median() function
The median() function is a special application of the quantile() function
that returns the median _value of an input table or all non-null records in the input table
with values that fall within the 0.5 quantile (50th percentile) depending on the method used.
median() behaves like an aggregate function
or a selector function depending on
the method used.
median(
column: "_value",
method: "estimate_tdigest",
compression: 0.0
)
When using the estimate_tdigest or exact_mean methods, it outputs non-null
records with values that fall within the 0.5 quantile.
When using the exact_selector method, it outputs the non-null record with the
value that represents the 0.5 quantile.
The median() function can only be used with float value types.
It is a special application of the quantile() function
which uses an approximation implementation that requires floats.
You can convert your value column to a float column using the toFloat() function.
Parameters
column
Column to use to compute the median.
Default is "_value".
method
Computation method.
Default is "estimate_tdigest".
The available options are:
estimate_tdigest
An aggregate method that uses a t-digest data structure to compute an accurate quantile estimate on large data sources.
exact_mean
An aggregate method that takes the average of the two points closest to the quantile value.
exact_selector
A selector method that returns the data point for which at least q points are less than.
compression
Number of centroids to use when compressing the dataset.
A larger number produces a more accurate result at the cost of increased memory requirements.
Default is 1000.0.
tables
Input data.
Default is piped-forward data (<-).
Examples
The following examples use data provided by the sampledata package
to show how median() transforms data.
Median as an aggregate
import "sampledata"
sampledata.float()
|> median()
Median as a selector
import "sampledata"
sampledata.float()
|> median(method: "exact_selector")
Function definition
median = (method="estimate_tdigest", compression=0.0, tables=<-) =>
quantile(
q:0.5,
method:method,
compression:compression
)
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