By Steven Lott
- Implement universal practical programming layout styles and strategies in Python
- Learn how one can choose from significant and sensible ways according to expressiveness, readability, and performance
- Apply sensible Python to universal Exploratory facts research (EDA) programming problems
- Use Python's generator services and generator expressions to paintings with collections in a non-strict (or lazy) manner
- Utilize Python library modules together with itertools, functools, multiprocessing, and concurrent.futures for effective practical programs
- Use Python strings with object-oriented suffix notation and prefix notation
- Avoid stateful sessions by way of using households of tuples
- Design and enforce decorators to create composite functions
- Use services like max(), min(), map(), filter(), and sorted()
- Write complex higher-order functions
Python’s easy-to-learn and extensible skills supply a few useful programming positive factors so you might deliver into your workflow, particularly within the realm of information science.
If you’re a Python developer who desires to observe find out how to take the ability of practical programming and convey it into your individual courses then this publication is key for you, whether you recognize subsequent to not anything in regards to the paradigm. beginning with a common evaluate of useful ideas you’ll discover universal practical positive factors corresponding to top notch and higher-order features, natural features and extra, and the way those are comprehensive in Python to provide you the middle foundations you’ll construct upon. After that, you’ll become aware of universal practical optimizations for Python to aid your apps achieve even greater speeds. You'll additionally discover facts coaching suggestions and knowledge exploration extensive, in addition to studying how the Python commonplace library matches the useful programming version. eventually, to replenish your trip into the realm of functionality Python you’ll at examine the PyMonad venture and a few higher examples to place every little thing into perspective.
With sensible Python Programming by means of your facet you’ll comprehend the middle techniques of functionality Python, its impression at the programming workflow, and the way to enforce it in Python, supplying you with the power to take your purposes to an excellent greater level.
What you are going to learn
About the Author
Steven F. Lott has been programming because the 70s, whilst pcs have been huge, dear, and infrequent. As a freelance software program developer and architect, he has labored on thousands of initiatives, from very small to large. He's been utilizing Python to unravel enterprise difficulties for over 10 years.
Table of Contents
- Introducing sensible Programming
- Introducing a few practical Features
- Functions, Iterators, and Generators
- Working with Collections
- Higher-order Functions
- Recursions and Reductions
- Additional Tuple Techniques
- The Itertools Module
- More Itertools Techniques
- The Functools Module
- Decorator layout Techniques
- The Multiprocessing and Threading Modules
- Conditional Expressions and the Operator Module
- The Pymonad Library
- A practical method of net Services
- Optimizations and Improvements
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Extra resources for Functional Python Programming
In the second example, we materialized a list object from the generator function. This is handy for seeing the output and writing unit test cases. In the third example, we saw one limitation of generator functions: there's no len(). [ 46 ] Chapter 3 The other limitation of generator functions is that they can only be used once. For example, look at the following command snippet: >>> result= pfactorsl(1560) >>> sum(result) 27 >>> sum(result) 0 The first evaluation of the sum() method performed evaluation of the generator.
Here's a function created by assigning lambda to a variable: >>> mersenne = lambda x: 2**x-1 >>> mersenne(17) 131071 We created a pure function using lambda and assigned this to the variable mersenne. This is a callable object with a single argument value that returns a single value. Because lambda's can't have assignment statements, they're always pure functions and suitable for functional programming. Higher-order functions We can achieve expressive, succinct programs using higher-order functions.
The original example of this approximation algorithm was shown in the Miranda language. It's easy to see that there are few profound differences between Miranda and Python. The biggest difference is Miranda's ability to construct cons, a value back into an iterable, doing a kind of unget. This parallelism between Miranda and Python gives us confidence that many kinds of functional programming can be easily done in Python. Exploratory Data Analysis Later in this book, we'll use the field of EDA as a source for concrete examples of functional programming.