Real Time Data exploration

Real time Data Exploration can be categorized into two types:

• Real time Univariate Data Exploration
• Real time Bivariate Data Exploration

Univariate exploration

Use univariate function to get the summarized univariate statistics for the data. Only argument needed for this function is the BET table for generating the summarized results. It returns a dataframe with all the univariate stats.

  #Importing artml explore module for calculating univariate
  from artml.explore import stats
  stats.univariate(BET)

Created univarate statistics table looks like the one below. As the BET gets updated with new data, table also updates in real time to display the stats

Stats	feature 1	feature 2	feature 3	feature 4	feature 5
Count	----------	---------	---------	---------	---------
Mean	----------	---------	---------	---------	---------
Variance	----------	---------	---------	---------	---------
Standard_deviation	----------	---------	---------	---------	---------
coeff_of_variation	----------	---------	---------	---------	---------
skewness	----------	---------	---------	---------	---------
Kurtosis	----------	---------	---------	---------	---------

Bivariate exploration

For getting bivariate stats for the data use covariance or correlation functions. These functions explores the concept of relationship between two attributes, whether there is an association and the strength of this association.

  ## Import artml explore module for calculating bivariate
  from artml.explore import stats
  stats.covariance(BET)
  stats.correlation(BET)

Also, for comparing whether there are differences between two attributes and the significance of these differences, use Ztest or Ttest functions.

  stats.Ztest(BET, 'feature1name','feature2name')
  stats.Ttest(BET, 'feature1name','feature2name')

Similarly, we can perform Ftest, ANOVA & Chi2 test using the syntaxes in the artml library.