how to preserve number of records in word2vec?
If you are splitting each entry into a list of words, that's essentially 'tokenization'. 
Word2Vec just learns vectors for each word, not for each text example ('record') – so there's nothing to 'preserve', no vectors for the 45,000 records are ever created. But if there are 26,000 unique words among the records (after applying min_count), you will have 26,000 vectors at the end. 
Gensim's Doc2Vec (the '
Paragraph Vector' algorithm) can create a vector for each text example, so you may want to try that. 
If you only have word-vectors, one simplistic way to create a vector for a larger text is to just add all the individual word vectors together. Further options include choosing between using the unit-normed word-vectors or raw word-vectors of many magnitudes; whether to then unit-norm the sum; and whether to otherwise weight the words by any other importance factor (such as TF/IDF). 
Note that unless your documents are very long, this is a quite small training set for either Word2Vec or Doc2Vec.
I have 45000 text records in my dataframe. I wanted to convert those 45000 records into word vectors so that I can train a classifier on the word vector. I am not tokenizing the sentences. I just split the each entry into list of words.
After training word2vec model with 300 features, the shape of the model resulted in only 26000. How can I preserve all of my 45000 records ? 
In the classifier model, I need all of those 45000 records, so that it can match 45000 output labels.