histogram() approximates the cumulative distribution of a dataset by counting
data frequencies for a list of bins.
A bin is defined by an upper bound where all data points that are less than or equal to the bound are counted in the bin. Bin counts are cumulative.
Each input table is converted into a single output table representing a single histogram. Each output table has the same group key as the corresponding input table. Columns not part of the group key are dropped. Output tables include additional columns for the upper bound and count of bins.
Function type signature
( <-tables: stream[A], bins: [float], ?column: string, ?countColumn: string, ?normalize: bool, ?upperBoundColumn: string, ) => stream[B] where A: Record, B: Record
Column containing input values. Column must be of type float.
Column to store bin upper bounds in. Default is
Column to store bin counts in. Default is
(Required) List of upper bounds to use when computing the histogram frequencies.
Bins should contain a bin whose bound is the maximum value of the data set. This value can be set to positive infinity if no maximum is known.
Bin helper functions
The following helper functions can be used to generated bins.
Convert counts into frequency values between 0 and 1.
Note: Normalized histograms cannot be aggregated by summing their counts.
Input data. Default is piped-forward data (
Create a cumulative histogram
import "sampledata" sampledata.float() |> histogram(bins: [0.0, 5.0, 10.0, 20.0])
Create a cumulative histogram with dynamically generated bins
import "sampledata" sampledata.float() |> histogram(bins: linearBins(start: 0.0, width: 4.0, count: 3))
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