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Python Essentials

You're reading from   Python Essentials Modernize existing Python code and plan code migrations to Python using this definitive guide

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Product type Paperback
Published in Jun 2015
Publisher Packt
ISBN-13 9781784390341
Length 298 pages
Edition 1st Edition
Languages
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Author (1):
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Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
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Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Simple Data Types 3. Expressions and Output 4. Variables, Assignment and Scoping Rules 5. Logic, Comparisons, and Conditions 6. More Complex Data Types 7. Basic Function Definitions 8. More Advanced Functions 9. Exceptions 10. Files, Databases, Networks, and Contexts 11. Class Definitions 12. Scripts, Modules, Packages, Libraries, and Applications 13. Metaprogramming and Decorators 14. Fit and Finish – Unit Testing, Packaging, and Documentation 15. Next Steps Index

Using generator expressions and comprehensions


We can think of simple generator expressions as an operator with three operands. The syntax for these three operands parallels the for statement:

(expression for target in source)

We specify an expression which is evaluated for each value assigned to a target variable from a source. There are more complex generators, which we'll look at later.

Generator expressions can be used freely in Python. They can be used anywhere in a sequence or a collection that is meaningful.

It's important to note that a generator expression is lazy, or "non-strict." It doesn't actually calculate anything until some consuming operation demands values from it. To see this, we can try to evaluate a generator expression at the REPL:

>>> (2*x+1 for x in range(5))
<generator object <genexpr> at 0x1023981e0>

Python tells us only that we've created a generator object. Since we didn't write an expression to consume the values, all we saw was the object, passively...

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