Overview

Dataset statistics

Number of variables14
Number of observations55
Missing cells190
Missing cells (%)24.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 KiB
Average record size in memory120.4 B

Variable types

Categorical6
Numeric6
Text2

Dataset

Description지능형 해상교통정보시스템(바다내비)에서 도선사예선지원서비스(SV51)를 위한 항만가이드라인 항로 이력에 대한 데이터 테이블임
Author해양수산부
URLhttps://www.data.go.kr/data/15121302/fileData.do

Alerts

이력 테이블 아이디 has constant value ""Constant
등록자 아이디 has constant value ""Constant
등록 일시 has constant value ""Constant
수정자 아이디 has constant value ""Constant
수정 일시 has constant value ""Constant
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
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 started2023-12-13 00:01:17.989943
Analysis finished2023-12-13 00:01:21.408267
Duration3.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

이력 테이블 아이디
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
2020-04-22 15:08
55 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-04-22 15:08
2nd row2020-04-22 15:08
3rd row2020-04-22 15:08
4th row2020-04-22 15:08
5th row2020-04-22 15:08

Common Values

ValueCountFrequency (%)
2020-04-22 15:08 55
100.0%

Length

2023-12-13T09:01:21.458206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:01:21.538226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-04-22 55
50.0%
15:08 55
50.0%

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
2023-12-13T09:01:21.620060image/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
2023-12-13T09:01:21.707855image/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

2023-12-13T09:01:21.800256image/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
2023-12-13T09:01:21.964111image/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%
2023-12-13T09:01:22.256835image/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
2023-12-13T09:01:22.363406image/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
2023-12-13T09:01:22.448312image/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
2023-12-13T09:01:22.532027image/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
2023-12-13T09:01:22.613636image/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
2023-12-13T09:01:22.701315image/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
2023-12-13T09:01:22.794708image/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
2023-12-13T09:01:22.887141image/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
2023-12-13T09:01:22.997617image/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
2023-12-13T09:01:23.103561image/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
2023-12-13T09:01:23.215550image/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
2023-12-13T09:01:23.373385image/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%
2023-12-13T09:01:23.647889image/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%

등록자 아이디
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
dsp
55 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
dsp 55
100.0%

Length

2023-12-13T09:01:23.761648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:01:23.832853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
dsp 55
100.0%

등록 일시
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
2020-04-22 15:08
55 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-04-22 15:08
2nd row2020-04-22 15:08
3rd row2020-04-22 15:08
4th row2020-04-22 15:08
5th row2020-04-22 15:08

Common Values

ValueCountFrequency (%)
2020-04-22 15:08 55
100.0%

Length

2023-12-13T09:01:23.909075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:01:23.981694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-04-22 55
50.0%
15:08 55
50.0%

수정자 아이디
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
dsp
55 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
dsp 55
100.0%

Length

2023-12-13T09:01:24.058656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:01:24.140160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
dsp 55
100.0%

수정 일시
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
2020-04-22 15:08
55 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-04-22 15:08
2nd row2020-04-22 15:08
3rd row2020-04-22 15:08
4th row2020-04-22 15:08
5th row2020-04-22 15:08

Common Values

ValueCountFrequency (%)
2020-04-22 15:08 55
100.0%

Length

2023-12-13T09:01:24.223201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:01:24.301635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-04-22 55
50.0%
15:08 55
50.0%

Interactions

2023-12-13T09:01:20.353674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.339551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.708757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.112343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.516156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.935184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.433088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.398021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.764560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.176388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.581767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.003107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.508854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.454155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.817780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.235314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.646055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.067536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.571808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.511609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.891834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.297635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.709652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.132020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.645425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.576023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.968346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.366332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.781125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.205993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.714844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:18.642149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.040967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.437333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:19.851382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:01:20.277745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:01:24.354858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번항구 명항로 명길이수심 최소값수심 최대값최소 너비최대 너비비고
순번1.0000.0000.9930.0000.0000.0000.4380.0000.895
항구 명0.0001.0000.5060.2280.5160.2290.0000.1071.000
항로 명0.9930.5061.0000.0000.0000.4800.0000.0001.000
길이0.0000.2280.0001.0000.5590.9300.5410.7120.331
수심 최소값0.0000.5160.0000.5591.0000.5750.7280.5231.000
수심 최대값0.0000.2290.4800.9300.5751.0000.5210.6761.000
최소 너비0.4380.0000.0000.5410.7280.5211.0000.9230.947
최대 너비0.0000.1070.0000.7120.5230.6760.9231.0001.000
비고0.8951.0001.0000.3311.0001.0000.9471.0001.000
2023-12-13T09:01:24.456355image/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

2023-12-13T09:01:20.815216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:01:21.242257image/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.
2023-12-13T09:01:21.343404image/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

이력 테이블 아이디순번항구 명항로 명길이수심 최소값수심 최대값최소 너비최대 너비비고등록자 아이디등록 일시수정자 아이디수정 일시
02020-04-22 15:081군산군산항로0.00.00.000<NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
12020-04-22 15:081대산제1항로<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
22020-04-22 15:081동해동해항로<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
32020-04-22 15:081마산제1항로<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
42020-04-22 15:081목포목포항로<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
52020-04-22 15:081부산제1항로 (부산항로)3.414.834.0340340북항dsp2020-04-22 15:08dsp2020-04-22 15:08
62020-04-22 15:081여수제1항로1.6528.028.07001200<NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
72020-04-22 15:081울산제1항로<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
82020-04-22 15:081인천제1항로47.08.076.04151850북항, 내항, 남항, 연안항, 신항dsp2020-04-22 15:08dsp2020-04-22 15:08
92020-04-22 15:081제주제주항로<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
이력 테이블 아이디순번항구 명항로 명길이수심 최소값수심 최대값최소 너비최대 너비비고등록자 아이디등록 일시수정자 아이디수정 일시
452020-04-22 15:087부산입항항로 (감천항로)0.00.00.000<NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
462020-04-22 15:087인천제4항로0.511.630.015951595제1항로-제3항로dsp2020-04-22 15:08dsp2020-04-22 15:08
472020-04-22 15:088부산출항항로 (감천항로)0.00.00.000<NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
482020-04-22 15:088인천연안여객선항로6.05.427.0750820연안항dsp2020-04-22 15:08dsp2020-04-22 15:08
492020-04-22 15:089부산분리대 (감천항로)0.00.00.000<NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
502020-04-22 15:089인천동측측경간항로3.310.917.5150150석탄부두, 남항(인천대교)dsp2020-04-22 15:08dsp2020-04-22 15:08
512020-04-22 15:0810인천서측측경간항로1.111.315.1160160연안부두, 내항, 북항(인천대교)dsp2020-04-22 15:08dsp2020-04-22 15:08
522020-04-22 15:0811인천제1항로(경인항)<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
532020-04-22 15:0812인천제2항로(경인항)<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08
542020-04-22 15:0813인천경인아라뱃길 항로<NA><NA><NA><NA><NA><NA>dsp2020-04-22 15:08dsp2020-04-22 15:08