Dask 数据帧中的多个聚合用户定义函数

Question

I'm processing a data set using Dask (considering it doesn't fit in memory) and I want to group the instances with a different aggregating function depending on the column and it's type.

Dask has a set of default aggregation functions for numerical data types, but not for strings/objects. Is there a way to implement a user defined aggregation function for strings somewhat similar to the example below?

atts_to_group = {'A', 'B'}
agg_fn = {
  'C': 'mean'  #int
  'D': 'concatenate_fn1'  #string - No default fn for strings - Doesn't work
  'E': 'concatenate_fn2'  #string
}
ddf = ddf.groupby(atts_to_group).agg(agg_fn).compute().reset_index()

At this point I'm able to read the whole data set in memory upon dropping irrelevant columns/rows, but I'd prefer continuing the processing in Dask considering it's faster performing the required operations.

Edit: Tried adding a custom function directly onto the dictionary:

def custom_concat(df):
    ...
    return df_concatd

agg_fn = {
  'C': 'mean'  #int
  'D': custom_concat(df)
}

-------------------------------------------------------
ValueError: unknown aggregate Dask DataFrame Structure:

Answer 1

Realised Dask provides with an Aggregation data structure . The custom aggregation can be done as follows:

# Concatenates the strings and separates them using ","
custom_concat = dd.Aggregation('custom_sum', lambda x: ",".join(str(x)), lambda x0: ",".join(str(x0)))
custom_concat_E = ...

atts_to_group = {'A', 'B'}
agg_fn = {
  'C': 'mean'  #int
  'D': custom_concat_D
  'E': custom_concat_E
}
ddf = ddf.groupby(atts_to_group).agg(agg_fn).compute().reset_index()

This can also be done with Dataframe.apply for a less verbose solution

def agg_fn(x):
    return pd.Series(
        dict(
            C = x['C'].mean(), # int
            D = "{%s}" % ', '.join(x['D']), # string (concat strings)
            E = ...
        )
    )

ddf = ddf.groupby(atts_to_group).apply(agg_fn).compute().reset_index