How to Create Interactive Plots in Matplotlib

In this Matplotlib article we want to learn How to Create Interactive Plots in Matplotlib, so Matplotlib library offers different tools for creating static plots, but some times what if you want to take your data visualization to the next level ? for example you want to create interactive plots. Interactive plots allows you to interact with your data, zoom in and out, pan across the plot and explore specific data points, so in this tutorial we create interactive plots in Matplotlib

First of all we need to install Python Matplotlib

pip install matplotlib

1	pip install matplotlib

Also we need to install the mplcursors library, which enhances interactivity with Matplotlib plots:

pip install mplcursors

1	pip install mplcursors

First of all we need to import the required modules from matplotlib.

import matplotlib.pyplot as plt
import numpy as np
import mplcursors

import matplotlib.pyplot as plt

import numpy as np

import mplcursors

For demonstrating interactive plots, we need to generate some random data using numpy. We will create two arrays, x and y, to represent the coordinates of the data points.

x = np.linspace(0, 10, 100)
y = np.sin(x)

1 2	x = np.linspace(0, 10, 100) y = np.sin(x)

Using Matplotlib plot() function, we can create the initial plot. After that we want to use the mplcursors.cursor() function to add interactivity to the plot, in here we have created a figure and axes object, plot the data, and after that created a cursor object. on_add function is a callback that specifies the behavior when the cursor is added to a specific data point. In this case, it displays the coordinates of the selected point.

fig, ax = plt.subplots()
ax.plot(x, y, 'b-', label='Data')

cursor = mplcursors.cursor(ax, hover=True)

@cursor.connect("add")
def on_add(sel):
    x, y = sel.target
    sel.annotation.set_text(f"({x:.2f}, {y:.2f})")
    sel.annotation.get_bbox_patch().set(fc="white", ec="black", lw=1, alpha=0.9)

fig, ax = plt.subplots()

ax.plot(x, y, 'b-', label='Data')

cursor = mplcursors.cursor(ax, hover=True)

@cursor.connect("add")

def on_add(sel):

x, y = sel.target

sel.annotation.set_text(f"({x:.2f}, {y:.2f})")

sel.annotation.get_bbox_patch().set(fc="white", ec="black", lw=1, alpha=0.9)

Matplotlib provides different customization options to enhance the appearance of the interactive plot. You can add labels, change colors, adjust the axis limits and many more.

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Interactive Plot')
ax.legend()

plt.show()

ax.set_xlabel('X')

ax.set_ylabel('Y')

ax.set_title('Interactive Plot')

ax.legend()

plt.show()

This is the complete code for this tutorial

import matplotlib.pyplot as plt
import numpy as np
import mplcursors

# Generate Sample Data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create the Interactive Plot
fig, ax = plt.subplots()
ax.plot(x, y, 'b-', label='Data')

cursor = mplcursors.cursor(ax, hover=True)

@cursor.connect("add")
def on_add(sel):
    x, y = sel.target
    sel.annotation.set_text(f"({x:.2f}, {y:.2f})")
    sel.annotation.get_bbox_patch().set(fc="white", ec="black", lw=1, alpha=0.9)

# Customize the Interactive Plot
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Interactive Plot')
ax.legend()

# Display the Interactive Plot
plt.show()

import matplotlib.pyplot as plt

import numpy as np

import mplcursors

# Generate Sample Data

x = np.linspace(0, 10, 100)

y = np.sin(x)

# Create the Interactive Plot