fix several broken links

mattmakai · mattmakai · commit 5a10de057525 · 2020-07-04T18:29:46.000-04:00
diff --git a/content/pages/03-data/17-scipy-numpy.markdown b/content/pages/03-data/17-scipy-numpy.markdown
@@ -70,7 +70,7 @@ following resources are broader walkthroughs for the SciPy ecosystem:
 * [Math to Code](https://mathtocode.com/) provides an interactive
   tutorial to learn how to implement math in NumPy.
 
-* [101 NumPy Exercises for Data Analysis](https://www.machinelearningplus.com/101-numpy-exercises-python/)
+* [101 NumPy Exercises for Data Analysis](https://www.machinelearningplus.com/python/101-numpy-exercises-python/)
 
 * [NumPy: creating and manipulating numerical data](http://www.scipy-lectures.org/intro/numpy/index.html)
 
diff --git a/content/pages/03-data/18-data-visualization.markdown b/content/pages/03-data/18-data-visualization.markdown
@@ -36,7 +36,6 @@ easier for humans to see patterns, trends and other useful information.
 
 * [Python & OpenGL for Scientific Visualization](https://www.labri.fr/perso/nrougier/python-opengl/)
   is a free book that shows how to combine open source tools such as 
-  [Pyglet](https://pyglet.readthedocs.io/en/stable/) and 
   [PyOpenGL](http://pyopengl.sourceforge.net/) with Python 
   [data analysis](/data-analysis.html) libraries to generate interactive
   scientific data visualizations.
@@ -163,8 +162,7 @@ visualization and gave me ideas for what to build.
 * [Engineering Intelligence Through Data Visualization at Uber](https://eng.uber.com/data-viz-intel/)
   explains how Uber's data visualization team grew from 1 person to 15
   and the output they created along the way, including the open source
-  tools [react-vis](https://uber.github.io/react-vis/) and 
-  [react-map-gl](https://uber.github.io/react-map-gl/).
+  tool [react-vis](https://uber.github.io/react-vis/).
 
 * The Practitioner's Guide to System Dashboard Design series covers a
   lot of ground for what you should consider when building one form
diff --git a/content/pages/04-web-development/39-code-metrics.markdown b/content/pages/04-web-development/39-code-metrics.markdown
@@ -151,10 +151,6 @@ diminishing returns.
   covers Pylint, PyFlakes and mypy with a short description of the 
   advantages and disadvantage for each one.
 
-* [Dynamic tracing a Pony + Python program with DTrace](https://blog.wallaroolabs.com/2017/12/dynamic-tracing-a-pony---python-program-with-dtrace/)
-  shows how to use dynamic tracing programs to analyze code
-  at runtime.
-
 * This [Stack Overflow question on Python static code analysis tools](http://stackoverflow.com/questions/1428872/pylint-pychecker-or-pyflakes)
   contains comparison discussions of PyLint, PyChecker and PyFlakes.
 
diff --git a/content/pages/04-web-development/47-bots.markdown b/content/pages/04-web-development/47-bots.markdown
@@ -53,7 +53,7 @@ often also called "chatbots", "assistants" or "agents."
 * [Making a Reddit + Facebook Messenger Bot](https://pythontips.com/2017/04/13/making-a-reddit-facebook-messenger-bot/)
   builds a bot for two platforms and shows how to deploy it to Heroku.
 
-* [Build a Slack Bot that Mimics Your Colleagues with Python](http://hirelofty.com/blog/how-build-slack-bot-mimics-your-colleague/)
+* [Build a Slack Bot that Mimics Your Colleagues with Python](https://hirelofty.com/blog/software-development/how-build-slack-bot-mimics-your-colleague/)
   is a humorous post that uses the 
   [markovify](https://github.com/jsvine/markovify) Markov Chains library to 
   generate responses that are similar to ones other Slack users have said.
diff --git a/content/pages/04-web-development/51-api-integration.markdown b/content/pages/04-web-development/51-api-integration.markdown
@@ -26,7 +26,7 @@ across many implementation areas.
   add them to applications with a bunch of useful code examples.
 
 * Some developers prefer to use 
-  [Requests](http://docs.python-requests.org/en/latest/) instead of an API's 
+  [Requests](https://requests.readthedocs.io/en/master/) instead of an API's 
   helper library. In that case check out this 
   [tutorial on using requests to access web APIs](http://engineering.hackerearth.com/2014/08/21/python-requests-module/).
 
@@ -98,7 +98,7 @@ across many implementation areas.
    requests and responses.
 
 1. Evaluate whether to use a helper library or work with 
-   [Requests](http://docs.python-requests.org/en/latest/). Helper libraries 
+   [Requests](https://requests.readthedocs.io/en/master/). Helper libraries 
    are usually easier to get started with while Requests gives you more 
    control over the HTTP calls.
 
diff --git a/content/pages/05-deployment/00-deployment.markdown b/content/pages/05-deployment/00-deployment.markdown
@@ -112,14 +112,6 @@ guide as they are considered advanced deployment techniques.
   defines delivery versus deployment and walks through a continuous deployment
   workflow.
 
-* [Automation for the People](https://gist.github.com/classam/9e07a36aa63624ca2dda75a1367a53c6)
-  provides an awesome overview of one developer's story for how he gradually
-  learned about deployment automation. The story follows my own journey in
-  many ways, from starting with brittle [Bash](/bourne-again-shell-bash.html) 
-  scripts, moving to Fabric and then on to [Ansible](/ansible.html) for 
-  higher-level [configuration management](/configuration-management.html)
-  and continuous deployment pipelines.
-
 * [5 ways to deploy your Python application in 2017](https://www.youtube.com/watch?v=vGphzPLemZE)
   is a talk from 
   [PyCon US 2017](https://www.youtube.com/channel/UCrJhliKNQ8g0qoE_zvL8eVg/videos)
