Overview

Dataset statistics

Number of variables10
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory16.1 KiB
Average record size in memory80.6 B

Variable types

NUM10

Reproduction

Analysis started2020-08-25 01:46:37.971376
Analysis finished2020-08-25 01:46:51.533209
Duration13.56 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 1 (0.5%) duplicate rows Duplicates
Mg has 39 (19.0%) zeros Zeros
K has 21 (10.2%) zeros Zeros
Ba has 167 (81.5%) zeros Zeros
Fe has 135 (65.9%) zeros Zeros
target has 13 (6.3%) zeros Zeros

Variables

RI
Real number (ℝ≥0)

Distinct count172
Unique (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5184053658536585
Minimum1.51131
Maximum1.53393
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:46:51.576871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.51131
5-th percentile1.515582
Q11.51652
median1.51764
Q31.51915
95-th percentile1.523682
Maximum1.53393
Range0.02262
Interquartile range (IQR)0.00263

Descriptive statistics

Standard deviation0.003034894043
Coefficient of variation (CV)0.001998737696
Kurtosis5.00712817
Mean1.518405366
Median Absolute Deviation (MAD)0.00121
Skewness1.754543998
Sum311.2731
Variance9.210581851e-06
2020-08-25T01:46:51.674768image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.5164531.5%
 
1.515931.5%
 
1.5215231.5%
 
1.5162321.0%
 
1.516121.0%
 
1.5176321.0%
 
1.5179321.0%
 
1.5176921.0%
 
1.5161821.0%
 
1.5167421.0%
 
1.5161321.0%
 
1.5159321.0%
 
1.5217221.0%
 
1.5174321.0%
 
1.5221321.0%
 
1.5177921.0%
 
1.5164621.0%
 
1.5159621.0%
 
1.5176121.0%
 
1.5175421.0%
 
1.5181121.0%
 
1.516421.0%
 
1.5217721.0%
 
1.5175521.0%
 
1.5184121.0%
 
Other values (147)15274.1%
 
ValueCountFrequency (%) 
1.5113110.5%
 
1.5121510.5%
 
1.5131610.5%
 
1.5132110.5%
 
1.5140910.5%
 
1.5150810.5%
 
1.5151421.0%
 
1.5153110.5%
 
1.5154510.5%
 
1.5155610.5%
 
ValueCountFrequency (%) 
1.5339310.5%
 
1.5312510.5%
 
1.5277710.5%
 
1.5273910.5%
 
1.5272510.5%
 
1.5266710.5%
 
1.5266410.5%
 
1.5261410.5%
 
1.5247510.5%
 
1.524110.5%
 

Na
Real number (ℝ≥0)

Distinct count134
Unique (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.353463414634147
Minimum10.73
Maximum15.79
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:46:51.783835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum10.73
5-th percentile12.37
Q112.88
median13.25
Q313.72
95-th percentile14.84
Maximum15.79
Range5.06
Interquartile range (IQR)0.84

Descriptive statistics

Standard deviation0.7612491057
Coefficient of variation (CV)0.05700761533
Kurtosis1.455735804
Mean13.35346341
Median Absolute Deviation (MAD)0.4
Skewness0.00241879876
Sum2737.46
Variance0.5795002009
2020-08-25T01:46:51.889874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1352.4%
 
13.0252.4%
 
13.2152.4%
 
13.6442.0%
 
12.8642.0%
 
13.2442.0%
 
13.3342.0%
 
12.8542.0%
 
12.7931.5%
 
12.8731.5%
 
13.4431.5%
 
12.9331.5%
 
13.4831.5%
 
13.231.5%
 
13.7231.5%
 
13.4131.5%
 
14.3621.0%
 
13.4321.0%
 
12.8121.0%
 
13.0521.0%
 
14.1421.0%
 
12.8221.0%
 
12.6421.0%
 
13.8921.0%
 
14.2121.0%
 
Other values (109)12862.4%
 
ValueCountFrequency (%) 
10.7310.5%
 
11.0210.5%
 
11.0310.5%
 
11.2310.5%
 
11.4510.5%
 
11.5610.5%
 
11.9510.5%
 
12.1610.5%
 
12.210.5%
 
12.310.5%
 
ValueCountFrequency (%) 
15.7910.5%
 
15.1510.5%
 
15.0110.5%
 
14.9521.0%
 
14.9410.5%
 
14.9210.5%
 
14.8621.0%
 
14.8521.0%
 
14.810.5%
 
14.7710.5%
 

Mg
Real number (ℝ≥0)

ZEROS

Distinct count88
Unique (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7450731707317075
Minimum0.0
Maximum4.49
Zeros39
Zeros (%)19.0%
Memory size1.7 KiB
2020-08-25T01:46:52.004132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.68
median3.49
Q33.61
95-th percentile3.85
Maximum4.49
Range4.49
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation1.427427282
Coefficient of variation (CV)0.5199960779
Kurtosis-0.1752913221
Mean2.745073171
Median Absolute Deviation (MAD)0.18
Skewness-1.258365771
Sum562.74
Variance2.037548647
2020-08-25T01:46:52.099288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03919.0%
 
3.5483.9%
 
3.4883.9%
 
3.5883.9%
 
3.5273.4%
 
3.6252.4%
 
3.6142.0%
 
3.542.0%
 
3.6642.0%
 
3.5742.0%
 
3.5642.0%
 
3.7431.5%
 
3.4331.5%
 
3.631.5%
 
3.4731.5%
 
3.6531.5%
 
3.5931.5%
 
3.931.5%
 
3.4931.5%
 
3.6731.5%
 
3.3431.5%
 
3.5531.5%
 
3.4531.5%
 
3.5321.0%
 
3.3921.0%
 
Other values (63)7034.1%
 
ValueCountFrequency (%) 
03919.0%
 
0.3310.5%
 
1.0110.5%
 
1.3510.5%
 
1.6110.5%
 
1.7110.5%
 
1.7810.5%
 
1.8310.5%
 
1.8510.5%
 
1.8810.5%
 
ValueCountFrequency (%) 
4.4910.5%
 
3.9810.5%
 
3.9710.5%
 
3.9310.5%
 
3.931.5%
 
3.8910.5%
 
3.8710.5%
 
3.8610.5%
 
3.8521.0%
 
3.8410.5%
 

Al
Real number (ℝ≥0)

Distinct count115
Unique (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4483414634146343
Minimum0.29
Maximum3.5
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:46:52.204327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile0.726
Q11.19
median1.36
Q31.63
95-th percentile2.412
Maximum3.5
Range3.21
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.497152855
Coefficient of variation (CV)0.3432566612
Kurtosis2.163738396
Mean1.448341463
Median Absolute Deviation (MAD)0.2
Skewness1.009296375
Sum296.91
Variance0.2471609613
2020-08-25T01:46:52.314538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.5473.4%
 
1.4352.4%
 
1.1952.4%
 
1.2952.4%
 
1.2352.4%
 
1.3642.0%
 
1.3542.0%
 
1.2842.0%
 
1.5642.0%
 
1.3331.5%
 
1.2731.5%
 
1.2631.5%
 
1.6331.5%
 
1.1831.5%
 
1.1131.5%
 
1.2531.5%
 
1.3231.5%
 
1.3131.5%
 
1.5231.5%
 
1.4731.5%
 
1.1231.5%
 
1.4931.5%
 
1.8721.0%
 
0.8721.0%
 
121.0%
 
Other values (90)11757.1%
 
ValueCountFrequency (%) 
0.2910.5%
 
0.4721.0%
 
0.5110.5%
 
0.5610.5%
 
0.5810.5%
 
0.6510.5%
 
0.6610.5%
 
0.6710.5%
 
0.7110.5%
 
0.7210.5%
 
ValueCountFrequency (%) 
3.510.5%
 
3.0410.5%
 
3.0210.5%
 
2.8810.5%
 
2.7910.5%
 
2.7410.5%
 
2.6810.5%
 
2.6610.5%
 
2.5410.5%
 
2.5110.5%
 

Si
Real number (ℝ≥0)

Distinct count128
Unique (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.62653658536584
Minimum69.81
Maximum75.18
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:46:52.437437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum69.81
5-th percentile71.27
Q172.25
median72.81
Q373.08
95-th percentile73.456
Maximum75.18
Range5.37
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.7526412348
Coefficient of variation (CV)0.01036317123
Kurtosis2.732078451
Mean72.62653659
Median Absolute Deviation (MAD)0.37
Skewness-1.062819367
Sum14888.44
Variance0.5664688283
2020-08-25T01:46:52.543975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
72.8642.0%
 
73.142.0%
 
73.2842.0%
 
72.9942.0%
 
73.1142.0%
 
72.7531.5%
 
71.9931.5%
 
72.9531.5%
 
73.0131.5%
 
72.6431.5%
 
73.0831.5%
 
72.9731.5%
 
72.9631.5%
 
73.2131.5%
 
73.3931.5%
 
72.6131.5%
 
72.7231.5%
 
72.8531.5%
 
72.8731.5%
 
72.8921.0%
 
73.2721.0%
 
72.0821.0%
 
72.8121.0%
 
72.7321.0%
 
73.221.0%
 
Other values (103)13163.9%
 
ValueCountFrequency (%) 
69.8110.5%
 
69.8910.5%
 
70.1610.5%
 
70.2610.5%
 
70.4310.5%
 
70.4810.5%
 
70.5710.5%
 
70.710.5%
 
71.1510.5%
 
71.2410.5%
 
ValueCountFrequency (%) 
75.1810.5%
 
74.4510.5%
 
73.8810.5%
 
73.8110.5%
 
73.7510.5%
 
73.7210.5%
 
73.710.5%
 
73.6110.5%
 
73.5510.5%
 
73.510.5%
 

K
Real number (ℝ≥0)

ZEROS

Distinct count65
Unique (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5188780487804878
Minimum0.0
Maximum6.21
Zeros21
Zeros (%)10.2%
Memory size1.7 KiB
2020-08-25T01:46:52.659038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.16
median0.56
Q30.61
95-th percentile0.76
Maximum6.21
Range6.21
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.6578308157
Coefficient of variation (CV)1.267794653
Kurtosis54.35373529
Mean0.5188780488
Median Absolute Deviation (MAD)0.1
Skewness6.587724994
Sum106.37
Variance0.4327413821
2020-08-25T01:46:52.979557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02110.2%
 
0.57125.9%
 
0.6115.4%
 
0.56115.4%
 
0.58104.9%
 
0.6483.9%
 
0.6183.9%
 
0.5973.4%
 
0.5462.9%
 
0.6262.9%
 
0.5562.9%
 
0.6942.0%
 
0.0642.0%
 
0.1142.0%
 
0.6742.0%
 
0.0842.0%
 
0.5142.0%
 
0.6842.0%
 
0.6531.5%
 
0.2331.5%
 
0.3931.5%
 
0.5231.5%
 
0.6631.5%
 
0.1231.5%
 
0.1931.5%
 
Other values (40)5024.4%
 
ValueCountFrequency (%) 
02110.2%
 
0.0210.5%
 
0.0310.5%
 
0.0421.0%
 
0.0510.5%
 
0.0642.0%
 
0.0710.5%
 
0.0842.0%
 
0.0921.0%
 
0.110.5%
 
ValueCountFrequency (%) 
6.2121.0%
 
2.710.5%
 
1.7610.5%
 
1.6810.5%
 
1.4610.5%
 
1.4110.5%
 
1.110.5%
 
0.9710.5%
 
0.8110.5%
 
0.7621.0%
 

Ca
Real number (ℝ≥0)

Distinct count135
Unique (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.939414634146342
Minimum5.43
Maximum16.19
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-08-25T01:46:53.097540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5.43
5-th percentile7.836
Q18.24
median8.59
Q39.08
95-th percentile11.602
Maximum16.19
Range10.76
Interquartile range (IQR)0.84

Descriptive statistics

Standard deviation1.42299589
Coefficient of variation (CV)0.1591822226
Kurtosis7.144036772
Mean8.939414634
Median Absolute Deviation (MAD)0.41
Skewness2.166544142
Sum1832.58
Variance2.024917303
2020-08-25T01:46:53.207323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8.0352.4%
 
8.4352.4%
 
8.7942.0%
 
8.4442.0%
 
8.5331.5%
 
8.8331.5%
 
8.3831.5%
 
8.5531.5%
 
8.6731.5%
 
9.8531.5%
 
9.5731.5%
 
8.3931.5%
 
8.2131.5%
 
8.5231.5%
 
8.631.5%
 
8.7631.5%
 
8.5621.0%
 
8.0521.0%
 
9.8221.0%
 
10.1721.0%
 
8.1221.0%
 
7.8321.0%
 
8.2721.0%
 
8.8121.0%
 
8.421.0%
 
Other values (110)13364.9%
 
ValueCountFrequency (%) 
5.4310.5%
 
5.7910.5%
 
5.8710.5%
 
6.4710.5%
 
6.9310.5%
 
6.9610.5%
 
7.0810.5%
 
7.3610.5%
 
7.7810.5%
 
7.8321.0%
 
ValueCountFrequency (%) 
16.1910.5%
 
14.9610.5%
 
14.6810.5%
 
14.410.5%
 
13.4410.5%
 
13.310.5%
 
13.2410.5%
 
12.510.5%
 
12.2410.5%
 
11.6410.5%
 

Ba
Real number (ℝ≥0)

ZEROS

Distinct count34
Unique (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18273170731707317
Minimum0.0
Maximum3.15
Zeros167
Zeros (%)81.5%
Memory size1.7 KiB
2020-08-25T01:46:53.314132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.57
Maximum3.15
Range3.15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5066782937
Coefficient of variation (CV)2.772798991
Kurtosis11.85601173
Mean0.1827317073
Median Absolute Deviation (MAD)0
Skewness3.328353312
Sum37.46
Variance0.2567228934
2020-08-25T01:46:53.430785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
016781.5%
 
1.5721.0%
 
0.6421.0%
 
0.0921.0%
 
1.5921.0%
 
0.1121.0%
 
0.1510.5%
 
1.5510.5%
 
0.6110.5%
 
0.6310.5%
 
1.0610.5%
 
2.8810.5%
 
0.5310.5%
 
0.5610.5%
 
0.6710.5%
 
0.8110.5%
 
0.6910.5%
 
0.1410.5%
 
1.6710.5%
 
1.6810.5%
 
1.3810.5%
 
1.7110.5%
 
0.5410.5%
 
1.6310.5%
 
2.210.5%
 
Other values (9)94.4%
 
ValueCountFrequency (%) 
016781.5%
 
0.0610.5%
 
0.0921.0%
 
0.1121.0%
 
0.1410.5%
 
0.1510.5%
 
0.2410.5%
 
0.2710.5%
 
0.410.5%
 
0.5310.5%
 
ValueCountFrequency (%) 
3.1510.5%
 
2.8810.5%
 
2.210.5%
 
1.7110.5%
 
1.6810.5%
 
1.6710.5%
 
1.6410.5%
 
1.6310.5%
 
1.5921.0%
 
1.5721.0%
 

Fe
Real number (ℝ≥0)

ZEROS

Distinct count32
Unique (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05951219512195122
Minimum0.0
Maximum0.51
Zeros135
Zeros (%)65.9%
Memory size1.7 KiB
2020-08-25T01:46:53.555347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile0.276
Maximum0.51
Range0.51
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.09881035558
Coefficient of variation (CV)1.660337942
Kurtosis2.411356938
Mean0.05951219512
Median Absolute Deviation (MAD)0
Skewness1.68902575
Sum12.2
Variance0.00976348637
2020-08-25T01:46:53.665616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
013565.9%
 
0.1773.4%
 
0.2473.4%
 
0.0962.9%
 
0.152.4%
 
0.1142.0%
 
0.0731.5%
 
0.1431.5%
 
0.2831.5%
 
0.1631.5%
 
0.1231.5%
 
0.2231.5%
 
0.1921.0%
 
0.0821.0%
 
0.1521.0%
 
0.2110.5%
 
0.2910.5%
 
0.2510.5%
 
0.3410.5%
 
0.0310.5%
 
0.3210.5%
 
0.2610.5%
 
0.3110.5%
 
0.1810.5%
 
0.0610.5%
 
Other values (7)73.4%
 
ValueCountFrequency (%) 
013565.9%
 
0.0110.5%
 
0.0310.5%
 
0.0510.5%
 
0.0610.5%
 
0.0731.5%
 
0.0821.0%
 
0.0962.9%
 
0.152.4%
 
0.1142.0%
 
ValueCountFrequency (%) 
0.5110.5%
 
0.3710.5%
 
0.3510.5%
 
0.3410.5%
 
0.3210.5%
 
0.3110.5%
 
0.310.5%
 
0.2910.5%
 
0.2831.5%
 
0.2610.5%
 

target
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8097560975609754
Minimum0
Maximum5
Zeros13
Zeros (%)6.3%
Memory size1.7 KiB
2020-08-25T01:46:53.779453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3532468
Coefficient of variation (CV)0.3552056261
Kurtosis1.600970029
Mean3.809756098
Median Absolute Deviation (MAD)1
Skewness-1.411014546
Sum781
Variance1.831276901
2020-08-25T01:46:53.882916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
57637.1%
 
47034.1%
 
32914.1%
 
2178.3%
 
0136.3%
 
ValueCountFrequency (%) 
0136.3%
 
2178.3%
 
32914.1%
 
47034.1%
 
57637.1%
 
ValueCountFrequency (%) 
57637.1%
 
47034.1%
 
32914.1%
 
2178.3%
 
0136.3%
 

Interactions

2020-08-25T01:46:38.379745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:38.493058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:38.608248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:38.719969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:38.838399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:38.979640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:39.100176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:39.214966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:39.332788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:39.447022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:39.563849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:39.681804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:39.799659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:39.914130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:40.036016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:40.152375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:40.282903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:40.402200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:40.535682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:40.672227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:40.989512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.099344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.212543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.321910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.436697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.546741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.658853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.770324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.885376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:41.996289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:42.105634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:42.228510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:42.352723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:42.471452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:42.598319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:42.727432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:42.852568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:42.976028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:43.104689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:43.234071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:43.356753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:43.489311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:43.608002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:43.723573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:43.855328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:43.979122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:44.098876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:44.216552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:44.339076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:44.457299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:44.573666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:44.693325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:45.019824image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:45.136982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:45.263105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:45.383999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:45.509711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:45.629743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:45.771177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:45.899043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.024571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.145312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.267728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.385427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.505800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.630494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.752424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.874570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:46.999026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:47.115859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:47.232419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:47.354313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:47.480664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:47.598356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:47.728583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:47.852633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:47.983177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:48.112088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:48.238834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:48.363010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:48.486937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:48.602711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:48.720098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:48.833693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:49.161508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:49.277976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:49.398596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:49.516234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:49.637870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:49.755818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:49.873437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:49.987966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:50.104265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:50.217023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:50.341372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:50.457650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:50.579126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:50.695504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:50.816474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:50.934134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:46:54.008094image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T01:46:54.218245image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T01:46:54.428854image/svg+xmlMatplotlib v3.3.1, 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.
2020-08-25T01:46:54.643042image/svg+xmlMatplotlib v3.3.1, 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.

Missing values

2020-08-25T01:46:51.166511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:46:51.418761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

RINaMgAlSiKCaBaFetarget
01.5210113.644.491.1071.780.068.750.00.004
11.5176113.893.601.3672.730.487.830.00.004
21.5161813.533.551.5472.990.397.780.00.004
31.5176613.213.691.2972.610.578.220.00.004
41.5174213.273.621.2473.080.558.070.00.004
51.5159612.793.611.6272.970.648.070.00.264
61.5174313.303.601.1473.090.588.170.00.004
71.5175613.153.611.0573.240.578.240.00.004
81.5191814.043.581.3772.080.568.300.00.004
91.5175513.003.601.3672.990.578.400.00.114

Last rows

RINaMgAlSiKCaBaFetarget
1951.5161714.950.02.2773.300.008.710.670.03
1961.5173214.950.01.8072.990.008.611.550.03
1971.5164514.940.01.8773.110.008.671.380.03
1981.5183114.390.01.8272.861.416.472.880.03
1991.5164014.370.02.7472.850.009.450.540.03
2001.5162314.140.02.8872.610.089.181.060.03
2011.5168514.920.01.9973.060.008.401.590.03
2021.5206514.360.02.0273.420.008.441.640.03
2031.5165114.380.01.9473.610.008.481.570.03
2041.5171114.230.02.0873.360.008.621.670.03

Duplicate rows

Most frequent

RINaMgAlSiKCaBaFetargetcount
01.5221314.213.820.4771.770.119.570.00.042