Creating an empty Pandas DataFrame, and then filling it

I'm starting from the pandas DataFrame documentation here: Introduction to data structures

I'd like to iteratively fill the DataFrame with values in a time series kind of calculation. I'd like to initialize the DataFrame with columns A, B, and timestamp rows, all 0 or all NaN.

I'd then add initial values and go over this data calculating the new row from the row before, say row[A][t] = row[A][t-1]+1 or so.

I'm currently using the code as below, but I feel it's kind of ugly and there must be a way to do this with a DataFrame directly or just a better way in general.

import pandas as pd
import datetime as dt
import scipy as s
base = dt.datetime.today().date()
dates = [ base - dt.timedelta(days=x) for x in range(9, -1, -1) ]

valdict = {}
symbols = ['A','B', 'C']
for symb in symbols:
    valdict[symb] = pd.Series( s.zeros(len(dates)), dates )

for thedate in dates:
    if thedate > dates[0]:
        for symb in valdict:
            valdict[symb][thedate] = 1 + valdict[symb][thedate - dt.timedelta(days=1)]