pandas DataFrame: replace nan values with average of columns

You can simply use DataFrame.fillna to fill the nan's directly:

In [27]: df Out[27]:           A         B         C0 -0.166919  0.979728 -0.6329551 -0.297953 -0.912674 -1.3654632 -0.120211 -0.540679 -0.6804813       NaN -2.027325  1.5335824       NaN       NaN  0.4618215 -0.788073       NaN       NaN6 -0.916080 -0.612343       NaN7 -0.887858  1.033826       NaN8  1.948430  1.025011 -2.9822249  0.019698 -0.795876 -0.046431In [28]: df.mean()Out[28]: A   -0.151121B   -0.231291C   -0.530307dtype: float64In [29]: df.fillna(df.mean())Out[29]:           A         B         C0 -0.166919  0.979728 -0.6329551 -0.297953 -0.912674 -1.3654632 -0.120211 -0.540679 -0.6804813 -0.151121 -2.027325  1.5335824 -0.151121 -0.231291  0.4618215 -0.788073 -0.231291 -0.5303076 -0.916080 -0.612343 -0.5303077 -0.887858  1.033826 -0.5303078  1.948430  1.025011 -2.9822249  0.019698 -0.795876 -0.046431

The docstring of fillna says that value should be a scalar or a dict, however, it seems to work with a Series as well. If you want to pass a dict, you could use df.mean().to_dict().

Try:

sub2['income'].fillna((sub2['income'].mean()), inplace=True)

In [16]: df = DataFrame(np.random.randn(10,3))In [17]: df.iloc[3:5,0] = np.nanIn [18]: df.iloc[4:6,1] = np.nanIn [19]: df.iloc[5:8,2] = np.nanIn [20]: dfOut[20]:           0         1         20  1.148272  0.227366 -2.3681361 -0.820823  1.071471 -0.7847132  0.157913  0.602857  0.6650343       NaN -0.985188 -0.3241364       NaN       NaN  0.2385125  0.769657       NaN       NaN6  0.141951  0.326064       NaN7 -1.694475 -0.523440       NaN8  0.352556 -0.551487 -1.6392989 -2.067324 -0.492617 -1.675794In [22]: df.mean()Out[22]: 0   -0.2515341   -0.0406222   -0.841219dtype: float64

Apply per-column the mean of that columns and fill

In [23]: df.apply(lambda x: x.fillna(x.mean()),axis=0)Out[23]:           0         1         20  1.148272  0.227366 -2.3681361 -0.820823  1.071471 -0.7847132  0.157913  0.602857  0.6650343 -0.251534 -0.985188 -0.3241364 -0.251534 -0.040622  0.2385125  0.769657 -0.040622 -0.8412196  0.141951  0.326064 -0.8412197 -1.694475 -0.523440 -0.8412198  0.352556 -0.551487 -1.6392989 -2.067324 -0.492617 -1.675794