Data-Driven Predictive Control (DPC) optimizes system behavior directly from measured trajectories without requiring an explicit model. However, its computational cost scales with dataset size, limiting real-time applicability to nonlinear robotic systems. For robotic tasks such as trajectory tracking and motion planning, real-time feasibility and numerical robustness are essential. Nonlinear DPC often relies on large datasets or learned nonlinear representations to ensure accuracy, both of which increase computational demand. We propose Contextual Sampling, a dynamic data selection strategy that adaptively selects the most relevant trajectories based on the current state and reference. By reducing dataset size while preserving representativeness, it improves computational efficiency. Experiments on a scaled autonomous vehicle and a quadrotor show that Contextual Sampling achieves comparable or better tracking than Random Sampling with fewer trajectories, enabling real-time feasibility. Compared with Select-DPC, it achieves similar tracking accuracy at lower computational cost. In comparison with the full DPC formulation without sampling, Contextual Sampling attains comparable tracking performance while requiring less computation, highlighting the benefit of efficient data selection in data-driven predictive control.