old_contents: #!/usr/bin/env python
  import os
  import click
  from sklearn.feature_extraction.text import CountVectorizer
  import numpy as np
  import pandas as pd
  
  from nlppln.utils import create_dirs, get_files
  from nlppln.liwc_tokenized import split
  
  
  @click.command()
  @click.argument('in_dir', type=click.Path(exists=True))
  @click.option('--out_dir', '-o', default=os.getcwd(), type=click.Path())
  @click.option('--name', '-n', default='freqs.csv')
  def freqs(in_dir, out_dir, name):
      out_file = os.path.join(out_dir, name)
      create_dirs(out_file)
  
      in_files = get_files(in_dir)
  
      vectorizer = CountVectorizer(input='filename', tokenizer=split)
      X = vectorizer.fit_transform(in_files)
      freqs = np.array(X.sum(axis=0)).squeeze()
      vocab_df = pd.DataFrame(
          {'word': vectorizer.get_feature_names(), 'freq': freqs})
      vocab_df['rank'] = vocab_df['freq'].rank(method='first', ascending=False)
      vocab_df = vocab_df.sort('rank')
      vocab_df.to_csv(out_file, encoding='utf-8', index=False)
  
  
  if __name__ == '__main__':
      freqs()
  
new_contents: #!/usr/bin/env python
  import os
  import click
  from sklearn.feature_extraction.text import CountVectorizer
  import numpy as np
  import pandas as pd
  
  from nlppln.utils import create_dirs, get_files, split
  
  
  @click.command()
  @click.argument('in_dir', type=click.Path(exists=True))
  @click.option('--out_dir', '-o', default=os.getcwd(), type=click.Path())
  @click.option('--name', '-n', default='freqs.csv')
  def freqs(in_dir, out_dir, name):
      out_file = os.path.join(out_dir, name)
      create_dirs(out_file)
  
      in_files = get_files(in_dir)
  
      vectorizer = CountVectorizer(input='filename', tokenizer=split)
      X = vectorizer.fit_transform(in_files)
      freqs = np.array(X.sum(axis=0)).squeeze()
      vocab_df = pd.DataFrame(
          {'word': vectorizer.get_feature_names(), 'freq': freqs})
      vocab_df['rank'] = vocab_df['freq'].rank(method='first', ascending=False)
      vocab_df = vocab_df.sort('rank')
      vocab_df.to_csv(out_file, encoding='utf-8', index=False)
  
  
  if __name__ == '__main__':
      freqs()
  
subject: Use utility function split for splitting text