We introduce mlpy, a high-performance Python package for predictive modeling. It makes extensive use of NumPy to provide fast N-dimensional array manipulation and easy integration of C code. Mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. The package includes tools to measure stability in sets of ranked feature lists, of special interest in bioinformatics for functional genomics, for which large scale experiments with up to 106 classifiers have been run on Linux clusters and on the Grid.