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content/pages/01-introduction/02-python-programming-language.markdown

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@@ -121,10 +121,6 @@ There's an entire page on [best Python resources](/best-python-resources.html)
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with links but the following resources are a better fit for when you're past
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the very beginner topics.
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* [The Python Ecosystem: An Introduction](http://mirnazim.org/writings/python-ecosystem-introduction/)
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provides context for virtual machines, Python packaging, pip, virtualenv
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and many other topics after learning the basic Python syntax.
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* The [Python Subreddit](http://www.reddit.com/r/python) rolls up great
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Python links and has an active community ready to answer questions from
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beginners and advanced Python developers alike.

content/pages/03-data/10-no-sql-datastore.markdown

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## NoSQL third-party services
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* [MongoHQ](http://www.mongohq.com/home) provides MongoDB as a service. It's
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* [Compose](https://www.compose.com/) provides MongoDB as a service. It's
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easy to set up with either a standard LAMP stack or on Heroku.
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* Relational databases such as MySQL and PostgreSQL have added features in
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more recent versions that mimic some of the capabilities of NoSQL data
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stores. For example, check out this blog post on
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[using MySQL as a key-value pair store](http://engineering.wix.com/2015/12/10/scaling-to-100m-mysql-is-a-better-nosql/)
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and this post on
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[storing JSON data in PostgreSQL](https://blog.codeship.com/unleash-the-power-of-storing-json-in-postgres/).
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content/pages/03-data/21-matplotlib.markdown

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is a long and comprehensive tutorial that walks through obtaining,
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cleaning and visualizing data.
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* The
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[Python cheat sheet for Matplotlib on GitHub](https://github.com/juliangaal/python-cheat-sheet/blob/master/Matplotlib/Matplotlib.md)
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has a slew of example code blocks for generating plots.
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* [Matplotlib Cheat Sheet: Plotting in Python](https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet)
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contains some handy snippets of code to perform common plotting operations
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in Matplotlib.

content/pages/04-web-development/37-mkdocs.markdown

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is likely the best place to go when you are just getting set up with your
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first site that uses this project.
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* [Building Markdown-based Sites with MkDocs](http://toolsfortheweb.net/cms/building-markdown-based-sites-with-mkdocs/)
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provides an initial perspective on using MkDocs to build a static website
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along with some of the advantages and disadvantages that come with using
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this static site generator.
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* [Mkdocs documentation](https://visibilityspots.org/mkdocs.html) is a quick
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tutorial to get MkDocs installed and modify the initial mkdocs.yml file.
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content/pages/04-web-development/38-testing.markdown

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* [Python Mocking 101: Fake It Before You Make It](https://blog.fugue.co/2016-02-11-python-mocking-101.html)
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explains what mocking is and is not, and shows how to use the `patch`
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function to accomplish it in your project.
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[Revisiting Unit Testing and Mocking in Python](https://blog.fugue.co/2017-07-18-revisiting-unit-testing-and-mocking-in-python.htmlZ)
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[Revisiting Unit Testing and Mocking in Python](https://blog.fugue.co/2017-07-18-revisiting-unit-testing-and-mocking-in-python.html)
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is a follow-up post that expands upon using the `patch` function
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along with dependency injection.
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content/posts/171113-devops-maintaining-contributing-open-source.markdown

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[this GitHub issue thread named "**thank you**"](https://github.com/jhund/filterrific/issues/147#issuecomment-341867147)
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is incredible to read. The issue ticket blew up on the front page of Hacker
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News as an example of how powerful genuine positive comments can be for
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project maintainers. Every time I get a thank you tweet
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([like this one](https://twitter.com/foozmeat/status/928834578864750592)),
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email or GitHub issue it certainly helps to motivate me to continue working
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hard on Full Stack Python.
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project maintainers.
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[**Contributing to open source**](https://talkpython.fm/episodes/show/132/contributing-to-open-source)
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is a recent [Talk Python to Me](https://talkpython.fm/) podcast episode in

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