>> masks = [[1,1],[0,0]]
>> [np.ma.masked_array(data=np.array([1.0,2.0]), mask=m, fill_value=np.nan).mean() for m in masks]
[masked, 1.5]
I'd like to replace the masked result with nan. Is there a way to do that directly with numpy's masked_array?
In [232]: M = np.ma.masked_array(data=np.array([1.0,2.0]),mask=[True, False])
filled method replaces the masked values with the fill value:
In [233]: M.filled()
Out[233]: array([1.e+20, 2.e+00])
In [234]: M.filled(np.nan) # or with a value of your choice.
Out[234]: array([nan, 2.])
Or as you do, specify the fill value when defining the array:
In [235]: M = np.ma.masked_array(data=np.array([1.0,2.0]),mask=[True, False],
...: fill_value=np.nan)
In [236]: M
Out[236]:
masked_array(data=[--, 2.0],
mask=[ True, False],
fill_value=nan)
In [237]: M.filled()
Out[237]: array([nan, 2.])
The masked mean method skips over the filled values:
In [238]: M.mean()
Out[238]: 2.0
In [239]: M.filled().mean()
Out[239]: nan
In [241]: np.nanmean(M.filled()) # so does the `nanmean` function
In [242]: M.data.mean() # mean of the underlying data
Out[242]: 1.5