Given the following (totally overkill) data frame example
import pandas as pd
import datetime as dt
df = pd.DataFrame({
"date" : [dt.date(2012, x, 1) for x in range(1, 11)],
"returns" : 0.05 * np.random.randn(10),
"dummy" : np.repeat(1, 10)
})
is there an existing built-in way to apply two different aggregating functions to the same column, without having to call agg multiple times?
The syntactically wrong, but intuitively right, way to do it would be:
# Assume `function1` and `function2` are defined for aggregating.
df.groupby("dummy").agg({"returns":function1, "returns":function2})
Obviously, Python doesn't allow duplicate keys. Is there any other manner for expressing the input to agg? Perhaps a list of tuples [(column, function)] would work better, to allow multiple functions applied to the same column? But it seems like it only accepts a dictionary.
Is there a workaround for this besides defining an auxiliary function that just applies both of the functions inside of it? (How would this work with aggregation anyway?)