I have data frame columns like language, region and country. In that data frame using language column to fill the country with country name.
My input is:
language region country
english a canada
chinese b china
english a usa
japanese a japan
english a usa
portugese b portugal
english a null
In above data frame, I want to fill the null country name with by using country names based on count which countries are using English. Let's suppose USA count has 2 and Canada count has 1. So, USA has highest count then we have to fill the USA country name in null place.
Required output should be:
language region country
english a canada
chinese b china
english a usa
japanese a japan
english a usa
portugese b portugal
english a usa
For above required output I used below code snippet. But it is not working. Can anyone help me for above required output data frame.
df.loc[df['language']=='english' & df['region']='ap' & df['country'].value_counts()[df['country'].value_counts() == df['country'].value_counts().max()]
In above code snippet i must need to be use df.loc[df['language']=='english' & df['region']='ap'.after that i have to find highest country count based on AP region and fill blank country as with highest country count country.
Assume your null
is NaN
or None
. If it is string null
, You need pre-process it to NaN
df = df.where(df.ne('null')) # doing this step if your `null` is string `null`
m = df.country.isna()
m1 = df.language.eq('english')
df.loc[m & m1, 'country'] = df.loc[m1, 'country'].mode()[0]
Out[194]:
language region country
0 english a canada
1 chinese b china
2 english a usa
3 japanese a japan
4 english a usa
5 portugese b portugal
6 english a usa
A more generalized solution would be to map
and fillna
d = df.groupby('language').country.apply(lambda s: s.mode()[0]).to_dict()
df['country'] = df.country.fillna(df.language.map(d))
language region country
0 english a canada
1 chinese b china
2 english a usa
3 japanese a japan
4 english a usa
5 portugese b portugal
6 english a usa
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