book

Learn Python Programming - Fourth Edition

Name: Learn Python Programming - Fourth Edition
ISBN: 9781835882948

by Fabrizio Romano, Heinrich Kruger

November 2024

Intermediate to advanced

620 pages

13h 46m

English

Packt Publishing

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Preface
A Gentle Introduction to Python
A brief introduction to programmingEnter the PythonAbout PythonPortabilityCoherenceDeveloper productivityAn extensive librarySoftware qualitySoftware integrationData scienceSatisfaction and enjoymentWhat are the drawbacks?Who is using Python today?Setting up the environmentInstalling PythonUseful installation resourcesInstalling Python on WindowsInstalling Python on macOSInstalling Python on LinuxThe Python consoleAbout virtual environmentsYour first virtual environmentInstalling third-party librariesThe consoleHow to run a Python programRunning Python scriptsRunning the Python interactive shellRunning Python as a serviceRunning Python as a GUI applicationHow is Python code organized?How do we use modules and packages?Python’s execution modelNames and namespacesScopesGuidelines for writing good codePython cultureA note on IDEsA word about AISummaryGet this book’s PDF version and more
Built-In Data Types
Everything is an objectMutabilityNumbersIntegersBooleansReal numbersComplex numbersFractions and decimalsImmutable sequencesStrings and bytesEncoding and decoding stringsIndexing and slicing stringsString formattingTuplesMutable sequencesListsBytearraysSet typesMapping types: dictionariesData typesDates and timesThe standard libraryThird-party librariesThe collections modulenamedtupledefaultdictChainMapEnumsFinal considerationsSmall value cachingHow to choose data structuresAbout indexing and slicingAbout namesSummary
Conditionals and Iteration
Conditional programmingThe if statementA specialized else: elifNesting if statementsThe ternary operatorPattern matchingLoopingThe for loopIterating over a rangeIterating over a sequenceIterators and iterablesIterating over multiple sequencesThe while loopThe break and continue statementsA special else clauseAssignment expressionsStatements and expressionsUsing the walrus operatorA word of warningPutting all this togetherA prime generatorApplying discountsA quick peek at the itertools moduleInfinite iteratorsIterators terminating on the shortest input sequenceCombinatoric generatorsSummaryGet this book’s PDF version and more
Functions, the Building Blocks of Code
Why use functions?Reducing code duplicationSplitting a complex taskHiding implementation detailsImproving readabilityImproving traceabilityScopes and name resolutionThe global and nonlocal statementsInput parametersArgument-passingAssignment to parameter namesChanging a mutable objectPassing argumentsPositional argumentsKeyword argumentsIterable unpackingDictionary unpackingCombining argument typesDefining parametersOptional parametersVariable positional parametersVariable keyword parametersPositional-only parametersKeyword-only parametersCombining input parametersMore signature examplesAvoid the trap! Mutable defaultsReturn valuesReturning multiple valuesA few useful tipsRecursive functionsAnonymous functionsFunction attributesBuilt-in functionsDocumenting your codeImporting objectsRelative importsOne final exampleSummary
Comprehensions and Generators
The map, zip, and filter functionsmapzipfilterComprehensionsNested comprehensionsFiltering a comprehensionDictionary comprehensionsSet comprehensionsGeneratorsGenerator functionsGoing beyond nextThe yield from expressionGenerator expressionsSome performance considerationsDo not overdo comprehensions and generatorsName localizationGeneration behavior in built-insOne last exampleSummaryGet this book’s PDF version and more
OOP, Decorators, and Iterators
DecoratorsA decorator factoryOOPThe simplest Python classClass and object namespacesAttribute shadowingThe self argumentInitializing an instanceOOP is about code reuseInheritance and compositionAccessing a base classMultiple inheritanceMethod resolution orderClass and static methodsStatic methodsClass methodsPrivate methods and name manglingThe property decoratorThe cached_property decoratorOperator overloadingPolymorphism—a brief overviewData classesWriting a custom iteratorSummary
Exceptions and Context Managers
ExceptionsRaising exceptionsDefining your own exceptionsTracebacksHandling exceptionsException groupsNot only for errorsContext managersClass-based context managersGenerator-based context managersSummaryGet this book’s PDF version and more
Files and Data Persistence
Working with files and directoriesOpening filesUsing a context manager to open a fileReading from and writing to a fileReading and writing in binary modeProtecting against overwriting an existing fileChecking for file and directory existenceManipulating files and directoriesManipulating pathnamesTemporary files and directoriesDirectory contentFile and directory compressionData interchange formatsWorking with JSONCustom encoding/decoding with JSONI/O, streams, and requestsUsing an in-memory streamMaking HTTP requestsPersisting data on diskSerializing data with pickleSaving data with shelveSaving data to a databaseConfiguration filesCommon formatsThe INI configuration formatThe TOML configuration formatSummary
Cryptography and Tokens
The need for cryptographyUseful guidelinesHashlibHMACSecretsRandom objectsToken generationDigest comparisonJSON Web TokensRegistered claimsTime-related claimsAuthentication-related claimsUsing asymmetric (public key) algorithmsUseful referencesSummaryGet this book’s PDF version and more

Testing
Testing your applicationThe anatomy of a testTesting guidelinesUnit testingWriting a unit testMock objects and patchingAssertionsTesting a CSV generatorBoundaries and granularityTesting the export functionFinal considerationsTest-driven developmentSummary
Debugging and Profiling
Debugging techniquesDebugging with printDebugging with a custom functionUsing the Python debuggerInspecting logsOther techniquesReading tracebacksAssertionsWhere to find informationTroubleshooting guidelinesWhere to inspectUsing tests to debugMonitoringProfiling PythonWhen to profileMeasuring execution timeSummaryGet this book’s PDF version and more
Introduction to Type Hinting
Python approach to typesDuck typingHistory of type hintingBenefits of type hintingType annotationsAnnotating functionsThe Any typeType aliasesSpecial formsOptionalUnionGenericsAnnotating variablesAnnotating containersAnnotating tuplesFixed-length tuplesTuples with named fieldsTuples of arbitrary lengthAbstract base classes (ABCs)Special typing primitivesThe Self typeAnnotating variable parametersProtocolsThe Mypy static type checkerSome useful resourcesSummary
Data Science in Brief
IPython and Jupyter NotebookUsing AnacondaStarting a NotebookDealing with dataSetting up the NotebookPreparing the dataCleaning the dataCreating the DataFrameUnpacking the campaign nameUnpacking the user dataRenaming columnsComputing some metricsCleaning everything upSaving the DataFrame to a fileVisualizing the resultsWhere do we go from here?SummaryGet this book’s PDF version and more
Introduction to API Development
The Hypertext Transfer ProtocolHow does HTTP work?Response status codesAPIs – An introductionWhat is an API?What is the purpose of an API?API protocolsAPI data-exchange formatsThe railway APIModeling the databaseMain setup and configurationApplication settingsStation endpointsReading dataCreating dataUpdating dataDeleting dataUser authenticationDocumenting the APIWhere do we go from here?Summary
CLI Applications
Command-line argumentsPositional argumentsOptionsSub-commandsArgument parsingBuilding a CLI client for the railway APIInteracting with the railway APICreating the command-line interfaceConfiguration files and secretsCreating sub-commandsImplementing sub-commandsOther resources and toolsSummaryGet this book’s PDF version and more
Packaging Python Applications
The Python Package IndexPackaging with SetuptoolsProject layoutDevelopment installationChangelogLicenseREADMEpyproject.tomlPackage metadataVersioning and dynamic metadataSpecifying dependenciesProject URLsScripts and entry pointsDefining the package contentsAccessing metadata in your codeBuilding and publishing packagesBuildingPublishingAdvice for starting new projectsOther filesAlternative toolsFurther readingSummary
Programming Challenges
Advent of CodeCamel CardsPart one – problem statementPart one – solutionPart two – problem statementPart two – solutionCosmic ExpansionPart one – problem statementPart one – solutionPart two – problem statementPart two – solutionFinal considerationsOther programming challenge websitesSummaryGet this book’s PDF version and more
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Content preview from Learn Python Programming - Fourth Edition

13 Data Science in Brief

”If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”

— Jim Barksdale, former Netscape CEO

Data science is a broad term that can have different meanings depending on the context, understanding, and tools, amongst other factors. To do proper data science, you need to, at the very least, know mathematics and statistics. Then, you may want to dig into other subjects, such as pattern recognition and machine learning, and, of course, there is a plethora of languages and tools you can use.

We will not be able to talk about everything here. Therefore, to render this chapter meaningful, we are going to work on a project together instead.

Around 2012/2013, Fabrizio was working for a top-tier ...

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Learn Python Programming - Third Edition

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ISBN: 9781835882948

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Data Science

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Learn Python Programming - Fourth Edition

by Fabrizio Romano, Heinrich Kruger

13

Data Science in Brief

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.