Python Pandas: How to create a binary matrix from column of lists?

Question

I have a Python Pandas DataFrame like the following:

      1
0  a, b
1     c
2     d
3     e

a, b is a string representing a list of user features

How can I convert this into a binary matrix of the user features like the following:

     a    b    c    d    e
0    1    1    0    0    0
1    0    0    1    0    0
2    0    0    0    1    0
3    0    0    0    0    1

I saw a similar question Creating boolean matrix from one column with pandas but the column does not contain entries which are lists.

I have tried these approaches, is there a way to merge the two:

pd.get_dummies()

pd.get_dummies(df[1])


   a, b  c  d  e
0     1  0  0  0
1     0  1  0  0
2     0  0  1  0
3     0  0  0  1

df[1].apply(lambda x: pd.Series(x.split()))

      1
0  a, b
1     c
2     d
3     e

Also interested in different ways to create this type of binary matrix!

Any help is appreciated!

Thanks

Answer 1

I think you can use:

df = df.iloc[:,0].str.split(', ', expand=True)
       .stack()
       .reset_index(drop=True)
       .str.get_dummies()

print df
   a  b  c  d  e
0  1  0  0  0  0
1  0  1  0  0  0
2  0  0  1  0  0
3  0  0  0  1  0
4  0  0  0  0  1

EDITED:

print df.iloc[:,0].str.replace(' ','').str.get_dummies(sep=',')
   a  b  c  d  e
0  1  1  0  0  0
1  0  0  1  0  0
2  0  0  0  1  0
3  0  0  0  0  1

Answer 2

I wrote a general function, with support for grouping, to do this a while back:

def sublist_uniques(data,sublist):
    categories = set()
    for d,t in data.iterrows():
        try:
            for j in t[sublist]:
                categories.add(j)
        except:
            pass
    return list(categories)

def sublists_to_dummies(f,sublist,index_key = None):
    categories = sublist_uniques(f,sublist)
    frame = pd.DataFrame(columns=categories)
    for d,i in f.iterrows():
        if type(i[sublist]) == list or np.array:
            try:
                if index_key != None:
                    key = i[index_key]
                    f =np.zeros(len(categories))
                    for j in i[sublist]:
                        f[categories.index(j)] = 1
                    if key in frame.index:
                        for j in i[sublist]:
                            frame.loc[key][j]+=1
                    else:
                        frame.loc[key]=f
                else:
                    f =np.zeros(len(categories))
                    for j in i[sublist]:
                        f[categories.index(j)] = 1
                    frame.loc[d]=f
            except:
                pass

    return frame

 In [15]: a Out[15]: a group labels 0 1 new [a, d] 1 2 old [a, g, h] 2 3 new [i, m, a] In [16]: sublists_to_dummies(a,'labels') Out[16]: adgihm 0 1 1 0 0 0 0 1 1 0 1 0 1 0 2 1 0 0 1 0 1 In [17]: sublists_to_dummies(a,'labels','group') Out[17]: adgihm new 2 1 0 1 0 1 old 1 0 1 0 1 0