import pandas as pd
from IPython.parallel import Client, error

class Load_balanced_view(object):
    def __init__(self, profile='default'):
        #initialise a direct and a load ballanced view
        self.c = Client(profile=profile)
        self.dview = self.c[:]
        self.lbview = self.c.load_balanced_view()
        
    def exec_on_engine(self, code, block=True):
        """ 
        Execute the given code on all engines 
        
        Parameters
        ----------
        to_execute: string or list of strings 
            command(s) to execute on all the nodes. Thought for short tasks,
            like importing modules
        block: bool
            whether or not to wait until done to return. default: True
        """
        #Six: Python 2 and 3 Compatibility Library
        from six import string_types  #appropriate string type
        if isinstance(code, string_types): # if it's a string
            code = [code,]  # convert to list
        # execute the required commands 
        # (better to do in block mode, avoids errors if command is slow)
        for te in code:
            try:
                self.dview.execute(te, block=block)
            except error.CompositeError as e:  # if an error occurs, print a single one, not one per engine
                e.raise_exception()


parallel_env = Load_balanced_view(profile='default')

#parallel_env = Load_balanced_view(profile='empty')

imports = ['import numpy as np',]
parallel_env.exec_on_engine(imports)

imports = ['import numpy as np',]
parallel_env.exec_on_engine(imports)

imports = ['import pandas as pd',]
parallel_env.exec_on_engine(imports)

imports = ['import pandas as pd',]
parallel_env.exec_on_engine(imports)