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My dataframe has a column with some values, these values can apper in all rows, but is not required. I want to split this values in different rows, putting 1 if the value apper in that column

I am using python 3 and the df is:

data = {'Amenities':[{'TV', 'Wifi'}, {'TV',"Cable TV",'Internet','Wifi'}, {'Internet','Wifi'}]} df2 = pd.DataFrame(data) df2 Amenities 0 {TV, Wifi} 1 {TV, Cable TV, Wifi, Internet} 2 {Wifi, Internet}

I would like to see a table in this way:

data = {'Amenities':[{'TV', 'Wifi'}, {'TV',"Cable TV",'Internet','Wifi'}, {'Internet','Wifi'}], 'TV':[1,1,0], 'Cable TV':[0,1,0], 'Wifi':[1,1,1], 'Internet':[0,1,1], } df2 = pd.DataFrame(data) df2 Amenities TV Cable TV Wifi Internet 0 {TV, Wifi} 1 0 1 0 1 {TV, Cable TV, Wifi, Internet} 1 1 1 1 2 {Wifi, Internet} 0 0 1 1
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In your case using get_dummies then concat 

s=pd.get_dummies(df2.Amenities.apply(list).apply(pd.Series),prefix = '',prefix_sep = '').sum(level=0,axis=1) s Out[66]: Cable TV Internet TV Wifi 0 0 0 1 1 1 1 1 1 1 2 0 1 0 1 df=pd.concat([df2,s],axis=1) df Out[68]: Amenities Cable TV Internet TV Wifi 0 {TV, Wifi} 0 0 1 1 1 {Cable TV, Internet, Wifi, TV} 1 1 1 1 2 {Internet, Wifi} 0 1 0 1
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