Aggregation Functions
PantherFlow aggregation functions
agg.avg()
agg.avg()
agg.avg(column: any) -> float
Returns the average of the values in the aggregation.
Example:
agg.count()
agg.count()
agg.count([column: any]) -> int
Returns the number of values in the aggregation.
Example:
agg.count_distinct()
agg.count_distinct()
agg.count_distinct(column: any) -> int
Returns the number of unique values in the aggregation.
Example:
agg.make_set()
agg.make_set()
agg.make_set(column: any) -> any
Returns a set of unique values from the column.
Example:
agg.max()
agg.max()
agg.max(column: any) -> float
Returns the maximum value in the aggregation.
Example:
agg.min()
agg.min()
agg.min(column: any) -> float
Returns the minimum value in the aggregation.
Example:
agg.percentile_cont()
agg.percentile_cont()
agg.percentile_cont(column: [any], percentile: number) -> float
For a given percentile
value between 0.0 and 1.0, return the value of the input column
based on a continuous distribution of rows. If no input row lies exactly at the desired percentile, the result is calculated using linear interpolation of the two nearest input values. If a group contains only one value, then that value will be returned for any specified percentile (e.g. both percentile 0.0 and percentile 1.0 will return that one row).
Example:
agg.stddev()
agg.stddev()
agg.stddev(column: [number]) -> float
Returns the sample standard deviation (square root of sample variance) of non-null values.
Example:
agg.sum()
agg.sum()
agg.sum(column: [any]) -> float
Returns the sum of the values in the aggregation.
Example:
agg.take_any()
agg.take_any()
agg.take_any(column: [any]) -> any
Returns any value from the aggregation.
Example:
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