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?
2 Answers
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])
What about
my_array = np.array(map(lambda x: numpy.nan if x==None else x, my_list))
