Seaborn — it is not only a visualization library, but also a provider of embedded datasets. Here we will be working with one such dataset in seaborn called "tips". The tips dataset contains information about people who likely had a meal at a restaurant and whether or not they tip. It also provides information about the gender of people, whether they smoke, day, time, etc.
Let’s take a look at the dataset first before we start with regression plots.
Load dataset
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Output
Now let’s start from the regression plots in the sea bay.
Regression plots in seaborn can be easily implemented using the lmplot () function. lmplot () can be thought of as a function that basically creates a line plot of a model. lmplot () creates a very simple linear regression plot. It creates a scatter plot with a linear fit on top of it.
Simple linear plot
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explanation
The x and y parameters are specified to provide values for the x and y axes. sns.set_style () is used to create a grid in the background instead of the default white background. The data parameter is used to specify the source of information for drawing graphs.
Line chart with additional parameters
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< p> Exit
explanation
To better analyze with these plots, we can specify the hue to have a categorical separation in our plot, and also use markers that are taken from matplotlib marker symbols. Since we have two separate categories, we need to specify a list of characters when specifying a bullet.
Setting the size and color of the scene
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explanation
In this example, what Seabron does is that he calls matplotlib parameters indirectly to affect scatter plots. We specify a parameter named scatter_kws. It should be noted that the scatter_kws parameter only resizes the scatter plots, not the regression lines. The regression lines remain intact. We also use the palette option to change the color of the graph. The rest of the things remain the same as described in the first example.
Display multiple graphs
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explanation
In the above code, we draw multiple plots, defining separation using rows and columns. Each row contains graphs of tips versus total score for different times specified in the dataset. Each column contains graphs of advice versus total score for different genders. Further separation is done by specifying a hue parameter based on whether the person smokes.
Plot size and aspect ratio
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explanation
Suppose we have a large number of graphs in the output, we you need to set its size and aspect in order to better visualize it.
aspect: scalar, an optional parameter specifies the aspect ratio of each facet, so "aspect * height" gives the width of each facet in inches.
