1. Course Introduction


In this lesson, we get a preview of the course ahead.

To explore more Kubicle data literacy subjects, please refer to our full library.


  1. Prerequisites (00:15)

    To complete this course, you’ll need to have a good understanding of the basics of functions, loops, visualizations, and Pandas dataframes.

  2. Course structure (02:30)

    This course has 3 main parts. 

    In the first part, we’ll learn how to connect to csv files that are stored on the internet.

    In the second part, we’ll learn how to convert tables on a web page into a Pandas dataframe.

    And in the 3rd part, we’ll learn how to connect to live and regularly updated data by connecting to an API.


Welcome to this first lesson on connecting to live data. Before we learn about these concepts, we'd like to quickly remind you of the process of using the Jupyter Notebook files we'll be providing for this course.

Most lessons come with before and after files that capture the notebook as it was at the beginning of the lesson, and at the end of the lesson.

To view these files, we first need to download them.

By default, Jupyter Notebook opens to our Personal folder, so we'll make sure the downloaded files are stored there.

In this case, we have a folder called Kubicle Lesson Files.

We'll save the file here and navigate to Jupyter Notebook.

Here we can open the Kubicle Lesson Files folder where we can find and open the file we just downloaded.

We'll now return to the course introduction.

The concepts covered in this course are advanced Python skills. We'll approach these concepts with the assumption that you, the learner, are already familiar with the basics of Python.

This includes performing basic calculations, understanding the different data types, storing values in variables, and adding multiple values to lists.

If you're unfamiliar with any of these concepts, we encourage you to check out our course on Python basics.

You should be quite familiar with conditionality and loops at this stage in your learning but if you're unfamiliar with these concepts, have a look at our course on functions, conditionality and loops.

Also, we'll make extensive use of the Pandas and MatPlotLib libraries in this course, so if you need a refresher on using them, make sure to check out our course on storing, transforming, and visualizing data.

Finally, we'll assume that you're familiar with data preparation in Python so make sure you check out our course on data preparation if you're unfamiliar with data joins and cleaning data in Python.

Let's now have a quick look at what we'll cover in this course.

In the first part of this course, we'll learn about connecting to files on the internet. We'll use functions that can connect to a CSV file stored on the web to import it directly as a pandas data frame without having to download it to our local machine first.

In the second part of the course, we'll learn about downloading HTML tables.

Web pages are compiled with code called HTML.

This contains all types of data, such as titles, headers and paragraphs, not unlike a text document.

They also contain data stored in formatted tables.

In this section of the course, we'll use functions that allow us to convert table stored on web pages to pandas data frames. In the final part of this course, we'll learn about connecting to APIs.

APIs enable us to send information from one machine to another.

We'll connect to an API designed to provide currency exchange rates for specific dates.

Let's stop the lesson here.

In the next lesson, we'll establish the business scenario that we'll consider in this course.

Python Basics
Connecting to Live Data