row sum and column mean works but row mean gives all NaN, on heterogenous data types
When different DataFrame rows are of different types, row sum works but row mean gives all NaN values.

```
df=pd.DataFrame({'a':[1,pd.to_timedelta('1D'),3.], 'b':[4,pd.to_timedelta('1.5D'),6.], 'c':[7,pd.to_timedelta('0.5D'),np.nan]})
# df=pd.DataFrame({'a':[1,2,3.], 'b':[4,5,6.], 'c':[7,8,np.nan]})
display(df.transpose())
display(df.transpose().sum(axis=0))
display(df.transpose().mean(axis=0))
display(df)
display(df.sum(axis=1))
display(df.mean(axis=1))  # bug
```
<img width="332" alt="image" src="https://user-images.githubusercontent.com/10172392/78124015-a4838380-7441-11ea-84fe-9bd45de54f1b.png">

As shown above, the last statement shows the bug by returning all NaN values which is incorrect. Interestingly, if the DataFrame entries are of the same type (uncomment the 2nd line), this bug does not occur.