book

The Python Workshop

Name: The Python Workshop
ISBN: 9781839218859

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

November 2019

Beginner

608 pages

11h 2m

English

Packt Publishing

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Preface
About the CourseAbout the ChaptersConventionsBefore You BeginInstalling Jupyter on your systemLaunching the Jupyter NotebookTo Install the Python Terminal on your systemA Few Important PackagesTo install DockerInstalling the Code Bundle
1. Vital Python – Math, Strings, Conditionals, and Loops
IntroductionVital PythonNumbers: Operations, Types, and VariablesTo Open a Jupyter NotebookPython as a CalculatorStandard Math OperationsBasic Math OperationsOrder of OperationsExercise 1: Getting to Know the Order of OperationsSpacing in PythonNumber Types: Integers and FloatsExercise 2: Integer and Float TypesComplex Number TypesErrors in PythonVariablesVariable AssignmentExercise 3: Assigning VariablesChanging TypesReassigning Variables in Terms of ThemselvesActivity 1: Assigning Values to VariablesVariable NamesExercise 4: Variable NamesMultiple VariablesExercise 5: Multiple Variables in PythonCommentsExercise 6: Comments in PythonDocstringsActivity 2: Finding a Solution Using the Pythagorean Theorem in PythonStrings: Concatenation, Methods, and input()String SyntaxExercise 7: String Error SyntaxEscape Sequences with QuotesMulti-Line StringsThe print() Function Exercise 8: Displaying StringsString Operations and ConcatenationExercise 9: String ConcatenationString InterpolationComma SeparatorsFormatThe len() FunctionString MethodsExercise 10: String MethodsCastingExercise 11: Types and CastingThe input() FunctionExercise 12: 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: Comparing StringsConditionalsThe if SyntaxIndentationExercise 16: Using the if Syntaxif elseExercise 17: Using the if-else SyntaxThe elif StatementLoopsThe while LoopsAn Infinite LoopbreakActivity 4: Finding the Least Common Multiple (LCM)ProgramsExercise 18: Calculating Perfect SquaresExercise 19: Real Estate OfferThe for LoopExercise 20: Using for LoopsThe continue KeywordActivity 5: Building Conversational Bots Using PythonSummary
2. Python Structures
IntroductionThe Power of ListsExercise 21: Working with Python ListsMatrices as Nested ListsExercise 22: Using a Nested List to Store Data from a MatrixActivity 6: Using a Nested List to Store Employee DataMatrix OperationsExercise 23: Implementing Matrix Operations (Addition and Subtraction)Matrix Multiplication OperationsExercise 24: Implementing Matrix Operations (Multiplication)List MethodsExercise 25: Basic List OperationsAccessing an Item from a ListExercise 26: Accessing an Item from Shopping List DataAdding an Item to a ListExercise 27: Adding Items to Our Shopping ListDictionary Keys and ValuesExercise 28: Using a Dictionary to Store a Movie RecordActivity 7: Storing Company Employee Table Data Using a List and a DictionaryZipping and Unzipping Dictionaries Using zip()Exercise 29: Using the zip() Method to Manipulate DictionariesDictionary MethodsExercise 30: Accessing a Dictionary Using Dictionary MethodsTuplesExercise 31: Exploring Tuple Properties in Our Shopping ListA Survey of SetsExercise 32: Using Sets in PythonSet OperationsExercise 33: Implementing Set OperationsChoosing TypesSummary
3. Executing Python – Programs, Algorithms, and Functions
IntroductionPython Scripts and ModulesExercise 34: Writing and Executing Our First ScriptExercise 35: Writing and Importing Our First ModuleShebangs in UbuntuDocstringsExercise 36: Adding a Docstring to my_module.pyImportsExercise 37: Finding the System DateThe if __name__ == "__main__" StatementActivity 8: What's the Time?Python AlgorithmsExercise 38: The Maximum NumberTime ComplexityTime Complexity for the Maximum Number AlgorithmSorting AlgorithmsExercise 39: Using Bubble Sort in PythonSearching AlgorithmsExercise 40: Linear Search in PythonExercise 41: Binary Search in PythonBasic FunctionsExercise 42: Defining and Calling the Function in ShellExercise 43: Defining and Calling the Function in Python ScriptExercise 44: Importing and Calling the Function from the ShellPositional ArgumentsKeyword ArgumentsExercise 45: Defining the Function with Keyword ArgumentsExercise 46: Defining the Function with Positional and Keyword ArgumentsExercise 47: Using **kwargsActivity 9: Formatting Customer NamesIterative FunctionsExercise 48: A Simple Function with a for LoopExiting EarlyExercise 49: Exiting the Function During the for LoopActivity 10: The Fibonacci Function with an IterationRecursive FunctionsA Terminating CaseExercise 50: Recursive CountdownExercise 51: Factorials with Iteration and RecursionActivity 11: The Fibonacci Function with RecursionDynamic ProgrammingExercise 52: Summing IntegersTiming Your CodeExercise 53: Timing Your CodeActivity 12: The Fibonacci Function with Dynamic ProgrammingHelper FunctionsDon't Repeat YourselfExercise 54: Helper Currency ConversionVariable ScopeVariablesDefining inside versus outside a FunctionThe Global KeywordThe Nonlocal KeywordLambda FunctionsExercise 55: The First Item in a ListMapping with Lambda FunctionsExercise 56: Mapping with a Logistic TransformFiltering with Lambda FunctionsExercise 57: Using the Filter LambdaSorting with Lambda FunctionsSummary
4. Extending Python, Files, Errors, and Graphs
IntroductionReading FilesExercise 58: Reading a Text File Using PythonExercise 59: Reading Partial Content from a Text FileWriting FilesExercise 60: Creating and Writing Content to Files to Record the Date and Time in a Text FilePreparing for Debugging (Defensive Code)Writing AssertionsExercise 61: Working with Incorrect Parameters to Find the Average Using Assert with FunctionsPlotting TechniquesExercise 62: Drawing a Scatter Plot to Study the Data between Ice Cream Sales versus TemperatureExercise 63: Drawing a Line Chart to Find the Growth in Stock PricesExercise 64: Plotting Bar Plots to Grade StudentsExercise 65: Creating a Pie Chart to Visualize the Number of Votes in a SchoolExercise 66: Generating a Heatmap to Visualize the Grades of StudentsExercise 67: Generating a Density Plot to Visualize the Score of StudentsExercise 68: Creating a Contour PlotExtending GraphsExercise 69: Generating 3D plots to Plot a Sine WaveThe Don'ts of Plotting GraphsManipulating the AxisCherry Picking Data Wrong Graph, Wrong ContextActivity 13: Visualizing the Titanic Dataset Using a Pie Chart and Bar PlotsSummary
5. Constructing Python – Classes and Methods
IntroductionClasses and ObjectsExercise 70: Exploring StringsDefining ClassesExercise 71: Creating a Pet ClassThe __init__ methodExercise 72: Creating a Circle ClassKeyword ArgumentsExercise 73: The Country Class with Keyword ArgumentsMethodsInstance MethodsExercise 74: Adding an Instance Method to Our Pet ClassAdding Arguments to Instance MethodsExercise 75: Computing the Size of Our CountryThe __str__ methodExercise 76: Adding an __str__ Method to the Country ClassStatic MethodsExercise 77: Refactoring Instance Methods Using a Static MethodClass MethodsExercise 78: Extending Our Pet Class with Class MethodsPropertiesThe Property DecoratorExercise 79: The Full Name PropertyThe Setter MethodExercise 80: Writing a Setter MethodValidation via the Setter MethodInheritanceThe DRY Principle RevisitedSingle InheritanceExercise 81: Inheriting from the Person ClassSub-Classing Classes from Python PackagesExercise 82: Sub-Classing the datetime.date ClassOverriding MethodsCalling the Parent Method with super()Exercise 83: Overriding Methods Using super() Multiple InheritanceExercise 84: Creating a Consultation Appointment System Method Resolution OrderActivity 14: Creating Classes and Inheriting from a Parent ClassSummary
6. The Standard Library
IntroductionThe Importance of the Standard LibraryHigh-Level ModulesLower-Level ModulesKnowing How to Navigate in the Standard LibraryExercise 85: Using the dataclass ModuleExercise 86: Extending the echo.py ExampleDates and TimesExercise 87: Comparing datetime across Time ZonesExercise 88: Calculating the Time Delta between Two datetime ObjectsExercise 89: Calculating the Unix Epoch TimeActivity 15: Calculating the Time Elapsed to Run a LoopInteracting with the OSOS InformationExercise 90: Inspecting the Current Process InformationUsing pathlibExercise 91: Using the glob Pattern to List Files within a DirectoryListing All Hidden Files in Your Home DirectoryUsing the subprocess ModuleExercise 92: Customizing Child Processes with env varsActivity 16: Testing Python CodeLoggingUsing LoggingLogger ObjectExercise 93: Using a logger ObjectLogging in warning, error, and fatal CategoriesConfiguring the Logging StackExercise 94: Configuring the Logging StackCollectionsCountersExercise 95: Counting Words in a Text DocumentdefaultdictExercise 96: Refactoring Code with defaultdictChainMapFunctoolsCaching with functools.lru_cacheExercise 97: Using lru_cache to Speed Up Our CodePartialExercise 98: Creating a print Function That Writes to stderrActivity 17: Using partial on class MethodsSummary
7. Becoming Pythonic
IntroductionUsing List ComprehensionsExercise 99: Introducing List ComprehensionsExercise 100: Using Multiple Input ListsActivity 18: Building a Chess TournamentSet and Dictionary ComprehensionsExercise 101: Using Set ComprehensionsExercise 102: Using Dictionary ComprehensionsActivity 19: Building a Scorecard Using Dictionary Comprehensions and Multiple ListsDefault DictionaryExercise 103: Adopting a Default DictIteratorsExercise 104: The Simplest IteratorExercise 105: A Custom IteratorExercise 106: Controlling the IterationItertoolsExercise 107: Using Infinite Sequences and takewhileExercise 108: Turning a Finite Sequence into an Infinite One, and Back AgainGeneratorsExercise 109: Generating a SieveActivity 20: Using Random Numbers to Find the Value of PiRegular ExpressionsExercise 110: Matching Text with Regular ExpressionsExercise 111: Using Regular Expressions to Replace TextActivity 21: Regular ExpressionsSummary
8. Software Development
IntroductionDebuggingExercise 112: Debugging a Salary CalculatorActivity 22: Debugging Sample Python Code for an ApplicationAutomated TestingTest CategorizationTest CoverageWriting Tests in Python with Unit TestingExercise 113: Checking Sample Code with Unit TestingWriting a Test with pytestCreating a PIP PackageExercise 114: Creating a Distribution That Includes Multiple Files within a PackageAdding More Information to Your PackageCreating Documentation the Easy WayDocstringsUsing SphinxExercise 115: Documenting a Divisible Code FileMore Complex DocumentationSource ManagementRepositoryCommitStaging AreaUndoing Local ChangesHistoryIgnoring FilesExercise 116: Making a Change in CPython Using gitSummary
9. Practical Python – Advanced Topics
IntroductionDeveloping CollaborativelyExercise 117: Writing Python on GitHub as a TeamDependency ManagementVirtual EnvironmentsExercise 118: Creating and Setting Up a conda Virtual Environment to Install numpy and pandasSaving and Sharing Virtual EnvironmentsExercise 119: Sharing Environments between a conda Server and Your Local SystemDeploying Code into ProductionExercise 120: Dockerizing Your Fizzbuzz ToolMultiprocessingMultiprocessing with execnetExercise 121: Working with execnet to Execute a Simple Python Squaring ProgramMultiprocessing with the Multiprocessing PackageExercise 122: Using the Multiprocessing Package to Execute a Simple Python ProgramMultiprocessing with the Threading PackageExercise 123: Using the Threading PackageParsing Command-Line Arguments in ScriptsExercise 124: Introducing argparse to Accept Input from the UserPositional ArgumentsExercise 125: Using Positional Arguments to Accept Source and Destination Inputs from a UserPerformance and ProfilingChanging Your PythonPyPyExercise 126: Using PyPy to Find the Time to Get a List of Prime NumbersCythonExercise 127: Adopting Cython to Find the Time Taken to get a List of Prime NumbersProfilingProfiling with cProfileActivity 23: Generating a List of Random Numbers in a Python Virtual EnvironmentSummary

10. Data Analytics with pandas and NumPy
IntroductionNumPy and Basic StatsExercise 128: Converting Lists to NumPy ArraysExercise 129: Calculating the Mean of the Test ScoreExercise 130: Finding the Median from a Collection of Income DataSkewed Data and OutliersStandard DeviationExercise 131: Finding the Standard Deviation from Income DataMatricesExercise 132: MatricesComputation Time for Large MatricesExercise 133: Creating an Array to Implement NumPy ComputationsThe pandas LibraryExercise 134: Using DataFrames to Manipulate Stored Student testscore DataExercise 135: DataFrame Computations with the Student testscore DataExercise 136: Computing DataFrames within DataFramesNew Rows and NaNExercise 137: Concatenating and Finding the Mean with Null Values for Our testscore DataCast Column TypesDataDownloading DataDownloading the Boston Housing Data from GitHubReading DataExercise 138: Reading and Viewing the Boston Housing DatasetExercise 139: Gaining Data Insights on the Boston Housing DatasetNull ValuesExercise 140: Null Value Operations on the DatasetReplacing Null ValuesVisual AnalysisThe matplotlib LibraryHistogramsExercise 141: Creating a Histogram Using the Boston Housing DatasetHistogram FunctionsScatter PlotsExercise 142: Creating a Scatter Plot for the Boston Housing DatasetCorrelationExercise 143: Correlation Values from the DatasetRegressionPlotting a Regression LineStatsModel Regression OutputAdditional ModelsExercise 144: Box PlotsViolin PlotsActivity 24: Data Analysis to Find the Outliers in Pay versus the Salary Report in the UK Statistics DatasetSummary
11. Machine Learning
IntroductionIntroduction to Linear RegressionSimplify the ProblemFrom One to N-DimensionsThe Linear Regression AlgorithmExercise 145: Using Linear Regression to Predict the Accuracy of the Median Values of Our DatasetLinear Regression FunctionCross-ValidationExercise 146: 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 147: Using K-Nearest Neighbors to Find the Median Value of the DatasetExercise 148: K-Nearest Neighbors with GridSearchCV to Find the Optimal Number of NeighborsDecision Trees and Random ForestsExercise 149: Decision Trees and Random ForestsRandom Forest HyperparametersExercise 150: Random Forest Tuned to Improve the Prediction on Our DatasetClassification ModelsExercise 151: Preparing the Pulsar Dataset and Checking for Null ValuesLogistic RegressionExercise 152: Using Logistic Regression to Predict Data AccuracyOther ClassifiersNaive BayesExercise 153: Using GaussianNB, KneighborsClassifier, DecisionTreeClassifier, and RandomForestClassifier to Predict Accuracy in Our DatasetConfusion MatrixExercise 154: Finding the Pulsar Percentage from the DatasetExercise 155: Confusion Matrix and Classification Report for the Pulsar DatasetBoosting MethodsExercise 156: Using AdaBoost to Predict the Best Optimal ValuesActivity 25: Using Machine Learning to Predict Customer Return Rate AccuracySummary
Appendix
1. Vital Python – Math, Strings, Conditionals, and LoopsActivity 1: Assigning Values to VariablesActivity 2: Finding a Solution Using Pythagorean Theorem in PythonActivity 3: Using the input() Function to Rate Your DayActivity 4: Finding the Least Common Multiple (LCM)Activity 5: Building Conversational Bots Using Python2. Python StructuresActivity 6: Using a Nested List to Store Employee DataActivity 7: Storing Company Employee Table Data Using a List and a Dictionary3. Executing Python – Programs, Algorithms, FunctionsActivity 8: What's the Time?Activity 9: Formatting Customer NamesActivity 10: The Fibonacci Function with an IterationActivity 11: The Fibonacci Function with RecursionActivity 12: The Fibonacci Function with Dynamic Programming4. Extending Python, Files, Errors, and GraphsActivity 13: Visualizing the Titanic Dataset Using a Pie Chart and Bar Plots5. Constructing Python – Classes and MethodsActivity 14: Creating Classes and Inheriting from a Parent ClassSolution:6. The Standard LibraryActivity 15: Calculating the Time Elapsed to Run a Loop Activity 16: Testing Python Code Activity 17: Using partial on class Methods Wrong Assumptions with Date and Time7. Becoming PythonicActivity 18: Building a Chess TournamentActivity 19: Building a Scorecard Using Dictionary Comprehensions and Multiple ListsActivity 20: Using Random Numbers to Find the Value of PiActivity 21: Regular Expressions8. Software DevelopmentActivity 22: Debugging Sample Python Code for an Application9. Practical Python – Advanced TopicsActivity 23: Generating a List of Random Numbers in a Python Virtual Environment10. Data Analytics with pandas and NumPyActivity 24: Data Analysis to Find the Outliers in Pay versus the Salary Report in the UK Statistics Dataset11. Machine LearningActivity 25: Using Machine Learning to Predict Customer Return Rate Accuracy

Overview

The Python Workshop is a complete guide to mastering Python from scratch, written to kickstart your journey in programming or data science. Whether your goal is automating tasks, analyzing data, or building robust applications, this book will provide you with the knowledge and hands-on experience to achieve it.

What this Book will help me do

Learn to write clean and maintainable Python code for professional use.
Understand core programming concepts, including data manipulation and algorithm implementation.
Get familiar with popular Python libraries like pandas, NumPy, Matplotlib, and Seaborn.
Build and analyze data visualizations and basic machine learning models.
Master debugging techniques and write reliable code with unit testing.

Author(s)

Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, and Corey Wade together bring decades of combined programming, teaching, and industry experience. Their collaborative expertise ensures this book is both practical and accessible, letting new learners grow their skills efficiently and effectively.

Who is it for?

This book is ideal for beginners who want to learn Python, whether for software development, data science, or related fields. It's most suited to people with minimal or no programming experience but with a desire to build practical skills. If you're aspiring to work in tech or just curious about coding, this book will get you started on the right path.

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