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Awesome GitHub RepositoriesSubplot Layouts
Techniques for arranging multiple individual plots within a single figure for comparative analysis.
Distinct from Plot Dimensions: Shortlist candidates focus on dimensions or composite plot types, not the organizational layout of subplots.
Explore 4 awesome GitHub repositories matching data & databases · Subplot Layouts. Refine with filters or upvote what's useful.
Awesome Subplot Layouts GitHub Repositories
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- iamseancheney/python_for_data_analysis_2nd_chinese_version
iamseancheney/python_for_data_analysis_2nd_chinese_version
8,937在 GitHub 上查看This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p
Organizes multiple individual plots within a single figure layout for comparative analysis.
matplotlibnumpypandas - mrdbourke/zero-to-mastery-ml
mrdbourke/zero-to-mastery-ml
5,839在 GitHub 上查看本项目是一个机器学习教育课程和学习平台，通过交互式 Jupyter Notebooks 提供。它作为掌握 Python 数据科学工具包的综合指南，为数值计算、表格数据操作和统计可视化提供结构化教程。 该课程包括 Scikit-Learn 的具体实现指南，以及关于构建、训练和部署神经网络及计算机视觉模型的 TensorFlow 实践课程。它涵盖了构建预测模型的端到端过程，从初始问题定义和任务分类，到通过交互式 Web 界面部署模型。 该项目涵盖了广泛的功能领域，包括多维数组的数值计算、探索性数据分析和数据预处理例程。它为监督和无监督学习、自动化机器学习流水线、超参数优化以及使用分类指标和交叉验证的模型评估提供了详细的工作流。 教育内容组织为一系列 Notebook，将 Python 代码与叙述性解释交织在一起，以记录数据科学工作流。
Shows how to arrange multiple individual plots within a single figure for side-by-side comparative analysis.
Jupyter Notebookdata-sciencedeep-learningmachine-learning - nyandwi/machine_learning_complete
Nyandwi/machine_learning_complete
4,983在 GitHub 上查看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
Organizes multiple individual plots into a single figure using subplots to compare datasets.
Jupyter Notebookcomputer-visiondata-analysisdata-science - mne-tools/mne-python
mne-tools/mne-python
3,243在 GitHub 上查看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
Ships topographical subplot layouts that arrange per-sensor traces according to their spatial positions on the scalp.
Pythonecogeegelectrocorticography
