How to merge two dataframe in pandas to replace nan

The official way promoted exactly to do this is A.combine_first(B). Further information are in the official documentation.

However, it gets outperformed massively with large databases from A.fillna(B) (performed tests with 25000 elements):

In[891]: %timeit df.fillna(df2)
1000 loops, best of 3: 333 µs per loop
In[892]: %timeit df.combine_first(df2)
100 loops, best of 3: 2.15 ms per loop
In[894]: (df.fillna(df2) == df.combine_first(df2)).all().all()
Out[890]: True

  • Get the numpy arrays for A and B.
  • Make a mask of A where A == numpy.NaN
  • Assign B to A using the mask as a boolean index for both.

Similar to this:

>>> a
array([[  0.,   1.,   2.],
       [  3.,  nan,   5.],
       [  6.,   7.,   8.]], dtype=float16)
>>> b
array([[ 1000.,  1000.,  1000.],
       [ 1000.,  1000.,  1000.],
       [ 1000.,  1000.,  1000.]])
>>> mask = np.isnan(a)
>>> mask
array([[False, False, False],
       [False,  True, False],
       [False, False, False]], dtype=bool)
>>> a[mask] = b[mask]
>>> a
array([[    0.,     1.,     2.],
       [    3.,  1000.,     5.],
       [    6.,     7.,     8.]], dtype=float16)

Alternatively, use numpy.where():

>>> a
array([[  0.,   1.,   2.],
       [  3.,  nan,   5.],
       [  6.,   7.,   8.]], dtype=float16)
>>> a = np.where(np.isnan(a), b, a)
>>> a
array([[    0.,     1.,     2.],
       [    3.,  1000.,     5.],
       [    6.,     7.,     8.]])
>>>

https://stackoverflow.com/a/13062410/2823755 suggests the first (boolean indexing) method may work with the dataframe itself. ... and it does (wasn't satisfied, so i installed pandas):

>>> a = pandas.DataFrame(np.arange(25, dtype = np.float16).reshape(5,5))
>>> a.values[3,2] = np.NaN
>>> b = pandas.DataFrame(np.arange(1000, 1025, dtype = np.float16).reshape(5,5))
>>> a[np.isnan(a)] = b[np.isnan(a)]
>>> a
    0   1     2   3   4
0   0   1     2   3   4
1   5   6     7   8   9
2  10  11    12  13  14
3  15  16  1017  18  19
4  20  21    22  23  24
>>>

pandas.DataFrame.where also works.

a.where(~np.isnan(a), other = b, inplace = True)

Tags:

Python

Pandas

Merge

Nan

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