Person re-identification (Re-ID) aims to retrieve specific individuals across non-overlapping camera views. In recent years, attention-based models contribute to many computer vision tasks due to their great ability for learning discriminative features. We propose the deep progressive attention (DPA) in a more natural manner for person Re-ID. Similar to human visual attention mechanism, the proposed DPA progressively selects the most discriminative parts of a specific individual and formulates feature representation for comparison purpose. Concretely, on the one hand, the proposed DPA uses a long-term reward to optimize the discriminative feature selection. On the other hand, a deep convolutional architecture is integrated into a recurrent model for feature representation learning. Extensive experiments on three person Re-ID benchmarks Market-1501, DukeMTMC-reID, and CUHK03-NP demonstrate the proposed DPA is on par with the state-of-the-art. Moreover, the experiments on partial person Re-ID datasets indicate the proposed DPA is competitive with the specially designed partial person Re-ID methods.