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=CT08OSN017

Alerts

EEZ_CD has constant value ""Constant
SHP_ID is highly overall correlated with LO and 1 other fieldsHigh correlation
RGN_FSHRS_MNGM_ORGZ_NM is highly overall correlated with EEZ_12_NMI_CDHigh correlation
EEZ_12_NMI_CD is highly overall correlated with LA and 10 other fieldsHigh correlation
LA is highly overall correlated with LO and 1 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 3 other fieldsHigh correlation
SHRLN_FROM_END_KM_DSTC is highly overall correlated with SHRLN_FROM_STR_KM_DSTC and 3 other fieldsHigh correlation
PRT_FROM_STR_KM_DSTC is highly overall correlated with SHRLN_FROM_STR_KM_DSTC and 3 other fieldsHigh correlation
PRT_FROM_END_KM_DSTC is highly overall correlated with SHRLN_FROM_STR_KM_DSTC and 3 other fieldsHigh correlation
TTL_KM_DSTC is highly overall correlated with AVG_KN and 1 other fieldsHigh correlation
AVG_KN is highly overall correlated with TTL_KM_DSTC and 1 other fieldsHigh correlation
AVG_CNTE_TM is highly overall correlated with EEZ_12_NMI_CDHigh correlation
SHRLN_FROM_STR_KM_DSTC has 5 (5.0%) zerosZeros
SHRLN_FROM_END_KM_DSTC has 22 (22.0%) zerosZeros
PRT_FROM_STR_KM_DSTC has 7 (7.0%) zerosZeros
PRT_FROM_END_KM_DSTC has 12 (12.0%) zerosZeros

Reproduction

Analysis started2024-01-14 07:00:22.942961
Analysis finished2024-01-14 07:00:32.593659
Duration9.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SHP_ID
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3a616fee9-9c70-dc46-a053-174b73f1af39
74 
1324aec35-54f7-e561-21d4-40db06bb0fc7
13 
b174c1f66-63fb-a19e-20ea-b5e358dad6a3
 
4
905fa0086-6f31-7c3c-3c13-6c9c8c35f677
 
4
a92683229-9e0b-7814-6d72-baac3ed6edf5
 
3

Length

Max length37
Median length37
Mean length37
Min length37

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3a616fee9-9c70-dc46-a053-174b73f1af39
2nd row3a616fee9-9c70-dc46-a053-174b73f1af39
3rd row3a616fee9-9c70-dc46-a053-174b73f1af39
4th row3a616fee9-9c70-dc46-a053-174b73f1af39
5th row3a616fee9-9c70-dc46-a053-174b73f1af39

Common Values

ValueCountFrequency (%)
3a616fee9-9c70-dc46-a053-174b73f1af39 74
74.0%
1324aec35-54f7-e561-21d4-40db06bb0fc7 13
 
13.0%
b174c1f66-63fb-a19e-20ea-b5e358dad6a3 4
 
4.0%
905fa0086-6f31-7c3c-3c13-6c9c8c35f677 4
 
4.0%
a92683229-9e0b-7814-6d72-baac3ed6edf5 3
 
3.0%
8307e75cb-bc03-db9a-31a4-abeb129de98b 2
 
2.0%

Length

2024-01-14T16:00:32.682566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:00:32.799992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3a616fee9-9c70-dc46-a053-174b73f1af39 74
74.0%
1324aec35-54f7-e561-21d4-40db06bb0fc7 13
 
13.0%
b174c1f66-63fb-a19e-20ea-b5e358dad6a3 4
 
4.0%
905fa0086-6f31-7c3c-3c13-6c9c8c35f677 4
 
4.0%
a92683229-9e0b-7814-6d72-baac3ed6edf5 3
 
3.0%
8307e75cb-bc03-db9a-31a4-abeb129de98b 2
 
2.0%

STR_DT
Date

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2013-01-04 07:29:00
Maximum2013-08-08 04:04:00
2024-01-14T16:00:32.940146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:33.097524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

END_DT
Date

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2013-01-04 07:57:00
Maximum2013-08-08 05:45:00
2024-01-14T16:00:33.239004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:33.376116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

LA
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.817866
Minimum34.2666
Maximum35.3361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:33.723191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.2666
5-th percentile34.36849
Q134.690025
median34.73375
Q335.104875
95-th percentile35.227415
Maximum35.3361
Range1.0695
Interquartile range (IQR)0.41485

Descriptive statistics

Standard deviation0.27857936
Coefficient of variation (CV)0.0080010465
Kurtosis-0.98401929
Mean34.817866
Median Absolute Deviation (MAD)0.204
Skewness0.10448013
Sum3481.7866
Variance0.077606462
MonotonicityNot monotonic
2024-01-14T16:00:33.857700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.7319 2
 
2.0%
34.5402 2
 
2.0%
35.184 2
 
2.0%
34.7148 2
 
2.0%
34.4663 1
 
1.0%
34.4427 1
 
1.0%
34.78 1
 
1.0%
34.7415 1
 
1.0%
34.7336 1
 
1.0%
34.7357 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
34.2666 1
1.0%
34.2834 1
1.0%
34.3113 1
1.0%
34.3249 1
1.0%
34.3341 1
1.0%
34.3703 1
1.0%
34.4056 1
1.0%
34.4088 1
1.0%
34.4427 1
1.0%
34.4454 1
1.0%
ValueCountFrequency (%)
35.3361 1
1.0%
35.2648 1
1.0%
35.2484 1
1.0%
35.2454 1
1.0%
35.2429 1
1.0%
35.2266 1
1.0%
35.2236 1
1.0%
35.218 1
1.0%
35.2088 1
1.0%
35.2008 1
1.0%

LO
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.66793
Minimum127.373
Maximum129.3759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:33.977832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.373
5-th percentile127.80906
Q1128.46308
median128.56675
Q3129.19038
95-th percentile129.32638
Maximum129.3759
Range2.0029
Interquartile range (IQR)0.7273

Descriptive statistics

Standard deviation0.49482402
Coefficient of variation (CV)0.0038457448
Kurtosis-0.59499745
Mean128.66793
Median Absolute Deviation (MAD)0.44645
Skewness-0.32915494
Sum12866.793
Variance0.24485081
MonotonicityNot monotonic
2024-01-14T16:00:34.099245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.5654 2
 
2.0%
128.5339 1
 
1.0%
127.7399 1
 
1.0%
128.4568 1
 
1.0%
128.5752 1
 
1.0%
128.5613 1
 
1.0%
128.5698 1
 
1.0%
128.5527 1
 
1.0%
128.5327 1
 
1.0%
128.5754 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
127.373 1
1.0%
127.7008 1
1.0%
127.7399 1
1.0%
127.7404 1
1.0%
127.7874 1
1.0%
127.8102 1
1.0%
127.8177 1
1.0%
127.8232 1
1.0%
127.8336 1
1.0%
127.8381 1
1.0%
ValueCountFrequency (%)
129.3759 1
1.0%
129.3443 1
1.0%
129.3324 1
1.0%
129.332 1
1.0%
129.3297 1
1.0%
129.3262 1
1.0%
129.3038 1
1.0%
129.3035 1
1.0%
129.3026 1
1.0%
129.295 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

2024-01-14T16:00:34.242980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:00:34.338627image/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
<NA>
62 
8327
38 

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 (%)
<NA> 62
62.0%
8327 38
38.0%

Length

2024-01-14T16:00:34.483433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:00:34.632115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 62
62.0%
8327 38
38.0%

RGN_FSHRS_MNGM_ORGZ_NM
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
WCPFC, ACAP, IWC, PICES
8 
IWC, ACAP, PICES, WCPFC
7 
PICES, ACAP, WCPFC, IWC
7 
ACAP, PICES, IWC, WCPFC
 
6
WCPFC, PICES, ACAP, IWC
 
6
Other values (27)
66 

Length

Max length30
Median length23
Mean length23.45
Min length12

Unique

Unique10 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
WCPFC, ACAP, IWC, PICES 8
 
8.0%
IWC, ACAP, PICES, WCPFC 7
 
7.0%
PICES, ACAP, WCPFC, IWC 7
 
7.0%
ACAP, PICES, IWC, WCPFC 6
 
6.0%
WCPFC, PICES, ACAP, IWC 6
 
6.0%
IWC, ACAP, WCPFC, PICES 6
 
6.0%
ACAP, IWC, WCPFC, PICES 5
 
5.0%
PICES, IWC, WCPFC, ACAP 5
 
5.0%
IWC, WCPFC, PICES, ACAP 4
 
4.0%
WCPFC, IWC, PICES, ACAP 4
 
4.0%
Other values (22) 42
42.0%

Length

2024-01-14T16:00:34.798962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wcpfc 100
24.6%
acap 99
24.4%
iwc 99
24.4%
pices 99
24.4%
apfic 9
 
2.2%

SHRLN_FROM_STR_KM_DSTC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.33
Minimum0
Maximum27
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:34.945369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q12
median3
Q36.25
95-th percentile18.05
Maximum27
Range27
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation5.7420213
Coefficient of variation (CV)1.0773023
Kurtosis3.8867281
Mean5.33
Median Absolute Deviation (MAD)2
Skewness2.0054695
Sum533
Variance32.970808
MonotonicityNot monotonic
2024-01-14T16:00:35.067586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 21
21.0%
1 17
17.0%
2 13
13.0%
5 10
10.0%
8 6
 
6.0%
4 6
 
6.0%
0 5
 
5.0%
11 4
 
4.0%
6 3
 
3.0%
25 2
 
2.0%
Other values (11) 13
13.0%
ValueCountFrequency (%)
0 5
 
5.0%
1 17
17.0%
2 13
13.0%
3 21
21.0%
4 6
 
6.0%
5 10
10.0%
6 3
 
3.0%
7 2
 
2.0%
8 6
 
6.0%
9 1
 
1.0%
ValueCountFrequency (%)
27 1
 
1.0%
25 2
2.0%
20 1
 
1.0%
19 1
 
1.0%
18 2
2.0%
17 1
 
1.0%
16 1
 
1.0%
13 1
 
1.0%
12 1
 
1.0%
11 4
4.0%

SHRLN_FROM_END_KM_DSTC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.81
Minimum0
Maximum25
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:35.183746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36.25
95-th percentile17.05
Maximum25
Range25
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation5.6078859
Coefficient of variation (CV)1.1658806
Kurtosis3.1890118
Mean4.81
Median Absolute Deviation (MAD)3
Skewness1.8061774
Sum481
Variance31.448384
MonotonicityNot monotonic
2024-01-14T16:00:35.301554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 22
22.0%
1 12
12.0%
3 10
10.0%
2 9
9.0%
5 8
 
8.0%
7 8
 
8.0%
4 8
 
8.0%
6 6
 
6.0%
9 3
 
3.0%
12 2
 
2.0%
Other values (10) 12
12.0%
ValueCountFrequency (%)
0 22
22.0%
1 12
12.0%
2 9
9.0%
3 10
10.0%
4 8
 
8.0%
5 8
 
8.0%
6 6
 
6.0%
7 8
 
8.0%
8 1
 
1.0%
9 3
 
3.0%
ValueCountFrequency (%)
25 2
2.0%
21 1
1.0%
20 1
1.0%
18 1
1.0%
17 1
1.0%
16 2
2.0%
15 1
1.0%
14 1
1.0%
12 2
2.0%
11 1
1.0%

PRT_FROM_STR_KM_DSTC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6373
Minimum0
Maximum35.22
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:35.431073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.355
median4.26
Q37.45
95-th percentile24.1315
Maximum35.22
Range35.22
Interquartile range (IQR)5.095

Descriptive statistics

Standard deviation7.5944975
Coefficient of variation (CV)1.1442149
Kurtosis4.8498439
Mean6.6373
Median Absolute Deviation (MAD)2.145
Skewness2.2325105
Sum663.73
Variance57.676392
MonotonicityNot monotonic
2024-01-14T16:00:35.591785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
1.44 6
 
6.0%
0.91 5
 
5.0%
4.54 5
 
5.0%
2.88 4
 
4.0%
3.8 3
 
3.0%
3.46 3
 
3.0%
6.19 3
 
3.0%
2.87 3
 
3.0%
4.26 2
 
2.0%
Other values (53) 59
59.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.91 5
5.0%
1.11 2
 
2.0%
1.44 6
6.0%
1.83 2
 
2.0%
2.14 2
 
2.0%
2.22 1
 
1.0%
2.4 1
 
1.0%
2.73 1
 
1.0%
2.74 1
 
1.0%
ValueCountFrequency (%)
35.22 1
1.0%
34.97 1
1.0%
32.73 1
1.0%
29.02 1
1.0%
25.3 1
1.0%
24.07 1
1.0%
23.39 1
1.0%
21.94 1
1.0%
19.07 1
1.0%
18.33 1
1.0%

PRT_FROM_END_KM_DSTC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0601
Minimum0
Maximum32.73
Zeros12
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:35.758892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.1375
median4.26
Q36.985
95-th percentile21.4935
Maximum32.73
Range32.73
Interquartile range (IQR)4.8475

Descriptive statistics

Standard deviation6.754632
Coefficient of variation (CV)1.1146074
Kurtosis4.5789732
Mean6.0601
Median Absolute Deviation (MAD)2.415
Skewness2.1232954
Sum606.01
Variance45.625054
MonotonicityNot monotonic
2024-01-14T16:00:35.926238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
12.0%
2.14 5
 
5.0%
1.83 4
 
4.0%
0.91 4
 
4.0%
2.4 4
 
4.0%
2.74 3
 
3.0%
4.26 3
 
3.0%
3.53 2
 
2.0%
3.8 2
 
2.0%
3.65 2
 
2.0%
Other values (53) 59
59.0%
ValueCountFrequency (%)
0.0 12
12.0%
0.91 4
 
4.0%
1.11 1
 
1.0%
1.44 2
 
2.0%
1.82 1
 
1.0%
1.83 4
 
4.0%
2.13 1
 
1.0%
2.14 5
5.0%
2.4 4
 
4.0%
2.74 3
 
3.0%
ValueCountFrequency (%)
32.73 1
1.0%
29.42 1
1.0%
27.59 1
1.0%
26.22 1
1.0%
25.55 1
1.0%
21.28 1
1.0%
19.1 1
1.0%
18.65 1
1.0%
17.01 1
1.0%
14.07 1
1.0%

TTL_KM_DSTC
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.057
Minimum0.67
Maximum69.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:36.094532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.67
5-th percentile1.078
Q14.065
median9.585
Q318.9825
95-th percentile54.294
Maximum69.32
Range68.65
Interquartile range (IQR)14.9175

Descriptive statistics

Standard deviation17.603691
Coefficient of variation (CV)1.096325
Kurtosis1.537903
Mean16.057
Median Absolute Deviation (MAD)6.205
Skewness1.5768394
Sum1605.7
Variance309.88992
MonotonicityNot monotonic
2024-01-14T16:00:36.279216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.87 2
 
2.0%
18.53 2
 
2.0%
68.11 1
 
1.0%
12.31 1
 
1.0%
3.72 1
 
1.0%
11.61 1
 
1.0%
52.23 1
 
1.0%
46.89 1
 
1.0%
18.25 1
 
1.0%
22.88 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.67 1
1.0%
0.87 1
1.0%
0.88 1
1.0%
1.0 1
1.0%
1.04 1
1.0%
1.08 1
1.0%
1.25 1
1.0%
1.36 1
1.0%
1.38 1
1.0%
1.7 1
1.0%
ValueCountFrequency (%)
69.32 1
1.0%
68.11 1
1.0%
67.29 1
1.0%
60.11 1
1.0%
54.75 1
1.0%
54.27 1
1.0%
54.07 1
1.0%
52.68 1
1.0%
52.23 1
1.0%
49.05 1
1.0%

AVG_KN
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1028
Minimum0.47
Maximum9.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:36.436819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.47
5-th percentile0.7425
Q11.6575
median2.42
Q34.12
95-th percentile7.4875
Maximum9.13
Range8.66
Interquartile range (IQR)2.4625

Descriptive statistics

Standard deviation2.063528
Coefficient of variation (CV)0.66505349
Kurtosis0.4279785
Mean3.1028
Median Absolute Deviation (MAD)1.08
Skewness1.0798971
Sum310.28
Variance4.2581476
MonotonicityNot monotonic
2024-01-14T16:00:36.586637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.07 5
 
5.0%
3.45 3
 
3.0%
0.6 2
 
2.0%
1.47 2
 
2.0%
1.33 2
 
2.0%
5.98 2
 
2.0%
2.48 2
 
2.0%
3.12 2
 
2.0%
1.75 2
 
2.0%
1.3 2
 
2.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.47 1
1.0%
0.55 1
1.0%
0.57 1
1.0%
0.6 2
2.0%
0.75 1
1.0%
0.78 1
1.0%
0.85 1
1.0%
0.9 1
1.0%
0.93 1
1.0%
0.98 1
1.0%
ValueCountFrequency (%)
9.13 1
1.0%
8.47 1
1.0%
8.07 1
1.0%
7.68 1
1.0%
7.63 1
1.0%
7.48 1
1.0%
7.39 1
1.0%
7.05 1
1.0%
7.02 1
1.0%
6.8 1
1.0%

AVG_CNTE_TM
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3748
Minimum0.21
Maximum1.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-14T16:00:36.778864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.21
Q10.2475
median0.29
Q30.435
95-th percentile0.6955
Maximum1.46
Range1.25
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.19499536
Coefficient of variation (CV)0.52026511
Kurtosis9.9572529
Mean0.3748
Median Absolute Deviation (MAD)0.07
Skewness2.6238794
Sum37.48
Variance0.038023192
MonotonicityNot monotonic
2024-01-14T16:00:36.928434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.23 8
 
8.0%
0.24 7
 
7.0%
0.27 6
 
6.0%
0.28 6
 
6.0%
0.21 6
 
6.0%
0.25 6
 
6.0%
0.29 6
 
6.0%
0.34 5
 
5.0%
0.41 5
 
5.0%
0.22 4
 
4.0%
Other values (25) 41
41.0%
ValueCountFrequency (%)
0.21 6
6.0%
0.22 4
4.0%
0.23 8
8.0%
0.24 7
7.0%
0.25 6
6.0%
0.26 3
 
3.0%
0.27 6
6.0%
0.28 6
6.0%
0.29 6
6.0%
0.32 1
 
1.0%
ValueCountFrequency (%)
1.46 1
1.0%
0.97 2
2.0%
0.81 1
1.0%
0.8 1
1.0%
0.69 1
1.0%
0.68 1
1.0%
0.61 2
2.0%
0.6 2
2.0%
0.58 1
1.0%
0.57 1
1.0%

Interactions

2024-01-14T16:00:31.290050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.126968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.888957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.877025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.686474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.487038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.695466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.643099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.521509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.371907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.205992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.976193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.978987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.780020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.565638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.785947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.728297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.585880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.471679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.288675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.070306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.077955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.884312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.685650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.886802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.831407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.668050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.571457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.369967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.158568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.162255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.964593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.789687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.989404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.936538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.746015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.656628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.453009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.268192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.235607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.056862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.161617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.110168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.042375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.828979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.748864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.543298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.364410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.318684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.157430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.258495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.224366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.154214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.934785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.837993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.639866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.489840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.448202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.240372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.387114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.333403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.247979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.028008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.965417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.721409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.630553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.531655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.327909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.510092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.445376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.340223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.123087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:32.088536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:24.796243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:25.748208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:26.604201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:27.404239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:28.601319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:29.535668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:30.425971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:00:31.202305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T16:00:37.037038image/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_ID1.0000.0000.9460.4740.7560.0000.0000.0000.0000.0000.0000.0000.288
STR_DT0.0001.0000.9990.9441.0000.9961.0001.0000.9941.0000.9770.9850.000
END_DT0.9460.9991.0001.0001.0000.9960.9951.0001.0001.0000.9931.0001.000
LA0.4740.9441.0001.0000.9340.0000.6450.8320.7890.8310.0000.0000.000
LO0.7561.0001.0000.9341.0000.0000.4110.5190.7860.7620.0000.2280.000
RGN_FSHRS_MNGM_ORGZ_NM0.0000.9960.9960.0000.0001.0000.0000.0000.0000.0000.5870.7080.000
SHRLN_FROM_STR_KM_DSTC0.0001.0000.9950.6450.4110.0001.0000.8850.8320.7780.0000.3040.000
SHRLN_FROM_END_KM_DSTC0.0001.0001.0000.8320.5190.0000.8851.0000.9320.9450.2580.0000.000
PRT_FROM_STR_KM_DSTC0.0000.9941.0000.7890.7860.0000.8320.9321.0000.9700.0000.0000.000
PRT_FROM_END_KM_DSTC0.0001.0001.0000.8310.7620.0000.7780.9450.9701.0000.0000.0000.000
TTL_KM_DSTC0.0000.9770.9930.0000.0000.5870.0000.2580.0000.0001.0000.6240.045
AVG_KN0.0000.9851.0000.0000.2280.7080.3040.0000.0000.0000.6241.0000.336
AVG_CNTE_TM0.2880.0001.0000.0000.0000.0000.0000.0000.0000.0000.0450.3361.000
2024-01-14T16:00:37.203708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SHP_IDRGN_FSHRS_MNGM_ORGZ_NMEEZ_12_NMI_CD
SHP_ID1.0000.0001.000
RGN_FSHRS_MNGM_ORGZ_NM0.0001.0001.000
EEZ_12_NMI_CD1.0001.0001.000
2024-01-14T16:00:37.303593image/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
LA1.0000.860-0.054-0.024-0.222-0.232-0.0270.014-0.2670.2651.0000.000
LO0.8601.0000.2010.216-0.019-0.025-0.059-0.017-0.4130.5161.0000.000
SHRLN_FROM_STR_KM_DSTC-0.0540.2011.0000.9060.6980.592-0.023-0.176-0.2980.0001.0000.000
SHRLN_FROM_END_KM_DSTC-0.0240.2160.9061.0000.6460.633-0.150-0.338-0.3470.0001.0000.000
PRT_FROM_STR_KM_DSTC-0.222-0.0190.6980.6461.0000.849-0.026-0.186-0.0580.0001.0000.000
PRT_FROM_END_KM_DSTC-0.232-0.0250.5920.6330.8491.000-0.037-0.284-0.1240.0001.0000.000
TTL_KM_DSTC-0.027-0.059-0.023-0.150-0.026-0.0371.0000.5460.1330.0001.0000.210
AVG_KN0.014-0.017-0.176-0.338-0.186-0.2840.5461.0000.2130.0001.0000.289
AVG_CNTE_TM-0.267-0.413-0.298-0.347-0.058-0.1240.1330.2131.0000.1731.0000.000
SHP_ID0.2650.5160.0000.0000.0000.0000.0000.0000.1731.0001.0000.000
EEZ_12_NMI_CD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
RGN_FSHRS_MNGM_ORGZ_NM0.0000.0000.0000.0000.0000.0000.2100.2890.0000.0001.0001.000

Missing values

2024-01-14T16:00:32.251335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T16:00:32.494679image/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
03a616fee9-9c70-dc46-a053-174b73f1af392013-01-04 7:292013-01-04 7:5734.4663128.533983278327WCPFC, ACAP, IWC, PICES131219.0718.651.250.60.24
13a616fee9-9c70-dc46-a053-174b73f1af392013-01-05 3:482013-01-05 5:1434.3113128.462483278327IWC, ACAP, WCPFC, PICES272534.9732.736.291.670.29
23a616fee9-9c70-dc46-a053-174b73f1af392013-01-05 6:052013-01-05 8:2334.3249128.474583278327ACAP, PICES, IWC, WCPFC252535.2229.4214.771.990.29
33a616fee9-9c70-dc46-a053-174b73f1af392013-01-06 22:262013-01-06 23:3134.3703128.480783278327IWC, PICES, WCPFC, ACAP202029.0227.593.681.60.27
43a616fee9-9c70-dc46-a053-174b73f1af392013-01-11 1:352013-01-11 4:5934.3341128.481383278327WCPFC, PICES, IWC, ACAP252132.7326.2221.743.450.68
53a616fee9-9c70-dc46-a053-174b73f1af392013-01-11 5:392013-01-11 8:2934.4088128.553683278327WCPFC, ACAP, IWC, PICES191821.9419.19.671.680.26
63a616fee9-9c70-dc46-a053-174b73f1af392013-01-12 5:012013-01-12 6:4434.5214128.57538327<NA>IWC, WCPFC, PICES, ACAP111114.8311.919.54.640.43
73a616fee9-9c70-dc46-a053-174b73f1af392013-01-12 7:322013-01-12 8:2134.4926128.607483278327IWC, WCPFC, PICES, ACAP161511.0210.622.780.550.27
83a616fee9-9c70-dc46-a053-174b73f1af392013-01-12 23:042013-01-12 23:3634.5184128.56478327<NA>PICES, ACAP, WCPFC, IWC101215.6112.875.11.470.27
93a616fee9-9c70-dc46-a053-174b73f1af392013-01-13 0:322013-01-13 1:3934.4056128.515383278327WCPFC, PICES, ACAP, IWC181724.0721.285.323.00.28
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
90905fa0086-6f31-7c3c-3c13-6c9c8c35f6772013-08-02 4:472013-08-02 6:2534.6832127.8458327<NA>IWC, ACAP, PICES, WCPFC343.662.741.362.070.81
91b174c1f66-63fb-a19e-20ea-b5e358dad6a32013-08-02 6:412013-08-02 10:0934.6937127.83368327<NA>IWC, ACAP, WCPFC, PICES333.461.839.970.750.69
92905fa0086-6f31-7c3c-3c13-6c9c8c35f6772013-08-02 20:572013-08-02 22:0234.6619127.87518327<NA>IWC, WCPFC, ACAP, PICES562.740.013.693.450.36
93905fa0086-6f31-7c3c-3c13-6c9c8c35f6772013-08-07 6:342013-08-07 9:3334.5504127.78748327<NA>ACAP, PICES, IWC, WCPFC123.345.638.360.780.42
94b174c1f66-63fb-a19e-20ea-b5e358dad6a32013-08-07 7:332013-08-07 9:5734.5402127.81778327<NA>ACAP, PICES, IWC, WCPFC343.545.767.890.850.48
953a616fee9-9c70-dc46-a053-174b73f1af392013-08-07 22:222013-08-08 1:2434.5226128.09648327<NA>IWC, ACAP, WCPFC, PICES1177.849.3114.263.140.34
96905fa0086-6f31-7c3c-3c13-6c9c8c35f6772013-08-08 0:512013-08-08 3:3534.5369127.82328327<NA>WCPFC, PICES, ACAP, IWC353.546.3812.751.820.23
973a616fee9-9c70-dc46-a053-174b73f1af392013-08-08 2:572013-08-08 3:2334.5459128.13478327<NA>PICES, ACAP, IWC, WCPFC4412.0312.40.670.930.22
98b174c1f66-63fb-a19e-20ea-b5e358dad6a32013-08-08 3:292013-08-08 5:4134.6702127.89118327<NA>WCPFC, PICES, ACAP, IWC770.910.010.41.180.24
99a92683229-9e0b-7814-6d72-baac3ed6edf52013-08-08 4:042013-08-08 5:4534.5402127.81028327<NA>WCPFC, ACAP, IWC, PICES242.886.6612.460.980.21