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To turn a Python function (REFPROP 9.1) into a vectorized version, you can use the NumPy module. NumPy provides a way to work with arrays of data efficiently and can be used to vectorize scalar functions. The general steps to vectorize a function using NumPy are as follows:

1. Convert the scalar function to operate on NumPy arrays of the same shape.
2. Use the NumPy vectorize() function to create a vectorized version of the function.
3. Use the vectorized function with NumPy arrays to perform element-wise operations.

Here is an example of how to vectorize a function for calculating the vapor pressure of water at a given temperature using the REFPROP 9.1 library:

import numpy as np
from ctypes import c_double, byref, CDLL

# Load REFPROP 9.1 shared library
RPP = CDLL('REFPROP.dll')

# Define function to calculate vapor pressure of water at a given temperature
def water_sat_p(T):
    T = c_double(T)
    D = c_double(0.0)
    P = c_double(0.0)
    Q = c_double(0.0)
    K = c_double(0.0)
    err = c_double(0.0)

    # Call REFPROP 9.1 function to calculate vapor pressure
    RPP.SATPdll(byref(T), byref(D), byref(P), byref(Q), byref(K), byref(err), 255)

    return P.value

# Vectorize the function using NumPy
water_sat_p = np.vectorize(water_sat_p)

# Define temperature array
T = np.linspace(273, 373, 101)

# Calculate vapor pressure at each temperature using vectorized function
P = water_sat_p(T)

# Print results
print('Temperature (K):', T)
print('Vapor pressure (Pa):', P)

In this example, the water_sat_p function is first defined to calculate the vapor pressure of water at a given temperature using the REFPROP 9.1 library. Next, the function is vectorized using the np.vectorize() function, which creates a new function that can be used with NumPy arrays. Finally, the vectorized function is used with a NumPy array of temperatures to calculate the vapor pressure at each temperature, and the results are printed to the console.