Unlock powerful data insights through APIs, gaining straightforward access to extensive publicly available information. Learn how mastering API structures and data handling techniques can significantly enhance your analytical skills in Python.
Key Insights
- Understanding and correctly using the appropriate API URL enables easy retrieval and manipulation of data, allowing activities like converting JSON responses into data frames or visual representations.
- The primary challenge with APIs is not writing the code but effectively comprehending the structure of the data, identifying suitable APIs, and possibly combining multiple sources to obtain relevant insights.
- A curated list on GitHub offers access to numerous constantly updated public APIs across diverse categories, demonstrating the vast availability and potential applications of publicly accessible data.
Note: These materials offer prospective students a preview of how our classes are structured. Students enrolled in this course will receive access to the full set of materials, including video lectures, project-based assignments, and instructor feedback.
Let's sum up our way of accessing data using APIs. First, remember that an API is an interface to data. And many APIs are public.
We'll take a look at that link here in a second. With our understanding of this particular API—which required spending time researching and understanding it—we were able to use the correct URL to access the data we wanted, make a request to that URL, convert it from JSON, and then do anything we wanted with it: explore it, understand its structure, put it into a data frame, graph it, plot it, and extract useful information from it.
All the things you're used to doing with data—we're able to do with this data using relatively little code. The hard part isn’t the code.
It’s understanding the shape of the data, understanding which API is right for you, which API has the data you're looking for, and maybe even combining multiple APIs. The fact that APIs are so powerful and that the data is so publicly available means you have great power and great access to information. But you also have to put in the work to ensure you're doing it correctly and that you can actually find, in that sea of data, the data you truly need.
The existence of that sea of data—and our ability to access it—is amazing and has really changed the nature of working with data in Python as APIs have expanded over the last 20 years. If you want to get started with APIs and explore what data is out there, I recommend the link we showed you earlier—the GitHub-curated list of public APIs. It’s updated constantly and contains a massive amount of resources.
These are just categories—let alone the actual data itself. Even if we just look at the "Documents and Productivity" category, there's a long list of public APIs.
There's a lot more to be said about APIs, but it’s far outside the scope of this course. They are the first of the two main ways we're going to look at acquiring data using Python. I hope you got a lot out of this introduction to APIs and accessing data.
We'll move on to our next section.