Overview

Dataset statistics

Number of variables10
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory94.1 B

Variable types

Categorical3
Numeric7

Dataset

DescriptionSample
Author한국해양과학기술원
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT00KST019

Alerts

WTCH_LO is highly overall correlated with WTCH_YMDHMS and 5 other fieldsHigh correlation
STA_NM is highly overall correlated with WTCH_YMDHMS and 5 other fieldsHigh correlation
WTCH_LA is highly overall correlated with WTCH_YMDHMS and 5 other fieldsHigh correlation
WTCH_YMDHMS is highly overall correlated with VRTL_WTEM and 5 other fieldsHigh correlation
VRTL_WTEM is highly overall correlated with WTCH_YMDHMS and 4 other fieldsHigh correlation
VRTL_SLNTY is highly overall correlated with WTCH_YMDHMS and 6 other fieldsHigh correlation
METER_WTDP is highly overall correlated with WTCH_YMDHMS and 3 other fieldsHigh correlation
VRTL_PH is highly overall correlated with VRTL_WTEM and 5 other fieldsHigh correlation
VRTL_TU is highly overall correlated with VRTL_PHHigh correlation
VRTL_DOXN is highly overall correlated with VRTL_WTEM and 6 other fieldsHigh correlation
WTCH_YMDHMS has unique valuesUnique
VRTL_WTEM has unique valuesUnique
VRTL_SLNTY has unique valuesUnique
METER_WTDP has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:42:08.370718
Analysis finished2024-03-13 12:42:16.467332
Duration8.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STA_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
2018-갈수기-A1-Ebb
13 
2018-갈수기-A2-Ebb
13 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-갈수기-A1-Ebb
2nd row2018-갈수기-A1-Ebb
3rd row2018-갈수기-A1-Ebb
4th row2018-갈수기-A1-Ebb
5th row2018-갈수기-A1-Ebb

Common Values

ValueCountFrequency (%)
2018-갈수기-A1-Ebb 13
50.0%
2018-갈수기-A2-Ebb 13
50.0%

Length

2024-03-13T21:42:16.552999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:16.687785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-갈수기-a1-ebb 13
50.0%
2018-갈수기-a2-ebb 13
50.0%

WTCH_LA
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
35.1116
13 
35.094987
13 

Length

Max length9
Median length8
Mean length8
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row35.1116
2nd row35.1116
3rd row35.1116
4th row35.1116
5th row35.1116

Common Values

ValueCountFrequency (%)
35.1116 13
50.0%
35.094987 13
50.0%

Length

2024-03-13T21:42:16.933599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:17.108898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
35.1116 13
50.0%
35.094987 13
50.0%

WTCH_LO
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
128.897975
13 
128.893659
13 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row128.897975
2nd row128.897975
3rd row128.897975
4th row128.897975
5th row128.897975

Common Values

ValueCountFrequency (%)
128.897975 13
50.0%
128.893659 13
50.0%

Length

2024-03-13T21:42:17.690747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:17.822105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
128.897975 13
50.0%
128.893659 13
50.0%

WTCH_YMDHMS
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0180418 × 1013
Minimum2.0180418 × 1013
Maximum2.0180418 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T21:42:17.965968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0180418 × 1013
5-th percentile2.0180418 × 1013
Q12.0180418 × 1013
median2.0180418 × 1013
Q32.0180418 × 1013
95-th percentile2.0180418 × 1013
Maximum2.0180418 × 1013
Range5400
Interquartile range (IQR)5335

Descriptive statistics

Standard deviation2730.3235
Coefficient of variation (CV)1.3529569 × 10-10
Kurtosis-2.1736506
Mean2.0180418 × 1013
Median Absolute Deviation (MAD)2668
Skewness-0.00014627843
Sum5.2469087 × 1014
Variance7454666.6
MonotonicityStrictly increasing
2024-03-13T21:42:18.128062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20180418125548 1
 
3.8%
20180418130926 1
 
3.8%
20180418130948 1
 
3.8%
20180418130946 1
 
3.8%
20180418130944 1
 
3.8%
20180418130942 1
 
3.8%
20180418130940 1
 
3.8%
20180418130938 1
 
3.8%
20180418130936 1
 
3.8%
20180418130934 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
20180418125548 1
3.8%
20180418125550 1
3.8%
20180418125552 1
3.8%
20180418125554 1
3.8%
20180418125556 1
3.8%
20180418125558 1
3.8%
20180418125600 1
3.8%
20180418125602 1
3.8%
20180418125604 1
3.8%
20180418125606 1
3.8%
ValueCountFrequency (%)
20180418130948 1
3.8%
20180418130946 1
3.8%
20180418130944 1
3.8%
20180418130942 1
3.8%
20180418130940 1
3.8%
20180418130938 1
3.8%
20180418130936 1
3.8%
20180418130934 1
3.8%
20180418130932 1
3.8%
20180418130930 1
3.8%

VRTL_WTEM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.659731
Minimum12.478
Maximum15.329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T21:42:18.291531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.478
5-th percentile12.53475
Q112.95425
median13.6605
Q314.21525
95-th percentile15.0375
Maximum15.329
Range2.851
Interquartile range (IQR)1.261

Descriptive statistics

Standard deviation0.8172569
Coefficient of variation (CV)0.059829649
Kurtosis-0.76674175
Mean13.659731
Median Absolute Deviation (MAD)0.694
Skewness0.32737019
Sum355.153
Variance0.66790884
MonotonicityNot monotonic
2024-03-13T21:42:18.456790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
14.534 1
 
3.8%
15.205 1
 
3.8%
12.478 1
 
3.8%
12.511 1
 
3.8%
12.606 1
 
3.8%
12.7 1
 
3.8%
12.942 1
 
3.8%
13.138 1
 
3.8%
13.49 1
 
3.8%
13.699 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
12.478 1
3.8%
12.511 1
3.8%
12.606 1
3.8%
12.7 1
3.8%
12.823 1
3.8%
12.916 1
3.8%
12.942 1
3.8%
12.991 1
3.8%
13.138 1
3.8%
13.162 1
3.8%
ValueCountFrequency (%)
15.329 1
3.8%
15.205 1
3.8%
14.535 1
3.8%
14.534 1
3.8%
14.528 1
3.8%
14.48 1
3.8%
14.217 1
3.8%
14.21 1
3.8%
14.045 1
3.8%
13.926 1
3.8%

VRTL_SLNTY
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.9395
Minimum5.828
Maximum26.883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T21:42:18.600587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.828
5-th percentile7.99725
Q113.12775
median19.7085
Q324.24225
95-th percentile26.81175
Maximum26.883
Range21.055
Interquartile range (IQR)11.1145

Descriptive statistics

Standard deviation7.0155102
Coefficient of variation (CV)0.39106498
Kurtosis-1.2952518
Mean17.9395
Median Absolute Deviation (MAD)6.3565
Skewness-0.23481698
Sum466.427
Variance49.217384
MonotonicityNot monotonic
2024-03-13T21:42:18.751535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7.998 1
 
3.8%
9.339 1
 
3.8%
26.883 1
 
3.8%
26.841 1
 
3.8%
26.724 1
 
3.8%
26.616 1
 
3.8%
26.323 1
 
3.8%
26.14 1
 
3.8%
24.747 1
 
3.8%
22.728 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
5.828 1
3.8%
7.997 1
3.8%
7.998 1
3.8%
8.072 1
3.8%
9.167 1
3.8%
9.339 1
3.8%
13.028 1
3.8%
13.427 1
3.8%
14.299 1
3.8%
14.688 1
3.8%
ValueCountFrequency (%)
26.883 1
3.8%
26.841 1
3.8%
26.724 1
3.8%
26.616 1
3.8%
26.323 1
3.8%
26.14 1
3.8%
24.747 1
3.8%
22.728 1
3.8%
21.447 1
3.8%
21.109 1
3.8%

METER_WTDP
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1670769
Minimum0.086
Maximum2.776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T21:42:18.904576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.086
5-th percentile0.0995
Q10.35725
median1.0305
Q31.66975
95-th percentile2.68175
Maximum2.776
Range2.69
Interquartile range (IQR)1.3125

Descriptive statistics

Standard deviation0.88250208
Coefficient of variation (CV)0.75616445
Kurtosis-1.045747
Mean1.1670769
Median Absolute Deviation (MAD)0.6805
Skewness0.43878415
Sum30.344
Variance0.77880991
MonotonicityNot monotonic
2024-03-13T21:42:19.088617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.086 1
 
3.8%
0.174 1
 
3.8%
2.776 1
 
3.8%
2.725 1
 
3.8%
2.552 1
 
3.8%
2.445 1
 
3.8%
2.188 1
 
3.8%
1.944 1
 
3.8%
1.116 1
 
3.8%
0.945 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
0.086 1
3.8%
0.092 1
3.8%
0.122 1
3.8%
0.174 1
3.8%
0.188 1
3.8%
0.266 1
3.8%
0.309 1
3.8%
0.502 1
3.8%
0.558 1
3.8%
0.664 1
3.8%
ValueCountFrequency (%)
2.776 1
3.8%
2.725 1
3.8%
2.552 1
3.8%
2.445 1
3.8%
2.188 1
3.8%
1.944 1
3.8%
1.67 1
3.8%
1.669 1
3.8%
1.66 1
3.8%
1.538 1
3.8%

VRTL_PH
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9969231
Minimum7.9
Maximum8.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T21:42:19.328891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.9
5-th percentile7.9
Q17.9325
median7.98
Q38.0375
95-th percentile8.125
Maximum8.22
Range0.32
Interquartile range (IQR)0.105

Descriptive statistics

Standard deviation0.081817809
Coefficient of variation (CV)0.010231161
Kurtosis0.63783372
Mean7.9969231
Median Absolute Deviation (MAD)0.055
Skewness0.8826124
Sum207.92
Variance0.0066941538
MonotonicityNot monotonic
2024-03-13T21:42:19.550838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8.08 4
15.4%
7.98 3
11.5%
7.9 3
11.5%
7.91 2
 
7.7%
8.01 2
 
7.7%
7.94 2
 
7.7%
8.03 2
 
7.7%
8.04 1
 
3.8%
7.95 1
 
3.8%
7.93 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
7.9 3
11.5%
7.91 2
7.7%
7.92 1
 
3.8%
7.93 1
 
3.8%
7.94 2
7.7%
7.95 1
 
3.8%
7.96 1
 
3.8%
7.98 3
11.5%
8.01 2
7.7%
8.02 1
 
3.8%
ValueCountFrequency (%)
8.22 1
 
3.8%
8.14 1
 
3.8%
8.08 4
15.4%
8.04 1
 
3.8%
8.03 2
7.7%
8.02 1
 
3.8%
8.01 2
7.7%
7.98 3
11.5%
7.96 1
 
3.8%
7.95 1
 
3.8%

VRTL_TU
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2777692
Minimum6.482
Maximum20.459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T21:42:19.746094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.482
5-th percentile6.543
Q17.1225
median7.642
Q37.93175
95-th percentile12.9055
Maximum20.459
Range13.977
Interquartile range (IQR)0.80925

Descriptive statistics

Standard deviation2.8742173
Coefficient of variation (CV)0.34722124
Kurtosis13.948656
Mean8.2777692
Median Absolute Deviation (MAD)0.489
Skewness3.6172548
Sum215.222
Variance8.2611253
MonotonicityNot monotonic
2024-03-13T21:42:19.946986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6.543 2
 
7.7%
7.092 2
 
7.7%
7.214 2
 
7.7%
7.764 2
 
7.7%
7.703 2
 
7.7%
6.482 1
 
3.8%
7.336 1
 
3.8%
7.52 1
 
3.8%
7.947 1
 
3.8%
7.825 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
6.482 1
3.8%
6.543 2
7.7%
6.726 1
3.8%
7.031 1
3.8%
7.092 2
7.7%
7.214 2
7.7%
7.336 1
3.8%
7.397 1
3.8%
7.52 1
3.8%
7.581 1
3.8%
ValueCountFrequency (%)
20.459 1
3.8%
14.172 1
3.8%
9.106 1
3.8%
8.496 1
3.8%
8.374 1
3.8%
8.252 1
3.8%
7.947 1
3.8%
7.886 1
3.8%
7.825 1
3.8%
7.764 2
7.7%

VRTL_DOXN
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8878846
Minimum8.322
Maximum9.958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T21:42:20.136373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.322
5-th percentile8.3925
Q18.52475
median8.7095
Q39.3165
95-th percentile9.6445
Maximum9.958
Range1.636
Interquartile range (IQR)0.79175

Descriptive statistics

Standard deviation0.47322226
Coefficient of variation (CV)0.05324352
Kurtosis-0.71809184
Mean8.8878846
Median Absolute Deviation (MAD)0.249
Skewness0.73153521
Sum231.085
Variance0.22393931
MonotonicityNot monotonic
2024-03-13T21:42:20.327146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
9.321 2
 
7.7%
9.303 1
 
3.8%
8.515 1
 
3.8%
8.523 1
 
3.8%
8.53 1
 
3.8%
8.533 1
 
3.8%
8.534 1
 
3.8%
8.545 1
 
3.8%
8.66 1
 
3.8%
8.808 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
8.322 1
3.8%
8.383 1
3.8%
8.421 1
3.8%
8.463 1
3.8%
8.486 1
3.8%
8.515 1
3.8%
8.523 1
3.8%
8.53 1
3.8%
8.533 1
3.8%
8.534 1
3.8%
ValueCountFrequency (%)
9.958 1
3.8%
9.646 1
3.8%
9.64 1
3.8%
9.502 1
3.8%
9.336 1
3.8%
9.321 2
7.7%
9.303 1
3.8%
9.189 1
3.8%
8.961 1
3.8%
8.873 1
3.8%

Interactions

2024-03-13T21:42:15.085796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:09.277216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:10.281742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:11.280569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:12.116155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:13.132930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:14.020214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:15.238245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:09.396617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:10.436229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:11.392364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:12.280591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:13.273956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:14.133590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:15.381651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:09.512580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:10.561504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:11.526486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:12.442892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:13.403243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:14.248099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:15.505634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:09.633165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:10.689391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:11.631398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:12.588344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:13.515982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:14.376385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:15.642809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:09.754761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:10.836573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:11.746711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:12.710596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:13.632084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:14.564815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:15.783164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:09.912189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:10.984288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:11.863080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:12.832896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:13.754244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:14.749616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:15.941947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:10.094264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:11.123662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:11.978911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:12.985664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:13.872430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:14.927429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:42:20.467014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STA_NMWTCH_LAWTCH_LOWTCH_YMDHMSVRTL_WTEMVRTL_SLNTYMETER_WTDPVRTL_PHVRTL_TUVRTL_DOXN
STA_NM1.0000.9920.9920.9910.4890.8920.5270.8150.5810.604
WTCH_LA0.9921.0000.9920.9910.4890.8920.5270.8150.5810.604
WTCH_LO0.9920.9921.0000.9910.4890.8920.5270.8150.5810.604
WTCH_YMDHMS0.9910.9910.9911.0000.4410.8830.5280.8020.6490.605
VRTL_WTEM0.4890.4890.4890.4411.0000.9060.7900.7060.2150.904
VRTL_SLNTY0.8920.8920.8920.8830.9061.0000.8250.8870.7970.861
METER_WTDP0.5270.5270.5270.5280.7900.8251.0000.6360.0000.630
VRTL_PH0.8150.8150.8150.8020.7060.8870.6361.0000.3910.763
VRTL_TU0.5810.5810.5810.6490.2150.7970.0000.3911.0000.000
VRTL_DOXN0.6040.6040.6040.6050.9040.8610.6300.7630.0001.000
2024-03-13T21:42:20.650305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_LOSTA_NMWTCH_LA
WTCH_LO1.0000.9200.920
STA_NM0.9201.0000.920
WTCH_LA0.9200.9201.000
2024-03-13T21:42:20.789539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_YMDHMSVRTL_WTEMVRTL_SLNTYMETER_WTDPVRTL_PHVRTL_TUVRTL_DOXNSTA_NMWTCH_LAWTCH_LO
WTCH_YMDHMS1.000-0.6540.7930.6810.0160.113-0.2610.9200.9200.920
VRTL_WTEM-0.6541.000-0.950-0.9810.517-0.2970.8710.4010.4010.401
VRTL_SLNTY0.793-0.9501.0000.955-0.4360.246-0.7430.6180.6180.618
METER_WTDP0.681-0.9810.9551.000-0.4930.260-0.8440.3130.3130.313
VRTL_PH0.0160.517-0.436-0.4931.000-0.7670.7200.5440.5440.544
VRTL_TU0.113-0.2970.2460.260-0.7671.000-0.4280.3770.3770.377
VRTL_DOXN-0.2610.871-0.743-0.8440.720-0.4281.0000.5040.5040.504
STA_NM0.9200.4010.6180.3130.5440.3770.5041.0000.9200.920
WTCH_LA0.9200.4010.6180.3130.5440.3770.5040.9201.0000.920
WTCH_LO0.9200.4010.6180.3130.5440.3770.5040.9200.9201.000

Missing values

2024-03-13T21:42:16.154737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:42:16.391353image/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

STA_NMWTCH_LAWTCH_LOWTCH_YMDHMSVRTL_WTEMVRTL_SLNTYMETER_WTDPVRTL_PHVRTL_TUVRTL_DOXN
02018-갈수기-A1-Ebb35.1116128.8979752018041812554814.5347.9980.0868.086.4829.321
12018-갈수기-A1-Ebb35.1116128.8979752018041812555014.5357.9970.0928.086.5439.321
22018-갈수기-A1-Ebb35.1116128.8979752018041812555214.5288.0720.1888.086.5439.303
32018-갈수기-A1-Ebb35.1116128.8979752018041812555414.489.1670.3098.046.7269.189
42018-갈수기-A1-Ebb35.1116128.8979752018041812555614.2113.0280.5587.987.0318.961
52018-갈수기-A1-Ebb35.1116128.8979752018041812555814.04514.2990.7187.957.3978.873
62018-갈수기-A1-Ebb35.1116128.8979752018041812560013.87616.2710.8797.937.8868.759
72018-갈수기-A1-Ebb35.1116128.8979752018041812560213.62219.2071.1487.928.2528.553
82018-갈수기-A1-Ebb35.1116128.8979752018041812560413.30920.211.417.98.3748.486
92018-갈수기-A1-Ebb35.1116128.8979752018041812560613.16220.5331.5387.98.4968.463
STA_NMWTCH_LAWTCH_LOWTCH_YMDHMSVRTL_WTEMVRTL_SLNTYMETER_WTDPVRTL_PHVRTL_TUVRTL_DOXN
162018-갈수기-A2-Ebb35.094987128.8936592018041813093013.92614.6880.5028.017.7649.502
172018-갈수기-A2-Ebb35.094987128.8936592018041813093213.88115.960.6647.987.8259.336
182018-갈수기-A2-Ebb35.094987128.8936592018041813093413.69922.7280.9457.947.7648.808
192018-갈수기-A2-Ebb35.094987128.8936592018041813093613.4924.7471.1167.947.9478.66
202018-갈수기-A2-Ebb35.094987128.8936592018041813093813.13826.141.9447.967.528.545
212018-갈수기-A2-Ebb35.094987128.8936592018041813094012.94226.3232.1887.987.3368.534
222018-갈수기-A2-Ebb35.094987128.8936592018041813094212.726.6162.4458.017.2148.533
232018-갈수기-A2-Ebb35.094987128.8936592018041813094412.60626.7242.5528.027.2148.53
242018-갈수기-A2-Ebb35.094987128.8936592018041813094612.51126.8412.7258.037.0928.523
252018-갈수기-A2-Ebb35.094987128.8936592018041813094812.47826.8832.7768.037.0928.515