Data Visualization in Python

At this point in the course, it is time to begin connecting the dots and applying visualization to your knowledge of statistics. Work through these programming examples to round out your knowledge of seaborn as it is applied to univariate and bivariate plots.

Customizing the Look

Themes

There are several themes available in the modern matplotlib, some of which borrow from seaborn . You can see the available themes and play around.

plt.style.available

['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast',
 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind',
 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted',
 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk',
 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']

See some examples below.

plt.style.use('fivethirtyeight')
sns.scatterplot(data = iris, x = 'sepal_width', y = 'sepal_length');
plt.show()

plt.style.use('bmh')
sns.scatterplot(data = iris, x = 'sepal_width', y = 'sepal_length');
plt.show()

plt.style.use('classic')
sns.scatterplot(data = iris, x = 'sepal_width', y = 'sepal_length');
plt.show()

plt.style.use('ggplot')
sns.scatterplot(data = iris, x = 'sepal_width', y = 'sepal_length');
plt.show()

plt.style.use('Solarize_Light2')
sns.scatterplot(data = iris, x = 'sepal_width', y = 'sepal_length');
plt.show()

One small syntax point. You may have noticed in your own work that you get a little annoying line in the output when you plot. You can prevent that from happening by putting a semi-colon ( ;) after the last plotting command.