#jupyter_notebook #altair #analytics #covid_19 #covid_data #covid19 #data_science #data_visualisation #fastai #fastpages #github_actions #github_pages #jupyter #matplotlib #nteract #papermill #pymc3 #python

https://github.com/github/covid19-dashboard

#python #matplotlib_figures #matplotlib_style_sheets #matplotlib_styles #scientific_papers #thesis_template

https://github.com/garrettj403/SciencePlots

#python #cheatsheet #matplotlib

https://github.com/matplotlib/cheatsheets

#jupyter_notebook #cartography #generative_art #jupyter_notebook #maps #matplotlib #openstreetmap #python

https://github.com/marceloprates/prettymaps

#python #book #dataviz #matplotlib #numpy #open_access #plotting #scientific_publications

https://github.com/rougier/scientific-visualization-book

#jupyter_notebook #data_analysis #data_science #data_science_tips #data_visualization #jupyter #jupyter_notebook #jupyter_tips #matplotlib #matplotlib_tips #numpy #pandas #pandas_tips #python #python_tips #sklearn

https://github.com/ChawlaAvi/Daily-Dose-of-Data-Science

#python #bokeh #control_panels #dashboards #dataapp #datascience #dataviz #gui #holoviews #holoviz #hvplot #jupyter #matplotlib #panel #plotly

https://github.com/holoviz/panel

#other #matplotlib #numpy #pandas

The book "利用Python进行数据分析" (Using Python for Data Analysis) has a new third edition with several improvements. It includes updated versions of Python (3.10) and Pandas (1.4.0), adding new methods and features. The book is more user-friendly for beginners, simplifying code readability by avoiding confusing shortcuts. There are also additional resources like video guides, study notes, and online versions available. This makes it easier for users to learn and apply data analysis techniques effectively.

For advanced users, the book "极速Python" (Fast Python) focuses on high-performance techniques for large datasets, covering topics like data structure optimization, high concurrency, and distributed data processing. It integrates technologies like Arrow and Ray, which are crucial for efficient data handling and analysis in modern applications. This helps users handle big data more efficiently and stay updated with the latest technological advancements.

https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version

#python #cjk_fonts #ieee_paper #latex #matplotlib_figures #matplotlib_style_sheets #matplotlib_styles #python #scientific_papers #thesis_template

SciencePlots is a tool that helps you make your graphs look professional for scientific papers and presentations. It uses Matplotlib styles to create simple, informative plots like those in academic journals. To use it, you need to install SciencePlots with `pip` and have LaTeX installed on your computer. Then, you can easily change the style of your plots by adding a few lines of code, such as `plt.style.use('science')`. This makes your figures consistent and high-quality, which is important when publishing research[1][2].

https://github.com/garrettj403/SciencePlots