code: def count_sentences(s: TextSeries) -> pd.Series:
      """
      Count the number of sentences per cell in a Pandas Series.
  
      Return a new Pandas Series with the number of sentences per cell.
  
      This makes use of the SpaCy `sentencizer
      <https://spacy.io/api/sentencizer>`_
  
      Examples
      --------
      >>> import texthero as hero
      >>> import pandas as pd
      >>> s = pd.Series(
      ...     ["Yesterday I was in NY with Bill de Blasio. Great story...",
      ...      "This is the F.B.I.! What? Open up!"])
      >>> hero.count_sentences(s)
      0    2
      1    3
      dtype: int64
      """
      number_of_sentences = []
  
      nlp = en_core_web_sm.load(disable=["tagger", "parser", "ner"])
  
      nlp.add_pipe(
          nlp.create_pipe("sentencizer")
      )  # Pipe is only "sentencizer"
  
      for doc in nlp.pipe(s.values, batch_size=32):
          sentences = len(list(doc.sents))
          number_of_sentences.append(sentences)
  
      return pd.Series(number_of_sentences, index=s.index)
  
file_path: texthero/texthero/nlp.py
imports: import spacy
  import pandas
  from nltk.stem import PorterStemmer, SnowballStemmer
  from texthero._types import TextSeries, InputSeries
label_desc: Use image node_id 3 for calling a global function with example usage: count_sentences(s) and returns: pd