python - Recode list values in dataframe column -

i'm trying recode values in dataframe column organized in list format. know how replace string values in dataframe column struggling how in list.

here snippet of data:

{0: '[crime, drama]',  1: '[crime, drama]',  2: '[crime, drama]',  3: '[action, crime, drama, thriller]',  4: '[crime, drama]',  5: '[biography, drama, history]',  6: '[crime, drama]',  7: '[adventure, drama, fantasy]',  8: '[western]',  9: '[drama]'} 

for example, i'd recode crimes thrillers , biography history.

i know below works replacing string values

df.loc[df['genre']=='crime']='thriller' 

but how modify list?

thanks!

edit: code used create dataframe (with data extracted imdb database) is:

# these variables want (ie able to) extract movie object metadata = ('title', 'rating', 'genre', "plot", "language", "runtime", "year", "color", "country" , "votes") #creates dataframe variable name headers df = pd.dataframe(np.random.randn(250, len(metadata)), columns=metadata)   #these different data types, including lists, makes compile df = df.astype('object') #populate df movie objects in range(250):     j in metadata:         df.loc[i, j] = movies_list[i].get(j)  # convert right data types: metadata_dict_dtypes = {"title": unicode,                         "rating": float,                         "genre":list,                         "plot": str,                         "language":list,                         "runtime":list,                         "year":int,                         "color":list,                         "country":list ,                         "votes":int} colname, my_dtype in metadata_dict_dtypes.iteritems():     df[colname] = df[colname].astype(my_dtype) 

assuming correctly formatted list in dataframe. write function takes row, , genre name change map arguments , apply dataframe. example

name_map = {'crime': 'thriller', 'biography': 'history'} def change_names(row, name_map):     name in name_map:         if name in row.genre:             row.genre[row.genre.index(name)] = name_map[name]     return row df = df.apply(lambda row: change_name(row, name_map), axis=1) 

it's not vectorized, job done.