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tons of new unit testing resources and update logos
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‎content/pages/02-development-environments/11-terminal-multiplexers.markdown‎

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@@ -42,11 +42,17 @@ Many terminal multiplexer implementations exist, including:
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* [tmux](/tmux.html)
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* screen
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* [screen](/screen.html)
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* byobu
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* pymux
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* [Pymux](https://pypi.org/project/pymux)
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([source code](https://github.com/jonathanslenders/pymux)) is a
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terminal multiplexer implementation
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written in Python that clones the functionality of [tmux](/tmux.html).
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Like tmux and [Screen](/screen.html), Pymux makes it easier for
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programmers to use many shells within a single terminal window during
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development.
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### Terminal multiplexer resources
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gives solid answers on this (now closed) question of the usefulness of
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the major terminal multiplexer implementations.
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* [Pymux discussion on Hacker News](https://news.ycombinator.com/item?id=10831149)
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‎content/pages/02-development-environments/14-pymux.markdown‎

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‎content/pages/03-data/05-object-relational-mappers.markdown‎

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1. [Peewee](/peewee.html)
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1. [The Django ORM](/django-orm.html)
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1. [PonyORM](/pony-orm.html)
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1. [SQLObject](/sqlobject.html)
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1. [SQLObject](http://sqlobject.org/)
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There are other ORMs, such as Canonical's
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[Storm](https://storm.canonical.com/), but most of them do not appear to
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### SQLObject ORM
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[SQLObject](http://sqlobject.org/) is an ORM that has been under active
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open source development since
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open source development for over 14 years, since
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[before 2003](http://sqlobject.org/News1.html#sqlobject-0-5).
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## Schema migrations
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Schema migrations, for example when you need to add a new column to an
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existing table in your database, are not technically part of ORMs. However,
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### SQLObject resources
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SQLObject resources can be found on the [SQLObject ORM page](/sqlobject.html).
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SQLObject has been around for a long time as an open source project but
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unfortunately there are not that many tutorials for it. The following
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talks and posts will get you started. If you take an interest in the project
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and write additional resources, file an
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[issue ticket](https://github.com/mattmakai/fullstackpython.com/issues)
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so we can get them added to this list.
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* This post on
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[Object-Relational Mapping with SQLObject](http://www.andypatterns.com/index.php/blog/object_relational_mapping_pattern_-_using_sqlobj/)
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explains the concept behind ORMs and shows the Python code for how they
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can be used.
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* Ian Bicking presented on SQLObject back in 2004 with a talk on
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[SQLObject and Database Programming in Python](http://www.ianbicking.org/docs/sqlobject-presentation/sqlobject-and-database-programming.html).
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* [Connecting databases to Python with SQLObject](https://www.ibm.com/developerworks/library/os-pythonsqlo/index.html)
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is an older post but still relevant with getting started basics.

‎content/pages/03-data/08-django-orm.markdown‎

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explains the difficulty of supporting Django 1.7 and maintaining South
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migrations for Django 1.6 then goes into how it can be done.
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* [Writing unit tests for Django migrations](https://www.caktusgroup.com/blog/2016/02/02/writing-unit-tests-django-migrations/)
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contains a ton of awesome code examples for testing your
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migrations to ensure data migrations work well throughout
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the lifecycle of your Django project.

‎content/pages/03-data/09-sqlobject.markdown‎

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‎content/pages/03-data/18-numpy.markdown‎

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* [Python NumPy Array Tutorial](https://www.datacamp.com/community/tutorials/python-numpy-tutorial)
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is a starter tutorial specifically focused on using and working
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with NumPy's powerful arrays.
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* [Beyond Numpy Arrays in Python](https://matthewrocklin.com/blog//work/2018/05/27/beyond-numpy)
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is a predecessor to a
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[Numpy Enhancement Proposal](https://github.com/numpy/numpy/pull/11189)
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that recommends how to prepare the scientific computing ecosystme for
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GPU, distributed and sparse arrays.

‎content/pages/03-data/19-scipy.markdown‎

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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
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incompatible in only a few years. The issue is that many science projects
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last decades and therefore cannot follow the rate of change as easily
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as typical software development projects.

‎content/pages/03-data/23-matplotlib.markdown‎

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of detail on customizing your plots and graphs after creating the
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initial visuals.
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* [Visualize World Trends using Seaborn in Python](https://towardsdatascience.com/visualize-world-trends-using-seaborn-in-python-2e563e7d35da)
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shows world life expectancy in plots generated by Matplotlib and Seaborn.
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* [Pandas & Seaborn - A guide to handle & visualize data in Python](https://tryolabs.com/blog/2017/03/16/pandas-seaborn-a-guide-to-handle-visualize-data-elegantly/)
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builds a visualization by starting with [pandas](/pandas.html) for data
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wrangling then outputs charts with Matplotlib and Seaborn.

‎content/pages/04-web-development/39-unit-testing.markdown‎

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Hypothesis is by going through the well-written
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[quickstart](http://hypothesis.readthedocs.io/en/latest/quickstart.html).
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* [mimesis](https://github.com/lk-geimfari/mimesis) generates synthetic test
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data which is useful to apply in your tests.
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* [testify](https://github.com/Yelp/Testify/) was a testing framework
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meant to replace the common unittest+nose combination. However, the team
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behind testify is transitioning to pytest so it's recommended you do
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not use testify for new projects.
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### Unit testing resources
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Unit tests are useful in every project regardless of programming language.
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The following resources provide a good overview of unit testing from
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several viewpoints and follow up with additional depth in testing
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Python-specific applications.
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* [Introduction to Unit Testing](https://qunitjs.com/intro/)
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provides a broad introduction to unit testing, its importance and
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how to get started in your projects.
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* [Dive into Python 3's chapter on unit testing](http://www.diveintopython3.net/unit-testing.html)
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has a complete example with code and a detailed explanation for creating
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unit testing with the
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While the article is from 2006, it remains relevant today for learning
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how to use nose with your projects.
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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 wonderful post with many code examples showing how and
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why to use dependency injection and `@property` to mock unit
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tests.
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* [Unit Testing Doesn’t Affect Codebases the Way You Would Think](https://blog.ndepend.com/unit-testing-affect-codebases/)
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presents research on how unit testing impacts project code and
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ways that it does not. It is only one research report but the findings
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on more unit tests leading to higher Cyclomatic Complexity per method
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are interesting. Perhaps more tests are needed to keep a project
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running due to the increased complexity.
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* [Python unittest with Robert Collins](http://pythontesting.net/transcripts/19-python-unittest-robert-collins-2/)
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is the transcript of an interview with Robert Collins who is a core
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committer of `unittest`.
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* [Why most unit testing is a waste](https://rbcs-us.com/documents/Why-Most-Unit-Testing-is-Waste.pdf)
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discusses how low risk unit tests rarely fail even as the code
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changes and why they do not matter as much to a project's health as
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many developers are led to believe based on the test-driven development
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dogma.
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* [Writing Unit Tests for Django Migrations](https://www.caktusgroup.com/blog/2016/02/02/writing-unit-tests-django-migrations/)
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digs into code examples for testing Django data migrations.

‎redirects/pymux.html‎

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<!DOCTYPE html>
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<html>
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<head>
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<meta http-equiv="refresh" content="0; url=https://www.fullstackpython.com/terminal-multiplexers.html">
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</head>
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<body>
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</body>
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</html>

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