python 2.7 - Find Indexes of Non-NaN Values in Pandas DataFrame

Question

I have a very large dataset (roughly 200000x400), however I have it filtered and only a few hundred values remain, the rest are NaN. I would like to create a list of indexes of those remaining values. I can't seem to find a simple enough solution.

    0     1     2
0   NaN   NaN   1.2
1   NaN   NaN   NaN   
2   NaN   1.1   NaN   
3   NaN   NaN   NaN
4   1.4   NaN   1.01

For instance, I would like a list of [(0,2), (2,1), (4,0), (4,2)].

Answer 1

Convert the dataframe to it's equivalent NumPy array representation and check for NaNs present. Later, take the negation of it's corresponding indices (indicating non nulls) using numpy.argwhere. Since the output required must be a list of tuples, you could then make use of generator map function applying tuple as function to every iterable of the resulting array.

>>> list(map(tuple, np.argwhere(~np.isnan(df.values))))
[(0, 2), (2, 1), (4, 0), (4, 2)]