original_docstring: 
          Takes as input a DataFrame with sentences in column 'text_col'.
          Appends at least one new column to the DataFrame containing lists of single word tokens.
          
docstring: Takes as input a DataFrame with sentences in column 'text_col'.
  Appends at least one new column to the DataFrame containing lists of single word tokens.
docstring_tokens: Takes as input a DataFrame with sentences in column ' text_col ' . Appends at least one new column to the DataFrame containing lists of single word tokens .
code: def tokenize(self, df: DataFrame, text_col="text") -> DataFrame:
          """
          Takes as input a DataFrame with sentences in column 'text_col'.
          Appends at least one new column to the DataFrame containing lists of single word tokens.
          """
  
          texts = df[text_col]
          if self.to_lower:
              texts = texts.apply(lambda text: text.lower())
  
          if self.use_remove_punctuation:
              texts = texts.apply(lambda text: self.remove_punctuation(text))
  
          # Split sentences into list of words
          df["tokenized"] = texts.apply(
              lambda text: nltk.tokenize.word_tokenize(text, language="german")
          )
  
          if self.use_lemmatize:
              df["lemmata"] = df["tokenized"].apply(lambda tokens: self.lemmatize(tokens))
  
          if self.use_stem:
              df["stems"] = df["tokenized"].apply(lambda tokens: self.stem(tokens))
  
          return df
code_tokens: def tokenize ( self , df : DataFrame , text_col = "text" ) -> DataFrame : texts = df [ text_col ] if self . to_lower : texts = texts . apply ( lambda text : text . lower ( ) ) if self . use_remove_punctuation : texts = texts . apply ( lambda text : self . remove_punctuation ( text ) ) df [ "tokenized" ] = texts . apply ( lambda text : nltk . tokenize . word_tokenize ( text , language = "german" ) ) if self . use_lemmatize : df [ "lemmata" ] = df [ "tokenized" ] . apply ( lambda tokens : self . lemmatize ( tokens ) ) if self . use_stem : df [ "stems" ] = df [ "tokenized" ] . apply ( lambda tokens : self . stem ( tokens ) ) return df
short_docstring: Takes as input a DataFrame with sentences in column 'text_col'.
short_docstring_tokens: Takes as input a DataFrame with sentences in column ' text_col ' .
comment: """
          Takes as input a DataFrame with sentences in column 'text_col'.
          Appends at least one new column to the DataFrame containing lists of single word tokens.
          """$SEP$# Split sentences into list of words