Dynamically adding dictionary values based on row count from pandas dataframe

Question

I am rewriting some code of mine and feel there must be a better more dynamic way to do the below. Currently as you can see I am creating a condition based directly on the row count and adding values from there. However I don't want to have to make static conditions for multiple values if row_count == 3: if row_count == 4: etc. I'm positive there must be a more efficient way to achieve this. Any pointers would be appreciated.

for root, dirs, files in os.walk(main):
    filters = '*specificname*.csv'
    for filename in fnmatch.filter(files, filters):
        df = pd.read_csv(os.path.join(root, filename),error_bad_lines=False)
        row_count = len(df.index)
        device_dic = collections.defaultdict()
        if row_count == 2:
            device_dic[df.iloc[0][1]]  = {}
            device_dic[df.iloc[0][1]]['item1'] = df.iloc[0][2]
            device_dic[df.iloc[0][1]]['item2'] = df.iloc[0][3]
            device_dic[df.iloc[1][1]] = {}
            device_dic[df.iloc[1][1]]['item1'] = df.iloc[1][2]
            device_dic[df.iloc[1][1]]['item2'] = df.iloc[1][3]
            for key in device_dic.iterkeys():
                device.append(key)

Answer 1

def func1(device_dict):

    device_dic[df.iloc[0][1]]  = {}
    device_dic[df.iloc[0][1]]['item1'] = df.iloc[0][2]
    device_dic[df.iloc[0][1]]['item2'] = df.iloc[0][3]
    device_dic[df.iloc[1][1]] = {}
    device_dic[df.iloc[1][1]]['item1'] = df.iloc[1][2]
    device_dic[df.iloc[1][1]]['item2'] = df.iloc[1][3]
    for key in device_dic.iterkeys():
        device.append(key)

    # Or whatever you want to return
    return device

def func2(device_dict):
    # your code here
    pass



# Store each function in a dict
process_map = {2 : func1, 3: func2, 4: func2, ...}


for root, dirs, files in os.walk(main):
   filters = '*specificname*.csv'
   for filename in fnmatch.filter(files, filters):
      df = pd.read_csv(os.path.join(root, filename),error_bad_lines=False)
      row_count = len(df.index)
      device_dic = collections.defaultdict()

      # Could also use get() to provide a default processing func
      process_func = process_map[row_count]

      result = process_func(device_dict)