Overview

Dataset statistics

Number of variables6
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory53.7 B

Variable types

Text1
Numeric3
DateTime2

Dataset

DescriptionSample
Author올시데이터
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_001321

Alerts

SHIP_CNT is highly overall correlated with NOXHigh correlation
NOX is highly overall correlated with SHIP_CNTHigh correlation
SHIP_OWNER_NM has unique valuesUnique
NOX has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:47:33.932582
Analysis finished2023-12-10 14:47:34.933618
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHIP_OWNER_NM
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:47:35.085531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length12.673469
Min length2

Characters and Unicode

Total characters621
Distinct characters42
Distinct categories2 ?
Distinct scripts1 ?
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 rowQD
2nd rowGhosklv
3rd rowQruglfKdpexuj
4th rowFdshVklsslqj
5th rowFrqwvklsvPdqdjhphqw
ValueCountFrequency (%)
qd 1
 
2.0%
pvf 1
 
2.0%
kvvfkliidkuwv 1
 
2.0%
hdvwhuqphg 1
 
2.0%
uhhghuhlqrug 1
 
2.0%
eruhdolvpdulwlph 1
 
2.0%
kdsdjoorbgfrqw 1
 
2.0%
yvklsvkdpexuj 1
 
2.0%
uhhghuhludperz 1
 
2.0%
qruwkhuqvklsslqj 1
 
2.0%
Other values (39) 39
79.6%
2023-12-10T23:47:35.429861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 72
 
11.6%
l 55
 
8.9%
d 44
 
7.1%
u 41
 
6.6%
v 34
 
5.5%
s 32
 
5.2%
r 29
 
4.7%
q 29
 
4.7%
w 24
 
3.9%
k 23
 
3.7%
Other values (32) 238
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 496
79.9%
Uppercase Letter 125
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 72
14.5%
l 55
11.1%
d 44
 
8.9%
u 41
 
8.3%
v 34
 
6.9%
s 32
 
6.5%
r 29
 
5.8%
q 29
 
5.8%
w 24
 
4.8%
k 23
 
4.6%
Other values (12) 113
22.8%
Uppercase Letter
ValueCountFrequency (%)
V 21
16.8%
F 20
16.0%
O 12
9.6%
P 10
8.0%
U 9
 
7.2%
Q 8
 
6.4%
D 6
 
4.8%
S 6
 
4.8%
E 5
 
4.0%
K 4
 
3.2%
Other values (10) 24
19.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 621
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 72
 
11.6%
l 55
 
8.9%
d 44
 
7.1%
u 41
 
6.6%
v 34
 
5.5%
s 32
 
5.2%
r 29
 
4.7%
q 29
 
4.7%
w 24
 
3.9%
k 23
 
3.7%
Other values (32) 238
38.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 621
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 72
 
11.6%
l 55
 
8.9%
d 44
 
7.1%
u 41
 
6.6%
v 34
 
5.5%
s 32
 
5.2%
r 29
 
4.7%
q 29
 
4.7%
w 24
 
3.9%
k 23
 
3.7%
Other values (32) 238
38.3%

SHIP_CNT
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.571429
Minimum1
Maximum1104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:35.546300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q332
95-th percentile112.2
Maximum1104
Range1103
Interquartile range (IQR)27

Descriptive statistics

Standard deviation161.9299
Coefficient of variation (CV)3.3338508
Kurtosis39.626442
Mean48.571429
Median Absolute Deviation (MAD)9
Skewness6.1172254
Sum2380
Variance26221.292
MonotonicityNot monotonic
2023-12-10T23:47:35.643791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 5
 
10.2%
9 4
 
8.2%
1 4
 
8.2%
7 3
 
6.1%
8 2
 
4.1%
13 2
 
4.1%
10 2
 
4.1%
32 2
 
4.1%
36 2
 
4.1%
4 2
 
4.1%
Other values (21) 21
42.9%
ValueCountFrequency (%)
1 4
8.2%
2 5
10.2%
3 1
 
2.0%
4 2
 
4.1%
5 1
 
2.0%
7 3
6.1%
8 2
 
4.1%
9 4
8.2%
10 2
 
4.1%
11 1
 
2.0%
ValueCountFrequency (%)
1104 1
2.0%
329 1
2.0%
113 1
2.0%
111 1
2.0%
61 1
2.0%
54 1
2.0%
42 1
2.0%
41 1
2.0%
39 1
2.0%
36 2
4.1%

DPTR_HMS
Date

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-01 06:00:00
Maximum2023-05-06 00:00:00
2023-12-10T23:47:35.734752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:35.816691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

ARVL_HMS
Date

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2023-01-28 18:00:00
Maximum2023-05-31 18:00:00
2023-12-10T23:47:35.923457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:36.018854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

NOX
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7626167 × 109
Minimum7494340
Maximum2.12989 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:47:36.126848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7494340
5-th percentile17536140
Q193969800
median3.80666 × 108
Q39.35726 × 108
95-th percentile7.088656 × 109
Maximum2.12989 × 1010
Range2.1291406 × 1010
Interquartile range (IQR)8.417562 × 108

Descriptive statistics

Standard deviation4.0408706 × 109
Coefficient of variation (CV)2.2925407
Kurtosis15.492739
Mean1.7626167 × 109
Median Absolute Deviation (MAD)3.381372 × 108
Skewness3.8354656
Sum8.6368219 × 1010
Variance1.6328635 × 1019
MonotonicityNot monotonic
2023-12-10T23:47:36.260782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
21298900000 1
 
2.0%
2111910000 1
 
2.0%
92071800 1
 
2.0%
278869000 1
 
2.0%
293968000 1
 
2.0%
839525000 1
 
2.0%
7253520000 1
 
2.0%
271757000 1
 
2.0%
42528800 1
 
2.0%
1810120000 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
7494340 1
2.0%
14591300 1
2.0%
16209700 1
2.0%
19525800 1
2.0%
20813000 1
2.0%
27049300 1
2.0%
32843300 1
2.0%
42528800 1
2.0%
42671100 1
2.0%
43613300 1
2.0%
ValueCountFrequency (%)
21298900000 1
2.0%
17642200000 1
2.0%
7253520000 1
2.0%
6841360000 1
2.0%
3871390000 1
2.0%
3800520000 1
2.0%
3087370000 1
2.0%
2907740000 1
2.0%
2394970000 1
2.0%
2111910000 1
2.0%

RN
Real number (ℝ)

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
2023-12-10T23:47:36.383938image/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
2023-12-10T23:47:36.508022image/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

2023-12-10T23:47:34.589844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:34.155540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:34.394417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:34.654981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:34.238646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:34.457749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:34.723809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:34.322013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:47:34.525875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:47:36.588152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSNOXRN
SHIP_OWNER_NM1.0001.0001.0001.0001.0001.000
SHIP_CNT1.0001.0000.0000.0000.9000.000
DPTR_HMS1.0000.0001.0000.4970.0000.325
ARVL_HMS1.0000.0000.4971.0000.0000.293
NOX1.0000.9000.0000.0001.0000.687
RN1.0000.0000.3250.2930.6871.000
2023-12-10T23:47:36.677352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHIP_CNTNOXRN
SHIP_CNT1.0000.9420.076
NOX0.9421.0000.168
RN0.0760.1681.000

Missing values

2023-12-10T23:47:34.813144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:47:34.897272image/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

SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSNOXRN
0QD110401-Jan-2023 12:00:0031-May-2023 18:00:00212989000002
1Ghosklv1601-Jan-2023 12:00:0023-May-2023 06:00:005388460003
2QruglfKdpexuj1301-Jan-2023 12:00:0031-May-2023 18:00:001478470004
3FdshVklsslqj1001-Jan-2023 12:00:0031-May-2023 18:00:001252280005
4FrqwvklsvPdqdjhphqw3101-Jan-2023 12:00:0031-May-2023 18:00:004674280006
5PduorzQdyljdwlrq1301-Jan-2023 12:00:0031-May-2023 18:00:003282690007
6MxqjhukdqvFr921-Apr-2023 18:00:0031-May-2023 18:00:00939698008
7GdqdrvVklsslqj5401-Jan-2023 12:00:0031-May-2023 18:00:0038005200009
8HoeghlfkUhhghuhl901-Jan-2023 12:00:0031-May-2023 18:00:0012555200010
9RfhdqlfPdulwlphOwg101-Jan-2023 12:00:0031-May-2023 18:00:001952580011
SHIP_OWNER_NMSHIP_CNTDPTR_HMSARVL_HMSNOXRN
39ZhvvhovUhhghuhl201-Jan-2023 12:00:0031-May-2023 18:00:004361330041
40DQO201-Jan-2023 12:00:0031-May-2023 18:00:0012124700042
41FVVFVksjOhdvlqj501-Jan-2023 12:00:0031-May-2023 18:00:0038066600043
42DundvVklsslqj4201-Jan-2023 12:00:0031-May-2023 18:00:0093572600044
43FdslwdoSurgxfw1201-Jan-2023 12:00:0031-May-2023 00:00:0087160900045
44DSO3601-Jan-2023 12:00:0031-May-2023 06:00:00308737000046
45FrvwdpduhVklsslqj6101-Apr-2023 12:00:0031-May-2023 18:00:00387139000047
46UhhghuhlFSRiihq3301-Jan-2023 12:00:0031-May-2023 18:00:00290774000048
47JoredoVklsOhdvh3902-Jan-2023 18:00:0030-May-2023 12:00:00239497000049
48OhswdVklsslqj401-Jan-2023 12:00:0031-May-2023 18:00:002704930050