Data visualization is extremely important in data science.

Although you often hear about the importance of data manipulation (i.e., “80% of data science is data manipulation”), data visualization is just as important.

I explained why in a recent blog post about why you need to master data visualization. That blog post has a detailed explanation of why data visualization is important, and I recommend that you read it if you haven’t already.

To summarize though, data visualization:

  • Is required for almost every step of the data science workflow
  • Is critical at every data science job level, but especially at junior and intermediate levels

Whether you’re doing data exploration, data analysis, finding insights, storytelling with data, or building a machine learning model, you will probably need to use data visualization. So, you need to make learning data visualization a priority.

The Problem of Data Manipulation in Python

If you’re a Python user though, you’re going to run into a bit of a problem when you try.

Frankly, data visualization in Python is a pain in the a**.

Matplotlib is hard to use

When I say this, I’m mostly talking about Matplotlib.

Matplotlib is the de facto standard for data visualization in Python. It’s the package that’s used in 90% of the books, videos, and courses that I’ve seen. It’s ubiquitous.

But the fact is, Matplotlib is hard to use for the most part.

Things like simple bar charts and scatterplots are somewhat easy to create, but that’s where the simplicity ends.

Doing relatively simple things like adding multiple lines to a line chart, or creating a “dodged” bar chart (with different bars for different categories) is often confusing.

And slightly more complicated charts, like a small multiple chart, require a lot of confusing syntax.

Additionally, matplotlib doesn’t seem to work well natively with Python dataframes, which are now a standard for data science in Python.

This is in stark contrast to R’s ggplot2. ggplot2, although a little confusing to beginners, is extremely powerful and easy to use once you understand how it works.

Matplotlib, on the other hand, is almost always complicated and confusing. When you use it, things just seem harder than they should be.

Other options are also challenging

In an effort to find something better, I’ve personally looked at other options.

I was initially interested in Bokeh, which is very powerful. But Bokeh is not really great for statistical visualization. Bokeh excels at interactive visualizations. It’s really designed to produce visualizations that live on the internet, not necessarily static visualizations that are used for data exploration and analysis.

It also doesn’t work as well natively with Pandas dataframes.

What we really need is a visualization toolkit that’s easy to understand, easy to use, works well with dataframes, and is capable of producing a wide range of statistical visualizations that we can use for data exploration and analysis.

After some research and experimentation, I discovered that such a package exists.

It’s Seaborn.

Learn Seaborn for Data Visualization

After quite a bit of experimentation with various Python data visualization packages, I discovered that Seaborn is the best option right now for statistical data visualization in Python.

There are a few major reasons for this.

Seaborn is Easy to Use

The biggest reason that I like Seaborn is that it’s easy to use.

In Seaborn, most common statistical visualizations can be created with a simple line or a few lines of code.

For example, if you want to create a scatterplot in Seaborn, once you have your data, the plot itself is very easy to create.

sns.scatterplot(data = norm_data
                ,x = 'x_var'
                ,y = 'y_var'
                )

I wrote that code on 3 lines to make it easier to read, but it could have been 1 line of code.

Notice as well that the parameters make sense.

There’s a data parameter to set the dataframe.

And there’s x and y parameters to set the variables that go on the x-axis and y-axis respectively.

The functions in Seaborn are fairly well designed, easy to understand, and easy to use.

Seaborn Enables You to Create a Wide Variety of Statistical Visualizations

Seaborn also enables you to create a wide variety of statistical data visualizations.

For example, bar charts are very straightforward to create.

So are line charts:

An image of a laptop with Seaborn code in an IDE, and a corresponding line chart.

But Seaborn also enables you to create slightly more exotic visualizations.

For example, you can use Seaborn to create violin plots:

A laptop showing Seaborn creating a violin chart.

Two variable density plots:

A laptop showing code that creates a 2-dimensional "jointplot" with Seaborn.

Small multiple charts:

A laptop showing Seaborn creating a Small Multiple chart.

As well as “pair plots,” heatmaps, and more.

Seaborn is a full toolkit for creating data visualizations that will help you analyze your data and find valuable insights.

And like I mentioned previously, all of these visualizations are relatively easy to create. Seaborn has simple functions for creating all of them.

Seaborn Works Natively with DataFrames

Pandas dataframes have become a standard for storing and manipulating data for data science projects.

Because of this, I’d argue that a great data visualization package should work seamlessly with Pandas dataframes.

That’s one of the reasons that I really like Seaborn.

Seaborn works natively with dataframes.

When you study the syntax, you’ll notice that most of the Seaborn functions have a data parameter. Many also have an x and y parameter.

A laptop explaining the syntax to create a Seaborn scatterplot.

In these Seaborn functions, the data parameter enables you to specify a Pandas dataframe as an input. They work seamlessly with dataframes.

Moreover, the x and y parameters enable you to directly specify dataframe variables (i.e., columns) as inputs to parameters like x and y. This also works for other parameters like the hue parameter which enables you to modify plot colors based on a third variable.

When you start working with Seaborn, you find that it “just works” with Pandas dataframes.

Seaborn Creates Beautiful, Multivariate Visualizations

Finally, you can use Seaborn to create beautiful, multivariate visualizations with only a few lines of code.

An image of a laptop, playing a video that explains how to create a scatterplot in Seaborn

The “beauty” of these visualizations is important, because it makes them engaging to your audience.

But what’s more important here is that we can use Seaborn’s tools to easily manipulate plot aesthetics like size, opacity, color, and position to create visualizations that naturally produce insights. We can use Seaborn’s functionality to create visualizations that literally enable us to see the important features of our data. We can also use these complex visualizations to communicate insights to our partners.

I’ll emphasize again that Seaborn makes this relatively easy to do. Due to the simplicity of the syntax, creating beautiful, insightful visualizations becomes relatively easy once you know how it works.

Learn Seaborn to do Data Visualization in Python

Seaborn is hands down my favorite data visualization package for Python right now.

It’s the primary tool that I use for statistical data visualization.

And it’s also the only Python data visualization package that I teach to our beginning students (although, at advanced levels, students may want to explore other tools for specific use cases).

If you’re looking for a Python data visualization package that’s easy to use, easy to understand, powerful, and works well with Pandas dataframes, I think that Seaborn is definitely the best option.

How to Master Data Visualization using Seaborn

If you’re ready to master data visualization in Python using Seaborn, you should join our new course, Seaborn Mastery.

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If you’re ready to master data visualization in Python, this is the course you’ve been waiting for.