Overview

Dataset statistics

Number of variables19
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory145.0 MiB
Average record size in memory152.0 B

Variable types

NUM11
BOOL6
CAT2

Reproduction

Analysis started2020-08-24 23:44:44.995967
Analysis finished2020-08-24 23:47:26.343829
Duration2 minutes and 41.35 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Variables

D
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
616741
0
383259
ValueCountFrequency (%) 
161674161.7%
 
038325938.3%
 

Z1
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
0
630121
1
369879
ValueCountFrequency (%) 
063012163.0%
 
136987937.0%
 

Z2
Real number (ℝ≥0)

Distinct count945079
Unique (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.71160959226799
Minimum22.47959899902344
Maximum84.88188934326172
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:26.895232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum22.479599
5-th percentile33.92495327
Q143.24672508
median50.78588867
Q357.68946648
95-th percentile68.41936836
Maximum84.88188934
Range62.40229034
Interquartile range (IQR)14.44274139

Descriptive statistics

Standard deviation10.45003446
Coefficient of variation (CV)0.2060678914
Kurtosis-0.7074187591
Mean50.71160959
Median Absolute Deviation (MAD)7.223049164
Skewness0.08102142839
Sum50711609.59
Variance109.2032202
2020-08-24T23:47:27.020565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
55.737743385< 0.1%
 
44.638706214< 0.1%
 
55.159839634< 0.1%
 
43.83425144< 0.1%
 
50.975349434< 0.1%
 
56.607677464< 0.1%
 
56.12178044< 0.1%
 
67.561241154< 0.1%
 
56.955833444< 0.1%
 
44.477882394< 0.1%
 
49.759601594< 0.1%
 
51.528518684< 0.1%
 
43.483917244< 0.1%
 
44.032207494< 0.1%
 
44.707157144< 0.1%
 
50.822044374< 0.1%
 
49.629207614< 0.1%
 
50.442367554< 0.1%
 
50.402202614< 0.1%
 
51.346904754< 0.1%
 
51.303379064< 0.1%
 
43.84188084< 0.1%
 
56.721740724< 0.1%
 
50.657493594< 0.1%
 
56.707752234< 0.1%
 
Other values (945054)999899> 99.9%
 
ValueCountFrequency (%) 
22.4795991< 0.1%
 
22.913326261< 0.1%
 
22.982337951< 0.1%
 
23.029253011< 0.1%
 
23.230480191< 0.1%
 
23.271060941< 0.1%
 
23.276325231< 0.1%
 
23.36311341< 0.1%
 
23.400424961< 0.1%
 
23.419639591< 0.1%
 
ValueCountFrequency (%) 
84.881889341< 0.1%
 
83.520484921< 0.1%
 
83.12557221< 0.1%
 
82.931495671< 0.1%
 
82.222320561< 0.1%
 
82.044738771< 0.1%
 
82.0410081< 0.1%
 
82.038621< 0.1%
 
81.822624211< 0.1%
 
81.810798651< 0.1%
 

Z3
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
0
908983
1
 
91017
ValueCountFrequency (%) 
090898390.9%
 
1910179.1%
 

Z4
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
933216
0
 
66784
ValueCountFrequency (%) 
193321693.3%
 
0667846.7%
 

Z5
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
0
635315
1
364685
ValueCountFrequency (%) 
063531563.5%
 
136468536.5%
 

Z6
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
778600
0
221400
ValueCountFrequency (%) 
177860077.9%
 
022140022.1%
 

Z7
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
832219
2
 
112578
0
 
55203
ValueCountFrequency (%) 
183221983.2%
 
211257811.3%
 
0552035.5%
 
2020-08-24T23:47:31.551387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters4
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 (%) 
0105520335.2%
 
.100000033.3%
 
183221927.7%
 
21125783.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number200000066.7%
 
Other Punctuation100000033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0105520352.8%
 
183221941.6%
 
21125785.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1000000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3000000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0105520335.2%
 
.100000033.3%
 
183221927.7%
 
21125783.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3000000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0105520335.2%
 
.100000033.3%
 
183221927.7%
 
21125783.8%
 

Z8
Real number (ℝ)

Distinct count862733
Unique (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.274871514263747
Minimum-14.14468479156494
Maximum35.740596771240234
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:32.118076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-14.14468479
5-th percentile0.4801328972
Q10.7664745152
median1.400047958
Q33.302636266
95-th percentile14.26304402
Maximum35.74059677
Range49.88528156
Interquartile range (IQR)2.536161751

Descriptive statistics

Standard deviation4.481907266
Coefficient of variation (CV)1.368574995
Kurtosis4.611595775
Mean3.274871514
Median Absolute Deviation (MAD)0.7992949486
Skewness2.234646919
Sum3274871.514
Variance20.08749274
2020-08-24T23:47:32.228100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.52224701647< 0.1%
 
0.56864798077< 0.1%
 
1.021564967< 0.1%
 
0.57481497537< 0.1%
 
0.52372497327< 0.1%
 
0.67983502156< 0.1%
 
0.60218900446< 0.1%
 
0.63149499896< 0.1%
 
0.91604602346< 0.1%
 
0.6313210136< 0.1%
 
0.85123401886< 0.1%
 
0.55511498456< 0.1%
 
0.69327199466< 0.1%
 
0.55514997246< 0.1%
 
0.65375298266< 0.1%
 
0.6605409986< 0.1%
 
0.59326100356< 0.1%
 
0.55546802286< 0.1%
 
0.56831502916< 0.1%
 
0.62173497686< 0.1%
 
0.57956701526< 0.1%
 
0.68109101066< 0.1%
 
0.58281201126< 0.1%
 
0.55742800246< 0.1%
 
0.59123599536< 0.1%
 
Other values (862708)999845> 99.9%
 
ValueCountFrequency (%) 
-14.144684791< 0.1%
 
-13.328173641< 0.1%
 
-13.213547711< 0.1%
 
-12.492196081< 0.1%
 
-11.863097191< 0.1%
 
-11.723362921< 0.1%
 
-11.513075831< 0.1%
 
-11.456810951< 0.1%
 
-11.370180131< 0.1%
 
-11.355071071< 0.1%
 
ValueCountFrequency (%) 
35.740596771< 0.1%
 
35.422725681< 0.1%
 
33.627540591< 0.1%
 
33.355476381< 0.1%
 
33.241249081< 0.1%
 
32.735130311< 0.1%
 
32.69422151< 0.1%
 
32.430999761< 0.1%
 
32.248451231< 0.1%
 
32.110713961< 0.1%
 

Z9
Real number (ℝ)

Distinct count954746
Unique (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372.7969093925877
Minimum-882.482177734375
Maximum2065.0244140625
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:32.842741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-882.4821777
5-th percentile186.1779961
Q1261.2439499
median342.0298462
Q3392.5618362
95-th percentile845.5699036
Maximum2065.024414
Range2947.506592
Interquartile range (IQR)131.3178864

Descriptive statistics

Standard deviation201.6037313
Coefficient of variation (CV)0.5407870242
Kurtosis8.531449867
Mean372.7969094
Median Absolute Deviation (MAD)63.73150635
Skewness2.580498224
Sum372796909.4
Variance40644.06446
2020-08-24T23:47:32.971936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
260.85537725< 0.1%
 
259.36145024< 0.1%
 
366.8307194< 0.1%
 
378.39944464< 0.1%
 
388.80596924< 0.1%
 
392.19689944< 0.1%
 
258.61953744< 0.1%
 
363.17562874< 0.1%
 
351.68270874< 0.1%
 
377.645054< 0.1%
 
379.93945314< 0.1%
 
310.67980964< 0.1%
 
365.98742684< 0.1%
 
358.72985844< 0.1%
 
308.18722534< 0.1%
 
371.84738164< 0.1%
 
266.29336554< 0.1%
 
392.44921884< 0.1%
 
396.93093874< 0.1%
 
305.68624884< 0.1%
 
385.26702884< 0.1%
 
256.69961554< 0.1%
 
376.27194214< 0.1%
 
376.15689094< 0.1%
 
391.76156624< 0.1%
 
Other values (954721)999899> 99.9%
 
ValueCountFrequency (%) 
-882.48217771< 0.1%
 
-785.82562261< 0.1%
 
-773.02124021< 0.1%
 
-744.71472171< 0.1%
 
-719.61260991< 0.1%
 
-699.51257321< 0.1%
 
-684.43096921< 0.1%
 
-681.92956541< 0.1%
 
-673.50781251< 0.1%
 
-668.07598881< 0.1%
 
ValueCountFrequency (%) 
2065.0244141< 0.1%
 
2024.8507081< 0.1%
 
2009.5955811< 0.1%
 
1992.2919921< 0.1%
 
1989.1872561< 0.1%
 
1981.4892581< 0.1%
 
1972.561891< 0.1%
 
1971.1140141< 0.1%
 
1970.6859131< 0.1%
 
1962.4565431< 0.1%
 

Z10
Real number (ℝ≥0)

Distinct count715569
Unique (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.499945929758191
Minimum1.7229160070419312
Maximum4.784540176391602
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:33.474797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.722916007
5-th percentile2.694155836
Q13.277952671
median3.53124094
Q33.760113955
95-th percentile4.165658045
Maximum4.784540176
Range3.061624169
Interquartile range (IQR)0.4821612835

Descriptive statistics

Standard deviation0.4223767999
Coefficient of variation (CV)0.12068095
Kurtosis0.06695382307
Mean3.49994593
Median Absolute Deviation (MAD)0.2404866219
Skewness-0.4180750386
Sum3499945.93
Variance0.1784021611
2020-08-24T23:47:33.597638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.72547411910< 0.1%
 
3.5465970049< 0.1%
 
3.5015408999< 0.1%
 
3.744476088< 0.1%
 
3.5113990318< 0.1%
 
3.3683669578< 0.1%
 
3.537168988< 0.1%
 
3.5049779428< 0.1%
 
3.3197760588< 0.1%
 
3.7636339668< 0.1%
 
3.5323290828< 0.1%
 
3.5559060578< 0.1%
 
3.5032699118< 0.1%
 
3.5536239158< 0.1%
 
3.2797598848< 0.1%
 
3.7418119918< 0.1%
 
3.5614149577< 0.1%
 
3.5388410097< 0.1%
 
3.6877439027< 0.1%
 
3.3142480857< 0.1%
 
3.5035769947< 0.1%
 
3.5280020247< 0.1%
 
3.5481820117< 0.1%
 
3.5534219747< 0.1%
 
3.5219628817< 0.1%
 
Other values (715544)999805> 99.9%
 
ValueCountFrequency (%) 
1.7229160071< 0.1%
 
1.7708630561< 0.1%
 
1.7726529841< 0.1%
 
1.7734190231< 0.1%
 
1.7884160281< 0.1%
 
1.796694041< 0.1%
 
1.8156620261< 0.1%
 
1.8180619481< 0.1%
 
1.8301039931< 0.1%
 
1.8317099811< 0.1%
 
ValueCountFrequency (%) 
4.7845401761< 0.1%
 
4.7661190031< 0.1%
 
4.7450990681< 0.1%
 
4.7346138951< 0.1%
 
4.7266201971< 0.1%
 
4.7053232191< 0.1%
 
4.6982088091< 0.1%
 
4.6964378361< 0.1%
 
4.695843221< 0.1%
 
4.6957249641< 0.1%
 

Z11
Real number (ℝ)

Distinct count980527
Unique (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.94101598177527
Minimum-156.5341033935547
Maximum632.386962890625
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:34.209916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-156.5341034
5-th percentile18.50917807
Q142.41050339
median56.86836052
Q3104.7587852
95-th percentile278.1340363
Maximum632.3869629
Range788.9210663
Interquartile range (IQR)62.34828186

Descriptive statistics

Standard deviation78.90432666
Coefficient of variation (CV)0.9075615895
Kurtosis4.594697272
Mean86.94101598
Median Absolute Deviation (MAD)22.13475609
Skewness2.127324533
Sum86941015.98
Variance6225.892765
2020-08-24T23:47:34.337734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
44.957267764< 0.1%
 
45.504253394< 0.1%
 
67.97230534< 0.1%
 
75.168594364< 0.1%
 
49.062133794< 0.1%
 
74.217475894< 0.1%
 
45.59278874< 0.1%
 
44.340042113< 0.1%
 
38.308429723< 0.1%
 
51.916545873< 0.1%
 
72.322006233< 0.1%
 
42.372863773< 0.1%
 
25.981357573< 0.1%
 
47.52585223< 0.1%
 
47.983474733< 0.1%
 
48.70434573< 0.1%
 
74.646774293< 0.1%
 
65.58155063< 0.1%
 
104.84647373< 0.1%
 
71.794563293< 0.1%
 
50.087894443< 0.1%
 
45.041519173< 0.1%
 
133.27325443< 0.1%
 
53.263301853< 0.1%
 
68.323173523< 0.1%
 
Other values (980502)999918> 99.9%
 
ValueCountFrequency (%) 
-156.53410341< 0.1%
 
-154.30926511< 0.1%
 
-145.36227421< 0.1%
 
-144.15478521< 0.1%
 
-142.96977231< 0.1%
 
-141.44169621< 0.1%
 
-139.42967221< 0.1%
 
-134.35798651< 0.1%
 
-131.74282841< 0.1%
 
-131.55345151< 0.1%
 
ValueCountFrequency (%) 
632.38696291< 0.1%
 
629.13330081< 0.1%
 
613.41345211< 0.1%
 
609.05682371< 0.1%
 
598.48657231< 0.1%
 
597.371461< 0.1%
 
596.16387941< 0.1%
 
595.66369631< 0.1%
 
590.89099121< 0.1%
 
589.86193851< 0.1%
 

Z12
Real number (ℝ)

Distinct count976926
Unique (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1833.7611590784807
Minimum-7466.93212890625
Maximum18792.521484375
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:35.118606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-7466.932129
5-th percentile546.5758514
Q1879.5376892
median1095.608337
Q31794.382599
95-th percentile6874.935352
Maximum18792.52148
Range26259.45361
Interquartile range (IQR)914.8449097

Descriptive statistics

Standard deviation2021.985799
Coefficient of variation (CV)1.102644032
Kurtosis7.454729209
Mean1833.761159
Median Absolute Deviation (MAD)351.0852661
Skewness2.66779536
Sum1833761159
Variance4088426.572
2020-08-24T23:47:35.241278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1077.6412355< 0.1%
 
997.13787844< 0.1%
 
1028.7980964< 0.1%
 
884.38867194< 0.1%
 
1021.81254< 0.1%
 
1028.9045414< 0.1%
 
1352.6292724< 0.1%
 
1062.3565674< 0.1%
 
1227.1685794< 0.1%
 
1025.690434< 0.1%
 
886.78277594< 0.1%
 
969.15899664< 0.1%
 
954.86663824< 0.1%
 
872.74414064< 0.1%
 
1126.7996834< 0.1%
 
1066.21463< 0.1%
 
1080.1643073< 0.1%
 
926.96380623< 0.1%
 
890.65930183< 0.1%
 
932.38433843< 0.1%
 
1000.2650153< 0.1%
 
804.76806643< 0.1%
 
1021.6099853< 0.1%
 
957.06158453< 0.1%
 
848.39184573< 0.1%
 
Other values (976901)999909> 99.9%
 
ValueCountFrequency (%) 
-7466.9321291< 0.1%
 
-7098.4282231< 0.1%
 
-7091.5649411< 0.1%
 
-6943.8759771< 0.1%
 
-6861.823731< 0.1%
 
-6821.9956051< 0.1%
 
-6764.1586911< 0.1%
 
-6662.9550781< 0.1%
 
-6385.2392581< 0.1%
 
-6335.7875981< 0.1%
 
ValueCountFrequency (%) 
18792.521481< 0.1%
 
18349.068361< 0.1%
 
17912.830081< 0.1%
 
17704.630861< 0.1%
 
17577.884771< 0.1%
 
17522.111331< 0.1%
 
17459.691411< 0.1%
 
17099.789061< 0.1%
 
17022.593751< 0.1%
 
16855.291021< 0.1%
 

Z13
Real number (ℝ≥0)

Distinct count969238
Unique (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.33384144998897
Minimum1.739421010017395
Maximum433.5694274902344
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:35.847024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.73942101
5-th percentile53.56120949
Q181.65940666
median105.1774139
Q3143.1369324
95-th percentile238.6270798
Maximum433.5694275
Range431.8300065
Interquartile range (IQR)61.47752571

Descriptive statistics

Standard deviation54.84641219
Coefficient of variation (CV)0.463488817
Kurtosis1.549024714
Mean118.3338414
Median Absolute Deviation (MAD)28.73896408
Skewness1.279470339
Sum118333841.4
Variance3008.12893
2020-08-24T23:47:35.965769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
88.821899414< 0.1%
 
64.622039794< 0.1%
 
86.586288454< 0.1%
 
87.675056464< 0.1%
 
86.311820984< 0.1%
 
85.306884774< 0.1%
 
83.571502694< 0.1%
 
86.310783394< 0.1%
 
142.37101754< 0.1%
 
84.32399754< 0.1%
 
94.848617554< 0.1%
 
83.595870974< 0.1%
 
146.05276494< 0.1%
 
84.364578254< 0.1%
 
88.149421694< 0.1%
 
87.993415834< 0.1%
 
141.101124< 0.1%
 
146.86007694< 0.1%
 
87.765808114< 0.1%
 
92.129508974< 0.1%
 
146.21539314< 0.1%
 
81.895462043< 0.1%
 
91.011665343< 0.1%
 
96.674873353< 0.1%
 
91.910392763< 0.1%
 
Other values (969213)999904> 99.9%
 
ValueCountFrequency (%) 
1.739421011< 0.1%
 
6.5890030861< 0.1%
 
7.8818359381< 0.1%
 
10.358643531< 0.1%
 
10.770044331< 0.1%
 
11.39250661< 0.1%
 
12.32072831< 0.1%
 
13.083864211< 0.1%
 
13.168775561< 0.1%
 
13.277343751< 0.1%
 
ValueCountFrequency (%) 
433.56942751< 0.1%
 
415.76162721< 0.1%
 
415.37728881< 0.1%
 
414.01342771< 0.1%
 
407.6884461< 0.1%
 
405.52020261< 0.1%
 
405.45050051< 0.1%
 
405.04870611< 0.1%
 
404.34396361< 0.1%
 
403.80584721< 0.1%
 

Z14
Real number (ℝ)

Distinct count966659
Unique (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.50148239126713
Minimum-91.03424072265624
Maximum588.9686889648438
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:36.571867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-91.03424072
5-th percentile56.19245663
Q186.44034767
median108.0734978
Q3139.4275131
95-th percentile265.6085342
Maximum588.968689
Range680.0029297
Interquartile range (IQR)52.98716545

Descriptive statistics

Standard deviation61.50756736
Coefficient of variation (CV)0.5020965148
Kurtosis3.813303719
Mean122.5014824
Median Absolute Deviation (MAD)25.09290695
Skewness1.833155147
Sum122501482.4
Variance3783.180843
2020-08-24T23:47:36.701346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
70.9327244< 0.1%
 
116.41072854< 0.1%
 
87.707527164< 0.1%
 
105.82516484< 0.1%
 
87.084213264< 0.1%
 
114.40189364< 0.1%
 
129.18865974< 0.1%
 
111.51779944< 0.1%
 
83.801666264< 0.1%
 
101.50604254< 0.1%
 
107.29636384< 0.1%
 
111.04732514< 0.1%
 
90.345939644< 0.1%
 
102.66997534< 0.1%
 
108.14550784< 0.1%
 
111.98252874< 0.1%
 
109.61067964< 0.1%
 
105.15418244< 0.1%
 
108.40135964< 0.1%
 
115.31206514< 0.1%
 
116.18309023< 0.1%
 
105.40885163< 0.1%
 
148.82159423< 0.1%
 
113.13593293< 0.1%
 
116.36624153< 0.1%
 
Other values (966634)999905> 99.9%
 
ValueCountFrequency (%) 
-91.034240721< 0.1%
 
-86.031478881< 0.1%
 
-81.641799931< 0.1%
 
-81.05320741< 0.1%
 
-78.707221981< 0.1%
 
-74.814353941< 0.1%
 
-69.447868351< 0.1%
 
-64.739685061< 0.1%
 
-62.541271211< 0.1%
 
-61.67291261< 0.1%
 
ValueCountFrequency (%) 
588.9686891< 0.1%
 
559.37799071< 0.1%
 
543.56445311< 0.1%
 
541.09710691< 0.1%
 
532.98626711< 0.1%
 
522.77789311< 0.1%
 
520.65441891< 0.1%
 
518.32476811< 0.1%
 
517.54003911< 0.1%
 
515.02587891< 0.1%
 

Z15
Real number (ℝ≥0)

Distinct count968420
Unique (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.4475520729866
Minimum12.134506225585938
Maximum730.5150756835938
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:37.314214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum12.13450623
5-th percentile110.0112743
Q1186.4013481
median247.3895035
Q3309.4531555
95-th percentile445.1947723
Maximum730.5150757
Range718.3805695
Interquartile range (IQR)123.0518074

Descriptive statistics

Standard deviation99.05169557
Coefficient of variation (CV)0.3892813853
Kurtosis-0.03711787
Mean254.4475521
Median Absolute Deviation (MAD)61.592659
Skewness0.5323944621
Sum254447552.1
Variance9811.238394
2020-08-24T23:47:37.445409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
198.7366185< 0.1%
 
241.86274724< 0.1%
 
311.26062014< 0.1%
 
289.37860114< 0.1%
 
333.35989384< 0.1%
 
196.77862554< 0.1%
 
293.95703124< 0.1%
 
262.61087044< 0.1%
 
303.77453614< 0.1%
 
299.73953254< 0.1%
 
258.22354134< 0.1%
 
293.50646974< 0.1%
 
245.08996584< 0.1%
 
263.05453494< 0.1%
 
284.08151254< 0.1%
 
318.48446664< 0.1%
 
269.10482794< 0.1%
 
260.39477544< 0.1%
 
273.00109864< 0.1%
 
312.18710334< 0.1%
 
258.98617554< 0.1%
 
301.5112614< 0.1%
 
292.64859014< 0.1%
 
308.30538944< 0.1%
 
261.88204964< 0.1%
 
Other values (968395)999899> 99.9%
 
ValueCountFrequency (%) 
12.134506231< 0.1%
 
13.464443211< 0.1%
 
15.198528291< 0.1%
 
19.823501591< 0.1%
 
21.638748171< 0.1%
 
24.509698871< 0.1%
 
25.497051241< 0.1%
 
25.630455021< 0.1%
 
27.368051531< 0.1%
 
28.035755161< 0.1%
 
ValueCountFrequency (%) 
730.51507571< 0.1%
 
700.52142331< 0.1%
 
685.10699461< 0.1%
 
670.95959471< 0.1%
 
670.54376221< 0.1%
 
669.21472171< 0.1%
 
667.58807371< 0.1%
 
666.62103271< 0.1%
 
665.79852291< 0.1%
 
662.92266851< 0.1%
 

Z16
Real number (ℝ≥0)

Distinct count819131
Unique (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.72546184243536
Minimum7.001218795776367
Maximum16.992422103881836
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:38.023355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7.001218796
5-th percentile9.552613115
Q110.04733443
median10.55051041
Q311.03195119
95-th percentile12.99760518
Maximum16.9924221
Range9.991203308
Interquartile range (IQR)0.9846167564

Descriptive statistics

Standard deviation1.017053943
Coefficient of variation (CV)0.09482612107
Kurtosis2.435096066
Mean10.72546184
Median Absolute Deviation (MAD)0.4919743538
Skewness1.476985115
Sum10725461.84
Variance1.034398723
2020-08-24T23:47:38.134703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10.896643648< 0.1%
 
11.003510487< 0.1%
 
10.063694957< 0.1%
 
11.126231197< 0.1%
 
10.527010927< 0.1%
 
10.591153147< 0.1%
 
10.08023937< 0.1%
 
10.155711177< 0.1%
 
10.497154247< 0.1%
 
10.542476656< 0.1%
 
10.686949736< 0.1%
 
10.573804866< 0.1%
 
10.603590016< 0.1%
 
10.070979126< 0.1%
 
10.535079966< 0.1%
 
10.635658266< 0.1%
 
10.125735286< 0.1%
 
10.053382876< 0.1%
 
10.542218216< 0.1%
 
10.126902586< 0.1%
 
11.009563456< 0.1%
 
10.129155166< 0.1%
 
10.142810826< 0.1%
 
10.157538416< 0.1%
 
10.557718286< 0.1%
 
Other values (819106)999840> 99.9%
 
ValueCountFrequency (%) 
7.0012187961< 0.1%
 
7.4327778821< 0.1%
 
7.6394329071< 0.1%
 
7.7109031681< 0.1%
 
7.7547979351< 0.1%
 
7.8251538281< 0.1%
 
7.8305668831< 0.1%
 
7.8336739541< 0.1%
 
7.8449811941< 0.1%
 
7.8468408581< 0.1%
 
ValueCountFrequency (%) 
16.99242211< 0.1%
 
16.962629321< 0.1%
 
16.866903311< 0.1%
 
16.827041631< 0.1%
 
16.747125631< 0.1%
 
16.706460951< 0.1%
 
16.705064771< 0.1%
 
16.672262191< 0.1%
 
16.666643141< 0.1%
 
16.648330691< 0.1%
 

Z17
Categorical

Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1
461203
0
253844
2
189283
3
95670
ValueCountFrequency (%) 
146120346.1%
 
025384425.4%
 
218928318.9%
 
3956709.6%
 
2020-08-24T23:47:42.565503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters5
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 (%) 
0125384441.8%
 
.100000033.3%
 
146120315.4%
 
21892836.3%
 
3956703.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number200000066.7%
 
Other Punctuation100000033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0125384462.7%
 
146120323.1%
 
21892839.5%
 
3956704.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1000000100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3000000100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0125384441.8%
 
.100000033.3%
 
146120315.4%
 
21892836.3%
 
3956703.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3000000100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0125384441.8%
 
.100000033.3%
 
146120315.4%
 
21892836.3%
 
3956703.2%
 

target
Real number (ℝ)

Distinct count978852
Unique (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1917.5776777154279
Minimum-756.8543701171875
Maximum5856.17919921875
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-08-24T23:47:43.166874image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-756.8543701
5-th percentile338.1485519
Q11133.135864
median1745.583008
Q32592.773132
95-th percentile3969.409265
Maximum5856.179199
Range6613.033569
Interquartile range (IQR)1459.637268

Descriptive statistics

Standard deviation1103.621876
Coefficient of variation (CV)0.5755291633
Kurtosis-0.5221327473
Mean1917.577678
Median Absolute Deviation (MAD)728.5579834
Skewness0.4665702559
Sum1917577678
Variance1217981.246
2020-08-24T23:47:43.294463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2371.0986335< 0.1%
 
2656.0671394< 0.1%
 
2358.6896974< 0.1%
 
3262.9294434< 0.1%
 
2517.1396484< 0.1%
 
1181.3945314< 0.1%
 
2433.8291024< 0.1%
 
1864.7502443< 0.1%
 
1699.382693< 0.1%
 
2437.6416023< 0.1%
 
1142.2941893< 0.1%
 
2349.0068363< 0.1%
 
1284.594363< 0.1%
 
2498.4877933< 0.1%
 
2236.1040043< 0.1%
 
1124.3571783< 0.1%
 
1195.8410643< 0.1%
 
1812.1712653< 0.1%
 
2638.8745123< 0.1%
 
2538.5219733< 0.1%
 
1492.1560063< 0.1%
 
2158.3557133< 0.1%
 
2350.1530763< 0.1%
 
1147.3883063< 0.1%
 
1156.761233< 0.1%
 
Other values (978827)999917> 99.9%
 
ValueCountFrequency (%) 
-756.85437011< 0.1%
 
-713.21093751< 0.1%
 
-665.69165041< 0.1%
 
-647.82647711< 0.1%
 
-630.99353031< 0.1%
 
-618.12609861< 0.1%
 
-616.68359381< 0.1%
 
-615.94421391< 0.1%
 
-610.25738531< 0.1%
 
-609.67022711< 0.1%
 
ValueCountFrequency (%) 
5856.1791991< 0.1%
 
5818.9965821< 0.1%
 
5775.7124021< 0.1%
 
5652.7495121< 0.1%
 
5640.8759771< 0.1%
 
5637.8574221< 0.1%
 
5634.9877931< 0.1%
 
5634.6225591< 0.1%
 
5621.4775391< 0.1%
 
5612.7119141< 0.1%
 

Interactions

2020-08-24T23:46:16.353796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:16.848378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:17.349296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:17.858396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:18.357881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:18.868550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:19.377272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:19.881312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:20.388048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:20.907448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:21.409243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:21.917392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:22.402382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:22.894257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:23.389605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:23.881469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:24.372605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:24.867115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:25.356653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:25.859158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:26.366405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:26.855660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:27.353389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:27.856651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:28.361183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:29.024485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:29.531852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:30.029709image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:30.527914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:31.030332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:31.544450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:32.056854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:32.554289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:33.078406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:33.573547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:34.081555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:34.580242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:35.077250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:35.570590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:36.084605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:36.584974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:37.083632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:37.584680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:38.070164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:38.586212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:39.083513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:39.583554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:40.083939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:40.581266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:41.088934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:41.603842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:42.109508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:42.620977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:43.281949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:43.789890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:44.315785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:44.796516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:45.283587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:45.771868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:46.269850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:46.776011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:47.268871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:47.754817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:48.271645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:48.769326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:49.253445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:49.755344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:50.233864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:50.724448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:51.220732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:51.701444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:52.196644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:52.690713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:53.193546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:53.712167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:54.221459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:54.716251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:55.222991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:55.726239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:56.236716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:56.746758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:57.276664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:58.031728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:58.542626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:59.051899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:46:59.564862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:00.080740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:00.586353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:01.102299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:01.607791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:02.119642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:02.628341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:03.131790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:03.647130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:04.146541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:04.649825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:05.164652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:05.668858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:06.175052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:06.686081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:07.170187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:07.650221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:08.141769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:08.632610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:09.114303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:09.604676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:10.091994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:10.588685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:11.123688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:11.605977image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:12.263746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:12.766980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:13.261759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:13.776475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:14.288587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:14.787595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:15.284815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:15.780876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:16.287653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:16.801859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:17.300090image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-24T23:47:43.452696image/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.
2020-08-24T23:47:43.764304image/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.
2020-08-24T23:47:44.073713image/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.
2020-08-24T23:47:44.367120image/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.
2020-08-24T23:47:44.605711image/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

2020-08-24T23:47:19.947293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-24T23:47:21.557796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

DZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z11Z12Z13Z14Z15Z16Z17target
01.01.031.7565311.01.01.01.01.00.720643204.7752084.12851141.5367621009.96441770.513832145.856323240.98498510.1707712.02382.476074
10.00.044.6268730.01.00.01.01.012.947697324.1353153.33176273.896744895.713806114.100945142.130829571.04370110.6199750.0135.560638
21.00.056.9202080.01.00.01.01.012.805899314.9739693.70868074.9532701840.04064963.579121109.543335299.95208710.0374800.02203.619629
30.00.053.4909520.01.00.01.01.03.575025358.3941043.63248343.917221949.666931108.45996188.847923308.77127110.1059531.01795.955200
40.00.041.3759570.01.00.01.01.016.583513427.3600773.70144852.308067887.63281291.052589105.256760326.0458689.4499021.01350.666992
51.00.052.5084920.01.00.01.01.01.556410360.0006714.21187190.175568925.069458100.229340114.589958208.41918911.0163370.01378.727173
60.01.047.0430530.01.00.00.01.03.322889261.0940552.83466024.3274592025.25378448.837574133.74105897.33284813.7951600.0475.944000
70.01.056.2403830.00.00.01.00.013.358179261.4468692.76800691.5102691064.851196142.189285322.775665133.04298412.0937281.0557.591492
80.01.037.0479130.01.01.00.02.07.866230263.5542603.56387478.839104643.43341178.586319106.826172459.2555249.4552930.01167.141724
91.01.035.8246990.01.01.01.01.00.544455227.5923463.22429733.839943568.074158141.423431129.546249251.20224010.6382581.03952.591553

Last rows

DZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z11Z12Z13Z14Z15Z16Z17target
9999901.00.057.6007000.01.00.01.01.01.651521356.0638733.74958166.0439991776.24365290.391586218.408691479.91427610.3246662.02202.633789
9999911.01.035.5360680.01.01.01.01.01.095892307.7799072.98747218.821051995.429993154.45188958.629288297.43051110.0178210.01142.842041
9999920.00.053.0566370.01.00.01.02.01.971753318.2034913.31117666.5368195109.550293136.856445111.354141326.1754769.7469900.0605.966980
9999931.00.070.4507140.01.01.01.01.00.775659360.4747923.78722030.4648093886.37353561.957294110.059967315.75491310.3574093.03174.704346
9999941.00.039.7555690.01.00.00.01.020.853916245.2101443.75983381.082932944.224243272.452515257.899109235.8740239.9083771.01689.774658
9999951.00.054.6486280.01.00.01.01.00.821357369.8595893.40611149.064838904.52710078.700981114.336380242.74235511.0913230.01141.450806
9999960.00.041.4962921.01.00.01.01.00.999576857.3906863.929982133.0713351798.910034250.418610109.321976267.24395811.7110253.02149.976562
9999970.01.033.8309520.01.00.01.01.013.906055312.3135383.54542873.0098951829.911377155.48819083.146812195.69686910.7705031.01678.040161
9999980.00.070.5231481.01.01.00.02.010.128497212.7667543.663546268.5291141046.63513286.70308797.095116239.90388511.0039210.0543.393616
9999991.00.062.1884120.01.00.01.01.09.892191404.3752753.783149121.0170291322.640259260.806000120.693352233.63179010.6186500.03439.468506