In order to analyze the influence of uncertain factors on power system, Polynomial Chaos Approximation (PCA) method, which is both fast and accurate, is widely used in probabilistic power flow calculation. The polynomial chaotic approximation method requires that the probability density function of the random input variable is known, and the random input variable must satisfy the independent condition. In this paper, a probabilistic power flow method based on Data Driven Polynomial Chaos Approximation (DDPCA) is proposed for the known random input variables which are historical data. First, DDPCA selects the optimal orthogonal polynomial according to the historical data, and then determines the Gaussian sample considering the nonlinear correlation of random input variables, and then computes the weights with Monte Carlo integral. Then, a small amount of power flow is calculated based on Gaussian samples, and the approximation coefficient is solved according to the power flow results and weights, and then the statistical characteristics of the random output variables are obtained. The proposed method is compared with the point estimation method, and the effectiveness of the proposed method is verified by the results of three examples.