Leverage the power of data visualization with Seaborn pair plots to clearly illustrate positive and negative correlations within datasets. Understand how to effectively use histograms to highlight data frequency where direct correlations are absent.
Key Insights
- Utilize Seaborn's pair plot feature to visually represent data correlations through scatter plots, clearly indicating positive correlations as upward-trending dots and negative correlations as downward-trending dots.
- Recognize the advantage of using histograms in cases where variables are compared to themselves, thus effectively depicting data frequency rather than meaningless correlation.
- Understand that while this content emphasizes visualization techniques closely related to data analysis, it does not serve as a dedicated data visualization training course.
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.
Whether you are a visual person or whether the person you're trying to demonstrate this data to is a visual learner, having something that visualizes your data is a very powerful tool to use.
Now, this is not a data visualization course, but they're very interrelated, and I think it can help to take a look at how these values are related to each other. We're going to use a pair plot, one of my very favorite plots, because, yeah, at this point, I have favorite plots.
A pair plot is going to be a correlation matrix, just like we looked at, but instead of just pure numbers, it's going to be scatter plots. It's going to be a plot of plots. And we'll use Seaborn to make this pair plot.
We'll see some positive correlations as dots trending upward from left to right. The more of one, the more of the other. And strong negative correlations will trend from left to right.
Down from left to right, right? Like the more of one, the less of the other. And when something is compared to itself, for example, sales in thousands being perfectly correlated with sales in thousands, which isn't very helpful. However, an alternative in cases where there's no real information to be gleaned is to show a histogram, showing the frequency distribution for that data.
We've already seen histograms. Seaborn has made a good choice by using this as the default option when there's no relationship, showing the data for itself. What do we show? Let's display more information about that variable.
Let's create one. Let's make a Seaborn pair plot. And here's how we're going to do it in the next video.