您可以pandas.Series.str.split像平常一样使用split。只需对string进行拆分'::'，并索引从该split方法创建的列表：
>>> df = pd.DataFrame({'text': ["vendor a::ProductA", "vendor b::ProductA", "vendor a::Productb"]})
>>> df
text
0 vendor a::ProductA
1 vendor b::ProductA
2 vendor a::Productb
>>> df['text_new'] = df['text'].str.split('::').str[0]
>>> df
text text_new
0 vendor a::ProductA vendor a
1 vendor b::ProductA vendor b
2 vendor a::Productb vendor a
>>> df['text_new1'] = [x.split('::')[0] for x in df['text']]
>>> df
text text_new text_new1
0 vendor a::ProductA vendor a vendor a
1 vendor b::ProductA vendor b vendor b
2 vendor a::Productb vendor a vendor a
编辑：这是pandas上面发生的情况的分步说明：
# Select the pandas.Series object you want
>>> df['text']
0 vendor a::ProductA
1 vendor b::ProductA
2 vendor a::Productb
Name: text, dtype: object
# using pandas.Series.str allows us to implement "normal" string methods
# (like split) on a Series
>>> df['text'].str
<pandas.core.strings.StringMethods object at 0x110af4e48>
# Now we can use the split method to split on our '::' string. You'll see that
# a Series of lists is returned (just like what you'd see outside of pandas)
>>> df['text'].str.split('::')
0 [vendor a, ProductA]
1 [vendor b, ProductA]
2 [vendor a, Productb]
Name: text, dtype: object
# using the pandas.Series.str method, again, we will be able to index through
# the lists returned in the prevIoUs step
>>> df['text'].str.split('::').str
<pandas.core.strings.StringMethods object at 0x110b254a8>
# Now we can grab the first item in each list above for our desired output
>>> df['text'].str.split('::').str[0]
0 vendor a
1 vendor b
2 vendor a
Name: text, dtype: object
我建议您查看pandas.Series.str文档，或者更好的方法是在pandas中使用文本数据。
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