book

Beginning Python 3 with Grok 2

Name: Beginning Python 3 with Grok 2
Author: Oswald Campesato
ISBN: 9781501522161

by Oswald Campesato

December 2024

Beginner to intermediate

310 pages

5h 34m

English

Mercury Learning and Information

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Cover
Half title
Title
Copyright
Dedication
Contents
Preface
Chapter 1: Introduction to Python
Tools for Pythoneasy_install and pipvirtualenvIPythonPython InstallationSetting the PATH Environment Variable (Windows Only)Launching Python on Your MachineThe Python Interactive InterpreterPython IdentifiersLines, Indentation, and Multi-LinesQuotations and Comments in PythonSaving Your Code in a ModuleSome Standard Modules in PythonThe help() and dir() FunctionsCompile-Time and Runtime Code CheckingSimple Data Types in PythonWorking with NumbersWorking with Other BasesThe chr() FunctionThe round() Function in PythonFormatting Numbers in PythonWorking with FractionsUnicode and UTF-8Working with UnicodeWorking with StringsComparing StringsFormatting Strings in PythonSlicing and Splicing StringsTesting for Digits and Alphabetic CharactersSearch and Replace a String in Other StringsRemove Leading and Trailing CharactersPrinting Text Without Newline CharactersText AlignmentWorking with DatesConverting Strings to DatesException Handling in PythonHandling User InputCommand-Line ArgumentsSummary
Chapter 2: Conditional Logic, Loops, and Functions
Precedence of Operators in PythonPython Reserved WordsWorking with Loops in PythonPython for LoopsA for Loop with try/except in PythonNumeric Exponents in PythonNested LoopsThe split() Function with for LoopsUsing the split() Function to Compare WordsUsing the split() Function to Print Justified TextUsing the split() Function to Print Fixed-Width TextUsing the split() Function to Compare Text StringsUsing the split() Function to Display Characters in a StringThe join() FunctionPython while LoopsConditional Logic in PythonThe break/continue/pass StatementsComparison and Boolean OperatorsThe in/not in/is/is not Comparison OperatorsThe and, or, and not Boolean OperatorsLocal and Global VariablesUninitialized Variables and the Value NoneThe Scope of VariablesPass by Reference Versus ValueArguments and ParametersUsing a while Loop to Find the Divisors of a NumberUsing a while Loop to Find Prime NumbersUser-Defined Functions in PythonSpecifying Default Values in a FunctionReturning Multiple Values from a FunctionFunctions with a Variable Number of ArgumentsLambda ExpressionsRecursionCalculating Factorial ValuesCalculating Fibonacci NumbersCalculating the GCD of Two NumbersCalculating the LCM of Two NumbersSummary
Chapter 3: Python Data Structures
Working with ListsLists and Basic OperationsReversing and Sorting a ListLists and Arithmetic OperationsLists and Filter-Related OperationsSorting Lists of Numbers and StringsExpressions in ListsConcatenating a List of WordsBubble Sort in PythonThe Python range() FunctionCounting Digits, Uppercase, and Lowercase LettersCounting Digits et al. with GrokArrays and the append() FunctionWorking with Lists and the split() FunctionCounting Words in a ListIterating Through Pairs of ListsOther List-Related FunctionsCreate a Stack Using GrokWorking with VectorsWorking with MatricesThe NumPy Library for MatricesCreate a Queue Using GrokUsing a List as a Stack and a QueueTuples (Immutable Lists)Working with SetsDictionariesCreating a DictionaryDisplaying the Contents of a DictionaryChecking for Keys in a DictionaryDeleting Keys from a DictionaryIterating Through a DictionaryInterpolating Data from a DictionaryDictionary Functions and MethodsDictionary FormattingOrdered DictionariesSorting DictionariesPython Multi DictionariesOther Sequence Types in PythonMutable and Immutable Types in PythonThe type() FunctionSummary

Chapter 4: Introduction to NumPy and Pandas
What Is NumPy?Useful NumPy FeaturesWhat Are NumPy Arrays?Working with LoopsAppending Elements to Arrays (1)Appending Elements to Arrays (2)Multiply Lists and ArraysDoubling the Elements in a ListLists and ExponentsArrays and ExponentsMath Operations and ArraysWorking with -1 Subranges with VectorsWorking with -1 Subranges with ArraysOther Useful NumPy MethodsArrays and Vector OperationsNumPy and Dot Products (1)NumPy and Dot Products (2)NumPy and the “Norm” of VectorsNumPy and Other OperationsNumPy and the reshape() MethodCalculating the Mean and Standard Deviation (1)Calculating the Mean and Standard Deviation (2)What Is Pandas?Pandas DataFramesDataFrames and Data-Cleaning TasksA Labeled Pandas DataFramePandas Numeric DataFramesPandas Boolean DataFramesTransposing a Pandas DataFramePandas DataFrames and Random NumbersCombining Pandas DataFrames (1)Combining Pandas DataFrames (2)Data Manipulation with Pandas DataFrames (1)Data Manipulation with Pandas DataFrames (2)Data Manipulation with Pandas DataFrames (3)Grok and NumPy DataFramesPandas DataFrames and CSV FilesPandas DataFrames and Excel SpreadsheetsSelect, Add, and Delete Columns in DataFramesPandas DataFrames and ScatterplotsGrok, DataFrames, and Bar ChartsPandas DataFrames and Simple StatisticsGrok, DataFrames, and Simple StatisticsUseful One-Line Commands in PandasSummary
Chapter 5: The Generative AI Landscape
What Is Generative AI?Key Features of Generative AIPopular Techniques in Generative AIWhat Makes Generative AI Different?Conversational AI Versus Generative AIPrimary ObjectiveApplicationsTechnologies UsedTraining and InteractionEvaluationData RequirementsWhat Are Generative AI Models?Is DALL-E Part of Generative AI?Are ChatGPT-3 and GPT-4 Part of Generative AI?Generative AI Versus ML, DL, and NLPWhich Fields Benefit the Most from Generative AI?Generative AI in the Enterprise SpaceThe Impact of Generative AI on JobsWhat Is Artificial General Intelligence (AGI)?When Will AGI Arrive?What Is the Path to AGI?How Can We Prepare for AGI?Will AGI Control the World?Should Humans Fear AGI?What Is Beyond AGI?Artificial General Intelligence Versus Generative AIWhat Are LLMs?What Is the Purpose of LLMs?Recent and Powerful LLMsDo LLMs Understand Language?Caveats Regarding LLMsModel Size Versus Training Set SizeMemory Requirements for LLMsMemory Types in LLMsLLMs Versus Deep Learning ModelsCost Comparison Among LLMsLLMs and DeceptionDeceptive Completions in LLMsLLMs and Intentional DeceptionSelecting an LLM: Factors to ConsiderPitfalls of Working with LLMsA Brief History of Modern LLMsAspects of LLM DevelopmentLLM Size Versus PerformanceEmergent Abilities of LLMsSkepticism Regarding Emergent AbilitiesWhat Are Hallucinations?Why Do LLMs Hallucinate?Hallucination Types in LLMsCan LLMs Detect Errors in Prompts?Are Intentional Hallucinations Possible?Reducing HallucinationsCauses of Hallucinations in LLMsIntrinsic Versus Extrinsic HallucinationsHallucination DetectionModel CalibrationKaplan and Under-Trained ModelsSuccess Stories in Generative AIReal-World Use Cases for Generative AIDeepMindDeepMind and GamesPlayer of Games (PoG)OpenAICohereHugging FaceHugging Face LibrariesHugging Face Model HubMeta AIAI21Anthropic and Claude 3Summary
Chapter 6: Grok and Python Code
What is Grok?CSV Files for GrokSimple Web ScrapingBasic ChatBotBasic Data VisualizationBasic Pandas and GrokGenerate Random DataRecursion: Fibonacci NumbersGenerate a Python ClassAsynchronous ProgrammingWorking With requests in PythonImage Processing with PILException HandlingGenerators in PythonRoll 7 or 11 with Two DiceRoll 7 or 11 with Three DiceRoll 7 or 11 with Four DiceMean and Standard DeviationSummary
Index

Content preview from Beginning Python 3 with Grok 2

CHAPTER 4

INTRODUCTION TO NUMPY AND PANDAS

The first half of this chapter starts with a quick introduction to the Python NumPy package, followed by a quick introduction to pandas and some of its useful features. The pandas package for Python provides a rich and powerful set of APIs for managing datasets. These APIs are very useful for machine learning and deep learning tasks that involve dynamically “slicing and dicing” subsets of datasets.

The first section contains examples of working arrays in NumPy, and contrasts some of the APIs for lists with the same APIs for arrays. In addition, you will see how easy it is to compute the exponent-related values (square, cube, and so forth) of elements in an array.

The second section introduces subranges, ...

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Beginning Python 3 with Grok 2

by Oswald Campesato

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,
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