# How to iterate over columns of pandas dataframe to run regression

I'm sure this is simple, but as a complete newbie to python, I'm having trouble figuring out how to iterate over variables in a `pandas`

dataframe and run a regression with each.

Here's what I'm doing:

```
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 suppose I want to do this for each column in the dataframe. In particular, I want to regress FIUIX on FSTMX, and then FSAIX on FSTMX, and then FSAVX on FSTMX. After each regression I want to store the residuals.

I've tried various versions of the following, but I must be getting the syntax wrong:

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

I think the problem is I don't know how to refer to the returns column by key, so `returns[k]`

is probably wrong.

Any guidance on the best way to do this would be much appreciated. Perhaps there's a common pandas approach I'm missing.

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

From: stackoverflow.com/q/28218698

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