For each chosen column (parameter) a reference population is used to estimate n different bins. The bins are based on the quantiles of the data. Then all data is grouped by the aggregation label (e.g. well) and the data of each group is applied to the bins. The percentage of data falling into a certain bin is calculated and used to estimate a z-score. The result contains a z-score, a percentage and an absolute count for each bin of each aggregation group and each parameter. It should help to detect minor distribution changes which would not be caught with a mean or median per aggregation group of the object data.


Aggregate object data by
object data of each subgroup will be applied to the bins (e.g. well)
Column selection
the data of the selected columns will be used for the binning analysis (e.g. different parameters)
Number of bins
the reference population is divided into the given number of bins
Column with reference label
the chosen column contains the label to describe the reference population. If all data should be used as reference population, the option can be set to "None"
Subset by
if a reference population should be used, the chosen label defines this subpopulation

Input Ports

Table containing object based data

Output Ports

Result of the binning analysis


This node has no views


  • No workflows found



You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.