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Select a table column

Select a specific table column using a Table Column expression.

Output column name

The name of column being selected.
import vitalx.aggregation as va

va.select(va.Sleep.col("efficiency"))

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Perform an analysis and select the output value

Perform data analysis with in-built algorithms using value macro expressions:

Output column name

The name of the value macro, e.g., sleep_score for the Chronotype value macro.
import vitalx.aggregation as va

va.select(va.Sleep.chronotype())

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Aggregate a table column

Aggregate a Table Column expression with respect to the group_by clause. The specified aggregate function is applied to each and every group created by the group_by clause. The result set is N aggregated values where N is the number of groups.

Statistical functions

Aggregate functionPython DSLJSON DSL
Minimum$EXPR.min(){ "aggregate": "min", "arg": $EXPR }
Maximum$EXPR.max(){ "aggregate": "max", "arg": $EXPR }
Mean$EXPR.mean(){ "aggregate": "mean", "arg": $EXPR }
Median$EXPR.median(){ "aggregate": "median", "arg": $EXPR }
Standard Deviation$EXPR.stddev(){ "aggregate": "stddev", "arg": $EXPR }
Count$EXPR.count(){ "aggregate": "count", "arg": $EXPR }
Oldest Value$EXPR.oldest(){ "aggregate": "oldest", "arg": $EXPR }
Newest Value$EXPR.newest(){ "aggregate": "newest", "arg": $EXPR }

Math functions

Aggregate functionPython DSLJSON DSL
Sum$EXPR.sum(){ "aggregate": "sum", "arg": $EXPR }

Output column name

  • the aggregation function name, e.g., sum; or
  • if the Split by Source mode is enabled — $FUNCTION_NAME.$SOURCE_COLUMN_NAME, e.g., mean.efficiency.
import vitalx.aggregation as va

va.select(
    va.Sleep.col("stage_asleep_second").newest()
)

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Select the Index Column

Select the primary datetime index of the table using the Index Column expression.

Output column name

timestamp (constant).
import vitalx.aggregation as va

va.select(va.Sleep.col("efficiency"))

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Select the Group Key Columns

Select the Group Key Columns associated with the group_by clause. You can select one specific group key column by offset, or select all group key columns with a * wildcard.

Output column name

group_key.$OFFSET, where $OFFSET corresponds to the N-th expression of the group_by clause.
import vitalx.aggregation as va

# Select all group key columns
va.select(va.group_key("*"))

# Select the 2nd group key column
va.select(va.group_key(2))