Overview

Dataset statistics

Number of variables7
Number of observations617
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.5 KiB
Average record size in memory62.2 B

Variable types

Categorical1
Numeric6

Dataset

DescriptionSample
Author㈜전략해양
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT01RNS004

Alerts

HB_NM has constant value ""Constant
PRDN_MODL_GRID_LA is highly overall correlated with MXM_FLDTD_CRDRCHigh correlation
MXM_FLDTD_CRSPD is highly overall correlated with MXM_EBBTD_CRSPDHigh correlation
MXM_FLDTD_CRDRC is highly overall correlated with PRDN_MODL_GRID_LAHigh correlation
MXM_EBBTD_CRSPD is highly overall correlated with MXM_FLDTD_CRSPDHigh correlation

Reproduction

Analysis started2024-03-13 12:30:20.018829
Analysis finished2024-03-13 12:30:28.346244
Duration8.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

HB_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
목포항
617 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
목포항 617
100.0%

Length

2024-03-13T21:30:28.479943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:30:28.684778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
목포항 617
100.0%

PRDN_MODL_GRID_LO
Real number (ℝ)

Distinct27
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.06615
Minimum125.72746
Maximum126.55861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-13T21:30:28.890124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.72746
5-th percentile125.72746
Q1125.85533
median126.04713
Q3126.23893
95-th percentile126.4627
Maximum126.55861
Range0.83115
Interquartile range (IQR)0.3836

Descriptive statistics

Standard deviation0.23166943
Coefficient of variation (CV)0.0018376816
Kurtosis-1.0631355
Mean126.06615
Median Absolute Deviation (MAD)0.1918
Skewness0.28642959
Sum77782.812
Variance0.053670726
MonotonicityIncreasing
2024-03-13T21:30:29.151849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
125.72746 33
 
5.3%
125.79139 33
 
5.3%
125.8873 33
 
5.3%
125.75943 33
 
5.3%
125.82336 32
 
5.2%
125.85533 31
 
5.0%
126.20697 28
 
4.5%
125.9832 28
 
4.5%
125.91926 27
 
4.4%
125.95123 26
 
4.2%
Other values (17) 313
50.7%
ValueCountFrequency (%)
125.72746 33
5.3%
125.75943 33
5.3%
125.79139 33
5.3%
125.82336 32
5.2%
125.85533 31
5.0%
125.8873 33
5.3%
125.91926 27
4.4%
125.95123 26
4.2%
125.9832 28
4.5%
126.01516 26
4.2%
ValueCountFrequency (%)
126.55861 6
 
1.0%
126.52664 7
 
1.1%
126.49467 10
1.6%
126.4627 15
2.4%
126.43074 16
2.6%
126.39877 19
3.1%
126.3668 21
3.4%
126.33484 21
3.4%
126.30287 14
2.3%
126.2709 21
3.4%

PRDN_MODL_GRID_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.461683
Minimum34.05773
Maximum34.98032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-13T21:30:29.426406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.05773
5-th percentile34.08671
Q134.20255
median34.43376
Q334.69311
95-th percentile34.92296
Maximum34.98032
Range0.92259
Interquartile range (IQR)0.49056

Descriptive statistics

Standard deviation0.27928557
Coefficient of variation (CV)0.0081042348
Kurtosis-1.1745595
Mean34.461683
Median Absolute Deviation (MAD)0.23121
Skewness0.28069519
Sum21262.859
Variance0.078000432
MonotonicityNot monotonic
2024-03-13T21:30:29.702915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
34.05773 27
 
4.4%
34.11569 27
 
4.4%
34.08671 27
 
4.4%
34.14465 25
 
4.1%
34.20255 25
 
4.1%
34.17361 24
 
3.9%
34.23149 24
 
3.9%
34.26042 24
 
3.9%
34.28933 24
 
3.9%
34.34713 24
 
3.9%
Other values (23) 366
59.3%
ValueCountFrequency (%)
34.05773 27
4.4%
34.08671 27
4.4%
34.11569 27
4.4%
34.14465 25
4.1%
34.17361 24
3.9%
34.20255 25
4.1%
34.23149 24
3.9%
34.26042 24
3.9%
34.28933 24
3.9%
34.31824 21
3.4%
ValueCountFrequency (%)
34.98032 15
2.4%
34.95164 15
2.4%
34.92296 15
2.4%
34.89426 16
2.6%
34.86556 12
1.9%
34.83684 17
2.8%
34.80811 18
2.9%
34.77938 17
2.8%
34.75063 13
2.1%
34.72187 14
2.3%

MXM_FLDTD_CRSPD
Real number (ℝ)

HIGH CORRELATION 

Distinct519
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.06759
Minimum9.8
Maximum347.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-13T21:30:29.992126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.8
5-th percentile46.96
Q1111.3
median152.3
Q3178.5
95-th percentile230.02
Maximum347.6
Range337.8
Interquartile range (IQR)67.2

Descriptive statistics

Standard deviation56.086322
Coefficient of variation (CV)0.38397515
Kurtosis0.90778347
Mean146.06759
Median Absolute Deviation (MAD)32
Skewness0.20421384
Sum90123.7
Variance3145.6755
MonotonicityNot monotonic
2024-03-13T21:30:30.311428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.8 4
 
0.6%
157.6 4
 
0.6%
179.9 3
 
0.5%
178.0 3
 
0.5%
168.6 3
 
0.5%
148.8 3
 
0.5%
155.9 3
 
0.5%
124.2 3
 
0.5%
162.1 3
 
0.5%
170.1 3
 
0.5%
Other values (509) 585
94.8%
ValueCountFrequency (%)
9.8 1
0.2%
10.7 1
0.2%
11.2 1
0.2%
11.9 1
0.2%
14.3 1
0.2%
15.8 1
0.2%
17.1 1
0.2%
18.7 1
0.2%
19.2 1
0.2%
22.2 1
0.2%
ValueCountFrequency (%)
347.6 1
0.2%
345.6 1
0.2%
343.9 1
0.2%
336.9 1
0.2%
334.9 1
0.2%
322.5 1
0.2%
321.9 1
0.2%
303.7 1
0.2%
290.4 1
0.2%
287.2 1
0.2%

MXM_FLDTD_CRDRC
Real number (ℝ)

HIGH CORRELATION 

Distinct204
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.64506
Minimum0
Maximum360
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-13T21:30:30.556141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.8
Q187
median107
Q3150
95-th percentile350
Maximum360
Range360
Interquartile range (IQR)63

Descriptive statistics

Standard deviation87.77938
Coefficient of variation (CV)0.6519317
Kurtosis1.2003402
Mean134.64506
Median Absolute Deviation (MAD)28
Skewness1.3922016
Sum83076
Variance7705.2196
MonotonicityNot monotonic
2024-03-13T21:30:30.832704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 16
 
2.6%
87 14
 
2.3%
90 12
 
1.9%
99 12
 
1.9%
96 11
 
1.8%
100 10
 
1.6%
89 9
 
1.5%
81 8
 
1.3%
88 8
 
1.3%
91 8
 
1.3%
Other values (194) 509
82.5%
ValueCountFrequency (%)
0 1
 
0.2%
1 1
 
0.2%
2 1
 
0.2%
3 5
0.8%
4 5
0.8%
5 4
0.6%
6 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
ValueCountFrequency (%)
360 3
0.5%
359 5
0.8%
358 2
 
0.3%
357 2
 
0.3%
356 3
0.5%
355 5
0.8%
354 2
 
0.3%
353 2
 
0.3%
352 4
0.6%
351 2
 
0.3%

MXM_EBBTD_CRSPD
Real number (ℝ)

HIGH CORRELATION 

Distinct517
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.69254
Minimum9.7
Maximum360.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-13T21:30:31.127177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.7
5-th percentile42.18
Q1105.9
median142
Q3169
95-th percentile223.4
Maximum360.2
Range350.5
Interquartile range (IQR)63.1

Descriptive statistics

Standard deviation55.131466
Coefficient of variation (CV)0.39750851
Kurtosis0.93636752
Mean138.69254
Median Absolute Deviation (MAD)30.5
Skewness0.20550602
Sum85573.3
Variance3039.4786
MonotonicityNot monotonic
2024-03-13T21:30:31.397734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134.1 3
 
0.5%
178.5 3
 
0.5%
195.2 3
 
0.5%
134.9 3
 
0.5%
145.3 3
 
0.5%
137.8 3
 
0.5%
148.5 3
 
0.5%
114.1 3
 
0.5%
145.8 3
 
0.5%
146.0 3
 
0.5%
Other values (507) 587
95.1%
ValueCountFrequency (%)
9.7 1
0.2%
11.2 1
0.2%
11.6 1
0.2%
11.9 1
0.2%
13.4 1
0.2%
14.5 1
0.2%
15.2 1
0.2%
15.5 1
0.2%
15.8 1
0.2%
16.1 1
0.2%
ValueCountFrequency (%)
360.2 1
0.2%
352.8 1
0.2%
351.5 1
0.2%
331.1 1
0.2%
300.9 1
0.2%
291.5 1
0.2%
271.7 1
0.2%
268.1 2
0.3%
266.4 1
0.2%
262.5 1
0.2%

MXM_EBBTD_CRDRC
Real number (ℝ)

Distinct192
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.57374
Minimum0
Maximum360
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-03-13T21:30:31.678861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.8
Q1249
median274
Q3311
95-th percentile351.2
Maximum360
Range360
Interquartile range (IQR)62

Descriptive statistics

Standard deviation90.036274
Coefficient of variation (CV)0.35229078
Kurtosis1.7708796
Mean255.57374
Median Absolute Deviation (MAD)32
Skewness-1.5475255
Sum157689
Variance8106.5307
MonotonicityNot monotonic
2024-03-13T21:30:31.907949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
271 17
 
2.8%
272 10
 
1.6%
347 10
 
1.6%
269 10
 
1.6%
268 10
 
1.6%
270 10
 
1.6%
274 9
 
1.5%
278 9
 
1.5%
267 9
 
1.5%
253 8
 
1.3%
Other values (182) 515
83.5%
ValueCountFrequency (%)
0 3
0.5%
1 3
0.5%
2 3
0.5%
3 2
 
0.3%
4 7
1.1%
5 4
0.6%
6 2
 
0.3%
7 2
 
0.3%
9 5
0.8%
10 2
 
0.3%
ValueCountFrequency (%)
360 3
0.5%
358 4
0.6%
357 5
0.8%
356 5
0.8%
355 3
0.5%
354 4
0.6%
353 5
0.8%
352 2
 
0.3%
351 6
1.0%
350 4
0.6%

Interactions

2024-03-13T21:30:26.765971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:20.471269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:21.709152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:22.784713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:23.994833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:25.614725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:26.922612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:20.726690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:21.872297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:23.014623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:24.170285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:25.805046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:27.129174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:20.963696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:22.034456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:23.221899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:24.313691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:25.967275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:27.323481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:21.146025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:22.204551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:23.385778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:24.481402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:26.162656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:27.533744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:21.313775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:22.364605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:23.567575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:24.656326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:26.357198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:27.759889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:21.496130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:22.547614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:23.790305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:25.393549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:30:26.528747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:30:32.074617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDN_MODL_GRID_LOPRDN_MODL_GRID_LAMXM_FLDTD_CRSPDMXM_FLDTD_CRDRCMXM_EBBTD_CRSPDMXM_EBBTD_CRDRC
PRDN_MODL_GRID_LO1.0000.2580.5760.4930.5500.593
PRDN_MODL_GRID_LA0.2581.0000.5940.5690.5800.681
MXM_FLDTD_CRSPD0.5760.5941.0000.3740.8680.574
MXM_FLDTD_CRDRC0.4930.5690.3741.0000.3940.702
MXM_EBBTD_CRSPD0.5500.5800.8680.3941.0000.496
MXM_EBBTD_CRDRC0.5930.6810.5740.7020.4961.000
2024-03-13T21:30:32.240829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDN_MODL_GRID_LOPRDN_MODL_GRID_LAMXM_FLDTD_CRSPDMXM_FLDTD_CRDRCMXM_EBBTD_CRSPDMXM_EBBTD_CRDRC
PRDN_MODL_GRID_LO1.000-0.233-0.4320.196-0.3310.207
PRDN_MODL_GRID_LA-0.2331.000-0.297-0.559-0.465-0.360
MXM_FLDTD_CRSPD-0.432-0.2971.0000.1320.7680.095
MXM_FLDTD_CRDRC0.196-0.5590.1321.0000.2370.425
MXM_EBBTD_CRSPD-0.331-0.4650.7680.2371.0000.137
MXM_EBBTD_CRDRC0.207-0.3600.0950.4250.1371.000

Missing values

2024-03-13T21:30:28.042530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:30:28.262043image/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

HB_NMPRDN_MODL_GRID_LOPRDN_MODL_GRID_LAMXM_FLDTD_CRSPDMXM_FLDTD_CRDRCMXM_EBBTD_CRSPDMXM_EBBTD_CRDRC
0목포항125.7274634.05773179.9301194.3323
1목포항125.7274634.08671181.8296198.1321
2목포항125.7274634.11569197.0141199.3320
3목포항125.7274634.14465210.7119199.2294
4목포항125.7274634.17361230.5121203.2289
5목포항125.7274634.20255290.4127200.7286
6목포항125.7274634.23149285.2121256.8112
7목포항125.7274634.26042249.1108205.5110
8목포항125.7274634.28933238.5105215.997
9목포항125.7274634.31824223.7100191.692
HB_NMPRDN_MODL_GRID_LOPRDN_MODL_GRID_LAMXM_FLDTD_CRSPDMXM_FLDTD_CRDRCMXM_EBBTD_CRSPDMXM_EBBTD_CRDRC
607목포항126.5266434.2025551.23950.748
608목포항126.5266434.2314974.8206110.419
609목포항126.5266434.26042189.8183170.81
610목포항126.5266434.28933168.317797.90
611목포항126.5586134.05773208.6353224.0343
612목포항126.5586134.08671186.0344218.4344
613목포항126.5586134.11569156.6172172.3357
614목포항126.5586134.2314944.73784.042
615목포항126.5586134.26042217.7185222.120
616목포항126.5586134.28933188.4182156.99