title: About using multiprocessing to read file
body: <p>I have many files in the folders,so I think I should use multiprocess ,
  then I use multiprocess to read txt file in the folder,
  But I compare the time if I used multiprocess or not ,
  I found if I don't use pool is more fast. I don't know why ,
  so what situation should I use Pool to read file( huge files?)</p>
  
  <pre><code>using Pool
  time:0.5836s
  not using Pool
  time:0.0076s
  </code></pre>
  
  <p>the code is ,</p>
  
  <pre><code>import pandas as pd
  from multiprocessing import Pool
  import glob2,os,time
  
  class PandasReadFile:
  
      def __init__(self):
          print('123')
  
      def readFilePool(self,path):
          n,t=0,time.time()
          print(t)
          pp = Pool(processes=1)
  
          # here is using pool
          df = pd.concat(pp.map(self.read_csv, glob2.iglob(os.path.join(path, "*.txt"))))
          # not using pool
          # df = pd.concat(map(pd.read_csv, glob2.iglob(os.path.join(path, "*.txt"))))
          t = time.time() - t
          print('%.4fs' % (t))
          print(df)
  
      @staticmethod
      def read_csv(filename):
          return pd.read_csv(filename)
  
  if __name__ == '__main__':
      p = PandasReadFile()
      p.readFilePool('D:/')
  </code></pre>