replace 'na' with 'nan' so it is then converted to np.nan, then use np.nanmean.

example:

test = np.array(['0','1','nan'], dtype=float)
np.where(np.isnan(test), np.nanmean(test), test)

array([ 0. ,  1. ,  0.5])

Use numpy.concatenate:

>>> arr = array([array([[2.4567]],dtype=object),array([[3.4567]],dtype=object),array([[4.4567]],dtype=object),array([[5.4567]],dtype=object),array([[6.4567]], dtype=object)])
>>> np.concatenate(arr).astype(None)
array([[ 2.4567],
       [ 3.4567],
       [ 4.4567],
       [ 5.4567],
       [ 6.4567]])

If you have a list of lists, you only needed to use ...

import numpy as np
...
npa = np.asarray(someListOfLists, dtype=np.float32)

per this LINK in the scipy / numpy documentation. You just needed to define dtype inside the call to asarray.

Nasty little problem... I have been fooling around with this toy example:

>>> arr = np.array([['one', [1, 2, 3]],['two', [4, 5, 6]]], dtype=np.object)
>>> arr
array([['one', [1, 2, 3]],
       ['two', [4, 5, 6]]], dtype=object)

My first guess was:

>>> np.array(arr[:, 1])
array([[1, 2, 3], [4, 5, 6]], dtype=object)

But that keeps the object dtype, so perhaps then:

>>> np.array(arr[:, 1], dtype=float)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: setting an array element with a sequence.

You can normally work around this doing the following:

>>> np.array(arr[:, 1], dtype=[('', float)]*3).view(float).reshape(-1, 3)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: expected a readable buffer object

Not here though, which was kind of puzzling. Apparently it is the fact that the objects in your array are lists that throws this off, as replacing the lists with tuples works:

>>> np.array([tuple(j) for j in arr[:, 1]],
...          dtype=[('', float)]*3).view(float).reshape(-1, 3)
array([[ 1.,  2.,  3.],
       [ 4.,  5.,  6.]])

Since there doesn't seem to be any entirely satisfactory solution, the easiest is probably to go with:

>>> np.array(list(arr[:, 1]), dtype=float)
array([[ 1.,  2.,  3.],
       [ 4.,  5.,  6.]])

Although that will not be very efficient, probably better to go with something like:

>>> np.fromiter((tuple(j) for j in arr[:, 1]), dtype=[('', float)]*3,
...             count=len(arr)).view(float).reshape(-1, 3)
array([[ 1.,  2.,  3.],
       [ 4.,  5.,  6.]])

The following will do it:

arr = np.array([(k,)+v for k,v in data_dict.iteritems()], \
         dtype=[('date', '|O4'), ('high', '<f8'), ('low', '<f8')])

If you then wish to use arr as a recarray, you could use:

arr = arr.view(np.recarray)

This will enable you to reference fields by name, e.g. arr.date.