@jreback I'm having trouble with a bit of this.

With a DataFrame

In [3]: df = DataFrame({'a': pd.to_datetime(['2010', '2011']), 'b': [0, 5]})

In [5]: df.dtypes
Out[5]: 
a    datetime64[ns]
b             int64
dtype: object

In [6]: df.quantile(.5, numeric_only=False)

For the implementation, I've got a function f that I apply to each column essentially via df.apply.
When f gets the first column a the dtype is object, and I'm not able to convert from object back to datetime64.

ipdb> arr
0    2010-01-01 00:00:00
1    2011-01-01 00:00:00
Name: a, dtype: object
ipdb> _values_from_object(arr)
array([Timestamp('2010-01-01 00:00:00'), Timestamp('2011-01-01 00:00:00')], dtype=object)
ipdb> _values_from_object(arr).view('i8')
*** TypeError: Cannot change data-type for object array.
ipdb> arr.convert_objects(convert_dates=True)
*** TypeError: Cannot change data-type for object array.

What's to best way to get from these timestamps to viewing as i8 so that the quantiles can be computed?

just iterate over with iteritems() and concat the results

apply has a lot of heuristics that u don't need

Thanks. That's a lot better.

no prob...this looks fine otherwise