Overview

Dataset statistics

Number of variables6
Number of observations250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory48.5 B

Variable types

NUM6

Reproduction

Analysis started2020-08-25 00:08:31.086360
Analysis finished2020-08-25 00:08:36.397662
Duration5.31 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.362104952335357e-10
Minimum-1.7267508506774902
Maximum1.7792969942092896
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:08:36.443506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.726750851
5-th percentile-1.586660832
Q1-0.8937306702
median-0.04383328557
Q30.9017361253
95-th percentile1.545355749
Maximum1.779296994
Range3.506047845
Interquartile range (IQR)1.795466796

Descriptive statistics

Standard deviation0.999999997
Coefficient of variation (CV)-1358307174
Kurtosis-1.145019652
Mean-7.362104952e-10
Median Absolute Deviation (MAD)0.8858536184
Skewness-0.0285719537
Sum-1.840526238e-07
Variance0.999999994
2020-08-25T00:08:36.549372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.67675399810.4%
 
1.02609825110.4%
 
0.905417025110.4%
 
-1.28552281910.4%
 
0.168411076110.4%
 
0.410918772210.4%
 
-0.684542000310.4%
 
0.290860831710.4%
 
-0.316495299310.4%
 
0.582343578310.4%
 
-0.909351408510.4%
 
0.262293130210.4%
 
-0.985947132110.4%
 
-0.105281412610.4%
 
0.105123050510.4%
 
-0.159218817910.4%
 
-0.575807273410.4%
 
-0.0076265172110.4%
 
-0.696805298310.4%
 
-0.972541928310.4%
 
-0.160070493810.4%
 
-0.00190762814610.4%
 
-0.328440189410.4%
 
0.273951321810.4%
 
0.897107660810.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.72675085110.4%
 
-1.71176874610.4%
 
-1.70593774310.4%
 
-1.70085704310.4%
 
-1.69836342310.4%
 
-1.69141805210.4%
 
-1.67675399810.4%
 
-1.67307090810.4%
 
-1.6689004910.4%
 
-1.63015341810.4%
 
ValueCountFrequency (%) 
1.77929699410.4%
 
1.74664139710.4%
 
1.74585032510.4%
 
1.74499535610.4%
 
1.74043679210.4%
 
1.6841696510.4%
 
1.6404606110.4%
 
1.63849544510.4%
 
1.61460924110.4%
 
1.60133802910.4%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4027464168009374e-09
Minimum-1.7675385475158691
Maximum1.7166963815689087
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:08:36.665299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.767538548
5-th percentile-1.600238967
Q1-0.8566597551
median0.05739217438
Q30.8800611198
95-th percentile1.543837994
Maximum1.716696382
Range3.484234929
Interquartile range (IQR)1.736720875

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)712887225.1
Kurtosis-1.165248479
Mean1.402746417e-09
Median Absolute Deviation (MAD)0.8835457563
Skewness-0.06355066026
Sum3.506866042e-07
Variance1.000000001
2020-08-25T00:08:36.775547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.269283205310.4%
 
1.03021264110.4%
 
-1.62303197410.4%
 
-0.0131090590710.4%
 
-1.19760835210.4%
 
0.231121733810.4%
 
0.0672204792510.4%
 
-0.230388119810.4%
 
-1.60439145610.4%
 
-0.516276776810.4%
 
0.862466812110.4%
 
-0.946446120710.4%
 
0.309439241910.4%
 
-0.475206643310.4%
 
1.47589218610.4%
 
0.253491848710.4%
 
1.3576713810.4%
 
-0.931297898310.4%
 
-1.17216646710.4%
 
0.267161577910.4%
 
-0.870748221910.4%
 
1.27762746810.4%
 
-1.26297676610.4%
 
1.2893414510.4%
 
0.877580165910.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.76753854810.4%
 
-1.76627409510.4%
 
-1.76295280510.4%
 
-1.7486958510.4%
 
-1.74792993110.4%
 
-1.69504535210.4%
 
-1.63775992410.4%
 
-1.63685655610.4%
 
-1.624936710.4%
 
-1.62303197410.4%
 
ValueCountFrequency (%) 
1.71669638210.4%
 
1.70716643310.4%
 
1.70491266310.4%
 
1.66141331210.4%
 
1.64528918310.4%
 
1.63581454810.4%
 
1.62179839610.4%
 
1.59898757910.4%
 
1.58782255610.4%
 
1.57247304910.4%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.972121238708496e-10
Minimum-1.8020213842391968
Maximum1.6682318449020386
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:08:36.889765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.802021384
5-th percentile-1.560503072
Q1-0.933549419
median-0.04310968518
Q30.9330197126
95-th percentile1.509705871
Maximum1.668231845
Range3.470253229
Interquartile range (IQR)1.866569132

Descriptive statistics

Standard deviation0.999999998
Coefficient of variation (CV)1254371287
Kurtosis-1.249046146
Mean7.972121239e-10
Median Absolute Deviation (MAD)0.9265892506
Skewness-0.01793209541
Sum1.99303031e-07
Variance0.9999999959
2020-08-25T00:08:36.999230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.73730289910.4%
 
-0.651364386110.4%
 
1.42878985410.4%
 
0.420007407710.4%
 
0.247845336810.4%
 
0.938156485610.4%
 
0.787275612410.4%
 
0.854656875110.4%
 
1.20442521610.4%
 
-0.753577828410.4%
 
1.11847615210.4%
 
-0.885897576810.4%
 
-0.363692671110.4%
 
0.899718642210.4%
 
-0.208411574410.4%
 
0.992815434910.4%
 
-0.193664044110.4%
 
0.733047902610.4%
 
1.61355555110.4%
 
1.4250711210.4%
 
-1.80202138410.4%
 
0.297672778410.4%
 
0.917609393610.4%
 
-1.11256146410.4%
 
-1.61939370610.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.80202138410.4%
 
-1.79843902610.4%
 
-1.7591890110.4%
 
-1.75235426410.4%
 
-1.74851691710.4%
 
-1.73730289910.4%
 
-1.73573017110.4%
 
-1.70959329610.4%
 
-1.64616525210.4%
 
-1.63841164110.4%
 
ValueCountFrequency (%) 
1.66823184510.4%
 
1.66744387110.4%
 
1.6470323810.4%
 
1.63729715310.4%
 
1.62295842210.4%
 
1.61355555110.4%
 
1.61331355610.4%
 
1.61297464410.4%
 
1.58810901610.4%
 
1.56905734510.4%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.8961727619171143e-09
Minimum-1.7556302547454834
Maximum1.7088327407836914
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:08:37.121755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.755630255
5-th percentile-1.612123245
Q1-0.8308269233
median0.09325461835
Q30.8238402903
95-th percentile1.513243765
Maximum1.708832741
Range3.464462996
Interquartile range (IQR)1.654667214

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)-527378107.7
Kurtosis-1.180831762
Mean-1.896172762e-09
Median Absolute Deviation (MAD)0.8138491288
Skewness-0.1125429906
Sum-4.740431905e-07
Variance1.000000006
2020-08-25T00:08:37.227638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.716304421410.4%
 
1.70883274110.4%
 
0.675480008110.4%
 
-1.34217035810.4%
 
-1.61462676510.4%
 
-1.09704601810.4%
 
-1.6304968610.4%
 
0.905933976210.4%
 
-0.326260298510.4%
 
-1.15809130710.4%
 
0.282558053710.4%
 
-0.315100938110.4%
 
-1.7445892110.4%
 
-1.10677564110.4%
 
-1.7210235610.4%
 
-0.608068764210.4%
 
0.505007684210.4%
 
1.20040750510.4%
 
0.120621532210.4%
 
-0.455979049210.4%
 
-0.00882187485710.4%
 
0.968353092710.4%
 
-1.59658551210.4%
 
1.21903181110.4%
 
0.833633780510.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.75563025510.4%
 
-1.7445892110.4%
 
-1.73922371910.4%
 
-1.73089349310.4%
 
-1.72192370910.4%
 
-1.7210235610.4%
 
-1.69912791310.4%
 
-1.6384917510.4%
 
-1.63058936610.4%
 
-1.6304968610.4%
 
ValueCountFrequency (%) 
1.70883274110.4%
 
1.68512785410.4%
 
1.68113803910.4%
 
1.65196919410.4%
 
1.64633429110.4%
 
1.60829770610.4%
 
1.59288394510.4%
 
1.59276020510.4%
 
1.57437038410.4%
 
1.57269024810.4%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.146729027989295e-10
Minimum-1.8355518579483032
Maximum1.7266043424606323
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:08:37.340478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.835551858
5-th percentile-1.657834846
Q1-0.7771964669
median0.02539300732
Q30.8876019567
95-th percentile1.515326589
Maximum1.726604342
Range3.5621562
Interquartile range (IQR)1.664798424

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)1626881547
Kurtosis-1.063988586
Mean6.146729028e-10
Median Absolute Deviation (MAD)0.8404287994
Skewness-0.1054926381
Sum1.536682257e-07
Variance1.000000006
2020-08-25T00:08:37.445372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.28027200710.4%
 
0.111105471810.4%
 
-0.420753955810.4%
 
0.334815651210.4%
 
-0.493994802210.4%
 
-1.66836106810.4%
 
0.953809142110.4%
 
0.946479439710.4%
 
0.0188362747410.4%
 
-0.644298076610.4%
 
-1.22009718410.4%
 
0.133101880610.4%
 
1.26793563410.4%
 
-0.864435136310.4%
 
0.312008351110.4%
 
-0.767261385910.4%
 
-0.912728428810.4%
 
-0.508640408510.4%
 
1.00521957910.4%
 
0.75504827510.4%
 
1.50656592810.4%
 
1.19758701310.4%
 
0.520179808110.4%
 
-0.837880313410.4%
 
0.723785936810.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.83555185810.4%
 
-1.8122693310.4%
 
-1.80692505810.4%
 
-1.79244065310.4%
 
-1.79188978710.4%
 
-1.78338944910.4%
 
-1.76993787310.4%
 
-1.76473414910.4%
 
-1.7525509610.4%
 
-1.73446404910.4%
 
ValueCountFrequency (%) 
1.72660434210.4%
 
1.70117056410.4%
 
1.6990182410.4%
 
1.66790771510.4%
 
1.6644619710.4%
 
1.65947294210.4%
 
1.65890610210.4%
 
1.64746129510.4%
 
1.63499605710.4%
 
1.61892557110.4%
 

target
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.030974086546621e-09
Minimum-2.2208375930786133
Maximum2.1689748764038086
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:08:37.568954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.220837593
5-th percentile-1.61452387
Q1-0.7655197382
median-0.002857592539
Q30.7351218015
95-th percentile1.638214481
Maximum2.168974876
Range4.389812469
Interquartile range (IQR)1.50064154

Descriptive statistics

Standard deviation0.9999999939
Coefficient of variation (CV)969956478
Kurtosis-0.7137644878
Mean1.030974087e-09
Median Absolute Deviation (MAD)0.745393306
Skewness-0.002055108121
Sum2.577435216e-07
Variance0.9999999877
2020-08-25T00:08:37.671432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.74414032710.4%
 
0.370442330810.4%
 
1.50233268710.4%
 
-1.34863090510.4%
 
-1.2015434510.4%
 
-0.652419209510.4%
 
-0.551898777510.4%
 
1.19952678710.4%
 
-0.616879999610.4%
 
-0.125814229210.4%
 
-0.465174585610.4%
 
-0.48568195110.4%
 
1.64487075810.4%
 
-1.21713161510.4%
 
-0.946605682410.4%
 
0.754191398610.4%
 
0.863892614810.4%
 
-2.01821398710.4%
 
1.86597573810.4%
 
-0.633943378910.4%
 
0.766752898710.4%
 
0.5997503410.4%
 
0.890764117210.4%
 
0.474922925210.4%
 
-0.771810889210.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.22083759310.4%
 
-2.21237659510.4%
 
-2.19737768210.4%
 
-2.13179278410.4%
 
-2.01821398710.4%
 
-1.9048659810.4%
 
-1.86753535310.4%
 
-1.84239423310.4%
 
-1.83958852310.4%
 
-1.81545269510.4%
 
ValueCountFrequency (%) 
2.16897487610.4%
 
2.10341858910.4%
 
1.93067705610.4%
 
1.91414225110.4%
 
1.86597573810.4%
 
1.83512306210.4%
 
1.81064915710.4%
 
1.77098524610.4%
 
1.74756336210.4%
 
1.73100411910.4%
 

Interactions

2020-08-25T00:08:31.308037image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:31.437675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:31.563933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:31.688892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:31.815125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:31.948778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:32.074852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:32.202777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:32.329300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:32.467807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:32.599085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:32.730966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:32.852133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:32.982242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:33.110796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:33.241074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:33.370486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:33.505564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:33.628097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:33.754941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:33.880260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:34.015263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:34.141667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:34.272274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:34.389662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:34.526113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:34.657480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:34.950944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:35.085257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:35.219131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:35.342534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:35.459862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:35.579409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:35.697706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:35.815126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:35.932829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:08:37.785271image/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.
2020-08-25T00:08:37.961142image/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.
2020-08-25T00:08:38.295253image/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.
2020-08-25T00:08:38.472282image/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

2020-08-25T00:08:36.140181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:08:36.326314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5target
0-0.8840430.7516010.756124-1.5215810.203984-1.292118
1-1.516162-0.778852-0.9355250.361520-1.306545-1.543313
21.2763500.022499-0.3741631.502670-0.6092271.810649
31.2820111.030213-0.326553-1.5389500.474845-0.541642
4-0.6190481.5605391.435116-0.297633-0.1206580.564376
5-0.1537741.572473-0.233760-0.900302-1.1117600.246153
6-1.2931730.109598-0.1123720.7475090.687816-0.328576
70.054722-0.863815-0.363693-0.326260-1.386199-0.925473
8-1.6767540.0405300.808882-0.835051-0.674255-1.759257
9-1.4268211.2703930.799673-0.0688210.312008-0.580568

Last rows

oz1oz2oz3oz4oz5target
240-0.1981280.0071841.668232-0.391066-1.1975720.408259
241-0.147316-0.7860491.613314-1.342831-0.263877-0.125814
242-0.904101-1.3107280.992815-0.4156650.273116-1.217132
243-0.065320-1.6368570.815962-0.779124-1.272264-1.815453
2440.4083461.645289-1.112561-0.291446-0.6502400.739030
245-0.5023471.120583-0.212220-0.750066-0.441486-0.794529
246-0.9400080.2799201.6135560.631651-0.3956550.692209
247-0.368071-0.593283-1.434136-0.5696530.8616190.036260
248-1.7008570.135705-0.8572811.592884-0.724478-0.336974
249-0.205241-0.9712581.5667810.065888-0.2622710.097324