Pandas Interpolate Returning NaNs - python-2.7

Question

I'm trying to do basic interpolation of position data at 60hz (~16ms) intervals. When I try to use pandas 0.14 interpolation over the dataframe, it tells me I only have NaNs in my data set (not true). When I try to run it over individual series pulled from the dataframe, it returns the same series without the NaNs filled in. I've tried setting the indices to integers, using different methods, fiddling with the axis and limit parameters of the interpolation function - no dice. What am I doing wrong?
df.head(5) :
x y ms
0 20.5815 14.1821 333.3333
1 NaN NaN 350
2 20.6112 14.2013 366.6667
3 NaN NaN 383.3333
4 20.5349 14.2232 400
df = df.set_index(df.ms) # set indices to milliseconds
When I try running
df.interpolate(method='values')
I get this error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-462-cb0f1f01eb84> in <module>()
12
13
---> 14 df.interpolate(method='values')
15
16
/Users/jsb/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in interpolate(self, method, axis, limit, inplace, downcast, **kwargs)
2511
2512 if self._data.get_dtype_counts().get('object') == len(self.T):
-> 2513 raise TypeError("Cannot interpolate with all NaNs.")
2514
2515 # create/use the index
TypeError: Cannot interpolate with all NaNs.
I've also tried running over individual series, which only return what I put in:
temp = df.x
temp.interpolate(method='values')
333.333333 20.5815
350.000000 NaN
366.666667 20.6112
383.333333 NaN
400.000000 20.5349 Name: x, dtype: object
EDIT :
Props to Jeff for inspiring the solution.
Adding:
df[['x','y','ms']] = df[['x','y','ms']].astype(float)
before
df.interpolate(method='values')
interpolation did the trick.

Answer 1

Based on your edit with props to Jeff for inspiring the solution.
Adding:
df = df.astype(float)
before
df.interpolate(method='values')
interpolation did the trick for me as well. Unless you're sub-selecting a column set, you don't need to specify the columns.

Answer 2

I'm not able to to reproduce the error (see below for a copy/paste-able example), can you make sure the the data you show is actually representative of your data?
In [137]: from StringIO import StringIO
In [138]: df = pd.read_csv(StringIO(""" x y ms
...: 0 20.5815 14.1821 333.3333
...: 1 NaN NaN 350
...: 2 20.6112 14.2013 366.6667
...: 3 NaN NaN 383.3333
...: 4 20.5349 14.2232 400"""), delim_whitespace=True)
In [140]: df = df.set_index(df.ms)
In [142]: df.interpolate(method='values')
Out[142]:
x y ms
ms
333.3333 20.58150 14.18210 333.3333
350.0000 20.59635 14.19170 350.0000
366.6667 20.61120 14.20130 366.6667
383.3333 20.57305 14.21225 383.3333
400.0000 20.53490 14.22320 400.0000