Overview

Dataset statistics

Number of variables13
Number of observations8192
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory832.1 KiB
Average record size in memory104.0 B

Variable types

NUM13

Reproduction

Analysis started2020-08-24 23:55:45.202931
Analysis finished2020-08-24 23:56:12.837218
Duration27.63 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

lread has 675 (8.2%) zeros Zeros
lwrite has 2684 (32.8%) zeros Zeros
target has 283 (3.5%) zeros Zeros

Variables

lread
Real number (ℝ≥0)

ZEROS

Distinct count235
Unique (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5596923828125
Minimum0.0
Maximum1845.0
Zeros675
Zeros (%)8.2%
Memory size64.1 KiB
2020-08-24T23:56:12.880466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q320
95-th percentile68
Maximum1845
Range1845
Interquartile range (IQR)18

Descriptive statistics

Standard deviation53.35379894
Coefficient of variation (CV)2.727742231
Kurtosis313.7492589
Mean19.55969238
Median Absolute Deviation (MAD)6
Skewness13.89785224
Sum160233
Variance2846.627861
2020-08-24T23:56:12.990206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1105012.8%
 
27328.9%
 
06758.2%
 
35396.6%
 
44085.0%
 
53644.4%
 
62873.5%
 
72733.3%
 
82142.6%
 
91952.4%
 
101922.3%
 
111672.0%
 
121591.9%
 
141571.9%
 
131551.9%
 
151251.5%
 
161131.4%
 
181121.4%
 
171061.3%
 
191031.3%
 
20991.2%
 
23760.9%
 
21750.9%
 
22700.9%
 
28610.7%
 
Other values (210)168520.6%
 
ValueCountFrequency (%) 
06758.2%
 
1105012.8%
 
27328.9%
 
35396.6%
 
44085.0%
 
53644.4%
 
62873.5%
 
72733.3%
 
82142.6%
 
91952.4%
 
ValueCountFrequency (%) 
18451< 0.1%
 
14861< 0.1%
 
9121< 0.1%
 
8691< 0.1%
 
8561< 0.1%
 
8251< 0.1%
 
8171< 0.1%
 
7961< 0.1%
 
7551< 0.1%
 
7531< 0.1%
 

lwrite
Real number (ℝ≥0)

ZEROS

Distinct count189
Unique (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.106201171875
Minimum0.0
Maximum575.0
Zeros2684
Zeros (%)32.8%
Memory size64.1 KiB
2020-08-24T23:56:13.121063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile66
Maximum575
Range575
Interquartile range (IQR)10

Descriptive statistics

Standard deviation29.89172584
Coefficient of variation (CV)2.280731498
Kurtosis50.11442202
Mean13.10620117
Median Absolute Deviation (MAD)1
Skewness5.277644526
Sum107366
Variance893.5152735
2020-08-24T23:56:13.239202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0268432.8%
 
1152918.7%
 
26157.5%
 
32843.5%
 
42533.1%
 
52212.7%
 
61511.8%
 
71261.5%
 
81131.4%
 
111051.3%
 
131001.2%
 
91001.2%
 
10770.9%
 
12750.9%
 
14620.8%
 
15530.6%
 
17430.5%
 
18430.5%
 
16410.5%
 
47380.5%
 
59350.4%
 
27340.4%
 
24330.4%
 
19330.4%
 
21320.4%
 
Other values (164)131216.0%
 
ValueCountFrequency (%) 
0268432.8%
 
1152918.7%
 
26157.5%
 
32843.5%
 
42533.1%
 
52212.7%
 
61511.8%
 
71261.5%
 
81131.4%
 
91001.2%
 
ValueCountFrequency (%) 
5751< 0.1%
 
5431< 0.1%
 
4041< 0.1%
 
3931< 0.1%
 
2981< 0.1%
 
2961< 0.1%
 
2851< 0.1%
 
2751< 0.1%
 
2701< 0.1%
 
2671< 0.1%
 

scall
Real number (ℝ≥0)

Distinct count4115
Unique (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2306.3182373046875
Minimum109.0
Maximum12493.0
Zeros0
Zeros (%)0.0%
Memory size64.1 KiB
2020-08-24T23:56:13.362632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile227
Q11012
median2051.5
Q33317.25
95-th percentile5365.9
Maximum12493
Range12384
Interquartile range (IQR)2305.25

Descriptive statistics

Standard deviation1633.617322
Coefficient of variation (CV)0.7083225963
Kurtosis0.9031412645
Mean2306.318237
Median Absolute Deviation (MAD)1135
Skewness0.9025312213
Sum18893359
Variance2668705.554
2020-08-24T23:56:13.466856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
220100.1%
 
158100.1%
 
26190.1%
 
23090.1%
 
19590.1%
 
41990.1%
 
20190.1%
 
31090.1%
 
16090.1%
 
16690.1%
 
21780.1%
 
26480.1%
 
20580.1%
 
19880.1%
 
18680.1%
 
46780.1%
 
21080.1%
 
18980.1%
 
15980.1%
 
20080.1%
 
32270.1%
 
191770.1%
 
16170.1%
 
24670.1%
 
21470.1%
 
Other values (4090)798597.5%
 
ValueCountFrequency (%) 
1091< 0.1%
 
1251< 0.1%
 
1282< 0.1%
 
1311< 0.1%
 
1322< 0.1%
 
1333< 0.1%
 
1341< 0.1%
 
1351< 0.1%
 
1361< 0.1%
 
1371< 0.1%
 
ValueCountFrequency (%) 
124931< 0.1%
 
122771< 0.1%
 
117101< 0.1%
 
103311< 0.1%
 
99341< 0.1%
 
93881< 0.1%
 
93041< 0.1%
 
91721< 0.1%
 
89651< 0.1%
 
88701< 0.1%
 

sread
Real number (ℝ≥0)

Distinct count794
Unique (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.47998046875
Minimum6.0
Maximum5318.0
Zeros0
Zeros (%)0.0%
Memory size64.1 KiB
2020-08-24T23:56:13.581250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile21
Q186
median166
Q3279
95-th percentile533
Maximum5318
Range5312
Interquartile range (IQR)193

Descriptive statistics

Standard deviation198.9801463
Coefficient of variation (CV)0.9453637625
Kurtosis89.10903826
Mean210.4799805
Median Absolute Deviation (MAD)91
Skewness5.459465962
Sum1724252
Variance39593.09861
2020-08-24T23:56:13.687748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
16430.5%
 
10410.5%
 
43400.5%
 
12380.5%
 
95370.5%
 
146370.5%
 
109360.4%
 
13360.4%
 
176360.4%
 
148350.4%
 
15350.4%
 
93350.4%
 
57340.4%
 
154340.4%
 
11340.4%
 
104340.4%
 
94340.4%
 
44330.4%
 
76330.4%
 
73320.4%
 
81320.4%
 
153320.4%
 
35320.4%
 
172320.4%
 
77310.4%
 
Other values (769)731689.3%
 
ValueCountFrequency (%) 
64< 0.1%
 
74< 0.1%
 
8260.3%
 
9260.3%
 
10410.5%
 
11340.4%
 
12380.5%
 
13360.4%
 
14300.4%
 
15350.4%
 
ValueCountFrequency (%) 
53181< 0.1%
 
44491< 0.1%
 
25031< 0.1%
 
23961< 0.1%
 
19841< 0.1%
 
19491< 0.1%
 
17001< 0.1%
 
16971< 0.1%
 
16941< 0.1%
 
16841< 0.1%
 

swrite
Real number (ℝ≥0)

Distinct count640
Unique (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.0582275390625
Minimum7.0
Maximum5456.0
Zeros0
Zeros (%)0.0%
Memory size64.1 KiB
2020-08-24T23:56:13.804212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile21
Q163
median117
Q3185
95-th percentile392.45
Maximum5456
Range5449
Interquartile range (IQR)122

Descriptive statistics

Standard deviation160.47898
Coefficient of variation (CV)1.069444726
Kurtosis234.1277983
Mean150.0582275
Median Absolute Deviation (MAD)59
Skewness9.605843698
Sum1229277
Variance25753.50302
2020-08-24T23:56:13.915486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
30560.7%
 
91560.7%
 
24530.6%
 
118510.6%
 
15500.6%
 
45500.6%
 
22500.6%
 
68490.6%
 
67490.6%
 
62490.6%
 
116490.6%
 
99480.6%
 
134480.6%
 
44480.6%
 
81480.6%
 
37470.6%
 
77460.6%
 
56460.6%
 
92460.6%
 
90450.5%
 
20450.5%
 
72450.5%
 
75440.5%
 
103440.5%
 
29440.5%
 
Other values (615)698685.3%
 
ValueCountFrequency (%) 
72< 0.1%
 
82< 0.1%
 
9170.2%
 
10190.2%
 
11100.1%
 
12150.2%
 
13380.5%
 
14380.5%
 
15500.6%
 
16350.4%
 
ValueCountFrequency (%) 
54561< 0.1%
 
45901< 0.1%
 
24851< 0.1%
 
18881< 0.1%
 
16451< 0.1%
 
15631< 0.1%
 
15451< 0.1%
 
15441< 0.1%
 
15321< 0.1%
 
14991< 0.1%
 

fork
Real number (ℝ≥0)

Distinct count228
Unique (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8845544471751055
Minimum0.0
Maximum20.1200008392334
Zeros21
Zeros (%)0.3%
Memory size64.1 KiB
2020-08-24T23:56:14.031982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.200000003
Q10.400000006
median0.8000000119
Q32.200000048
95-th percentile7.400000095
Maximum20.12000084
Range20.12000084
Interquartile range (IQR)1.800000042

Descriptive statistics

Standard deviation2.479493423
Coefficient of variation (CV)1.315692113
Kurtosis5.434380895
Mean1.884554447
Median Absolute Deviation (MAD)0.6000000089
Skewness2.249689151
Sum15438.27003
Variance6.147887633
2020-08-24T23:56:14.145127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.200000003199924.4%
 
0.40000000696611.8%
 
0.60000002387168.7%
 
0.80000001195636.9%
 
13984.9%
 
1.2000000483053.7%
 
1.3999999762613.2%
 
1.7999999522513.1%
 
22292.8%
 
1.6000000242162.6%
 
2.2000000481762.1%
 
2.4000000951351.6%
 
2.5999999051131.4%
 
2.799999952851.0%
 
3750.9%
 
3.200000048680.8%
 
3.400000095620.8%
 
3.599999905490.6%
 
4.199999809460.6%
 
3.799999952450.5%
 
4450.5%
 
4.599999905380.5%
 
5340.4%
 
7.199999809340.4%
 
6.400000095310.4%
 
Other values (203)125215.3%
 
ValueCountFrequency (%) 
0210.3%
 
0.18999999761< 0.1%
 
0.200000003199924.4%
 
0.38999998571< 0.1%
 
0.40000000696611.8%
 
0.58999997381< 0.1%
 
0.60000002387168.7%
 
0.80000001195636.9%
 
0.97000002861< 0.1%
 
0.990000009560.1%
 
ValueCountFrequency (%) 
20.120000841< 0.1%
 
17.030000691< 0.1%
 
16.170000081< 0.1%
 
15.800000191< 0.1%
 
15.399999621< 0.1%
 
15.199999811< 0.1%
 
15.170000081< 0.1%
 
152< 0.1%
 
14.399999622< 0.1%
 
14.199999811< 0.1%
 

exec
Real number (ℝ≥0)

Distinct count386
Unique (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.791998294113
Minimum0.0
Maximum59.56000137329102
Zeros21
Zeros (%)0.3%
Memory size64.1 KiB
2020-08-24T23:56:14.269317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.200000003
Q10.200000003
median1.200000048
Q32.799999952
95-th percentile11.58900013
Maximum59.56000137
Range59.56000137
Interquartile range (IQR)2.599999949

Descriptive statistics

Standard deviation5.212455993
Coefficient of variation (CV)1.86692664
Kurtosis19.42134755
Mean2.791998294
Median Absolute Deviation (MAD)1.000000045
Skewness4.06923771
Sum22872.05003
Variance27.16969748
2020-08-24T23:56:14.382542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.200000003206025.1%
 
0.4000000065957.3%
 
0.60000002385717.0%
 
0.80000001194535.5%
 
13444.2%
 
1.7999999523254.0%
 
22973.6%
 
1.2000000482903.5%
 
2.2000000482763.4%
 
1.3999999762372.9%
 
1.6000000242242.7%
 
2.4000000952022.5%
 
2.5999999051101.3%
 
2.7999999521011.2%
 
3961.2%
 
3.200000048961.2%
 
3.400000095770.9%
 
3.599999905760.9%
 
3.799999952620.8%
 
4550.7%
 
4.400000095530.6%
 
4.199999809490.6%
 
4.800000191410.5%
 
2.99000001360.4%
 
4.599999905350.4%
 
Other values (361)143117.5%
 
ValueCountFrequency (%) 
0210.3%
 
0.18999999761< 0.1%
 
0.200000003206025.1%
 
0.38999998571< 0.1%
 
0.4000000065957.3%
 
0.58999997382< 0.1%
 
0.60000002385717.0%
 
0.79000002152< 0.1%
 
0.80000001194535.5%
 
0.99000000953< 0.1%
 
ValueCountFrequency (%) 
59.560001371< 0.1%
 
49.900001531< 0.1%
 
40.599998471< 0.1%
 
38.799999242< 0.1%
 
37.799999241< 0.1%
 
37.599998471< 0.1%
 
37.400001531< 0.1%
 
37.130001071< 0.1%
 
371< 0.1%
 
36.930000311< 0.1%
 

rchar
Real number (ℝ≥0)

Distinct count7997
Unique (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197013.66833496094
Minimum278.0
Maximum2526649.0
Zeros0
Zeros (%)0.0%
Memory size64.1 KiB
2020-08-24T23:56:14.504077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile3149.3
Q133864.25
median124779.5
Q3267669.25
95-th percentile649268.05
Maximum2526649
Range2526371
Interquartile range (IQR)233805

Descriptive statistics

Standard deviation239480.8308
Coefficient of variation (CV)1.215554397
Kurtosis13.04849526
Mean197013.6683
Median Absolute Deviation (MAD)103680.5
Skewness2.848257232
Sum1613935971
Variance5.735106831e+10
2020-08-24T23:56:14.610851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
45260.1%
 
699450.1%
 
700150.1%
 
4254< 0.1%
 
70184< 0.1%
 
70263< 0.1%
 
4233< 0.1%
 
4313< 0.1%
 
69983< 0.1%
 
4293< 0.1%
 
4443< 0.1%
 
52743< 0.1%
 
4393< 0.1%
 
4243< 0.1%
 
4263< 0.1%
 
4303< 0.1%
 
13553< 0.1%
 
4163< 0.1%
 
20853< 0.1%
 
70073< 0.1%
 
30272< 0.1%
 
1283052< 0.1%
 
39872< 0.1%
 
4462< 0.1%
 
894092< 0.1%
 
Other values (7972)811399.0%
 
ValueCountFrequency (%) 
2781< 0.1%
 
4163< 0.1%
 
4171< 0.1%
 
4191< 0.1%
 
4201< 0.1%
 
4211< 0.1%
 
4221< 0.1%
 
4233< 0.1%
 
4243< 0.1%
 
4254< 0.1%
 
ValueCountFrequency (%) 
25266491< 0.1%
 
24862031< 0.1%
 
24083251< 0.1%
 
22148831< 0.1%
 
22143651< 0.1%
 
22070091< 0.1%
 
22046361< 0.1%
 
21345291< 0.1%
 
21046701< 0.1%
 
20072791< 0.1%
 

wchar
Real number (ℝ≥0)

Distinct count7939
Unique (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95898.29064941406
Minimum1498.0
Maximum1801623.0
Zeros0
Zeros (%)0.0%
Memory size64.1 KiB
2020-08-24T23:56:14.732596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1498
5-th percentile7815.85
Q122935.5
median46620
Q3106148
95-th percentile361894.6
Maximum1801623
Range1800125
Interquartile range (IQR)83212.5

Descriptive statistics

Standard deviation140756.8623
Coefficient of variation (CV)1.46777238
Kurtosis21.57375872
Mean95898.29065
Median Absolute Deviation (MAD)29512
Skewness3.848024602
Sum785598797
Variance1.981249428e+10
2020-08-24T23:56:14.856530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
187094< 0.1%
 
84823< 0.1%
 
219623< 0.1%
 
254733< 0.1%
 
263773< 0.1%
 
135543< 0.1%
 
267763< 0.1%
 
157492< 0.1%
 
223902< 0.1%
 
135532< 0.1%
 
60982< 0.1%
 
178442< 0.1%
 
120802< 0.1%
 
247022< 0.1%
 
291482< 0.1%
 
135362< 0.1%
 
69832< 0.1%
 
396152< 0.1%
 
183792< 0.1%
 
341772< 0.1%
 
357782< 0.1%
 
400222< 0.1%
 
135282< 0.1%
 
215632< 0.1%
 
260992< 0.1%
 
Other values (7914)813499.3%
 
ValueCountFrequency (%) 
14981< 0.1%
 
15221< 0.1%
 
16121< 0.1%
 
16681< 0.1%
 
19411< 0.1%
 
19721< 0.1%
 
19851< 0.1%
 
22201< 0.1%
 
22311< 0.1%
 
22331< 0.1%
 
ValueCountFrequency (%) 
18016231< 0.1%
 
15264101< 0.1%
 
15234131< 0.1%
 
15207691< 0.1%
 
14692731< 0.1%
 
14564411< 0.1%
 
13126951< 0.1%
 
12927981< 0.1%
 
12807881< 0.1%
 
12608051< 0.1%
 

runqsz
Real number (ℝ≥0)

Distinct count302
Unique (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.630676271015545
Minimum1.0
Maximum2823.0
Zeros0
Zeros (%)0.0%
Memory size64.1 KiB
2020-08-24T23:56:14.984795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.200000048
median2
Q33
95-th percentile6.400000095
Maximum2823
Range2822
Interquartile range (IQR)1.799999952

Descriptive statistics

Standard deviation125.7420851
Coefficient of variation (CV)6.40538733
Kurtosis173.9568321
Mean19.63067627
Median Absolute Deviation (MAD)0.7999999523
Skewness11.71721895
Sum160814.5
Variance15811.07196
2020-08-24T23:56:15.093251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1161819.8%
 
28009.8%
 
2.2000000484695.7%
 
1.2000000484545.5%
 
33324.1%
 
2.4000000953304.0%
 
1.3999999762993.6%
 
1.52933.6%
 
1.6000000242873.5%
 
1.7999999522863.5%
 
2.5999999052853.5%
 
3.2000000482713.3%
 
2.7999999522603.2%
 
3.4000000951692.1%
 
1.2999999521652.0%
 
3.5999999051602.0%
 
3.7999999521461.8%
 
1.7000000481291.6%
 
41281.6%
 
4.1999998091071.3%
 
2.51051.3%
 
4.4000000951011.2%
 
4.599999905770.9%
 
4.800000191690.8%
 
5660.8%
 
Other values (277)7869.6%
 
ValueCountFrequency (%) 
1161819.8%
 
1.2000000484545.5%
 
1.2999999521652.0%
 
1.3999999762993.6%
 
1.52933.6%
 
1.6000000242873.5%
 
1.7000000481291.6%
 
1.7999999522863.5%
 
28009.8%
 
2.2000000484695.7%
 
ValueCountFrequency (%) 
28231< 0.1%
 
24361< 0.1%
 
24261< 0.1%
 
23901< 0.1%
 
21701< 0.1%
 
21571< 0.1%
 
20351< 0.1%
 
19751< 0.1%
 
19631< 0.1%
 
18861< 0.1%
 

freemem
Real number (ℝ≥0)

Distinct count3165
Unique (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1763.456298828125
Minimum55.0
Maximum12027.0
Zeros0
Zeros (%)0.0%
Memory size64.1 KiB
2020-08-24T23:56:15.213397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile128
Q1231
median579
Q32002.25
95-th percentile7567.7
Maximum12027
Range11972
Interquartile range (IQR)1771.25

Descriptive statistics

Standard deviation2482.104511
Coefficient of variation (CV)1.407522552
Kurtosis2.226633567
Mean1763.456299
Median Absolute Deviation (MAD)420
Skewness1.807554653
Sum14446234
Variance6160842.803
2020-08-24T23:56:15.322507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
132370.5%
 
159310.4%
 
168290.4%
 
139280.3%
 
136280.3%
 
133270.3%
 
181250.3%
 
152250.3%
 
150240.3%
 
138240.3%
 
158240.3%
 
143240.3%
 
137240.3%
 
134230.3%
 
155230.3%
 
147230.3%
 
156230.3%
 
90230.3%
 
165230.3%
 
151230.3%
 
89220.3%
 
141220.3%
 
153220.3%
 
172210.3%
 
135200.2%
 
Other values (3140)757492.5%
 
ValueCountFrequency (%) 
551< 0.1%
 
622< 0.1%
 
643< 0.1%
 
661< 0.1%
 
682< 0.1%
 
691< 0.1%
 
703< 0.1%
 
713< 0.1%
 
721< 0.1%
 
7360.1%
 
ValueCountFrequency (%) 
120271< 0.1%
 
120121< 0.1%
 
119911< 0.1%
 
119821< 0.1%
 
119721< 0.1%
 
119581< 0.1%
 
119431< 0.1%
 
118841< 0.1%
 
118131< 0.1%
 
117801< 0.1%
 

freeswap
Real number (ℝ≥0)

Distinct count7658
Unique (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1328125.9598388672
Minimum2.0
Maximum2243187.0
Zeros0
Zeros (%)0.0%
Memory size64.1 KiB
2020-08-24T23:56:15.441201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile969854.85
Q11042623.5
median1289289.5
Q31730379.5
95-th percentile1865881.3
Maximum2243187
Range2243185
Interquartile range (IQR)687756

Descriptive statistics

Standard deviation422019.427
Coefficient of variation (CV)0.3177555742
Kurtosis1.166533636
Mean1328125.96
Median Absolute Deviation (MAD)282671
Skewness-0.7916644439
Sum1.088000786e+10
Variance1.781003967e+11
2020-08-24T23:56:15.547750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11250.3%
 
10230.3%
 
9220.3%
 
7190.2%
 
12190.2%
 
13180.2%
 
5180.2%
 
8160.2%
 
6160.2%
 
16140.2%
 
15130.2%
 
18100.1%
 
19100.1%
 
1490.1%
 
187532870.1%
 
187161670.1%
 
2160.1%
 
2360.1%
 
187511260.1%
 
177688860.1%
 
460.1%
 
182840850.1%
 
1750.1%
 
175992050.1%
 
18655844< 0.1%
 
Other values (7633)789796.4%
 
ValueCountFrequency (%) 
21< 0.1%
 
32< 0.1%
 
460.1%
 
5180.2%
 
6160.2%
 
7190.2%
 
8160.2%
 
9220.3%
 
10230.3%
 
11250.3%
 
ValueCountFrequency (%) 
22431871< 0.1%
 
21617791< 0.1%
 
20422801< 0.1%
 
19672751< 0.1%
 
18925711< 0.1%
 
18924711< 0.1%
 
18910691< 0.1%
 
18906761< 0.1%
 
18890141< 0.1%
 
18887341< 0.1%
 

target
Real number (ℝ≥0)

ZEROS

Distinct count56
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.9688720703125
Minimum0.0
Maximum99.0
Zeros283
Zeros (%)3.5%
Memory size64.1 KiB
2020-08-24T23:56:15.825314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q181
median89
Q394
95-th percentile98
Maximum99
Range99
Interquartile range (IQR)13

Descriptive statistics

Standard deviation18.40190451
Coefficient of variation (CV)0.2191515028
Kurtosis12.72518587
Mean83.96887207
Median Absolute Deviation (MAD)6
Skewness-3.416749603
Sum687873
Variance338.6300894
2020-08-24T23:56:15.931359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
904595.6%
 
914485.5%
 
924265.2%
 
944215.1%
 
934115.0%
 
974105.0%
 
964105.0%
 
954054.9%
 
883844.7%
 
983784.6%
 
893764.6%
 
873384.1%
 
862833.5%
 
02833.5%
 
852543.1%
 
842523.1%
 
832302.8%
 
812012.5%
 
821872.3%
 
801662.0%
 
791501.8%
 
771441.8%
 
781261.5%
 
761191.5%
 
751041.3%
 
Other values (31)82710.1%
 
ValueCountFrequency (%) 
02833.5%
 
1100.1%
 
21< 0.1%
 
461< 0.1%
 
481< 0.1%
 
491< 0.1%
 
504< 0.1%
 
514< 0.1%
 
522< 0.1%
 
5350.1%
 
ValueCountFrequency (%) 
99600.7%
 
983784.6%
 
974105.0%
 
964105.0%
 
954054.9%
 
944215.1%
 
934115.0%
 
924265.2%
 
914485.5%
 
904595.6%
 

Interactions

2020-08-24T23:55:46.120653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:46.283842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:46.438744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:46.594107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:46.745451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:46.899760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:47.062954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:47.216408image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:47.368029image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:47.529280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:47.682555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:47.838315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:47.984272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:48.128555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:48.279677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:48.425793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:48.572702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:48.717127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:48.863163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:49.021521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:49.166279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:49.314021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:49.469252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:49.627578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:49.780471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:49.922475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:50.064530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:50.214404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:50.545004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:50.692200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:50.841096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:51.000206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:51.155962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:51.303694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:51.450819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:51.607658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:51.753110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:51.909916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:52.051776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:52.199773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:52.349115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:52.489554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:52.632211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:52.771676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:52.909927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:53.058709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:53.199053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:53.339883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:53.489737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:53.628374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:53.771579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:53.918980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:54.056163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:54.201629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:54.345038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:54.498562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:54.635942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:54.942111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:55.094520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:55.235037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:55.377767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:55.525723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:55.661981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:55.802375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:55.946875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:56.083994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:56.248001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:56.431351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:56.604680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:56.793064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:56.969110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:57.147318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:57.303131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:57.462108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:57.623981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:57.778550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:57.937922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:58.088451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:58.237744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:58.397503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:58.544756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:58.691662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:58.839095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:58.981420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:59.140373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:59.441419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:59.582857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:59.735706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:55:59.878880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:00.029953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:00.172096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:00.314938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:00.466892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:00.609760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:00.756205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:00.897796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:01.047898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:01.206571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:01.354863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:01.499104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:01.651090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:01.790213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:01.938363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:02.081552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:02.224709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:02.384387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:02.544621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:02.702639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:02.858062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:03.013044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:03.176410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:03.331984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:03.486252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:03.819252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:03.974414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:04.129488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:04.279143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:04.430591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:04.577722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:04.719031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:04.860980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:05.005880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:05.144179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:05.301512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:05.442279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:05.585283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:05.733109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:05.870265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:06.016165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:06.152076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:06.288670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:06.440660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:06.588154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:06.738058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:06.885004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:07.038355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:07.195767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:07.345130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:07.492028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:07.653212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:07.801190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:07.949357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:08.257993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:08.397926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:08.540411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:08.684150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:08.824861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:08.959772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:09.099184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:09.248276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:09.388744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:09.526700image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:09.687101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:09.826927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:09.967330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:10.105560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:10.249319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:10.394369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:10.533838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:10.679522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:10.816042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:10.952246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:11.105314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:11.248550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:11.389705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:11.538522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:11.677740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:11.819413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:11.956047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-24T23:56:16.067232image/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.
2020-08-24T23:56:16.343023image/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.
2020-08-24T23:56:16.592992image/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.
2020-08-24T23:56:16.852761image/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

2020-08-24T23:56:12.225116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:56:12.709564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

lreadlwritescallsreadswriteforkexecrcharwcharrunqszfreememfreeswaptarget
06.02.01036.0103.0114.01.001.00172076.0355965.02.06527.01851864.090.0
11.00.02165.0205.0101.00.401.2043107.044139.03.0130.01131931.088.0
262.077.03806.0258.0166.01.401.40492142.0268706.05.2256.01314590.085.0
35.00.04721.0256.0177.00.992.58524787.0174964.01.0233.0972606.081.0
442.055.03949.0249.0244.02.604.60197289.0529200.03.4331.01013805.079.0
55.01.01692.0132.087.00.401.80220194.0107031.02.22291.01010703.092.0
63.00.0635.065.047.03.003.0087465.040740.01.0289.01806587.082.0
77.05.01341.0240.0120.00.400.60718437.0672290.04.82532.01037078.090.0
8159.040.02443.0299.0262.01.001.00240375.0209450.02.8536.01069565.087.0
91.00.03322.0271.0170.01.003.20399277.0128680.01.0579.01120168.086.0

Last rows

lreadlwritescallsreadswriteforkexecrcharwcharrunqszfreememfreeswaptarget
81823.00.03580.0278.0230.03.191.000000292840.055884.04.21006.01388792.084.0
81830.00.0160.013.018.00.200.2000002085.04636.01.07426.01866406.098.0
81842.01.0637.0102.095.00.400.20000037606.049019.01.5391.01092827.096.0
818510.01.01564.0157.0137.00.600.600000184137.059736.01.3485.01067194.093.0
818615.011.02599.0277.0234.03.612.200000133465.0102035.02.0139.01731732.085.0
818774.049.02688.0176.0103.011.0032.20000157714.038484.07.0314.01096333.069.0
818829.040.01906.0118.090.00.802.0000008175.027313.03.6166.01107088.088.0
81893.00.0926.090.067.00.601.0000005411.019322.01.01177.01020400.092.0
81904.00.0418.030.029.00.801.0000003959.010679.02.46355.01702592.096.0
81915.00.01888.0248.0215.06.201.800000216420.039346.04.61628.01757696.080.0