x= refers to the column to use as your x-axis.In the example above, you only passed in three different variables: Let’s now create a basic scatter plot using the Seaborn relplot function: # Creating Your First Seaborn Plot However, there are actually over twenty-five different parameters to help you customize your plot! In the code block above, seven parameters of the relplot() function are described. Kind='scatter', # Either 'scatter' or 'line' Size=None, # A grouping variable to define the size of data pointsĭata=None, # The input data structure, such as a DataFrame Hue=None, # A grouping variable to use to color data points Y=None, # The variable to use as the y-axis X=None, # The variable to use as the x-axis Let’s take a look at some of the arguments the function provides: # A highlight of the parameters of the sns.relplot() function It provides a high-level wrapper to create scatter plots and line plots. The function technically lets you create more than scatter plots. Seaborn lets you create relational plots using the relplot() function. In this section, you’ll learn how to create your first Seaborn plot – a scatter plot. In the next section, you’ll learn how to create your first Seaborn plot: a scatter plot. head() method to return the first five records in the dataset. # species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex Because Seaborn works closely with Pandas, we can import the dataset directly as a DataFrame: # Loading a Sample DataFrame We’ll use the 'penguins' dataset throughout this tutorial. This function is aptly-named as load_dataset(). Seaborn comes with a function to load datasets built into the library. Let’s load all the libraries we’ll need: # Import libraries However, since Seaborn is built on top of Matplotlib, you’ll need some of the features to customize your plot. It may seem redundant to need to import Matplotlib. In order to follow along, you’ll need to import both pandas and matplotlib.pyplot. For example, the datasets have unique statistical attributes that allow you to visualize them. These datasets are built deliberately to highlight some of the features of the library. To follow along with this tutorial, we’ll be using a dataset built into the Seaborn library. If this code runs without a problem, then you successfully installed and imported Seaborn! Let’s get started with using the library. Conventionally, the alias sns is used for Seaborn: # Importing Seaborn Once the installation is complete, you can import seaborn in your Python script. The package installer will install any dependencies for the library. To install Seaborn, simply use either of the commands below: # Installing Seaborn Seaborn can be installed using either the pip package manager or the conda package manager. Check it out now! Installing and Loading Seaborn in Python This post is part of the Seaborn learning path! The learning path will take you from a beginner in Seaborn to creating beautiful, customized visualizations. It aims to let you understand your data easily, finding nuances that may otherwise not be apparent.
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