Overview

Dataset statistics

Number of variables4
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory39.6 B

Variable types

Categorical2
Numeric2

Dataset

DescriptionSample
Author㈜해안해양기술
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT04CMT006

Alerts

TYPHN_NM is highly overall correlated with MXM_SGNFCT_WVHGHHigh correlation
MXM_SGNFCT_WVHGH is highly overall correlated with TYPHN_NM and 1 other fieldsHigh correlation
WVDRCT_BRNG is highly overall correlated with MXM_SGNFCT_WVHGHHigh correlation

Reproduction

Analysis started2024-01-14 07:02:04.095744
Analysis finished2024-01-14 07:02:04.707194
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WVDRCT_BRNG
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size364.0 B
N
3 
NNE
3 
NE
3 
ENE
3 
E
3 
Other values (5)
14 

Length

Max length3
Median length2
Mean length2.1724138
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowNNE
5th rowNNE

Common Values

ValueCountFrequency (%)
N 3
10.3%
NNE 3
10.3%
NE 3
10.3%
ENE 3
10.3%
E 3
10.3%
ESE 3
10.3%
SE 3
10.3%
SSE 3
10.3%
S 3
10.3%
SSW 2
6.9%

Length

2024-01-14T16:02:04.788959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:02:04.956213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3
10.3%
nne 3
10.3%
ne 3
10.3%
ene 3
10.3%
e 3
10.3%
ese 3
10.3%
se 3
10.3%
sse 3
10.3%
s 3
10.3%
ssw 2
6.9%

TYPHN_RANK
Categorical

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
1
10 
2
10 
3
9 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 10
34.5%
2 10
34.5%
3 9
31.0%

Length

2024-01-14T16:02:05.127936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:02:05.245347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10
34.5%
2 10
34.5%
3 9
31.0%

TYPHN_NM
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3054.1034
Minimum-999
Maximum9414
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)13.8%
Memory size393.0 B
2024-01-14T16:02:05.363881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11214
median1913
Q36411
95-th percentile7264.6
Maximum9414
Range10413
Interquartile range (IQR)5197

Descriptive statistics

Standard deviation3050.9978
Coefficient of variation (CV)0.99898311
Kurtosis-0.92163537
Mean3054.1034
Median Absolute Deviation (MAD)699
Skewness0.58703152
Sum88569
Variance9308587.4
MonotonicityNot monotonic
2024-01-14T16:02:05.504909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1913 5
17.2%
2008 5
17.2%
1214 4
13.8%
-999 4
13.8%
7207 3
10.3%
7011 2
 
6.9%
1105 1
 
3.4%
6411 1
 
3.4%
6409 1
 
3.4%
7303 1
 
3.4%
Other values (2) 2
 
6.9%
ValueCountFrequency (%)
-999 4
13.8%
1105 1
 
3.4%
1214 4
13.8%
1819 1
 
3.4%
1913 5
17.2%
2008 5
17.2%
6409 1
 
3.4%
6411 1
 
3.4%
7011 2
 
6.9%
7207 3
10.3%
ValueCountFrequency (%)
9414 1
 
3.4%
7303 1
 
3.4%
7207 3
10.3%
7011 2
 
6.9%
6411 1
 
3.4%
6409 1
 
3.4%
2008 5
17.2%
1913 5
17.2%
1819 1
 
3.4%
1214 4
13.8%

MXM_SGNFCT_WVHGH
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-133.92759
Minimum-999
Maximum6.8
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)13.8%
Memory size393.0 B
2024-01-14T16:02:05.621199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q13.1
median4.1
Q35.2
95-th percentile6.64
Maximum6.8
Range1005.8
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation352.15581
Coefficient of variation (CV)-2.6294494
Kurtosis3.1229453
Mean-133.92759
Median Absolute Deviation (MAD)1
Skewness-2.2162743
Sum-3883.9
Variance124013.72
MonotonicityNot monotonic
2024-01-14T16:02:05.738500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-999.0 4
 
13.8%
5.5 3
 
10.3%
6.8 2
 
6.9%
4.4 2
 
6.9%
3.1 2
 
6.9%
3.3 1
 
3.4%
6.4 1
 
3.4%
4.8 1
 
3.4%
6.1 1
 
3.4%
3.2 1
 
3.4%
Other values (11) 11
37.9%
ValueCountFrequency (%)
-999.0 4
13.8%
2.4 1
 
3.4%
2.5 1
 
3.4%
3.1 2
6.9%
3.2 1
 
3.4%
3.3 1
 
3.4%
3.6 1
 
3.4%
3.7 1
 
3.4%
3.8 1
 
3.4%
3.9 1
 
3.4%
ValueCountFrequency (%)
6.8 2
6.9%
6.4 1
 
3.4%
6.1 1
 
3.4%
5.5 3
10.3%
5.2 1
 
3.4%
5.1 1
 
3.4%
4.8 1
 
3.4%
4.7 1
 
3.4%
4.4 2
6.9%
4.2 1
 
3.4%

Interactions

2024-01-14T16:02:04.399030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:04.240336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:04.477845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:04.324182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T16:02:05.815633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WVDRCT_BRNGTYPHN_RANKTYPHN_NMMXM_SGNFCT_WVHGH
WVDRCT_BRNG1.0000.0000.597NaN
TYPHN_RANK0.0001.0000.397NaN
TYPHN_NM0.5970.3971.000NaN
MXM_SGNFCT_WVHGHNaNNaNNaN1.000
2024-01-14T16:02:05.903289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TYPHN_RANKWVDRCT_BRNG
TYPHN_RANK1.0000.000
WVDRCT_BRNG0.0001.000
2024-01-14T16:02:05.977751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TYPHN_NMMXM_SGNFCT_WVHGHWVDRCT_BRNGTYPHN_RANK
TYPHN_NM1.0000.6200.4680.143
MXM_SGNFCT_WVHGH0.6201.0000.5500.118
WVDRCT_BRNG0.4680.5501.0000.000
TYPHN_RANK0.1430.1180.0001.000

Missing values

2024-01-14T16:02:04.590632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T16:02:04.674239image/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

WVDRCT_BRNGTYPHN_RANKTYPHN_NMMXM_SGNFCT_WVHGH
0N119134.2
1N212143.8
2N370113.7
3NNE120084.7
4NNE219134.4
5NNE370113.1
6NE119134.4
7NE220083.9
8NE312143.6
9ENE120085.2
WVDRCT_BRNGTYPHN_RANKTYPHN_NMMXM_SGNFCT_WVHGH
19SE211054.1
20SE372073.2
21SSE164116.1
22SSE272075.5
23SSE312144.8
24S164096.8
25S273036.8
26S372076.4
27SSW118195.5
28SSW294145.5