فهرست مطالب

• Preface
• I. Building APIs for Data Science
• 1. Creating APIs That Data Scientists Will Love
• How Do Data Scientists Use APIs?
• What Tools Do Data Scientists Use?
• Designing APIs for Data Scientists
• Introducing Your Part I Portfolio Project
• Every API Has a Story
• Selecting the First API Products
• Additional Resources
• Summary
• 2. Selecting Your API Architecture
• API Architectural Styles
• Technology Architecture
• Software Used in This Chapter
• Getting Started with Your GitHub Codespace
• Additional Resources
• Summary
• 3. Creating Your Database
• Components of Your API
• Software Used in This Chapter
• Creating Your SQLite Database
• Accessing Your Data Using Python
• Additional Resources
• Summary
• 4. Developing the FastAPI Code
• Continuing Your Portfolio Project
• Software Used in This Chapter
• Copying Files from Chapter 3
• Installing the New Libraries in Your Codespace
• Creating Python Files for Your API
• Testing Your API
• Launching Your API
• Additional Resources
• Summary
• 5. Documenting Your API
• Sending a Signal of Trust
• Making Great API Docs
• Reviewing Examples of API Documentation
• Viewing Your API’s Built-in Documentation
• Working with Your OpenAPI Specification File
• Continuing Your Portfolio Project
• Updating Your README.md
• Additional Resources
• Summary
• 6. Deploying Your API to the Cloud
• Benefits and Responsibilities of Cloud Deployment
• Choosing a Cloud Host for Your Project
• Setting Up Your Project Directory
• Using GitHub Codespaces as a Cloud Host
• Deploying to Render
• Shipping Your Application in a Docker Container
• Deploying to AWS
• Updating Your API Documentation
• Additional Resources
• Summary
• 7. Batteries Included: Creating a Python SDK
• SDKs Bridge the Gap
• Picking a Language for Your SDK
• Starting with a Minimum Viable SDK
• Building a Feature-Rich SDK
• Completing Your Part I Portfolio Project
• Additional Resources
• Summary
• II. Using APIs in Your Data Science Project
• 8. What Data Scientists Should Know About APIs
• Using a Variety of API Styles
• HTTP Basics
• How to Consume APIs Responsibly
• Separation of Concerns: Using SDKs or Creating API Clients
• How to Build APIs
• How to Test APIs
• API Deployment and Containerization
• Using Version Control
• Introducing Your Part II Portfolio Project
• Getting Started with Your GitHub Codespace
• Running the SportsWorldCentral (SWC) API Locally
• Additional Resources
• Summary
• 9. Using APIs for Data Analytics
• Custom Metrics for Sports Analytics
• Using APIs as Data Sources for Fantasy Custom Metrics
• Creating a Custom Metric: The Shark League Score
• Software Used in This Chapter
• Installing the New Libraries in Your Codespace
• Launching Your API in Codespaces
• Creating an API Client File
• Creating Your Jupyter Notebook
• Adding General Configuration to Your Notebook
• Working with Your API Data
• Calculating the League Balance Score
• Calculating the League Juice Score
• Creating the Shark League Score
• Additional Resources
• Summary
• 10. Using APIs in Data Pipelines
• Types of Data Sources for Data Pipelines
• Planning Your Data Pipeline
• Orchestrating the Data Pipeline with Apache Airflow
• Installing Apache Airflow in GitHub Codespaces
• Creating Your Local Analytics Database
• Launching Your API in Codespaces
• Configuring Airflow Connections
• Creating Your First DAG
• Coding a Shared Function
• Running Your DAG
• Summary
• 11. Using APIs in Streamlit Data Apps
• Engaging Users with Interactive Visualizations
• Software Used in This Chapter
• Installing Streamlit and nfl_data_py
• Launching Your API in Codespaces
• Reusing the Chapter 9 API Client File
• Creating Your Streamlit App
• Updating the Entrypoint File
• Running Your Streamlit App
• Creating the Team Rosters Page
• Creating the Team Stats Page
• Deploying Your Streamlit App
• Completing Your Part II Portfolio Project
• Additional Resources
• Summary
• III. Using APIs with Artificial Intelligence
• 12. Using APIs with Artificial Intelligence
• The Overlap of AI and APIs
• Designing APIs to Use with Generative AI and LLMs
• Defining Artificial Intelligence
• Generative AI and Large Language Models (LLMs)
• Creating Agentic AI Applications
• Introducing Your Part III Portfolio Project
• Getting Started with Your GitHub Codespace
• Additional Resources
• Summary
• 13. Deploying a Machine Learning API
• Training Machine Learning Models
• New Software Used in This Chapter
• Installing the New Libraries in Your Codespace
• Using the CRISP-DM Process
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deployment
• Additional Resources
• Summary
• 14. Using APIs with LangChain
• Calling AI Using APIs (via LangChain)
• Creating a LangGraph Agent
• Installing the New Libraries in Your Codespace
• Creating Your Jupyter Notebook
• Chatting with the LangGraph Agent
• Running the SportsWorldCentral (SWC) API Locally
• Installing the swcpy Software Development Kit (SDK)
• Creating a LangChain Toolkit
• Calling APIs Using AI (with LangGraph)
• Chatting with Your Agent (with Tools)
• Additional Resources
• Summary
• 15. Using ChatGPT to Call Your API
• Architecture of Your Application
• Getting Started with ChatGPT
• Creating a Custom GPT
• Launching Your GitHub Codespace
• Running the SportsWorldCentral (SWC) API in GitHub Codespaces
• Adding the Servers Section to Your OAS File
• Creating a GPT Action
• Testing the APIs in Your GPT
• Chatting with Your Custom GPT
• Completing Your Part III Portfolio Project
• Summary
• Index
• About the Author