NA><NA>8000FTMSL
4A582VASLIMAKDU<NA><NA><NA>8000FTMSL
5A582SOTOSPOT<NA><NA><NA>8000FTMSL
6A582PSNAPELA<NA><NA><NA>4000FTMSL
7A582KALODPSN<NA><NA><NA>4000FTMSL
8A582MAKDUBITUX<NA><NA><NA>8000FTMSL
9A582SELPOLEG<NA><NA><NA>4500FTMSL
항로명시작FIX종료FIX상위제한고도상위제한고도단위상위제한고도참조표면하위제한고도하위제한고도단위하위제한고도참조표면
282Z82CJUPANSI<NA><NA><NA>140FL<NA>
283Z83TGUMASTA<NA><NA><NA>5000FT<NA>
284Z83MASTASARAM<NA><NA><NA>5000FT<NA>
285Z83ENGOTANROD<NA><NA><NA>5000FT<NA>
286Z83SARAMENGOT<NA><NA><NA>5000FT<NA>
287Z84PSNKALEK<NA><NA><NA>8000FT<NA>
288Z85BILUMPAPLU<NA><NA><NA>170FL<NA>
289Z85PAPLURUGMA<NA><NA><NA>170FL<NA>
290Z86BONSOATOTI<NA><NA><NA>140FL<NA>
291Z91PSNINVOK<NA><NA><NA>10000FT<NA>