Select a specific table column using a Table Column expression.
Output column name
The name of column being selected.
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.
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 function | Python DSL | JSON 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 function | Python DSL | JSON DSL |
---|---|---|
Sum | $EXPR.sum() | { "aggregate": "sum", "arg": $EXPR } |
Output column name
sum
; or$FUNCTION_NAME.$SOURCE_COLUMN_NAME
, e.g., mean.efficiency
.Select the primary datetime index of the table using the Index Column expression.
Output column name
timestamp
(constant).
Select the Group Key Columns associated
with the group_by
clause.
You can select one specific group key column by offset, or select all group key columns with a *
wildcard.
Output column name
group_key.$OFFSET
, where $OFFSET
corresponds to the N-th expression of the group_by
clause.
Select a specific table column using a Table Column expression.
Output column name
The name of column being selected.
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.
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 function | Python DSL | JSON 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 function | Python DSL | JSON DSL |
---|---|---|
Sum | $EXPR.sum() | { "aggregate": "sum", "arg": $EXPR } |
Output column name
sum
; or$FUNCTION_NAME.$SOURCE_COLUMN_NAME
, e.g., mean.efficiency
.Select the primary datetime index of the table using the Index Column expression.
Output column name
timestamp
(constant).
Select the Group Key Columns associated
with the group_by
clause.
You can select one specific group key column by offset, or select all group key columns with a *
wildcard.
Output column name
group_key.$OFFSET
, where $OFFSET
corresponds to the N-th expression of the group_by
clause.