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Find centralized, trusted content and collaborate around the technologies you use most. Connect and share knowledge within a single location that is structured and easy to search. Each one of these accepts an rc keyword parameter dictionary. In each individual API page for the above three functions, it says:.

This only updates parameters that are considered part of the style definition. If you want to see what parameters are included, you can just call the function with no arguments, which will return the current settings:. However, using this on the other functions always returns None. So, for example, I am using the following mix of matplotlib and seaborn to set parameters:.

This means that they each have mutually exclusively characteristics that can be edited. However, this is not outlined anywhere in the docs. I want to know which one of these three functions will accept a parameter for figsize. I'd also be curious to see what else they accept that might help me fine-tune things. My goal is to exclusively use the seaborn interface as often as possible. I don't need the fine tune control of things matplotlib provides, and often find it awkward anyway. It would appear that the answer is 'none of the above'.

So you will have to use matplotlib. Stack Overflow for Teams — Start collaborating and sharing organizational knowledge. Create a free Team Why Teams? Collectives on Stack Overflow. Learn more. Asked 2 years, 1 month ago. Modified 2 years, 1 month ago. Viewed 5k times. Because of this, Seaborn places a strong emphasis on exploratory data analysis. It aims to let you understand your data easily, finding nuances that may otherwise not be apparent. Seaborn can be installed using either the pip package manager or the conda package manager.

To install Seaborn, simply use either of the commands below:. The package installer will install any dependencies for the library. Once the installation is complete, you can import seaborn in your Python script. Conventionally, the alias sns is used for Seaborn:. If this code runs without a problem, then you successfully installed and imported Seaborn!

These datasets are built deliberately to highlight some of the features of the library. For example, the datasets have unique statistical attributes that allow you to visualize them. It may seem redundant to need to import Matplotlib. Seaborn comes with a function to load datasets built into the library. Because Seaborn works closely with Pandas, we can import the dataset directly as a DataFrame:.

Here, we used the. Seaborn lets you create relational plots using the relplot function. The function technically lets you create more than scatter plots. It provides a high-level wrapper to create scatter plots and line plots. In the code block above, seven parameters of the relplot function are described. However, there are actually over twenty-five different parameters to help you customize your plot! This example highlights the deep integration that Seaborn has with Pandas.

From there, making use of the variables available in that DataFrame became a matter of only referencing them by name. Another thing you may notice is how much more modern the resulting graph is. Seaborn immediately styles the graph in a much more pleasant aesthetic! While the visualizations are immediately nicer to look at than those produced with Matplotlib, Seaborn also provides an array of customization options.

In fact, because Seaborn is built on top of Matplotlib, you can actually use the customization options from Matplotlib to customize your graph. Similar to Matplotlib, Seaborn comes with a number of built-in styles. Seaborn comes with five different styles built-in:. The function takes one of these styles as its argument. In this example, you applied the darkgrid style to your Seaborn visualization.

Everything else in the code remained exactly the same! Your graph now looks like this:. Because Seaborn uses Matplotlib under the hood, you can use any of the same Matplotlib attributes to customize your graph. Right now, you can see that as flipper length increases, as does the body mass.

But the dataset you loaded provides significantly more information than just that. For example, you can use each dot on the visualization to represent a species and the sex of the penguin. This will allow you to see different trends in the data. The parameter expects a column name.

So, since we want to color our data points using the species column, we simply pass in that column:. By coloring the data points, the visualization makes a lot more sense. Larger penguins almost exclusively belong to one species. Because Seaborn can work readily with long DataFrames, passing in the hue parameter immediately created a legend.

We can take this one step further by changing the shape of each data point to reflect another data attribute. For example, we can split the dataset by the sex variable to see if there are trends and differences in sex. The parameter expects a DataFrame column being passed in. Doing this modifies the legend to add a hierarchy to it. Now, both the colors and shapes are differentiated. This allows us to see that for the Gentoo species there are significantly higher variations in flipper length and body mass between the two sexes.

Seaborn also makes creating multiple charts incredibly easy. Rather than needing to worry about creating figures and axes, you can easily split your data by a Pandas column.