I have a pandas dataframe as:
word_list
[\'nuclear\',\'election\',\'usa\',\'baseball\']
[\'football\',\'united\',\'thriller\']
[\'marvels\',\'hollywood\',\'s
First of all, I think you should take advantage of O(1) lookup from sets and dictionaries. That said, I'd set the data as (notice that values are sets):
d = dict(movies={'spiderman','marvels','thriller'},
sports={'baseball','hockey','football'},
politics={'election','china','usa'})
Then, you can transform your series using your custom logic
def f(r):
def m(r_):
_ = [k for (k, v) in d.items() if r_ in v]
return _ if _ else ['Misc']
return {item for z in [m(r_) for r_ in r] for item in z}
df.word_list.transform(f)
0 {Misc, sports, politics}
1 {Misc, sports, movies}
2 {Misc, movies}
For 300000 rows,
%timeit df.word_list.transform(f)
1.1 s ± 22.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
which is not great but doable..
You can flatten dictionary of lists first and then lookup by .get with miscellaneous for non matched values, then convert to sets for unique categories and convert to strings by join:
movies=['spiderman','marvels','thriller']
sports=['baseball','hockey','football']
politics=['election','china','usa']
d = {'movies':movies, 'sports':sports, 'politics':politics}
d1 = {k: oldk for oldk, oldv in d.items() for k in oldv}
f = lambda x: ','.join(set([d1.get(y, 'miscellaneous') for y in x]))
df['matched_list_names'] = df['word_list'].apply(f)
print (df)
word_list matched_list_names
0 [nuclear, election, usa, baseball] politics,miscellaneous,sports
1 [football, united, thriller] miscellaneous,sports,movies
2 [marvels, hollywood, spiderman, budget] miscellaneous,movies
Similar solution with list comprehension:
df['matched_list_names'] = [','.join(set([d1.get(y, 'miscellaneous') for y in x]))
for x in df['word_list']]