We add a new function hero.filter_extremes(s: TokenSeries, max_words=None, min_df=1, max_df=1.0) to remove words from all documents that are above or below a document frequency threshold; additionally only keep max_words many words. Naming from gensim's similar function here.

Excerpt from docstring to explain functionality:

Decrease the size of your documents by
filtering out words by their frequency.

It is often useful to reduce the size of your dataset
by dropping words in order to
reduce noise and improve performance.
This function removes all words/tokens from
all documents where the
document frequency (=number of documents a term appears in) is

• below min_df
• above max_df.

When min_df or max_df is an integer, then document frequency
is the absolute number of documents that a term
appears in. When it's a float, it is the
proportion of documents a term appears in.

Additionally, only max_words many words are kept.

Parameters

max_words : int, default to None
The maximum number of words/tokens that
are kept, according to term frequency descending.
If None, will consider all features.

min_df : int or float, default to 1
Remove words that have a document frequency
lower than min_df. If float, it represents a
proportion of documents, integer absolute counts.

max_df : int or float, default to 1
Remove words that have a document frequency
higher than max_df. If float, it represents a
proportion of documents, integer absolute counts.

Example

>>> import texthero as hero
>>> import pandas as pd
>>> s = pd.Series(
...        [
...         "Here one two one one one go there",
...         "two go one one one two two two is important",
...     ]
... )
>>> s.pipe(hero.tokenize).pipe(hero.filter_extremes, 3)
0              [one, two, one, one, one, go]
1    [two, go, one, one, one, two, two, two]
dtype: object

Note: only so many lines changed as this builds upon the DocumentTermDF (see #156)