Numba won't accept numpy array datatype

Question

I have following dataframe:

                      time         t            sp
598258 2017-01-02 00:00:00 -2.634766  89892.492188
598259 2017-01-02 01:00:00 -2.753906  89921.398438
598260 2017-01-02 02:00:00 -2.730469  89896.914062
598261 2017-01-02 03:00:00 -2.765625  89874.468750
598262 2017-01-02 04:00:00 -2.855469  89864.609375
598263 2017-01-02 05:00:00 -3.005859  89846.929688
598264 2017-01-02 06:00:00 -3.011719  89877.875000
598265 2017-01-02 07:00:00 -2.900391  89873.109375
598266 2017-01-02 08:00:00 -2.416016  89891.812500
598267 2017-01-02 09:00:00 -2.126953  89882.289062

and following code:

import pandas as pd
import numpy as np
from numba import jit
@jit(nopython=True)
def en(t,p):
    dd = np.empty((t.size),np.float16)
    for i in range(len(t)):
        dd[i]=p[i]/287.05/(t[i]+273.15)
    return dd

ex=en(dfx['t'].values,dfx['sp'].values)

I get error:

TypingError: non-precise type pyobject
[1] During: typing of argument at dd = np.empty((t.size),np.float16)

I know I must define precise type for numba to accept it, i think I did just that with np.float16 but error is present. Any help to resolve this is appreciated

Answer 1

You have to change to float 64 or 32 depending on the precision you want:

from numba import jit
@jit(nopython=True)
def en(t,p):
    dd = np.empty((t.size),np.float64)
    for i in range(len(t)):
        dd[i]=p[i]/287.05/(t[i]+273.15)
    return dd

ex=en(dfx['t'].values,dfx['sp'].values)

output:

array([1.15764165, 1.15852414, 1.15810831, 1.1579697 , 1.15822752,
       1.15864432, 1.15906853, 1.15852962, 1.15669754, 1.15534144])

That will fix it!