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=CT04CMT007

Reproduction

Analysis started2024-01-14 07:02:06.363743
Analysis finished2024-01-14 07:02:06.987799
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

WVDRCT_BRNG
Categorical

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

2024-01-14T16:02:07.065151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:02:07.192145image/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%

NON_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

2024-01-14T16:02:07.331670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T16:02:07.456540image/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)

Distinct19
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19977309
Minimum19790111
Maximum20210920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-14T16:02:07.581583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19790111
5-th percentile19800625
Q119880315
median19930602
Q320080822
95-th percentile20206711
Maximum20210920
Range420809
Interquartile range (IQR)200507

Descriptive statistics

Standard deviation142911.36
Coefficient of variation (CV)0.0071536839
Kurtosis-1.1567384
Mean19977309
Median Absolute Deviation (MAD)79800
Skewness0.54096073
Sum5.7934197 × 108
Variance2.0423656 × 1010
MonotonicityNot monotonic
2024-01-14T16:02:07.726583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
19850802 3
 
10.3%
19930602 3
 
10.3%
19890830 2
 
6.9%
19971125 2
 
6.9%
20200723 2
 
6.9%
19800625 2
 
6.9%
19880827 2
 
6.9%
20080822 2
 
6.9%
20051022 1
 
3.4%
20180820 1
 
3.4%
Other values (9) 9
31.0%
ValueCountFrequency (%)
19790111 1
 
3.4%
19800625 2
6.9%
19830702 1
 
3.4%
19850802 3
10.3%
19880315 1
 
3.4%
19880827 2
6.9%
19890608 1
 
3.4%
19890830 2
6.9%
19930602 3
10.3%
19971125 2
6.9%
ValueCountFrequency (%)
20210920 1
3.4%
20210703 1
3.4%
20200724 1
3.4%
20200723 2
6.9%
20180820 1
3.4%
20121104 1
3.4%
20080822 2
6.9%
20051022 1
3.4%
19980823 1
3.4%
19971125 2
6.9%

MXM_SGNFCT_WVHGH
Real number (ℝ)

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5965517
Minimum2.5
Maximum5.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-01-14T16:02:07.858628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile2.74
Q13
median3.5
Q34
95-th percentile4.8
Maximum5.2
Range2.7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.70583424
Coefficient of variation (CV)0.19625305
Kurtosis-0.54932897
Mean3.5965517
Median Absolute Deviation (MAD)0.5
Skewness0.45939371
Sum104.3
Variance0.49820197
MonotonicityNot monotonic
2024-01-14T16:02:08.000108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4.2 3
10.3%
3.8 3
10.3%
3.5 3
10.3%
2.9 3
10.3%
4.0 3
10.3%
3.0 2
 
6.9%
2.8 2
 
6.9%
3.1 2
 
6.9%
4.8 2
 
6.9%
3.7 1
 
3.4%
Other values (5) 5
17.2%
ValueCountFrequency (%)
2.5 1
 
3.4%
2.7 1
 
3.4%
2.8 2
6.9%
2.9 3
10.3%
3.0 2
6.9%
3.1 2
6.9%
3.2 1
 
3.4%
3.5 3
10.3%
3.7 1
 
3.4%
3.8 3
10.3%
ValueCountFrequency (%)
5.2 1
 
3.4%
4.8 2
6.9%
4.4 1
 
3.4%
4.2 3
10.3%
4.0 3
10.3%
3.8 3
10.3%
3.7 1
 
3.4%
3.5 3
10.3%
3.2 1
 
3.4%
3.1 2
6.9%

Interactions

2024-01-14T16:02:06.675267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:06.508083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:06.758880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T16:02:06.592896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T16:02:08.097115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WVDRCT_BRNGNON_TYPHN_RANKTYPHN_NMMXM_SGNFCT_WVHGH
WVDRCT_BRNG1.0000.0000.0000.884
NON_TYPHN_RANK0.0001.0000.0000.000
TYPHN_NM0.0000.0001.0000.527
MXM_SGNFCT_WVHGH0.8840.0000.5271.000
2024-01-14T16:02:08.190392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NON_TYPHN_RANKWVDRCT_BRNG
NON_TYPHN_RANK1.0000.000
WVDRCT_BRNG0.0001.000
2024-01-14T16:02:08.273224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TYPHN_NMMXM_SGNFCT_WVHGHWVDRCT_BRNGNON_TYPHN_RANK
TYPHN_NM1.0000.0450.0000.000
MXM_SGNFCT_WVHGH0.0451.0000.4570.000
WVDRCT_BRNG0.0000.4571.0000.000
NON_TYPHN_RANK0.0000.0000.0001.000

Missing values

2024-01-14T16:02:06.869901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T16:02:06.954502image/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_BRNGNON_TYPHN_RANKTYPHN_NMMXM_SGNFCT_WVHGH
0N1200510224.2
1N2198803153.8
2N3197901113.7
3NNE1198908303.5
4NNE2199306023.0
5NNE3199711252.9
6NE1198908303.5
7NE2199711252.9
8NE3202007242.8
9ENE1202007233.1
WVDRCT_BRNGNON_TYPHN_RANKTYPHN_NMMXM_SGNFCT_WVHGH
19SE2200808223.8
20SE3198906083.8
21SSE1198508024.2
22SSE2201211044.0
23SSE3200808224.0
24S1198508024.8
25S2202109204.4
26S3199808234.2
27SSW1201808205.2
28SSW2198508024.8