Overview

Dataset statistics

Number of variables18
Number of observations744
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory112.7 KiB
Average record size in memory155.2 B

Variable types

Categorical7
Numeric11

Dataset

DescriptionSample
Author㈜전략해양
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT01RNS017

Alerts

SAR_SE_NM has constant value ""Constant
HHS_WTCH_TDLV_VAL has constant value ""Constant
PH_VAL has constant value ""Constant
DOXN_DGRSTR_VAL has constant value ""Constant
CHPLA_MSQT_VAL has constant value ""Constant
TN_MSQT_VAL has constant value ""Constant
TP_MSQT_VAL has constant value ""Constant
WTCH_YMDHM is highly overall correlated with WTCH_WTEM and 1 other fieldsHigh correlation
WTCH_WTEM is highly overall correlated with WTCH_YMDHM and 2 other fieldsHigh correlation
HHS_WTCH_WNSPD_VAL is highly overall correlated with HHS_WTCH_WNDRCT_VALHigh correlation
HHS_WTCH_WNDRCT_VAL is highly overall correlated with HHS_WTCH_WNSPD_VALHigh correlation
HHS_WTCH_ATEM_VAL is highly overall correlated with WTCH_WTEMHigh correlation
HHS_SWTRSF_ARCSR_VAL is highly overall correlated with WTCH_YMDHM and 1 other fieldsHigh correlation
WTCH_YMDHM has unique valuesUnique
HHS_PRAMT_VAL has 711 (95.6%) zerosZeros

Reproduction

Analysis started2024-03-13 12:37:32.765942
Analysis finished2024-03-13 12:37:53.979692
Duration21.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SAR_SE_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
WANDO
744 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
WANDO 744
100.0%

Length

2024-03-13T21:37:54.061981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:37:54.219034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
wando 744
100.0%

WTCH_YMDHM
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct744
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0220516 × 1011
Minimum2.0220501 × 1011
Maximum2.0220531 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:54.381240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0220501 × 1011
5-th percentile2.0220502 × 1011
Q12.0220508 × 1011
median2.0220516 × 1011
Q32.0220524 × 1011
95-th percentile2.022053 × 1011
Maximum2.0220531 × 1011
Range302300
Interquartile range (IQR)158750

Descriptive statistics

Standard deviation89505.57
Coefficient of variation (CV)4.426473 × 10-7
Kurtosis-1.2023675
Mean2.0220516 × 1011
Median Absolute Deviation (MAD)79400
Skewness0
Sum1.5044064 × 1014
Variance8.011247 × 109
MonotonicityStrictly increasing
2024-03-13T21:37:54.600263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202205010000 1
 
0.1%
202205212000 1
 
0.1%
202205211100 1
 
0.1%
202205211200 1
 
0.1%
202205211300 1
 
0.1%
202205211400 1
 
0.1%
202205211500 1
 
0.1%
202205211600 1
 
0.1%
202205211700 1
 
0.1%
202205211800 1
 
0.1%
Other values (734) 734
98.7%
ValueCountFrequency (%)
202205010000 1
0.1%
202205010100 1
0.1%
202205010200 1
0.1%
202205010300 1
0.1%
202205010400 1
0.1%
202205010500 1
0.1%
202205010600 1
0.1%
202205010700 1
0.1%
202205010800 1
0.1%
202205010900 1
0.1%
ValueCountFrequency (%)
202205312300 1
0.1%
202205312200 1
0.1%
202205312100 1
0.1%
202205312000 1
0.1%
202205311900 1
0.1%
202205311800 1
0.1%
202205311700 1
0.1%
202205311600 1
0.1%
202205311500 1
0.1%
202205311400 1
0.1%

WTCH_WTEM
Real number (ℝ)

HIGH CORRELATION 

Distinct531
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.32827
Minimum13.29
Maximum18.017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:54.773880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.29
5-th percentile13.857
Q114.72825
median15.287
Q315.88475
95-th percentile16.7964
Maximum18.017
Range4.727
Interquartile range (IQR)1.1565

Descriptive statistics

Standard deviation0.8998205
Coefficient of variation (CV)0.05870333
Kurtosis-0.25323386
Mean15.32827
Median Absolute Deviation (MAD)0.5845
Skewness0.15738744
Sum11404.233
Variance0.80967693
MonotonicityNot monotonic
2024-03-13T21:37:54.971234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.22 7
 
0.9%
14.735 5
 
0.7%
14.897 4
 
0.5%
15.06 4
 
0.5%
15.4 4
 
0.5%
15.41 4
 
0.5%
15.39 4
 
0.5%
15.46 4
 
0.5%
15.225 3
 
0.4%
15.48 3
 
0.4%
Other values (521) 702
94.4%
ValueCountFrequency (%)
13.29 1
 
0.1%
13.36 3
0.4%
13.365 1
 
0.1%
13.38 1
 
0.1%
13.395 1
 
0.1%
13.415 1
 
0.1%
13.425 1
 
0.1%
13.44 1
 
0.1%
13.45 1
 
0.1%
13.475 1
 
0.1%
ValueCountFrequency (%)
18.017 1
0.1%
17.93 1
0.1%
17.873 1
0.1%
17.815 1
0.1%
17.693 1
0.1%
17.633 1
0.1%
17.605 1
0.1%
17.423 1
0.1%
17.39 1
0.1%
17.37 1
0.1%

WTCH_SLNTY
Real number (ℝ)

Distinct296
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.48922
Minimum29.54
Maximum33.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:55.167184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.54
5-th percentile29.996
Q130.73
median31.51
Q332.24
95-th percentile32.8785
Maximum33.2
Range3.66
Interquartile range (IQR)1.51

Descriptive statistics

Standard deviation0.91943554
Coefficient of variation (CV)0.029198422
Kurtosis-0.93111258
Mean31.48922
Median Absolute Deviation (MAD)0.74
Skewness-0.15691288
Sum23427.98
Variance0.84536171
MonotonicityNot monotonic
2024-03-13T21:37:55.393550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.42 13
 
1.7%
32.12 9
 
1.2%
31.48 8
 
1.1%
32.13 7
 
0.9%
31.51 7
 
0.9%
30.19 7
 
0.9%
31.43 6
 
0.8%
30.79 6
 
0.8%
31.49 6
 
0.8%
29.57 6
 
0.8%
Other values (286) 669
89.9%
ValueCountFrequency (%)
29.54 1
 
0.1%
29.55 1
 
0.1%
29.56 1
 
0.1%
29.57 6
0.8%
29.58 3
0.4%
29.6 1
 
0.1%
29.61 1
 
0.1%
29.62 3
0.4%
29.63 1
 
0.1%
29.64 2
 
0.3%
ValueCountFrequency (%)
33.2 1
 
0.1%
33.19 1
 
0.1%
33.18 1
 
0.1%
33.17 1
 
0.1%
33.16 2
0.3%
33.15 2
0.3%
33.12 3
0.4%
33.11 1
 
0.1%
33.1 1
 
0.1%
33.09 1
 
0.1%

HHS_SGNFCT_WVHGH_VAL
Real number (ℝ)

Distinct115
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29202957
Minimum0.105
Maximum0.895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:56.102078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.105
5-th percentile0.125
Q10.19
median0.255
Q30.35
95-th percentile0.585
Maximum0.895
Range0.79
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.13923186
Coefficient of variation (CV)0.47677317
Kurtosis1.6628654
Mean0.29202957
Median Absolute Deviation (MAD)0.07
Skewness1.3414691
Sum217.27
Variance0.019385512
MonotonicityNot monotonic
2024-03-13T21:37:56.304887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 39
 
5.2%
0.175 29
 
3.9%
0.18 27
 
3.6%
0.21 25
 
3.4%
0.185 22
 
3.0%
0.3 20
 
2.7%
0.19 20
 
2.7%
0.165 16
 
2.2%
0.17 16
 
2.2%
0.265 15
 
2.0%
Other values (105) 515
69.2%
ValueCountFrequency (%)
0.105 1
 
0.1%
0.11 9
1.2%
0.115 8
1.1%
0.12 12
1.6%
0.125 13
1.7%
0.13 5
 
0.7%
0.135 2
 
0.3%
0.14 3
 
0.4%
0.155 2
 
0.3%
0.16 4
 
0.5%
ValueCountFrequency (%)
0.895 1
 
0.1%
0.835 1
 
0.1%
0.775 3
0.4%
0.745 1
 
0.1%
0.73 2
0.3%
0.725 2
0.3%
0.705 2
0.3%
0.695 2
0.3%
0.685 1
 
0.1%
0.675 1
 
0.1%

WTCH_CRDRC
Real number (ℝ)

Distinct30
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.86425
Minimum0
Maximum338
Zeros3
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:56.517068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1146
median191
Q3203
95-th percentile315
Maximum338
Range338
Interquartile range (IQR)57

Descriptive statistics

Standard deviation58.329912
Coefficient of variation (CV)0.31552835
Kurtosis1.2243418
Mean184.86425
Median Absolute Deviation (MAD)23
Skewness0.33285382
Sum137539
Variance3402.3786
MonotonicityNot monotonic
2024-03-13T21:37:56.696579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
191 119
16.0%
203 104
14.0%
169 63
8.5%
146 60
8.1%
158 60
8.1%
135 58
7.8%
180 52
 
7.0%
214 36
 
4.8%
315 27
 
3.6%
124 25
 
3.4%
Other values (20) 140
18.8%
ValueCountFrequency (%)
0 3
 
0.4%
11 4
 
0.5%
23 4
 
0.5%
34 3
 
0.4%
45 1
 
0.1%
56 2
 
0.3%
68 1
 
0.1%
79 2
 
0.3%
90 17
2.3%
101 3
 
0.4%
ValueCountFrequency (%)
338 3
 
0.4%
326 9
 
1.2%
315 27
3.6%
304 23
3.1%
293 17
2.3%
281 4
 
0.5%
270 3
 
0.4%
259 1
 
0.1%
236 8
 
1.1%
225 20
2.7%

WTCH_CRSPD
Real number (ℝ)

Distinct698
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.495108
Minimum3.99
Maximum79.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:56.929280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.99
5-th percentile13.0545
Q126.0425
median36.19
Q349.995
95-th percentile62.6685
Maximum79.47
Range75.48
Interquartile range (IQR)23.9525

Descriptive statistics

Standard deviation15.466628
Coefficient of variation (CV)0.41249723
Kurtosis-0.7121899
Mean37.495108
Median Absolute Deviation (MAD)11.35
Skewness0.12349417
Sum27896.36
Variance239.21658
MonotonicityNot monotonic
2024-03-13T21:37:57.206156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.86 3
 
0.4%
32.48 2
 
0.3%
52.92 2
 
0.3%
46.21 2
 
0.3%
49.09 2
 
0.3%
33.75 2
 
0.3%
53.1 2
 
0.3%
36.19 2
 
0.3%
42.87 2
 
0.3%
51.09 2
 
0.3%
Other values (688) 723
97.2%
ValueCountFrequency (%)
3.99 1
0.1%
4.64 1
0.1%
4.87 1
0.1%
5.9 1
0.1%
6.21 1
0.1%
6.5 1
0.1%
6.6 1
0.1%
6.85 1
0.1%
7.58 1
0.1%
7.62 1
0.1%
ValueCountFrequency (%)
79.47 1
0.1%
76.56 1
0.1%
75.07 1
0.1%
74.68 1
0.1%
71.69 1
0.1%
71.62 1
0.1%
69.94 1
0.1%
69.63 1
0.1%
69.53 1
0.1%
69.35 1
0.1%

HHS_WTCH_TDLV_VAL
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
NaN
744 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row NaN
2nd row NaN
3rd row NaN
4th row NaN
5th row NaN

Common Values

ValueCountFrequency (%)
NaN 744
100.0%

Length

2024-03-13T21:37:57.403389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:37:57.545884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nan 744
100.0%

HHS_WTCH_WNSPD_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct568
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5013212
Minimum0.403
Maximum5.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:57.710433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.403
5-th percentile0.74
Q11.49925
median2.497
Q33.37075
95-th percentile4.4421
Maximum5.97
Range5.567
Interquartile range (IQR)1.8715

Descriptive statistics

Standard deviation1.1840676
Coefficient of variation (CV)0.47337688
Kurtosis-0.69072307
Mean2.5013212
Median Absolute Deviation (MAD)0.94
Skewness0.21703557
Sum1860.983
Variance1.4020162
MonotonicityNot monotonic
2024-03-13T21:37:57.958309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9 4
 
0.5%
3.737 4
 
0.5%
1.663 4
 
0.5%
0.78 4
 
0.5%
2.793 3
 
0.4%
2.15 3
 
0.4%
1.477 3
 
0.4%
1.55 3
 
0.4%
2.817 3
 
0.4%
3.51 3
 
0.4%
Other values (558) 710
95.4%
ValueCountFrequency (%)
0.403 1
0.1%
0.413 1
0.1%
0.417 1
0.1%
0.437 1
0.1%
0.483 1
0.1%
0.493 1
0.1%
0.497 1
0.1%
0.517 1
0.1%
0.527 1
0.1%
0.53 1
0.1%
ValueCountFrequency (%)
5.97 1
0.1%
5.78 1
0.1%
5.627 1
0.1%
5.613 1
0.1%
5.457 2
0.3%
5.363 1
0.1%
5.297 1
0.1%
5.287 1
0.1%
5.277 2
0.3%
5.117 1
0.1%

HHS_WTCH_WNDRCT_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct740
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.85338
Minimum33.357
Maximum307.393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:58.162819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.357
5-th percentile81.187
Q1127.88925
median190.03
Q3238.62425
95-th percentile286.756
Maximum307.393
Range274.036
Interquartile range (IQR)110.735

Descriptive statistics

Standard deviation65.218842
Coefficient of variation (CV)0.3528139
Kurtosis-1.1042254
Mean184.85338
Median Absolute Deviation (MAD)53.91
Skewness-0.11887592
Sum137530.92
Variance4253.4974
MonotonicityNot monotonic
2024-03-13T21:37:58.373483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206.523 2
 
0.3%
184.847 2
 
0.3%
81.187 2
 
0.3%
107.967 2
 
0.3%
281.277 1
 
0.1%
281.897 1
 
0.1%
216.53 1
 
0.1%
206.44 1
 
0.1%
230.073 1
 
0.1%
272.93 1
 
0.1%
Other values (730) 730
98.1%
ValueCountFrequency (%)
33.357 1
0.1%
44.587 1
0.1%
54.473 1
0.1%
57.99 1
0.1%
58.427 1
0.1%
59.577 1
0.1%
64.727 1
0.1%
65.053 1
0.1%
67.263 1
0.1%
68.277 1
0.1%
ValueCountFrequency (%)
307.393 1
0.1%
306.907 1
0.1%
304.64 1
0.1%
303.957 1
0.1%
302.207 1
0.1%
301.447 1
0.1%
301.37 1
0.1%
300.533 1
0.1%
299.39 1
0.1%
299.33 1
0.1%

HHS_WTCH_ATEM_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct675
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.760933
Minimum8.37
Maximum26.537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:58.573499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.37
5-th percentile11.74705
Q115.486
median17.637
Q320.00775
95-th percentile23.494
Maximum26.537
Range18.167
Interquartile range (IQR)4.52175

Descriptive statistics

Standard deviation3.4232681
Coefficient of variation (CV)0.19274146
Kurtosis-0.1643779
Mean17.760933
Median Absolute Deviation (MAD)2.227
Skewness-0.054912321
Sum13214.134
Variance11.718764
MonotonicityNot monotonic
2024-03-13T21:37:58.793063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.983 3
 
0.4%
16.297 3
 
0.4%
17.87 3
 
0.4%
16.967 3
 
0.4%
16.91 2
 
0.3%
18.177 2
 
0.3%
16.763 2
 
0.3%
19.69 2
 
0.3%
18.007 2
 
0.3%
14.7 2
 
0.3%
Other values (665) 720
96.8%
ValueCountFrequency (%)
8.37 1
0.1%
8.523 1
0.1%
8.53 1
0.1%
8.613 1
0.1%
8.92 1
0.1%
9.07 1
0.1%
9.263 1
0.1%
9.443 1
0.1%
9.533 1
0.1%
9.883 1
0.1%
ValueCountFrequency (%)
26.537 1
0.1%
26.497 1
0.1%
25.927 1
0.1%
25.76 1
0.1%
25.657 1
0.1%
25.027 1
0.1%
24.76 1
0.1%
24.727 1
0.1%
24.707 1
0.1%
24.683 1
0.1%

HHS_SWTRSF_ARCSR_VAL
Real number (ℝ)

HIGH CORRELATION 

Distinct691
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.0488
Minimum1001.35
Maximum1021.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:58.999756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001.35
5-th percentile1003.913
Q11009.1157
median1014.068
Q31016.8697
95-th percentile1020.1655
Maximum1021.78
Range20.43
Interquartile range (IQR)7.754

Descriptive statistics

Standard deviation4.9588022
Coefficient of variation (CV)0.0048949294
Kurtosis-0.702298
Mean1013.0488
Median Absolute Deviation (MAD)3.552
Skewness-0.41639768
Sum753708.27
Variance24.589719
MonotonicityNot monotonic
2024-03-13T21:37:59.225226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014.92 3
 
0.4%
1018.937 2
 
0.3%
1013.923 2
 
0.3%
1013.96 2
 
0.3%
1014.697 2
 
0.3%
1007.06 2
 
0.3%
1021.23 2
 
0.3%
1015.407 2
 
0.3%
1019.083 2
 
0.3%
1015.54 2
 
0.3%
Other values (681) 723
97.2%
ValueCountFrequency (%)
1001.35 1
0.1%
1001.37 1
0.1%
1001.43 1
0.1%
1001.477 1
0.1%
1001.483 1
0.1%
1001.67 1
0.1%
1001.677 1
0.1%
1001.763 1
0.1%
1001.86 1
0.1%
1001.893 1
0.1%
ValueCountFrequency (%)
1021.78 1
0.1%
1021.543 1
0.1%
1021.527 1
0.1%
1021.457 2
0.3%
1021.4 1
0.1%
1021.383 1
0.1%
1021.323 1
0.1%
1021.23 2
0.3%
1021.107 1
0.1%
1021.093 1
0.1%

HHS_PRAMT_VAL
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01109543
Minimum0
Maximum1.045
Zeros711
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2024-03-13T21:37:59.457293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.045
Range1.045
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.073853573
Coefficient of variation (CV)6.6562154
Kurtosis97.371815
Mean0.01109543
Median Absolute Deviation (MAD)0
Skewness9.2031384
Sum8.255
Variance0.0054543502
MonotonicityNot monotonic
2024-03-13T21:37:59.660419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 711
95.6%
0.06 4
 
0.5%
0.13 3
 
0.4%
0.165 2
 
0.3%
0.08 2
 
0.3%
0.03 2
 
0.3%
0.085 2
 
0.3%
0.57 1
 
0.1%
0.105 1
 
0.1%
0.16 1
 
0.1%
Other values (15) 15
 
2.0%
ValueCountFrequency (%)
0.0 711
95.6%
0.03 2
 
0.3%
0.05 1
 
0.1%
0.06 4
 
0.5%
0.065 1
 
0.1%
0.08 2
 
0.3%
0.085 2
 
0.3%
0.105 1
 
0.1%
0.13 3
 
0.4%
0.15 1
 
0.1%
ValueCountFrequency (%)
1.045 1
0.1%
0.82 1
0.1%
0.75 1
0.1%
0.57 1
0.1%
0.565 1
0.1%
0.49 1
0.1%
0.415 1
0.1%
0.395 1
0.1%
0.39 1
0.1%
0.33 1
0.1%

PH_VAL
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
NaN
744 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row NaN
2nd row NaN
3rd row NaN
4th row NaN
5th row NaN

Common Values

ValueCountFrequency (%)
NaN 744
100.0%

Length

2024-03-13T21:37:59.849733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:37:59.985906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nan 744
100.0%

DOXN_DGRSTR_VAL
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
NaN
744 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row NaN
2nd row NaN
3rd row NaN
4th row NaN
5th row NaN

Common Values

ValueCountFrequency (%)
NaN 744
100.0%

Length

2024-03-13T21:38:00.186283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:38:00.356061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nan 744
100.0%

CHPLA_MSQT_VAL
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
NaN
744 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row NaN
2nd row NaN
3rd row NaN
4th row NaN
5th row NaN

Common Values

ValueCountFrequency (%)
NaN 744
100.0%

Length

2024-03-13T21:38:00.496198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:38:00.615979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nan 744
100.0%

TN_MSQT_VAL
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
NaN
744 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row NaN
2nd row NaN
3rd row NaN
4th row NaN
5th row NaN

Common Values

ValueCountFrequency (%)
NaN 744
100.0%

Length

2024-03-13T21:38:00.747211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:38:00.875041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nan 744
100.0%

TP_MSQT_VAL
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
NaN
744 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row NaN
2nd row NaN
3rd row NaN
4th row NaN
5th row NaN

Common Values

ValueCountFrequency (%)
NaN 744
100.0%

Length

2024-03-13T21:38:00.997115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:38:01.152678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nan 744
100.0%

Interactions

2024-03-13T21:37:51.538011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:33.586659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:35.263682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:36.949881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:38.505353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:40.775696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:42.532917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:44.209237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:45.836142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:47.636518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:49.877647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:51.665107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:33.719511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:35.408525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:37.115544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:38.657951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:40.911880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:42.692183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:44.361112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:45.977723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:47.799570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:50.030920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:51.820531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:33.850878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:35.546175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:37.250709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:38.787925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:41.046962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:42.816990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:44.492644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:46.152089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:47.944834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:50.159843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:52.002746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:34.017450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:35.741361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:37.386878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:38.957463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:41.197933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:42.947531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:44.639827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:46.363272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:48.594613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:50.316570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:52.162944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:34.168279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:35.902150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:37.520712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:39.148560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:41.355814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:43.089695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:44.790307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:46.537244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:48.811050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:50.453644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:52.371155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:34.290531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:36.025769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:37.651301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:39.785523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:41.549824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:43.227637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:44.963513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:46.699410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:48.983367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:50.610682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:52.532725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:34.443628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:36.162253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:37.797721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:39.955675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:41.771098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:43.371433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:45.111754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:46.845303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:49.164534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:50.816498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:52.692371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:34.626796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:36.285707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:37.960067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:40.127244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:41.924754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:43.520029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:45.258948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:46.985516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:49.328361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:50.987741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:52.861025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:34.798506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:36.455814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:38.083480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:40.319704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:42.078900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:43.665945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:45.368254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:47.139490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:49.446875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:51.102949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:53.098166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:34.948794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:36.593551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:38.221281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:40.476395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:42.235823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:43.880509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:45.552931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:47.297632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:49.589328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:51.235121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:53.318713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:35.113282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:36.767053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:38.369890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:40.635574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:42.396061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:44.074854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:45.696300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:47.477713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:49.726126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:37:51.381837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:38:01.254635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_YMDHMWTCH_WTEMWTCH_SLNTYHHS_SGNFCT_WVHGH_VALWTCH_CRDRCWTCH_CRSPDHHS_WTCH_WNSPD_VALHHS_WTCH_WNDRCT_VALHHS_WTCH_ATEM_VALHHS_SWTRSF_ARCSR_VALHHS_PRAMT_VAL
WTCH_YMDHM1.0000.8170.8530.7100.4460.5490.5360.6870.6550.8590.211
WTCH_WTEM0.8171.0000.4850.2800.2360.1920.2640.2730.7320.6770.000
WTCH_SLNTY0.8530.4851.0000.6160.3400.2340.4260.5540.5130.7050.150
HHS_SGNFCT_WVHGH_VAL0.7100.2800.6161.0000.2020.3160.5020.4830.3520.6760.468
WTCH_CRDRC0.4460.2360.3400.2021.0000.5010.1910.1600.3400.2860.000
WTCH_CRSPD0.5490.1920.2340.3160.5011.0000.0000.2870.1640.3030.000
HHS_WTCH_WNSPD_VAL0.5360.2640.4260.5020.1910.0001.0000.6380.3960.4550.000
HHS_WTCH_WNDRCT_VAL0.6870.2730.5540.4830.1600.2870.6381.0000.4410.4820.159
HHS_WTCH_ATEM_VAL0.6550.7320.5130.3520.3400.1640.3960.4411.0000.5790.000
HHS_SWTRSF_ARCSR_VAL0.8590.6770.7050.6760.2860.3030.4550.4820.5791.0000.310
HHS_PRAMT_VAL0.2110.0000.1500.4680.0000.0000.0000.1590.0000.3101.000
2024-03-13T21:38:01.473891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_YMDHMWTCH_WTEMWTCH_SLNTYHHS_SGNFCT_WVHGH_VALWTCH_CRDRCWTCH_CRSPDHHS_WTCH_WNSPD_VALHHS_WTCH_WNDRCT_VALHHS_WTCH_ATEM_VALHHS_SWTRSF_ARCSR_VALHHS_PRAMT_VAL
WTCH_YMDHM1.0000.7820.121-0.073-0.0770.0070.0310.1360.487-0.7430.154
WTCH_WTEM0.7821.0000.1330.091-0.075-0.1000.1080.0960.630-0.6830.128
WTCH_SLNTY0.1210.1331.000-0.3730.0250.024-0.099-0.0660.0950.0020.107
HHS_SGNFCT_WVHGH_VAL-0.0730.091-0.3731.000-0.009-0.2320.286-0.1670.011-0.2300.240
WTCH_CRDRC-0.077-0.0750.025-0.0091.000-0.010-0.051-0.025-0.1700.0750.014
WTCH_CRSPD0.007-0.1000.024-0.232-0.0101.000-0.0070.180-0.073-0.006-0.011
HHS_WTCH_WNSPD_VAL0.0310.108-0.0990.286-0.051-0.0071.0000.5310.295-0.2260.041
HHS_WTCH_WNDRCT_VAL0.1360.096-0.066-0.167-0.0250.1800.5311.0000.226-0.246-0.225
HHS_WTCH_ATEM_VAL0.4870.6300.0950.011-0.170-0.0730.2950.2261.000-0.400-0.023
HHS_SWTRSF_ARCSR_VAL-0.743-0.6830.002-0.2300.075-0.006-0.226-0.246-0.4001.000-0.189
HHS_PRAMT_VAL0.1540.1280.1070.2400.014-0.0110.041-0.225-0.023-0.1891.000

Missing values

2024-03-13T21:37:53.550861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:37:53.843632image/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

SAR_SE_NMWTCH_YMDHMWTCH_WTEMWTCH_SLNTYHHS_SGNFCT_WVHGH_VALWTCH_CRDRCWTCH_CRSPDHHS_WTCH_TDLV_VALHHS_WTCH_WNSPD_VALHHS_WTCH_WNDRCT_VALHHS_WTCH_ATEM_VALHHS_SWTRSF_ARCSR_VALHHS_PRAMT_VALPH_VALDOXN_DGRSTR_VALCHPLA_MSQT_VALTN_MSQT_VALTP_MSQT_VAL
0WANDO20220501000013.96732.280.2419158.42NaN3.55281.27712.3631016.6670.0NaNNaNNaNNaNNaN
1WANDO20220501010013.4432.250.2613551.13NaN4.16301.44712.1371016.280.0NaNNaNNaNNaNNaN
2WANDO20220501020013.9332.230.2312435.94NaN4.55247.1911.9631015.9530.0NaNNaNNaNNaNNaN
3WANDO20220501030013.91332.210.22520332.36NaN3.6268.51311.6531015.7370.0NaNNaNNaNNaNNaN
4WANDO20220501040013.3632.20.1919156.55NaN3.643286.78311.2271015.4530.0NaNNaNNaNNaNNaN
5WANDO20220501050013.87732.150.19519162.77NaN3.793295.03310.911015.0630.0NaNNaNNaNNaNNaN
6WANDO20220501060013.8332.210.29518054.7NaN4.117246.310.9071015.1730.0NaNNaNNaNNaNNaN
7WANDO20220501070013.2932.190.24514648.4NaN3.867206.52311.3971015.4370.0NaNNaNNaNNaNNaN
8WANDO20220501080013.7632.170.26532625.64NaN3.997208.56312.3231015.6970.0NaNNaNNaNNaNNaN
9WANDO20220501090013.8332.170.2622521.6NaN3.377253.413.471015.7130.0NaNNaNNaNNaNNaN
SAR_SE_NMWTCH_YMDHMWTCH_WTEMWTCH_SLNTYHHS_SGNFCT_WVHGH_VALWTCH_CRDRCWTCH_CRSPDHHS_WTCH_TDLV_VALHHS_WTCH_WNSPD_VALHHS_WTCH_WNDRCT_VALHHS_WTCH_ATEM_VALHHS_SWTRSF_ARCSR_VALHHS_PRAMT_VALPH_VALDOXN_DGRSTR_VALCHPLA_MSQT_VALTN_MSQT_VALTP_MSQT_VAL
734WANDO20220531140017.87333.10.2214635.58NaN3.51213.3624.491009.130.0NaNNaNNaNNaNNaN
735WANDO20220531150017.9333.080.2114629.48NaN4.187242.80324.5931008.7870.0NaNNaNNaNNaNNaN
736WANDO20220531160017.60532.960.20519141.75NaN3.813257.95324.2871008.480.0NaNNaNNaNNaNNaN
737WANDO20220531170018.01732.820.25519155.64NaN3.137255.924.0231008.30.0NaNNaNNaNNaNNaN
738WANDO20220531180017.69332.690.19519146.99NaN2.623240.33723.5331008.2870.0NaNNaNNaNNaNNaN
739WANDO20220531190015.8832.610.2115844.23NaN1.927213.27322.3271008.4370.0NaNNaNNaNNaNNaN
740WANDO20220531200016.73332.560.2231534.58NaN1.23200.3720.5931008.3370.0NaNNaNNaNNaNNaN
741WANDO20220531210016.6232.520.23514628.27NaN1.317141.11319.291008.7970.0NaNNaNNaNNaNNaN
742WANDO20220531220016.28532.510.2319127.51NaN1.277186.51718.5471009.6670.0NaNNaNNaNNaNNaN
743WANDO20220531230016.46732.50.22519144.2NaN1.09367.26317.7071009.9470.0NaNNaNNaNNaNNaN