# based on code provided by Jeff Reback at PyCon 2015 in Montreal

from pandas import Timestamp
import numpy as np

from pandas.util import testing
df = testing.makeMixedDataFrame()
df.iloc[0] = np.nan


blocks = df._data.blocks
ncols = sum(b.shape[0] for b in blocks)
data = [None] * ncols


for b in blocks:
    # convert blocks into lists with conversion of datetimes
    # expands contiguous blocks

    if b.dtype.type == np.datetime64:
        # cast to timestamp, direct conversion to serial might be preferable
        values = np.array([Timestamp(v) for v in b.values.ravel()])
        values = values.reshape(b.shape)

    else:
        values = b.values

    result = values.tolist()

    for col_loc, col in zip(b.mgr_locs, result):
        data[col_loc] = col


for i in df.index:
    row = [data[j][i] for j in range(ncols)] # could be appended to an openpyxl worksheet