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.

Asked By: Peter D

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Source

Answer 1

setup

df = pd.DataFrame(dict(name=['Smith, Sean']))
print(df)

          name
0  Smith, Sean

using replace

df.name.str.replace(r'(w+),s*(w+)', r'2 1')

0    Sean Smith
Name: name, dtype: object

using extract
split to two columns

df.name.str.extract('(?P<Last>w+),s*(?P<First>w+)', expand=True)

    Last First
0  Smith  Sean

Answered By: piRSquared

Answer 2

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

Answered By: MaxU – stop genocide of UA

Using regex matched groups in pandas dataframe replace function

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