Pandas - 如果不是数字，则替换列中的子字符串

Question

I have a list of suffixes I want to remove in a list, say suffixes = ['inc','co','ltd'] . I want to remove these from a column in a Pandas dataframe, and I have been doing this: df['name'] = df['name'].str.replace('|'.join(suffixes), '') .

This works, but I do NOT want to remove the suffice if what remains is numeric. For example, if the name is 123 inc , I don't want to strip the 'inc'. Is there a way to add this condition in the code?

Answer 1

Using Regex --> negative lookbehind .

Ex:

suffixes = ['inc','co','ltd']

df = pd.DataFrame({"Col": ["Abc inc", "123 inc", "Abc co", "123 co"]})
df['Col_2'] = df['Col'].str.replace(r"(?<!\d) \b(" + '|'.join(suffixes) + r")\b", '', regex=True)
print(df)

Output:

       Col    Col_2
0  Abc inc      Abc
1  123 inc  123 inc
2   Abc co      Abc
3   123 co   123 co

Answer 2

Try adding ^[^0-9]+ to the suffixes. It is a REGEX that literally means "at least one not numeric char before". The code would look like this:

non_numeric_regex = r"^[^0-9]+"
suffixes = ['inc','co','ltd']
regex_w_suffixes = [non_numeric_regex + suf for suf in suffixes]
df['name'] = df['name'].str.replace('|'.join(regex_w_suffixes ), '')