write a function called lag() which accepts two arguments:

• x, a numpy array.
• num_places, the number of places by which to lag x.

The result of lag(x, num_places) should be a new array y which is a copy of x that has been "shifted over" by num_places. You may assume that the input x is a 1d numpy array of floats. The first num_places elements of y should be nan.

A bit more technically, y[i] = x[i - num_places] if i >= num_places, and y[i] = np.nan if i < num_places.

Here's an example:

x = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) lag(x, 2)

# output array([nan, nan, 1., 2., 3.])

• For full credit, please do not use for loops.