What is the most efficient way to loop through dataframes with pandas?

Question

I want to perform my own complex operations on financial data in dataframes in a sequential manner.

For example I am using the following MSFT CSV file taken from Yahoo Finance:

Date,Open,High,Low,Close,Volume,Adj Close
2011-10-19,27.37,27.47,27.01,27.13,42880000,27.13
2011-10-18,26.94,27.40,26.80,27.31,52487900,27.31
2011-10-17,27.11,27.42,26.85,26.98,39433400,26.98
2011-10-14,27.31,27.50,27.02,27.27,50947700,27.27

....

I then do the following:

#!/usr/bin/env python
from pandas import *

df = read_csv('table.csv')

for i, row in enumerate(df.values):
    date = df.index[i]
    open, high, low, close, adjclose = row
    #now perform analysis on open/close based on date, etc..

Is that the most efficient way? Given the focus on speed in pandas, I would assume there must be some special function to iterate through the values in a manner that one also retrieves the index (possibly through a generator to be memory efficient)? df.iteritems unfortunately only iterates column by column.

Answer 1

The newest versions of pandas now include a built-in function for iterating over rows.

for index, row in df.iterrows():

    # do some logic here

Or, if you want it faster use itertuples()

But, unutbu's suggestion to use numpy functions to avoid iterating over rows will produce the fastest code.