Power flow (PF) is one of the most important calculations in power systems. The widely-used PF methods are the Newton-Raphson PF (NRPF) method and the fast-decoupled PF (FDPF) method. In smart grids, power generations and loads become intermittent and much more uncertain, and the topology also changes more frequently, which may result in significant state shifts and further make NRPF or FDPF difficult to converge. To address this problem, we propose a data-driven PF (DDPF) method based on historical/simulated data that includes an offline learning stage and an online computing stage. In the offline learning stage, a learning model is constructed based on the proposed exact linear regression equations, and then the proposed learning model is solved by the ridge regression (RR) method to suppress the effect of data collinearity. In online computing stage, the nonlinear iterative calculation is not needed. Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.