Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory117.0 B

Variable types

Numeric5
Categorical8

Dataset

DescriptionSample
Author경북대학교 산학협력단
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT02KNU003

Alerts

SRC_DATA_INST_NM has constant value ""Constant
QLITY_MNAGN_NM has constant value ""Constant
OPNG_SITE_NM has constant value ""Constant
UOM_NM has constant value ""Constant
SRC_DATA_VER_NM has constant value ""Constant
DATA_KND_NM has constant value ""Constant
DATA_RELN has constant value ""Constant
SRC_DATA_FMT_NM has constant value ""Constant
WTCH_LA is highly overall correlated with WTCH_WTEM and 1 other fieldsHigh correlation
WTCH_WTEM is highly overall correlated with WTCH_LA and 1 other fieldsHigh correlation
REVISN_WTEM is highly overall correlated with WTCH_LA and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-01-14 06:57:13.535046
Analysis finished2024-01-14 06:57:18.333476
Duration4.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WTCH_YMD
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19870107
Minimum19870101
Maximum19870113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-14T15:57:18.418457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870101
5-th percentile19870101
Q119870104
median19870107
Q319870110
95-th percentile19870112
Maximum19870113
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6300451
Coefficient of variation (CV)1.8268876 × 10-7
Kurtosis-1.1856577
Mean19870107
Median Absolute Deviation (MAD)3
Skewness0.022786164
Sum1.9870107 × 1011
Variance13.177227
MonotonicityNot monotonic
2024-01-14T15:57:18.630624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
19870102 825
8.2%
19870108 824
8.2%
19870107 823
8.2%
19870103 799
8.0%
19870109 796
 
8.0%
19870112 790
 
7.9%
19870106 790
 
7.9%
19870104 786
 
7.9%
19870105 786
 
7.9%
19870111 784
 
7.8%
Other values (3) 1997
20.0%
ValueCountFrequency (%)
19870101 767
7.7%
19870102 825
8.2%
19870103 799
8.0%
19870104 786
7.9%
19870105 786
7.9%
19870106 790
7.9%
19870107 823
8.2%
19870108 824
8.2%
19870109 796
8.0%
19870110 757
7.6%
ValueCountFrequency (%)
19870113 473
4.7%
19870112 790
7.9%
19870111 784
7.8%
19870110 757
7.6%
19870109 796
8.0%
19870108 824
8.2%
19870107 823
8.2%
19870106 790
7.9%
19870105 786
7.9%
19870104 786
7.9%

WTCH_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.38335
Minimum34.125
Maximum51.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-14T15:57:18.835812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.125
5-th percentile34.625
Q137.375
median39.875
Q342.625
95-th percentile47.875
Maximum51.875
Range17.75
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation3.9908393
Coefficient of variation (CV)0.098823878
Kurtosis-0.20273683
Mean40.38335
Median Absolute Deviation (MAD)2.5
Skewness0.56297553
Sum403833.5
Variance15.926798
MonotonicityNot monotonic
2024-01-14T15:57:19.077350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.125 283
 
2.8%
38.875 274
 
2.7%
39.875 263
 
2.6%
38.375 262
 
2.6%
39.625 249
 
2.5%
41.375 248
 
2.5%
39.125 244
 
2.4%
39.375 244
 
2.4%
38.625 243
 
2.4%
40.875 242
 
2.4%
Other values (62) 7448
74.5%
ValueCountFrequency (%)
34.125 237
2.4%
34.375 209
2.1%
34.625 214
2.1%
34.875 162
1.6%
35.125 146
1.5%
35.375 118
1.2%
35.625 160
1.6%
35.875 183
1.8%
36.125 165
1.7%
36.375 210
2.1%
ValueCountFrequency (%)
51.875 9
 
0.1%
51.625 15
0.1%
51.375 26
0.3%
51.125 28
0.3%
50.875 36
0.4%
50.625 25
0.2%
50.375 25
0.2%
50.125 34
0.3%
49.875 24
0.2%
49.625 36
0.4%

WTCH_LO
Real number (ℝ)

Distinct64
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.4165
Minimum127.125
Maximum142.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-14T15:57:19.317304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.125
5-th percentile129.625
Q1132.625
median136.875
Q3140.125
95-th percentile142.375
Maximum142.875
Range15.75
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.2318369
Coefficient of variation (CV)0.031021445
Kurtosis-1.1547104
Mean136.4165
Median Absolute Deviation (MAD)3.75
Skewness-0.21898187
Sum1364165
Variance17.908444
MonotonicityNot monotonic
2024-01-14T15:57:19.526284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141.625 288
 
2.9%
141.875 258
 
2.6%
141.375 257
 
2.6%
141.125 238
 
2.4%
140.875 234
 
2.3%
142.875 228
 
2.3%
138.625 217
 
2.2%
142.125 211
 
2.1%
138.125 209
 
2.1%
142.625 209
 
2.1%
Other values (54) 7651
76.5%
ValueCountFrequency (%)
127.125 10
 
0.1%
127.375 12
 
0.1%
127.625 33
 
0.3%
127.875 25
 
0.2%
128.125 43
0.4%
128.375 49
0.5%
128.625 64
0.6%
128.875 73
0.7%
129.125 73
0.7%
129.375 89
0.9%
ValueCountFrequency (%)
142.875 228
2.3%
142.625 209
2.1%
142.375 181
1.8%
142.125 211
2.1%
141.875 258
2.6%
141.625 288
2.9%
141.375 257
2.6%
141.125 238
2.4%
140.875 234
2.3%
140.625 192
1.9%

WTCH_WTEM
Real number (ℝ)

HIGH CORRELATION 

Distinct2070
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.148654
Minimum-1.5
Maximum20.21
Zeros1
Zeros (%)< 0.1%
Negative350
Negative (%)3.5%
Memory size166.0 KiB
2024-01-14T15:57:19.686627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.5
5-th percentile0.4995
Q13.12
median7.64
Q312.62
95-th percentile17.5605
Maximum20.21
Range21.71
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation5.5266569
Coefficient of variation (CV)0.67822942
Kurtosis-1.0947358
Mean8.148654
Median Absolute Deviation (MAD)4.74
Skewness0.2334023
Sum81486.54
Variance30.543936
MonotonicityNot monotonic
2024-01-14T15:57:19.829590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 18
 
0.2%
2.6 15
 
0.1%
2.13 15
 
0.1%
2.82 15
 
0.1%
3.2 14
 
0.1%
2.07 14
 
0.1%
2.29 14
 
0.1%
1.33 14
 
0.1%
6.2 14
 
0.1%
1.44 14
 
0.1%
Other values (2060) 9853
98.5%
ValueCountFrequency (%)
-1.5 1
< 0.1%
-1.46 1
< 0.1%
-1.45 1
< 0.1%
-1.43 2
< 0.1%
-1.34 2
< 0.1%
-1.33 1
< 0.1%
-1.27 1
< 0.1%
-1.26 1
< 0.1%
-1.25 2
< 0.1%
-1.23 1
< 0.1%
ValueCountFrequency (%)
20.21 1
< 0.1%
20.17 1
< 0.1%
20.13 2
< 0.1%
20.1 1
< 0.1%
20.07 1
< 0.1%
20.05 1
< 0.1%
20.04 1
< 0.1%
20.03 1
< 0.1%
20.02 1
< 0.1%
19.98 1
< 0.1%

REVISN_WTEM
Real number (ℝ)

HIGH CORRELATION 

Distinct2070
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.736782
Minimum-1.6943
Maximum20.21
Zeros0
Zeros (%)0.0%
Negative507
Negative (%)5.1%
Memory size166.0 KiB
2024-01-14T15:57:20.010334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.6943
5-th percentile-0.021135
Q12.3823
median7.0207
Q312.4711
95-th percentile17.731215
Maximum20.21
Range21.9043
Interquartile range (IQR)10.0888

Descriptive statistics

Standard deviation5.8031128
Coefficient of variation (CV)0.75006803
Kurtosis-1.1164604
Mean7.736782
Median Absolute Deviation (MAD)5.0021
Skewness0.28669666
Sum77367.82
Variance33.676119
MonotonicityNot monotonic
2024-01-14T15:57:20.155757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7924 18
 
0.2%
1.8866 15
 
0.1%
1.4465 15
 
0.1%
2.0953 15
 
0.1%
2.4594 14
 
0.1%
1.3908 14
 
0.1%
1.5955 14
 
0.1%
0.7148 14
 
0.1%
5.4881 14
 
0.1%
0.8141 14
 
0.1%
Other values (2060) 9853
98.5%
ValueCountFrequency (%)
-1.6943 1
< 0.1%
-1.6622 1
< 0.1%
-1.6541 1
< 0.1%
-1.638 2
< 0.1%
-1.5655 2
< 0.1%
-1.5574 1
< 0.1%
-1.5088 1
< 0.1%
-1.5007 1
< 0.1%
-1.4926 2
< 0.1%
-1.4764 1
< 0.1%
ValueCountFrequency (%)
20.21 1
< 0.1%
20.1897 1
< 0.1%
20.17 1
< 0.1%
20.1697 2
< 0.1%
20.1597 1
< 0.1%
20.1496 1
< 0.1%
20.13 2
< 0.1%
20.1296 1
< 0.1%
20.1196 2
< 0.1%
20.1 1
< 0.1%

SRC_DATA_INST_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
National Centers for Environmental Information National Oceanic and Atmospheric Administration
10000 

Length

Max length94
Median length94
Mean length94
Min length94

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational Centers for Environmental Information National Oceanic and Atmospheric Administration
2nd rowNational Centers for Environmental Information National Oceanic and Atmospheric Administration
3rd rowNational Centers for Environmental Information National Oceanic and Atmospheric Administration
4th rowNational Centers for Environmental Information National Oceanic and Atmospheric Administration
5th rowNational Centers for Environmental Information National Oceanic and Atmospheric Administration

Common Values

ValueCountFrequency (%)
National Centers for Environmental Information National Oceanic and Atmospheric Administration 10000
100.0%

Length

2024-01-14T15:57:20.295434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:20.391371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
national 20000
20.0%
centers 10000
10.0%
for 10000
10.0%
environmental 10000
10.0%
information 10000
10.0%
oceanic 10000
10.0%
and 10000
10.0%
atmospheric 10000
10.0%
administration 10000
10.0%

QLITY_MNAGN_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Korea Autonomous Ocean observing System from Kyungbook national University (KAOS)
10000 

Length

Max length81
Median length81
Mean length81
Min length81

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)
2nd rowKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)
3rd rowKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)
4th rowKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)
5th rowKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)

Common Values

ValueCountFrequency (%)
Korea Autonomous Ocean observing System from Kyungbook national University (KAOS) 10000
100.0%

Length

2024-01-14T15:57:20.495221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:20.596113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
korea 10000
10.0%
autonomous 10000
10.0%
ocean 10000
10.0%
observing 10000
10.0%
system 10000
10.0%
from 10000
10.0%
kyungbook 10000
10.0%
national 10000
10.0%
university 10000
10.0%
kaos 10000
10.0%

OPNG_SITE_NM
Categorical

CONSTANT 

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

Length

Max length22
Median length22
Mean length22
Min length22

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOAST BIGDATA PLATFORM
2nd rowCOAST BIGDATA PLATFORM
3rd rowCOAST BIGDATA PLATFORM
4th rowCOAST BIGDATA PLATFORM
5th rowCOAST BIGDATA PLATFORM

Common Values

ValueCountFrequency (%)
COAST BIGDATA PLATFORM 10000
100.0%

Length

2024-01-14T15:57:20.705692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:20.814821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
coast 10000
33.3%
bigdata 10000
33.3%
platform 10000
33.3%

UOM_NM
Categorical

CONSTANT 

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

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Celsius 10000
100.0%

Length

2024-01-14T15:57:20.940703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:21.061501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
celsius 10000
100.0%

SRC_DATA_VER_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
OISST version 2.0
10000 

Length

Max length17
Median length17
Mean length17
Min length17

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOISST version 2.0
2nd rowOISST version 2.0
3rd rowOISST version 2.0
4th rowOISST version 2.0
5th rowOISST version 2.0

Common Values

ValueCountFrequency (%)
OISST version 2.0 10000
100.0%

Length

2024-01-14T15:57:21.193243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:21.321545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
oisst 10000
33.3%
version 10000
33.3%
2.0 10000
33.3%

DATA_KND_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Optimum Interpolation Sea Surface Temperature (OISST)
10000 

Length

Max length53
Median length53
Mean length53
Min length53

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOptimum Interpolation Sea Surface Temperature (OISST)
2nd rowOptimum Interpolation Sea Surface Temperature (OISST)
3rd rowOptimum Interpolation Sea Surface Temperature (OISST)
4th rowOptimum Interpolation Sea Surface Temperature (OISST)
5th rowOptimum Interpolation Sea Surface Temperature (OISST)

Common Values

ValueCountFrequency (%)
Optimum Interpolation Sea Surface Temperature (OISST) 10000
100.0%

Length

2024-01-14T15:57:21.454888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:21.557603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
optimum 10000
16.7%
interpolation 10000
16.7%
sea 10000
16.7%
surface 10000
16.7%
temperature 10000
16.7%
oisst 10000
16.7%

DATA_RELN
Categorical

CONSTANT 

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

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowper day
2nd rowper day
3rd rowper day
4th rowper day
5th rowper day

Common Values

ValueCountFrequency (%)
per day 10000
100.0%

Length

2024-01-14T15:57:21.650392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:21.735132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
per 10000
50.0%
day 10000
50.0%

SRC_DATA_FMT_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
netCDF WOD(World Ocean Database) format
10000 

Length

Max length39
Median length39
Mean length39
Min length39

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownetCDF WOD(World Ocean Database) format
2nd rownetCDF WOD(World Ocean Database) format
3rd rownetCDF WOD(World Ocean Database) format
4th rownetCDF WOD(World Ocean Database) format
5th rownetCDF WOD(World Ocean Database) format

Common Values

ValueCountFrequency (%)
netCDF WOD(World Ocean Database) format 10000
100.0%

Length

2024-01-14T15:57:21.821688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:57:21.902797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
netcdf 10000
20.0%
wod(world 10000
20.0%
ocean 10000
20.0%
database 10000
20.0%
format 10000
20.0%

Interactions

2024-01-14T15:57:17.132967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:14.478355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:14.998080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:15.885930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:16.462943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:17.247313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:14.569903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:15.106514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:15.984849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:16.579453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:17.369038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:14.678212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:15.523848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:16.089289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:16.705098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:17.525060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:14.781865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:15.635035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:16.213128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:16.831338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:17.649259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:14.907139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:15.735152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:16.342972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:57:16.964930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:57:21.958829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_YMDWTCH_LAWTCH_LOWTCH_WTEMREVISN_WTEM
WTCH_YMD1.0000.0720.0000.1440.130
WTCH_LA0.0721.0000.6080.9260.922
WTCH_LO0.0000.6081.0000.5790.582
WTCH_WTEM0.1440.9260.5791.0000.995
REVISN_WTEM0.1300.9220.5820.9951.000
2024-01-14T15:57:22.337464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_YMDWTCH_LAWTCH_LOWTCH_WTEMREVISN_WTEM
WTCH_YMD1.000-0.054-0.0280.0030.003
WTCH_LA-0.0541.0000.358-0.951-0.951
WTCH_LO-0.0280.3581.000-0.190-0.190
WTCH_WTEM0.003-0.951-0.1901.0001.000
REVISN_WTEM0.003-0.951-0.1901.0001.000

Missing values

2024-01-14T15:57:17.865196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:57:18.201927image/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

WTCH_YMDWTCH_LAWTCH_LOWTCH_WTEMREVISN_WTEMSRC_DATA_INST_NMQLITY_MNAGN_NMOPNG_SITE_NMUOM_NMSRC_DATA_VER_NMDATA_KND_NMDATA_RELNSRC_DATA_FMT_NM
69561987010436.875141.12513.5913.5265National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
257011987011245.125139.1251.671.023National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
47811987010336.875135.87514.7514.7758National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
6121987010137.875132.87513.1713.0706National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
138781987010739.125131.8759.178.6838National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
83771987010445.375138.6253.542.7892National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
78781987010441.375142.8755.084.3278National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
106441987010546.625138.3751.220.6159National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
70291987010437.375134.87512.3112.1322National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
114541987010637.875133.37513.1513.0488National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
WTCH_YMDWTCH_LAWTCH_LOWTCH_WTEMREVISN_WTEMSRC_DATA_INST_NMQLITY_MNAGN_NMOPNG_SITE_NMUOM_NMSRC_DATA_VER_NMDATA_KND_NMDATA_RELNSRC_DATA_FMT_NM
75001987010439.625136.1256.896.2175National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
138351987010738.875134.87511.1810.8923National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
247461987011239.125138.87511.0410.7383National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
272521987011340.875133.6252.982.248National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
194241987010948.625140.3750.43-0.0817National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
51141987010338.625135.87512.3112.1322National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
30711987010239.125141.8759.989.5725National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
228131987011140.375131.8755.054.2972National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
82571987010444.125136.6252.441.736National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format
142841987010741.125131.1253.993.2314National Centers for Environmental Information National Oceanic and Atmospheric AdministrationKorea Autonomous Ocean observing System from Kyungbook national University (KAOS)COAST BIGDATA PLATFORMCelsiusOISST version 2.0Optimum Interpolation Sea Surface Temperature (OISST)per daynetCDF WOD(World Ocean Database) format