Applying Regex across entire column of a Dataframe

Question

I have a Dataframe with 3 columns:

id,name,team 
101,kevin, marketing
102,scott,admin\n
103,peter,finance\n

I am trying to apply a regex function such that I remove the unnecessary spaces. I have got the code that removes these spaces how ever I am unable loop it through the entire Dataframe.

This is what I have tried thus far:

df['team'] = re.sub(r'[\n\r]*','',df['team'])

But this throws an error AttributeError: 'Series' object has no attribute 're'

Could anyone advice how could I loop this regex through the entire Dataframe df['team'] column

Answer 1

You are almost there, there are two simple ways of doing this:

# option 1 - faster way
df['team'] =  [re.sub(r'[\n\r]*','', str(x)) for x in df['team']]

# option 2
df['team'] =  df['team'].apply(lambda x: re.sub(r'[\n\r]*','', str(x)))

Answer 2

As long it's a dataframe check replace https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html

df['team'].replace( { r"[\n\r]+" : '' }, inplace= True, regex = True)

Regarding the regex, '*' means 0 or more, you should need '+' which is 1 or more