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add a few new resources
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content/pages/03-data/17-scipy-numpy.markdown

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that recommends how to prepare the scientific computing ecosystme for
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GPU, distributed and sparse arrays.
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* [Probability distribution explorer](http://bois.caltech.edu/distribution_explorer/)
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contains graphs for understanding how different probabilities look
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when plotted. There is also code for implementing the visuals in NumPy
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and SciPy.
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### Example NumPy code
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* [SmoothLife](https://github.com/duckythescientist/SmoothLife) is an

content/pages/04-web-development/35-testing.markdown

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then your tests will be run to check that everything is still working
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as intended.
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* [8 great pytest plugins](https://opensource.com/article/18/6/pytest-plugins)
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is a list of plugins that go with PyTest and help with tasks like reducing
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the friction around testing [Django](/django.html), as well as running
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tests in parallel.
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* This test-driven development series shows you how to write an interpreter
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in Python and contains a ton of great code samples to learn from:
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content/pages/06-devops/06-web-app-performance.markdown

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database queries, page size and many other factors.
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### Performance and load testing tools
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* [Falco](https://github.com/theodo/falco)
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### Load testing resources
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* [Load Testing with Locust.io & Docker Swarm](https://wheniwork.engineering/load-testing-with-locust-io-docker-swarm-d78a2602997a)
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shows you how to set up load tests using [Docker](/docker.html) containers

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