CODEWITHSUNDEEP
pythonpandas
ComplexData
ComplexData

Reputation: 1103

List Index Out of Range error - pandas

I have two data frames. df1 looks like - 

MovieName    Actors
lights out   Maria Bello
legend       Tom Hardy*Emily Browning*Christopher Eccleston*David Thewlis

df2 looks like - 

ActorName    Gender
Tom          male
Emily        female
Christopher  male

I want to add two columns in df1 'female_actors' and 'male_actors' which contains the count of female and male actors in that particular movie respectively. Whether an actor is male or female is done based on df2.

Here is what I am doing - 

def func(actors, gender):
    actors = [act.split()[0] for act in actors.split('*')]      
    n_gender = df2.Gender[df2.Gender==gender][df2.ActorName.isin(actors)].count()
    return n_gender

df1['male_actors'] = df1.Actors.apply(lambda x: func(x, 'male'))
df1['female_actors'] = df1.Actors.apply(lambda x: func(x, 'female'))

This code gives me list index out of range error.

Please note that -

If particular name isn't present in gender.csv, don't count it in the total. If there is just one actor in a movie, and it isn't present in gender.csv, then it's count should be zero.

Result should be - 

MovieName    Actors      male_actors    female_actors
lights out   Maria Bello    0              0
legend       Tom Hardy*Emily Browning*Christopher Eccleston*David Thewlis    2    1

Feel free to suggest some other approach.

Upvotes: 1

Views: 2424

Answers (2)

piRSquared
piRSquared

Reputation: 294546

using str and join

d2 = df2.set_index('ActorName')
d1 = df1.set_index('MovieName')

method 1
split

d1.join(d1.Actors.str.split('*', expand=True).stack() \
    .str.split(expand=True)[0].map(d2.Gender) \
    .groupby(level='MovieName') \
    .value_counts().unstack()).fillna(0).reset_index()

method 2
extractall

d1.join(d1.Actors.str.extractall('((?P<first>[^*]+)\s+(?P<last>[^*]+))') \
    ['first'].map(d2.Gender).groupby(level='MovieName') \
    .value_counts().unstack()).fillna(0).reset_index()

enter image description here

Upvotes: 1

Alex
Alex

Reputation: 12943

How about this?

df1['Male'] = df1.Actors.apply(lambda x: len(pd.concat( [df2[(df2.ActorName == name) & (df2.Gender == 'male')] for name in x.split('*')] )))
df1['Female'] = df1.Actors.apply(lambda x: len(pd.concat( [df2[(df2.ActorName == name) & (df2.Gender == 'female')] for name in x.split('*')] )))

Upvotes: 2

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