Numpy masked arrays - indicating missing values
import numpy as np
import num开发者_运维问答py.ma as ma
"""This operates as expected with one value masked"""
a = [0., 1., 1.e20, 9.]
error_value = 1.e20
b = ma.masked_values(a, error_value)
print b
"""This does not, all values are masked """
d = [0., 1., 'NA', 9.]
error_value = 'NA'
e = ma.masked_values(d, error_value)
print e
How can I use 'nan', 'NA', 'None', or some similar value to indicate missing data?
Are you getting your data from a text file or similar? If so, I'd suggest using the genfromtxt
function directly to specify your masked value:
In [149]: f = StringIO('0.0, 1.0, NA, 9.0')
In [150]: a = np.genfromtxt(f, delimiter=',', missing_values='NA', usemask=True)
In [151]: a
Out[151]:
masked_array(data = [0.0 1.0 -- 9.0],
mask = [False False True False],
fill_value = 1e+20)
I think the problem in your example is that the python list you're using to initialize the numpy array has heterogeneous types (floats and a string). The values are coerced to a strings in a numpy array, but the masked_values
function uses floating point equality yielding the strange results.
Here's one way to overcome this by creating an array with object dtype:
In [152]: d = np.array([0., 1., 'NA', 9.], dtype=object)
In [153]: e = ma.masked_values(d, 'NA')
In [154]: e
Out[154]:
masked_array(data = [0.0 1.0 -- 9.0],
mask = [False False True False],
fill_value = ?)
You may prefer the first solution since the result has a float dtype.
This solution works, it does force the creation of a copy of the array.
a_true = (a == 'NA')
a[a_true] = 1.e20
a = a.astype(float)
print a
error_value = 1.e20
b = ma.masked_values(a, error_value)
print b
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