#Seaborn scatter plot with size color how to

Seaborn doesn't come with any built-in 3D functionality, unfortunately. We've also added a legend in the end, to help identify the colors. This results in 10 different scatter plots, each with the related x and y data, separated by region. Specifically, we specified a sns.scatterplot as the type of plot we'd like, as well as the x and y variables we want to plot in these scatter plots. To this grid object, we map() our arguments. Finally, we've set the col_wrap argument to 5 so that the entire figure isn't too wide - it breaks on every 5 columns into a new row. We've also assigned the hue to depend on the region, so each region has a different color. By specifying the col argument as 'Region', we've told Seaborn that we'd like to facet the data into regions and plot a scatter plot for each region in the dataset. Here, we've created a FacetGrid, passing our data ( df) to it. Here, we've supplied the df as the data argument, and provided the features we want to visualize as the x and y arguments. We don't need to fiddle with the Figure object, Axes instances or set anything up, although, we can if we want to.

Seaborn makes it really easy to plot basic graphs like scatter plots. Sns.scatterplot(data = df, x = 'Economy (GDP per Capita)', y = 'Happiness Score') We'll plot the Happiness Score against the country's Economy (GDP per Capita): import matplotlib.pyplot as plt Now, with the dataset loaded, let's import PyPlot, which we'll use to show the graph, as well as Seaborn. We'll use the World Happiness dataset, and compare the Happiness Score against varying features to see what influences perceived happiness in the world: import pandas as pdĭf = pd.read_csv( 'worldHappiness2016.csv') We'll cover simple scatter plots, multiple scatter plots with FacetGrid as well as 3D scatter plots.

#Seaborn scatter plot with size color how to

In this tutorial, we'll take a look at how to plot a scatter plot in Seaborn. It offers a simple, intuitive, yet highly customizable API for data visualization.

Seaborn is one of the most widely used data visualization libraries in Python, as an extension to Matplotlib.