book

Learning Python

Name: Learning Python
Author: Fabrizio Romano
ISBN: 9781783551712

by Fabrizio Romano

December 2015

Beginner

442 pages

10h 12m

English

Packt Publishing

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Learning Python
Table of Contents
Learning Python
Credits
About the Author
Acknowledgements
About the Reviewers
www.PacktPub.com
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Preface
What this book covers
What you need for this book

Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example codeErrataPiracyQuestions
1. Introduction and First Steps – Take a Deep Breath
A proper introduction
Enter the Python
About Python
PortabilityCoherenceDeveloper productivityAn extensive librarySoftware qualitySoftware integrationSatisfaction and enjoyment
What are the drawbacks?
Who is using Python today?
Setting up the environment
Python 2 versus Python 3 – the great debate
Installing Python
Setting up the Python interpreterAbout virtualenvYour first virtual environmentYour friend, the console
How you can run a Python program
Running Python scriptsRunning the Python interactive shellRunning Python as a serviceRunning Python as a GUI application
How is Python code organized
How do we use modules and packages
Python's execution model
Names and namespacesScopesObject and classes
Guidelines on how to write good code
The Python culture
A note on the IDEs
Summary
2. Built-in Data Types
Everything is an object
Mutable or immutable? That is the question
Numbers
IntegersBooleansRealsComplex numbersFractions and decimals
Immutable sequences
Strings and bytesEncoding and decoding stringsIndexing and slicing stringsTuples
Mutable sequences
ListsByte arrays
Set types
Mapping types – dictionaries
The collections module
Named tuplesDefaultdictChainMap
Final considerations
Small values cachingHow to choose data structuresAbout indexing and slicingAbout the names
Summary
3. Iterating and Making Decisions
Conditional programmingA specialized else: elifThe ternary operator
Looping
The for loopIterating over a rangeIterating over a sequenceIterators and iterablesIterating over multiple sequencesThe while loopThe break and continue statementsA special else clause
Putting this all together
Example 1 – a prime generatorExample 2 – applying discounts
A quick peek at the itertools module
Infinite iteratorsIterators terminating on the shortest input sequenceCombinatoric generators
Summary
4. Functions, the Building Blocks of Code
Why use functions?Reduce code duplicationSplitting a complex taskHide implementation detailsImprove readabilityImprove traceability
Scopes and name resolution
The global and nonlocal statements
Input parameters
Argument passingAssignment to argument names don't affect the callerChanging a mutable affects the callerHow to specify input parametersPositional argumentsKeyword arguments and default valuesVariable positional argumentsVariable keyword argumentsKeyword-only argumentsCombining input parametersAvoid the trap! Mutable defaults
Return values
Returning multiple values
A few useful tips
Recursive functions
Anonymous functions
Function attributes
Built-in functions
One final example
Documenting your code
Importing objects
Relative imports
Summary
5. Saving Time and Memory
map, zip, and filtermapzipfilter
Comprehensions
Nested comprehensionsFiltering a comprehensiondict comprehensionsset comprehensions
Generators
Generator functionsGoing beyond nextThe yield from expressionGenerator expressions
Some performance considerations
Don't overdo comprehensions and generators
Name localization
Generation behavior in built-ins
One last example
Summary
6. Advanced Concepts – OOP, Decorators, and Iterators
DecoratorsA decorator factory
Object-oriented programming
The simplest Python classClass and object namespacesAttribute shadowingI, me, and myself – using the self variableInitializing an instanceOOP is about code reuseInheritance and compositionAccessing a base classMultiple inheritanceMethod resolution orderStatic and class methodsStatic methodsClass methodsPrivate methods and name manglingThe property decoratorOperator overloadingPolymorphism – a brief overview
Writing a custom iterator
Summary
7. Testing, Profiling, and Dealing with Exceptions
Testing your applicationThe anatomy of a testTesting guidelinesUnit testingWriting a unit testMock objects and patchingAssertionsA classic unit test exampleMaking a test failInterface testingComparing tests with and without mocksBoundaries and granularityA more interesting example
Test-driven development
Exceptions
Profiling Python
When to profile?
Summary
8. The Edges – GUIs and Scripts
First approach – scriptingThe importsParsing argumentsThe business logic
Second approach – a GUI application
The importsThe layout logicThe business logicFetching the web pageSaving the imagesAlerting the userHow to improve the application?
Where do we go from here?
The tkinter.tix moduleThe turtle modulewxPython, PyQt, and PyGTKThe principle of least astonishmentThreading considerations
Summary
9. Data Science
IPython and Jupyter notebook
Dealing with data
Setting up the notebookPreparing the dataCleaning the dataCreating the DataFrameUnpacking the campaign nameUnpacking the user dataCleaning everything upSaving the DataFrame to a fileVisualizing the results
Where do we go from here?
Summary
10. Web Development Done Right
What is the Web?
How does the Web work?
The Django web framework
Django design philosophyThe model layerThe view layerThe template layerThe Django URL dispatcherRegular expressions
A regex website
Setting up DjangoStarting the projectCreating usersAdding the Entry modelCustomizing the admin panelCreating the formWriting the viewsThe home viewThe entry list viewThe form viewTying up URLs and viewsWriting the templates
The future of web development
Writing a Flask viewBuilding a JSON quote server in Falcon
Summary
11. Debugging and Troubleshooting
Debugging techniquesDebugging with printDebugging with a custom functionInspecting the tracebackUsing the Python debuggerInspecting log filesOther techniquesProfilingAssertionsWhere to find information
Troubleshooting guidelines
Using console editorsWhere to inspectUsing tests to debugMonitoring
Summary
12. Summing Up – A Complete Example
The challenge
Our implementation
Implementing the Django interface
The setupThe model layerA simple formThe view layerImports and home viewListing all recordsCreating recordsUpdating recordsDeleting recordsSetting up the URLsThe template layerHome and footer templatesListing all recordsCreating and editing recordsTalking to the APIDeleting records
Implementing the Falcon API
The main applicationWriting the helpersCoding the password validatorCoding the password generatorWriting the handlersCoding the password validator handlerCoding the password generator handlerRunning the APITesting the APITesting the helpersTesting the handlers
Where do you go from here?
Summary
A word of farewell
Index

Content preview from Learning Python

Where do we go from here?

Data science is indeed a fascinating subject. As I said in the introduction, those who want to delve into its meanders need to be well trained in mathematics and statistics. Working with data that has been interpolated incorrectly renders any result about it useless. The same goes for data that has been extrapolated incorrectly or sampled with the wrong frequency. To give you an example, imagine a population of individuals that are aligned in a queue. If, for some reason, the gender of that population alternated between male and female, the queue would be something like this: F-M-F-M-F-M-F-M-F...

If you sampled it taking only the even elements, you would draw the conclusion that the population was made up only of males, ...

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Learning Python

by Fabrizio Romano

Where do we go from here?

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