Overview

Dataset statistics

Number of variables20
Number of observations959
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory150.0 KiB
Average record size in memory160.1 B

Variable types

NUM16
CAT3
BOOL1

Reproduction

Analysis started2020-08-25 01:58:51.395043
Analysis finished2020-08-25 01:59:34.046999
Duration42.65 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

net_avg_write is highly correlated with net_max_writeHigh correlation
net_max_write is highly correlated with net_avg_writeHigh correlation
cpu_max_wait is highly correlated with cpu_avg_waitHigh correlation
cpu_avg_wait is highly correlated with cpu_max_waitHigh correlation
disk_frac_active has 33 (3.4%) zeros Zeros
cpu_frac_busy has 842 (87.8%) zeros Zeros
cpu_avg_wait has 20 (2.1%) zeros Zeros
cpu_max_wait has 20 (2.1%) zeros Zeros

Variables

net_max_write
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count844
Unique (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.9597705943691
Minimum22.15
Maximum5012.0
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:34.092624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum22.15
5-th percentile49.465
Q194.85
median114.4
Q3194.2
95-th percentile624.49
Maximum5012
Range4989.85
Interquartile range (IQR)99.35

Descriptive statistics

Standard deviation403.8629652
Coefficient of variation (CV)1.878783942
Kurtosis68.44865965
Mean214.9597706
Median Absolute Deviation (MAD)42.53
Skewness7.521706028
Sum206146.42
Variance163105.2947
2020-08-25T01:59:34.183917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100.450.5%
 
102.240.4%
 
101.740.4%
 
122.230.3%
 
101.230.3%
 
112.230.3%
 
104.630.3%
 
99.4730.3%
 
94.6530.3%
 
101.530.3%
 
194.730.3%
 
10030.3%
 
94.8530.3%
 
19530.3%
 
116.930.3%
 
99.2520.2%
 
179.920.2%
 
99.220.2%
 
175.520.2%
 
195.820.2%
 
94.920.2%
 
198.920.2%
 
114.920.2%
 
197.120.2%
 
193.120.2%
 
Other values (819)89092.8%
 
ValueCountFrequency (%) 
22.1510.1%
 
24.8710.1%
 
28.2510.1%
 
30.4210.1%
 
30.8310.1%
 
32.410.1%
 
32.4710.1%
 
32.5710.1%
 
32.7710.1%
 
33.410.1%
 
ValueCountFrequency (%) 
501210.1%
 
467010.1%
 
460510.1%
 
378010.1%
 
340010.1%
 
306110.1%
 
280610.1%
 
249110.1%
 
240710.1%
 
229310.1%
 

disk_max_active
Categorical

Distinct count4
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
0
662
0.1667
179
0.08333
 
116
0.25
 
2
ValueCountFrequency (%) 
066269.0%
 
0.166717918.7%
 
0.0833311612.1%
 
0.2520.2%
 
2020-08-25T01:59:34.322885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.045881126
Min length3

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0173744.8%
 
.95924.7%
 
63589.2%
 
33489.0%
 
11794.6%
 
71794.6%
 
81163.0%
 
220.1%
 
520.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number292175.3%
 
Other Punctuation95924.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0173759.5%
 
635812.3%
 
334811.9%
 
11796.1%
 
71796.1%
 
81164.0%
 
220.1%
 
520.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.959100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3880100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0173744.8%
 
.95924.7%
 
63589.2%
 
33489.0%
 
11794.6%
 
71794.6%
 
81163.0%
 
220.1%
 
520.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3880100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0173744.8%
 
.95924.7%
 
63589.2%
 
33489.0%
 
11794.6%
 
71794.6%
 
81163.0%
 
220.1%
 
520.1%
 

disk_frac_active
Real number (ℝ≥0)

ZEROS

Distinct count770
Unique (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8431647851929087
Minimum0.0
Maximum9.865
Zeros33
Zeros (%)3.4%
Memory size7.6 KiB
2020-08-25T01:59:34.621708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002778
Q11.718
median2.243
Q34.0605
95-th percentile6.9443
Maximum9.865
Range9.865
Interquartile range (IQR)2.3425

Descriptive statistics

Standard deviation2.03117445
Coefficient of variation (CV)0.7144061648
Kurtosis-0.02388213252
Mean2.843164785
Median Absolute Deviation (MAD)1.219
Skewness0.6799005746
Sum2726.595029
Variance4.125669646
2020-08-25T01:59:34.721940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0333.4%
 
0.001389111.1%
 
0.00277890.9%
 
0.00694450.5%
 
0.0111140.4%
 
0.0333340.4%
 
0.037540.4%
 
0.0277840.4%
 
1.76840.4%
 
2.08340.4%
 
0.00972230.3%
 
1.74230.3%
 
0.0194430.3%
 
2.23830.3%
 
1.91830.3%
 
2.22530.3%
 
0.0208330.3%
 
0.0361130.3%
 
0.00555630.3%
 
2.08130.3%
 
0.00416730.3%
 
1.64730.3%
 
1.92430.3%
 
2.10720.2%
 
0.0888920.2%
 
Other values (745)83487.0%
 
ValueCountFrequency (%) 
0333.4%
 
0.001389111.1%
 
0.00277890.9%
 
0.00416730.3%
 
0.00555630.3%
 
0.00694450.5%
 
0.00833310.1%
 
0.00972230.3%
 
0.0111140.4%
 
0.012520.2%
 
ValueCountFrequency (%) 
9.86510.1%
 
8.77910.1%
 
8.56910.1%
 
8.55610.1%
 
8.32610.1%
 
8.31910.1%
 
8.25610.1%
 
8.2510.1%
 
8.20810.1%
 
8.18910.1%
 

net_max_read
Real number (ℝ≥0)

Distinct count819
Unique (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.20786652763295
Minimum7.85
Maximum395.9
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:34.826069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7.85
5-th percentile9.3153
Q116.34
median29.13
Q340.71
95-th percentile182.99
Maximum395.9
Range388.05
Interquartile range (IQR)24.37

Descriptive statistics

Standard deviation52.08058599
Coefficient of variation (CV)1.152024857
Kurtosis7.654480753
Mean45.20786653
Median Absolute Deviation (MAD)12.27
Skewness2.656956252
Sum43354.344
Variance2712.387437
2020-08-25T01:59:34.934583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13.940.4%
 
8.5540.4%
 
30.2740.4%
 
3940.4%
 
38.1530.3%
 
25.9230.3%
 
16.3830.3%
 
8.31730.3%
 
15.0330.3%
 
36.1330.3%
 
21.630.3%
 
15.3530.3%
 
33.6330.3%
 
34.8730.3%
 
37.8330.3%
 
12.6830.3%
 
26.5830.3%
 
24.2820.2%
 
15.1320.2%
 
22.2820.2%
 
39.8220.2%
 
38.3320.2%
 
15.0220.2%
 
9.5520.2%
 
39.120.2%
 
Other values (794)88892.6%
 
ValueCountFrequency (%) 
7.8510.1%
 
7.88310.1%
 
7.9510.1%
 
7.98310.1%
 
8.120.2%
 
8.16720.2%
 
8.18310.1%
 
8.21710.1%
 
8.310.1%
 
8.31730.3%
 
ValueCountFrequency (%) 
395.910.1%
 
346.110.1%
 
29310.1%
 
287.810.1%
 
286.110.1%
 
284.810.1%
 
279.910.1%
 
256.810.1%
 
247.610.1%
 
244.210.1%
 

net_avg_write
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count829
Unique (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.68338581856101
Minimum9.447000000000001
Maximum931.4
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:35.065954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9.447
5-th percentile13.922
Q119.78
median24.08
Q338.595
95-th percentile107.81
Maximum931.4
Range921.953
Interquartile range (IQR)18.815

Descriptive statistics

Standard deviation77.32051978
Coefficient of variation (CV)1.854948159
Kurtosis65.69687082
Mean41.68338582
Median Absolute Deviation (MAD)6.46
Skewness7.658640578
Sum39974.367
Variance5978.462779
2020-08-25T01:59:35.155303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13.4140.4%
 
22.8940.4%
 
19.7340.4%
 
26.5240.4%
 
22.0630.3%
 
18.2830.3%
 
21.9330.3%
 
21.9930.3%
 
18.5730.3%
 
23.7730.3%
 
19.7830.3%
 
21.8930.3%
 
2030.3%
 
20.3220.2%
 
20.0220.2%
 
22.7320.2%
 
21.1920.2%
 
40.5220.2%
 
20.5120.2%
 
17.4620.2%
 
13.9320.2%
 
17.8120.2%
 
22.0920.2%
 
44.3820.2%
 
18.8920.2%
 
Other values (804)89293.0%
 
ValueCountFrequency (%) 
9.44710.1%
 
10.510.1%
 
11.4210.1%
 
11.5410.1%
 
11.6310.1%
 
11.6720.2%
 
11.9410.1%
 
12.0210.1%
 
12.0310.1%
 
12.0610.1%
 
ValueCountFrequency (%) 
931.410.1%
 
849.810.1%
 
754.410.1%
 
718.710.1%
 
685.410.1%
 
675.410.1%
 
608.810.1%
 
598.410.1%
 
597.810.1%
 
545.810.1%
 

mem_swap
Real number (ℝ≥0)

Distinct count483
Unique (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.03200834202293
Minimum9.217
Maximum768.7
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:35.251320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9.217
5-th percentile9.217
Q19.25
median11.9
Q352.09
95-th percentile624.28
Maximum768.7
Range759.483
Interquartile range (IQR)42.84

Descriptive statistics

Standard deviation207.6657954
Coefficient of variation (CV)1.715792361
Kurtosis1.127230436
Mean121.0320083
Median Absolute Deviation (MAD)2.683
Skewness1.654229631
Sum116069.696
Variance43125.08258
2020-08-25T01:59:35.365366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9.21715916.6%
 
9.233626.5%
 
9.283282.9%
 
20.38242.5%
 
9.25232.4%
 
9.267161.7%
 
9.417121.3%
 
9.467111.1%
 
9.317111.1%
 
23.6590.9%
 
10.1380.8%
 
11.7270.7%
 
9.48370.7%
 
9.370.7%
 
10.1270.7%
 
20.460.6%
 
21.2860.6%
 
16.0750.5%
 
13.650.5%
 
11.7350.5%
 
9.4550.5%
 
10.1840.4%
 
10.8240.4%
 
9.43340.4%
 
9.440.4%
 
Other values (458)52054.2%
 
ValueCountFrequency (%) 
9.21715916.6%
 
9.233626.5%
 
9.25232.4%
 
9.267161.7%
 
9.283282.9%
 
9.370.7%
 
9.317111.1%
 
9.33320.2%
 
9.3530.3%
 
9.36730.3%
 
ValueCountFrequency (%) 
768.710.1%
 
732.620.2%
 
720.810.1%
 
71310.1%
 
694.610.1%
 
693.510.1%
 
693.310.1%
 
687.310.1%
 
685.410.1%
 
684.910.1%
 

cpu_avg_idle
Real number (ℝ≥0)

Distinct count895
Unique (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.31333503232534
Minimum0.0015
Maximum8859.0
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:35.485751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.012199
Q10.1409
median0.4435
Q30.59945
95-th percentile0.978
Maximum8859
Range8858.9985
Interquartile range (IQR)0.45855

Descriptive statistics

Standard deviation805.6301421
Coefficient of variation (CV)10.84099027
Kurtosis115.4908157
Mean74.31333503
Median Absolute Deviation (MAD)0.256
Skewness10.8281986
Sum71266.4883
Variance649039.9258
2020-08-25T01:59:35.589436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
885350.5%
 
0.134630.3%
 
0.980230.3%
 
0.963630.3%
 
0.153620.2%
 
0.946520.2%
 
0.50720.2%
 
0.156920.2%
 
0.959320.2%
 
0.111320.2%
 
0.964920.2%
 
0.564820.2%
 
0.958320.2%
 
0.166820.2%
 
0.65520.2%
 
0.140720.2%
 
0.550720.2%
 
0.149720.2%
 
0.967320.2%
 
0.980920.2%
 
0.538120.2%
 
0.565520.2%
 
0.175520.2%
 
0.115820.2%
 
0.526720.2%
 
Other values (870)90394.2%
 
ValueCountFrequency (%) 
0.001510.1%
 
0.00237510.1%
 
0.00272910.1%
 
0.00283310.1%
 
0.0032110.1%
 
0.003510.1%
 
0.00379210.1%
 
0.00383310.1%
 
0.00418810.1%
 
0.00431310.1%
 
ValueCountFrequency (%) 
885910.1%
 
885350.5%
 
885220.2%
 
7.34910.1%
 
7.23210.1%
 
6.67410.1%
 
6.57810.1%
 
6.36410.1%
 
6.34710.1%
 
6.32310.1%
 

cpu_frac_busy
Real number (ℝ≥0)

ZEROS

Distinct count9
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.052528675703858184
Minimum0.0
Maximum1.0
Zeros842
Zeros (%)87.8%
Memory size7.6 KiB
2020-08-25T01:59:35.695573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.375
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1894103943
Coefficient of variation (CV)3.605847506
Kurtosis16.18504477
Mean0.0525286757
Median Absolute Deviation (MAD)0
Skewness4.122127406
Sum50.375
Variance0.03587629745
2020-08-25T01:59:35.799588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
084287.8%
 
0.125596.2%
 
1202.1%
 
0.875121.3%
 
0.2580.8%
 
0.62570.7%
 
0.540.4%
 
0.7540.4%
 
0.37530.3%
 
ValueCountFrequency (%) 
084287.8%
 
0.125596.2%
 
0.2580.8%
 
0.37530.3%
 
0.540.4%
 
0.62570.7%
 
0.7540.4%
 
0.875121.3%
 
1202.1%
 
ValueCountFrequency (%) 
1202.1%
 
0.875121.3%
 
0.7540.4%
 
0.62570.7%
 
0.540.4%
 
0.37530.3%
 
0.2580.8%
 
0.125596.2%
 
084287.8%
 

cpu_max_busy
Real number (ℝ≥0)

Distinct count897
Unique (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean517.5754389468196
Minimum0.02083
Maximum70870.0
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:35.908309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.02083
5-th percentile0.03712
Q10.08381
median0.1982
Q30.45625
95-th percentile0.78541
Maximum70870
Range70869.97917
Interquartile range (IQR)0.37244

Descriptive statistics

Standard deviation6031.533435
Coefficient of variation (CV)11.65343828
Kurtosis132.704513
Mean517.5754389
Median Absolute Deviation (MAD)0.1255
Skewness11.59428926
Sum496354.8459
Variance36379395.57
2020-08-25T01:59:36.017410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.0348340.4%
 
7087030.3%
 
0.127130.3%
 
0.0669830.3%
 
0.0459820.2%
 
0.0566820.2%
 
0.263620.2%
 
0.315520.2%
 
0.0793520.2%
 
0.215820.2%
 
0.101320.2%
 
0.43320.2%
 
0.144820.2%
 
0.0601720.2%
 
0.0909820.2%
 
0.0598520.2%
 
0.0769720.2%
 
0.0779720.2%
 
0.151320.2%
 
0.030520.2%
 
0.165120.2%
 
0.0783320.2%
 
0.115120.2%
 
0.105920.2%
 
0.0871720.2%
 
Other values (872)90494.3%
 
ValueCountFrequency (%) 
0.0208310.1%
 
0.0238310.1%
 
0.02410.1%
 
0.0241710.1%
 
0.024510.1%
 
0.02510.1%
 
0.025510.1%
 
0.0268310.1%
 
0.027510.1%
 
0.0275210.1%
 
ValueCountFrequency (%) 
7087030.3%
 
7084010.1%
 
7079010.1%
 
7077010.1%
 
7073010.1%
 
136.510.1%
 
101.710.1%
 
76.6110.1%
 
29.1910.1%
 
2.7910.1%
 

cpu_avg_wait
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count868
Unique (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3509087993326382
Minimum0.0
Maximum0.8849
Zeros20
Zeros (%)2.1%
Memory size7.6 KiB
2020-08-25T01:59:36.133629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0004975
Q10.2186
median0.3591
Q30.46245
95-th percentile0.75173
Maximum0.8849
Range0.8849
Interquartile range (IQR)0.24385

Descriptive statistics

Standard deviation0.2152540632
Coefficient of variation (CV)0.6134188244
Kurtosis-0.3322692481
Mean0.3509087993
Median Absolute Deviation (MAD)0.1196
Skewness0.1505594877
Sum336.5215386
Variance0.0463343117
2020-08-25T01:59:36.233908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0202.1%
 
0.000166740.4%
 
0.366430.3%
 
0.40230.3%
 
0.399430.3%
 
0.000395830.3%
 
0.43130.3%
 
0.407330.3%
 
0.378530.3%
 
0.345920.2%
 
0.375620.2%
 
0.323720.2%
 
0.000833320.2%
 
0.551120.2%
 
0.24420.2%
 
0.321920.2%
 
0.205720.2%
 
0.391520.2%
 
0.343320.2%
 
0.369320.2%
 
0.472720.2%
 
0.266820.2%
 
0.297720.2%
 
0.40820.2%
 
0.376120.2%
 
Other values (843)88292.0%
 
ValueCountFrequency (%) 
0202.1%
 
1.667e-0520.2%
 
2.083e-0510.1%
 
4.167e-0510.1%
 
4.792e-0510.1%
 
0.000104210.1%
 
0.000145810.1%
 
0.000166740.4%
 
0.000229210.1%
 
0.000237510.1%
 
ValueCountFrequency (%) 
0.884910.1%
 
0.879610.1%
 
0.879410.1%
 
0.878310.1%
 
0.874210.1%
 
0.872410.1%
 
0.87210.1%
 
0.867110.1%
 
0.86610.1%
 
0.86110.1%
 

cpu_max_user
Real number (ℝ≥0)

Distinct count624
Unique (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0839606079249218
Minimum0.002833
Maximum0.6458
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:36.336121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.002833
5-th percentile0.0055
Q10.011
median0.023
Q30.15145
95-th percentile0.26436
Maximum0.6458
Range0.642967
Interquartile range (IQR)0.14045

Descriptive statistics

Standard deviation0.09917454854
Coefficient of variation (CV)1.181203316
Kurtosis3.072745881
Mean0.08396060792
Median Absolute Deviation (MAD)0.017167
Skewness1.561395431
Sum80.518223
Variance0.009835591077
2020-08-25T01:59:36.435744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.009667121.3%
 
0.011121.3%
 
0.009167111.1%
 
0.008667101.0%
 
0.010590.9%
 
0.007580.8%
 
0.00480.8%
 
0.0111770.7%
 
0.00970.7%
 
0.0121770.7%
 
0.0108360.6%
 
0.00816760.6%
 
0.00416760.6%
 
0.0103360.6%
 
0.012560.6%
 
0.0106760.6%
 
0.00683360.6%
 
0.00933360.6%
 
0.0143260.6%
 
0.0133350.5%
 
0.0118350.5%
 
0.00783350.5%
 
0.015550.5%
 
0.00466750.5%
 
0.00883350.5%
 
Other values (599)78481.8%
 
ValueCountFrequency (%) 
0.00283310.1%
 
0.003520.2%
 
0.00366720.2%
 
0.00383340.4%
 
0.00480.8%
 
0.00416760.6%
 
0.00433330.3%
 
0.004530.3%
 
0.00466750.5%
 
0.00483350.5%
 
ValueCountFrequency (%) 
0.645810.1%
 
0.610810.1%
 
0.603410.1%
 
0.519510.1%
 
0.469210.1%
 
0.460710.1%
 
0.434110.1%
 
0.433510.1%
 
0.419610.1%
 
0.417910.1%
 

disk_avg_write
Real number (ℝ≥0)

Distinct count807
Unique (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.297862158498436
Minimum0.052779999999999994
Maximum15.02
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:36.543876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.05278
5-th percentile0.09167
Q14.958
median6.172
Q38.722
95-th percentile11.581
Maximum15.02
Range14.96722
Interquartile range (IQR)3.764

Descriptive statistics

Standard deviation3.380814266
Coefficient of variation (CV)0.5368193493
Kurtosis-0.415644023
Mean6.297862158
Median Absolute Deviation (MAD)1.762
Skewness-0.2949051698
Sum6039.64981
Variance11.4299051
2020-08-25T01:59:36.654485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.106960.6%
 
0.0722260.6%
 
0.0986140.4%
 
0.0777840.4%
 
10.1540.4%
 
0.123640.4%
 
0.0736140.4%
 
0.0819430.3%
 
0.169430.3%
 
0.134730.3%
 
10.1430.3%
 
10.5930.3%
 
10.0530.3%
 
10.330.3%
 
5.59630.3%
 
0.0916730.3%
 
10.9530.3%
 
0.112530.3%
 
0.161130.3%
 
0.136120.2%
 
6.1520.2%
 
0.0680620.2%
 
9.30720.2%
 
5.05620.2%
 
0.109720.2%
 
Other values (782)87991.7%
 
ValueCountFrequency (%) 
0.0527810.1%
 
0.0583310.1%
 
0.062520.2%
 
0.0652820.2%
 
0.0666710.1%
 
0.0680620.2%
 
0.0694410.1%
 
0.0708320.2%
 
0.0722260.6%
 
0.0736140.4%
 
ValueCountFrequency (%) 
15.0210.1%
 
14.0610.1%
 
13.5310.1%
 
13.4610.1%
 
13.4110.1%
 
13.210.1%
 
13.1510.1%
 
13.0810.1%
 
13.0410.1%
 
12.9910.1%
 

disk_max_total
Categorical

Distinct count3
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
0
682
0.1667
221
0.08333
 
56
ValueCountFrequency (%) 
068271.1%
 
0.166722123.0%
 
0.08333565.8%
 
2020-08-25T01:59:36.805492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.924921794
Min length3

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0169745.1%
 
.95925.5%
 
644211.7%
 
12215.9%
 
72215.9%
 
31684.5%
 
8561.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number280574.5%
 
Other Punctuation95925.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0169760.5%
 
644215.8%
 
12217.9%
 
72217.9%
 
31686.0%
 
8562.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.959100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3764100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0169745.1%
 
.95925.5%
 
644211.7%
 
12215.9%
 
72215.9%
 
31684.5%
 
8561.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3764100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0169745.1%
 
.95925.5%
 
644211.7%
 
12215.9%
 
72215.9%
 
31684.5%
 
8561.5%
 

io_phwrite
Real number (ℝ≥0)

Distinct count279
Unique (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37592.6228362878
Minimum750.9
Maximum321700.0
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:36.923833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum750.9
5-th percentile750.9
Q1750.9
median819.2
Q320870
95-th percentile196520
Maximum321700
Range320949.1
Interquartile range (IQR)20119.1

Descriptive statistics

Standard deviation70384.66313
Coefficient of variation (CV)1.872299877
Kurtosis1.600565622
Mean37592.62284
Median Absolute Deviation (MAD)68.3
Skewness1.726174301
Sum36051325.3
Variance4954000804
2020-08-25T01:59:37.035113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
750.944446.3%
 
955.7697.2%
 
819.2353.6%
 
887.5313.2%
 
1024171.8%
 
20870151.6%
 
768111.1%
 
204890.9%
 
836.380.8%
 
853.360.6%
 
116160.6%
 
1079060.6%
 
109240.4%
 
136530.3%
 
184330.3%
 
413030.3%
 
143430.3%
 
129730.3%
 
17780020.2%
 
12260020.2%
 
18740020.2%
 
17810020.2%
 
14840020.2%
 
16840020.2%
 
785.120.2%
 
Other values (254)26928.1%
 
ValueCountFrequency (%) 
750.944446.3%
 
768111.1%
 
785.120.2%
 
802.110.1%
 
819.2353.6%
 
836.380.8%
 
853.360.6%
 
887.5313.2%
 
921.610.1%
 
955.7697.2%
 
ValueCountFrequency (%) 
32170010.1%
 
31860010.1%
 
30260010.1%
 
26510010.1%
 
25920010.1%
 
25800010.1%
 
25200020.2%
 
24640010.1%
 
24600010.1%
 
24310010.1%
 

io_lwrite
Categorical

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
0
958
546.1
 
1
ValueCountFrequency (%) 
095899.9%
 
546.110.1%
 
2020-08-25T01:59:37.185502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.002085506
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0191666.6%
 
.95933.3%
 
51< 0.1%
 
41< 0.1%
 
61< 0.1%
 
11< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number192066.7%
 
Other Punctuation95933.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0191699.8%
 
510.1%
 
410.1%
 
610.1%
 
110.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.959100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2879100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0191666.6%
 
.95933.3%
 
51< 0.1%
 
41< 0.1%
 
61< 0.1%
 
11< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2879100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0191666.6%
 
.95933.3%
 
51< 0.1%
 
41< 0.1%
 
61< 0.1%
 
11< 0.1%
 

io_iget
Real number (ℝ≥0)

Distinct count840
Unique (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean496661.8550573514
Minimum3004.0
Maximum4151000.0
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:37.313219image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3004
5-th percentile3224.3
Q1124850
median206600
Q3564100
95-th percentile2265200
Maximum4151000
Range4147996
Interquartile range (IQR)439250

Descriptive statistics

Standard deviation714634.9134
Coefficient of variation (CV)1.438876181
Kurtosis7.442678518
Mean496661.8551
Median Absolute Deviation (MAD)149650
Skewness2.627837177
Sum476298719
Variance5.107030595e+11
2020-08-25T01:59:37.413244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3004293.0%
 
307230.3%
 
15370030.3%
 
14740030.3%
 
327730.3%
 
12270030.3%
 
14840030.3%
 
13850030.3%
 
11800030.3%
 
12700030.3%
 
320930.3%
 
314030.3%
 
14830030.3%
 
302120.2%
 
13380020.2%
 
14010020.2%
 
23160020.2%
 
12060020.2%
 
416420.2%
 
20660020.2%
 
14430020.2%
 
375520.2%
 
12160020.2%
 
39340020.2%
 
12490020.2%
 
Other values (815)87090.7%
 
ValueCountFrequency (%) 
3004293.0%
 
302120.2%
 
303810.1%
 
307230.3%
 
308920.2%
 
310620.2%
 
312310.1%
 
314030.3%
 
315710.1%
 
317410.1%
 
ValueCountFrequency (%) 
415100010.1%
 
405500010.1%
 
405000010.1%
 
400000010.1%
 
394300010.1%
 
391200010.1%
 
382200010.1%
 
377000010.1%
 
370800010.1%
 
367900010.1%
 

mem_fault
Real number (ℝ≥0)

Distinct count30
Unique (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4960264859228363
Minimum0.1333
Maximum97.88
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:37.519721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1333
5-th percentile0.1333
Q10.1333
median0.1333
Q30.1333
95-th percentile0.1333
Maximum97.88
Range97.7467
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.309434401
Coefficient of variation (CV)8.687911882
Kurtosis360.5799066
Mean0.4960264859
Median Absolute Deviation (MAD)0
Skewness18.03267102
Sum475.6894
Variance18.57122486
2020-08-25T01:59:37.619842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.133391495.3%
 
0.933350.5%
 
1.33340.4%
 
2.420.2%
 
1.46720.2%
 
0.266720.2%
 
2.13320.2%
 
2.820.2%
 
0.533320.2%
 
1.06720.2%
 
1.86720.2%
 
3.06720.2%
 
31.0710.1%
 
3.73310.1%
 
21.610.1%
 
0.410.1%
 
8.93310.1%
 
18.5710.1%
 
1.210.1%
 
4.66710.1%
 
33.5810.1%
 
2.66710.1%
 
0.666710.1%
 
97.8810.1%
 
72.6210.1%
 
Other values (5)50.5%
 
ValueCountFrequency (%) 
0.133391495.3%
 
0.266720.2%
 
0.410.1%
 
0.533320.2%
 
0.666710.1%
 
0.933350.5%
 
1.06720.2%
 
1.210.1%
 
1.33340.4%
 
1.46720.2%
 
ValueCountFrequency (%) 
97.8810.1%
 
72.6210.1%
 
33.5810.1%
 
31.0710.1%
 
21.610.1%
 
18.5710.1%
 
8.93310.1%
 
4.66710.1%
 
3.73310.1%
 
3.610.1%
 

io_bwrite
Real number (ℝ≥0)

Distinct count914
Unique (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1856418.196037539
Minimum18790.0
Maximum9368000.0
Zeros0
Zeros (%)0.0%
Memory size7.6 KiB
2020-08-25T01:59:37.904092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum18790
5-th percentile31120
Q1536250
median1178000
Q32954000
95-th percentile5465900
Maximum9368000
Range9349210
Interquartile range (IQR)2417750

Descriptive statistics

Standard deviation1780407.023
Coefficient of variation (CV)0.9590549297
Kurtosis1.249058131
Mean1856418.196
Median Absolute Deviation (MAD)980000
Skewness1.240798265
Sum1780305050
Variance3.169849166e+12
2020-08-25T01:59:37.993731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
51570030.3%
 
52340030.3%
 
100200020.2%
 
300500020.2%
 
3524020.2%
 
52970020.2%
 
54460020.2%
 
56260020.2%
 
248300020.2%
 
56750020.2%
 
3357020.2%
 
258000020.2%
 
236700020.2%
 
334500020.2%
 
50510020.2%
 
59100020.2%
 
60020020.2%
 
165700020.2%
 
103100020.2%
 
199400020.2%
 
199700020.2%
 
362600020.2%
 
297100020.2%
 
53230020.2%
 
490200020.2%
 
Other values (889)90794.6%
 
ValueCountFrequency (%) 
1879010.1%
 
2039010.1%
 
2065010.1%
 
2067010.1%
 
2183010.1%
 
2186010.1%
 
2217010.1%
 
2246010.1%
 
2255010.1%
 
2261010.1%
 
ValueCountFrequency (%) 
936800010.1%
 
884700010.1%
 
880600010.1%
 
845000010.1%
 
842300010.1%
 
799800010.1%
 
774500010.1%
 
772000010.1%
 
735000010.1%
 
721700010.1%
 

cpu_max_wait
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count843
Unique (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3937321655265901
Minimum0.0
Maximum0.9646
Zeros20
Zeros (%)2.1%
Memory size7.6 KiB
2020-08-25T01:59:38.097929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0006567
Q10.2633
median0.3903
Q30.528
95-th percentile0.80889
Maximum0.9646
Range0.9646
Interquartile range (IQR)0.2647

Descriptive statistics

Standard deviation0.2373273376
Coefficient of variation (CV)0.6027633969
Kurtosis-0.4086592678
Mean0.3937321655
Median Absolute Deviation (MAD)0.1323
Skewness0.09699525135
Sum377.5891467
Variance0.05632426516
2020-08-25T01:59:38.207324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0202.1%
 
0.000166780.8%
 
0.000560.6%
 
0.000333360.6%
 
0.00140.4%
 
0.363330.3%
 
0.426930.3%
 
0.000833330.3%
 
0.00216730.3%
 
0.00483330.3%
 
0.429130.3%
 
0.00220.2%
 
0.400820.2%
 
0.40820.2%
 
0.365520.2%
 
0.427320.2%
 
0.470220.2%
 
0.338120.2%
 
0.410620.2%
 
0.604620.2%
 
0.383120.2%
 
0.369720.2%
 
0.388720.2%
 
0.394320.2%
 
0.377320.2%
 
Other values (818)86990.6%
 
ValueCountFrequency (%) 
0202.1%
 
1.667e-0520.2%
 
5e-0510.1%
 
0.000166780.8%
 
0.0002520.2%
 
0.000333360.6%
 
0.000560.6%
 
0.000516710.1%
 
0.0005510.1%
 
0.000566710.1%
 
ValueCountFrequency (%) 
0.964610.1%
 
0.96110.1%
 
0.959510.1%
 
0.957710.1%
 
0.956910.1%
 
0.95610.1%
 
0.952410.1%
 
0.94710.1%
 
0.945310.1%
 
0.943910.1%
 

target
Boolean

Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
1
613
0
346
ValueCountFrequency (%) 
161363.9%
 
034636.1%
 

Interactions

2020-08-25T01:58:52.495914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:52.640130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:52.787033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:52.945930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:53.090986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:53.244868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:53.393065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:53.710113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:53.868382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:54.010737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:54.159350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:54.313969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:54.469270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:58:54.609798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:59:27.639544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:27.782660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:27.928671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:28.078880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:28.225889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:28.371775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:28.517168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:28.677587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:28.815490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:28.976460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:29.119948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:29.270576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:29.614071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:29.753679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:29.904206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:30.055993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:30.208544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:30.352912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:30.499840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:30.649894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:30.793117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:30.939899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:31.086546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:31.244919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:31.392221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:31.557765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:31.702886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:31.849986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:32.007363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:32.154020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:32.308117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:32.467738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:32.623208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:32.769944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:32.916008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:33.063230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:59:38.358491image/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.
2020-08-25T01:59:38.700463image/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.
2020-08-25T01:59:39.041988image/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.
2020-08-25T01:59:39.382696image/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.
2020-08-25T01:59:39.668813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-08-25T01:59:33.364226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:59:33.852944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

net_max_writedisk_max_activedisk_frac_activenet_max_readnet_avg_writemem_swapcpu_avg_idlecpu_frac_busycpu_max_busycpu_avg_waitcpu_max_userdisk_avg_writedisk_max_totalio_phwriteio_lwriteio_igetmem_faultio_bwritecpu_max_waittarget
0710.400.166702.0210197.40124.5010.5700.12700.0000.423000.422900.02283010.89000.1667819.20.0205000.00.13332577000.00.570200
1108.700.000003.108016.9021.159.2170.37800.0000.323700.478200.2680006.44600.0000750.90.0215700.00.1333931800.00.544301
293.470.166703.249011.5716.589.2500.32470.0000.198600.602200.01333010.21000.1667750.90.0129700.00.13331124000.00.626301
3316.700.000001.6610158.4043.51527.3000.33600.0000.597800.137900.2096002.92900.0000168700.00.0612000.00.13332342000.00.178201
458.570.000000.037516.2313.269.2330.96550.0000.056330.010270.0106700.14170.0000750.90.022410.00.133333570.00.011021
5165.200.000002.387033.5036.4511.7700.48200.0000.065200.454300.0096836.09400.0000887.50.0160200.00.1333567500.00.476701
6430.600.000001.924038.5267.029.7000.58560.0000.090480.332600.0116704.92500.0000750.90.0129300.00.1333475800.00.359201
754.500.000001.946014.8317.229.2170.59400.0000.109300.360100.0169805.07400.0000750.90.0130800.00.1333493400.00.373801
897.280.166702.289012.6819.2323.6500.28220.0000.129200.622000.0209809.91900.166720870.00.0221800.00.13332023000.00.663301
995.470.083335.826010.3519.73433.8000.03890.12576.610000.378500.1922009.71800.0000132800.00.01900000.00.13337998000.00.458400

Last rows

net_max_writedisk_max_activedisk_frac_activenet_max_readnet_avg_writemem_swapcpu_avg_idlecpu_frac_busycpu_max_busycpu_avg_waitcpu_max_userdisk_avg_writedisk_max_totalio_phwriteio_lwriteio_igetmem_faultio_bwritecpu_max_waittarget
94973.000.000002.081035.1815.9511.7200.53230.00.071670.4188000.0090005.774000.0000887.50.0142600.00.1333601200.00.4334001
950170.500.166704.919053.4732.469.2170.20100.00.310200.6217000.1895007.908000.1667750.90.0327300.00.13331593000.00.7244000
95166.950.000001.676020.2815.139.2500.60610.00.089330.3480000.0121705.054000.0000750.90.0104200.00.1333519900.00.3601001
952173.800.083335.642027.8727.5313.6000.15640.00.251700.6496000.1301006.847000.00002048.00.0383200.00.13333626000.00.7601000
953189.400.000000.161135.5532.859.2670.95800.00.095820.0095630.0184800.240300.0000750.90.010530.00.133366850.00.0106701
954286.700.000002.8710143.3061.88501.4000.20030.00.655000.1993000.1852005.549000.0000216200.00.0587800.00.13332788000.00.2500000
955119.700.000001.849016.2820.929.2170.59240.00.077680.3571000.0113505.060000.0000750.90.0122700.00.1333546300.00.3735001
95630.830.000000.00009.5511.679.3000.98150.00.030670.0001460.0038330.077780.0000750.90.03004.00.133324490.00.0001671
957143.900.000002.518012.9722.9120.3800.50170.00.053870.4451000.0073335.765000.0000955.70.0167200.00.1333598400.00.4638001
958117.300.083334.840011.6218.7911.9000.12890.00.199500.7869000.0503208.458000.0000750.90.02272000.00.13332572000.00.8347001