Pandas: aggregate when column contains numpy arrays

Question

I'm using a pandas DataFrame in which one column contains numpy arrays. When trying to sum that column via aggregation I get an error stating 'Must produce aggregated value'.

e.g.

import pandas as pd
import numpy as np

DF = pd.DataFrame([[1,np.array([10,20,30])],
               [1,np.array([40,50,60])], 
               [2,np.array([20,30,40])],], columns=['category','arraydata'])

This works the way I would expect it to:

DF.groupby('category').agg(sum)

output:

             arraydata
category 1   [50 70 90]
         2   [20 30 40]

However, since my real data frame has multiple numeric columns, arraydata is not chosen as the default column to aggregate on, and I have to select it manually. Here is one approach I tried:

g=DF.groupby('category')
g.agg({'arraydata':sum})

Here is another:

g=DF.groupby('category')
g['arraydata'].agg(sum)

Both give the same output:

Exception: must produce aggregated value

However if I have a column that uses numeric rather than array data, it works fine. I can work around this, but it's confusing and I'm wondering if this is a bug, or if I'm doing something wrong. I feel like the use of arrays here might be a bit of an edge case and indeed wasn't sure if they were supported. Ideas?

Thanks

Answer 1

One, perhaps more clunky way to do it would be to iterate over the GroupBy object (it generates (grouping_value, df_subgroup) tuples. For example, to achieve what you want here, you could do:

grouped = DF.groupby("category")
aggregate = list((k, v["arraydata"].sum()) for k, v in grouped)
new_df = pd.DataFrame(aggregate, columns=["category", "arraydata"]).set_index("category")

This is very similar to what pandas is doing under the hood anyways [groupby, then do some aggregation, then merge back in], so you aren't really losing out on much.

---

Diving into the Internals

The problem here is that pandas is checking explicitly that the output not be an ndarray because it wants to intelligently reshape your array, as you can see in this snippet from _aggregate_named where the error occurs.

def _aggregate_named(self, func, *args, **kwargs):
    result = {}

    for name, group in self:
        group.name = name
        output = func(group, *args, **kwargs)
        if isinstance(output, np.ndarray):
            raise Exception('Must produce aggregated value')
        result[name] = self._try_cast(output, group)

    return result

My guess is that this happens because groupby is explicitly set up to try to intelligently put back together a DataFrame with the same indexes and everything aligned nicely. Since it's rare to have nested arrays in a DataFrame like that, it checks for ndarrays to make sure that you are actually using an aggregate function. In my gut, this feels like a job for Panel, but I'm not sure how to transform it perfectly. As an aside, you can sidestep this problem by converting your output to a list, like this:

DF.groupby("category").agg({"arraydata": lambda x: list(x.sum())})

Pandas doesn't complain, because now you have an array of Python objects. [but this is really just cheating around the typecheck]. And if you want to convert back to array, just apply np.array to it.

result = DF.groupby("category").agg({"arraydata": lambda x: list(x.sum())})
result["arraydata"] = result["arraydata"].apply(np.array)

How you want to resolve this issue really depends on why you have columns of ndarray and whether you want to aggregate anything else at the same time. That said, you can always iterate over GroupBy like I've shown above.