Overview

Dataset statistics

Number of variables11
Number of observations5607
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory509.4 KiB
Average record size in memory93.0 B

Variable types

Numeric5
Categorical4
Text2

Dataset

Description공단의 선박검사 접수현황에 데이터로 아래와 같은 데이터를 제공하고 있습니다.일반선 검사현황(구분, 총톤수, 길이, 너비, 깊이, 선질, 용도, 세부용도, 항행구역, 선적항)공공데이터 활용도 제고를 위한 목록(항행구역) 추가 후 업데이트
Author한국해양교통안전공단
URLhttps://www.data.go.kr/data/15049853/fileData.do

Alerts

구분 has constant value ""Constant
총톤수 is highly overall correlated with 길이 and 2 other fieldsHigh correlation
길이 is highly overall correlated with 총톤수 and 2 other fieldsHigh correlation
너비 is highly overall correlated with 총톤수 and 2 other fieldsHigh correlation
깊이 is highly overall correlated with 총톤수 and 2 other fieldsHigh correlation
선질 is highly imbalanced (53.1%)Imbalance
총톤수 is highly skewed (γ1 = 39.18198818)Skewed
순번 has unique valuesUnique
너비 has 203 (3.6%) zerosZeros
깊이 has 209 (3.7%) zerosZeros

Reproduction

Analysis started2023-12-12 02:47:10.903502
Analysis finished2023-12-12 02:47:14.856855
Duration3.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct5607
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2804
Minimum1
Maximum5607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.4 KiB
2023-12-12T11:47:14.971383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile281.3
Q11402.5
median2804
Q34205.5
95-th percentile5326.7
Maximum5607
Range5606
Interquartile range (IQR)2803

Descriptive statistics

Standard deviation1618.7458
Coefficient of variation (CV)0.57729879
Kurtosis-1.2
Mean2804
Median Absolute Deviation (MAD)1402
Skewness0
Sum15722028
Variance2620338
MonotonicityStrictly increasing
2023-12-12T11:47:15.174121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3736 1
 
< 0.1%
3744 1
 
< 0.1%
3743 1
 
< 0.1%
3742 1
 
< 0.1%
3741 1
 
< 0.1%
3740 1
 
< 0.1%
3739 1
 
< 0.1%
3738 1
 
< 0.1%
3737 1
 
< 0.1%
Other values (5597) 5597
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5607 1
< 0.1%
5606 1
< 0.1%
5605 1
< 0.1%
5604 1
< 0.1%
5603 1
< 0.1%
5602 1
< 0.1%
5601 1
< 0.1%
5600 1
< 0.1%
5599 1
< 0.1%
5598 1
< 0.1%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
일반선
5607 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반선 5607
100.0%

Length

2023-12-12T11:47:15.371037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:15.495254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반선 5607
100.0%

총톤수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1537
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.15678
Minimum0
Maximum137746
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.4 KiB
2023-12-12T11:47:15.620183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.98
Q19.16
median29
Q3136
95-th percentile1146.7
Maximum137746
Range137746
Interquartile range (IQR)126.84

Descriptive statistics

Standard deviation2536.0478
Coefficient of variation (CV)8.393152
Kurtosis1853.0741
Mean302.15678
Median Absolute Deviation (MAD)27
Skewness39.181988
Sum1694193
Variance6431538.3
MonotonicityNot monotonic
2023-12-12T11:47:15.785036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.0 172
 
3.1%
24.0 116
 
2.1%
12.0 99
 
1.8%
10.0 90
 
1.6%
17.0 88
 
1.6%
13.0 85
 
1.5%
21.0 71
 
1.3%
19.0 70
 
1.2%
11.0 64
 
1.1%
14.0 60
 
1.1%
Other values (1527) 4692
83.7%
ValueCountFrequency (%)
0.0 6
0.1%
0.16 1
 
< 0.1%
0.18 1
 
< 0.1%
0.23 2
 
< 0.1%
0.25 2
 
< 0.1%
0.27 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3 2
 
< 0.1%
0.31 2
 
< 0.1%
0.32 3
0.1%
ValueCountFrequency (%)
137746.0 1
< 0.1%
88594.0 1
< 0.1%
48676.0 1
< 0.1%
45293.0 1
< 0.1%
37308.0 1
< 0.1%
26014.0 1
< 0.1%
24639.0 1
< 0.1%
11598.0 1
< 0.1%
11572.0 1
< 0.1%
9794.0 1
< 0.1%

길이
Real number (ℝ)

HIGH CORRELATION 

Distinct2656
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.29147
Minimum0
Maximum302
Zeros26
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size49.4 KiB
2023-12-12T11:47:15.962118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.713
Q111.95
median20.08
Q331.04
95-th percentile65.29
Maximum302
Range302
Interquartile range (IQR)19.09

Descriptive statistics

Standard deviation20.254829
Coefficient of variation (CV)0.80085615
Kurtosis18.784381
Mean25.29147
Median Absolute Deviation (MAD)8.94
Skewness2.7913721
Sum141809.27
Variance410.2581
MonotonicityNot monotonic
2023-12-12T11:47:16.125235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
0.5%
57.62 24
 
0.4%
5.86 23
 
0.4%
21.16 21
 
0.4%
8.0 19
 
0.3%
48.02 18
 
0.3%
38.42 18
 
0.3%
43.22 17
 
0.3%
6.84 16
 
0.3%
87.86 16
 
0.3%
Other values (2646) 5409
96.5%
ValueCountFrequency (%)
0.0 26
0.5%
3.65 1
 
< 0.1%
3.8 1
 
< 0.1%
3.91 1
 
< 0.1%
4.02 1
 
< 0.1%
4.08 1
 
< 0.1%
4.1 1
 
< 0.1%
4.24 1
 
< 0.1%
4.26 1
 
< 0.1%
4.37 1
 
< 0.1%
ValueCountFrequency (%)
302.0 1
< 0.1%
283.74 1
< 0.1%
253.5 1
< 0.1%
216.0 1
< 0.1%
190.05 1
< 0.1%
177.97 1
< 0.1%
174.7 1
< 0.1%
150.35 1
< 0.1%
145.5 1
< 0.1%
133.14 1
< 0.1%

너비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct592
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6808008
Minimum0
Maximum58
Zeros203
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size49.4 KiB
2023-12-12T11:47:16.303070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7
Q13.3
median4.8
Q38
95-th percentile19
Maximum58
Range58
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation5.5594202
Coefficient of variation (CV)0.83214878
Kurtosis6.3777559
Mean6.6808008
Median Absolute Deviation (MAD)2.03
Skewness2.0906997
Sum37459.25
Variance30.907153
MonotonicityNot monotonic
2023-12-12T11:47:16.447285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 203
 
3.6%
4.0 114
 
2.0%
4.5 109
 
1.9%
6.0 108
 
1.9%
5.0 107
 
1.9%
9.0 102
 
1.8%
7.0 101
 
1.8%
4.2 94
 
1.7%
8.0 87
 
1.6%
3.5 86
 
1.5%
Other values (582) 4496
80.2%
ValueCountFrequency (%)
0.0 203
3.6%
0.74 1
 
< 0.1%
1.0 1
 
< 0.1%
1.19 1
 
< 0.1%
1.4 2
 
< 0.1%
1.44 2
 
< 0.1%
1.45 3
 
0.1%
1.46 1
 
< 0.1%
1.48 1
 
< 0.1%
1.5 3
 
0.1%
ValueCountFrequency (%)
58.0 1
< 0.1%
50.0 1
< 0.1%
48.64 1
< 0.1%
47.17 1
< 0.1%
45.0 2
< 0.1%
41.0 1
< 0.1%
36.0 2
< 0.1%
35.0 1
< 0.1%
32.26 1
< 0.1%
32.2 1
< 0.1%

깊이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct439
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2423506
Minimum0
Maximum31.05
Zeros209
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size49.4 KiB
2023-12-12T11:47:16.621346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11.15
median2
Q33
95-th percentile4.5
Maximum31.05
Range31.05
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation1.6667555
Coefficient of variation (CV)0.74330725
Kurtosis58.607821
Mean2.2423506
Median Absolute Deviation (MAD)0.9
Skewness4.8415817
Sum12572.86
Variance2.7780738
MonotonicityNot monotonic
2023-12-12T11:47:16.783189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 209
 
3.7%
3.0 182
 
3.2%
2.0 153
 
2.7%
2.5 149
 
2.7%
3.5 141
 
2.5%
1.8 122
 
2.2%
2.2 111
 
2.0%
1.6 110
 
2.0%
2.3 108
 
1.9%
2.1 102
 
1.8%
Other values (429) 4220
75.3%
ValueCountFrequency (%)
0.0 209
3.7%
0.03 2
 
< 0.1%
0.05 1
 
< 0.1%
0.08 1
 
< 0.1%
0.2 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3 1
 
< 0.1%
0.32 2
 
< 0.1%
0.34 2
 
< 0.1%
0.35 3
 
0.1%
ValueCountFrequency (%)
31.05 1
< 0.1%
28.3 1
< 0.1%
27.0 1
< 0.1%
24.1 1
< 0.1%
24.0 1
< 0.1%
23.0 1
< 0.1%
21.0 1
< 0.1%
20.2 2
< 0.1%
15.75 1
< 0.1%
12.0 1
< 0.1%

선질
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
강
3476 
FRP
1847 
알루미늄합금
 
229
목
 
53
시멘트
 
1

Length

Max length6
Median length1
Mean length1.8635634
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowFRP
2nd rowFRP
3rd row강
4th rowFRP
5th rowFRP

Common Values

ValueCountFrequency (%)
강 3476
62.0%
FRP 1847
32.9%
알루미늄합금 229
 
4.1%
목 53
 
0.9%
시멘트 1
 
< 0.1%
기타 1
 
< 0.1%

Length

2023-12-12T11:47:16.961729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:17.101679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강 3476
62.0%
frp 1847
32.9%
알루미늄합금 229
 
4.1%
목 53
 
0.9%
시멘트 1
 
< 0.1%
기타 1
 
< 0.1%

용도
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
기타선
3479 
부선
825 
여객선
529 
유조선
529 
화물선
 
213
Other values (4)
 
32

Length

Max length5
Median length3
Mean length2.859283
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row기타선
2nd row기타선
3rd row여객선
4th row기타선
5th row기타선

Common Values

ValueCountFrequency (%)
기타선 3479
62.0%
부선 825
 
14.7%
여객선 529
 
9.4%
유조선 529
 
9.4%
화물선 213
 
3.8%
경찰용선박 15
 
0.3%
예인선 14
 
0.2%
일반화물선 2
 
< 0.1%
산적화물선 1
 
< 0.1%

Length

2023-12-12T11:47:17.250837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:17.391643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타선 3479
62.0%
부선 825
 
14.7%
여객선 529
 
9.4%
유조선 529
 
9.4%
화물선 213
 
3.8%
경찰용선박 15
 
0.3%
예인선 14
 
0.2%
일반화물선 2
 
< 0.1%
산적화물선 1
 
< 0.1%

용도1
Text

Distinct78
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
2023-12-12T11:47:17.657480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length4.1635456
Min length2

Characters and Unicode

Total characters23345
Distinct characters107
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.2%

Sample

1st row기타선
2nd row기타선
3rd row유선
4th row기타선
5th row기타선
ValueCountFrequency (%)
기타선 1020
16.5%
예인선 705
 
11.4%
통선 521
 
8.4%
석유제품운반선 460
 
7.5%
유선 425
 
6.9%
작업선 364
 
5.9%
일반부선 275
 
4.5%
도선 191
 
3.1%
및 163
 
2.6%
플레저보트 151
 
2.4%
Other values (72) 1891
30.7%
2023-12-12T11:47:18.124862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
선 5531
23.7%
기 1342
 
5.7%
타 1114
 
4.8%
반 1057
 
4.5%
유 1021
 
4.4%
부 774
 
3.3%
인 770
 
3.3%
예 749
 
3.2%
운 665
 
2.8%
작 629
 
2.7%
Other values (97) 9693
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22786
97.6%
Space Separator 559
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 5531
24.3%
기 1342
 
5.9%
타 1114
 
4.9%
반 1057
 
4.6%
유 1021
 
4.5%
부 774
 
3.4%
인 770
 
3.4%
예 749
 
3.3%
운 665
 
2.9%
작 629
 
2.8%
Other values (96) 9134
40.1%
Space Separator
ValueCountFrequency (%)
559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22786
97.6%
Common 559
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 5531
24.3%
기 1342
 
5.9%
타 1114
 
4.9%
반 1057
 
4.6%
유 1021
 
4.5%
부 774
 
3.4%
인 770
 
3.4%
예 749
 
3.3%
운 665
 
2.9%
작 629
 
2.8%
Other values (96) 9134
40.1%
Common
ValueCountFrequency (%)
559
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22786
97.6%
ASCII 559
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
선 5531
24.3%
기 1342
 
5.9%
타 1114
 
4.9%
반 1057
 
4.6%
유 1021
 
4.5%
부 774
 
3.4%
인 770
 
3.4%
예 749
 
3.3%
운 665
 
2.9%
작 629
 
2.8%
Other values (96) 9134
40.1%
ASCII
ValueCountFrequency (%)
559
100.0%

항행구역
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
평수
2688 
연해
2259 
평수(호소,하천)
597 
근해
 
55
원양
 
8

Length

Max length9
Median length2
Mean length2.7453184
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row평수(호소,하천)
2nd row평수(호소,하천)
3rd row평수(호소,하천)
4th row평수
5th row평수

Common Values

ValueCountFrequency (%)
평수 2688
47.9%
연해 2259
40.3%
평수(호소,하천) 597
 
10.6%
근해 55
 
1.0%
원양 8
 
0.1%

Length

2023-12-12T11:47:18.277026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:47:18.406527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평수 2688
47.9%
연해 2259
40.3%
평수(호소,하천 597
 
10.6%
근해 55
 
1.0%
원양 8
 
0.1%

선적항
Text

Distinct317
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size43.9 KiB
2023-12-12T11:47:18.843805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length6.0315677
Min length3

Characters and Unicode

Total characters33819
Distinct characters185
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)2.3%

Sample

1st row충북 청주시
2nd row충북 청주시
3rd row충북 충주시
4th row충북 충주시
5th row충북 충주시
ValueCountFrequency (%)
부산광역시 1532
16.6%
전남 995
 
10.8%
경남 649
 
7.0%
인천광역시 607
 
6.6%
여수시 442
 
4.8%
울산광역시 263
 
2.9%
목포시 250
 
2.7%
강원 241
 
2.6%
전북 221
 
2.4%
통영시 206
 
2.2%
Other values (341) 3819
41.4%
2023-12-12T11:47:19.345267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 5040
14.9%
3618
 
10.7%
광 2485
 
7.3%
역 2414
 
7.1%
산 2247
 
6.6%
남 1976
 
5.8%
부 1583
 
4.7%
전 1225
 
3.6%
경 1027
 
3.0%
천 980
 
2.9%
Other values (175) 11224
33.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30147
89.1%
Space Separator 3618
 
10.7%
Uppercase Letter 54
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5040
16.7%
광 2485
 
8.2%
역 2414
 
8.0%
산 2247
 
7.5%
남 1976
 
6.6%
부 1583
 
5.3%
전 1225
 
4.1%
경 1027
 
3.4%
천 980
 
3.3%
군 791
 
2.6%
Other values (170) 10379
34.4%
Uppercase Letter
ValueCountFrequency (%)
A 27
50.0%
N 9
 
16.7%
P 9
 
16.7%
M 9
 
16.7%
Space Separator
ValueCountFrequency (%)
3618
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30147
89.1%
Common 3618
 
10.7%
Latin 54
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5040
16.7%
광 2485
 
8.2%
역 2414
 
8.0%
산 2247
 
7.5%
남 1976
 
6.6%
부 1583
 
5.3%
전 1225
 
4.1%
경 1027
 
3.4%
천 980
 
3.3%
군 791
 
2.6%
Other values (170) 10379
34.4%
Latin
ValueCountFrequency (%)
A 27
50.0%
N 9
 
16.7%
P 9
 
16.7%
M 9
 
16.7%
Common
ValueCountFrequency (%)
3618
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30147
89.1%
ASCII 3672
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 5040
16.7%
광 2485
 
8.2%
역 2414
 
8.0%
산 2247
 
7.5%
남 1976
 
6.6%
부 1583
 
5.3%
전 1225
 
4.1%
경 1027
 
3.4%
천 980
 
3.3%
군 791
 
2.6%
Other values (170) 10379
34.4%
ASCII
ValueCountFrequency (%)
3618
98.5%
A 27
 
0.7%
N 9
 
0.2%
P 9
 
0.2%
M 9
 
0.2%

Interactions

2023-12-12T11:47:14.091866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:11.900309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.421886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.914917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.610325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:14.183469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:11.987087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.520020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.001416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.694037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:14.284004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.130526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.616683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.101966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.804448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:14.372599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.237012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.705886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.189976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.908000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:14.481029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.324946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:12.821491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.518688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:47:13.997900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:47:19.447917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번총톤수길이너비깊이선질용도용도1항행구역
순번1.0000.0480.3330.3910.2840.3130.3430.5710.473
총톤수0.0481.0000.9600.8210.8300.0000.5630.9380.507
길이0.3330.9601.0000.8930.8290.3490.6620.9370.793
너비0.3910.8210.8931.0000.6950.4350.5960.8890.629
깊이0.2840.8300.8290.6951.0000.3280.6330.8990.604
선질0.3130.0000.3490.4350.3281.0000.4260.7150.250
용도0.3430.5630.6620.5960.6330.4261.0000.9990.360
용도10.5710.9380.9370.8890.8990.7150.9991.0000.859
항행구역0.4730.5070.7930.6290.6040.2500.3600.8591.000
2023-12-12T11:47:19.556818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선질항행구역용도
선질1.0000.1720.227
항행구역0.1721.0000.217
용도0.2270.2171.000
2023-12-12T11:47:19.658086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번총톤수길이너비깊이선질용도항행구역
순번1.0000.0910.0900.0660.1300.1700.1630.215
총톤수0.0911.0000.9740.9150.8820.0000.3200.374
길이0.0900.9741.0000.9000.8680.1920.3760.449
너비0.0660.9150.9001.0000.9090.2440.3210.311
깊이0.1300.8820.8680.9091.0000.1690.2490.404
선질0.1700.0000.1920.2440.1691.0000.2270.172
용도0.1630.3200.3760.3210.2490.2271.0000.217
항행구역0.2150.3740.4490.3110.4040.1720.2171.000

Missing values

2023-12-12T11:47:14.626824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:47:14.782447image/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

순번구분총톤수길이너비깊이선질용도용도1항행구역선적항
01일반선1.075.010.00.0FRP기타선기타선평수(호소,하천)충북 청주시
12일반선3.397.42.481.32FRP기타선기타선평수(호소,하천)충북 청주시
23일반선293.00.00.00.0강여객선유선평수(호소,하천)충북 충주시
34일반선0.970.00.00.0FRP기타선기타선평수충북 충주시
45일반선0.640.00.00.0FRP기타선기타선평수충북 충주시
56일반선89.026.880.00.0강여객선유선평수(호소,하천)충북 충주시
67일반선0.85.160.00.0알루미늄합금기타선기타선평수(호소,하천)충북 충주시
78일반선5.177.952.770.87알루미늄합금기타선기타선평수(호소,하천)충북 충주시
89일반선4.397.12.580.76알루미늄합금기타선기타선평수(호소,하천)충북 충주시
910일반선1.227.683.10.78FRP기타선기타선평수(호소,하천)충북 충주시
순번구분총톤수길이너비깊이선질용도용도1항행구역선적항
55975598일반선2.670.00.00.0FRP기타선기타선평수부산광역시
55985599일반선1.025.00.00.0강기타선기타선연해부산광역시
55995600일반선10.011.536.11.4강부선일반부선평수부산광역시
56005601일반선0.011.23.551.21강부선일반부선평수(호소,하천)부산광역시
56015602일반선0.08.01.911.51강부선일반부선평수(호소,하천)부산광역시
56025603일반선0.010.00.00.0강부선준설 및 운반부선평수(호소,하천)부산광역시
56035604일반선0.08.01.911.51강부선일반부선평수(호소,하천)부산광역시
56045605일반선777.00.00.00.0FRP기타선기타선평수부산광역시
56055606일반선11.00.00.00.0FRP기타선기타선평수부산광역시
56065607일반선137746.0302.058.028.3강유조선원유운반선원양부산광역시