In this paper gradient and Hamiltonian dynamics are investigated in both discrete-time and sampled-data contexts. At first, the discrete gradient function is profitably employed to define discrete gradient and Hamiltonian dynamics. On these bases, it is shown that representations of these forms can be recovered when computing the sampled-data equivalent models to gradient and Hamiltonian continuous-time dynamics.