How to make dummy variables with comma separated valued columns? [closed] - python-3.x

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I am working on data preprocessing for machine learning and faced a problem.
Here is what I want to do.
table image:
Table's type is pandas dataframe.
My current table is left one, and I want to transform my table to right one.
The number of movies and actors are not fixed.
EDIT :
Data input
df=pd.DataFrame({'name':['A','B','C'],'actors':['a,b','b,d','c,m']})
Expected output :
a b c d m
A 1 1 0 0 0
B 0 1 0 1 0
C 0 0 1 0 1

Answer 1

Try this ? (BTW , kaggle movie dataset, better using LabelEncoder)
PS: I did not add the column name, you can simply do out['name']=df.name
Option 1 pd.crosstab
df.actors=df.actors.str.split(',')
df1=df.set_index('name').actors.apply(pd.Series).stack()
pd.crosstab(df1.index.get_level_values(0),df1).rename_axis(None).rename_axis(None,1)
Out[246]:
a b c d m
A 1 1 0 0 0
B 0 1 0 1 0
C 0 0 1 0 1
Option 2
get_dummies
pd.get_dummies(df.actors.str.split(',').apply(pd.Series).stack()).sum(level=0)
Out[230]:
a b c d m
0 1 1 0 0 0
1 0 1 0 1 0
2 0 0 1 0 1
Option 3
MultiLabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
pd.DataFrame(mlb.fit_transform(df.actors.str.split(',')),columns=mlb.classes_,index=df.name).reset_index()
Out[238]:
name a b c d m
0 A 1 1 0 0 0
1 B 0 1 0 1 0
2 C 0 0 1 0 1
Data Input
df=pd.DataFrame({'name':['A','B','C'],'actors':['a,b','b,d','c,m']})