What is the most efficient way to create a dictionary of two pandas Dataframe columns?

What is the most efficient way to organise the following pandas Dataframe:

data =

    Position    Letter
    1           a
    2           b
    3           c
    4           d
    5           e

into a dictionary like alphabet[1 : 'a', 2 : 'b', 3 : 'c', 4 : 'd', 5 : 'e']?

    In [9]: Series(df.Letter.values,index=df.Position).to_dict()
    Out[9]: {1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e'}

Speed comparion (using Wouter's method)

    In [6]: df = DataFrame(randint(0,10,10000).reshape(5000,2),columns=list('AB'))

    In [7]: %timeit dict(zip(df.A,df.B))
    1000 loops, best of 3: 1.27 ms per loop

    In [8]: %timeit Series(df.A.values,index=df.B).to_dict()
    1000 loops, best of 3: 987 us per loop

From: stackoverflow.com/q/17426292