Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells13488
Missing cells (%)19.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory654.3 KiB
Average record size in memory67.0 B

Variable types

Text4
Numeric3

Dataset

Description2023년 8월 17일 기준 우리나라 연안 여객선 항로 위치 데이터를 제공하고 있습니다.다만, 동 데이터는 법령상 고시된 항로가 아니며, 각 지방해양수산청에서 여객운송면허사업자에게 허가해준 면허 항로상의 항로 위치(위경도)일 뿐이며,해상 기상, 해상교통 상황에 따라 운항 항로 위치 변동 가능성이 큼을 알려 드리오니, 해상풍력발전사업 등 해상 개발사업 입지 검토에 참고자료로 활용 바랍니다.
Author한국해양교통안전공단
URLhttps://www.data.go.kr/data/15126739/fileData.do

Alerts

변침점명 has 5260 (52.6%) missing valuesMissing
기항지명 has 8228 (82.3%) missing valuesMissing
변침점순번 has 105 (1.1%) zerosZeros

Reproduction

Analysis started2024-03-14 11:40:36.970294
Analysis finished2024-03-14 11:40:41.403853
Duration4.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류(ID)
Text

Distinct1025
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T20:40:42.551743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9875
Min length7

Characters and Unicode

Total characters79875
Distinct characters23
Distinct categories3 ?
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 (%)0.4%

Sample

1st rowQ02-15-2
2nd rowF05-03-2
3rd rowF13-07-2
4th rowQ02-09-2
5th rowF24-26-2
ValueCountFrequency (%)
f13-10-1 55
 
0.5%
f05-05-1 53
 
0.5%
f05-05-2 52
 
0.5%
h02-12-2 51
 
0.5%
f13-08-1 50
 
0.5%
f13-10-2 49
 
0.5%
f14-06-2 46
 
0.5%
f14-06-1 46
 
0.5%
f05-04-1 45
 
0.4%
f05-04-2 45
 
0.4%
Other values (1015) 9508
95.1%
2024-03-14T20:40:44.285922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 20000
25.0%
0 12059
15.1%
1 11946
15.0%
2 11166
14.0%
4 3307
 
4.1%
3 3303
 
4.1%
F 3074
 
3.8%
5 2521
 
3.2%
6 1791
 
2.2%
L 1640
 
2.1%
Other values (13) 9068
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49875
62.4%
Dash Punctuation 20000
25.0%
Uppercase Letter 10000
 
12.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 3074
30.7%
L 1640
16.4%
H 1457
14.6%
Q 879
 
8.8%
D 720
 
7.2%
J 705
 
7.0%
E 703
 
7.0%
K 541
 
5.4%
M 140
 
1.4%
G 83
 
0.8%
Other values (2) 58
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 12059
24.2%
1 11946
24.0%
2 11166
22.4%
4 3307
 
6.6%
3 3303
 
6.6%
5 2521
 
5.1%
6 1791
 
3.6%
7 1466
 
2.9%
8 1280
 
2.6%
9 1036
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69875
87.5%
Latin 10000
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 3074
30.7%
L 1640
16.4%
H 1457
14.6%
Q 879
 
8.8%
D 720
 
7.2%
J 705
 
7.0%
E 703
 
7.0%
K 541
 
5.4%
M 140
 
1.4%
G 83
 
0.8%
Other values (2) 58
 
0.6%
Common
ValueCountFrequency (%)
- 20000
28.6%
0 12059
17.3%
1 11946
17.1%
2 11166
16.0%
4 3307
 
4.7%
3 3303
 
4.7%
5 2521
 
3.6%
6 1791
 
2.6%
7 1466
 
2.1%
8 1280
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 20000
25.0%
0 12059
15.1%
1 11946
15.0%
2 11166
14.0%
4 3307
 
4.1%
3 3303
 
4.1%
F 3074
 
3.8%
5 2521
 
3.2%
6 1791
 
2.2%
L 1640
 
2.1%
Other values (13) 9068
11.4%

항로명
Text

Distinct500
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T20:40:45.070902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.9058
Min length3

Characters and Unicode

Total characters89058
Distinct characters227
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row우두금당-비견
2nd row목포쉬미(기상특보)
3rd row팽목-죽도(짝수일)
4th row우두동송(충도 x)
5th row목읍-사-수-가-도
ValueCountFrequency (%)
군산-말도(순환 146
 
1.4%
통영-용초(2호 139
 
1.3%
목포-율목-서거차(신 105
 
1.0%
임시 104
 
1.0%
팽목죽도(동거 104
 
1.0%
여수-역포(오전순환2 95
 
0.9%
진도-죽도(홀수일 92
 
0.9%
목포율목-서거차(구 90
 
0.9%
팽목죽도(홀수일 87
 
0.8%
진도-죽도(짝수일 80
 
0.8%
Other values (499) 9398
90.0%
2024-03-14T20:40:46.247102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 9739
 
10.9%
) 6486
 
7.3%
( 6467
 
7.3%
도 4464
 
5.0%
목 3018
 
3.4%
포 1997
 
2.2%
수 1656
 
1.9%
통 1585
 
1.8%
동 1490
 
1.7%
산 1475
 
1.7%
Other values (217) 50681
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 62900
70.6%
Dash Punctuation 9739
 
10.9%
Close Punctuation 6486
 
7.3%
Open Punctuation 6467
 
7.3%
Decimal Number 2207
 
2.5%
Other Punctuation 662
 
0.7%
Space Separator 440
 
0.5%
Lowercase Letter 100
 
0.1%
Uppercase Letter 35
 
< 0.1%
Connector Punctuation 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 4464
 
7.1%
목 3018
 
4.8%
포 1997
 
3.2%
수 1656
 
2.6%
통 1585
 
2.5%
동 1490
 
2.4%
산 1475
 
2.3%
순 1342
 
2.1%
오 1129
 
1.8%
거 1053
 
1.7%
Other values (197) 43691
69.5%
Decimal Number
ValueCountFrequency (%)
2 908
41.1%
1 390
17.7%
3 350
 
15.9%
6 159
 
7.2%
0 127
 
5.8%
4 78
 
3.5%
5 65
 
2.9%
7 65
 
2.9%
8 45
 
2.0%
9 20
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 492
74.3%
/ 130
 
19.6%
. 40
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 9739
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6486
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6467
100.0%
Space Separator
ValueCountFrequency (%)
440
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 100
100.0%
Uppercase Letter
ValueCountFrequency (%)
O 35
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 62900
70.6%
Common 26023
29.2%
Latin 135
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 4464
 
7.1%
목 3018
 
4.8%
포 1997
 
3.2%
수 1656
 
2.6%
통 1585
 
2.5%
동 1490
 
2.4%
산 1475
 
2.3%
순 1342
 
2.1%
오 1129
 
1.8%
거 1053
 
1.7%
Other values (197) 43691
69.5%
Common
ValueCountFrequency (%)
- 9739
37.4%
) 6486
24.9%
( 6467
24.9%
2 908
 
3.5%
, 492
 
1.9%
440
 
1.7%
1 390
 
1.5%
3 350
 
1.3%
6 159
 
0.6%
/ 130
 
0.5%
Other values (8) 462
 
1.8%
Latin
ValueCountFrequency (%)
x 100
74.1%
O 35
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 62900
70.6%
ASCII 26158
29.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 9739
37.2%
) 6486
24.8%
( 6467
24.7%
2 908
 
3.5%
, 492
 
1.9%
440
 
1.7%
1 390
 
1.5%
3 350
 
1.3%
6 159
 
0.6%
/ 130
 
0.5%
Other values (10) 597
 
2.3%
Hangul
ValueCountFrequency (%)
도 4464
 
7.1%
목 3018
 
4.8%
포 1997
 
3.2%
수 1656
 
2.6%
통 1585
 
2.5%
동 1490
 
2.4%
산 1475
 
2.3%
순 1342
 
2.1%
오 1129
 
1.8%
거 1053
 
1.7%
Other values (197) 43691
69.5%

변침점순번
Real number (ℝ)

ZEROS 

Distinct144
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.3463
Minimum0
Maximum144
Zeros105
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T20:40:46.481959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median18
Q334
95-th percentile78
Maximum144
Range144
Interquartile range (IQR)26

Descriptive statistics

Standard deviation24.425062
Coefficient of variation (CV)0.96365393
Kurtosis3.3487778
Mean25.3463
Median Absolute Deviation (MAD)12
Skewness1.7430045
Sum253463
Variance596.58363
MonotonicityNot monotonic
2024-03-14T20:40:46.745283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 345
 
3.5%
4 337
 
3.4%
2 335
 
3.4%
3 330
 
3.3%
8 309
 
3.1%
6 303
 
3.0%
5 301
 
3.0%
7 296
 
3.0%
9 282
 
2.8%
11 278
 
2.8%
Other values (134) 6884
68.8%
ValueCountFrequency (%)
0 105
 
1.1%
1 345
3.5%
2 335
3.4%
3 330
3.3%
4 337
3.4%
5 301
3.0%
6 303
3.0%
7 296
3.0%
8 309
3.1%
9 282
2.8%
ValueCountFrequency (%)
144 1
 
< 0.1%
143 1
 
< 0.1%
141 1
 
< 0.1%
140 2
< 0.1%
139 1
 
< 0.1%
138 2
< 0.1%
137 4
< 0.1%
136 4
< 0.1%
135 3
< 0.1%
134 4
< 0.1%

위도(LAT)
Real number (ℝ)

Distinct2660
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.007882
Minimum33.1205
Maximum37.8423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T20:40:47.005911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.1205
5-th percentile34.204895
Q134.4424
median34.6933
Q334.92285
95-th percentile37.4404
Maximum37.8423
Range4.7218
Interquartile range (IQR)0.48045

Descriptive statistics

Standard deviation0.94748769
Coefficient of variation (CV)0.027064982
Kurtosis1.5586019
Mean35.007882
Median Absolute Deviation (MAD)0.2434
Skewness1.5957311
Sum350078.82
Variance0.89773292
MonotonicityNot monotonic
2024-03-14T20:40:47.288442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.8375 76
 
0.8%
34.8348 72
 
0.7%
34.8389 54
 
0.5%
34.8287 48
 
0.5%
34.8106 43
 
0.4%
34.671 40
 
0.4%
34.7651 40
 
0.4%
34.6331 40
 
0.4%
34.6206 39
 
0.4%
34.782 39
 
0.4%
Other values (2650) 9509
95.1%
ValueCountFrequency (%)
33.1205 2
< 0.1%
33.1225 1
 
< 0.1%
33.1229 1
 
< 0.1%
33.1258 3
< 0.1%
33.1287 1
 
< 0.1%
33.1365 1
 
< 0.1%
33.1665 3
< 0.1%
33.1754 1
 
< 0.1%
33.1777 2
< 0.1%
33.1971 1
 
< 0.1%
ValueCountFrequency (%)
37.8423 1
 
< 0.1%
37.8309 3
< 0.1%
37.8285 2
< 0.1%
37.7812 1
 
< 0.1%
37.7783 2
< 0.1%
37.7727 3
< 0.1%
37.7699 1
 
< 0.1%
37.7666 1
 
< 0.1%
37.7591 1
 
< 0.1%
37.7309 2
< 0.1%

경도(LON)
Real number (ℝ)

Distinct2762
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97581
Minimum124.7111
Maximum131.8744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T20:40:47.716123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum124.7111
5-th percentile125.9597
Q1126.2118
median126.5304
Q3127.6489
95-th percentile128.5118
Maximum131.8744
Range7.1633
Interquartile range (IQR)1.4371

Descriptive statistics

Standard deviation1.0137456
Coefficient of variation (CV)0.007983769
Kurtosis2.4836675
Mean126.97581
Median Absolute Deviation (MAD)0.5072
Skewness1.2865933
Sum1269758.1
Variance1.0276801
MonotonicityNot monotonic
2024-03-14T20:40:48.178203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.4209 76
 
0.8%
128.4299 72
 
0.7%
128.5742 60
 
0.6%
128.4204 54
 
0.5%
128.4446 50
 
0.5%
128.4475 43
 
0.4%
127.7338 43
 
0.4%
126.3851 41
 
0.4%
127.642 41
 
0.4%
126.3996 38
 
0.4%
Other values (2752) 9482
94.8%
ValueCountFrequency (%)
124.7111 1
 
< 0.1%
124.7164 2
 
< 0.1%
124.7205 3
< 0.1%
124.7366 1
 
< 0.1%
124.7367 1
 
< 0.1%
124.7464 2
 
< 0.1%
125.1238 5
0.1%
125.1242 5
0.1%
125.1333 2
 
< 0.1%
125.1543 4
< 0.1%
ValueCountFrequency (%)
131.8744 5
 
0.1%
131.874 3
 
< 0.1%
131.871 3
 
< 0.1%
131.8667 4
 
< 0.1%
131.8656 15
0.1%
131.8641 3
 
< 0.1%
131.8619 8
0.1%
130.9357 2
 
< 0.1%
130.926 9
0.1%
130.9252 1
 
< 0.1%

변침점명
Text

MISSING 

Distinct734
Distinct (%)15.5%
Missing5260
Missing (%)52.6%
Memory size156.2 KiB
2024-03-14T20:40:49.663576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length9
Mean length4.1409283
Min length1

Characters and Unicode

Total characters19628
Distinct characters293
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)3.1%

Sample

1st row청등도 북동
2nd row금당
3rd row삼도
4th row목포(북항)
5th row금호충무마리나콘도
ValueCountFrequency (%)
진입 531
 
8.3%
방파제 251
 
3.9%
북단 190
 
3.0%
북서 132
 
2.1%
통영항 130
 
2.0%
남단 114
 
1.8%
입구 101
 
1.6%
장자도 100
 
1.6%
북동 84
 
1.3%
공주섬 72
 
1.1%
Other values (630) 4696
73.4%
2024-03-14T20:40:51.505740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 1920
 
9.8%
1661
 
8.5%
진 794
 
4.0%
입 747
 
3.8%
항 609
 
3.1%
단 509
 
2.6%
북 507
 
2.6%
서 474
 
2.4%
동 460
 
2.3%
방 413
 
2.1%
Other values (283) 11534
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16848
85.8%
Space Separator 1661
 
8.5%
Decimal Number 606
 
3.1%
Close Punctuation 189
 
1.0%
Open Punctuation 189
 
1.0%
Uppercase Letter 75
 
0.4%
Connector Punctuation 43
 
0.2%
Other Punctuation 9
 
< 0.1%
Lowercase Letter 6
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1920
 
11.4%
진 794
 
4.7%
입 747
 
4.4%
항 609
 
3.6%
단 509
 
3.0%
북 507
 
3.0%
서 474
 
2.8%
동 460
 
2.7%
방 413
 
2.5%
제 397
 
2.4%
Other values (251) 10018
59.5%
Uppercase Letter
ValueCountFrequency (%)
S 31
41.3%
T 12
 
16.0%
A 6
 
8.0%
C 5
 
6.7%
N 4
 
5.3%
D 3
 
4.0%
B 3
 
4.0%
M 3
 
4.0%
K 3
 
4.0%
G 1
 
1.3%
Other values (4) 4
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 173
28.5%
2 129
21.3%
6 71
11.7%
3 53
 
8.7%
7 52
 
8.6%
4 49
 
8.1%
5 39
 
6.4%
8 33
 
5.4%
9 7
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
o 4
66.7%
y 1
 
16.7%
u 1
 
16.7%
Space Separator
ValueCountFrequency (%)
1661
100.0%
Close Punctuation
ValueCountFrequency (%)
) 189
100.0%
Open Punctuation
ValueCountFrequency (%)
( 189
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 43
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16848
85.8%
Common 2699
 
13.8%
Latin 81
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1920
 
11.4%
진 794
 
4.7%
입 747
 
4.4%
항 609
 
3.6%
단 509
 
3.0%
북 507
 
3.0%
서 474
 
2.8%
동 460
 
2.7%
방 413
 
2.5%
제 397
 
2.4%
Other values (251) 10018
59.5%
Latin
ValueCountFrequency (%)
S 31
38.3%
T 12
 
14.8%
A 6
 
7.4%
C 5
 
6.2%
N 4
 
4.9%
o 4
 
4.9%
D 3
 
3.7%
B 3
 
3.7%
M 3
 
3.7%
K 3
 
3.7%
Other values (7) 7
 
8.6%
Common
ValueCountFrequency (%)
1661
61.5%
) 189
 
7.0%
( 189
 
7.0%
1 173
 
6.4%
2 129
 
4.8%
6 71
 
2.6%
3 53
 
2.0%
7 52
 
1.9%
4 49
 
1.8%
_ 43
 
1.6%
Other values (5) 90
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16848
85.8%
ASCII 2780
 
14.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 1920
 
11.4%
진 794
 
4.7%
입 747
 
4.4%
항 609
 
3.6%
단 509
 
3.0%
북 507
 
3.0%
서 474
 
2.8%
동 460
 
2.7%
방 413
 
2.5%
제 397
 
2.4%
Other values (251) 10018
59.5%
ASCII
ValueCountFrequency (%)
1661
59.7%
) 189
 
6.8%
( 189
 
6.8%
1 173
 
6.2%
2 129
 
4.6%
6 71
 
2.6%
3 53
 
1.9%
7 52
 
1.9%
4 49
 
1.8%
_ 43
 
1.5%
Other values (22) 171
 
6.2%

기항지명
Text

MISSING 

Distinct288
Distinct (%)16.3%
Missing8228
Missing (%)82.3%
Memory size156.2 KiB
2024-03-14T20:40:53.056993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.6044018
Min length2

Characters and Unicode

Total characters4615
Distinct characters207
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)2.3%

Sample

1st row금당
2nd row목포(북항)
3rd row여수
4th row산양
5th row율도(진도)
ValueCountFrequency (%)
통영 54
 
3.0%
목포 39
 
2.2%
백야 39
 
2.2%
낭도 24
 
1.4%
여수 24
 
1.4%
녹동 23
 
1.3%
흑일 21
 
1.2%
제도 21
 
1.2%
손죽 21
 
1.2%
개도 20
 
1.1%
Other values (278) 1486
83.9%
2024-03-14T20:40:55.567353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 519
 
11.2%
( 153
 
3.3%
) 153
 
3.3%
산 110
 
2.4%
동 109
 
2.4%
목 97
 
2.1%
진 87
 
1.9%
포 79
 
1.7%
항 78
 
1.7%
대 74
 
1.6%
Other values (197) 3156
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4286
92.9%
Open Punctuation 153
 
3.3%
Close Punctuation 153
 
3.3%
Other Punctuation 15
 
0.3%
Decimal Number 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 519
 
12.1%
산 110
 
2.6%
동 109
 
2.5%
목 97
 
2.3%
진 87
 
2.0%
포 79
 
1.8%
항 78
 
1.8%
대 74
 
1.7%
화 72
 
1.7%
리 61
 
1.4%
Other values (192) 3000
70.0%
Decimal Number
ValueCountFrequency (%)
1 6
75.0%
2 2
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 153
100.0%
Close Punctuation
ValueCountFrequency (%)
) 153
100.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4286
92.9%
Common 329
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 519
 
12.1%
산 110
 
2.6%
동 109
 
2.5%
목 97
 
2.3%
진 87
 
2.0%
포 79
 
1.8%
항 78
 
1.8%
대 74
 
1.7%
화 72
 
1.7%
리 61
 
1.4%
Other values (192) 3000
70.0%
Common
ValueCountFrequency (%)
( 153
46.5%
) 153
46.5%
. 15
 
4.6%
1 6
 
1.8%
2 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4286
92.9%
ASCII 329
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 519
 
12.1%
산 110
 
2.6%
동 109
 
2.5%
목 97
 
2.3%
진 87
 
2.0%
포 79
 
1.8%
항 78
 
1.8%
대 74
 
1.7%
화 72
 
1.7%
리 61
 
1.4%
Other values (192) 3000
70.0%
ASCII
ValueCountFrequency (%)
( 153
46.5%
) 153
46.5%
. 15
 
4.6%
1 6
 
1.8%
2 2
 
0.6%

Interactions

2024-03-14T20:40:39.442321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:40:37.797680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:40:38.634574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:40:39.723455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:40:38.080891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:40:38.907315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:40:39.990791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:40:38.351578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:40:39.166512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:40:55.724929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
변침점순번위도(LAT)경도(LON)
변침점순번1.0000.2710.205
위도(LAT)0.2711.0000.641
경도(LON)0.2050.6411.000
2024-03-14T20:40:55.872908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
변침점순번위도(LAT)경도(LON)
변침점순번1.000-0.077-0.130
위도(LAT)-0.0771.000-0.005
경도(LON)-0.130-0.0051.000

Missing values

2024-03-14T20:40:40.362713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:40:40.955680image/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.
2024-03-14T20:40:41.263951image/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

분류(ID)항로명변침점순번위도(LAT)경도(LON)변침점명기항지명
27368Q02-15-2우두금당-비견534.4298127.0774<NA><NA>
5311F05-03-2목포쉬미(기상특보)4634.6291126.192<NA><NA>
7130F13-07-2팽목-죽도(짝수일)1934.2598126.0849청등도 북동<NA>
27135Q02-09-2우두동송(충도 x)2534.4253127.0757금당금당
10280F24-26-2목읍-사-수-가-도1134.7694126.0507삼도<NA>
6899F11-06-1진도-창유-관매634.3172126.0232<NA><NA>
11486F36-13-2목포(남강가산수치)4934.8046126.3643목포(북항)목포(북항)
23832L14-03-1삼연-욕-삼(순)3134.652128.3473<NA><NA>
22826L06-05-2통제-의-관-통(순)434.8287128.4446금호충무마리나콘도<NA>
4262F02-02-2목포도초734.7812126.08<NA><NA>
분류(ID)항로명변침점순번위도(LAT)경도(LON)변침점명기항지명
9526F24-02-2목포남강가산수치남강734.7926126.3353<NA><NA>
26785Q02-02-1녹동-동송(2,5)734.464127.0949<NA><NA>
26481Q01-09-1녹동거문(서도)1534.2412127.2347<NA><NA>
12448F48-04-1봉리-재원(목섬)2035.0883126.1532<NA><NA>
5360F05-04-1목포율목-서거차(구)3834.5355126.1028<NA><NA>
22863L06-08-1통제-관-통(순)1634.8028128.4688<NA><NA>
26992Q02-07-1우두동송(신도 x)834.425127.0778비견비견
28230Q05-08-2녹동초도-손죽1334.2415127.2427대동1<NA>
16278H04-83-1낭도하화2934.5971127.6172<NA><NA>
12925F50-03-1제주추자233.5332126.5429제주항 입구<NA>