How to add different graphs (as an inset) in another Python graph?

To add different graphs (as an inset) in another Python graph, we can use Matplotlib's add_axes() method to create a smaller subplot within the main plot. This technique is useful for showing detailed views or related data alongside the primary visualization.

Steps to Create an Inset Graph

• Create x and y data points using NumPy

• Using subplots() method, create a figure and a set of subplots

• Add a new axis to the existing figure using add_axes()

• Plot data on both the main axis and the inset axis

• Use show() method to display the figure

Basic Example

Here's how to create a simple inset graph ?

import numpy as np
import matplotlib.pyplot as plt

# Set figure size
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True

# Create data
x = np.linspace(-1, 1, 100)
y = np.sin(x)

# Create main plot
fig, ax = plt.subplots()

# Define inset position: [left, bottom, width, height]
left, bottom, width, height = [0.30, 0.6, 0.2, 0.25]
ax_inset = fig.add_axes([left, bottom, width, height])

# Plot on main axis
ax.plot(x, y, color='red', label='Main plot')
ax.set_title('Main Graph with Inset')
ax.set_xlabel('X values')
ax.set_ylabel('Y values')

# Plot on inset axis
ax_inset.plot(x, y, color='green', linewidth=2)
ax_inset.set_title('Inset', fontsize=10)

plt.show()

Advanced Example with Different Data

You can also display different data in the inset, such as a zoomed-in view or a different function ?

import numpy as np
import matplotlib.pyplot as plt

# Create data
x = np.linspace(0, 10, 100)
y_main = np.cos(x)
y_inset = x**2 / 100  # Different function for inset

fig, ax = plt.subplots(figsize=(8, 5))

# Main plot
ax.plot(x, y_main, 'b-', linewidth=2, label='cos(x)')
ax.set_title('Main Plot: Cosine Function')
ax.set_xlabel('X')
ax.set_ylabel('cos(x)')
ax.grid(True, alpha=0.3)

# Create inset at different position
inset_ax = fig.add_axes([0.15, 0.15, 0.3, 0.3])  # Bottom-left corner
inset_ax.plot(x, y_inset, 'r-', linewidth=2)
inset_ax.set_title('Inset: x²/100', fontsize=10)
inset_ax.set_xlabel('X', fontsize=8)
inset_ax.set_ylabel('Y', fontsize=8)
inset_ax.tick_params(labelsize=8)

plt.show()

Understanding add_axes() Parameters

The add_axes() method takes a list of four values: [left, bottom, width, height]. All values are in figure coordinates (0 to 1) ?

Multiple Insets Example

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(-5, 5, 200)
y = np.exp(-x**2)  # Gaussian function

fig, ax = plt.subplots(figsize=(10, 6))

# Main plot
ax.plot(x, y, 'purple', linewidth=2)
ax.set_title('Gaussian Function with Multiple Insets')
ax.set_xlabel('X')
ax.set_ylabel('exp(-x²)')

# First inset - top right
inset1 = fig.add_axes([0.65, 0.65, 0.25, 0.25])
inset1.plot(x, y, 'red', linewidth=1.5)
inset1.set_xlim(-1, 1)  # Zoom in
inset1.set_title('Zoomed Center', fontsize=9)

# Second inset - bottom left  
inset2 = fig.add_axes([0.15, 0.15, 0.25, 0.25])
inset2.plot(x, np.gradient(y), 'green', linewidth=1.5)
inset2.set_title('Derivative', fontsize=9)

plt.show()

Conclusion

Use fig.add_axes() to create inset graphs within your main plot. Specify position and size using figure coordinates (0-1). This technique is perfect for showing detailed views, different data, or supplementary information alongside your primary visualization.