You can see a list of the freely available files on their GitHub repository. To begin with, because you’re working with one of the seaborn sample datasets, seaborn allows you online access to these using its load_dataset() function. The usual way of loading data is to use the pandas read_csv() function to read data from a file on disk. To work with data in seaborn, you usually load it into a pandas DataFrame, although other data structures can also be used. Although you can use any alias you like, sns is a nod to the fictional character the library was named after. show () Copied!įirst, you import seaborn into your Python code. So now it’s time for you to dive right in and learn how to use seaborn to produce your Python plots. Knowing the difference between a pandas DataFrame and Series will also prove useful. You might also like to learn how a pandas DataFrame stores its data. Although you can follow along with this seaborn tutorial using your favorite Python environment, Jupyter Notebook is preferred. Create Python plots using seaborn’s objectsīefore you start, you should familiarize yourself with the Jupyter Notebook data analysis tool available in JupyterLab.Create Python plots using seaborn’s functions.Understand the principles of seaborn’s more contemporary Python objects interface.Understand the principles of seaborn’s classic Python functional interface.Make an informed judgment as to whether or not seaborn meets your data visualization needs.If you want to take your statistical visualizations to the next level, you should master the Python seaborn library to produce impressive statistical analysis plots that will display your data. Most likely you’ll have used a library such as Matplotlib to produce these. If you have some experience using Python for data analysis, chances are you’ve produced some data plots to explain your analysis to other people.

Set colors on seaborn scatter plot