def get_term_and_background_counts(self):
		'''
		Returns
		-------
		A pd.DataFrame consisting of unigram term counts of words occurring
		 in the TermDocumentMatrix and their corresponding background corpus
		 counts.  The dataframe has two columns, corpus and background.

		>>> corpus.get_unigram_corpus().get_term_and_background_counts()
		                  corpus  background
		obama              702.0    565739.0
		romney             570.0    695398.0
		barack             248.0    227861.0
		...
		'''
		background_df = self._get_background_unigram_frequencies()
		corpus_freq_df = pd.DataFrame({'corpus': self.term_category_freq_df.sum(axis=1)})
		corpus_unigram_freq = corpus_freq_df.loc[[w for w in corpus_freq_df.index if ' ' not in w]]
		df = corpus_unigram_freq.join(background_df, how='outer').fillna(0)
		return df

		Returns
		-------
		A pd.DataFrame consisting of unigram term counts of words occurring
		 in the TermDocumentMatrix and their corresponding background corpus
		 counts.  The dataframe has two columns, corpus and background.

		>>> corpus.get_unigram_corpus().get_term_and_background_counts()
		                  corpus  background
		obama              702.0    565739.0
		romney             570.0    695398.0
		barack             248.0    227861.0
		...
		
scattertext/TermCategoryFrequencies.py