title: pandas better runtime, going trough dataframe
body: <p>I have a pandas dataframe, there I wanna search in one column for numbers, find it and put it in a new column.</p>
  <pre><code>import pandas
  import regex as re
  import numpy as np
  data = {'numbers':['134.ABBC,189.DREB, 134.TEB', '256.EHBE, 134.RHECB, 345.DREBE', '456.RHN,256.REBN,864.TREBNSE', '256.DREB, 134.ETNHR,245.DEBHTECM'],
          'rate':[434, 456, 454256, 2334544]}
    
  
  df = pd.DataFrame(data)
    
  
  print(df)
  
  pattern = '134.[A-Z]{2,}'
  
  
  df['mynumbers'] = None
  
  
  index_numbers = df.columns.get_loc('numbers')
  index_mynumbers = df.columns.get_loc('mynumbers')
  
  
  length = np.array([])
  
  
  for row in range(0, len(df)):
      number = re.findall(pattern, df.iat[row, index_numbers])
  
      
      
      df.iat[row, index_mynumbers] = number
  
  
  print(df)
  
  
  </code></pre>
  <p>I get my numbers: {'mynumbers': ['[134.ABBC, 134.TEB]', '[134.RHECB]', '[134.RHECB]']}. My dataframe is huge. Is there a better, faster method in pandas for going trough my df?</p>