Latest version
Released:
Python implementation of Markdown.
Project description
私はRStudio 0.98.1103を使っています。リリースノートでは、XML、YAML、SQL、Python、およびシェルスクリプト用の構文強調表示モードが追加されていると言われています。しかし、私はこのようなものを書くとき：r. Knitrでは、kableパッケージを使用して（小さな）データフレームをテーブルとして追加します：r kabledset1-read.csv（homerunlevel0edxstatsAPmod1dcordset01.csv）knitr:: kable（dset1、format = html）.
This is a Python implementation of John Gruber's Markdown.It is almost completely compliant with the reference implementation,though there are a few known issues. See Features for informationon what exactly is supported and what is not. Additional features aresupported by the Available Extensions.
Documentation
For more advanced installation and usage documentation, see the docs/ directoryof the distribution or the project website at https://Python-Markdown.github.io/.
See the change log at https://Python-Markdown.github.io/change_log.
What Is R Markdown
Support
You may report bugs, ask for help, and discuss various other issues on the bug tracker.
Code of Conduct
Everyone interacting in the Python-Markdown project's codebases, issue trackers,and mailing lists is expected to follow the Code of Conduct.
Release historyRelease notifications | RSS feed
3.3.4
3.3.3
3.3.2
3.3.1
3.3
3.2.2
3.2.1
3.2
3.1.1
3.1
3.0.1
3.0
2.6.11
2.6.10
2.6.9
2.6.8
2.6.7
2.6.6
2.6.5
2.6.4
2.6.3
2.6.2
2.6.1
2.6
R Markdown Python Code
2.5.2
2.5.1
2.5
2.4.1
2.4
2.3.1
2.3
2.2.1
2.2.0
2.1.1
2.1.0
2.0.3
2.0.2
2.0.1
R Markdown Python Program
2.0
1.7
1.6
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| Filename, size Markdown-3.3.4.tar.gz (322.2 kB) | File type Source | Python version None | Upload date | Hashes |
Hashes for Markdown-3.3.4-py3-none-any.whl
R Markdown Python Equivalent
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Hashes for Markdown-3.3.4.tar.gz
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R Markdown Python
As a DevOps engineer or an IT Admin, you often find it time-consuming and difficult to support separate environments for Data Scientists using a variety of tools (R, Python, RStudio, and Jupyter plus supporting packages). You’ve seen your Data Science teams struggle with unfamiliar tools and concepts for deployment, production, and scaling. Instead of using the infrastructure you provide for scaling out computation, such as Kubernetes or Slurm, data scientists continue to ask for help troubleshooting their desktop environments--and your team is forced to acquire expertise in supporting multiple open source platforms.
With RStudio products, you can maintain a single infrastructure for provisioning, scaling, and managing environments for both R and Python users, meaning that you only need to configure, maintain and secure a single system. This makes it easy to leverage your existing automation tools to provide data scientists with access to your servers or Kubernetes/Slurm clusters in a transparent way, directly from the development tools they prefer. Access, monitoring, and environment management are easily configured, and RStudio’s Support, Customer Success, and Solutions Engineering teams are poised to offer advice as you scale.
Learn more:
Using Python In R Markdown
- RStudio Team enables the Data Science team you support to develop, collaborate, manage and share their data science work, while providing you the tools you need to administer, maintain and scale.
- For a deeper view on how RStudio professional products work with Python, Jupyter, and VS Code see Using Python with RStudio.