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Home > Programming > Programming Language > Python Essentials
Python Essentials
Python Essentials

Python Essentials: Modernize existing Python code and plan code migrations to Python using this definitive guide

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Python Essentials

Chapter 2. Simple Data Types

Now we'll look at a number of data types that are built-in as well as some that are part of Python's standard library. We'll start with Python's numeric types. These include three built-in types: int, float, and complex, plus the standard library types Fraction and Decimal.

We'll also look at strings, str, and simple collections, tuple. These are more complex than numbers because they contain multiple items. Since their behavior is less complex than the kinds of objects we'll see in later chapters, they serve as a good introduction to the general concept of sequences in Python.

Note the capitalization of the names of Fraction and Decimal. The built-in type names start with a lowercase letter. Types that we must import have a module name that starts with a lowercase letter, but the type name starts with a capital letter. This convention is widespread, but not universal.

All of the types we'll look at in this chapter have...

Introducing the built-in operators

Before looking at the various kinds of numbers available, we'll introduce the Python operators. The operators fall into three broad groups:

Group

Operators

Arithmetic

+, -, *, **, /, //, %

Bit-oriented

<<, >>, &, |, ^, ~

Comparison

<, >, <=, >=, ==, !=

The differences between these groups are partly subjective. There's only a small technical difference in the way the comparison operators work. Most of the operators are binary, only one (~) is unary, and a few (+, -, *, **) can be used in either context.

The +, -, *, /, and % operators have meanings similar to those used other programming languages. There is an arithmetic meaning for – and +. Python adds the ** operator when raising a number to a power. The ** operator takes higher precedence than the unary form -; this means that -2**4 is -16.

Bit-oriented operators apply only to integers. They also apply to sets. These are emphatically not logical...

The numeric tower

We've seen Python's three built-in numeric types: int, float, complex, plus two more types—Fraction and Decimal—imported from the standard library. The numbers module in the standard library provides four base class definitions for the numeric types. We rarely need to use this module explicitly; it's a convention that we need when we have to implement our own numeric types.

The numeric types form a kind of "tower" that parallels the various kinds of numbers seen in conventional mathematics. The foundation of the tower is integers. Rational numbers are above integers. Floating-point values are still further up, and complex numbers are at the top of the tower.

A common expectation is that a language will automatically coerce numeric values to permit expressions such as 2*2.718 to work properly and produce a useful result. When multiplying an integer by a float value, we expect integers to be coerced to a floating-point value.

In order...

The math libraries

The Python library has six modules relevant to mathematical work. These are described in Chapter 9, Numeric and Mathematical Modules, of the Python Standard Library document. Beyond this, we have external libraries such as NumPy (http://www.numpy.org) and SciPy (http://www.scipy.org). These libraries include vast collections of sophisticated algorithms. For an even more sophisticated toolset, the Anaconda project (https://store.continuum.io/cshop/anaconda/) combines NumPy, SciPy, and 18 more packages.

These are the relevant built-in numeric packages:

  • numbers: This module defines the essential numeric abstractions. We rarely need this unless we're going to invent an entirely new kind of number.
  • math: This module has a large collection of functions. It includes basic sqrt(), the various trigonometric functions (sine, cosine, and so on) and the various log-related functions. It has functions for working with the internals of floating-point numbers. It also has the gamma...

Using bits and Boolean values

As noted earlier, the bit-oriented operators &, |, ^, and ~ have nothing to do with Python's actual Boolean operators and, or, not, and if-else. We'll look at Boolean values, logic operators, and related programming in Chapter 5, Logic, Comparisons, and Conditions.

If we misuse the bit-oriented operators & or | in place of a logical and or or, things may appear very peculiar:

>>> 5 > 6 & 3 > 1
True
>>> (5 > 6) & (3 > 1)
False

The first example is clearly wrong. Why? This is because the & operator has relatively high priority. It's not a logical connective, it's more like an arithmetic operator. The & operator is performed first: 6&3 evaluates to 2. Given this, the resulting expression, 5 > 2 > 1, is True.

When we group the comparisons to perform them first, we'll get a False for 5>6, and a True for 3>1. When we apply the & operator the result will be False, which...

Working with sequences

In this chapter, we'll introduce Python sequence collections. We'll look at strings and tuples as the first two examples of this class. Python offers a number of other sequence collections; we'll look at them in Chapter 6, More Complex Data Types. All of these sequences have common features.

Python sequences identify the individual elements by position. Position numbers start with zero. Here's a tuple collection with five elements:

>>> t=("hello", 3.14, 23, None, True)
>>> t[0]
'hello'
>>> t[4]
True

In addition to the expected ascending numbers, Python also offers reverse numbering. Position -1 is the end of the sequence:

>>> t[-1]
True
>>> t[-2]
>>> t[-5]
'hello'

Note that position 3 (or -2) has a value of None. The REPL doesn't display the None object, so the value of t[-2] appears to be missing. For more visible evidence that this value is None, use this:

&gt...

Introducing the built-in operators

Before looking at the various kinds of numbers available, we'll introduce the Python operators. The operators fall into three broad groups:

Group

Operators

Arithmetic

+, -, *, **, /, //, %

Bit-oriented

<<, >>, &, |, ^, ~

Comparison

<, >, <=, >=, ==, !=

The differences between these groups are partly subjective. There's only a small technical difference in the way the comparison operators work. Most of the operators are binary, only one (~) is unary, and a few (+, -, *, **) can be used in either context.

The +, -, *, /, and % operators have meanings similar to those used other programming languages. There is an arithmetic meaning for – and +. Python adds the ** operator when raising a number to a power. The ** operator takes higher precedence than the unary form -; this means that -2**4 is -16.

Bit-oriented operators apply only to integers. They also apply to sets. These are emphatically not logical operators. The actual...

The numeric tower

We've seen Python's three built-in numeric types: int, float, complex, plus two more types—Fraction and Decimal—imported from the standard library. The numbers module in the standard library provides four base class definitions for the numeric types. We rarely need to use this module explicitly; it's a convention that we need when we have to implement our own numeric types.

The numeric types form a kind of "tower" that parallels the various kinds of numbers seen in conventional mathematics. The foundation of the tower is integers. Rational numbers are above integers. Floating-point values are still further up, and complex numbers are at the top of the tower.

A common expectation is that a language will automatically coerce numeric values to permit expressions such as 2*2.718 to work properly and produce a useful result. When multiplying an integer by a float value, we expect integers to be coerced to a floating-point value.

In order for this to work, there are two general...

The math libraries

The Python library has six modules relevant to mathematical work. These are described in Chapter 9, Numeric and Mathematical Modules, of the Python Standard Library document. Beyond this, we have external libraries such as NumPy (http://www.numpy.org) and SciPy (http://www.scipy.org). These libraries include vast collections of sophisticated algorithms. For an even more sophisticated toolset, the Anaconda project (https://store.continuum.io/cshop/anaconda/) combines NumPy, SciPy, and 18 more packages.

These are the relevant built-in numeric packages:

  • numbers: This module defines the essential numeric abstractions. We rarely need this unless we're going to invent an entirely new kind of number.

  • math: This module has a large collection of functions. It includes basic sqrt(), the various trigonometric functions (sine, cosine, and so on) and the various log-related functions. It has functions for working with the internals of floating-point numbers. It also has the gamma function...

Using bits and Boolean values

As noted earlier, the bit-oriented operators &, |, ^, and ~ have nothing to do with Python's actual Boolean operators and, or, not, and if-else. We'll look at Boolean values, logic operators, and related programming in Chapter 5, Logic, Comparisons, and Conditions.

If we misuse the bit-oriented operators & or | in place of a logical and or or, things may appear very peculiar:

>>> 5 > 6 & 3 > 1
True
>>> (5 > 6) & (3 > 1)
False

The first example is clearly wrong. Why? This is because the & operator has relatively high priority. It's not a logical connective, it's more like an arithmetic operator. The & operator is performed first: 6&3 evaluates to 2. Given this, the resulting expression, 5 > 2 > 1, is True.

When we group the comparisons to perform them first, we'll get a False for 5>6, and a True for 3>1. When we apply the & operator the result will be False, which is what we expected. Using...

Working with sequences

In this chapter, we'll introduce Python sequence collections. We'll look at strings and tuples as the first two examples of this class. Python offers a number of other sequence collections; we'll look at them in Chapter 6, More Complex Data Types. All of these sequences have common features.

Python sequences identify the individual elements by position. Position numbers start with zero. Here's a tuple collection with five elements:

>>> t=("hello", 3.14, 23, None, True)
>>> t[0]
'hello'
>>> t[4]
True

In addition to the expected ascending numbers, Python also offers reverse numbering. Position -1 is the end of the sequence:

>>> t[-1]
True
>>> t[-2]
>>> t[-5]
'hello'

Note that position 3 (or -2) has a value of None. The REPL doesn't display the None object, so the value of t[-2] appears to be missing. For more visible evidence that this value is None, use this:

>>> t[3] is None
True

The sequences use an extra comparison...

Using string and bytes values

Python string values are similar—in some respects—to simple numeric types. There are a few arithmetic-like operators available and all of the comparisons are defined. Strings are immutable: we cannot change a string. We can, however, easily build new strings from existing strings, making the mutability question as irrelevant for string objects as it is for number objects. Python has two kinds of string values:

  • Unicode: These strings use the entire Unicode character set. These are the default strings Python uses. The input-output libraries are all capable of a wide variety of Unicode encoding and decoding. The name for this type is str. It's a built-in type, so it starts with a lowercase letter.

  • Bytes: Many file formats and network protocols are defined over bytes, not Unicode characters. Python uses ASCII encoding for bytes. Special arrangements must be made to process bytes. The internal type name is bytes.

We can easily encode Unicode into a sequence of bytes...

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Description

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.

What you will learn

  • Use Python interactively and master the art of writing efficient Python scripts
  • Learn techniques for working with string data, including how to use regular expressions to parse strings
  • Get to know how the shortcircuit and ifelse logic operators work
  • Explore mutable data structures including list, set, and dict
  • Define functions with positional and optional parameters, and learn to use keyword argument values when calling a function
  • Write generator functions, generator expressions, and comprehensions
  • Master the use of exceptions for handling errors and other unexpected conditions
  • Understand the importance of working with files, context manager, and the with statement

Product Details

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Publication date : Jun 30, 2015
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781784390341
Category :
Programming
Languages :
Python
Concepts :
Programming Language

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Product Details

Publication date : Jun 30, 2015
Length: 298 pages
Edition : 1st
Language : English
ISBN-13 : 9781784390341
Category :
Programming
Languages :
Python
Concepts :
Programming Language

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Table of Contents

16 Chapters
1. Getting Started Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(2 Ratings)
5 star 50%
4 star 50%
3 star 0%
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1 star 0%
ruben Aug 26, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book has develop the skills on how to program in a very structured way so that it covers all the necessary to be ready to use Python3 in order to be updated to the next version.It covers the main idea of how to use the tools of programming, functions, libraries, structures and the codes to develop your own programming.You will learn how to used the main structure in a programit covers the main topics to be ready and program your own codesThe main topics1: getting started2: simple data types3: expressions and output4: variables, assignment and scoping rules5: logic, comparisons, and conditions6: more complex data types7: basic function definitions8: more advanced functions9: exceptions10: files, databases, networks, and contexts11: class definitions12: scripts, modules, packages, libraries, and applications13: metaprogramming and decorators14: fit and finish – unit testing, packaging, and documentation15: next stepsI really recomend this title has the necessary to learn the basics of this important language and tool to jump to the next generation
Amazon Verified review Amazon
Tim Crothers Aug 21, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I found Python Essentials to be a very solid book depending on your background and intent. This book is best suited if you are an experienced programmer looking to learn Python to add it to your toolkit. To that end the chapters are all matter of fact and straight to the point. Coverage of Python is quite broad despite being just over 250 pages. On the other hand if you're new to programming then this book is unlikely to get you coding in Python successfully. There is very little discussion of "why and when" to use things, just how. The book also lacks any examples of how to tie the pieces all together. All of the Python code is done discretely in snippets illustrating just the necessary information.I gave the book four stars instead of five for a few reasons. Mostly it boils down to the book sending some confusing messages. For instance, the Amazon description "a rapid guide to the fundamental features of Python" is spot on whereas the cover of the book displays "Modernize existing Python code and plan code migrations to Python using this definitive guide". The latter from the cover is definitely not accomplished in my opinion. There is no discussion of code migration or modernization beyond the initial chapters reasons for focusing on Python 3 rather than 2.x. There are a fair number of similar inconsistencies which lead me confused a bit. Ultimately they were mostly just distracting rather than serious flaws.All in all an excellent book for this with solid programming experience looking to learn Python quickly.
Amazon Verified review Amazon

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The Python Workshop Second Edition
The Python Workshop Second Edition
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Nov 2022 600 pages
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Template Metaprogramming with C++
Template Metaprogramming with C++
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Aug 2022 480 pages
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Domain-Driven Design with Golang
Domain-Driven Design with Golang
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Dec 2022 204 pages
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About the author

Profile icon Steven F. Lott
Steven F. Lott
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Steven Lott has been programming since computers were large, expensive, and rare. Working for decades in high tech has given him exposure to a lot of ideas and techniques, some bad, but most are helpful to others. Since the 1990s, Steven has been engaged with Python, crafting an array of indispensable tools and applications. His profound expertise has led him to contribute significantly to Packt Publishing, penning notable titles like "Mastering Object-Oriented," "The Modern Python Cookbook," and "Functional Python Programming." A self-proclaimed technomad, Steven's unconventional lifestyle sees him residing on a boat, often anchored along the vibrant east coast of the US. He tries to live by the words “Don't come home until you have a story.”
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