How to use Gensim Doc2vec infer_vector() for large DataFrame?
Doc2Vec infer_vector() only takes individual text examples, as lists-of-word-tokens. So you can't pass in a batch of examples. (And, you shouldn't be passing in non-tokenized strings – but lists-of-tokens, preprocessed in the same manner as your training data was preprocessed.)
But, you might be able to use a function that multiply-applies infer_vector() for you, as the @COLDSPEED comment suggests. Still, the column should have lists-of-tokens, rather than strings-of-characters, if you want meaningful results. 
Also, most users find infer_vector() works much better using non-default values for its steps parameter (much larger than its default of 5), and perhaps smaller values for its starting alpha parameter (such as more like the training default of 0.025 than the inference default of 0.1).
I have created document vectors for a large corpus using Gensim's doc2vec.
sentences=gensim.models.doc2vec.TaggedLineDocument('file.csv')
model = gensim.models.doc2vec.Doc2Vec(sentences,size = 10, window = 800, min_count = 1, workers=40, iter=10, dm=0)
Now I am using Gensim's infer_vector() using those document vectors to create document vectors for another sample corpus
Eg: model.infer_vector('This is a string')
Is there a way to pass the entire DataFrame through infer_vector and get the output vectors for each line in the DataFrame?