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removing link rot
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content/pages/03-data/16-pandas.markdown

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@@ -82,10 +82,6 @@ is a data structures and analysis library.
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[Tabula](https://tabula.technology/) library that makes it easier to
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extract table data from PDF files.
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* [Analyzing Browser History Using Python and Pandas](https://applecrazy.github.io/blog/2017-11-12/analyzing-browser-hist-using-python)
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shows how to take data from Google Chrome and start to visualize it
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with pandas and [matplotlib](/matplotlib.html).
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* [Time Series Forecast Case Study with Python: Monthly Armed Robberies in Boston](https://machinelearningmastery.com/time-series-forecast-case-study-python-monthly-armed-robberies-boston/)
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walks through the data wrangling, analysis and visualization steps
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with a public data set of murders in Boston from 1966 to 1975. This

content/pages/03-data/17-scipy-numpy.markdown

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plotter. This is a very cool example project that ties together
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the scientific world and the art world.
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* [Lectures in Quantitative Economics: SciPy](https://lectures.quantecon.org/py/scipy.html)
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provides a good overview of SciPy compared to the specific NumPy
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project, as well as explanations for the wrappers SciPy provides
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over lower-level FORTRAN libraries.
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* [A plea for stability in the SciPy ecosystem](http://blog.khinsen.net/posts/2017/11/16/a-plea-for-stability-in-the-scipy-ecosystem/)
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presents concerns from one scientist's perspective about how fast the
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Python programming ecosystem changes and that code can become backwards

content/pages/03-data/21-matplotlib.markdown

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is an awesome getting started tutorial that breaks through the confusing
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beginner steps so you can quick start using the plotting library.
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* [Web Scraping With Python: Scrapy, SQL, Matplotlib To Gain Web Data Insights](http://www.scrapingauthority.com/python-scrapy-mysql-and-matplotlib-to-gain-web-data-insights/)
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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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* [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/05-deployment/32-configuration-management.markdown

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is an openly biased but detailed post on why to choose SaltStack over
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Ansible in certain situations.
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* [Ansible vs. Shell Scripts](https://valdhaus.co/writings/ansible-vs-shell-scripts/)
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provides some perspective on why using a configuration management tool is a
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better choice than venerable but brittle shell scripts.
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* [Ansible vs. Chef](http://tjheeta.github.io/2015/04/15/ansible-vs-chef/)
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is a comparsion of Ansible with the Chef configuration management tool.
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content/pages/05-deployment/33-ansible.markdown

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configures macOS with various applications and developer tools such as
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[Docker](/docker.html), Homebrew and [Sublime Text](/sublime-text.html).
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* [ansible-nginx-haproxy-elasticsearch](https://github.com/gp187/ansible-nginx-haproxy-elasticsearch)
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sets up a server with [Nginx](/nginx.html), HAProxy and ElasticSearch.
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### Specific Ansible topics
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* [An Ansible2 Tutorial](https://serversforhackers.com/c/an-ansible2-tutorial)
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* [DevOps from Scratch, Part 1: Vagrant & Ansible](https://www.kevinlondon.com/2016/09/19/devops-from-scratch-pt-1.html)
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* [Ansible: Post-Install Setup](https://valdhaus.co/writings/ansible-post-install/)
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* [How To Use Vault to Protect Sensitive Ansible Data on Ubuntu 16.04](https://www.digitalocean.com/community/tutorials/how-to-use-vault-to-protect-sensitive-ansible-data-on-ubuntu-16-04)
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* [How to use Ansible Variables and Vaults](https://www.expressvpn.com/blog/ansible-variables-vaults/)
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* [CI for Ansible playbooks which require Ansible Vault protected variables](https://www.jeffgeerling.com/blog/2017/ci-ansible-playbooks-which-require-ansible-vault-protected-variables)

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