Aggregation functions for Grizzly Series

[sppalkia]

This PR adds aggregation function support for Grizzly Series. The API follows that of Pandas, with the exception of the index of the returned value: Pandas assigns labels based on the aggregation function, while Grizzly (which does not support custom indexes at the moment) assigns numerical labels.

Examples of aggregations with Grizzly

>>> s = GrizzlySeries([1,2,3,4])
>>> s.agg('sum')
10
>>> s.agg(['sum', 'mean']).evaluate()
0    10.0
1     2.5
dtype: float64

Multiple aggregations are co-optimized to prevent redundant computation, e.g., a requesting a variance and a mean will only compute the mean once and use the result for variance computation.

Currently supports:

• count
• min
• max
• sum
• product
• mean
• variance
• standard deviation

Some of the IR code is taken from https://www.github.com/weld-project/baloo.