Python Tutorial: Regression Plots in Seaborn

Key Takeaways

now that we have taken a look at distribution plots in Seaborn we will transition to another basic visualization process by plotting linear regression lines the previous analysis we perform is often referred to as univariate analysis because we only look at one variable regression analysis is bivariate because we are looking for relationships between two variables the red plot function is the basis for building regression plots and Seabourn the basic function call is similar to dis plot but we explicitly define the x and y variables as well as the source of the data since we are using a panda's data frame the x and y variables refer to the columns in the data frame this basic process is similar for many other Seabourn plots so it is good to make sure you understand this well before moving on to more complex plots in this specific plot we are interested in any relationship between the alcohol content of the wine and the pH levels the regression line hints that there might be a slight increase in pH values as the alcohol content increases one of the confusing points about Seabourn is that it may seem like there's more than one way to do the same plot in the previous exercises we looked at dist plots and briefly discussed KDE plots as a building block for the more robust dis plot in a similar manner the lower level reg plot and higher level Ln plot are related they are called the same way and produce similar output however the LM plot is much more powerful in this example we can look at the relationship between alcohol content and quality using both of these plot types the output looks similar except for the aspect ratio in the next slide I will show how the LM plot is much more flexible the use of hue and columns is a powerful concept that is present throughout many of Seaborn's functions the use of adding multiple graphs while changing a single variable is often called faceting in this case faceting can be accomplished by using the LM plot function the base function is very similar to red plot but it provides much more power by allowing you to add additional information using columns colors or rows there are an entire class of functions in Seaborn that support this type of faceting and we will continue to explore them throughout the course it is time to put these concepts into practice in the following exercises we will go through some more examples of using the Reg plot and LM plot functions to analyze the data set by the end of the exercises you should have a good understanding of how Seaborn works and be prepared for learning about additional plot types supported by Seaborn