Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory673.8 KiB
Average record size in memory69.0 B

Variable types

Categorical3
Numeric4

Dataset

DescriptionSample
Author경북대학교 산학협력단
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT02KNU002

Alerts

NDWT_WTCH_EQPMN_ID has constant value ""Constant
SAR_NM has constant value ""Constant
WTCH_LA has constant value ""Constant
WTCH_LO is highly overall correlated with WTCH_YMDHigh correlation
WTCH_YMD is highly overall correlated with WTCH_LOHigh correlation

Reproduction

Analysis started2024-03-13 12:45:52.654449
Analysis finished2024-03-13 12:45:55.981777
Duration3.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

NDWT_WTCH_EQPMN_ID
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
KG_679
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKG_679
2nd rowKG_679
3rd rowKG_679
4th rowKG_679
5th rowKG_679

Common Values

ValueCountFrequency (%)
KG_679 10000
100.0%

Length

2024-03-13T21:45:56.095489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:45:56.269863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kg_679 10000
100.0%

SAR_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
EAST_SEA
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
EAST_SEA 10000
100.0%

Length

2024-03-13T21:45:56.495692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:45:56.658464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
east_sea 10000
100.0%

WTCH_LA
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
37.88631507
10000 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row37.88631507
2nd row37.88631507
3rd row37.88631507
4th row37.88631507
5th row37.88631507

Common Values

ValueCountFrequency (%)
37.88631507 10000
100.0%

Length

2024-03-13T21:45:56.798303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:45:56.932146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
37.88631507 10000
100.0%

WTCH_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.33871
Minimum129.24562
Maximum131.43344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:45:57.087216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.24562
5-th percentile129.35957
Q1129.76978
median130.33953
Q3130.88649
95-th percentile131.34228
Maximum131.43344
Range2.1878233
Interquartile range (IQR)1.1167015

Descriptive statistics

Standard deviation0.64234168
Coefficient of variation (CV)0.0049282496
Kurtosis-1.2199501
Mean130.33871
Median Absolute Deviation (MAD)0.5697456
Skewness-0.0050765554
Sum1303387.1
Variance0.41260283
MonotonicityNot monotonic
2024-03-13T21:45:57.278267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.4279372 131
 
1.3%
130.5674285 126
 
1.3%
129.3595677 124
 
1.2%
131.2055436 123
 
1.2%
129.4735169 120
 
1.2%
130.7497471 119
 
1.2%
129.7697846 117
 
1.2%
129.724205 117
 
1.2%
129.5646762 116
 
1.2%
131.159964 115
 
1.1%
Other values (87) 8792
87.9%
ValueCountFrequency (%)
129.2456186 101
1.0%
129.2684084 96
1.0%
129.2911983 103
1.0%
129.3139881 98
1.0%
129.3367779 92
0.9%
129.3595677 124
1.2%
129.3823576 114
1.1%
129.4051474 108
1.1%
129.4279372 131
1.3%
129.450727 113
1.1%
ValueCountFrequency (%)
131.4334419 98
1.0%
131.4106521 103
1.0%
131.3878622 111
1.1%
131.3650724 110
1.1%
131.3422826 105
1.1%
131.3194928 106
1.1%
131.2967029 98
1.0%
131.2739131 93
0.9%
131.2511233 95
0.9%
131.2283335 100
1.0%

WTCH_YMD
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20221205
Minimum20221201
Maximum20221208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:45:57.452164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20221201
5-th percentile20221201
Q120221203
median20221205
Q320221206
95-th percentile20221207
Maximum20221208
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9934723
Coefficient of variation (CV)9.8583262 × 10-8
Kurtosis-1.0789067
Mean20221205
Median Absolute Deviation (MAD)2
Skewness-0.3258182
Sum2.0221205 × 1011
Variance3.9739318
MonotonicityNot monotonic
2024-03-13T21:45:57.625946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20221207 1928
19.3%
20221205 1820
18.2%
20221206 1691
16.9%
20221204 1360
13.6%
20221202 1171
11.7%
20221201 1019
10.2%
20221203 910
9.1%
20221208 101
 
1.0%
ValueCountFrequency (%)
20221201 1019
10.2%
20221202 1171
11.7%
20221203 910
9.1%
20221204 1360
13.6%
20221205 1820
18.2%
20221206 1691
16.9%
20221207 1928
19.3%
20221208 101
 
1.0%
ValueCountFrequency (%)
20221208 101
 
1.0%
20221207 1928
19.3%
20221206 1691
16.9%
20221205 1820
18.2%
20221204 1360
13.6%
20221203 910
9.1%
20221202 1171
11.7%
20221201 1019
10.2%

PRSR
Real number (ℝ)

Distinct801
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396.7671
Minimum0
Maximum800
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:45:57.829765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q1195
median397
Q3596
95-th percentile760
Maximum800
Range800
Interquartile range (IQR)401

Descriptive statistics

Standard deviation231.53474
Coefficient of variation (CV)0.58355327
Kurtosis-1.2051631
Mean396.7671
Median Absolute Deviation (MAD)200
Skewness0.0054913578
Sum3967671
Variance53608.335
MonotonicityNot monotonic
2024-03-13T21:45:58.035026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
471 25
 
0.2%
465 23
 
0.2%
487 23
 
0.2%
516 21
 
0.2%
720 21
 
0.2%
112 21
 
0.2%
314 20
 
0.2%
505 20
 
0.2%
580 20
 
0.2%
392 20
 
0.2%
Other values (791) 9786
97.9%
ValueCountFrequency (%)
0 8
0.1%
1 18
0.2%
2 10
0.1%
3 15
0.1%
4 10
0.1%
5 15
0.1%
6 14
0.1%
7 9
0.1%
8 13
0.1%
9 12
0.1%
ValueCountFrequency (%)
800 17
0.2%
799 11
0.1%
798 18
0.2%
797 10
0.1%
796 4
 
< 0.1%
795 11
0.1%
794 16
0.2%
793 9
0.1%
792 15
0.1%
791 13
0.1%

SVEL
Real number (ℝ)

Distinct9996
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1471.5906
Minimum1456.2546
Maximum1516.7655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:45:58.238413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1456.2546
5-th percentile1457.787
Q11459.6178
median1461.3884
Q31476.1966
95-th percentile1514.6739
Maximum1516.7655
Range60.510881
Interquartile range (IQR)16.57884

Descriptive statistics

Standard deviation19.757622
Coefficient of variation (CV)0.013426031
Kurtosis0.24557991
Mean1471.5906
Median Absolute Deviation (MAD)2.149217
Skewness1.3889311
Sum14715906
Variance390.36362
MonotonicityNot monotonic
2024-03-13T21:45:58.444546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1460.373978 2
 
< 0.1%
1460.325418 2
 
< 0.1%
1459.627503 2
 
< 0.1%
1458.660586 2
 
< 0.1%
1461.555863 1
 
< 0.1%
1460.651283 1
 
< 0.1%
1460.36791 1
 
< 0.1%
1485.647987 1
 
< 0.1%
1483.266559 1
 
< 0.1%
1498.182173 1
 
< 0.1%
Other values (9986) 9986
99.9%
ValueCountFrequency (%)
1456.254625 1
< 0.1%
1456.272942 1
< 0.1%
1456.283604 1
< 0.1%
1456.29217 1
< 0.1%
1456.313754 1
< 0.1%
1456.328053 1
< 0.1%
1456.333018 1
< 0.1%
1456.337696 1
< 0.1%
1456.346513 1
< 0.1%
1456.347643 1
< 0.1%
ValueCountFrequency (%)
1516.765506 1
< 0.1%
1516.670274 1
< 0.1%
1516.662104 1
< 0.1%
1516.657378 1
< 0.1%
1516.655195 1
< 0.1%
1516.607502 1
< 0.1%
1516.588665 1
< 0.1%
1516.583185 1
< 0.1%
1516.573198 1
< 0.1%
1516.567675 1
< 0.1%

Interactions

2024-03-13T21:45:55.182729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:53.393264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:53.900680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:54.593208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:55.300220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:53.493089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:54.124826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:54.729305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:55.428680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:53.634722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:54.307504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:54.880452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:55.575108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:53.772596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:54.458154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:55.040743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:45:58.596236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LOWTCH_YMDPRSRSVEL
WTCH_LO1.0000.9850.0000.308
WTCH_YMD0.9851.0000.0000.230
PRSR0.0000.0001.0000.897
SVEL0.3080.2300.8971.000
2024-03-13T21:45:58.802447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LOWTCH_YMDPRSRSVEL
WTCH_LO1.000-0.988-0.0120.192
WTCH_YMD-0.9881.0000.010-0.186
PRSR-0.0120.0101.000-0.372
SVEL0.192-0.186-0.3721.000

Missing values

2024-03-13T21:45:55.739486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:45:55.897828image/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

NDWT_WTCH_EQPMN_IDSAR_NMWTCH_LAWTCH_LOWTCH_YMDPRSRSVEL
75979KG_679EAST_SEA37.886315131.387862202212016851461.589447
23621KG_679EAST_SEA37.886315129.906524202212063921458.197923
27769KG_679EAST_SEA37.886315130.020473202212065351460.000429
13757KG_679EAST_SEA37.886315129.633046202212071401478.337816
48498KG_679EAST_SEA37.886315130.613008202212044381460.010716
10461KG_679EAST_SEA37.886315129.54188620221207481516.607502
54440KG_679EAST_SEA37.886315130.772537202212037731463.139181
24723KG_679EAST_SEA37.886315129.929313202212066931461.63075
35824KG_679EAST_SEA37.886315130.248371202212055801460.582943
69049KG_679EAST_SEA37.886315131.205544202212021631481.049806
NDWT_WTCH_EQPMN_IDSAR_NMWTCH_LAWTCH_LOWTCH_YMDPRSRSVEL
45394KG_679EAST_SEA37.886315130.521849202212045381460.426113
22281KG_679EAST_SEA37.886315129.860944202212066541461.124822
55882KG_679EAST_SEA37.886315130.818117202212036131461.200119
60120KG_679EAST_SEA37.886315130.95485620221203451514.203603
76935KG_679EAST_SEA37.886315131.43344220221201391513.414644
627KG_679EAST_SEA37.886315129.245619202212086271460.61553
37969KG_679EAST_SEA37.886315130.31674202212053221461.248149
63590KG_679EAST_SEA37.886315131.046015202212023111459.777464
26758KG_679EAST_SEA37.886315129.997683202212063251458.249714
39270KG_679EAST_SEA37.886315130.3623220221205211512.429791