Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory429.7 KiB
Average record size in memory44.0 B

Variable types

Categorical1
Numeric3

Dataset

DescriptionSample
Author㈜전략해양
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT01RNS002

Alerts

WTDP_GRID_LO is highly overall correlated with METER_WTDPHigh correlation
METER_WTDP is highly overall correlated with WTDP_GRID_LOHigh correlation

Reproduction

Analysis started2024-03-13 12:45:16.326164
Analysis finished2024-03-13 12:45:18.386248
Duration2.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WTCH_YMDH
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021080103
2488 
2021080100
2482 
2021080101
2454 
2021080102
2396 
2021080104
 
180

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021080100
2nd row2021080101
3rd row2021080102
4th row2021080103
5th row2021080103

Common Values

ValueCountFrequency (%)
2021080103 2488
24.9%
2021080100 2482
24.8%
2021080101 2454
24.5%
2021080102 2396
24.0%
2021080104 180
 
1.8%

Length

2024-03-13T21:45:18.477122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:45:18.625591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021080103 2488
24.9%
2021080100 2482
24.8%
2021080101 2454
24.5%
2021080102 2396
24.0%
2021080104 180
 
1.8%

WTDP_GRID_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct260
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.47427
Minimum126.33185
Maximum126.75388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:45:18.805225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.33185
5-th percentile126.34163
Q1126.39377
median126.46221
Q3126.53716
95-th percentile126.65286
Maximum126.75388
Range0.42203
Interquartile range (IQR)0.14339

Descriptive statistics

Standard deviation0.098462337
Coefficient of variation (CV)0.00077851675
Kurtosis-0.41451174
Mean126.47427
Median Absolute Deviation (MAD)0.0717
Skewness0.57984049
Sum1264742.7
Variance0.0096948318
MonotonicityNot monotonic
2024-03-13T21:45:18.975890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.34326 82
 
0.8%
126.35141 79
 
0.8%
126.34652 78
 
0.8%
126.33511 78
 
0.8%
126.34163 78
 
0.8%
126.34978 78
 
0.8%
126.38562 76
 
0.8%
126.5062 76
 
0.8%
126.34489 75
 
0.8%
126.33674 74
 
0.7%
Other values (250) 9226
92.3%
ValueCountFrequency (%)
126.33185 72
0.7%
126.33348 74
0.7%
126.33511 78
0.8%
126.33674 74
0.7%
126.33837 69
0.7%
126.34 62
0.6%
126.34163 78
0.8%
126.34326 82
0.8%
126.34489 75
0.8%
126.34652 78
0.8%
ValueCountFrequency (%)
126.75388 1
 
< 0.1%
126.75225 5
 
0.1%
126.75062 10
0.1%
126.74899 3
 
< 0.1%
126.74736 8
0.1%
126.74574 11
0.1%
126.74411 8
0.1%
126.74248 8
0.1%
126.74085 14
0.1%
126.73922 7
0.1%

WTDP_GRID_LA
Real number (ℝ)

Distinct150
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.985829
Minimum35.87204
Maximum36.07357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:45:19.155893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.87204
5-th percentile35.88557
Q135.94102
median35.99377
Q336.03164
95-th percentile36.06545
Maximum36.07357
Range0.20153
Interquartile range (IQR)0.09062

Descriptive statistics

Standard deviation0.055678919
Coefficient of variation (CV)0.0015472457
Kurtosis-0.93845023
Mean35.985829
Median Absolute Deviation (MAD)0.04328
Skewness-0.3529496
Sum359858.29
Variance0.003100142
MonotonicityNot monotonic
2024-03-13T21:45:19.428526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.99242 120
 
1.2%
36.00189 116
 
1.2%
35.99377 111
 
1.1%
36.00594 107
 
1.1%
36.00324 106
 
1.1%
35.99918 106
 
1.1%
35.9843 105
 
1.1%
36.04111 104
 
1.0%
35.99106 104
 
1.0%
35.99647 102
 
1.0%
Other values (140) 8919
89.2%
ValueCountFrequency (%)
35.87204 44
0.4%
35.87339 48
0.5%
35.87475 58
0.6%
35.8761 46
0.5%
35.87745 32
0.3%
35.8788 40
0.4%
35.88016 58
0.6%
35.88151 47
0.5%
35.88286 43
0.4%
35.88421 42
0.4%
ValueCountFrequency (%)
36.07357 83
0.8%
36.07222 81
0.8%
36.07087 82
0.8%
36.06951 82
0.8%
36.06816 81
0.8%
36.06681 81
0.8%
36.06545 75
0.8%
36.0641 75
0.8%
36.06275 65
0.7%
36.0614 67
0.7%

METER_WTDP
Real number (ℝ)

HIGH CORRELATION 

Distinct2610
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.771793
Minimum-1.94
Maximum41.55
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)0.1%
Memory size166.0 KiB
2024-03-13T21:45:19.712846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.94
5-th percentile3.21
Q112.2
median16.86
Q320.22
95-th percentile24.7305
Maximum41.55
Range43.49
Interquartile range (IQR)8.02

Descriptive statistics

Standard deviation6.5187524
Coefficient of variation (CV)0.41331714
Kurtosis-0.12696976
Mean15.771793
Median Absolute Deviation (MAD)3.79
Skewness-0.4412631
Sum157717.93
Variance42.494133
MonotonicityNot monotonic
2024-03-13T21:45:20.278344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.12 17
 
0.2%
18.0 17
 
0.2%
18.7 15
 
0.1%
16.56 15
 
0.1%
19.0 15
 
0.1%
21.34 15
 
0.1%
18.37 14
 
0.1%
15.56 14
 
0.1%
16.79 14
 
0.1%
15.98 14
 
0.1%
Other values (2600) 9850
98.5%
ValueCountFrequency (%)
-1.94 1
< 0.1%
-1.2 1
< 0.1%
-1.12 1
< 0.1%
-0.92 1
< 0.1%
-0.84 1
< 0.1%
-0.76 1
< 0.1%
-0.64 1
< 0.1%
-0.35 1
< 0.1%
-0.32 1
< 0.1%
-0.15 1
< 0.1%
ValueCountFrequency (%)
41.55 1
< 0.1%
40.85 1
< 0.1%
40.08 1
< 0.1%
39.96 1
< 0.1%
39.8 1
< 0.1%
39.7 1
< 0.1%
39.66 1
< 0.1%
38.63 1
< 0.1%
37.75 1
< 0.1%
37.46 1
< 0.1%

Interactions

2024-03-13T21:45:17.733341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:16.757917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:17.263576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:17.858593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:16.884670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:17.406714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:18.020077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:17.119587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:45:17.589731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:45:20.388787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTCH_YMDHWTDP_GRID_LOWTDP_GRID_LAMETER_WTDP
WTCH_YMDH1.0000.3340.0000.310
WTDP_GRID_LO0.3341.0000.5110.871
WTDP_GRID_LA0.0000.5111.0000.568
METER_WTDP0.3100.8710.5681.000
2024-03-13T21:45:20.542463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WTDP_GRID_LOWTDP_GRID_LAMETER_WTDPWTCH_YMDH
WTDP_GRID_LO1.0000.268-0.9380.145
WTDP_GRID_LA0.2681.000-0.2480.000
METER_WTDP-0.938-0.2481.0000.134
WTCH_YMDH0.1450.0000.1341.000

Missing values

2024-03-13T21:45:18.203357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:45:18.329334image/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_YMDHWTDP_GRID_LOWTDP_GRID_LAMETER_WTDP
236402021080100126.6626335.992426.24
282162021080101126.3709635.9437323.61
614412021080102126.4654735.9369614.92
913152021080103126.5257635.943735.51
942462021080103126.5893136.008654.61
170972021080100126.5176136.0032414.55
454952021080101126.5958236.00734.07
430632021080101126.5420536.037054.76
23402021080100126.3562935.9518425.08
588802021080102126.4377735.9518418.26
WTCH_YMDHWTDP_GRID_LOWTDP_GRID_LAMETER_WTDP
607542021080102126.4589536.0546317.59
716152021080102126.6316736.034354.4
717002021080102126.633336.012712.91
460662021080101126.6088636.018124.11
595972021080102126.4459135.9964718.13
684022021080102126.5599836.004597.57
688352021080102126.5697536.027587.67
994872021080104126.3465235.9964720.8
693452021080102126.5811636.037055.95
635082021080102126.4882835.981617.47