Inappropriate stemming in nltk.stem I was starting to learn stemming with nltk and a few words were quite inappropriately stemmed. 
For example:- very was stemmed to 'veri', important to 'import', once to 'onc', poorly to 'poorli' , etc.

I was just thinking that during data analysis, does it cause any error or these errors can be ignored?
Or is there a better option for the same purpose
The renown algorithm for stemming is Porter stemming algorithm. Hence, you can use stemmer = nltk.stemmer.PorterStemmer() for the stemming. you can test it using stemmer.stem('poorly').

Moreover, you can see this post for more details.