pandas shift rows NaNs

It's possible this is a bug, you can use np.roll to achieve this:

In[11]:x.apply(lambda x: np.roll(x, 2), axis=1)Out[11]:    col0  col1  col2  col3  col4  col5  col6  col70   NaN   NaN   6.0   5.0   1.0   5.0   2.0   4.01   NaN   NaN   8.0   8.0   9.0   6.0   7.0   2.02   NaN   NaN   8.0   3.0   9.0   6.0   6.0   6.03   NaN   NaN   8.0   4.0   4.0   4.0   8.0   9.04   NaN   NaN   5.0   3.0   4.0   3.0   8.0   7.0

Speedwise, it's probably quicker to construct a df and reuse the existing columns and pass the result of np.roll as the data arg to the constructor to DataFrame:

In[12]:x = pd.DataFrame(np.roll(x, 2, axis=1), columns = x.columns)xOut[12]:    col0  col1  col2  col3  col4  col5  col6  col70   NaN   NaN   6.0   5.0   1.0   5.0   2.0   4.01   NaN   NaN   8.0   8.0   9.0   6.0   7.0   2.02   NaN   NaN   8.0   3.0   9.0   6.0   6.0   6.03   NaN   NaN   8.0   4.0   4.0   4.0   8.0   9.04   NaN   NaN   5.0   3.0   4.0   3.0   8.0   7.0

timings

In[13]:%timeit pd.DataFrame(np.roll(x, 2, axis=1), columns = x.columns)%timeit x.fillna(0).astype(int).shift(2, axis=1)10000 loops, best of 3: 117 µs per loop1000 loops, best of 3: 418 µs per loop

So constructing a new df with the result of np.roll is quicker than first filling the NaN values, cast to int, and then shifting.