Search Results for "matplotlib-1.5.2"

...The repository covers a wide range of Python topics, including data types, control flow, functions, object-oriented programming, error handling, modules, and advanced concepts like decorators and generators. In addition, it includes applied exercises in areas such as web scraping, working with APIs, and using Python libraries like NumPy, pandas, Matplotlib, and Seaborn for data analysis and visualization. Learners can progress from beginner-friendly basics to more advanced programming skills while reinforcing their knowledge with practice problems and projects. Because it mirrors the course content, this repository is widely used by students taking the Udemy course.

The Scientific Visualization book is a freely available open-access textbook that introduces how to produce effective scientific visualizations using Python, focusing especially on leveraging the popular plotting library Matplotlib (and related tools). It goes beyond simple plotting tutorials and emphasizes design principles: how to choose colors, layout subplots, annotate graphs, and present data in a way that is both accurate and visually compelling. As such, it serves as a guide for researchers, data scientists, and academic authors who need to create publication-quality figures or explanatory graphics, rather than quick exploratory plots. ...

The Python Data Science Handbook is a comprehensive collection of Jupyter notebooks written by Jake VanderPlas covering fundamental Python libraries for data science, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and more. The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. Each chapter is a standalone Jupyter notebook, with runnable code, explanatory prose, visuals, and examples showing how to handle data-wrangling, exploratory data analysis, machine learning workflows, and visualization. ...

...This includes foundational Python concepts, data processing with libraries like NumPy and pandas, threading and multiprocessing for concurrency, and practical use of libraries such as Matplotlib for data visualization. It also provides tutorials on machine learning frameworks and concepts, including TensorFlow, PyTorch, Keras, Scikit-Learn, and reinforcement learning techniques. Each section contains organized code and explanations designed to help learners understand the underlying mechanics of Python and common computational approaches.

Python course (in Spanish) for Proteomics analysis using basically Jupyter NoteBooks. For more information, you can have a look at the readme.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/p4p/code/ci/default/tree/readme.md

...The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. Although the GitHub repository has been archived and is read-only, it is still a valuable snapshot of early, hands-on teaching material for scikit-learn and machine learning in Python.

Basic and intermediate examples to read, modify and write DICOM files with Python code using Jupyter - To install Jupyter - https://jupyter.org/install ====== All examples are based on Pydicom. An open source library - https://pydicom.github.io/

The Matplotlib tutorial repository is designed as a hands-on learning resource to help users — especially Python beginners — get started with Matplotlib for creating plots and charts. It provides a sequence of example scripts and notebooks that cover fundamental plotting tasks: line graphs, histograms, scatter plots, bar charts, customizing axes, labels, legends, and styling.