I have some issue using `sympy.lambdify`

. I have a rather simple symbolic expression involving only square root, sine and cosine and some large numbers (which get generated by other parts of the program not shown here). Lambdify does work for single floats, but not for numpy arrays. However, these would be very helpful for plotting later.

The error I get is

```
AttributeError: 'float' object has no attribute 'sqrt'
```

Here is a mwe. Note that `expr1`

works just fine whereas `expr2`

does not. Any help to fix the issue would be well appreciated.

```
import sympy
import numpy
x = sympy.symbols('x', real=True)
expr1 = -sympy.sqrt(4*sympy.sin(3*x/4)**2 - 2*sympy.cos(3*x/83) + 5*sympy.cos(2*x/3)**2 + 2)
expr2 = -sympy.sqrt(2.14881349445107e+30*sympy.sin(209178661335919*x/10000000000000)**2 + 13456000000000000000000000000*sympy.cos(209178661335919*x/10000000000000)**2 - 1.40793126300373e+29*sympy.cos(209178661335919*x/10000000000000) + 4.73607234789273e+30)
func1 = sympy.lambdify(x, expr1, modules='numpy')
func2 = sympy.lambdify(x, expr2, modules='numpy')
array = numpy.arange(2)
print(func1(array))
print(func2(array[0]))
print(func2(array[1])) #works fine until here
print(func2(array)) #fails
```

python 3.7.3

numpy 1.16.3

sympy 1.14

**EDIT:**

I cannot directly modify `expr2`

. It just appears here in this form to provide a mwe. However, in the real code it get generated as eigenvalue of a matrix and takes rather long to calculate.

```
eigenvalues = Matrix.eigenvals()
expr2 = list(eigenvalues.keys())[0]
```

Try to apply

`nfloat`

to the expression before passing it to`lambdify`

:`expr2 = sympy.nfloat(expr1)`

.