title: Adding value to pandas dataframe with multiprocessing
body: <pre><code>import pandas as pd
  import numpy as np
  import multiprocessing
  d = {'col1': [1, 2], 'col2': [3, 4]}
  df = pandas.DataFrame(data = d)
  list = [(0,3,1,5), ...]
  def function(x):
      df.loc[x[0],"col1"] = x[1]
      df.loc[x[2],"col2"] = x[3]
  pool = multiprocessing.Pool()
  pool.map(function, list)
  df.to_csv("tset.csv")
  </code></pre>
  
  <p>Finally, the output of the dataframe is empty. I know for dictionary we can use multiprocessing.Manager().dict to share memory. But this can't be applied to pandas dataframe. Is there any easy way to put the dataframe into shared memory? </p>