How to use .rolling() on each row of a Pandas dataframe?

I create a Pandas dataframe df:

df.head()
Out[1]: 
                    A           B   DateTime 
2010-01-01  50.662365  101.035099 2010-01-01             
2010-01-02  47.652424   99.274288 2010-01-02            
2010-01-03  51.387459   99.747135 2010-01-03               
2010-01-04  52.344788   99.621896 2010-01-04               
2010-01-05  47.106364   98.286224 2010-01-05               

I can add a moving average of column A:

df['A_moving_average'] = df.A.rolling(window=50, axis="rows") \
                             .apply(lambda x: np.mean(x))

Question: how do I add a moving average of columns A and B?

This should work, but it gives an error:

df['A_B_moving_average'] = df.rolling(window=50, axis="rows") \
                             .apply(lambda row: (np.mean(row.A) + np.mean(row.B)) / 2)

The error is:

NotImplementedError: ops for Rolling for this dtype datetime64[ns] are not implemented

Appendix A: Code to create Pandas dataframe

Here is how I created the test Pandas dataframe df:

import numpy.random as rnd
import pandas as pd
import numpy as np

count = 1000

dates = pd.date_range('1/1/2010', periods=count, freq='D')

df = pd.DataFrame(
    {
        'DateTime': dates,
        'A': rnd.normal(50, 2, count), # Mean 50, standard deviation 2
        'B': rnd.normal(100, 4, count) # Mean 100, standard deviation 4
    }, index=dates
)