Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 KiB
Average record size in memory132.3 B

Variable types

Categorical4
DateTime2
Numeric9

Dataset

DescriptionSample
Author(사)동아시아바다공동체오션
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT08OSN018

Alerts

EEZ_CD has constant value ""Constant
SHP_ID is highly overall correlated with LA and 6 other fieldsHigh correlation
EEZ_12_NMI_CD is highly overall correlated with LA and 10 other fieldsHigh correlation
RGN_FSHRS_MNGM_ORGZ_NM is highly overall correlated with EEZ_12_NMI_CDHigh correlation
LA is highly overall correlated with LO and 2 other fieldsHigh correlation
LO is highly overall correlated with LA and 2 other fieldsHigh correlation
SHRLN_FROM_STR_KM_DSTC is highly overall correlated with SHRLN_FROM_END_KM_DSTC and 4 other fieldsHigh correlation
SHRLN_FROM_END_KM_DSTC is highly overall correlated with SHRLN_FROM_STR_KM_DSTC and 4 other fieldsHigh correlation
PRT_FROM_STR_KM_DSTC is highly overall correlated with SHRLN_FROM_STR_KM_DSTC and 4 other fieldsHigh correlation
PRT_FROM_END_KM_DSTC is highly overall correlated with SHRLN_FROM_STR_KM_DSTC and 4 other fieldsHigh correlation
TTL_KM_DSTC is highly overall correlated with EEZ_12_NMI_CDHigh correlation
AVG_KN is highly overall correlated with EEZ_12_NMI_CDHigh correlation
AVG_CNTE_TM is highly overall correlated with EEZ_12_NMI_CDHigh correlation
STR_DT has unique valuesUnique
END_DT has unique valuesUnique
LO has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:40:51.015907
Analysis finished2024-03-13 12:41:03.663390
Duration12.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHP_ID
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
75cd15faf-f61a-913c-ff72-e2a2a16995a5
18 
e86a5fa07-7a24-55f4-346f-8290a8bdb786
17 
9b6297659-9f0a-a179-acd9-b2c1b4f6e4d4
16 
56a8702e4-471b-a4f0-732a-a169e21f9618
13 
0d6a9bbed-d88c-c4ff-2116-bb6ac6bf7736
13 
Other values (6)
23 

Length

Max length37
Median length37
Mean length37
Min length37

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row56a8702e4-471b-a4f0-732a-a169e21f9618
2nd row0d6a9bbed-d88c-c4ff-2116-bb6ac6bf7736
3rd row0d6a9bbed-d88c-c4ff-2116-bb6ac6bf7736
4th rowa793b69d4-4e28-bffe-414c-e23755ec4c6e
5th rowe86a5fa07-7a24-55f4-346f-8290a8bdb786

Common Values

ValueCountFrequency (%)
75cd15faf-f61a-913c-ff72-e2a2a16995a5 18
18.0%
e86a5fa07-7a24-55f4-346f-8290a8bdb786 17
17.0%
9b6297659-9f0a-a179-acd9-b2c1b4f6e4d4 16
16.0%
56a8702e4-471b-a4f0-732a-a169e21f9618 13
13.0%
0d6a9bbed-d88c-c4ff-2116-bb6ac6bf7736 13
13.0%
870157f5e-e146-1892-1cbb-be8e30b13aac 9
9.0%
9bbe86252-2cf3-fcf2-7e15-6b5cc44c5ab1 8
8.0%
717ba2ece-ed66-1a5f-18f2-b5c28db23589 2
 
2.0%
84b84a929-9ccf-c36c-e2f0-beb77bcf82f8 2
 
2.0%
a793b69d4-4e28-bffe-414c-e23755ec4c6e 1
 
1.0%

Length

2024-03-13T21:41:03.741623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
75cd15faf-f61a-913c-ff72-e2a2a16995a5 18
18.0%
e86a5fa07-7a24-55f4-346f-8290a8bdb786 17
17.0%
9b6297659-9f0a-a179-acd9-b2c1b4f6e4d4 16
16.0%
56a8702e4-471b-a4f0-732a-a169e21f9618 13
13.0%
0d6a9bbed-d88c-c4ff-2116-bb6ac6bf7736 13
13.0%
870157f5e-e146-1892-1cbb-be8e30b13aac 9
9.0%
9bbe86252-2cf3-fcf2-7e15-6b5cc44c5ab1 8
8.0%
717ba2ece-ed66-1a5f-18f2-b5c28db23589 2
 
2.0%
84b84a929-9ccf-c36c-e2f0-beb77bcf82f8 2
 
2.0%
a793b69d4-4e28-bffe-414c-e23755ec4c6e 1
 
1.0%

STR_DT
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2013-01-01 19:04:00
Maximum2013-01-29 19:34:00
2024-03-13T21:41:03.915142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:04.577658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

END_DT
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2013-01-01 22:58:00
Maximum2013-01-30 04:10:00
2024-03-13T21:41:04.756547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:05.004152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

LA
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.742268
Minimum34.1061
Maximum35.1561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:05.189111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.1061
5-th percentile34.12309
Q134.600925
median34.91445
Q334.969425
95-th percentile35.118405
Maximum35.1561
Range1.05
Interquartile range (IQR)0.3685

Descriptive statistics

Standard deviation0.36312732
Coefficient of variation (CV)0.010452033
Kurtosis-0.80772641
Mean34.742268
Median Absolute Deviation (MAD)0.1237
Skewness-0.93614906
Sum3474.2268
Variance0.13186145
MonotonicityNot monotonic
2024-03-13T21:41:05.462960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.9292 2
 
2.0%
34.7917 2
 
2.0%
35.1196 2
 
2.0%
34.125 1
 
1.0%
34.1092 1
 
1.0%
34.1174 1
 
1.0%
34.1368 1
 
1.0%
34.9681 1
 
1.0%
35.0943 1
 
1.0%
34.8006 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
34.1061 1
1.0%
34.1092 1
1.0%
34.1136 1
1.0%
34.1174 1
1.0%
34.121 1
1.0%
34.1232 1
1.0%
34.125 1
1.0%
34.1283 1
1.0%
34.13 1
1.0%
34.1354 1
1.0%
ValueCountFrequency (%)
35.1561 1
1.0%
35.1196 2
2.0%
35.1193 1
1.0%
35.1185 1
1.0%
35.1184 1
1.0%
35.1165 1
1.0%
35.1054 1
1.0%
35.0943 1
1.0%
35.0925 1
1.0%
35.0883 1
1.0%

LO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.00278
Minimum128.4656
Maximum129.5686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:05.688557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.4656
5-th percentile128.50939
Q1128.73085
median129.20865
Q3129.2425
95-th percentile129.40856
Maximum129.5686
Range1.103
Interquartile range (IQR)0.51165

Descriptive statistics

Standard deviation0.33071047
Coefficient of variation (CV)0.0025635918
Kurtosis-1.405997
Mean129.00278
Median Absolute Deviation (MAD)0.1722
Skewness-0.37509346
Sum12900.278
Variance0.10936941
MonotonicityNot monotonic
2024-03-13T21:41:05.890743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.2536 1
 
1.0%
129.2185 1
 
1.0%
128.5546 1
 
1.0%
129.2431 1
 
1.0%
129.3325 1
 
1.0%
128.8056 1
 
1.0%
129.2832 1
 
1.0%
129.2089 1
 
1.0%
129.2084 1
 
1.0%
128.5305 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
128.4656 1
1.0%
128.4658 1
1.0%
128.4661 1
1.0%
128.4775 1
1.0%
128.4883 1
1.0%
128.5105 1
1.0%
128.5227 1
1.0%
128.5268 1
1.0%
128.5305 1
1.0%
128.5343 1
1.0%
ValueCountFrequency (%)
129.5686 1
1.0%
129.5556 1
1.0%
129.4607 1
1.0%
129.4478 1
1.0%
129.4362 1
1.0%
129.4071 1
1.0%
129.3818 1
1.0%
129.3799 1
1.0%
129.3729 1
1.0%
129.3325 1
1.0%

EEZ_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
8327
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8327
2nd row8327
3rd row8327
4th row8327
5th row8327

Common Values

ValueCountFrequency (%)
8327 100
100.0%

Length

2024-03-13T21:41:06.077208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:41:06.196708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8327 100
100.0%

EEZ_12_NMI_CD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
8327
53 
<NA>
47 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8327
2nd row8327
3rd row8327
4th row8327
5th row<NA>

Common Values

ValueCountFrequency (%)
8327 53
53.0%
<NA> 47
47.0%

Length

2024-03-13T21:41:06.317389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:41:06.454066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8327 53
53.0%
na 47
47.0%

RGN_FSHRS_MNGM_ORGZ_NM
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
PICES, IWC, ACAP, WCPFC
9 
WCPFC, IWC, PICES, ACAP
8 
ACAP, PICES, IWC, WCPFC
7 
IWC, ACAP, PICES, WCPFC
7 
ACAP, IWC, PICES, WCPFC
 
6
Other values (19)
63 

Length

Max length23
Median length23
Mean length23
Min length23

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st rowWCPFC, PICES, IWC, ACAP
2nd rowIWC, ACAP, PICES, WCPFC
3rd rowWCPFC, PICES, ACAP, IWC
4th rowACAP, PICES, WCPFC, IWC
5th rowPICES, IWC, ACAP, WCPFC

Common Values

ValueCountFrequency (%)
PICES, IWC, ACAP, WCPFC 9
 
9.0%
WCPFC, IWC, PICES, ACAP 8
 
8.0%
ACAP, PICES, IWC, WCPFC 7
 
7.0%
IWC, ACAP, PICES, WCPFC 7
 
7.0%
ACAP, IWC, PICES, WCPFC 6
 
6.0%
WCPFC, PICES, IWC, ACAP 5
 
5.0%
ACAP, WCPFC, IWC, PICES 5
 
5.0%
WCPFC, ACAP, PICES, IWC 5
 
5.0%
IWC, PICES, WCPFC, ACAP 5
 
5.0%
IWC, WCPFC, PICES, ACAP 5
 
5.0%
Other values (14) 38
38.0%

Length

2024-03-13T21:41:06.574622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pices 100
25.0%
iwc 100
25.0%
acap 100
25.0%
wcpfc 100
25.0%

SHRLN_FROM_STR_KM_DSTC
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.75
Minimum4
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:06.722940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q110.75
median19.5
Q335.75
95-th percentile52
Maximum54
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation16.216574
Coefficient of variation (CV)0.68280312
Kurtosis-0.89741761
Mean23.75
Median Absolute Deviation (MAD)12
Skewness0.70124044
Sum2375
Variance262.97727
MonotonicityNot monotonic
2024-03-13T21:41:06.876135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
18 9
 
9.0%
7 8
 
8.0%
17 7
 
7.0%
6 7
 
7.0%
22 7
 
7.0%
5 7
 
7.0%
21 6
 
6.0%
20 5
 
5.0%
52 4
 
4.0%
50 3
 
3.0%
Other values (22) 37
37.0%
ValueCountFrequency (%)
4 1
 
1.0%
5 7
7.0%
6 7
7.0%
7 8
8.0%
8 1
 
1.0%
10 1
 
1.0%
11 1
 
1.0%
12 1
 
1.0%
14 1
 
1.0%
15 2
 
2.0%
ValueCountFrequency (%)
54 2
2.0%
53 1
 
1.0%
52 4
4.0%
51 3
3.0%
50 3
3.0%
49 3
3.0%
48 2
2.0%
47 2
2.0%
46 2
2.0%
45 2
2.0%

SHRLN_FROM_END_KM_DSTC
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.13
Minimum4
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:07.031713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q111.5
median18
Q335.75
95-th percentile45
Maximum47
Range43
Interquartile range (IQR)24.25

Descriptive statistics

Standard deviation13.228111
Coefficient of variation (CV)0.59774565
Kurtosis-1.0443557
Mean22.13
Median Absolute Deviation (MAD)9
Skewness0.49820502
Sum2213
Variance174.98293
MonotonicityNot monotonic
2024-03-13T21:41:07.198321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
18 13
 
13.0%
6 9
 
9.0%
41 7
 
7.0%
22 7
 
7.0%
8 6
 
6.0%
17 6
 
6.0%
40 4
 
4.0%
42 4
 
4.0%
19 3
 
3.0%
23 3
 
3.0%
Other values (22) 38
38.0%
ValueCountFrequency (%)
4 1
 
1.0%
5 3
 
3.0%
6 9
9.0%
7 3
 
3.0%
8 6
6.0%
9 2
 
2.0%
10 1
 
1.0%
12 2
 
2.0%
13 1
 
1.0%
14 1
 
1.0%
ValueCountFrequency (%)
47 1
 
1.0%
46 2
 
2.0%
45 3
3.0%
43 2
 
2.0%
42 4
4.0%
41 7
7.0%
40 4
4.0%
39 1
 
1.0%
38 1
 
1.0%
35 1
 
1.0%

PRT_FROM_STR_KM_DSTC
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2036
Minimum4.45
Maximum56.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:07.378376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.45
5-th percentile6.334
Q19.9625
median16.25
Q336.35
95-th percentile52.9965
Maximum56.72
Range52.27
Interquartile range (IQR)26.3875

Descriptive statistics

Standard deviation17.087923
Coefficient of variation (CV)0.73643414
Kurtosis-0.86380732
Mean23.2036
Median Absolute Deviation (MAD)8.79
Skewness0.86020961
Sum2320.36
Variance291.99712
MonotonicityNot monotonic
2024-03-13T21:41:07.627448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.04 5
 
5.0%
7.06 4
 
4.0%
6.37 4
 
4.0%
14.1 3
 
3.0%
16.25 3
 
3.0%
14.81 3
 
3.0%
16.69 3
 
3.0%
14.75 3
 
3.0%
49.54 2
 
2.0%
14.94 2
 
2.0%
Other values (62) 68
68.0%
ValueCountFrequency (%)
4.45 1
 
1.0%
5.08 1
 
1.0%
5.56 2
 
2.0%
5.65 1
 
1.0%
6.37 4
4.0%
6.41 1
 
1.0%
6.67 1
 
1.0%
7.04 5
5.0%
7.06 4
4.0%
7.77 1
 
1.0%
ValueCountFrequency (%)
56.72 1
1.0%
55.68 1
1.0%
54.68 1
1.0%
53.37 1
1.0%
53.12 1
1.0%
52.99 1
1.0%
52.7 1
1.0%
52.42 1
1.0%
52.04 1
1.0%
51.94 1
1.0%

PRT_FROM_END_KM_DSTC
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.2806
Minimum0.91
Maximum46.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:07.870126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.91
5-th percentile3.34
Q111.03
median16.505
Q335.815
95-th percentile45.04
Maximum46.14
Range45.23
Interquartile range (IQR)24.785

Descriptive statistics

Standard deviation13.931732
Coefficient of variation (CV)0.65466821
Kurtosis-1.0290326
Mean21.2806
Median Absolute Deviation (MAD)7.915
Skewness0.52018922
Sum2128.06
Variance194.09316
MonotonicityNot monotonic
2024-03-13T21:41:08.055358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.45 7
 
7.0%
3.34 5
 
5.0%
41.26 4
 
4.0%
15.73 4
 
4.0%
15.84 4
 
4.0%
14.94 3
 
3.0%
40.24 3
 
3.0%
9.36 3
 
3.0%
16.11 3
 
3.0%
24.42 2
 
2.0%
Other values (56) 62
62.0%
ValueCountFrequency (%)
0.91 1
 
1.0%
2.22 1
 
1.0%
3.34 5
5.0%
3.46 1
 
1.0%
3.8 1
 
1.0%
4.45 7
7.0%
5.56 1
 
1.0%
8.48 1
 
1.0%
9.15 1
 
1.0%
9.33 1
 
1.0%
ValueCountFrequency (%)
46.14 1
1.0%
45.79 1
1.0%
45.69 1
1.0%
45.5 1
1.0%
45.04 2
2.0%
44.72 1
1.0%
44.44 1
1.0%
43.39 1
1.0%
42.96 2
2.0%
41.64 1
1.0%

TTL_KM_DSTC
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5324
Minimum2.94
Maximum375.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:08.255959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.94
5-th percentile3.729
Q111.73
median20.295
Q347.2775
95-th percentile172.179
Maximum375.76
Range372.82
Interquartile range (IQR)35.5475

Descriptive statistics

Standard deviation67.897009
Coefficient of variation (CV)1.4284364
Kurtosis8.258493
Mean47.5324
Median Absolute Deviation (MAD)12.755
Skewness2.7342664
Sum4753.24
Variance4610.0038
MonotonicityNot monotonic
2024-03-13T21:41:08.437397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.74 2
 
2.0%
6.93 1
 
1.0%
6.54 1
 
1.0%
31.85 1
 
1.0%
5.81 1
 
1.0%
11.61 1
 
1.0%
74.38 1
 
1.0%
5.68 1
 
1.0%
5.73 1
 
1.0%
14.87 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
2.94 1
1.0%
2.96 1
1.0%
2.97 1
1.0%
3.47 1
1.0%
3.52 1
1.0%
3.74 1
1.0%
4.64 1
1.0%
5.68 1
1.0%
5.73 1
1.0%
5.81 1
1.0%
ValueCountFrequency (%)
375.76 1
1.0%
303.93 1
1.0%
295.5 1
1.0%
238.97 1
1.0%
200.85 1
1.0%
170.67 1
1.0%
159.17 1
1.0%
151.57 1
1.0%
145.96 1
1.0%
144.59 1
1.0%

AVG_KN
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4442
Minimum0.92
Maximum5.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:08.635116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.92
5-th percentile1.169
Q11.8675
median2.355
Q32.9125
95-th percentile3.822
Maximum5.05
Range4.13
Interquartile range (IQR)1.045

Descriptive statistics

Standard deviation0.8645898
Coefficient of variation (CV)0.3537312
Kurtosis0.51111844
Mean2.4442
Median Absolute Deviation (MAD)0.54
Skewness0.58103829
Sum244.42
Variance0.74751552
MonotonicityNot monotonic
2024-03-13T21:41:08.854365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.18 3
 
3.0%
2.26 3
 
3.0%
2.92 2
 
2.0%
2.3 2
 
2.0%
2.47 2
 
2.0%
2.9 2
 
2.0%
2.22 2
 
2.0%
1.34 2
 
2.0%
3.27 2
 
2.0%
2.79 2
 
2.0%
Other values (72) 78
78.0%
ValueCountFrequency (%)
0.92 1
1.0%
0.94 2
2.0%
1.1 1
1.0%
1.15 1
1.0%
1.17 1
1.0%
1.24 1
1.0%
1.25 1
1.0%
1.3 1
1.0%
1.32 1
1.0%
1.33 1
1.0%
ValueCountFrequency (%)
5.05 1
1.0%
4.94 1
1.0%
4.57 1
1.0%
4.28 1
1.0%
4.24 1
1.0%
3.8 1
1.0%
3.71 2
2.0%
3.64 1
1.0%
3.49 1
1.0%
3.43 1
1.0%

AVG_CNTE_TM
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.388
Minimum0.21
Maximum0.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-13T21:41:09.049556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.23
Q10.28
median0.34
Q30.44
95-th percentile0.673
Maximum0.91
Range0.7
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.14989559
Coefficient of variation (CV)0.38632883
Kurtosis3.3868227
Mean0.388
Median Absolute Deviation (MAD)0.07
Skewness1.7396518
Sum38.8
Variance0.022468687
MonotonicityNot monotonic
2024-03-13T21:41:09.312783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.27 7
 
7.0%
0.33 5
 
5.0%
0.31 5
 
5.0%
0.34 5
 
5.0%
0.23 4
 
4.0%
0.26 4
 
4.0%
0.25 4
 
4.0%
0.38 4
 
4.0%
0.3 4
 
4.0%
0.42 4
 
4.0%
Other values (31) 54
54.0%
ValueCountFrequency (%)
0.21 1
 
1.0%
0.22 1
 
1.0%
0.23 4
4.0%
0.24 2
 
2.0%
0.25 4
4.0%
0.26 4
4.0%
0.27 7
7.0%
0.28 3
3.0%
0.29 2
 
2.0%
0.3 4
4.0%
ValueCountFrequency (%)
0.91 3
3.0%
0.83 1
 
1.0%
0.73 1
 
1.0%
0.67 1
 
1.0%
0.65 1
 
1.0%
0.61 1
 
1.0%
0.58 1
 
1.0%
0.57 1
 
1.0%
0.56 2
2.0%
0.55 1
 
1.0%

Interactions

2024-03-13T21:41:02.020766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:51.981885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:53.125329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:54.400845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:55.923562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:57.300204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:58.602637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.713311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:00.906481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:02.160869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:52.096436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:53.232328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:54.526528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:56.086747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:57.458472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:58.722318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.843761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:01.028086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:02.267449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:52.229389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:53.356362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:54.662522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:56.253183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:57.607604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:58.884746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.977126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:01.141584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:02.375867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:52.394417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:53.473161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:54.777376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:56.386666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:57.744163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.007503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:00.085609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:01.237519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:02.507177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:52.521774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:53.627090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:54.908485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:56.507726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:57.861423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.139029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:00.201765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:01.315506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:02.629151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:52.645465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:53.784689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:55.036112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:56.659082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:57.990633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.258979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:00.329580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:01.445636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:02.744773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:52.753920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:53.948421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:55.134297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:56.775932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:58.127978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.368759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:00.429319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:01.590113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:02.894115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:52.884462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:54.122987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:55.682136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:56.902439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:58.252420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.507575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:00.558635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:01.734549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:03.052526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:52.990913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:54.259947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:55.784638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:57.091796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:58.425420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:40:59.589303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:00.733239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:41:01.863153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:41:09.472794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHP_IDSTR_DTEND_DTLALORGN_FSHRS_MNGM_ORGZ_NMSHRLN_FROM_STR_KM_DSTCSHRLN_FROM_END_KM_DSTCPRT_FROM_STR_KM_DSTCPRT_FROM_END_KM_DSTCTTL_KM_DSTCAVG_KNAVG_CNTE_TM
SHP_ID1.0001.0001.0000.9300.9080.3770.8460.8530.8530.8780.4520.4670.481
STR_DT1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
END_DT1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
LA0.9301.0001.0001.0000.8630.2350.8560.9350.8290.8850.5720.5050.537
LO0.9081.0001.0000.8631.0000.3700.8160.8210.8400.8640.0000.4490.529
RGN_FSHRS_MNGM_ORGZ_NM0.3771.0001.0000.2350.3701.0000.5110.4840.5100.3430.7510.0000.519
SHRLN_FROM_STR_KM_DSTC0.8461.0001.0000.8560.8160.5111.0000.8770.9850.9680.4220.5420.372
SHRLN_FROM_END_KM_DSTC0.8531.0001.0000.9350.8210.4840.8771.0000.8580.9030.6780.4300.483
PRT_FROM_STR_KM_DSTC0.8531.0001.0000.8290.8400.5100.9850.8581.0000.9650.2070.5320.255
PRT_FROM_END_KM_DSTC0.8781.0001.0000.8850.8640.3430.9680.9030.9651.0000.4990.4980.380
TTL_KM_DSTC0.4521.0001.0000.5720.0000.7510.4220.6780.2070.4991.0000.0000.000
AVG_KN0.4671.0001.0000.5050.4490.0000.5420.4300.5320.4980.0001.0000.439
AVG_CNTE_TM0.4811.0001.0000.5370.5290.5190.3720.4830.2550.3800.0000.4391.000
2024-03-13T21:41:09.697814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHP_IDEEZ_12_NMI_CDRGN_FSHRS_MNGM_ORGZ_NM
SHP_ID1.0001.0000.121
EEZ_12_NMI_CD1.0001.0001.000
RGN_FSHRS_MNGM_ORGZ_NM0.1211.0001.000
2024-03-13T21:41:09.857660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LALOSHRLN_FROM_STR_KM_DSTCSHRLN_FROM_END_KM_DSTCPRT_FROM_STR_KM_DSTCPRT_FROM_END_KM_DSTCTTL_KM_DSTCAVG_KNAVG_CNTE_TMSHP_IDEEZ_12_NMI_CDRGN_FSHRS_MNGM_ORGZ_NM
LA1.0000.903-0.457-0.450-0.438-0.407-0.3240.2050.2010.8071.0000.091
LO0.9031.000-0.297-0.291-0.270-0.253-0.4210.2820.2280.7301.0000.085
SHRLN_FROM_STR_KM_DSTC-0.457-0.2971.0000.9690.9790.9490.091-0.439-0.3610.5961.0000.214
SHRLN_FROM_END_KM_DSTC-0.450-0.2910.9691.0000.9490.9730.040-0.471-0.3710.5801.0000.176
PRT_FROM_STR_KM_DSTC-0.438-0.2700.9790.9491.0000.9570.081-0.435-0.3410.6061.0000.195
PRT_FROM_END_KM_DSTC-0.407-0.2530.9490.9730.9571.0000.018-0.489-0.3470.6531.0000.115
TTL_KM_DSTC-0.324-0.4210.0910.0400.0810.0181.000-0.091-0.4350.2071.0000.356
AVG_KN0.2050.282-0.439-0.471-0.435-0.489-0.0911.0000.2560.2281.0000.000
AVG_CNTE_TM0.2010.228-0.361-0.371-0.341-0.347-0.4350.2561.0000.2191.0000.212
SHP_ID0.8070.7300.5960.5800.6060.6530.2070.2280.2191.0001.0000.121
EEZ_12_NMI_CD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
RGN_FSHRS_MNGM_ORGZ_NM0.0910.0850.2140.1760.1950.1150.3560.0000.2120.1211.0001.000

Missing values

2024-03-13T21:41:03.299934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:41:03.565276image/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

SHP_IDSTR_DTEND_DTLALOEEZ_CDEEZ_12_NMI_CDRGN_FSHRS_MNGM_ORGZ_NMSHRLN_FROM_STR_KM_DSTCSHRLN_FROM_END_KM_DSTCPRT_FROM_STR_KM_DSTCPRT_FROM_END_KM_DSTCTTL_KM_DSTCAVG_KNAVG_CNTE_TM
056a8702e4-471b-a4f0-732a-a169e21f96182013-01-01 19:042013-01-01 22:5834.9772129.253683278327WCPFC, PICES, IWC, ACAP171715.2416.556.931.940.33
10d6a9bbed-d88c-c4ff-2116-bb6ac6bf77362013-01-01 19:202013-01-01 23:3034.9679129.236983278327IWC, ACAP, PICES, WCPFC171714.7515.8410.852.490.27
20d6a9bbed-d88c-c4ff-2116-bb6ac6bf77362013-01-03 19:002013-01-04 0:0434.9554129.16283278327WCPFC, PICES, ACAP, IWC14129.889.336.722.360.56
3a793b69d4-4e28-bffe-414c-e23755ec4c6e2013-01-04 13:122013-01-04 15:5734.7419129.000383278327ACAP, PICES, WCPFC, IWC212617.0421.9812.091.650.91
4e86a5fa07-7a24-55f4-346f-8290a8bdb7862013-01-04 23:152013-01-05 4:0334.7932128.79228327<NA>PICES, IWC, ACAP, WCPFC657.063.3453.092.520.48
575cd15faf-f61a-913c-ff72-e2a2a16995a52013-01-05 22:102013-01-06 0:3734.8933129.212883278327PICES, IWC, ACAP, WCPFC222217.2519.079.133.710.41
69bbe86252-2cf3-fcf2-7e15-6b5cc44c5ab12013-01-06 0:342013-01-06 4:4135.1185129.232483278327WCPFC, IWC, ACAP, PICES777.048.4866.091.240.26
7e86a5fa07-7a24-55f4-346f-8290a8bdb7862013-01-06 1:072013-01-06 3:2734.7798128.78778327<NA>IWC, PICES, WCPFC, ACAP556.374.4528.892.320.58
80d6a9bbed-d88c-c4ff-2116-bb6ac6bf77362013-01-06 18:592013-01-06 22:4035.0118129.195283278327ACAP, WCPFC, IWC, PICES11129.1811.296.051.860.61
99bbe86252-2cf3-fcf2-7e15-6b5cc44c5ab12013-01-06 19:142013-01-07 2:4735.1165129.248483278327ACAP, IWC, WCPFC, PICES787.0410.25159.172.30.31
SHP_IDSTR_DTEND_DTLALOEEZ_CDEEZ_12_NMI_CDRGN_FSHRS_MNGM_ORGZ_NMSHRLN_FROM_STR_KM_DSTCSHRLN_FROM_END_KM_DSTCPRT_FROM_STR_KM_DSTCPRT_FROM_END_KM_DSTCTTL_KM_DSTCAVG_KNAVG_CNTE_TM
9075cd15faf-f61a-913c-ff72-e2a2a16995a52013-01-28 20:272013-01-29 0:2635.0765129.46078327<NA>ACAP, IWC, PICES, WCPFC242625.3526.1711.251.540.36
9156a8702e4-471b-a4f0-732a-a169e21f96182013-01-28 20:432013-01-29 0:2035.0592129.407183278327WCPFC, IWC, PICES, ACAP222423.725.3510.672.50.33
92e86a5fa07-7a24-55f4-346f-8290a8bdb7862013-01-28 21:162013-01-29 3:2334.7934128.81268327<NA>ACAP, PICES, IWC, WCPFC777.783.4687.263.390.34
93870157f5e-e146-1892-1cbb-be8e30b13aac2013-01-28 22:392013-01-29 3:1734.13128.46618327<NA>ACAP, PICES, IWC, WCPFC504253.1246.1445.01.930.52
94870157f5e-e146-1892-1cbb-be8e30b13aac2013-01-29 6:352013-01-29 9:0634.1532128.46568327<NA>WCPFC, ACAP, IWC, PICES464249.8545.6916.340.940.42
95870157f5e-e146-1892-1cbb-be8e30b13aac2013-01-29 12:142013-01-29 22:1934.1061128.52688327<NA>IWC, WCPFC, PICES, ACAP504051.2241.6433.750.940.35
969b6297659-9f0a-a179-acd9-b2c1b4f6e4d42013-01-29 15:412013-01-29 21:2934.1232128.55278327<NA>IWC, WCPFC, PICES, ACAP524652.9945.7929.412.260.36
970d6a9bbed-d88c-c4ff-2116-bb6ac6bf77362013-01-29 19:122013-01-30 3:3935.0408129.379983278327IWC, WCPFC, PICES, ACAP222322.5624.4223.382.480.27
9856a8702e4-471b-a4f0-732a-a169e21f96182013-01-29 19:172013-01-29 23:1934.9657129.230583278327ACAP, IWC, PICES, WCPFC161613.9114.958.652.220.25
9975cd15faf-f61a-913c-ff72-e2a2a16995a52013-01-29 19:342013-01-30 4:1034.9026129.216183278327PICES, IWC, ACAP, WCPFC212116.4117.6824.612.890.29