Concatenating and merging operations over DataFrames

This recipe shows how to concatenate, merge/join, and perform complex operations over Pandas DataFrames as well as Spark DataFrames.

Getting ready

To step through this recipe, you will need a running Spark cluster either in pseudo distributed mode or in one of the distributed modes, that is, standalone, YARN, or Mesos. Also, have Python and IPython installed on the Linux machine, that is, Ubuntu 14.04.

How to do it…

1. Invoke ipython console -profile=pyspark:

         In [1]: from pyspark import SparkConf, SparkContext, SQLContext 
         In [2]: import pandas as pd 
         In [3]: sqlcontext = SQLContext(sc) 
         In [4]: pdf1 = pd.DataFrame({'A':['A0','A1','A2','A3'], 'B':   
         ['B0','B1','B2','B3'], 'C':['C0','C1','C2','C3'],'D': 
         ['D0','D1','D2','D3']},index=[0,1,2,3]) 
         In [5]: pdf2 = pd.DataFrame({'A':['A4','A5','A6','A7'], 'B':
         ['B4','B5','B6','B7'], 'C':['C4','C5','C6','C7'],'D':    ...