Dipy is a free and open-source Python library for analyzing diffusion MRI (dMRI) data. It provides a unified programming interface for various dMRI analysis steps, including diffusion signal preprocessing, diffusion distribution reconstruction in individual voxels, fiber tractography, and visualization. Dipy implements classical and cutting-edge methods for dMRI analysis, such as diffusion tensor modeling, deterministic fiber tractography, constrained spherical deconvolution, and diffusion spectrum imaging (DSI). It also includes utility functions for statistical analysis, visualization, and file handling. Dipy is developed as an open project, encouraging contributions from scientists and developers worldwide. It is supported by a diverse international team of contributors from seven academic institutions across five countries and three continents. Dipy is built on top of Python libraries such as NumPy, SciPy, and Cython, and uses tools like Matplotlib and VTK for visualization. It also uses Sphinx for documentation and Nose for testing. Dipy's API is designed to be intuitive and well-documented, enabling researchers to build complex computational experiments with simple Python scripts. Dipy is hosted on GitHub, with continuous integration and testing systems ensuring code quality. It supports various dMRI data formats, including NIfTI, and provides tools for data loading, preprocessing, and analysis. Dipy includes modules for diffusion tensor imaging (DTI), diffusion spectrum imaging (DSI), q-ball imaging, and constrained spherical deconvolution. It also provides functions for extracting peaks from ODFs, calculating fiber directions, and visualizing results. Dipy is widely used in the neuroscience community for analyzing dMRI data and has been cited in over 5000 papers.Dipy is a free and open-source Python library for analyzing diffusion MRI (dMRI) data. It provides a unified programming interface for various dMRI analysis steps, including diffusion signal preprocessing, diffusion distribution reconstruction in individual voxels, fiber tractography, and visualization. Dipy implements classical and cutting-edge methods for dMRI analysis, such as diffusion tensor modeling, deterministic fiber tractography, constrained spherical deconvolution, and diffusion spectrum imaging (DSI). It also includes utility functions for statistical analysis, visualization, and file handling. Dipy is developed as an open project, encouraging contributions from scientists and developers worldwide. It is supported by a diverse international team of contributors from seven academic institutions across five countries and three continents. Dipy is built on top of Python libraries such as NumPy, SciPy, and Cython, and uses tools like Matplotlib and VTK for visualization. It also uses Sphinx for documentation and Nose for testing. Dipy's API is designed to be intuitive and well-documented, enabling researchers to build complex computational experiments with simple Python scripts. Dipy is hosted on GitHub, with continuous integration and testing systems ensuring code quality. It supports various dMRI data formats, including NIfTI, and provides tools for data loading, preprocessing, and analysis. Dipy includes modules for diffusion tensor imaging (DTI), diffusion spectrum imaging (DSI), q-ball imaging, and constrained spherical deconvolution. It also provides functions for extracting peaks from ODFs, calculating fiber directions, and visualizing results. Dipy is widely used in the neuroscience community for analyzing dMRI data and has been cited in over 5000 papers.