Well, if you're reading the data in as a list, just do np.array(map(float, list_of_strings)) (or equivalently, use a list comprehension). (In Python 3, you'll need to call list on the map return value if you use map, since map returns an iterator now.)

However, if it's already a numpy array of strings, there's a better way. Use astype().

import numpy as np
x = np.array(['1.1', '2.2', '3.3'])
y = x.astype(np.float)

Didn't find a function to make it work, so I wrote something that works for you.

def myArrayConverter(arr):

    convertArr = []
    for s in arr.ravel():    
        try:
            value = float32(s)
        except ValueError:
            value = s

        convertArr.append(value)

    return array(convertArr,dtype=object).reshape(arr.shape)

Cheers

You seem a bit confused as to how numpy arrays work behind the scenes. Each item in an array must be the same size.

The string representation of a float doesn't work this way. For example, repr(1.3) yields '1.3', but repr(1.33) yields '1.3300000000000001'.

A accurate string representation of a floating point number produces a variable length string.

Because numpy arrays consist of elements that are all the same size, numpy requires you to specify the length of the strings within the array when you're using string arrays.

If you use x.astype('str'), it will always convert things to an array of strings of length 1.

For example, using x = np.array(1.344566), x.astype('str') yields '1'!

You need to be more explict and use the '|Sx' dtype syntax, where x is the length of the string for each element of the array.

For example, use x.astype('|S10') to convert the array to strings of length 10.

Even better, just avoid using numpy arrays of strings altogether. It's usually a bad idea, and there's no reason I can see from your description of your problem to use them in the first place...

Type checking is not the only option to do what you want, but definitely one of the easiest:

import numpy as np

def to_str(var):
    if type(var) is list:
        return str(var)[1:-1] # list
    if type(var) is np.ndarray:
        try:
            return str(list(var[0]))[1:-1] # numpy 1D array
        except TypeError:
            return str(list(var))[1:-1] # numpy sequence
    return str(var) # everything else

EDIT: Another easy way, which does not use type checking (thanks to jtaylor for giving me that idea), is to convert everything into the same type (np.array) and then convert it to a string:

import numpy as np

def to_str(var):
    return str(list(np.reshape(np.asarray(var), (1, np.size(var)))[0]))[1:-1]

Example use (both methods give same results):

>>> to_str(1.) #float
'1.0'
>>> to_str([1., 1., 1.]) #list
'1.0, 1.0, 1.0'
>>> to_str(np.ones((1,3))) #np.array
'1.0, 1.0, 1.0'