It is time for a hands-on walkthrough to collect data and turn it into useful plots. For this exercise we are going to collect metadata from YouTube and run it through Seaborn to figure out if my hunch is correct or not.
Sometimes we only want to work with part of the data in a DataFrame. In this post, we explore the different ways that Pandas gives us to filter the data we want.
While working on my upcoming blog post on filtering data in Pandas, I noticed a little gap in my knowledge: How can we create a DataFrame without the help of a CSV file? Let us find out what options we have.
When we work with larger datasets in Jupyter, we will notice a slowdown in the execution time. Let us look at 4 magic commands that Jupyter offers us to check the performance of our statements.
JupyterLab offers us many little tricks to work more effectively. In this post we explore a few helpful tips I would no longer want to miss.
Seaborn is a powerful library that we can use to explore data. Once we have figured out what we want to show, it is time to adapt the plots to our requirements. In this post we explore the options we have for styling our plots.
Making sense of the data is an important first step before we can visualise the data. In this post we continue with Seaborn and explore the various ways it can help us to better understand what is going on.
Seaborn is a statistical data visualization library based on Matplotlib. We will use Seaborn to find interesting relationships in our data and turn them into informative graphs. As you will see shortly, this involves less code than if we were to use Matplotlib directly.
So far, I printed the Pandas data frame in Jupyter to see what data is in it. That works for small data frames but is rather useless for large ones. Let us find better ways to explore our data in Pandas.
The data we get is not always in a useful format. With a bit of Pandas magic we can clean-up a lot and automate the process along the way.