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3 dépôts

Awesome GitHub RepositoriesJoint Distribution Visualizers

Tools for combining bivariate plots with marginal univariate representations.

Distinct from Statistical Plotting Libraries: Distinct from Statistical Plotting Libraries: focuses on the specific joint-marginal layout pattern.

Explore 3 awesome GitHub repositories matching data & databases · Joint Distribution Visualizers. Refine with filters or upvote what's useful.

Awesome Joint Distribution Visualizers GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • mwaskom/seabornAvatar de mwaskom

    mwaskom/seaborn

    13,739Voir sur GitHub↗

    Seaborn is a Python library designed for statistical data visualization. It functions as a high-level interface built on the Matplotlib ecosystem, providing specialized routines to explore and communicate complex patterns within datasets. The framework enables users to generate informative graphics through automated statistical aggregation, multi-plot faceting, and integrated regression modeling. The library distinguishes itself through a declarative approach to data mapping, which translates raw inputs into visual properties like color, size, and position. It includes a robust statistical tr

    Combines bivariate plots with marginal univariate representations to show relationships and individual distributions simultaneously.

    Pythondata-sciencedata-visualizationmatplotlib
    Voir sur GitHub↗13,739
  • nyandwi/machine_learning_completeAvatar de Nyandwi

    Nyandwi/machine_learning_complete

    4,983Voir sur GitHub↗

    This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

    Implements joint distribution visualizers that combine bivariate plots with marginal univariate representations.

    Jupyter Notebookcomputer-visiondata-analysisdata-science
    Voir sur GitHub↗4,983
  • mne-tools/mne-pythonAvatar de mne-tools

    mne-tools/mne-python

    3,243Voir sur GitHub↗

    MNE-Python is an open-source Python library for processing, visualizing, and analyzing human neurophysiological data, including MEG, EEG, sEEG, ECoG, and NIRS recordings. It provides a comprehensive framework for loading data from over 30 proprietary file formats into a common hierarchical FIF data structure, and represents all time-series data as NumPy arrays for seamless integration with the scientific Python ecosystem. The library is built around object-oriented data containers that encapsulate raw, epoched, evoked, and source data with built-in preprocessing and visualization methods. The

    Combines butterfly plots with automatically placed scalp topographies for a comprehensive overview of evoked data.

    Pythonecogeegelectrocorticography
    Voir sur GitHub↗3,243
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  6. Joint Distribution Visualizers

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  • Neuroimaging Joint PlotsCombines butterfly plots with automatically placed scalp topographies for a comprehensive overview of evoked neurophysiological data. **Distinct from Joint Distribution Visualizers:** Distinct from Joint Distribution Visualizers: focuses on neuroimaging-specific layouts (butterfly + scalp topography) rather than general bivariate-marginal statistical plots.