Pandas nested sort and NaN

Question

I\'m trying to understand the expected behavior of DataFrame.sort on columns with NaN values.

Given this DataFrame:

In [36]: df
Out[36]: 
    a   b
0         


        

Answer 1

Until fixed in Pandas, this is what I'm using for sorting for my needs, with a subset of the functionality of the original DataFrame.sort function. This will work for numerical values only:

def dataframe_sort(df, columns, ascending=True):
    a = np.array(df[columns])

    # ascending/descending array - -1 if descending, 1 if ascending
    if isinstance(ascending, bool):
        ascending = len(columns) * [ascending]
    ascending = map(lambda x: x and 1 or -1, ascending)

    ind = np.lexsort([ascending[i] * a[:, i] for i in reversed(range(len(columns)))])
    return df.iloc[[ind]]

Usage example:

In [4]: df
Out[4]: 
     a   b   c
10   1   9   7
11 NaN NaN   1
12   2 NaN   6
13 NaN   5   6
14   1   2   6
15   6   5 NaN
16   8   4   4
17   4   5   3

In [5]: dataframe_sort(df, ['a', 'c'], False)
Out[5]: 
     a   b   c
16   8   4   4
15   6   5 NaN
17   4   5   3
12   2 NaN   6
10   1   9   7
14   1   2   6
13 NaN   5   6
11 NaN NaN   1

In [6]: dataframe_sort(df, ['b', 'a'], [False, True])
Out[6]: 
     a   b   c
10   1   9   7
17   4   5   3
15   6   5 NaN
13 NaN   5   6
16   8   4   4
14   1   2   6
12   2 NaN   6
11 NaN NaN   1