Creating an empty Pandas DataFrame, then filling it?

Question

I'm starting from the pandas DataFrame docs here: http://pandas.pydata.org/pandas-docs/stable/dsintro.html I'd like to iteratively fill the DataFrame with values in a time series kind of calculation. So basically, 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 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. Note: I'm using Python 2.7. import datetime as dt import pandas as pd import scipy as s if __name__ == '__main__': base = dt.datetime.today().date() dates = [ base - dt.timedelta(days=x) for x in range(0,10) ] dates.sort() 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)] print valdict Select the correct answer from above options

Answer 1

You can simply use pandas.DataFrame() method: To create an empty dataframe: import pandas as pd Newdf = pd.DataFrame() The above will create an empty dataframe of the Newdf variable. To write the previous dataframe into a new one: NewDF = NewDF.append(OldDF) NewDF.head() OldDF is the name of the previous dataframe, which you want to append in a new dataframe. In the above code .head() method will print the first five rows of the dataframe. Hope this answer helps.