GroupBy of a dataframe by comparison against a set of items - python-3.x

Question

So I have a dataframe of movies with about 10K rows. Have a column that captures its genre in a comma separated string. Since a movie can be classified in multiple genres, I needed to create a set of genres that contained all possible genres in the 10K rows. So I went about it by doing as follows:
simplist = []
for i in df.genres.values:
vlist = i.split(', ')
for item in vlist:
simplist.append(item)
gset = set(simplist)
This gets me a list of 24 genres from the 27K items in the simplist which is awesome. But heres the pinch:
I want to groupby genres by comparing genres to the set and then do aggregation and other operations AND
I want the output to be 24 distinct groups such that if a movie has more than one of the genres in the set - it should show up in both groups (removes sorting or tagging bias in the data gathering phase)
Is groupby even the right way to go about this?
Thanks for your input/thoughts/options/approach in advance.

Answer 1

Ok, so I made some headway but still unable to get to put the puzzle pieces together.
Started off by making a list and set (don't know which I will end up using) of unique values:
simplist = []
for i in df.genres.values:
vlist = i.split(', ')
for item in vlist:
simplist.append(item)
gset = set(simplist)
g_list = list(gset)
Then, separately use df.pivot to structure the analysis:
table7 = df.pivot_table(index=['release_year'], values=['runtime'],aggfunc={'runtime': [np.median, ], 'popularity': [np.mean]}, fill_value=0, dropna=True)
But here's the thing:
it would be awesome if I could index by g_list or check 'genres' against the gset of 24 distinct items but df.pivot_table does not support that. Leaving it at Genres creates ~2000 rows and is not meaningful.

Answer 2

Got it!! Wanted to thank a bunch of offline folks and Pythonistas who helped me in the right direction. Turns out I'd been spinning my wheels with sets and lists when one single Pandas command (well 3 to be precise) does the trick!!
df2 = pd.DataFrame(df.genres.str.split(', ').tolist(), index=[df.col1, df.col2, df.coln]).stack()
df2 = df2.reset_index()[[0, 'col1', 'col2', 'coln',]]
df2.columns = ['Genre', 'col1', "col2", 'coln']
This should create a 2nd dataframe (df2) that has the key columns for analysis from the original dataframe and the rows duplicated/attributed to each genre. You see the true value of this when you turnaround and do something like:
revenue_table = df2.pivot_table(index=['Release Year','Genre'], values=['Profit'],aggfunc={'Profit': np.sum},fill_value=0,dropna=True) or anything to similar effect or use case.
Closing this but would appreciate any notes on more efficient ways to do this.