7. Basic Function Definitions | Python Essentials

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Python Essentials

By : Steven F. Lott

4.5 (2)

Python Essentials

4.5 (2)

By: Steven F. Lott

Overview of this book

This book is designed for Python 2 developers who want to get to grips with Python 3 in a short period of time. It covers the key features of Python, assuming you are familiar with the fundamentals of Python 2.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started

1. Getting Started

Installation or upgrade

Using the Read-Evaluate-Print Loop (REPL)

Interacting with the help subsystem

Creating simple script files

The Python ecosystem

Looking at other Python interpreters

Summary

2. Simple Data Types

2. Simple Data Types

Introducing the built-in operators

The numeric tower

The math libraries

Using bits and Boolean values

Working with sequences

Using string and bytes values

Using the tuple collection

The None object

The consequences of immutability

Using the built-in conversion functions

Summary

3. Expressions and Output

3. Expressions and Output

Expressions, operators, and data types

The print() function

Examining syntax rules

Splitting, partitioning, and joining strings

Summary of the standard string libraries

Summary

4. Variables, Assignment and Scoping Rules

4. Variables, Assignment and Scoping Rules

Simple assignment and variables

Multiple assignment

Using the head, *tail assignment

Augmented assignment

The input() function

Python language concepts

The Python namespace concept

Summary

5. Logic, Comparisons, and Conditions

5. Logic, Comparisons, and Conditions

Boolean data and the bool() function

Comparison operators

Logic operators – and, or, not, if-else

The if-elif-else statement

The pass statement as a placeholder

The assert statement

The logic of the None object

Summary

6. More Complex Data Types

6. More Complex Data Types

The mutability and immutability distinction

Using the list collection

Using collection functions

Using the set collection

Mappings

Processing collections with the for statement

Iterating with the while statement

The continue and break statements

Using the else clause on a loop

Summary

7. Basic Function Definitions

7. Basic Function Definitions

Looking at the five kinds of callables

Defining functions with positional parameters

Mutable and immutable argument values

Defining optional parameters via default values

Using the "everything else" notations of * and **

Nested function definitions

Working with namespaces

Defining lambdas

Writing additional function annotations

Summary

8. More Advanced Functions

8. More Advanced Functions

Using the for statement with iterable collections

Consequences and next steps

Using generator expressions and comprehensions

Defining generator functions with the yield statement

Using the higher-order functions

Using the built-in reductions – max, min, and reduce

Three ways to sort a sequence

Functional programming design patterns

Summary

9. Exceptions

9. Exceptions

The core exception concept

Using the try and except statements

Matching exception classes in an except clause

Creating our own exceptions

Using a finally clause

Use cases for exceptions

Issuing warnings instead of exceptions

Permission versus forgiveness – a Pythonic approach

Summary

10. Files, Databases, Networks, and Contexts

10. Files, Databases, Networks, and Contexts

The essential file concept

Using a context manager via the with statement

Using the shelve module as a database

Web services and Internet protocols

Physical format considerations

Summary

11. Class Definitions

11. Class Definitions

Creating a class

Writing the suite of statements in a class

Using instance variables and methods

Pythonic object-oriented programming

Using properties

Using inheritance to simplify class definitions

Using class methods and attributes

Using __slots__ to save storage

The ABCs of abstract base classes

Summary

12. Scripts, Modules, Packages, Libraries, and Applications

12. Scripts, Modules, Packages, Libraries, and Applications

Script file rules

Running a script by the filename

Creating a reusable module

Creating a hybrid library/application module

Creating a package

Designing alternative implementations

Seeing the package search path

Summary

13. Metaprogramming and Decorators

13. Metaprogramming and Decorators

Simple metaprogramming with decorators

Defining our own decorator

More complex metaprogramming with metaclasses

Summary

14. Fit and Finish – Unit Testing, Packaging, and Documentation

14. Fit and Finish – Unit Testing, Packaging, and Documentation

Writing docstrings

Writing unit tests with doctest

Using the unittest library for testing

Using other add-on test libraries

Logging events and conditions

Writing documentation with RST markup

Organizing Python code

Summary

15. Next Steps

15. Next Steps

Leveraging the standard library

Leveraging PyPI – the Python Package Index

Types of applications

Building CLI applications

Building GUI applications

Building web applications

Plugging into a MapReduce framework

Summary

Index

Index

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Defining functions with positional parameters

The essential Python function definition is built with the def statement. We provide a name, the names of the parameters, and an indented suite of statements that is the body of the function. The return statement provides the range of values.

The syntax looks like this:

def prod(sequence):
    p= 1
    for item in sequence:p *= item
    return p

We've defined a name, prod, and provided a list of only one parameter, sequence. The body of the function includes three statements: assignment, for, and return. The expression in the return statement provides the resulting value.

This fits the mathematical idea of a function reasonably well. The domain of values is any numeric sequence, the range will be a value of the a type which reflects the data types in the sequence.

We evaluate a function by simply using the name and a specific value for the argument in an expression:

>>> prod([1,2,3,4])
24
>>> prod(range(1,6))
120

In the first example...

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