Using regex matched groups in pandas dataframe replace function

Question

I'm just learning python/pandas and like how powerful and concise it is.

During data cleaning I want to use replace on a column in a dataframe with regex but I want to reinsert parts of the match (groups).

Simple Example: lastname, firstname -> firstname lastname

I tried something like the following (actual case is more complex so excuse the simple regex):

df['Col1'].replace({'([A-Za-z])+, ([A-Za-z]+)' : '\2 \1'}, inplace=True, regex=True) 

However, this results in empty values. The match part works as expected, but the value part doesn't. I guess this could be achieved by some splitting and merging, but I am looking for a general answer as to whether the regex group can be used in replace.

Answer 1

I think you have a few issues with the RegEx's.

As @Abdou just said use either '\\2 \\1' or better r'\2 \1', as '\1' is a symbol with ASCII code 1

Your solution should work if you will use correct RegEx's:

In [193]: df Out[193]:               name 0        John, Doe 1  Max, Mustermann  In [194]: df.name.replace({r'(\w+),\s+(\w+)' : r'\2 \1'}, regex=True) Out[194]: 0          Doe John 1    Mustermann Max Name: name, dtype: object  In [195]: df.name.replace({r'(\w+),\s+(\w+)' : r'\2 \1', 'Max':'Fritz'}, regex=True) Out[195]: 0            Doe John 1    Mustermann Fritz Name: name, dtype: object