How to unset 'sharex' or 'sharey' from two axes in Matplotlib?

When creating multiple subplots in Matplotlib, you might want each subplot to have independent X and Y axes instead of sharing them. You can unset sharex and sharey by setting them to 'none' or False.

Basic Syntax

To create subplots without shared axes ?

fig, axes = plt.subplots(rows, cols, sharex='none', sharey='none')
# or
fig, axes = plt.subplots(rows, cols, sharex=False, sharey=False)

Example with Independent Axes

Let's create a 2x4 grid of subplots where each has independent X and Y axes ?

import matplotlib.pyplot as plt
import numpy as np

# Set figure size
plt.rcParams["figure.figsize"] = [10, 6]
plt.rcParams["figure.autolayout"] = True

# Define grid dimensions
rows = 2
cols = 4

# Create subplots with no shared axes
fig, axes = plt.subplots(rows, cols, sharex='none', sharey='none', squeeze=False)

# Plot different data on each subplot
for row in range(rows):
    for col in range(cols):
        # Generate different random data for each subplot
        x_data = np.random.rand(10) * (col + 1)
        y_data = np.random.rand(10) * (row + 1)
        axes[row][col].plot(x_data, y_data, marker='o')
        axes[row][col].set_title(f'Subplot ({row},{col})')

plt.tight_layout()
plt.show()

Comparison of Sharing Options

Practical Example with Different Data Ranges

Here's why independent axes are useful when plotting data with different scales ?

import matplotlib.pyplot as plt
import numpy as np

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False)

# First subplot: small values
x1 = np.linspace(0, 1, 100)
y1 = np.sin(2 * np.pi * x1)
ax1.plot(x1, y1)
ax1.set_title('Small Scale Data')
ax1.set_xlabel('X (0-1)')
ax1.set_ylabel('Y (-1 to 1)')

# Second subplot: large values  
x2 = np.linspace(0, 1000, 100)
y2 = x2**2
ax2.plot(x2, y2)
ax2.set_title('Large Scale Data')
ax2.set_xlabel('X (0-1000)')
ax2.set_ylabel('Y (0-1M)')

plt.tight_layout()
plt.show()

Conclusion

Use sharex='none' and sharey='none' when each subplot needs independent axis scaling. This is essential when plotting data with different ranges or units across subplots.