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19 mar 2024 · Throughout this tutorial, we'll demonstrate how to work with APIs using Python and the requests library. You'll learn how to make API requests, handle response data, and integrate API data into your AI and data science workflows.
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This comprehensive guide offers everything you need for an introduction to Python, with tutorials that build foundational skills, practice problem exercises to reinforce learning, a handy cheat sheet for quick reference, and projects that provide Python practice in real-world data science scenarios.
In this edition, explore our Python API tutorial to learn how to handle real-time data and integrate it into AI projects, including a hands-on example with the “International Space Station” tracking API.
The document provides an introduction to using APIs in Python. It discusses how APIs allow retrieval of frequently updated or large datasets through requests rather than downloading entire files. The document then demonstrates making a GET request to the Open Notify API to retrieve astronaut data in JSON format and working with the JSON response.
This repository contains files, notebooks, and data used for live project walkthroughs on Dataquest. You can watch the project walkthroughs on Youtube. These walkthroughs help you build complete end-to-end projects that can go into your portfolio.
Dataquest does a great job remediating those pitfalls. Concepts like data storytelling, effective use of command-line, and working with SQL are a few examples of Dataquest’s awareness of what data scientists need to know.
Application Program Interfaces, or APIs, are commonly used to retrieve data from remote websites. Sites like Reddit, Twitter, and Facebook all offer certain data through their APIs. To use an API, you make a request to a remote web server, and retrieve the data you need.
