Animation

⏱ Quick start

An animated chart can be build with python using theFuncAnimation() function from matplotlib. We follow these steps:

• Load libraries
• Open a dataset
• Define an update function used to build each frame of the animation
• Create the animation with FuncAnimation() and save it with save()

# libraries
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

# initiate figure
fig, ax = plt.subplots(figsize=(10, 8), dpi=120)

def update(frame):
    ax.clear()
    ax.scatter(
      1+frame, 10+frame*10,
      s=600, alpha=0.5,
      edgecolors="black"
    )
    ax.set_xlim(0, 10)
    ax.set_ylim(0, 100)
    return fig, ax

ani = FuncAnimation(fig, update, frames=range(10))
ani.save("my_animation.gif", fps=5)

Matplotlib and FuncAnimation()

The FuncAnimation() function, a component of the matplotlib.animation module, facilitates the creation of animations by repeatedly invoking a function that updates the plot content with each iteration.

Typically, you start by establishing a figure and an axis. Following this, an update() function is defined to modify the plot's content at every animation cycle.

To complete the process, you generate the animation using FuncAnimation() and subsequently store it using the save() function.

The process is pretty much the same for a 3d chart. Here is an example with an animated volcano plot. Each iteration of the loop changes the camera angle, giving this feeling of travelling around the volcano.

Advanced uses cases

Animated charts can be highly impactful when illustrating aprocess or change over time. Here are a few examples that demonstrate their effectiveness.

This real life example illustrates the power of animated charts. The chart is a stacked area chart with a text that is written through the animation.

See code

This line chart, or race chart, shows the evolution of the of global plastic production over time with a smooth animation and highlighted text.

See code