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1

I am trying to convert a list that contains numeric values and None values to numpy.array, such that None is replaces with numpy.nan.

For example:

my_list = [3,5,6,None,6,None]

# My desired result: 
my_array = numpy.array([3,5,6,np.nan,6,np.nan])

Naive approach fails:

>>> my_list
[3, 5, 6, None, 6, None]
>>> np.array(my_list)
array([3, 5, 6, None, 6, None], dtype=object) # very limited 
>>> _ * 2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for *: 'NoneType' and 'int'

>>> my_array # normal array can handle these operations
array([  3.,   5.,   6.,  nan,   6.,  nan])
>>> my_array * 2
array([  6.,  10.,  12.,  nan,  12.,  nan])

What is the best way to solve this problem?

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2 Answers

up vote 4 down vote accepted

You simply have to explicitly declare the data type:

>>> my_list = [3, 5, 6, None, 6, None]
>>> np.array(my_list, dtype=np.float)
array([  3.,   5.,   6.,  nan,   6.,  nan])
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I had a feeling that I was missing something simple...Thank You –  Akavall Oct 18 '13 at 18:12
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up vote 0 down vote

What about

my_array = np.array(map(lambda x: numpy.nan if x==None else x, my_list))
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