Pandas Dataframe groupby on 3 columns and make one column lowercase - pandas

Question

I have a dataframe:
country rating owner
0 England a John Smith
1 England b John Smith
2 France a Frank Foo
3 France a Frank foo
4 France a Frank Foo
5 France b Frank Foo
I'd like to produce a count of owners after grouping by country and rating and
ignoring case
gnoring any spaces ( leading, trailing or inbetween)
I am expecting:
country rating owner count
0 England a John Smith 1
1 England b John Smith 1
2 France a Frank Foo 3
3 France b Frank Foo 1
I have tried:
df.group_by(['rating','owner'])['owner'].count()
and
df.group_by(['rating','owner'].str.lower())['owner'].count()

Answer 1

Use title and replace to rework the string and groupby.size to aggregate:
out = (df.groupby(['country', 'rating',
df['owner'].str.title().str.replace(r'\s+', ' ', regex=True)])
.size().reset_index(name='count')
)
Output:
country rating owner count
0 England a John Smith 1
1 England b John Smith 1
2 France a Frank Foo 3
3 France b Frank Foo 1

Answer 2

Use Series.str.strip, Series.str.title and remove multiple spaces by Series.str.replace with aggregate GroupBy.size:
DataFrameGroupBy.count is used for count exclude missing values, seems not necessary here.
df1 = (df.groupby(['county','rating',
df['owner'].str.strip().str.title().str.replace('\s+',' ', regex=True)])
.size()
.reset_index(name='count'))