Replacing error with default value while coercing in numpy array

Question

I'm trying to coerce the values in my NumPy array to float. However, in my array, there might be some values which might not coerce successfully and I want to replace those values with a default value. I do want the speed of NumPy though. I do not want to do the python loop. What's the best route to achieve this behavior?

For instance:

import numpy as np
my_array = np.array(["1", "2", "3", "NA"])
new_array = magic_coerce(my_array, float, -1.0) # I want to implement this
print (new_array) # should print [1., 2., 3. -1.] 

I'm trying to write my own ufunc in c, and I have the following:

int is_float(const char* c)
{
    regex_t regex;
    regcomp(&regex, "^[+-]?([0-9]*[.])?[0-9]+$", REG_EXTENDED);
    return regexec(&regex, c, 0, NULL, 0) == 0; 
}

float to_float(const char *c, float default_value)
{
    float result = default_value;
    if (is_float(c))
    {
        result = atof(c);
    }
    return result;
}


static PyMethodDef LogitMethods[] = {
        {NULL, NULL, 0, NULL}
};

/* The loop definition must precede the PyMODINIT_FUNC. */

static void double_logitprod(char **args, npy_intp *dimensions,
                            npy_intp* steps, void* data)
{
    npy_intp i;
    npy_intp n = dimensions[0];
    char *in1 = args[0], *in2 = args[1];
    char *out = args[2];
    npy_intp in1_step = steps[0]; 
    npy_intp out_step = steps[2];

    double tmp;

    for (i = 0; i < n; i++) {
        /*BEGIN main ufunc computation*/
        char *tmp1 = (char *) in1;
        tmp = *((double *)in2);
        *((double *) out) = to_float(tmp1, tmp);
        /*END main ufunc computation*/

        in1 += in1_step;
        out += out_step;
    }
}


/*This a pointer to the above function*/
PyUFuncGenericFunction funcs[1] = {&double_logitprod};

/* These are the input and return dtypes of logit.*/

static char types[3] = {NPY_OBJECT, NPY_DOUBLE,
                        NPY_DOUBLE};

But it looks like it's not working correctly. What's the type for UNICODE in numpy? NPY_UNICODE gives an error, so I coerced it to NPY_OBJECT, but this does not seem to play with it.