Python: handling a large set of data. Scipy or Rpy? And how?
I don't know anything about Rpy.  I do know that SciPy is used to do serious number-crunching with truly large data sets, so it should work for your problem.
As zephyr noted, you may not need either one; if you just need to keep some running sums, you can probably do it in Python.  If it is a CSV file or other common file format, check and see if there is a Python module that will parse it for you, and then write a loop that sums the appropriate values. 
I'm not sure how to get the top ten rows.  Can you gather them on the fly as you go, or do you need to compute the sums and then choose the rows?  To gather them you might want to use a dictionary to keep track of the current 10 best rows, and use the keys to store the metric you used to rank them (to make it easy to find and toss out a row if another row supersedes it).  If you need to find the rows after the computation is done, slurp all the data into a numpy.array, or else just take a second pass through the file to pull out the ten rows.
In my python environment, the Rpy and Scipy packages are already installed. 
The problem I want to tackle is such:
1) A huge set of financial data are stored in a text file. Loading into Excel is not possible
2) I need to sum a certain fields and get the totals.
3) I need to show the top 10 rows based on the totals.
Which package (Scipy or Rpy) is best suited for this task? 
If so, could you provide me some pointers (e.g. documentation or online example) that can help me to implement a solution?
Speed is a concern. Ideally scipy and Rpy can handle the large files when even when the files are so large that they cannot be fitted into memory