Hands-On APIs for AI and Data Science: Python Development with FastAPІ

Cover....1

Copyright....8

Table of Contents....11

Preface....19

Why Should You Read This Book?....19

Who This Book Is For....19

Data Scientists....19

API Developers and Designers....20

Job Seekers and Role Changers....20

Creating Portfolio Projects....20

Using This Book....21

What This Book Is Not....21

Why Fantasy Football?....22

Get More Tips on APIs, AI, and Data Science....23

Conventions Used in This Book....23

Using Code Examples....24

O’Reilly Online Learning....24

How to Contact Us....25

Acknowledgments....25

Part I. Building APIs for Data Science....27

Chapter 1. Creating APIs That Data Scientists Will Love....29

How Do Data Scientists Use APIs?....29

What Tools Do Data Scientists Use?....30

Designing APIs for Data Scientists....31

Introducing Your Part I Portfolio Project....32

Every API Has a Story....32

Meeting Your Company: SportsWorldCentral....33

SWC Needs an API....35

Selecting the First API Products....36

Identifying Potential Users....36

Creating User Stories....37

Additional Resources....39

Summary....39

Chapter 2. Selecting Your API Architecture....41

API Architectural Styles....41

Representational State Transfer (REST)....42

Graph Query Language (GraphQL)....43

gRPC....43

Your Choice: REST....44

Technology Architecture....45

Software Used in This Chapter....47

Python....47

GitHub....47

Getting Started with Your GitHub Codespace....48

Creating Your GitHub Account....48

Cloning the Part I Repository....48

Launching Your GitHub Codespace....49

Touring Your New Codespace....50

Making Your First Commit....51

Additional Resources....53

Summary....54

Chapter 3. Creating Your Database....55

Components of Your API....55

Software Used in This Chapter....56

SQLite....56

SQLAlchemy....57

pytest....57

Creating Your SQLite Database....58

Creating Database Tables....58

Understanding Table Structure....61

Loading Your Data....62

Accessing Your Data Using Python....63

Installing SQLAlchemy in Your Environment....63

Creating Python Files for Database Access....65

Creating the Database Configuration File....70

Creating SQLAlchemy Helper Functions....71

Installing pytest in Your Environment....75

Testing Your SQLAchemy Code....75

Additional Resources....78

Summary....79

Chapter 4. Developing the FastAPI Code....81

Continuing Your Portfolio Project....81

Software Used in This Chapter....82

FastAPI....82

HTTPX....83

Pydantic....83

Uvicorn....84

Copying Files from Chapter 3....84

Installing the New Libraries in Your Codespace....85

Creating Python Files for Your API....85

Creating Pydantic Schemas....85

Creating Your FastAPI Controller....90

Testing Your API....96

Launching Your API....99

Additional Resources....101

Summary....102

Chapter 5. Documenting Your API....103

Sending a Signal of Trust....103

Making Great API Docs....104

Core Features....104

Extra Features....105

Reviewing Examples of API Documentation....106

Sleeper App....106

MyFantasyLeague....107

Yahoo! Fantasy Football....109

Viewing Your API’s Built-in Documentation....109

Copying Files from Chapter 4....110

Documentation Option 1: Swagger UI....111

Documentation Option 2: Redoc....117

Working with Your OpenAPI Specification File....118

Continuing Your Portfolio Project....121

Adding Details to the OAS info Object....122

Adding Tags to Categorize Your Paths....123

Adding More Details to Individual Endpoints....123

Adding Parameter Descriptions....124

Viewing the Changes in Swagger UI....125

Regression-Testing Your API....126

Updating Your README.md....127

Additional Resources....129

Summary....130

Chapter 6. Deploying Your API to the Cloud....131

Benefits and Responsibilities of Cloud Deployment....131

Benefits....132

Responsibilities....132

Choosing a Cloud Host for Your Project....133

Setting Up Your Project Directory....134

Using GitHub Codespaces as a Cloud Host....134

Deploying to Render....135

Signing Up for Render....136

Creating a New Web Service....136

Auto-Deploying a Change to Your API....138

Shipping Your Application in a Docker Container....139

Verifying Docker Installation....140

Creating a Dockerfile....140

Creating a .dockerignore File....141

Building a Container Image....142

Running Your Container Image Locally....142

Deploying to AWS....143

Creating a Lightsail Container Service....143

Installing the AWS CLI....145

Installing the Amazon Lightsail Container Services Plug-in....145

Configuring Your Login Credentials....145

Pushing Your Container Image to Lightsail....145

Creating a Lightsail Deployment....147

Updating Your API Documentation....151

Additional Resources....151

Summary....152

Chapter 7. Batteries Included: Creating a Python SDK....153

SDKs Bridge the Gap....154

Picking a Language for Your SDK....157

Starting with a Minimum Viable SDK....158

Expert Tip: Making Your SDK Easy to Install....158

Expert Tip: Making the SDK Consistent and Idiomatic....160

Building a Feature-Rich SDK....162

Expert Tip: Using Sane Defaults....163

Expert Tip: Providing Rich Functionality....165

Expert Tip: Performing Logging....170

Expert Tip: Hiding Your API’s Complicated Details....172

Expert Tip: Supporting Bulk Downloads....174

Expert Tip: Documenting Your SDK....177

Testing Your SDK....179

Expert Tip: Supporting Every Task the API Supports....183

Completing Your Part I Portfolio Project....184

Additional Resources....186

Summary....187

Part II. Using APIs in Your Data Science Project....189

Chapter 8. What Data Scientists Should Know About APIs....191

Using a Variety of API Styles....191

HTTP Basics....193

How to Consume APIs Responsibly....195

Separation of Concerns: Using SDKs or Creating API Clients....196

How to Build APIs....198

How to Test APIs....198

API Deployment and Containerization....199

Using Version Control....199

Introducing Your Part II Portfolio Project....200

Getting Started with Your GitHub Codespace....200

Cloning the Part II Repository....200

Launching Your GitHub Codespace....201

Running the SportsWorldCentral (SWC) API Locally....202

Additional Resources....203

Summary....204

Chapter 9. Using APIs for Data Analytics....205

Custom Metrics for Sports Analytics....205

Using APIs as Data Sources for Fantasy Custom Metrics....206

Creating a Custom Metric: The Shark League Score....208

Software Used in This Chapter....209

httpx....209

Jupyter Notebooks....209

pandas....210

Installing the New Libraries in Your Codespace....210

Launching Your API in Codespaces....210

Creating an API Client File....211

Creating Your Jupyter Notebook....212

Adding General Configuration to Your Notebook....214

Working with Your API Data....215

Calculating the League Balance Score....218

Calculating the League Juice Score....219

Creating the Shark League Score....221

Additional Resources....222

Summary....222

Chapter 10. Using APIs in Data Pipelines....223

Types of Data Sources for Data Pipelines....224

Planning Your Data Pipeline....224

Orchestrating the Data Pipeline with Apache Airflow....225

Installing Apache Airflow in GitHub Codespaces....226

Creating Your Local Analytics Database....230

Launching Your API in Codespaces....231

Configuring Airflow Connections....231

Creating Your First DAG....232

Coding a Shared Function....235

Running Your DAG....237

Summary....239

Chapter 11. Using APIs in Streamlit Data Apps....241

Engaging Users with Interactive Visualizations....241

Software Used in This Chapter....242

nfl_data_py....243

Streamlit....243

Installing Streamlit and nfl_data_py....243

Launching Your API in Codespaces....243

Reusing the Chapter 9 API Client File....244

Creating Your Streamlit App....244

Updating the Entrypoint File....245

Running Your Streamlit App....246

Creating the Team Rosters Page....247

Creating the Team Stats Page....250

Deploying Your Streamlit App....254

Completing Your Part II Portfolio Project....254

Additional Resources....255

Summary....256

Part III. Using APIs with Artificial Intelligence....257

Chapter 12. Using APIs with Artificial Intelligence....259

The Overlap of AI and APIs....259

Designing APIs to Use with Generative AI and LLMs....261

Defining Artificial Intelligence....263

Generative AI and Large Language Models (LLMs)....264

Creating Agentic AI Applications....264

Introducing Your Part III Portfolio Project....266

Getting Started with Your GitHub Codespace....266

Cloning the Part III Repository....266

Launching Your GitHub Codespace....267

Additional Resources....268

Summary....268

Chapter 13. Deploying a Machine Learning API....269

Training Machine Learning Models....270

New Software Used in This Chapter....272

ONNX Runtime....272

scikit-learn....272

sklearn-onnx....272

Installing the New Libraries in Your Codespace....273

Using the CRISP-DM Process....273

Business Understanding....274

Data Understanding....275

Data Preparation....277

Modeling....277

Evaluation....280

Deployment....280

Additional Resources....289

Summary....289

Chapter 14. Using APIs with LangChain....291

Calling AI Using APIs (via LangChain)....292

Creating a LangGraph Agent....293

Signing Up for Anthropic....294

Launching Your GitHub Codespace....295

Installing the New Libraries in Your Codespace....296

Creating Your Jupyter Notebook....296

Chatting with the LangGraph Agent....299

Running the SportsWorldCentral (SWC) API Locally....301

Installing the swcpy Software Development Kit (SDK)....302

Creating a LangChain Toolkit....302

Calling APIs Using AI (with LangGraph)....306

Chatting with Your Agent (with Tools)....308

Additional Resources....309

Summary....310

Chapter 15. Using ChatGPT to Call Your API....311

Architecture of Your Application....311

Getting Started with ChatGPT....312

Creating a Custom GPT....313

Launching Your GitHub Codespace....316

Running the SportsWorldCentral (SWC) API in GitHub Codespaces....317

Adding the Servers Section to Your OAS File....318

Creating a GPT Action....319

Testing the APIs in Your GPT....321

Chatting with Your Custom GPT....322

Completing Your Part III Portfolio Project....324

Summary....326

Index....327

About the Author....352

Colophon....352

Are you ready to grow your skills in AI and data science? A great place to start is learning to build and use APIs in real-world data and AI projects. API skills have become essential for AI and data science success, because they are used in a variety of ways in these fields. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit.

As you complete the chapters in the book, you'll be creating portfolio projects that teach you how to:

• Design APIs that data scientists and AIs love
• Develop APIs using Python and FastAPI
• Deploy APIs using multiple cloud providers
• Create data science projects such as visualizations and models using APIs as a data source
• Access APIs using generative AI and LLMs