I'm trying to use the Pandas groupby function with a dict.update() function to each element. An example in a data frame (just for illustration):
A B
0 icon1 {'ap1': {'item' : 1}, 'ap2': {'item' : 2}}
1 icon1 {'ap3': {'item' : 3}}
What I'm trying to do is set something like
df = df.groupby('A')['B'].apply(', '.join).reset_index()
But instead of using python', '.join, I need to groupby the 'A' column and update each element in the 'B' column. I've tried using the map function, but I was not able to achieve anything useful.
The outcome should be:
A B
0 icon1 {'ap1': {'item' : 1}, 'ap2': {'item' : 2}, 'ap3': {'item' : 3}}
Is that even possible without changing the item type from dict?
Using dict comprehension
df.groupby('A').B.agg(lambda s: {k:v for a in s for k, v in a.items()}).reset_index()
A B
0 icon1 {'ap1': {'item' : 1}, 'ap2': {'item' : 2}, 'ap3': {'item' : 3}}
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