mpl_interactions: Easy interactive Matplotlib plots

mpl_interactions’ aims to make it as easy as possible to create responsive Matplotlib plots. In particular, you can:

  • Better understand a function’s change with respect to a parameter.

  • Visualize your data interactively.

To achieve this, mpl_interactions provides:

  • A way to control the output of pyplot functions (e.g. plot and hist) with sliders

  • A function to compare horizontal and vertical slices of heatmaps.

  • A function allowing zooming using the scroll wheel.


To install, simply run: pip install mpl_interactions

Further instructions for installation from JupyterLab can be found on the Installation page.

Basic example

To control a plot with a slider:

# if running this code in a Jupter notbeook or JupyterLab
%matplotlib ipympl
from mpl_interactions import interactive_plot
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,np.pi,100)
tau = np.linspace(.5, 10, 100)
def f1(x, tau, beta):
    return np.sin(x*tau)*x*beta
def f2(x, tau, beta):
    return np.sin(x*beta)*x*tau
fig, ax, sliders = interactive_plot([f1, f2], x=x, tau = tau, beta = (1, 10, 100))
_ = plt.legend()

If you are in a Jupyter Notebook the output will look like this:


and from a script or ipython the output will use Matplotlib sliders:


For other functionality and more detailed examples, visit the Examples page.