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 person. Having something that visualizes your data is very strong. It's a very strong 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 gonna use a pair plot. One of my very favorite plots, because, yeah, at this point I have favorite plots.
Pair plot is going to be a correlation matrix, just like we looked at, but instead of just pure numbers, it's gonna be scatter plots. It's gonna be a plot of plots. And we'll use Seaborn to make this pair plot.
We'll see some positive correlations as dots treading upward from left to right. The more of this, the more of that. And strong negative correlations from left to right.
Down from left to right, right? Like the more of this, the less of this. And when something is compared to itself, when it's like, yeah, that sales of thousands is perfectly correlated to sales of thousands, like that's not very helpful. But what is helpful, an alternative in those cases where there's no real information to be gleaned, is to show a histogram, show the data frequency for that thing.
We've already seen histograms. It's a good choice that Seaborn has made as the default thing for, hey, there's no relationship here, it's itself. What do we show? Let's show more information about that one thing.
Let's make one. Let's make a Seaborn pair plot. And here's how we're gonna do it in the next video.