def filter_count(self, words_compact, min_count=15, max_count=0,
                     max_replacement=None, min_replacement=None):
        """ Replace word indices below min_count with the pad index.

        Arguments
        ---------
        words_compact: int array
            Source array whose values will be replaced. This is assumed to
            already be converted into a compact array with `to_compact`.
        min_count : int
            Replace words less frequently occuring than this count. This
            defines the threshold for what words are very rare
        max_count : int
            Replace words occuring more frequently than this count. This
            defines the threshold for very frequent words
        min_replacement : int, default is out_of_vocabulary
            Replace words less than min_count with this.
        max_replacement : int, default is out_of_vocabulary
            Replace words greater than max_count with this.

        Examples
        --------
        >>> corpus = Corpus()
        >>> # Make 1000 word indices with index < 100 and
        >>> # update the word counts.
        >>> word_indices = np.random.randint(100, size=1000)
        >>> corpus.update_word_count(word_indices)
        >>> corpus.finalize()  # any word indices above 99 will be filtered
        >>> # Now create a new text, but with some indices above 100
        >>> word_indices = np.random.randint(200, size=1000)
        >>> word_indices.max() < 100
        False
        >>> # Remove words that have never appeared in the original corpus.
        >>> filtered = corpus.filter_count(word_indices, min_count=1)
        >>> filtered.max() < 100
        True
        >>> # We can also remove highly frequent words.
        >>> filtered = corpus.filter_count(word_indices, max_count=2)
        >>> len(np.unique(word_indices)) > len(np.unique(filtered))
        True
        """
        self._check_finalized()
        ret = words_compact.copy()
        if min_replacement is None:
            min_replacement = self.specials_to_compact['out_of_vocabulary']
        if max_replacement is None:
            max_replacement = self.specials_to_compact['out_of_vocabulary']
        not_specials = np.ones(self.keys_counts.shape[0], dtype='bool')
        not_specials[:self.n_specials] = False
        if min_count:
            # Find first index with count less than min_count
            min_idx = np.argmax(not_specials & (self.keys_counts < min_count))
            # Replace all indices greater than min_idx
            ret[ret is min_idx] = min_replacement
        if max_count:
            # Find first index with count less than max_count
            max_idx = np.argmax(not_specials & (self.keys_counts < max_count))
            # Replace all indices less than max_idx
            ret[ret < max_idx] = max_replacement
        return ret
dataset/ETHPy150Open cemoody/lda2vec/lda2vec/corpus.py/Corpus.filter_count/operator `>` -> `is`