seaborn: statistical data visualization — seaborn 0.13.2 …
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to …
Example gallery — seaborn 0.13.2 documentation
Explore various examples of visualizations using seaborn, a Python data visualization library.
Overview of seaborn plotting functions
Two important plotting functions in seaborn don’t fit cleanly into the classification scheme discussed above. These functions, jointplot() and pairplot(), employ multiple kinds of plots from …
An introduction to seaborn — seaborn 0.13.2 documentation
Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping …
seaborn.lineplot — seaborn 0.13.2 documentation
ax matplotlib.axes.Axes. Pre-existing axes for the plot. Otherwise, call matplotlib.pyplot.gca() internally.. kwargs key, value mappings. Other keyword arguments are ...
User guide and tutorial — seaborn 0.13.2 documentation
Overview of seaborn plotting functions. Similar functions for similar tasks; Figure-level vs. axes-level functions; Combining multiple views on the data
API reference — seaborn 0.13.2 documentation
Set aspects of the visual theme for all matplotlib and seaborn plots. axes_style. Get the parameters that control the general style of the plots. set_style. Set the parameters that control …
seaborn.scatterplot — seaborn 0.13.2 documentation
Examples. These examples will use the “tips” dataset, which has a mixture of numeric and categorical variables:
Building structured multi-plot grids — seaborn 0.13.2 …
Matplotlib offers good support for making figures with multiple axes; seaborn builds on top of this to directly link the structure of the plot to the structure of your dataset. The figure-level …
Installing and getting started — seaborn 0.13.2 documentation
Once you have seaborn installed, you’re ready to get started. To test it out, you could load and plot one of the example datasets: import seaborn as sns df = sns . load_dataset ( "penguins" ) …