I have a dataframe with a bunch of columns labelled in 'YYYY-MM' format, along with several other columns. I need to collapse the date columns into calendar quarters and take the mean; I was able to do it manually, but there are a few hundred date columns in my real data and I'd like to not have to map every single one of them by hand. I'm generating the initial df from a CSV; I didn't see anything in `read_csv` that seemed like it would help, but if there's anything I can leverage there that would be great. I found `dataframe.dt.to_period("Q")` that will convert a datetime object to quarter, but I'm not quite sure how to apply that here, if I can at all.

Here's a sample df (code below):

```
   foo  bar  2016-04  2016-05  2016-06  2016-07  2016-08
0    6    5        3        3        5        8        1
1    9    3        6        9        9        7        8
2    8    5        8        1        9        9        4
3    5    8        1        2        3        5        6
4    4    5        1        2        7        2        6

```

This code will do what I'm looking for, but I had to generate mapping by hand:

```py
mapping = {'2016-04':'2016q2', '2016-05':'2016q2', '2016-06':'2016q2', '2016-07':'2016q3', '2016-08':'2016q3'}
df = df.set_index(['foo', 'bar']).groupby(mapping, axis=1).mean().reset_index()

```

New df:

```py
    foo bar 2016q2  2016q3
0   6   5   3.666667    4.5
1   9   3   8.000000    7.5
2   8   5   6.000000    6.5
3   5   8   2.000000    5.5
4   4   5   3.333333    4.0

```

Code to generate the initial df:

```py
df = pd.DataFrame(np.random.randint(1, 11, size=(5, 7)), columns=('foo', 'bar', '2016-04', '2016-05', '2016-06', '2016-07', '2016-08'))                                                        '2016-07', '2016-08'))

```