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")
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