book

The Python Workshop Second Edition - Second Edition

Name: The Python Workshop Second Edition - Second Edition
ISBN: 9781804610619

by Corey Wade, Mario Corchero Jiménez, Andrew Bird, Dr. Lau Cher Han, Graham Lee

November 2022

Beginner

600 pages

13h 12m

English

Packt Publishing

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The Python Workshop
ContributorsAbout the authorsAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookInstalling Jupyter on your systemLaunching the Jupyter NotebookTo install the Python terminal on your systemA few important packagesTo install DockerDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Chapter 1: Python Fundamentals – Math, Strings, Conditionals, and Loops
OverviewIntroductionTechnical requirementsOpening a Jupyter NotebookPython as a calculatorStandard math operationsBasic math operationsOrder of operationsExercise 1 – getting to know the order of operationsPython concept – spacingNumber types – integers and floatsExercise 2 – integer and float typesComplex number typesErrors in PythonVariable assignmentExercise 3 – assigning variablesCasting – changing typesActivity 1 – assigning values to variablesVariable namesExercise 4 – naming variablesMultiple variablesExercise 5 – assigning multiple variablesCommentsExercise 6 – comments in PythonDocstringsActivity 2 – finding the area of a triangleStrings – concatenation, methods, and input()String syntaxExercise 7 – string error syntaxEscape charactersMultiline stringsThe print() functionExercise 8 – displaying stringsString operations and concatenationExercise 9 – string concatenationString interpolationComma separatorsf-stringsThe len() functionString methodsExercise 10 – implementing string methodsCastingExercise 11 – types and castingThe input() functionExercise 12 – using the input() functionActivity 3 – using the input() function to rate your dayString indexing and slicingIndexingSlicingStrings and their methodsBooleans and conditionalsBooleansExercise 13 – Boolean variablesLogical operatorsComparison operatorsExercise 14 – comparison operatorsComparing stringsExercise 15 – practicing comparing stringsConditionalsThe if syntaxIndentationExercise 16 – using the if syntaxif elseExercise 17 – using the if-else syntaxThe elif statementLoopswhile loopsThe break keywordActivity 4 – finding the least common multiple (LCM)ProgramsExercise 18 – calculating perfect squaresExercise 19 – real estate offerfor loopsExercise 20 – using for loopsThe continue keywordActivity 5 – building conversational bots using PythonSummary
Chapter 2: Python Data Structures
OverviewIntroductionTechnical requirementsThe power of listsExercise 21 – working with Python listsList methodsExercise 22 – basic list operationsAccessing an item from a listExercise 23 – accessing an item from shopping list dataAdding an item to a listExercise 24 – adding items to our shopping listExercise 25 – looping through a listMatrices as nested listsExercise 26 – using a nested list to store data from a matrixActivity 6 – using a nested list to store employee dataMatrix operationsExercise 27 – implementing matrix operations (addition and subtraction)Matrix multiplication operationsExercise 28 – implementing matrix operations (multiplication)Dictionary keys and valuesExercise 29 – using a dictionary to store a movie recordActivity 7 – storing company employee table data using a list and a dictionaryDictionary methodsExercise 30 – accessing a dictionary using dictionary methodsTuplesExercise 31 – exploring tuple properties in a dance genre listZipping and unzipping dictionaries and lists using zip()Exercise 32 – using the zip() method to manipulate dictionariesA survey of setsExercise 33 – using sets in PythonSet operationsExercise 34 – implementing set operationsChoosing typesSummary
Chapter 3: Executing Python – Programs, Algorithms, and Functions
OverviewIntroductionTechnical requirementsPython scripts and modulesExercise 35 – writing and executing our first scriptPython function exampleExercise 36 – writing and importing our first moduleShebangs in UbuntuDocstringsExercise 37 – adding a docstring to my_module.pyImporting librariesExercise 38 – finding the system dateThe if __name__ == ‘__main__’ statementActivity 8 – what’s the time?Python algorithmsExercise 39 – finding the maximum numberTime complexitySorting algorithmsExercise 40 – using bubble sort in PythonSearching algorithmsExercise 41 – linear search in PythonExercise 42 – binary search in PythonBasic functionsExercise 43 – defining and calling a function in the shellExercise 44 – defining and calling a function in a Python scriptExercise 45 – importing and calling the function from the shellPositional argumentsKeyword argumentsExercise 46 – defining a function with keyword argumentsExercise 47 – defining a function with positional and keyword argumentsExercise 48 – using **kwargsActivity 9 – formatting customer namesIterative functionsExercise 49 – a simple function with a for loopExiting earlyExercise 50 – exiting the function during the for loopActivity 10 – the Fibonacci function with an iterationRecursive functionsA terminating caseExercise 51 – recursive countdownExercise 52 – factorials with iteration and recursionActivity 11 – the Fibonacci function with recursionDynamic programmingExercise 53 – summing integersTiming your codeExercise 54 – calculating your code’s timingActivity 12 – the Fibonacci function with dynamic programmingHelper functionsDon’t Repeat YourselfExercise 55 – helper currency conversionVariable scopeVariablesDefining inside versus outside a functionThe global keywordThe nonlocal keywordLambda functionsExercise 56 – the first item in a listMapping with lambda functionsExercise 57 – mapping with a logistic transformFiltering with lambda functionsExercise 58 – using a filter lambdaSorting with lambda functionsSummary
Chapter 4: Extending Python, Files, Errors, and Graphs
OverviewIntroductionTechnical requirementsReading filesExercise 59 – reading a text file using PythonExercise 60 – reading partial content from a text fileWriting filesExercise 61 – creating and writing content to files to record the date and time in a text filePreparing for debugging (defensive code)Writing assertionsExercise 62 – working with incorrect parameters to find the average using assert with functionsPlotting techniquesExercise 63 – drawing a scatter plot to study the data between ice cream sales versus temperatureExercise 64 – drawing a line chart to find the growth in stock pricesExercise 65 – plotting bar plot to grade studentsExercise 66 – creating a pie chart to visualize the number of votes in a schoolExercise 67 – generating a heatmap to visualize the grades of studentsExercise 68 – generating a density plot to visualize the scores of studentsExercise 69 – creating a contour plotExtending graphsExercise 70 – generating 3D plots to plot a sine waveThe don’ts of plotting graphsManipulating the axisCherry picking dataWrong graph, wrong contextActivity 13 – visualizing the Titanic dataset using a pie chart and bar plotsSummary
Chapter 5: Constructing Python – Classes and Methods
OverviewIntroductionTechnical requirementsClasses and objectsExercise 71 – exploring stringsDefining classesExercise 72 – creating a Pet classThe __init__ methodExercise 73 – creating a Circle classKeyword argumentsExercise 74 – the Country class with keyword argumentsMethodsInstance methodsExercise 75 – adding an instance method to our Pet classAdding arguments to instance methodsExercise 76 – computing the size of our countryThe __str__ methodExercise 77 – adding an __str__ method to the Country classStatic methodsExercise 78 – refactoring instance methods using a static methodClass methodsExercise 79 – extending our Pet class with class methodsPropertiesThe property decoratorExercise 80 – the full name propertyThe setter methodExercise 81 – writing a setter methodValidation via the setter methodInheritanceThe DRY principle revisitedSingle inheritanceExercise 82 – inheriting from the Person classSubclassing classes from Python packagesExercise 83 – subclassing the datetime.date classOverriding methodsCalling the parent method with super()Exercise 84 – overriding methods using super()Multiple inheritancesExercise 85 – creating a consultation appointment systemMethod resolution orderActivity 14 – creating classes and inheriting from a parent classSummary
Chapter 6: The Standard Library
OverviewIntroductionTechnical requirementsThe importance of the Standard LibraryHigh-level modulesLower-level modulesKnowing how to navigate the Standard LibraryExercise 86 – using the dataclass moduleExercise 87 – extending the echo.py exampleWorking with dates and timesExercise 88 – comparing datetime across time zonesExercise 89 – calculating the time delta between two datetime objectsExercise 90 – calculating the Unix epoch timeActivity 15 – calculating the time elapsed to run a loopInteracting with the OSOS informationExercise 91 – inspecting the current process informationUsing pathlibExercise 92 – using the glob pattern to list files within a directoryListing all hidden files in your home directoryUsing the subprocess moduleExercise 93 – customizing child processes with env varsActivity 16 – testing Python codeLogging in PythonUsing loggingLogger objectExercise 94 – using a logger objectLogging in warning, error, and fatal categoriesConfiguring the logging stackExercise 95 – configuring the logging stackUsing collections in PythonThe counter classExercise 96 – counting words in a text documentThe defaultdict classExercise 97 – refactoring code with defaultdictThe ChainMap classUsing functoolsCaching with functools.lru_cacheExercise 98 – using lru_cache to speed up our codeAdapting functions with partialExercise 99 – creating a print function that writes to stderrActivity 17 – using partial on class methodsSummary
Chapter 7: Becoming Pythonic
OverviewIntroductionTechnical requirementsUsing list comprehensionsExercise 100 – introducing list comprehensionsExercise 101 – using multiple input listsActivity 18 – building a chess tournamentSet and dictionary comprehensionsExercise 102 – using set comprehensionsExercise 103 – using dictionary comprehensionsActivity 19 – building a scorecard using dictionary comprehensions and multiple listsUsing defaultdict to get default valuesExercise 104 – adopting a default dictCreating custom iteratorsExercise 105 – the simplest iteratorExercise 106 – a custom iteratorExercise 107 – controlling the iterationLeveraging itertoolsExercise 108 – using infinite sequences and takewhile()Exercise 109 – turning a finite sequence into an infinite one, and back againLazy evaluations with generatorsExercise 110 – generating a SieveActivity 20 – using random numbers to find the value of PiUsing regular expressionsExercise 111 – matching text with regular expressionsExercise 112 – using regular expressions to replace textActivity 21 – finding a winner for The X-FilesSummary
Chapter 8: Software Development
OverviewIntroductionTechnical requirementsHow to debugExercise 113 – debugging a salary calculatorActivity 22 – debugging sample Python code for an applicationAutomated testingTest categorizationTest coverageWriting tests in Python with unit testingExercise 114 – checking sample code with unit testingWriting a test with pytestCreating a pip packageExercise 115 – creating a distribution that includes multiple files within a packageAdding more information to your packageCreating documentation the easy wayUsing docstringsUsing SphinxExercise 116 – documenting a divisible code fileMore complex documentationSource code managementRepositoryCommitStaging areaUndoing local changesHistoryIgnoring filesExercise 117 – making a change in CPython using GitSummary

Chapter 9: Practical Python – Advanced Topics
OverviewIntroductionTechnical requirementsDeveloping collaborativelyExercise 118 – writing Python on GitHub as a teamDependency managementVirtual environmentsExercise 119 – creating and setting up a conda virtual environment to install numpy and pandasSaving and sharing virtual environmentsExercise 120 – sharing environments between a conda server and your local systemDeploying code into productionExercise 121 – Dockerizing your Fizzbuzz toolRunning code in parallel with multiprocessingMultiprocessing with execnetExercise 122 – working with execnet to execute a simple Python squaring programMultiprocessing with the multiprocessing packageExercise 123 – using the multiprocessing package to execute a simple Python programMultiprocessing with the threading packageExercise 124 – using the threading packageParsing command-line arguments in scriptsExercise 125 – introducing argparse to accept input from the userPositional argumentsExercise 126 – using positional arguments to accept source and destination inputs from a userPerformance and profilingChanging your Python environmentPyPyExercise 127 – using PyPy to find the time to get a list of prime numbersCythonExercise 128 – adopting Cython to find the time taken to get a list of prime numbersProfiling codeProfiling with cProfileActivity 23 – generating a list of random numbers in a Python virtual environmentSummary
Chapter 10: Data Analytics with pandas and NumPy
OverviewIntroductionTechnical requirementsNumPy and basic statsExercise 129 – converting lists into NumPy arraysExercise 130 – calculating the mean of the test scoreExercise 131 – finding the median from a collection of income dataSkewed data and outliersStandard deviationExercise 132 – finding the standard deviation from income dataFinding the min, max, and sumMatricesExercise 133 – working with matricesComputation time for large matricesExercise 134 – creating an array to implement NumPy computationsThe pandas libraryExercise 135 – using DataFrames to manipulate stored student test score dataExercise 136 – DataFrame computations with the student test score dataExercise 137 – more computations on DataFramesNew rows and NaNExercise 138 – concatenating and finding the mean with null values for our test score dataCasting column typesWorking with big dataDownloading dataDownloading the Boston Housing data from GitHubReading dataExercise 139 – reading and viewing the Boston Housing datasetExercise 140 – gaining data insights on the Boston Housing datasetNull valuesExercise 141 – viewing null valuesReplacing null valuesCreating statistical graphsHistogramsExercise 142 – creating a histogram using the Boston Housing datasetExercise 143 – creating histogram functionsScatter plotsExercise 144 – creating a scatter plot for the Boston Housing datasetCorrelationExercise 145 – correlation values from the datasetRegressionBox plots and violin plotsExercise 146 – creating box plotsExercise 147 – creating violin plotsActivity 24 – performing data analysis to find the outliers in pay versus the salary report in the UK statistics datasetSummary
Chapter 11: Machine Learning
OverviewIntroductionTechnical requirementsIntroduction to linear regressionSimplifying the problemFrom one to N-dimensionsThe linear regression algorithmExercise 148 – using linear regression to predict the accuracy of the median values of our datasetLinear regression functionTesting data with cross-validationExercise 149 – using the cross_val_score function to get accurate results on the datasetRegularization – Ridge and LassoK-nearest neighbors, decision trees, and random forestsK-nearest neighborsExercise 150 – using k-nearest neighbors to find the median value of the datasetExercise 151 – K-nearest neighbors with GridSearchCV to find the optimal number of neighborsDecision trees and random forestsExercise 152 – building decision trees and random forestsRandom forest hyperparametersExercise 153 – tuning a random forest using RandomizedSearchCVClassification modelsExercise 154 – preparing the pulsar dataset and checking for null valuesLogistic regressionExercise 155 – using logistic regression to predict data accuracyOther classifiersNaive BayesExercise 156 – using GaussianNB, KNeighborsClassifier, DecisionTreeClassifier, and RandomForestClassifier to predict the accuracy of our datasetConfusion matrixExercise 157 – finding the pulsar percentage from the datasetExercise 158 – confusion matrix and classification report for the pulsar datasetBoosting algorithmsAdaBoostXGBoostExercise 159 – using AdaBoost and XGBoost to predict pulsarsExercise 160 –using AdaBoost and XGBoost to predict median house values in BostonActivity 25 – using ML to predict customer return rate accuracySummary
Chapter 12: Deep Learning with Python
OverviewIntroductionTechnical requirementsColab notebooksJupyter NotebookIntroduction to deep learningYour first deep learning modelFirst deep learning librariesExercise 161 – preparing the Boston Housing dataset for deep learningExercise 162 – using sequential deep learning to predict the accuracy of the median house values of our datasetTuning Keras modelsExercise 163 – modifying densely connected layers in a neural network to improve the scoreNumber of epochsExercise 164 – modifying the number of epochs in the neural network to improve the scoreEarly StoppingExercise 165 – optimizing the number of epochs with Early StoppingAdditional regularization technique – DropoutExercise 166 – using Dropout in a neural network to improve the scoreBuilding neural networks for classificationExercise 167 – building a neural network for classificationActivity 26 – building your own neural network to predict whether a patient has heart diseaseConvolutional neural networksMNISTExercise 168 – preparing MNIST data for machine learningCNN kernelExercise 169 – building a CNN to predict handwritten digitsActivity 27 – classifying MNIST Fashion images using CNNsSummary
Chapter 13: The Evolution of Python – Discovering New Python Features
OverviewIntroductionPython Enhancement ProposalsPython 3.7Built-in breakpointModule dynamic attributesNanosecond support in a time moduleThe dict insertion order is preservedDataclassesImportlib.resourcesPython 3.8Assignment expressionfunctools.cached_propertyimportlib.metadatatyping.TypedDict, typing.Final, and typing.Literalf-string debug support via =Positional-only parametersPython 3.9PEG parserSupport for the IANA databaseMerge (|) and update (|=) syntax for dictsstr.removeprefix and str.removesuffixType hints with standard collectionsPython 3.10Pattern matching – PEP 634Parenthesized context managersBetter error messagesType union operator (|) – PEP 604Statistics – covariance, correlation, and linear_regressionPython 3.11Faster runtimeEnhanced errors in tracebacksThe new tomllib packageRequired keys in dictsThe new LiteralString typeExceptions notes – PEP 678Summary
Index
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Content preview from The Python Workshop Second Edition - Second Edition

11 Machine Learning

Overview

By the end of this chapter, you will be able to apply machine learning (ML) algorithms to solve different problems; compare, contrast, and apply different types of ML algorithms, including linear regression, logistic regression, decision trees, random forests, Naive Bayes, Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost); analyze overfitting and implement regularization; work with GridSearchCV and RandomizedSearchCV to adjust hyperparameters; evaluate algorithms using a confusion matrix and cross-validation, and solve real-world problems using the ML algorithms outlined here.

Introduction

Computer algorithms enable machines to learn from data. The more data an algorithm receives, the more capable ...

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The Python Workshop Second Edition - Second Edition

by Corey Wade, Mario Corchero Jiménez, Andrew Bird, Dr. Lau Cher Han, Graham Lee

11

Machine Learning

Overview

Introduction

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.