Question

I’m having trouble getting numpy.ma to work with my data. I’m sure I’ve used it before to mask blank values but can’t figure out how. Here’s a snippet of code that shows the problem I’m having.

import numpy as np
import numpy.ma as ma

x = np.array([[0.0, 1.1, '', 2.2, ''],[3.3,'', 4.4, '', 5.5]])
for index, value in np.ndenumerate(x):
    if value == '':
        x[index] = None
x = ma.masked_values(x, None)
print x

This prints:

[['0' '1' 'N' '2' 'N']
 ['3' 'N' '4' 'N' '5']]

What I’m trying to do is get a masked array of floats with any missing values masked out. The final print should produce:

[[0.0 1.0 -- 2.0 --]
 [3.0 -- 4.0 -- 5.0]]

Answer 1

If you replaced empty strings with NaN, the following would work:

In [19]: x = np.array([[0.0, 1.1, np.nan, 2.2, np.nan],[3.3,np.nan, 4.4, np.nan, 5.5]])

In [20]: m = ma.masked_array(x, np.isnan(x))

In [21]: print(m)
[[0.0 1.1 -- 2.2 --]
 [3.3 -- 4.4 -- 5.5]]

The easiest place to change the '' to np.nan is before you place them in np.array.