title: Parallel loading of Input Files in Pandas Dataframe
body: <p>I have a Requirement, where I have three Input files and need to load them inside the Pandas Data Frame, before merging two of the files into one single Data Frame.</p>
  
  <p>The File extension always changes, it could be .txt one time and .xlsx or .csv another time.</p>
  
  <p>How Can I run this process parallel, in order to save the waiting/ loading time ?</p>
  
  <p>This is my code at the moment, </p>
  
  <pre><code>from time import time # to measure the time taken to run the code
  start_time = time()
  
  Primary_File = "//ServerA/Testing Folder File Open/Report.xlsx"
  Secondary_File_1 = "//ServerA/Testing Folder File Open/Report2.csv"
  Secondary_File_2 = "//ServerA/Testing Folder File Open/Report2.csv"
  
  import pandas as pd # to work with the data frames
  Primary_df = pd.read_excel (Primary_File)
  Secondary_1_df = pd.read_csv (Secondary_File_1)
  Secondary_2_df = pd.read_csv (Secondary_File_2)
  
  Secondary_df = Secondary_1_df.merge(Secondary_2_df, how='inner', on=['ID'])
  end_time = time()
  
  print(end_time - start_time)
  </code></pre>
  
  <p>It takes around 20 minutes for me to load my primary_df and secondary_df. So, I am looking for an efficient solution possibly using parallel processing to save time.
  I timed by Reading operation and it takes most of the time approximately 18 minutes 45 seconds.</p>
  
  <p><strong>Hardware Config :- Intel i5 Processor, 16 GB Ram and 64-bit OS</strong></p>
  
  <blockquote>
    <p>Question Made Eligible for bounty :- As I am looking for a working
    code with detailed steps - using a <strong>package with in anaconda
    environment</strong> that supports loading my input files Parallel and
    storing them in a pandas data frame separately. This should eventually
    save time.</p>
  </blockquote>