title: python multiprocessing dataframe rows
body: <pre><code>  def main():     
      df_master = read_bb_csv(file)
                  p = Pool(2)
                  if len(df_master.index) &gt;= 1:
                      for row in df_master.itertuples(index=True, name='Pandas'):
                           p.map((partial(check_option, arg1=row), df_master))
      
      
      def check_option(row):
         get_price(row)
  </code></pre>
  <p>I am using Pandas to read a CSV, to loop through rows and process the informaiton. Give the function get_price() need to make serveral http calls, I want to use multiprocess to process all rows at once (depends on the CPU cores) to speed up the function.</p>
  <p>The issue I am having is that, I am new to multi process and don't know how to use
  <strong>p.map((check_option, arg1=row), df_master)</strong> to process all rows in the dataframe.
  There is no need to return the <strong>row</strong> value back to the function. Just need to allow to rows to processes to handle.</p>
  <p>Thank you for your help.</p>