human
I am working on the file "Content/ODEs/ODEsTour.ipynb".
The first task is:
Note, this same function can be written more compactly as
python
    def deriv(vals,t):  # return derivatives of the array vals
        g = 9.8 #m/s^2
        ell = 0.5 #m
        return np.array([ vals[1], -(g/ell)*np.sin(vals[0]) ])
But writing it the way we did in the code cell makes it very explicit what each step involves.  The odeint solver will take the derivative function, along with an array of current values for $[\theta,\omega]$ and return the result of 1) differentiating them via deriv, which produces $[d\theta/dt,d\omega/dt]$ and then 2) integrating $[d\theta/dt,d\omega/dt]$ at each time step, to give back $[\theta(t),\omega(t)]$.  
Many numerical ODE solvers do it exactly the same way (e.g. MATLAB).
Now we import odeint from scipy.integrate, set up our initial conditions, and solve:
Can you write Python code for it?
assistant
#Here each step involves.

from scipy.integrate import odeint

#Try the help for more info
#help(odeint)

t = np.arange(0.,10.+0.01,0.01)
th0 = 20.*np.pi/180 # 20 degrees, but must use radians
omega0 = 0. #starting from rest, omega = 0

th_init = np.array([th0, omega0]) # initial values

#pass the function deriv to the solver with initial values and integration variable
theta = odeint(deriv,th_init,t)