Visualization with Seaborn
atplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired. There are several valid complaints about Matplotlib that often come up:
- Prior to version 2.0, Matplotlib’s defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.
- Matplotlib’s API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a lot of boilerplate code.
- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas
DataFrames. In order to visualize data from a Pandas
DataFrame, you must extract each
Seriesand often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the
DataFramelabels in a plot.
An answer to these problems is Seaborn. Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas
To be fair, the Matplotlib team is addressing this: it has recently added the
plt.style tools discussed in Customizing Matplotlib: Configurations and Style Sheets, and is starting to handle Pandas data more seamlessly. The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo. But for all the reasons just discussed, Seaborn remains an extremely useful addon.
Seaborn Versus Matplotlib
Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors. We start with the typical imports:In :
import matplotlib.pyplot as plt plt.style.use('classic') %matplotlib inline import numpy as np import pandas as pd
Now we create some random walk data:In :
# Create some data rng = np.random.RandomState(0) x = np.linspace(0, 10, 500) y = np.cumsum(rng.randn(500, 6), 0)
And do a simple plot:In :
# Plot the data with Matplotlib defaults plt.plot(x, y) plt.legend('ABCDEF', ncol=2, loc='upper left');
Although the result contains all the information we’d like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.
Now let’s take a look at how it works with Seaborn. As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib’s default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output. We can set the style by calling Seaborn’s
set() method. By convention, Seaborn is imported as
import seaborn as sns sns.set()
Now let’s rerun the same two lines as before:In :
# same plotting code as above! plt.plot(x, y) plt.legend('ABCDEF', ncol=2, loc='upper left');
Ah, much better!
Exploring Seaborn Plots
The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.
Let’s take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following could be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient.
Histograms, KDE, and densities
Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables. We have seen that this is relatively straightforward in Matplotlib:In :
data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000) data = pd.DataFrame(data, columns=['x', 'y']) for col in 'xy': plt.hist(data[col], normed=True, alpha=0.5)
Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with
for col in 'xy': sns.kdeplot(data[col], shade=True)
Histograms and KDE can be combined using
If we pass the full two-dimensional dataset to
kdeplot, we will get a two-dimensional visualization of the data:In :
We can see the joint distribution and the marginal distributions together using
sns.jointplot. For this plot, we’ll set the style to a white background:In :
with sns.axes_style('white'): sns.jointplot("x", "y", data, kind='kde');
There are other parameters that can be passed to
jointplot—for example, we can use a hexagonally based histogram instead:In :
with sns.axes_style('white'): sns.jointplot("x", "y", data, kind='hex')
When you generalize joint plots to datasets of larger dimensions, you end up with pair plots. This is very useful for exploring correlations between multidimensional data, when you’d like to plot all pairs of values against each other.
We’ll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:In :
iris = sns.load_dataset("iris") iris.head()
Visualizing the multidimensional relationships among the samples is as easy as calling
sns.pairplot(iris, hue='species', size=2.5);