Axes Spines

Matplotlib Axes Spines refer to the borders of the plot — top, bottom, left, and right. These spines can be customized to improve the clarity and aesthetics of your visualizations.

The code below demonstrates the presence and location of spines by changing the visibility of each spine by plot.

import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

fig, ax = plt.subplots( figsize=(10,8), dpi=500, nrows=2, ncols=2)

ax[0, 0].spines['top'].set_visible(False)
ax[0, 0].set_title('Top Spine Revomed')

ax[0, 1].spines['left'].set_visible(False)
ax[0, 1].set_title('Left Spine Revomed')

ax[1, 0].spines['right'].set_visible(False)
ax[1, 0].set_title('Right Spine Revomed')

ax[1, 1].spines['bottom'].set_visible(False)
ax[1, 1].set_title('Bottom Spine Revomed')

Working with Spines

Working with spines can help you create a cleaner visual for your needs. The example below generates a time series plot with only the bottom spine.

Removing specific spines to create a cleaner visual. The example below retains only the bottom spine.

# Generate time series data
num_points = 100
time = np.arange(num_points)
data = np.cumsum(np.random.randn(num_points))

# Create a Figure and Axes
fig, ax = plt.figure(figsize=(8,5), dpi=500), plt.axes()

# Plot the time series data
ax.plot(time, data, c ='seagreen')
ax.spines[['top', 'left', 'right']].set_visible(False)

# set title of the plot
ax.set_title('Time Series with Bottom Spine Only')

Moving the Spines Position

Repositioning the spines can create a different visual effect on your charts. The example below converts a sine curve from a plot into an x-y plane.

# Create a Figure and Axes
fig, ax = plt.figure(figsize=(5,5), dpi=500), plt.axes()

x = np.linspace(-5, 5, 50)
y = np.sin(x)

ax.plot(x, y)
ax.spines[['top', 'right']].set_visible(False)
ax.spines[['bottom', 'left']].set_position('zero')
ax.set_title('Sine Curve on X-Y Plane')