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Learn Web Development with Python

You're reading from   Learn Web Development with Python Get hands-on with Python Programming and Django web development

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Product type Course
Published in Dec 2018
Publisher
ISBN-13 9781789953299
Length 796 pages
Edition 1st Edition
Languages
Tools
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Authors (3):
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Fabrizio Romano Fabrizio Romano
Author Profile Icon Fabrizio Romano
Fabrizio Romano
Gaston C. Hillar Gaston C. Hillar
Author Profile Icon Gaston C. Hillar
Gaston C. Hillar
Arun Ravindran Arun Ravindran
Author Profile Icon Arun Ravindran
Arun Ravindran
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Toc

Table of Contents (33) Chapters Close

Title Page
About Packt
Contributors
Preface
1. A Gentle Introduction to Python FREE CHAPTER 2. Built-in Data Types 3. Iterating and Making Decisions 4. Functions, the Building Blocks of Code 5. Saving Time and Memory 6. OOP, Decorators, and Iterators 7. Files and Data Persistence 8. Testing, Profiling, and Dealing with Exceptions 9. Concurrent Execution 10. Debugging and Troubleshooting 11. Installing the Required Software and Tools 12. Working with Models, Migrations, Serialization, and Deserialization 13. Creating API Views 14. Using Generalized Behavior from the APIView Class 15. Understanding and Customizing the Browsable API Feature 16. Using Constraints, Filtering, Searching, Ordering, and Pagination 17. Securing the API with Authentication and Permissions 18. Applying Throttling Rules and Versioning Management 19. Automating Tests 20. Solutions 21. Templates 22. Admin Interface 23. Forms 24. Security 25. Working Asynchronously 26. Creating APIs 27. Production-Ready 1. Other Books You May Enjoy Index

What are the drawbacks?


Probably, the only drawback that one could find in Python, which is not due to personal preferences, is its execution speed. Typically, Python is slower than its compiled brothers. The standard implementation of Python produces, when you run an application, a compiled version of the source code called byte code (with the extension .pyc), which is then run by the Python interpreter.

The advantage of this approach is portability, which we pay for with a slowdown due to the fact that Python is not compiled down to machine level as are other languages.

However, Python speed is rarely a problem today, hence its wide use regardless of this suboptimal feature. What happens is that, in real life, hardware cost is no longer a problem, and usually it's easy enough to gain speed by parallelizing tasks. Moreover, many programs spend a great proportion of the time waiting for IO operations to complete; therefore, the raw execution speed is often a secondary factor to the overall performance. When it comes to number crunching though, one can switch to faster Python implementations, such as PyPy, which provides an average five-fold speedup by implementing advanced compilation techniques (check http://pypy.org/ for reference).

When doing data science, you'll most likely find that the libraries that you use with Python, such as Pandas and NumPy, achieve native speed due to the way they are implemented.

If that wasn't a good-enough argument, you can always consider that Python has been used to drive the backend of services such as Spotify and Instagram, where performance is a concern. Nonetheless, Python has done its job perfectly adequately.

You have been reading a chapter from
Learn Web Development with Python
Published in: Dec 2018
Publisher:
ISBN-13: 9781789953299
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