您可以使用

You can use str.get_dummies mainly:

df = df['Products'].str.get_dummies(';').add_prefix('Has_')
print (df)
   Has_A  Has_B  Has_C  Has_D
0      1      1      0      0
1      1      0      0      0
2      1      0      1      1

示例:

还添加了带有 replace的解决方案由使用list comprehension创建的dict并添加了NaN和None.

There is also add solution with replace by dict created with list comprehension and added NaN and None.

df = pd.DataFrame({'Products': ['A;B', 'A', 'D;A;C', 'No prods', np.nan, 'None']})
print (df)
   Products
0       A;B
1         A
2     D;A;C
3  No prods
4       NaN
5      None

L = ['No prods','None']
d = {x :'No product' for x in L + [None, np.nan]}
df['Products'] = df['Products'].replace(d)
df = df['Products'].str.get_dummies(';').add_prefix('Has_')
print (df)
   Has_A  Has_B  Has_C  Has_D  Has_No product
0      1      1      0      0               0
1      1      0      0      0               0
2      1      0      1      1               0
3      0      0      0      0               1
4      0      0      0      0               1
5      0      0      0      0               1