How to iterate over columns of a pandas dataframe

I have this code using Pandas in Python:

all_data = {}
for ticker in ['FIUIX', 'FSAIX', 'FSAVX', 'FSTMX']:
    all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2010', '1/1/2015')

prices = DataFrame({tic: data['Adj Close'] for tic, data in all_data.iteritems()})  
returns = prices.pct_change()

I know I can run a regression like this:

regs = sm.OLS(returns.FIUIX,returns.FSTMX).fit()

but how can I do this for each column in the dataframe? Specifically, how can I iterate over columns, in order to run the regression on each?

Specifically, I want to regress each other ticker symbol (FIUIX, FSAIX and FSAVX) on FSTMX, and store the residuals for each regression.

I've tried various versions of the following, but nothing I've tried gives the desired result:

resids = {}
for k in returns.keys():
    reg = sm.OLS(returns[k],returns.FSTMX).fit()
    resids[k] = reg.resid

Is there something wrong with the returns[k] part of the code? How can I use the k value to access a column? Or else is there a simpler approach?

Old answer:

for column in df:
    print(df[column])

The previous answer still works, but was added around the time of pandas 0.16.0. Better versions are available.

Now you can do:

for series_name, series in df.items():
    print(series_name)
    print(series)