I expect there is an answer somewhere but I've looked and not found one that specifically shows me where I'm going wrong. I saved some previous (and not easily reproduced) data in a pickle storing a 'groupby' pandas object.
mygrpobj = pickle.load(srcfile)
So there it is, only a groupby object is available. Now I want to reduce this groupby object based on a regex match against the indices.
It should be easy, but, both
mydf = mygrpobj.filter(lambda x:pd.notna(re.match('string', x.name)))
mydf = mygrpobj.filter(any)
lose the grouping index that I want to filter by, so now I can't re-groupby using the original grouping name.
I understand that 'filter' is not really a tool for filtering the group but for the dataframes within each group, but I had hoped that filter(any) would get back my original dataframe.
So I know that I can 'hack' this by using mygrpobj.obj, but I'd like to understand why this is so difficult doing it by using 'proper' methods.
The principal problem seems to be the ungrouping throws away the original indexing data. Perhaps this is already not a problem ? My pandas version is 1.1.3.
If I understand your problem correctly, this will help you get the original DataFrame back:
import pandas as pd
# Example data
df = pd.DataFrame({'group': ['A', 'A', 'B', 'B'], 'value': [1, 2, 3, 4]})
# Group by 'group' column
grouped = df.groupby('group')
# Get the original DataFrame back
df = pd.concat([group for _, group in grouped])