Overview

Dataset statistics

Number of variables10
Number of observations55
Missing cells193
Missing cells (%)35.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory88.4 B

Variable types

Numeric6
Categorical1
Text3

Dataset

Description지능형 해상교통정보시스템(바다내비)에서 도선사예선지원서비스(SV51)를 위한 항만가이드라인 항로(GIS)에 대한 데이터 테이블임
Author해양수산부
URLhttps://www.data.go.kr/data/15121382/fileData.do

Alerts

길이 is highly overall correlated with 수심 최소값 and 3 other fieldsHigh correlation
수심 최소값 is highly overall correlated with 길이 and 3 other fieldsHigh correlation
수심 최대값 is highly overall correlated with 길이 and 3 other fieldsHigh correlation
최소 너비 is highly overall correlated with 길이 and 3 other fieldsHigh correlation
최대 너비 is highly overall correlated with 길이 and 3 other fieldsHigh correlation
길이 has 32 (58.2%) missing valuesMissing
수심 최소값 has 29 (52.7%) missing valuesMissing
수심 최대값 has 29 (52.7%) missing valuesMissing
최소 너비 has 30 (54.5%) missing valuesMissing
최대 너비 has 30 (54.5%) missing valuesMissing
비고 has 40 (72.7%) missing valuesMissing
좌표 값(geometry) has 3 (5.5%) missing valuesMissing
길이 has 6 (10.9%) zerosZeros
수심 최소값 has 6 (10.9%) zerosZeros
수심 최대값 has 6 (10.9%) zerosZeros
최소 너비 has 6 (10.9%) zerosZeros
최대 너비 has 6 (10.9%) zerosZeros

Reproduction

Analysis started2024-03-16 04:13:30.092038
Analysis finished2024-03-16 04:13:34.637709
Duration4.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

Distinct13
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9090909
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-03-16T13:13:34.687005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile10.3
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0627555
Coefficient of variation (CV)0.7834956
Kurtosis1.100687
Mean3.9090909
Median Absolute Deviation (MAD)2
Skewness1.3131623
Sum215
Variance9.3804714
MonotonicityIncreasing
2024-03-16T13:13:34.791542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 12
21.8%
2 12
21.8%
3 8
14.5%
4 6
10.9%
5 5
9.1%
6 2
 
3.6%
7 2
 
3.6%
8 2
 
3.6%
9 2
 
3.6%
10 1
 
1.8%
Other values (3) 3
 
5.5%
ValueCountFrequency (%)
1 12
21.8%
2 12
21.8%
3 8
14.5%
4 6
10.9%
5 5
9.1%
6 2
 
3.6%
7 2
 
3.6%
8 2
 
3.6%
9 2
 
3.6%
10 1
 
1.8%
ValueCountFrequency (%)
13 1
 
1.8%
12 1
 
1.8%
11 1
 
1.8%
10 1
 
1.8%
9 2
 
3.6%
8 2
 
3.6%
7 2
 
3.6%
6 2
 
3.6%
5 5
9.1%
4 6
10.9%

항구 명
Categorical

Distinct12
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
인천
13 
부산
9 
대산
5 
여수
5 
울산
5 
Other values (7)
18 

Length

Max length5
Median length2
Mean length2.2181818
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row군산
2nd row대산
3rd row동해
4th row마산
5th row목포

Common Values

ValueCountFrequency (%)
인천 13
23.6%
부산 9
16.4%
대산 5
 
9.1%
여수 5
 
9.1%
울산 5
 
9.1%
평택·당진 4
 
7.3%
목포 3
 
5.5%
포항 3
 
5.5%
군산 2
 
3.6%
동해 2
 
3.6%
Other values (2) 4
 
7.3%

Length

2024-03-16T13:13:34.896577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천 13
23.6%
부산 9
16.4%
대산 5
 
9.1%
여수 5
 
9.1%
울산 5
 
9.1%
평택·당진 4
 
7.3%
목포 3
 
5.5%
포항 3
 
5.5%
군산 2
 
3.6%
동해 2
 
3.6%
Other values (2) 4
 
7.3%

항로 명
Text

Distinct42
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-03-16T13:13:35.139266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.5272727
Min length4

Characters and Unicode

Total characters359
Distinct characters84
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

Unique38 ?
Unique (%)69.1%

Sample

1st row군산항로
2nd row제1항로
3rd row동해항로
4th row제1항로
5th row목포항로
ValueCountFrequency (%)
제1항로 6
 
9.0%
제2항로 6
 
9.0%
제3항로 5
 
7.5%
제4항로 4
 
6.0%
감천항로 4
 
6.0%
항로 2
 
3.0%
신항항로 2
 
3.0%
제5항로 2
 
3.0%
시멘트부두진입항로 1
 
1.5%
북항항로 1
 
1.5%
Other values (34) 34
50.7%
2024-03-16T13:13:35.501646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 77
21.4%
로 61
17.0%
제 27
 
7.5%
( 15
 
4.2%
) 15
 
4.2%
12
 
3.3%
2 7
 
1.9%
1 7
 
1.9%
신 6
 
1.7%
천 6
 
1.7%
Other values (74) 126
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 292
81.3%
Decimal Number 25
 
7.0%
Open Punctuation 15
 
4.2%
Close Punctuation 15
 
4.2%
Space Separator 12
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 77
26.4%
로 61
20.9%
제 27
 
9.2%
신 6
 
2.1%
천 6
 
2.1%
경 5
 
1.7%
부 5
 
1.7%
측 4
 
1.4%
감 4
 
1.4%
두 4
 
1.4%
Other values (66) 93
31.8%
Decimal Number
ValueCountFrequency (%)
2 7
28.0%
1 7
28.0%
3 5
20.0%
4 4
16.0%
5 2
 
8.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 292
81.3%
Common 67
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 77
26.4%
로 61
20.9%
제 27
 
9.2%
신 6
 
2.1%
천 6
 
2.1%
경 5
 
1.7%
부 5
 
1.7%
측 4
 
1.4%
감 4
 
1.4%
두 4
 
1.4%
Other values (66) 93
31.8%
Common
ValueCountFrequency (%)
( 15
22.4%
) 15
22.4%
12
17.9%
2 7
10.4%
1 7
10.4%
3 5
 
7.5%
4 4
 
6.0%
5 2
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 292
81.3%
ASCII 67
 
18.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 77
26.4%
로 61
20.9%
제 27
 
9.2%
신 6
 
2.1%
천 6
 
2.1%
경 5
 
1.7%
부 5
 
1.7%
측 4
 
1.4%
감 4
 
1.4%
두 4
 
1.4%
Other values (66) 93
31.8%
ASCII
ValueCountFrequency (%)
( 15
22.4%
) 15
22.4%
12
17.9%
2 7
10.4%
1 7
10.4%
3 5
 
7.5%
4 4
 
6.0%
5 2
 
3.0%

길이
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)73.9%
Missing32
Missing (%)58.2%
Infinite0
Infinite (%)0.0%
Mean3.6421739
Minimum0
Maximum47
Zeros6
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-03-16T13:13:35.686300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median1.6
Q32.225
95-th percentile7.17
Maximum47
Range47
Interquartile range (IQR)2.125

Descriptive statistics

Standard deviation9.6375284
Coefficient of variation (CV)2.6460923
Kurtosis21.027016
Mean3.6421739
Median Absolute Deviation (MAD)1.4
Skewness4.5111507
Sum83.77
Variance92.881954
MonotonicityNot monotonic
2024-03-16T13:13:35.843062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 6
 
10.9%
1.6 2
 
3.6%
1.65 1
 
1.8%
47.0 1
 
1.8%
2.4 1
 
1.8%
1.0 1
 
1.8%
1.2 1
 
1.8%
1.77 1
 
1.8%
7.3 1
 
1.8%
3.4 1
 
1.8%
Other values (7) 7
 
12.7%
(Missing) 32
58.2%
ValueCountFrequency (%)
0.0 6
10.9%
0.2 1
 
1.8%
0.5 1
 
1.8%
1.0 1
 
1.8%
1.1 1
 
1.8%
1.2 1
 
1.8%
1.6 2
 
3.6%
1.65 1
 
1.8%
1.7 1
 
1.8%
1.77 1
 
1.8%
ValueCountFrequency (%)
47.0 1
1.8%
7.3 1
1.8%
6.0 1
1.8%
3.4 1
1.8%
3.3 1
1.8%
2.4 1
1.8%
2.05 1
1.8%
1.77 1
1.8%
1.7 1
1.8%
1.65 1
1.8%

수심 최소값
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)73.1%
Missing29
Missing (%)52.7%
Infinite0
Infinite (%)0.0%
Mean8.7653846
Minimum0
Maximum28
Zeros6
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-03-16T13:13:35.973930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.075
median8.5
Q312.525
95-th percentile20.5
Maximum28
Range28
Interquartile range (IQR)9.45

Descriptive statistics

Standard deviation7.2410465
Coefficient of variation (CV)0.82609569
Kurtosis0.53078958
Mean8.7653846
Median Absolute Deviation (MAD)4.95
Skewness0.70124692
Sum227.9
Variance52.432754
MonotonicityNot monotonic
2024-03-16T13:13:36.113883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 6
 
10.9%
5.6 2
 
3.6%
12.0 2
 
3.6%
28.0 1
 
1.8%
5.5 1
 
1.8%
11.3 1
 
1.8%
10.9 1
 
1.8%
5.4 1
 
1.8%
11.6 1
 
1.8%
9.0 1
 
1.8%
Other values (9) 9
 
16.4%
(Missing) 29
52.7%
ValueCountFrequency (%)
0.0 6
10.9%
2.3 1
 
1.8%
5.4 1
 
1.8%
5.5 1
 
1.8%
5.6 2
 
3.6%
6.0 1
 
1.8%
8.0 1
 
1.8%
9.0 1
 
1.8%
10.9 1
 
1.8%
11.3 1
 
1.8%
ValueCountFrequency (%)
28.0 1
1.8%
22.0 1
1.8%
16.0 1
1.8%
15.0 1
1.8%
14.8 1
1.8%
14.2 1
1.8%
12.7 1
1.8%
12.0 2
3.6%
11.6 1
1.8%
11.3 1
1.8%

수심 최대값
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)76.9%
Missing29
Missing (%)52.7%
Infinite0
Infinite (%)0.0%
Mean16.376923
Minimum0
Maximum76
Zeros6
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-03-16T13:13:36.261137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.15
median15.55
Q320.95
95-th percentile34.75
Maximum76
Range76
Interquartile range (IQR)14.8

Descriptive statistics

Standard deviation16.287131
Coefficient of variation (CV)0.99451718
Kurtosis6.2745684
Mean16.376923
Median Absolute Deviation (MAD)8.75
Skewness2.0068812
Sum425.8
Variance265.27065
MonotonicityNot monotonic
2024-03-16T13:13:36.432068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 6
 
10.9%
9.0 2
 
3.6%
16.0 1
 
1.8%
15.1 1
 
1.8%
17.5 1
 
1.8%
27.0 1
 
1.8%
30.0 1
 
1.8%
17.7 1
 
1.8%
5.5 1
 
1.8%
16.6 1
 
1.8%
Other values (10) 10
 
18.2%
(Missing) 29
52.7%
ValueCountFrequency (%)
0.0 6
10.9%
5.5 1
 
1.8%
8.1 1
 
1.8%
9.0 2
 
3.6%
10.0 1
 
1.8%
15.0 1
 
1.8%
15.1 1
 
1.8%
16.0 1
 
1.8%
16.5 1
 
1.8%
16.6 1
 
1.8%
ValueCountFrequency (%)
76.0 1
1.8%
35.0 1
1.8%
34.0 1
1.8%
30.0 1
1.8%
28.0 1
1.8%
27.0 1
1.8%
22.0 1
1.8%
17.8 1
1.8%
17.7 1
1.8%
17.5 1
1.8%

최소 너비
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)76.0%
Missing30
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean350.2
Minimum0
Maximum1595
Zeros6
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-03-16T13:13:36.665103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q155
median250
Q3450
95-th percentile1074
Maximum1595
Range1595
Interquartile range (IQR)395

Descriptive statistics

Standard deviation395.68632
Coefficient of variation (CV)1.1298867
Kurtosis3.1163044
Mean350.2
Median Absolute Deviation (MAD)200
Skewness1.6745425
Sum8755
Variance156567.67
MonotonicityNot monotonic
2024-03-16T13:13:36.804956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 6
 
10.9%
150 2
 
3.6%
700 1
 
1.8%
415 1
 
1.8%
160 1
 
1.8%
750 1
 
1.8%
1595 1
 
1.8%
100 1
 
1.8%
1150 1
 
1.8%
440 1
 
1.8%
Other values (9) 9
 
16.4%
(Missing) 30
54.5%
ValueCountFrequency (%)
0 6
10.9%
55 1
 
1.8%
100 1
 
1.8%
125 1
 
1.8%
150 2
 
3.6%
160 1
 
1.8%
250 1
 
1.8%
275 1
 
1.8%
340 1
 
1.8%
400 1
 
1.8%
ValueCountFrequency (%)
1595 1
1.8%
1150 1
1.8%
770 1
1.8%
750 1
1.8%
700 1
1.8%
480 1
1.8%
450 1
1.8%
440 1
1.8%
415 1
1.8%
400 1
1.8%

최대 너비
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)72.0%
Missing30
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean530.2
Minimum0
Maximum1915
Zeros6
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-03-16T13:13:36.984941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q155
median340
Q3820
95-th percentile1799
Maximum1915
Range1915
Interquartile range (IQR)765

Descriptive statistics

Standard deviation607.28302
Coefficient of variation (CV)1.1453848
Kurtosis0.2064871
Mean530.2
Median Absolute Deviation (MAD)340
Skewness1.168895
Sum13255
Variance368792.67
MonotonicityNot monotonic
2024-03-16T13:13:37.141236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 6
 
10.9%
400 2
 
3.6%
150 2
 
3.6%
1200 1
 
1.8%
1160 1
 
1.8%
160 1
 
1.8%
820 1
 
1.8%
1595 1
 
1.8%
100 1
 
1.8%
1915 1
 
1.8%
Other values (8) 8
 
14.5%
(Missing) 30
54.5%
ValueCountFrequency (%)
0 6
10.9%
55 1
 
1.8%
100 1
 
1.8%
150 2
 
3.6%
160 1
 
1.8%
275 1
 
1.8%
340 1
 
1.8%
400 2
 
3.6%
450 1
 
1.8%
500 1
 
1.8%
ValueCountFrequency (%)
1915 1
1.8%
1850 1
1.8%
1595 1
1.8%
1210 1
1.8%
1200 1
1.8%
1160 1
1.8%
820 1
1.8%
525 1
1.8%
500 1
1.8%
450 1
1.8%

비고
Text

MISSING 

Distinct14
Distinct (%)93.3%
Missing40
Missing (%)72.7%
Memory size572.0 B
2024-03-16T13:13:37.373275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length7.9333333
Min length2

Characters and Unicode

Total characters119
Distinct characters41
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

Unique13 ?
Unique (%)86.7%

Sample

1st row북항
2nd row북항, 내항, 남항, 연안항, 신항
3rd row남항
4th row경인항, 내항 (갑문 진출입 항로)
5th row감천항
ValueCountFrequency (%)
신항 4
 
13.8%
내항 3
 
10.3%
남항 2
 
6.9%
연안항 2
 
6.9%
북항 2
 
6.9%
부산항 1
 
3.4%
연안부두 1
 
3.4%
남항(인천대교 1
 
3.4%
석탄부두 1
 
3.4%
제1항로-제3항로 1
 
3.4%
Other values (11) 11
37.9%
2024-03-16T13:13:37.757253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 24
20.2%
14
 
11.8%
, 8
 
6.7%
신 4
 
3.4%
로 4
 
3.4%
북 4
 
3.4%
남 4
 
3.4%
연 4
 
3.4%
부 4
 
3.4%
두 3
 
2.5%
Other values (31) 46
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
73.9%
Space Separator 14
 
11.8%
Other Punctuation 8
 
6.7%
Close Punctuation 3
 
2.5%
Open Punctuation 3
 
2.5%
Decimal Number 2
 
1.7%
Dash Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 24
27.3%
신 4
 
4.5%
로 4
 
4.5%
북 4
 
4.5%
남 4
 
4.5%
연 4
 
4.5%
부 4
 
4.5%
두 3
 
3.4%
천 3
 
3.4%
인 3
 
3.4%
Other values (24) 31
35.2%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
73.9%
Common 31
 
26.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 24
27.3%
신 4
 
4.5%
로 4
 
4.5%
북 4
 
4.5%
남 4
 
4.5%
연 4
 
4.5%
부 4
 
4.5%
두 3
 
3.4%
천 3
 
3.4%
인 3
 
3.4%
Other values (24) 31
35.2%
Common
ValueCountFrequency (%)
14
45.2%
, 8
25.8%
) 3
 
9.7%
( 3
 
9.7%
3 1
 
3.2%
- 1
 
3.2%
1 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
73.9%
ASCII 31
 
26.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 24
27.3%
신 4
 
4.5%
로 4
 
4.5%
북 4
 
4.5%
남 4
 
4.5%
연 4
 
4.5%
부 4
 
4.5%
두 3
 
3.4%
천 3
 
3.4%
인 3
 
3.4%
Other values (24) 31
35.2%
ASCII
ValueCountFrequency (%)
14
45.2%
, 8
25.8%
) 3
 
9.7%
( 3
 
9.7%
3 1
 
3.2%
- 1
 
3.2%
1 1
 
3.2%

좌표 값(geometry)
Text

MISSING 

Distinct52
Distinct (%)100.0%
Missing3
Missing (%)5.5%
Memory size572.0 B
2024-03-16T13:13:38.114221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length785
Median length213.5
Mean length178.21154
Min length92

Characters and Unicode

Total characters9267
Distinct characters21
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)100.0%

Sample

1st rowPOLYGON((126.3583 35.9703,126.4644 35.9703,126.4911 35.9775,126.5086 35.9842,126.5311 35.985,126.5494 35.9836,126.5692 35.9828,126.5936 35.9839,126.6339 35.9853,126.6353 35.9822,126.5503 35.9772,126.5317 35.9767,126.5133 35.9761,126.4644 35.9569,126.3583 35.9569,126.3583 35.9703))
2nd rowPOLYGON((126.2804 36.9984,126.3283 37.0276,126.3369 37.0283,126.3808 37.0456,126.3907 37.0451,126.4017 37.0333,126.4133 37.0267,126.4236 37.022,126.4211 37.02,126.3975 37.0296,126.3862 37.0321,126.3696 37.0324,126.3546 37.0262,126.2885 36.9918,126.3756 37.0349,126.3846 37.0387,126.3871 37.039,126.3912 37.0344,126.2804 36.9984))
3rd rowPOLYGON((129.1497 37.4975,129.1594 37.4939,129.1728 37.4939,129.1728 37.4914,129.1594 37.4914,129.1447 37.4967,129.1497 37.4975))
4th rowPOLYGON((128.6006 35.146,128.5936 35.1614,128.5883 35.1686,128.5742 35.1777,128.5756 35.1792,128.5875 35.1714,128.5943 35.1672,128.5953 35.1649,128.5967 35.1631,128.6053 35.1464,128.6006 35.146))
5th rowPOLYGON((126.3183 34.7578,126.3189 34.7583,126.3397 34.7811,126.3436 34.7986,126.35 34.7986,126.35 34.7939,126.3594 34.7911,126.3689 34.7803,126.3744 34.7783,126.3744 34.7725,126.3647 34.7756,126.3553 34.7867,126.3492 34.7867,126.3458 34.7792,126.3283 34.7597,126.3283 34.7536,126.3383 34.7486,126.3383 34.7425,126.3175 34.7525,126.3183 34.7578))
ValueCountFrequency (%)
37.51,126.6004 3
 
0.5%
37.3408,126.6414 2
 
0.4%
35.9569,126.3583 2
 
0.4%
37.4939,129.1728 2
 
0.4%
37.5606 2
 
0.4%
37.0536,126.3433 2
 
0.4%
34.8653,127.7531 2
 
0.4%
37.0361,126.3425 2
 
0.4%
35.040833 2
 
0.4%
37.0106,126.7044 2
 
0.4%
Other values (538) 541
96.3%
2024-03-16T13:13:38.643544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 1053
11.4%
. 1020
11.0%
1 889
9.6%
6 826
8.9%
2 806
8.7%
7 731
7.9%
5 642
 
6.9%
510
 
5.5%
4 506
 
5.5%
9 480
 
5.2%
Other values (11) 1804
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6707
72.4%
Other Punctuation 1478
 
15.9%
Space Separator 510
 
5.5%
Uppercase Letter 364
 
3.9%
Close Punctuation 104
 
1.1%
Open Punctuation 104
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1053
15.7%
1 889
13.3%
6 826
12.3%
2 806
12.0%
7 731
10.9%
5 642
9.6%
4 506
7.5%
9 480
7.2%
8 406
 
6.1%
0 368
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
O 104
28.6%
N 52
14.3%
G 52
14.3%
Y 52
14.3%
L 52
14.3%
P 52
14.3%
Other Punctuation
ValueCountFrequency (%)
. 1020
69.0%
, 458
31.0%
Space Separator
ValueCountFrequency (%)
510
100.0%
Close Punctuation
ValueCountFrequency (%)
) 104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8903
96.1%
Latin 364
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1053
11.8%
. 1020
11.5%
1 889
10.0%
6 826
9.3%
2 806
9.1%
7 731
8.2%
5 642
7.2%
510
 
5.7%
4 506
 
5.7%
9 480
 
5.4%
Other values (5) 1440
16.2%
Latin
ValueCountFrequency (%)
O 104
28.6%
N 52
14.3%
G 52
14.3%
Y 52
14.3%
L 52
14.3%
P 52
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1053
11.4%
. 1020
11.0%
1 889
9.6%
6 826
8.9%
2 806
8.7%
7 731
7.9%
5 642
 
6.9%
510
 
5.5%
4 506
 
5.5%
9 480
 
5.2%
Other values (11) 1804
19.5%

Interactions

2024-03-16T13:13:33.355778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:30.591652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.200755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.667235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.345778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.877089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.644161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:30.708240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.276699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.763591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.438675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.962666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.709308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:30.808343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.341194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.884002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.542566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.031326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.781377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:30.881458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.413159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.030348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.618851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.098151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.854016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:30.969140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.493074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.155324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.697643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.176810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.942500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.080058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:31.578880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.260879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:32.780288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:33.254087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:13:38.832630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번항구 명항로 명길이수심 최소값수심 최대값최소 너비최대 너비비고좌표 값(geometry)
순번1.0000.0000.9930.0000.0000.0000.4380.0000.8951.000
항구 명0.0001.0000.5060.2280.5160.2290.0000.1071.0001.000
항로 명0.9930.5061.0000.0000.0000.4800.0000.0001.0001.000
길이0.0000.2280.0001.0000.5590.9300.5410.7120.3311.000
수심 최소값0.0000.5160.0000.5591.0000.5750.7280.5231.0001.000
수심 최대값0.0000.2290.4800.9300.5751.0000.5210.6761.0001.000
최소 너비0.4380.0000.0000.5410.7280.5211.0000.9230.9471.000
최대 너비0.0000.1070.0000.7120.5230.6760.9231.0001.0001.000
비고0.8951.0001.0000.3311.0001.0000.9471.0001.0001.000
좌표 값(geometry)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-03-16T13:13:39.081671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번길이수심 최소값수심 최대값최소 너비최대 너비항구 명
순번1.000-0.247-0.254-0.286-0.147-0.2620.000
길이-0.2471.0000.5010.8180.6250.6790.196
수심 최소값-0.2540.5011.0000.6670.7570.6870.350
수심 최대값-0.2860.8180.6671.0000.7750.8720.120
최소 너비-0.1470.6250.7570.7751.0000.9290.000
최대 너비-0.2620.6790.6870.8720.9291.0000.000
항구 명0.0000.1960.3500.1200.0000.0001.000

Missing values

2024-03-16T13:13:34.109270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:13:34.404506image/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.
2024-03-16T13:13:34.548137image/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

순번항구 명항로 명길이수심 최소값수심 최대값최소 너비최대 너비비고좌표 값(geometry)
01군산군산항로0.00.00.000<NA>POLYGON((126.3583 35.9703,126.4644 35.9703,126.4911 35.9775,126.5086 35.9842,126.5311 35.985,126.5494 35.9836,126.5692 35.9828,126.5936 35.9839,126.6339 35.9853,126.6353 35.9822,126.5503 35.9772,126.5317 35.9767,126.5133 35.9761,126.4644 35.9569,126.3583 35.9569,126.3583 35.9703))
11대산제1항로<NA><NA><NA><NA><NA><NA>POLYGON((126.2804 36.9984,126.3283 37.0276,126.3369 37.0283,126.3808 37.0456,126.3907 37.0451,126.4017 37.0333,126.4133 37.0267,126.4236 37.022,126.4211 37.02,126.3975 37.0296,126.3862 37.0321,126.3696 37.0324,126.3546 37.0262,126.2885 36.9918,126.3756 37.0349,126.3846 37.0387,126.3871 37.039,126.3912 37.0344,126.2804 36.9984))
21동해동해항로<NA><NA><NA><NA><NA><NA>POLYGON((129.1497 37.4975,129.1594 37.4939,129.1728 37.4939,129.1728 37.4914,129.1594 37.4914,129.1447 37.4967,129.1497 37.4975))
31마산제1항로<NA><NA><NA><NA><NA><NA>POLYGON((128.6006 35.146,128.5936 35.1614,128.5883 35.1686,128.5742 35.1777,128.5756 35.1792,128.5875 35.1714,128.5943 35.1672,128.5953 35.1649,128.5967 35.1631,128.6053 35.1464,128.6006 35.146))
41목포목포항로<NA><NA><NA><NA><NA><NA>POLYGON((126.3183 34.7578,126.3189 34.7583,126.3397 34.7811,126.3436 34.7986,126.35 34.7986,126.35 34.7939,126.3594 34.7911,126.3689 34.7803,126.3744 34.7783,126.3744 34.7725,126.3647 34.7756,126.3553 34.7867,126.3492 34.7867,126.3458 34.7792,126.3283 34.7597,126.3283 34.7536,126.3383 34.7486,126.3383 34.7425,126.3175 34.7525,126.3183 34.7578))
51부산제1항로 (부산항로)3.414.834.0340340북항POLYGON((129.061056 35.105889,129.109972 35.077389,129.114111 35.070056,129.119389 35.078361,129.113028 35.079389,129.063306 35.108389,129.061056 35.105889))
61여수제1항로1.6528.028.07001200<NA>POLYGON((127.7936 34.8433,127.7769 34.8633,127.7675 34.8689,127.7858 34.8653,127.7931 34.8594,127.8014 34.8544,127.8022 34.8464,127.7936 34.8433))
71울산제1항로<NA><NA><NA><NA><NA><NA>POLYGON((129.4181 35.4031,129.3942 35.4664,129.392 35.4912,129.3891 35.5139,129.3923 35.5064,129.3951 35.4927,129.3996 35.4664,129.3908 35.4031,129.4181 35.4031))
81인천제1항로47.08.076.04151850북항, 내항, 남항, 연안항, 신항POLYGON((126.4589 37.3394,126.5164 37.3475,126.5247 37.3578,126.5293 37.3554,126.5194 37.3434,126.4731 37.3344,126.4689 37.3289,126.5196 37.3604,126.5125 37.3519,126.4525 37.3489,126.5196 37.3604,126.5677 37.4204,126.5885 37.4842,126.5957 37.4989,126.5957 37.51,126.6004 37.51,126.6004 37.4947,126.5935 37.4828,126.5772 37.4317,126.5741 37.4187,126.5293 37.3554,126.4589 37.3394))
91제주제주항로<NA><NA><NA><NA><NA><NA>POLYGON((126.5358 33.5233,126.5391 33.5255,126.5399 33.5298,126.5421 33.533,126.5424 33.5319,126.5408 33.5294,126.5399 33.5249,126.5363 33.5228,126.5358 33.5233))
순번항구 명항로 명길이수심 최소값수심 최대값최소 너비최대 너비비고좌표 값(geometry)
457부산입항항로 (감천항로)0.00.00.000<NA><NA>
467인천제4항로0.511.630.015951595제1항로-제3항로POLYGON((126.4791 37.3356,126.4897 37.3293,126.5081 37.3303,126.5 37.3396,126.4791 37.3356))
478부산출항항로 (감천항로)0.00.00.000<NA>POLYGON((129.015194 35.04675,129.028139 35.03775,129.025694 35.031722,129.016667 35.040833,129.015194 35.04675))
488인천연안여객선항로6.05.427.0750820연안항POLYGON((126.5623 37.4137,126.5111 37.3903,126.4448 37.3569,126.4496 37.3512,126.5111 37.3819,126.5514 37.4001,126.5623 37.4137))
499부산분리대 (감천항로)0.00.00.000<NA>POLYGON((129.016667 35.040833,129.022889 35.022694,129.015722 35.021278,129.01025 35.04575,129.016667 35.040833))
509인천동측측경간항로3.310.917.5150150석탄부두, 남항(인천대교)POLYGON((126.5851 37.6231,126.5747 37.414,126.5707 37.4083,126.5621 37.4017,126.5649 37.4058,126.5692 37.409,126.5731 37.4144,126.5741 37.4187,126.5772 37.4317,126.5851 37.6231))
5110인천서측측경간항로1.111.315.1160160연안부두, 내항, 북항(인천대교)POLYGON((126.5707 37.4297,126.5585 37.414,126.5541 37.4099,126.5594 37.4123,126.5692 37.425,126.5707 37.4297))
5211인천제1항로(경인항)<NA><NA><NA><NA><NA><NA>POLYGON((126.5914 37.5606,126.5817 37.5605,126.5797 37.5602,126.5776 37.559,126.5766 37.5574,126.5764 37.5558,126.5769 37.5544,126.5833 37.5418,126.5832 37.5409,126.5976 37.5181,126.5957 37.51,126.6004 37.51,126.5982 37.5181,126.5863 37.5419,126.5854 37.5425,126.5824 37.5484,126.5852 37.5536,126.5937 37.5567,126.5914 37.5606))
5312인천제2항로(경인항)<NA><NA><NA><NA><NA><NA>POLYGON((126.5914 37.5606,126.5937 37.5567,126.6017 37.5601,126.6014 37.5608,126.5914 37.5606))
5413인천경인아라뱃길 항로<NA><NA><NA><NA><NA><NA>POLYGON((126.6171 37.5648,126.6261 37.5673,126.6322 37.569,126.6362 37.5701,126.6375 37.5704,126.6389 37.5707,126.6405 37.5708,126.6446 37.5711,126.6476 37.5712,126.6509 37.5714,126.6572 37.5719,126.6635 37.5724,126.665 37.5724,126.6667 37.5723,126.6699 37.5721,126.6756 37.5715,126.6781 37.5714,126.6804 37.5713,126.6846 37.5713,126.6896 37.5714,126.6911 37.5714,126.6927 37.5715,126.7008 37.5718,126.7091 37.5722,126.7161 37.5726,126.7198 37.5728,126.7229 37.5729,126.729 37.5731,126.7315 37.5733,126.7341 37.5734,126.7372 37.5736,126.7391 37.5737,126.7456 37.5738,126.7635 37.5739,126.7533 37.5744,126.7555 37.5746,126.7569 37.5748,126.7584 37.5751,126.7617 37.5764,126.7627 37.577,126.764 37.5779,126.7667 37.5799,126.775 37.5863,126.781 37.5908,126.7834 37.5927,126.6171 37.5648))