Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory507.8 KiB
Average record size in memory52.0 B

Variable types

Categorical1
Numeric4

Dataset

DescriptionSample
Author㈜전략해양
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT01RNS010

Alerts

PRDN_YMDH has constant value ""Constant

Reproduction

Analysis started2024-03-13 12:51:37.900704
Analysis finished2024-03-13 12:51:42.873715
Duration4.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PRDN_YMDH
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021080700
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021080700
2nd row2021080700
3rd row2021080700
4th row2021080700
5th row2021080700

Common Values

ValueCountFrequency (%)
2021080700 10000
100.0%

Length

2024-03-13T21:51:42.973775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:51:43.126272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021080700 10000
100.0%

WTHR_PRDN_MODL_GRID_LO
Real number (ℝ)

Distinct9957
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.31284
Minimum123.00033
Maximum131.7067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:51:43.341100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123.00033
5-th percentile123.42179
Q1125.13433
median127.32533
Q3129.50546
95-th percentile131.18331
Maximum131.7067
Range8.70637
Interquartile range (IQR)4.3711275

Descriptive statistics

Standard deviation2.5022292
Coefficient of variation (CV)0.019654178
Kurtosis-1.204853
Mean127.31284
Median Absolute Deviation (MAD)2.18529
Skewness-0.0018870784
Sum1273128.4
Variance6.2611512
MonotonicityNot monotonic
2024-03-13T21:51:43.563138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.00561 3
 
< 0.1%
129.85594 2
 
< 0.1%
125.98933 2
 
< 0.1%
124.9736 2
 
< 0.1%
125.98934 2
 
< 0.1%
129.07856 2
 
< 0.1%
131.46654 2
 
< 0.1%
126.00574 2
 
< 0.1%
126.70856 2
 
< 0.1%
126.68425 2
 
< 0.1%
Other values (9947) 9979
99.8%
ValueCountFrequency (%)
123.00033 1
< 0.1%
123.00036 1
< 0.1%
123.00101 1
< 0.1%
123.00154 1
< 0.1%
123.00177 1
< 0.1%
123.00196 1
< 0.1%
123.00275 1
< 0.1%
123.00396 1
< 0.1%
123.00605 1
< 0.1%
123.00621 1
< 0.1%
ValueCountFrequency (%)
131.7067 1
< 0.1%
131.70194 1
< 0.1%
131.70078 1
< 0.1%
131.69959 1
< 0.1%
131.69601 1
< 0.1%
131.69487 1
< 0.1%
131.68897 1
< 0.1%
131.68661 1
< 0.1%
131.68418 1
< 0.1%
131.68411 1
< 0.1%

WTHR_PRDN_MODL_GRID_LA
Real number (ℝ)

Distinct9843
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.607602
Minimum32.19181
Maximum34.94673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:51:43.793181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.19181
5-th percentile32.436568
Q132.96067
median33.60382
Q334.255118
95-th percentile34.77161
Maximum34.94673
Range2.75492
Interquartile range (IQR)1.2944475

Descriptive statistics

Standard deviation0.75093261
Coefficient of variation (CV)0.02234413
Kurtosis-1.1868293
Mean33.607602
Median Absolute Deviation (MAD)0.64687
Skewness-0.0032440136
Sum336076.02
Variance0.56389978
MonotonicityNot monotonic
2024-03-13T21:51:44.031846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.08334 3
 
< 0.1%
34.1661 3
 
< 0.1%
34.21733 3
 
< 0.1%
34.05581 3
 
< 0.1%
32.81423 2
 
< 0.1%
32.7978 2
 
< 0.1%
34.53053 2
 
< 0.1%
34.05414 2
 
< 0.1%
32.90729 2
 
< 0.1%
33.93206 2
 
< 0.1%
Other values (9833) 9976
99.8%
ValueCountFrequency (%)
32.19181 1
< 0.1%
32.20353 1
< 0.1%
32.20626 1
< 0.1%
32.20723 1
< 0.1%
32.22173 1
< 0.1%
32.22718 1
< 0.1%
32.22766 1
< 0.1%
32.22852 1
< 0.1%
32.22976 1
< 0.1%
32.23276 1
< 0.1%
ValueCountFrequency (%)
34.94673 1
< 0.1%
34.94605 1
< 0.1%
34.9453 1
< 0.1%
34.94306 1
< 0.1%
34.94273 1
< 0.1%
34.94185 1
< 0.1%
34.9411 1
< 0.1%
34.93901 1
< 0.1%
34.93688 1
< 0.1%
34.93393 1
< 0.1%

TYPHN_PRDN_WNSPD
Real number (ℝ)

Distinct104
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.0966897
Minimum0.1
Maximum18.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:51:44.237456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.3
Q12.8
median4.3
Q35.3
95-th percentile6.5
Maximum18.2
Range18.1
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.6868596
Coefficient of variation (CV)0.41176161
Kurtosis0.47865895
Mean4.0966897
Median Absolute Deviation (MAD)1.2
Skewness0.13965969
Sum40962.8
Variance2.8454951
MonotonicityNot monotonic
2024-03-13T21:51:44.426930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.7 315
 
3.1%
5.4 285
 
2.9%
5.3 278
 
2.8%
4.8 278
 
2.8%
5.1 262
 
2.6%
5.0 260
 
2.6%
4.9 252
 
2.5%
5.2 240
 
2.4%
5.6 236
 
2.4%
4.6 230
 
2.3%
Other values (94) 7363
73.6%
ValueCountFrequency (%)
0.1 8
 
0.1%
0.2 8
 
0.1%
0.3 14
 
0.1%
0.4 24
0.2%
0.5 34
0.3%
0.6 37
0.4%
0.7 36
0.4%
0.8 33
0.3%
0.9 33
0.3%
1.0 50
0.5%
ValueCountFrequency (%)
18.2 1
 
< 0.1%
14.0 1
 
< 0.1%
13.0 1
 
< 0.1%
12.9 1
 
< 0.1%
11.2 1
 
< 0.1%
10.0 1
 
< 0.1%
9.9 4
< 0.1%
9.8 2
 
< 0.1%
9.6 6
0.1%
9.5 6
0.1%

TYPHN_PRDN_WNDRCT
Real number (ℝ)

Distinct354
Distinct (%)3.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean214.51965
Minimum0
Maximum360
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:51:44.655586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile99
Q1162
median235
Q3251.5
95-th percentile302
Maximum360
Range360
Interquartile range (IQR)89.5

Descriptive statistics

Standard deviation63.672748
Coefficient of variation (CV)0.29681545
Kurtosis0.17428156
Mean214.51965
Median Absolute Deviation (MAD)29
Skewness-0.72147009
Sum2144982
Variance4054.2188
MonotonicityNot monotonic
2024-03-13T21:51:45.305571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
244 198
 
2.0%
233 181
 
1.8%
236 180
 
1.8%
238 177
 
1.8%
234 174
 
1.7%
246 171
 
1.7%
239 164
 
1.6%
237 159
 
1.6%
243 158
 
1.6%
231 156
 
1.6%
Other values (344) 8281
82.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 4
< 0.1%
3 3
< 0.1%
5 1
 
< 0.1%
6 5
0.1%
7 4
< 0.1%
8 3
< 0.1%
9 6
0.1%
10 3
< 0.1%
11 5
0.1%
ValueCountFrequency (%)
360 1
 
< 0.1%
359 5
0.1%
358 5
0.1%
357 2
 
< 0.1%
356 9
0.1%
355 4
< 0.1%
354 4
< 0.1%
353 2
 
< 0.1%
352 1
 
< 0.1%
351 2
 
< 0.1%

Interactions

2024-03-13T21:51:41.595933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:38.573734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:39.309426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:40.022129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:41.743018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:38.747132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:39.450663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:40.216196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:41.899197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:38.912150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:39.617211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:40.915767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:42.292416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:39.155136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:39.856691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:41.295248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:51:45.479645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTHR_PRDN_MODL_GRID_LOWTHR_PRDN_MODL_GRID_LATYPHN_PRDN_WNSPDTYPHN_PRDN_WNDRCT
WTHR_PRDN_MODL_GRID_LO1.0000.1170.4890.838
WTHR_PRDN_MODL_GRID_LA0.1171.0000.2160.454
TYPHN_PRDN_WNSPD0.4890.2161.0000.440
TYPHN_PRDN_WNDRCT0.8380.4540.4401.000
2024-03-13T21:51:45.749956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTHR_PRDN_MODL_GRID_LOWTHR_PRDN_MODL_GRID_LATYPHN_PRDN_WNSPDTYPHN_PRDN_WNDRCT
WTHR_PRDN_MODL_GRID_LO1.000-0.045-0.387-0.430
WTHR_PRDN_MODL_GRID_LA-0.0451.0000.0820.227
TYPHN_PRDN_WNSPD-0.3870.0821.0000.010
TYPHN_PRDN_WNDRCT-0.4300.2270.0101.000

Missing values

2024-03-13T21:51:42.501271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:51:42.657099image/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.
2024-03-13T21:51:42.809307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PRDN_YMDHWTHR_PRDN_MODL_GRID_LOWTHR_PRDN_MODL_GRID_LATYPHN_PRDN_WNSPDTYPHN_PRDN_WNDRCT
218712021080700126.3794832.906652.354
516832021080700123.2278133.646335.6226
178022021080700128.8928332.754320.437
289202021080700129.6047733.016824.686
891712021080700127.9077734.640613.9313
826582021080700123.9588234.473194.5220
758452021080700123.7818434.290965.2233
675562021080700125.3604834.094925.5237
995462021080700126.7431134.931110.6304
590212021080700123.0221833.83196.1228
PRDN_YMDHWTHR_PRDN_MODL_GRID_LOWTHR_PRDN_MODL_GRID_LATYPHN_PRDN_WNSPDTYPHN_PRDN_WNDRCT
597712021080700126.7314833.888811.8287
422592021080700123.8927333.416956.1234
934722021080700129.9140134.690486.8145
214572021080700128.2313632.867652.2211
147672021080700131.0395132.582826.0134
112042021080700124.6459732.624994.4232
398182021080700126.9876333.366665.5290
343942021080700124.3399333.22076.1242
593202021080700127.9513133.858153.8273
654272021080700124.8155234.034985.9240