Data Visualization in Python

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()