I assume you want to plot the function, f(x), rather than the results of integrating it. To do that you will want to create a list of x values (sounds like you did this), evaluate f for each of those values, and then use matplotlib's plot function to display the result.
The documentation for arange says that "When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases." You probably want to plot with a non-integer step in x, otherwise you will use most of the details of your plot. So I would suggest switching to linspace.
import numpy as np
from matplotlib.pyplot import plot
xvals = np.linspace(0,6,100) #100 points from 0 to 6 in ndarray
yvals = list(map(f, xvals)) #evaluate f for each point in xvals
plot(xvals, yvals)
Most likely where you ran into a problem was directly applying your function f to an ndarray. The way it is written, f expects a single value as an input rather than an array. Map solves this problem by applying f to each value in your ndarray individually.
Edit: To use a sympy symbolic function:
You can also define a piecewise function in sympy. For the things you are trying to accomplish in your question, this won't be any different from using the original method described. However, if you want to do further symbolic manipulations with your function this could be useful.
import sympy
x = sympy.symbols('x')
f = sympy.Piecewise((0, x>4),(4, x>2) ,(x**2, x>=0)) #defines f as a symbolic function
sympy.plot(f, (x, 0,6)) #Plots f on the interval 0 to 6
Note in the definition of a piecewise function, the conditions are evaluated in order, so the order you define them in does matter. If, for example you swapper the first two conditions, the x>4 condition would never be reached because x>2 would always be satisfied first. This is why the conditions are defined in the reverse order from your original function.
