new matplotlib, bash and git resources

mattmakai · mattmakai · commit bfba4d9bb727 · 2019-04-28T12:44:27.000-04:00
diff --git a/content/pages/02-development-environments/08-bash-shell.markdown b/content/pages/02-development-environments/08-bash-shell.markdown
@@ -84,3 +84,7 @@ during Python software development as part of a programmer's
 * [Creating a bash completion script](https://iridakos.com/tutorials/2018/03/01/bash-programmable-completion-tutorial.html)
   is a great tutorial that walks you through a reasonably complex Bash
   script for completing syntax in other Bash shell scripts.
+
+* [6 Tips Before You Write Your Next Bash Cronjob](https://yasoob.me/posts/6-tips-before-you-write-your-next-bash-cronjob/)
+  covers starting your scripts with shebang, redirecting output, timeouts
+  and sudo privileges.
diff --git a/content/pages/02-development-environments/19-git.markdown b/content/pages/02-development-environments/19-git.markdown
@@ -146,6 +146,11 @@ workflow. These resources will come in handy for specific Git subjects.
   of tips to get you out of binds you may find yourself in when you get too 
   tricky with Git commands. 
 
+* [Tips for a disciplined git workflow](https://drewdevault.com/2019/02/25/Using-git-with-discipline.html)
+  is less about workflow and more about how to write self-explaining commit
+  messages, self-containing each commit and modifying branch history
+  when you muff up before it is merged into master.
+
 * [Another Git catastrophe cleaned up](http://blog.plover.com/prog/git-tastrophe.html) 
   goes through a difficult merge scenario that required deep Git 
   understanding to properly fix.
diff --git a/content/pages/03-data/21-matplotlib.markdown b/content/pages/03-data/21-matplotlib.markdown
@@ -49,3 +49,7 @@ toolkits.
 * [Matplotlib Tips and Demos](https://nbviewer.jupyter.org/urls/gist.githubusercontent.com/Jwink3101/e6b57eba3beca4b05ec146d9e38fc839/raw/f486ca3dcad44c33fc4e7ddedc1f83b82c02b492/Matplotlib_Cheatsheet)
   is a long [Jupyter Notebook](/jupyter-notebook.html) with a ton of example
   code that shows how to use Matplotlib in many ways.
+
+* [Animation with Matplotlib](https://towardsdatascience.com/animations-with-matplotlib-d96375c5442c)
+  explains the `animation` base class and the main interfaces for
+  creating animations in your visualizations.
diff --git a/content/pages/05-deployment/40-azure-functions.markdown b/content/pages/05-deployment/40-azure-functions.markdown
@@ -23,6 +23,10 @@ in other Azure services.
   is the official quickstart guide by Microsoft and has some good high-level
   information on their platform's services as well.
 
+* [Deploy Python to Azure Functions](https://code.visualstudio.com/docs/python/tutorial-azure-functions)
+  provides the step-by-step instructions needed to get Python code running 
+  on Azure Functions.
+
 * [Azure Functions vs AWS Lambda – Scaling Face Off](https://www.azurefromthetrenches.com/azure-functions-vs-aws-lambda-scaling-face-off/)
   contains metrics from comparing AWS Lambda with Azure Functions in
   response time, user load, requests per second and error rate
diff --git a/content/pages/06-devops/14-logging.markdown b/content/pages/06-devops/14-logging.markdown
@@ -121,6 +121,11 @@ certain threshold.
   structured logging to reduce Mean Time To Resolve (MTTR) and how they
   arranged their log levels to support their scale.
 
+* [How to collect, customize, and centralize Python logs](https://www.datadoghq.com/blog/python-logging-best-practices/)
+  covers the standard library `logging` module and how to configure
+  it for various ways you are likely to use it with one or more 
+  Python applications.
+
 * [A guide to logging in Python](https://opensource.com/article/17/9/python-logging)
   has some clear, simple diagrams to explain how logging works in Python
   applications.
