Animating a Seaborn bubble chart using FuncAnimation

Question

I have a data-set that contains the income and life expectancy per country across time. In the year 1800, it looks like this:

I would like to make an animated chart that shows how life expectancy and income evolve over time (from 1800 until 2019). Here's my code so far for a static plot:

import matplotlib
fig, ax = plt.subplots(figsize=(12, 7))

chart = sns.scatterplot(x="Income",
                        y="Life Expectancy",
                        size="Population",
                        data=gapminder_df[gapminder_df["Year"]==1800],
                        hue="Region", 
                        ax=ax,
                        alpha=.7,
                        sizes=(50, 3000)
                       )

ax.set_xscale('log')
ax.set_ylim(25, 90)
ax.set_xlim(100, 100000)

scatters = [c for c in ax.collections if isinstance(c, matplotlib.collections.PathCollection)]

handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[:5], labels[:5])

def animate(i):
    data = gapminder_df[gapminder_df["Year"]==i+1800]
    for c in scatters:
        # do whatever do get the new data to plot
        x = data["Income"]
        y = data["Life Expectancy"]
        xy = np.hstack([x,y])
        # update PathCollection offsets
        c.set_offsets(xy)
        c.set_sizes(data["Population"])
        c.set_array(data["Region"])
    return scatters

ani = matplotlib.animation.FuncAnimation(fig, animate, frames=10, blit=True)
ani.save("test.mp4")

Here's the link to the data: https://github.com/abdennouraissaoui/Animated-bubble-chart

Thank you!

Answer 1

You can loop over years of your data through the i counter, which increases by 1 at each loop (at each frame). You can define a year variable, that depends on i , then filter your data by this year and plot the filtered dataframe. At each loop you have to erase the previous scatterplot with ax.cla() . Finally, I choose 220 frames in order to have a frame for each year, from 1800 to 2019.
Check this code as a reference:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.animation import FuncAnimation

gapminder_df = pd.read_csv('data.csv')

fig, ax = plt.subplots(figsize = (12, 7))

def animate(i):
    ax.cla()
    year = 1800 + i
    sns.scatterplot(x = 'Income',
                    y = 'Life Expectancy',
                    size = 'Population',
                    data = gapminder_df[gapminder_df['Year'] == year],
                    hue = 'Region',
                    ax = ax,
                    alpha = 0.7,
                    sizes = (50, 3000))
    ax.set_title(f'Year {year}')
    ax.set_xscale('log')
    ax.set_ylim(25, 90)
    ax.set_xlim(100, 100000)
    handles, labels = ax.get_legend_handles_labels()
    ax.legend(handles[:5], labels[:5], loc = 'upper left')

ani = FuncAnimation(fig = fig, func = animate, frames = 220, interval = 100)
plt.show()

which reproduce this animation:

(I cut the above animation in order to have a lighter file, less than 2 MB, in fact the data increases at a step of 5 years. However the code above reproduces the complete animation, with a step of 1 year)