Python & Pandas: How to query if a list-type column contains something?

Question

I have a dataframe, which contains info about movies. It has a column called genre, which contains a list of genres it belongs to. For example:

df['genre']  ## returns   0       ['comedy', 'sci-fi'] 1       ['action', 'romance', 'comedy'] 2       ['documentary'] 3       ['crime','horror'] ... 

I want to know how can I query the dataframe, so it returns the movie belongs to a cerain genre?

For example, something may like df['genre'].contains('comedy') returns 0 or 1.

I know for a list, I can do things like:

'comedy' in  ['comedy', 'sci-fi'] 

However, in pandas, I didn't find something similar, the only thing I know is df['genre'].str.contains(), but it didn't work for the list type.

Answer 1

You can use apply for create mask and then boolean indexing:

mask = df.genre.apply(lambda x: 'comedy' in x) df1 = df[mask] print (df1)                        genre 0           [comedy, sci-fi] 1  [action, romance, comedy] 

Answer 2

using sets

df.genre.map(set(['comedy']).issubset)  0     True 1     True 2    False 3    False dtype: bool 

---

df.genre[df.genre.map(set(['comedy']).issubset)]  0             [comedy, sci-fi] 1    [action, romance, comedy] dtype: object 

---

presented in a way I like better

comedy = set(['comedy']) iscomedy = comedy.issubset df[df.genre.map(iscomedy)] 

more efficient

comedy = set(['comedy']) iscomedy = comedy.issubset df[[iscomedy(l) for l in df.genre.values.tolist()]] 

using str in two passes
slow! and not perfectly accurate!

df[df.genre.str.join(' ').str.contains('comedy')]