نمای کلی (مجموعه داده‌ی Abalone)

Dataset statistics

Number of variables9
Number of observations4177
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory497.8 KiB
Average record size in memory122.0 B

Variable types

NUM8
CAT1

Warnings

Diameter is highly correlated with Length and 2 other fieldsHigh correlation
Length is highly correlated with Diameter and 2 other fieldsHigh correlation
WholeWeight is highly correlated with Length and 4 other fieldsHigh correlation
ShuckedWeight is highly correlated with WholeWeight and 1 other fieldsHigh correlation
VisceraWeight is highly correlated with Length and 3 other fieldsHigh correlation
ShellWeight is highly correlated with Diameter and 2 other fieldsHigh correlation

Reproduction

Analysis started2020-10-05 21:42:50.161189
Analysis finished2020-10-05 21:43:05.138262
Duration14.98 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

متغیرها (ابعاد مسئله)

Sex
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.8 KiB
M
1528 
I
1342 
F
1307 
ValueCountFrequency (%) 
M152836.6%
 
I134232.1%
 
F130731.3%
 
2020-10-06T01:13:05.246258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-06T01:13:05.372374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:05.461195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
M152836.6%
 
I134232.1%
 
F130731.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter4177100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M152836.6%
 
I134232.1%
 
F130731.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4177100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M152836.6%
 
I134232.1%
 
F130731.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4177100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
M152836.6%
 
I134232.1%
 
F130731.3%
 

Length
Real number (ℝ≥0)

HIGH CORRELATION

Distinct134
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5239920996
Minimum0.075
Maximum0.815
Zeros0
Zeros (%)0.0%
Memory size32.8 KiB
2020-10-06T01:13:05.637602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.075
5-th percentile0.295
Q10.45
median0.545
Q30.615
95-th percentile0.69
Maximum0.815
Range0.74
Interquartile range (IQR)0.165

Descriptive statistics

Standard deviation0.1200929126
Coefficient of variation (CV)0.2291884031
Kurtosis0.06462097389
Mean0.5239920996
Median Absolute Deviation (MAD)0.08
Skewness-0.639873269
Sum2188.715
Variance0.01442230765
MonotocityNot monotonic
2020-10-06T01:13:05.812746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.625942.3%
 
0.55942.3%
 
0.575932.2%
 
0.58922.2%
 
0.62872.1%
 
0.6872.1%
 
0.5811.9%
 
0.57791.9%
 
0.63781.9%
 
0.61751.8%
 
0.525741.8%
 
0.65731.7%
 
0.53731.7%
 
0.52721.7%
 
0.645701.7%
 
0.59701.7%
 
0.595671.6%
 
0.56661.6%
 
0.615661.6%
 
0.475651.6%
 
0.565651.6%
 
0.605641.5%
 
0.585641.5%
 
0.635631.5%
 
0.515621.5%
 
Other values (109)230355.1%
 
ValueCountFrequency (%) 
0.0751< 0.1%
 
0.111< 0.1%
 
0.132< 0.1%
 
0.1351< 0.1%
 
0.142< 0.1%
 
0.151< 0.1%
 
0.15530.1%
 
0.1640.1%
 
0.16550.1%
 
0.1730.1%
 
ValueCountFrequency (%) 
0.8151< 0.1%
 
0.81< 0.1%
 
0.782< 0.1%
 
0.7752< 0.1%
 
0.7730.1%
 
0.7652< 0.1%
 
0.762< 0.1%
 
0.75530.1%
 
0.7580.2%
 
0.74550.1%
 

Diameter
Real number (ℝ≥0)

HIGH CORRELATION

Distinct111
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4078812545
Minimum0.055
Maximum0.65
Zeros0
Zeros (%)0.0%
Memory size32.8 KiB
2020-10-06T01:13:05.961385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.055
5-th percentile0.22
Q10.35
median0.425
Q30.48
95-th percentile0.545
Maximum0.65
Range0.595
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.09923986613
Coefficient of variation (CV)0.2433057784
Kurtosis-0.04547558144
Mean0.4078812545
Median Absolute Deviation (MAD)0.065
Skewness-0.6091981423
Sum1703.72
Variance0.00984855103
MonotocityNot monotonic
2020-10-06T01:13:06.099132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.451393.3%
 
0.4751202.9%
 
0.41112.7%
 
0.51102.6%
 
0.471002.4%
 
0.48912.2%
 
0.455902.2%
 
0.46892.1%
 
0.44872.1%
 
0.485832.0%
 
0.42822.0%
 
0.375822.0%
 
0.425811.9%
 
0.525811.9%
 
0.465811.9%
 
0.35801.9%
 
0.49791.9%
 
0.435771.8%
 
0.38761.8%
 
0.51731.7%
 
0.43721.7%
 
0.355711.7%
 
0.495701.7%
 
0.405691.7%
 
0.505681.6%
 
Other values (86)201548.2%
 
ValueCountFrequency (%) 
0.0551< 0.1%
 
0.091< 0.1%
 
0.0951< 0.1%
 
0.12< 0.1%
 
0.10540.1%
 
0.1140.1%
 
0.1152< 0.1%
 
0.1250.1%
 
0.12570.2%
 
0.1380.2%
 
ValueCountFrequency (%) 
0.651< 0.1%
 
0.6330.1%
 
0.6251< 0.1%
 
0.621< 0.1%
 
0.6151< 0.1%
 
0.611< 0.1%
 
0.60530.1%
 
0.680.2%
 
0.59540.1%
 
0.5960.1%
 

Height
Real number (ℝ≥0)

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1395163993
Minimum0
Maximum1.13
Zeros2
Zeros (%)< 0.1%
Memory size32.8 KiB
2020-10-06T01:13:06.257682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.075
Q10.115
median0.14
Q30.165
95-th percentile0.2
Maximum1.13
Range1.13
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.04182705661
Coefficient of variation (CV)0.2998002873
Kurtosis76.02550923
Mean0.1395163993
Median Absolute Deviation (MAD)0.025
Skewness3.128817379
Sum582.76
Variance0.001749502664
MonotocityNot monotonic
2020-10-06T01:13:06.400373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.152676.4%
 
0.142205.3%
 
0.1552175.2%
 
0.1752115.1%
 
0.162054.9%
 
0.1252024.8%
 
0.1651934.6%
 
0.1351894.5%
 
0.1451824.4%
 
0.131694.0%
 
0.121694.0%
 
0.171603.8%
 
0.11453.5%
 
0.111353.2%
 
0.1151333.2%
 
0.181313.1%
 
0.091243.0%
 
0.1051142.7%
 
0.1851032.5%
 
0.191032.5%
 
0.095912.2%
 
0.195781.9%
 
0.08761.8%
 
0.085741.8%
 
0.2681.6%
 
Other values (26)41810.0%
 
ValueCountFrequency (%) 
02< 0.1%
 
0.011< 0.1%
 
0.0152< 0.1%
 
0.022< 0.1%
 
0.02550.1%
 
0.0360.1%
 
0.03560.1%
 
0.04130.3%
 
0.045110.3%
 
0.05180.4%
 
ValueCountFrequency (%) 
1.131< 0.1%
 
0.5151< 0.1%
 
0.2530.1%
 
0.2440.1%
 
0.23560.1%
 
0.23100.2%
 
0.225130.3%
 
0.22170.4%
 
0.215310.7%
 
0.21230.6%
 

WholeWeight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2429
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8287421594
Minimum0.002
Maximum2.8255
Zeros0
Zeros (%)0.0%
Memory size32.8 KiB
2020-10-06T01:13:06.545426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.1259
Q10.4415
median0.7995
Q31.153
95-th percentile1.6949
Maximum2.8255
Range2.8235
Interquartile range (IQR)0.7115

Descriptive statistics

Standard deviation0.4903890182
Coefficient of variation (CV)0.5917268871
Kurtosis-0.02364350427
Mean0.8287421594
Median Absolute Deviation (MAD)0.3565
Skewness0.5309585633
Sum3461.656
Variance0.2404813892
MonotocityNot monotonic
2020-10-06T01:13:06.683836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.222580.2%
 
0.19670.2%
 
0.9770.2%
 
0.477570.2%
 
1.134570.2%
 
0.676560.1%
 
0.1860.1%
 
0.324560.1%
 
0.580560.1%
 
0.49460.1%
 
0.633550.1%
 
1.49450.1%
 
0.367550.1%
 
1.21750.1%
 
0.10650.1%
 
0.87450.1%
 
1.06550.1%
 
0.48750.1%
 
0.71750.1%
 
0.19750.1%
 
0.579550.1%
 
0.643550.1%
 
0.449550.1%
 
0.872550.1%
 
1.11550.1%
 
Other values (2404)403696.6%
 
ValueCountFrequency (%) 
0.0021< 0.1%
 
0.0081< 0.1%
 
0.01051< 0.1%
 
0.0131< 0.1%
 
0.0141< 0.1%
 
0.01452< 0.1%
 
0.0151< 0.1%
 
0.01551< 0.1%
 
0.01751< 0.1%
 
0.0182< 0.1%
 
ValueCountFrequency (%) 
2.82551< 0.1%
 
2.77951< 0.1%
 
2.6571< 0.1%
 
2.5551< 0.1%
 
2.551< 0.1%
 
2.5481< 0.1%
 
2.5261< 0.1%
 
2.51551< 0.1%
 
2.50851< 0.1%
 
2.5051< 0.1%
 

ShuckedWeight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1515
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3593674886
Minimum0.001
Maximum1.488
Zeros0
Zeros (%)0.0%
Memory size32.8 KiB
2020-10-06T01:13:06.838002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0524
Q10.186
median0.336
Q30.502
95-th percentile0.7402
Maximum1.488
Range1.487
Interquartile range (IQR)0.316

Descriptive statistics

Standard deviation0.221962949
Coefficient of variation (CV)0.6176489417
Kurtosis0.5951236784
Mean0.3593674886
Median Absolute Deviation (MAD)0.1585
Skewness0.7190979218
Sum1501.078
Variance0.04926755074
MonotocityNot monotonic
2020-10-06T01:13:07.027634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.175110.3%
 
0.2505100.2%
 
0.16590.2%
 
0.09690.2%
 
0.202590.2%
 
0.41990.2%
 
0.30290.2%
 
0.294590.2%
 
0.290.2%
 
0.2190.2%
 
0.09790.2%
 
0.125580.2%
 
0.216580.2%
 
0.238580.2%
 
0.26180.2%
 
0.35880.2%
 
0.157580.2%
 
0.201580.2%
 
0.2580.2%
 
0.21580.2%
 
0.33670.2%
 
0.298570.2%
 
0.3270.2%
 
0.170.2%
 
0.189570.2%
 
Other values (1490)396895.0%
 
ValueCountFrequency (%) 
0.0011< 0.1%
 
0.00251< 0.1%
 
0.00452< 0.1%
 
0.00530.1%
 
0.00552< 0.1%
 
0.006530.1%
 
0.0071< 0.1%
 
0.007540.1%
 
0.0081< 0.1%
 
0.00851< 0.1%
 
ValueCountFrequency (%) 
1.4881< 0.1%
 
1.3511< 0.1%
 
1.34851< 0.1%
 
1.2531< 0.1%
 
1.24551< 0.1%
 
1.23952< 0.1%
 
1.2321< 0.1%
 
1.19651< 0.1%
 
1.19451< 0.1%
 
1.17051< 0.1%
 

VisceraWeight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct880
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1805936079
Minimum0.0005
Maximum0.76
Zeros0
Zeros (%)0.0%
Memory size32.8 KiB
2020-10-06T01:13:07.228462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.027
Q10.0935
median0.171
Q30.253
95-th percentile0.3796
Maximum0.76
Range0.7595
Interquartile range (IQR)0.1595

Descriptive statistics

Standard deviation0.1096142503
Coefficient of variation (CV)0.6069663902
Kurtosis0.084011749
Mean0.1805936079
Median Absolute Deviation (MAD)0.0795
Skewness0.5918521514
Sum754.3395
Variance0.01201528386
MonotocityNot monotonic
2020-10-06T01:13:07.418747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.1715150.4%
 
0.196140.3%
 
0.061130.3%
 
0.037130.3%
 
0.2195130.3%
 
0.0575130.3%
 
0.159120.3%
 
0.1905120.3%
 
0.0265120.3%
 
0.1625120.3%
 
0.207120.3%
 
0.15120.3%
 
0.096120.3%
 
0.156120.3%
 
0.099120.3%
 
0.1405120.3%
 
0.318110.3%
 
0.057110.3%
 
0.0735110.3%
 
0.127110.3%
 
0.2145110.3%
 
0.1725110.3%
 
0.171110.3%
 
0.034110.3%
 
0.1455110.3%
 
Other values (855)387792.8%
 
ValueCountFrequency (%) 
0.00052< 0.1%
 
0.0021< 0.1%
 
0.00252< 0.1%
 
0.00330.1%
 
0.003530.1%
 
0.0041< 0.1%
 
0.004540.1%
 
0.00570.2%
 
0.005560.1%
 
0.0062< 0.1%
 
ValueCountFrequency (%) 
0.761< 0.1%
 
0.64151< 0.1%
 
0.591< 0.1%
 
0.5751< 0.1%
 
0.57451< 0.1%
 
0.5641< 0.1%
 
0.551< 0.1%
 
0.5412< 0.1%
 
0.52651< 0.1%
 
0.5261< 0.1%
 

ShellWeight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct926
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2388308595
Minimum0.0015
Maximum1.005
Zeros0
Zeros (%)0.0%
Memory size32.8 KiB
2020-10-06T01:13:07.620103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0384
Q10.13
median0.234
Q30.329
95-th percentile0.48
Maximum1.005
Range1.0035
Interquartile range (IQR)0.199

Descriptive statistics

Standard deviation0.1392026695
Coefficient of variation (CV)0.5828504316
Kurtosis0.5319261262
Mean0.2388308595
Median Absolute Deviation (MAD)0.0995
Skewness0.6209268251
Sum997.5965
Variance0.0193773832
MonotocityNot monotonic
2020-10-06T01:13:07.820992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.275431.0%
 
0.25421.0%
 
0.265401.0%
 
0.315401.0%
 
0.185401.0%
 
0.285370.9%
 
0.17370.9%
 
0.175360.9%
 
0.3360.9%
 
0.22360.9%
 
0.24350.8%
 
0.335350.8%
 
0.07350.8%
 
0.295350.8%
 
0.12340.8%
 
0.235330.8%
 
0.195320.8%
 
0.255320.8%
 
0.26320.8%
 
0.31320.8%
 
0.355320.8%
 
0.19310.7%
 
0.32310.7%
 
0.135300.7%
 
0.155300.7%
 
Other values (901)330179.0%
 
ValueCountFrequency (%) 
0.00151< 0.1%
 
0.0031< 0.1%
 
0.00351< 0.1%
 
0.0042< 0.1%
 
0.005120.3%
 
0.0061< 0.1%
 
0.00651< 0.1%
 
0.0071< 0.1%
 
0.00751< 0.1%
 
0.00840.1%
 
ValueCountFrequency (%) 
1.0051< 0.1%
 
0.8971< 0.1%
 
0.8852< 0.1%
 
0.851< 0.1%
 
0.8151< 0.1%
 
0.79751< 0.1%
 
0.781< 0.1%
 
0.761< 0.1%
 
0.7261< 0.1%
 
0.72530.1%
 

Rings
Real number (ℝ≥0)

Distinct28
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.933684463
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Memory size32.8 KiB
2020-10-06T01:13:08.005288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median9
Q311
95-th percentile16
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.224169032
Coefficient of variation (CV)0.324569302
Kurtosis2.330687427
Mean9.933684463
Median Absolute Deviation (MAD)2
Skewness1.114101898
Sum41493
Variance10.39526595
MonotocityNot monotonic
2020-10-06T01:13:08.176374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
968916.5%
 
1063415.2%
 
856813.6%
 
1148711.7%
 
73919.4%
 
122676.4%
 
62596.2%
 
132034.9%
 
141263.0%
 
51152.8%
 
151032.5%
 
16671.6%
 
17581.4%
 
4571.4%
 
18421.0%
 
19320.8%
 
20260.6%
 
3150.4%
 
21140.3%
 
2390.2%
 
2260.1%
 
242< 0.1%
 
272< 0.1%
 
11< 0.1%
 
251< 0.1%
 
Other values (3)30.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
3150.4%
 
4571.4%
 
51152.8%
 
62596.2%
 
73919.4%
 
856813.6%
 
968916.5%
 
1063415.2%
 
ValueCountFrequency (%) 
291< 0.1%
 
272< 0.1%
 
261< 0.1%
 
251< 0.1%
 
242< 0.1%
 
2390.2%
 
2260.1%
 
21140.3%
 
20260.6%
 
19320.8%
 

اثرات متقابل داده‌ها

2020-10-06T01:12:56.019176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:56.182116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:56.311173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:56.520749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:56.649767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:56.787251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:56.925692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:57.057118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:57.188422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:57.315914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:57.436589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:57.557537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:57.679280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:57.808182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:57.935805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:58.057746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:58.180405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:58.307106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:58.427075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:58.546578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:58.668340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:58.795610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:58.922067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:59.042772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:59.164160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:59.290783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:59.410912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:59.530699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:59.650513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:59.779001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:12:59.905477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:00.104792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:00.227134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:00.362363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:00.492037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:00.620923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:00.750103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:00.886735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:01.023379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:01.153153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:01.285275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:01.420099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:01.549490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:01.678340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:01.813822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:01.949127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:02.085527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:02.216118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:02.347916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:02.474773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:02.595791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:02.717403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:02.841651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:02.968590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:03.095909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:03.219781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:03.342169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:03.471744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:03.595125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:03.718056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:03.842004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:03.971216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:04.100678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:04.224211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

همبستگی‌ها

2020-10-06T01:13:08.351306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-06T01:13:08.673705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-06T01:13:09.015103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-06T01:13:09.214517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

مقادیر گم‌شده

2020-10-06T01:13:04.464552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-06T01:13:04.945110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

نمونه‌ی داده‌ها

First rows

SexLengthDiameterHeightWholeWeightShuckedWeightVisceraWeightShellWeightRings
0M0.4550.3650.0950.51400.22450.10100.15015
1M0.3500.2650.0900.22550.09950.04850.0707
2F0.5300.4200.1350.67700.25650.14150.2109
3M0.4400.3650.1250.51600.21550.11400.15510
4I0.3300.2550.0800.20500.08950.03950.0557
5I0.4250.3000.0950.35150.14100.07750.1208
6F0.5300.4150.1500.77750.23700.14150.33020
7F0.5450.4250.1250.76800.29400.14950.26016
8M0.4750.3700.1250.50950.21650.11250.1659
9F0.5500.4400.1500.89450.31450.15100.32019

Last rows

SexLengthDiameterHeightWholeWeightShuckedWeightVisceraWeightShellWeightRings
4167M0.5000.3800.1250.57700.26900.12650.15359
4168F0.5150.4000.1250.61500.28650.12300.17658
4169M0.5200.3850.1650.79100.37500.18000.181510
4170M0.5500.4300.1300.83950.31550.19550.240510
4171M0.5600.4300.1550.86750.40000.17200.22908
4172F0.5650.4500.1650.88700.37000.23900.249011
4173M0.5900.4400.1350.96600.43900.21450.260510
4174M0.6000.4750.2051.17600.52550.28750.30809
4175F0.6250.4850.1501.09450.53100.26100.296010
4176M0.7100.5550.1951.94850.94550.37650.495012