Divides the given set into bins and calculates statistics of the values of a
value-column in each bin. The bins are based on the values of a given
binning-column. The statistics that may be requested are mean, stdv (standard
deviation), variance, skew, kurtosis, sum, min, max, first, last and weighted
average. In addition to the requested statistics the count of total samples in
each bin is returned. This counts vector is just the histogram of the column
used to divide the set members into bins. The weighted average statistic
requires a weight column to be specified in weight_column_name. The weighted
average is then defined as the sum of the products of the value column times
the weight column divided by the sum of the weight column.There are two methods for binning the set members. In the first, which can be
used for numeric valued binning-columns, a min, max and interval are specified.
The number of bins, nbins, is the integer upper bound of (max-min)/interval.
Values that fall in the range [min+n*interval,min+(n+1)*interval) are placed in
the nth bin where n ranges from 0..nbin-2. The final bin is
[min+(nbin-1)*interval,max]. In the second method, bin_values specifies a
list of binning column values. Binning-columns whose value matches the nth
member of the bin_values list are placed in the nth bin. When a list is
provided, the binning-column must be of type string or int.NOTE: The Kinetica instance being accessed must be running a CUDA (GPU-based)
build to service this request.
Name of the table on which the ranged-statistics operation will be performed, in [schema_name.]table_name format, using standard name resolution rules.
A string of comma separated list of the statistics to calculate, e.g. ‘sum,mean’. Available statistics: mean, stdv (standard deviation), variance, skew, kurtosis, sum.
The Kinetica server embeds the endpoint response inside a standard response structure which contains status information and the actual response to the query. Here is a description of the various fields of the wrapper:
A map with a key for each statistic in the stats input parameter having a value that is a vector of the corresponding value-column bin statistics. In a addition the key count has a value that is a histogram of the binning-column.