The article provides an in-depth overview of MNE-Python, an open-source software package for magnetoencephalography (MEG) and electroencephalography (EEG) data analysis. MNE-Python is designed to address the complex task of characterizing and locating neural activation in the brain by providing state-of-the-art algorithms implemented in Python. The package covers multiple methods for data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. It integrates seamlessly with core Python libraries such as NumPy, SciPy, matplotlib, and Mayavi, and is compatible with other neuroimaging tools like Nibabel. MNE-Python offers a comprehensive set of features, including time-frequency analysis, non-parametric statistics, connectivity estimation, independent component analysis (ICA), and decoding (multivariate pattern analysis or supervised learning). The article details the standard analysis pipeline, advanced examples, and the benefits of scripting-based data processing, emphasizing reproducibility and flexibility in handling various experimental setups. The development process includes rigorous testing, peer review, and extensive documentation to ensure accuracy and efficiency.The article provides an in-depth overview of MNE-Python, an open-source software package for magnetoencephalography (MEG) and electroencephalography (EEG) data analysis. MNE-Python is designed to address the complex task of characterizing and locating neural activation in the brain by providing state-of-the-art algorithms implemented in Python. The package covers multiple methods for data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. It integrates seamlessly with core Python libraries such as NumPy, SciPy, matplotlib, and Mayavi, and is compatible with other neuroimaging tools like Nibabel. MNE-Python offers a comprehensive set of features, including time-frequency analysis, non-parametric statistics, connectivity estimation, independent component analysis (ICA), and decoding (multivariate pattern analysis or supervised learning). The article details the standard analysis pipeline, advanced examples, and the benefits of scripting-based data processing, emphasizing reproducibility and flexibility in handling various experimental setups. The development process includes rigorous testing, peer review, and extensive documentation to ensure accuracy and efficiency.