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I am performing topic modelling and using functions to get the top keywords in the topic models as shown below.

def getTopKWords(self, K):

 results = 
 """
 returns top K discriminative words for topic t
 ie words v for which p(v|t) is maximum
 """
 index = 
 key_terms = 



 pseudocounts = np.copy(self.n_vt)
 normalizer = np.sum(pseudocounts, (0))
 pseudocounts /= normalizer[np.newaxis, :]
 for t in range(self.numTopics):
 topWordIndices = pseudocounts[:, t].argsort()[-1:-(K+1):-1]
 vocab = self.vectorizer.get_feature_names()
 print (t, [vocab[i] for i in topWordIndices])
 ## Code for storing the values in a single list
 return results

The above functions gives me the code as shown in the fig

0 ['computer', 'laptop', 'mac', 'use', 'bought', 'like', 'warranty', 'screen', 'way', 'just']
1 ['laptop', 'computer', 'use', 'just', 'like', 'time', 'great', 'windows', 'macbook', 'months']
2 ['computer', 'great', 'laptop', 'mac', 'buy', 'just', 'macbook', 'use', 'pro', 'windows']
3 ['laptop', 'computer', 'great', 'time', 'battery', 'use', 'apple', 'love', 'just', 'work']

It results from the 4 time the loop runs and print index and all keywords in each vocab.

Now, I want to return a single list from the function which returns me the following output.

return [keyword1, keyword2, keyword3, keyword4]

where, keyword1/2/3/4 are the words which were occuring the most in vocab lists with index 0, 1,2,3 in output.

You can use collection.Counter:

from collections import Counter

a = ['computer', 'laptop', 'mac', 'use', 'bought', 'like', 
 'warranty', 'screen', 'way', 'just']
b = ['laptop', 'computer', 'use', 'just', 'like', 'time', 
 'great', 'windows', 'macbook', 'months']
c = ['computer', 'great', 'laptop', 'mac', 'buy', 'just', 
 'macbook', 'use', 'pro', 'windows']
d = ['laptop', 'computer', 'great', 'time', 'battery', 'use', 
 'apple', 'love', 'just', 'work']

def get_most_common(*kwargs):
 """Accepts iterables, feeds all into Counter and returns the Counter instance"""
 c = Counter()
 for k in kwargs:
 c.update(k)
 return c

# get the most common ones 
mc = get_most_common(a,b,c,d).most_common()

# print top 4 keys
top4 = [k for k,v in mc[0:4]]
print (top4)

Output:

['computer', 'laptop', 'use', 'just']

 some_results = # store stuff

for t in range(self.numTopics):
 topWordIndices = pseudocounts[:, t].argsort()[-1:-(K+1):-1]
 vocab = self.vectorizer.get_feature_names()
 print (t, [vocab[i] for i in topWordIndices])

 some_results.append( [vocab[i] for i in topWordIndices] )

 mc = get_most_common(*some_results).most_common()
 return [k for k,v in mc[0:4]]