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adding new sentry exception handling blog post
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‎content/pages/02-development-environments/06-jupyter-notebook.markdown‎

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examines the growth of the Jupyter ecosystem and the jump from exploratory
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analysis notebooks to production notebooks.
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* [Jupyter Notebook Best Practices](https://levelup.gitconnected.com/jupyter-notebook-best-practices-fc326eb5cd22)
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contains tips for beginners such as learning shortcuts and properly
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documenting the analysis you work on.
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* [How to Version Control Jupyter Notebooks](https://nextjournal.com/schmudde/how-to-version-control-jupyter)
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explains how Jupyter Notebooks are stored in JSON, the issues with that
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format for [source control](/source-control.html) and how to get

‎content/pages/02-development-environments/10-powershell.markdown‎

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* [Invoke-Build](https://github.com/nightroman/Invoke-Build) is a build
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automation tool. There is also extensive documentation on their
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[wiki](https://github.com/nightroman/Invoke-Build/wiki).
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* [Two months with Powershell on a UNIX](https://code.joejag.com/2020/a-month-with-powershell.html)
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examines one developer's experience using PowerShell not on Windows
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but on a \*nix [operating system](/operating-systems.html). The author
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covers the advantages and disadvantages he found during his experience
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and some of the bugs he hopes are fixed in PowerShell 7.

‎content/pages/02-development-environments/17-localhost-tunnels.markdown‎

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* [ngrok](https://ngrok.com/) is the service I use most often. It is
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easy and worth the small fee to upgrade your account with a few
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extra features such as fixed, customizable subdomains.
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extra features such as fixed, customizable subdomains. There is
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also a
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[Python wrapper for ngrok called pyngrok](https://github.com/alexdlaird/pyngrok)
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that makes it easy to programmatically access the ngrok client
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from Python applications.
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* [Localtunnel](https://localtunnel.github.io/www/) is a localhost
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tunnel written in Node.js.

‎content/pages/03-data/00-data.markdown‎

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[Jupyter Notebook](/jupyter-notebook.html) that [Stripe](/stripe.html)
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for their data analysis across the company.
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* [The Definitive Data Scientist Environment Setup](https://davidadrian.cc/definitive-data-scientist-setup/)
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explains how to set up both a hardware and software configuration that
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is conducive to data science research and analysis.
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‎content/pages/03-data/02-postgresql.markdown‎

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past five years. It's amazing to see how far this project has come and
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how it continues to evolve.
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* [The Internals of PostgreSQL](http://www.interdb.jp/pg/) is a book
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that goes into how PostgreSQL works, including core topics such as
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[query processing](http://www.interdb.jp/pg/pgsql03.html),
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[concurrency control](http://www.interdb.jp/pg/pgsql05.html) and the
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[layout of heap table files](http://www.interdb.jp/pg/pgsql01.html).
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* [PostgreSQL Weekly](http://postgresweekly.com/) is a weekly newsletter of
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PostgreSQL content from around the web.
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* [My Favorite PostgreSQL Extensions - Part One](https://severalnines.com/database-blog/my-favorite-postgresql-extensions-part-one)
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and
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[part two](https://severalnines.com/database-blog/my-favorite-postgresql-extensions-part-two)
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are roundups of useful PostgreSQL extensions that augment the
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standard PostgreSQL functionality.
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* [An introduction to PostgreSQL physical storage](http://rachbelaid.com/introduction-to-postgres-physical-storage/)
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provides a solid walkthrough of where PostgreSQL files are located on
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disk, how the files store your data and what mappings are important for

‎content/pages/03-data/03-mysql.markdown‎

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goes into details for why the company Userlike migrated from their MySQL
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database setup to PostgreSQL.
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* [MySQL sharding at Quora](https://www.quora.com/q/quoraengineering/MySQL-sharding-at-Quora)
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provides details behind Quora's at-scale infrastructure and
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how their MySQL sharding evolved over time.
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* [Growing up with MySQL](https://nylas.com/blog/growing-up-with-mysql/) is
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a story about how one company went through dramatic growth and had to keep
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up with it by quickly scaling their MySQL database.

‎content/pages/03-data/16-pandas.markdown‎

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shows how to use the `crosstab` function in pandas so you can summarize
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and group data.
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* [Calculating streaks in pandas](https://joshdevlin.com/blog/calculate-streaks-in-pandas/)
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shows how to measure and report on streaks in data, which is where
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several events happen in a row consecutively.
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* [How to Convert a Python Dictionary to a Pandas DataFrame](https://www.marsja.se/how-to-convert-a-python-dictionary-to-a-pandas-dataframe/)
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is a straightforward tutorial with example code for loading and adding
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data stored in a typical Python dictionary into a DataFrame.
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* [How to use Pandas read_html to Scrape Data from HTML Tables](https://www.marsja.se/how-to-use-pandas-read_html-to-scrape-data-from-html-tables/)
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has a bunch of great code examples that show how to load
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data from HTML directly into your DataFrames.
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* [How to download fundamentals data with Python](http://theautomatic.net/2020/05/05/how-to-download-fundamentals-data-with-python/)
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shows how to obtain and use financial data, such as balance sheets,
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stock prices, and various ratios to perform your own analysis on.

‎content/pages/04-web-development/00-web-development.markdown‎

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that encompasses all the activities involved with websites and web
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applications.
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## How does Python fit into web development?
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Python can be used to build server-side web applications. While a
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[web framework](/web-frameworks.html) is not required to build web apps,
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that the visualization unfortunately stops around the beginning of 2013 but
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it's a good way to explore what happened in the first 24 years.
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* [What happens when?](https://github.com/alex/what-happens-when) is an
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incredibly detailed answer to the questions "What happens when you
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type google.com into your browser's address box and press enter?" that
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seems straightforward on the surface until you really dig in.
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* [How browsers work](https://www.html5rocks.com/en/tutorials/internals/howbrowserswork/)
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provides an overview with solid detail on how browsers take the HTML,
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CSS, JavaScript, images and other files as input and render webpages as

‎content/pages/04-web-development/02-django.markdown‎

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order to build and deploy a RESTful API powered by Python, Django,
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* [User Interaction With Forms](https://www.mattlayman.com/understand-django/user-interaction-forms/)
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explains general web form input, how Django handles forms via POST requests,
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different types of input such as CharFields, DateFields and EmailFields,
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and validating that input.
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* This 3-part Django project optimization guide covers a wide range of
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advanced topics such as
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[Profiling and Django settings](https://dizballanze.com/django-project-optimization-part-1/),
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[working with databases](https://dizballanze.com/django-project-optimization-part-2/)
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and [caching](https://dizballanze.com/django-project-optimization-part-3/).
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* [Caching in Django](https://testdriven.io/blog/django-caching/) is a detailed
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look at the configuration required for caching and how to measure the
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performance improvements once you have it in place.
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* [Mental Models for Class Based Views](https://djangodeconstructed.com/2020/01/03/mental-models-for-class-based-views/)
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provides some comparison points between class based views (CBVs) and
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function based views and the author's opinions for how you can better
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is a good post to read for figuring out how to use [Gulp](http://gulpjs.com/)
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for handling front end tools in development and production Django sites.
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* [Django Testing Cheat Sheet](https://www.valentinog.com/blog/testing-django/)
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covers many common scenarios for Django applications such as testing
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POST requests, request headers, authentication, and large numbers of
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model fields in the [Django ORM](/django-orm.html).
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* [Getting Started with Django Testing](http://howchoo.com/g/mjkwmtu5zdl/getting-started-with-django-testing)
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will help you stop procrastinating on testing your Django projects if you're
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uncertain where to begin.

‎content/pages/04-web-development/03-flask.markdown‎

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[Docker](/docker.html). It shows how to [deploy](/deployment.html) it to an
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Amazon EC2 instance then scale the services on Amazon EC2 Container Service (ECS).
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* [Build a Video Chat Application with Python, JavaScript and Twilio Programmable Video](https://www.twilio.com/blog/build-video-chat-application-python-javascript-twilio-programmable-video)
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shows how to use [Twilio](/twilio.html)
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[Programmable Video](https://www.twilio.com/video) to build cross-platform
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(web, iOS and Android) video into Flask applications.
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* [Why and how to handle exceptions in Python Flask](https://opensource.com/article/17/3/python-flask-exceptions)
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has some great example code and reasons why you should code defensively
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by anticipating and handling the unhappy path exceptions in your Flask

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