You'll have to test for the type, or use exception handling, to switch approaches:

def find_max(a):
    if isinstance(a, list):
        return max(list)
    return a.values.max()

or

def find_max(a):
    try:
        return a.values.max()
    except AttributeError:
        # assume a Python sequence
        return max(a)

Which one you pick depends on a few factors:

• Do you need to support other iterables, like tuples or sets or generators?
• What is the more common case; the try...except case is faster if the majority of calls pass in a Pandas series.

Another option is to use np.max(), which works on either type:

import numpy as np

def find_max(a):
    return np.max(a)

Note that there is no need to use the .values attribute here!

First slice your list and then use index on the max function:

searchArray = [10,20,30,40,50,60,100,80,90,110]
slicedArray = searchArray[3:9]
print slicedArray.index(max(slicedArray))+3

This returns the index of the sliced array, plus the added beginSlice