Overview

Dataset statistics

Number of variables8
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory71.7 B

Variable types

Text1
Numeric5
Categorical2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000464

Alerts

SHIP_CNT is highly overall correlated with FUEL_CNSMP_QTY and 3 other fieldsHigh correlation
PRFMC is highly overall correlated with RNHigh correlation
FUEL_CNSMP_QTY is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
NVGTN_DIST is highly overall correlated with SHIP_CNT and 3 other fieldsHigh correlation
RN is highly overall correlated with PRFMCHigh correlation
DPTR_HMS is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
ARVL_HMS is highly overall correlated with SHIP_CNT and 1 other fieldsHigh correlation
DPTR_HMS is highly imbalanced (69.6%)Imbalance
SHPYRD_NM has unique valuesUnique
PRFMC has unique valuesUnique
FUEL_CNSMP_QTY has unique valuesUnique
NVGTN_DIST has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:37:33.985728
Analysis finished2023-12-10 14:37:37.080110
Duration3.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHPYRD_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:37:37.266137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length23
Min length3

Characters and Unicode

Total characters1127
Distinct characters37
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

Unique49 ?
Unique (%)100.0%

Sample

1st rowHANJIN HEAVY INDUSTRIES - PUSAN, KR
2nd rowKAWASAKI SAKAIDE WORKS
3rd rowSEDEF SHIPBUILDING
4th rowSHIN KURUSHIMA ONISHI SHIPYARD
5th rowTONGSHUN SHIPBUILDING & REPAIR
ValueCountFrequency (%)
shipbuilding 18
 
11.8%
shipyard 12
 
7.8%
heavy 8
 
5.2%
7
 
4.6%
industries 6
 
3.9%
industry 5
 
3.3%
group 3
 
2.0%
jiangsu 2
 
1.3%
shipping 2
 
1.3%
engineering 2
 
1.3%
Other values (84) 88
57.5%
2023-12-10T23:37:37.799744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 147
13.0%
104
 
9.2%
N 95
 
8.4%
A 90
 
8.0%
S 85
 
7.5%
H 66
 
5.9%
E 53
 
4.7%
D 53
 
4.7%
U 53
 
4.7%
R 51
 
4.5%
Other values (27) 330
29.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1002
88.9%
Space Separator 104
 
9.2%
Lowercase Letter 9
 
0.8%
Other Punctuation 8
 
0.7%
Dash Punctuation 3
 
0.3%
Decimal Number 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 147
14.7%
N 95
 
9.5%
A 90
 
9.0%
S 85
 
8.5%
H 66
 
6.6%
E 53
 
5.3%
D 53
 
5.3%
U 53
 
5.3%
R 51
 
5.1%
G 44
 
4.4%
Other values (15) 265
26.4%
Lowercase Letter
ValueCountFrequency (%)
i 2
22.2%
u 2
22.2%
t 1
11.1%
s 1
11.1%
r 1
11.1%
o 1
11.1%
p 1
11.1%
Other Punctuation
ValueCountFrequency (%)
& 5
62.5%
, 3
37.5%
Space Separator
ValueCountFrequency (%)
104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1011
89.7%
Common 116
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 147
14.5%
N 95
 
9.4%
A 90
 
8.9%
S 85
 
8.4%
H 66
 
6.5%
E 53
 
5.2%
D 53
 
5.2%
U 53
 
5.2%
R 51
 
5.0%
G 44
 
4.4%
Other values (22) 274
27.1%
Common
ValueCountFrequency (%)
104
89.7%
& 5
 
4.3%
, 3
 
2.6%
- 3
 
2.6%
3 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 147
13.0%
104
 
9.2%
N 95
 
8.4%
A 90
 
8.0%
S 85
 
7.5%
H 66
 
5.9%
E 53
 
4.7%
D 53
 
4.7%
U 53
 
4.7%
R 51
 
4.5%
Other values (27) 330
29.3%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.714286
Minimum1
Maximum1724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:37:37.936626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q328
95-th percentile96.6
Maximum1724
Range1723
Interquartile range (IQR)26

Descriptive statistics

Standard deviation244.90381
Coefficient of variation (CV)4.3957094
Kurtosis47.640206
Mean55.714286
Median Absolute Deviation (MAD)6
Skewness6.8596079
Sum2730
Variance59977.875
MonotonicityNot monotonic
2023-12-10T23:37:38.056172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 9
18.4%
3 5
 
10.2%
2 4
 
8.2%
19 2
 
4.1%
4 2
 
4.1%
30 2
 
4.1%
5 2
 
4.1%
23 2
 
4.1%
6 2
 
4.1%
1724 1
 
2.0%
Other values (18) 18
36.7%
ValueCountFrequency (%)
1 9
18.4%
2 4
8.2%
3 5
10.2%
4 2
 
4.1%
5 2
 
4.1%
6 2
 
4.1%
7 1
 
2.0%
11 1
 
2.0%
12 1
 
2.0%
15 1
 
2.0%
ValueCountFrequency (%)
1724 1
2.0%
106 1
2.0%
99 1
2.0%
93 1
2.0%
92 1
2.0%
68 1
2.0%
56 1
2.0%
54 1
2.0%
42 1
2.0%
41 1
2.0%

DPTR_HMS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
01-Jan-2021 00:00:00
43 
05-May-2021 00:00:00
 
1
02-Jul-2021 00:00:00
 
1
09-Jan-2021 00:00:00
 
1
17-May-2021 00:00:00
 
1
Other values (2)
 
2

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique6 ?
Unique (%)12.2%

Sample

1st row01-Jan-2021 00:00:00
2nd row01-Jan-2021 00:00:00
3rd row01-Jan-2021 00:00:00
4th row01-Jan-2021 00:00:00
5th row01-Jan-2021 00:00:00

Common Values

ValueCountFrequency (%)
01-Jan-2021 00:00:00 43
87.8%
05-May-2021 00:00:00 1
 
2.0%
02-Jul-2021 00:00:00 1
 
2.0%
09-Jan-2021 00:00:00 1
 
2.0%
17-May-2021 00:00:00 1
 
2.0%
11-Jan-2021 00:00:00 1
 
2.0%
24-Jan-2021 00:00:00 1
 
2.0%

Length

2023-12-10T23:37:38.177078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:37:38.284368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 49
50.0%
01-jan-2021 43
43.9%
05-may-2021 1
 
1.0%
02-jul-2021 1
 
1.0%
09-jan-2021 1
 
1.0%
17-may-2021 1
 
1.0%
11-jan-2021 1
 
1.0%
24-jan-2021 1
 
1.0%

ARVL_HMS
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
13-Oct-2021 18:00:00
30 
13-Oct-2021 12:00:00
 
2
13-Oct-2021 06:00:00
 
2
28-Apr-2021 18:00:00
 
1
11-Oct-2021 00:00:00
 
1
Other values (13)
13 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique15 ?
Unique (%)30.6%

Sample

1st row13-Oct-2021 18:00:00
2nd row13-Oct-2021 18:00:00
3rd row13-Oct-2021 18:00:00
4th row13-Oct-2021 18:00:00
5th row10-Oct-2021 12:00:00

Common Values

ValueCountFrequency (%)
13-Oct-2021 18:00:00 30
61.2%
13-Oct-2021 12:00:00 2
 
4.1%
13-Oct-2021 06:00:00 2
 
4.1%
28-Apr-2021 18:00:00 1
 
2.0%
11-Oct-2021 00:00:00 1
 
2.0%
19-Aug-2021 12:00:00 1
 
2.0%
12-Mar-2021 06:00:00 1
 
2.0%
11-Oct-2021 18:00:00 1
 
2.0%
02-Oct-2021 06:00:00 1
 
2.0%
10-Oct-2021 12:00:00 1
 
2.0%
Other values (8) 8
 
16.3%

Length

2023-12-10T23:37:38.415895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13-oct-2021 34
34.7%
18:00:00 34
34.7%
12:00:00 7
 
7.1%
06:00:00 5
 
5.1%
00:00:00 3
 
3.1%
11-oct-2021 2
 
2.0%
10-oct-2021 2
 
2.0%
10-may-2021 1
 
1.0%
06-jan-2021 1
 
1.0%
12-oct-2021 1
 
1.0%
Other values (8) 8
 
8.2%

PRFMC
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.044551629
Minimum0.0254728
Maximum0.0663895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:37:38.557375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0254728
5-th percentile0.02645708
Q10.0338897
median0.0422298
Q30.0537786
95-th percentile0.06397494
Maximum0.0663895
Range0.0409167
Interquartile range (IQR)0.0198889

Descriptive statistics

Standard deviation0.012014608
Coefficient of variation (CV)0.26967832
Kurtosis-1.1189473
Mean0.044551629
Median Absolute Deviation (MAD)0.0102066
Skewness0.11149932
Sum2.1830298
Variance0.00014435081
MonotonicityStrictly increasing
2023-12-10T23:37:38.707904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0254728 1
 
2.0%
0.0537841 1
 
2.0%
0.0481025 1
 
2.0%
0.0482068 1
 
2.0%
0.0482387 1
 
2.0%
0.048489 1
 
2.0%
0.0492178 1
 
2.0%
0.0517192 1
 
2.0%
0.0524975 1
 
2.0%
0.0526418 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
0.0254728 1
2.0%
0.0259741 1
2.0%
0.0263004 1
2.0%
0.0266921 1
2.0%
0.0281747 1
2.0%
0.0285951 1
2.0%
0.0296972 1
2.0%
0.0310069 1
2.0%
0.0320232 1
2.0%
0.0323009 1
2.0%
ValueCountFrequency (%)
0.0663895 1
2.0%
0.0651659 1
2.0%
0.0651403 1
2.0%
0.0622269 1
2.0%
0.0612658 1
2.0%
0.0611875 1
2.0%
0.0598967 1
2.0%
0.0582215 1
2.0%
0.0581971 1
2.0%
0.0573319 1
2.0%

FUEL_CNSMP_QTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108319.19
Minimum641.034
Maximum1937210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:37:38.853387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum641.034
5-th percentile1462.118
Q14458.08
median23267.8
Q379764
95-th percentile353004.2
Maximum1937210
Range1936569
Interquartile range (IQR)75305.92

Descriptive statistics

Standard deviation288262.71
Coefficient of variation (CV)2.6612341
Kurtosis35.196237
Mean108319.19
Median Absolute Deviation (MAD)21076.74
Skewness5.6188123
Sum5307640.1
Variance8.309539 × 1010
MonotonicityNot monotonic
2023-12-10T23:37:39.010595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
38717.6 1
 
2.0%
1255.37 1
 
2.0%
121322.0 1
 
2.0%
4150.53 1
 
2.0%
125554.0 1
 
2.0%
308462.0 1
 
2.0%
313142.0 1
 
2.0%
2631.96 1
 
2.0%
2247.92 1
 
2.0%
36950.9 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
641.034 1
2.0%
1176.61 1
2.0%
1255.37 1
2.0%
1772.24 1
2.0%
1792.38 1
2.0%
1912.27 1
2.0%
2191.06 1
2.0%
2247.92 1
2.0%
2464.1 1
2.0%
2631.96 1
2.0%
ValueCountFrequency (%)
1937210.0 1
2.0%
521831.0 1
2.0%
379579.0 1
2.0%
313142.0 1
2.0%
308462.0 1
2.0%
186397.0 1
2.0%
178658.0 1
2.0%
174467.0 1
2.0%
143050.0 1
2.0%
125554.0 1
2.0%

NVGTN_DIST
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2674308
Minimum22752.1
Maximum57412200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:37:39.181962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22752.1
5-th percentile34207.68
Q194651.9
median541221
Q31865030
95-th percentile7432044
Maximum57412200
Range57389448
Interquartile range (IQR)1770378.1

Descriptive statistics

Standard deviation8282320.7
Coefficient of variation (CV)3.0969958
Kurtosis41.861378
Mean2674308
Median Absolute Deviation (MAD)493144.7
Skewness6.2713349
Sum1.3104109 × 108
Variance6.8596837 × 1013
MonotonicityNot monotonic
2023-12-10T23:37:39.350739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1519960.0 1
 
2.0%
23340.9 1
 
2.0%
2522170.0 1
 
2.0%
86098.4 1
 
2.0%
2602770.0 1
 
2.0%
6361490.0 1
 
2.0%
6362360.0 1
 
2.0%
50889.4 1
 
2.0%
42819.6 1
 
2.0%
701932.0 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
22752.1 1
2.0%
23340.9 1
2.0%
28466.4 1
2.0%
42819.6 1
2.0%
45680.7 1
2.0%
48076.3 1
2.0%
50889.4 1
2.0%
64652.6 1
2.0%
67384.6 1
2.0%
74259.9 1
2.0%
ValueCountFrequency (%)
57412200.0 1
2.0%
8010880.0 1
2.0%
7908760.0 1
2.0%
6716970.0 1
2.0%
6362360.0 1
2.0%
6361490.0 1
2.0%
5359270.0 1
2.0%
4318750.0 1
2.0%
2807630.0 1
2.0%
2602770.0 1
2.0%

RN
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:37:39.794589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q114
median26
Q338
95-th percentile47.6
Maximum50
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.54956501
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)12
Skewness0
Sum1274
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:37:39.934422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Interactions

2023-12-10T23:37:36.435593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.274045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.643344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.968713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:35.596852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:36.583702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.348314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.713612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:35.046703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:35.900556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:36.655983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.413111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.769273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:35.116994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:36.119431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:36.738358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.494980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.840455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:35.211262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:36.275280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:36.812789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.575983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:34.905438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:35.365644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:37:36.366307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:37:40.033247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
SHPYRD_NM1.0001.0001.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0001.0001.0000.0001.0001.0000.000
DPTR_HMS1.0001.0001.0000.6220.0000.6320.7350.000
ARVL_HMS1.0001.0000.6221.0000.0000.7900.8720.206
PRFMC1.0000.0000.0000.0001.0000.5820.3940.963
FUEL_CNSMP_QTY1.0001.0000.6320.7900.5821.0000.8670.309
NVGTN_DIST1.0001.0000.7350.8720.3940.8671.0000.140
RN1.0000.0000.0000.2060.9630.3090.1401.000
2023-12-10T23:37:40.141634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DPTR_HMSARVL_HMS
DPTR_HMS1.0000.276
ARVL_HMS0.2761.000
2023-12-10T23:37:40.219909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTPRFMCFUEL_CNSMP_QTYNVGTN_DISTRNDPTR_HMSARVL_HMS
SHIP_CNT1.0000.0740.9700.9800.0740.9450.812
PRFMC0.0741.0000.2050.0591.0000.0000.000
FUEL_CNSMP_QTY0.9700.2051.0000.9850.2050.4750.464
NVGTN_DIST0.9800.0590.9851.0000.0590.6310.600
RN0.0741.0000.2050.0591.0000.0000.000
DPTR_HMS0.9450.0000.4750.6310.0001.0000.276
ARVL_HMS0.8120.0000.4640.6000.0000.2761.000

Missing values

2023-12-10T23:37:36.918837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:37:37.032629image/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

SHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
0HANJIN HEAVY INDUSTRIES - PUSAN, KR1901-Jan-2021 00:00:0013-Oct-2021 18:00:000.02547338717.61519960.02
1KAWASAKI SAKAIDE WORKS5601-Jan-2021 00:00:0013-Oct-2021 18:00:000.025974174467.06716970.03
2SEDEF SHIPBUILDING101-Jan-2021 00:00:0013-Oct-2021 18:00:000.02631772.2467384.64
3SHIN KURUSHIMA ONISHI SHIPYARD6801-Jan-2021 00:00:0013-Oct-2021 18:00:000.026692143050.05359270.05
4TONGSHUN SHIPBUILDING & REPAIR101-Jan-2021 00:00:0010-Oct-2021 12:00:000.028175641.03422752.16
5HALLA ENGINEERING & HEAVY INDUSTRIES605-May-2021 00:00:0013-Oct-2021 18:00:000.0285956280.09219622.07
6YANGZHOU RYUWA SHIPBUILDING201-Jan-2021 00:00:0013-Oct-2021 06:00:000.0296972810.994651.98
7KEJIN SHIPYARD1601-Jan-2021 00:00:0013-Oct-2021 18:00:000.03100721833.5704149.09
8JES INTERNATIONAL301-Jan-2021 00:00:0011-Oct-2021 00:00:000.0320234458.08139214.010
9TROGIR SHIPYARD1901-Jan-2021 00:00:0013-Oct-2021 18:00:000.03230131129.0963719.011
SHPYRD_NMSHIP_CNTDPTR_HMSARVL_HMSPRFMCFUEL_CNSMP_QTYNVGTN_DISTRN
39DAE SUN SHIPBUILDING & ENGINEERING2201-Jan-2021 00:00:0013-Oct-2021 18:00:000.05733279764.01391270.041
40LONGXUE SHIPBUILDING601-Jan-2021 00:00:0013-Oct-2021 18:00:000.05819727330.8469624.042
41JIANGNAN CHANGXING SHIPBUILDING1801-Jan-2021 00:00:0013-Oct-2021 18:00:000.05822270330.51207980.043
42NASSCO724-Jan-2021 00:00:0013-Oct-2021 18:00:000.05989734781.1580685.044
43PAL INDONESIA PERWAKILAN YARD301-Jan-2021 00:00:0013-Oct-2021 18:00:000.0611877378.17120583.045
44ZIJINSHAN SHIPYARD401-Jan-2021 00:00:0013-Oct-2021 18:00:000.06126610058.5164178.046
45ZHEJIANG CHENYE SHIPBUILDING401-Jan-2021 00:00:0010-Oct-2021 00:00:000.0622275439.087405.947
46JMU10601-Jan-2021 00:00:0013-Oct-2021 18:00:000.06514521831.08010880.048
47MHI KOBE SHIPYARD & MACHINERY WORKS301-Jan-2021 00:00:0025-Feb-2021 12:00:000.06516610242.6157177.049
48JIANGSU RONGSHENG HEAVY INDUSTRIES3001-Jan-2021 00:00:0013-Oct-2021 18:00:000.06639186397.02807630.050