Creating Dynamic Charts in Python with Matplotlib FuncAnimation

In the era of massive data, analysts need to convey insights clearly, and animated visualizations are an effective way to do so. This article demonstrates a simple method for creating dynamic charts in Python using Matplotlib's FuncAnimation class.

Matplotlib is a widely used open‑source plotting library for Python. Its FuncAnimation extension allows static figures to be turned into animations by repeatedly redrawing the canvas.

How FuncAnimation works

The animation starts with two lines of code:

<code>import matplotlib.animation as ani
animator = ani.FuncAnimation(fig, chartfunc, interval=100)</code>

The main arguments are:

fig : the Figure object that holds the plot.

chartfunc : a function that receives a frame index and updates the plot.

interval : delay between frames in milliseconds (default 200 ms).

After defining a function that draws the chart for a given frame, FuncAnimation repeatedly calls it, creating the animation.

Data preparation

<code>import matplotlib.animation as ani
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
df = pd.read_csv(url, delimiter=',', header='infer')
# Filter for selected countries and drop unnecessary columns
df_interest = df.loc[
    df['Country/Region'].isin(['United Kingdom', 'US', 'Italy', 'Germany']) &
    df['Province/State'].isna()
]
# Transpose and clean the DataFrame
df1 = df_interest.transpose()
df1 = df1.drop(['Province/State', 'Country/Region', 'Lat', 'Long'])
df1 = df1.loc[(df1 != 0).any(1)]
df1.index = pd.to_datetime(df1.index)
</code>

Animated line chart

First set up the figure and static styling, then define a function that draws the line up to the current frame and let FuncAnimation animate it.

<code>import numpy as np
import matplotlib.pyplot as plt
color = ['red', 'green', 'blue', 'orange']
fig = plt.figure()
plt.xticks(rotation=45, ha="right", rotation_mode="anchor")
plt.subplots_adjust(bottom=0.2, top=0.9)
plt.ylabel('No of Deaths')
plt.xlabel('Dates')
</code>

The update function:

<code>def buildmebarchart(i=int):
    plt.legend(df1.columns)
    p = plt.plot(df1[:i].index, df1[:i].values)
    for i in range(0,4):
        p[i].set_color(color[i])
animator = ani.FuncAnimation(fig, buildmebarchart, interval=100)
plt.show()
</code>

Animated pie chart

The structure is similar to the line chart, but each frame draws a pie of the cumulative data up to that point.

<code>import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
explode = [0.01, 0.01, 0.01, 0.01]

def getmepie(i):
    def absolute_value(val):
        a = np.round(val/100.*df1.head(i).max().sum(), 0)
        return int(a)
    ax.clear()
    plot = df1.head(i).max().plot.pie(y=df1.columns, autopct=absolute_value,
                                      label='', explode=explode, shadow=True)
    plot.set_title('Total Number of Deaths\n' + str(df1.index[min(i, len(df1.index)-1)].strftime('%y-%m-%d')),
                   fontsize=12)
    import matplotlib.animation as ani
    animator = ani.FuncAnimation(fig, getmepie, interval=200)
    plt.show()
</code>

Animated bar chart

The bar chart can be drawn vertically or horizontally; the example shows both options.

<code>fig = plt.figure()
bar = ''

def buildmebarchart(i=int):
    iv = min(i, len(df1.index)-1)
    objects = df1.max().index
    y_pos = np.arange(len(objects))
    performance = df1.iloc[[iv]].values.tolist()[0]
    if bar == 'vertical':
        plt.bar(y_pos, performance, align='center',
                color=['red','green','blue','orange'])
        plt.xticks(y_pos, objects)
        plt.ylabel('Deaths')
        plt.xlabel('Countries')
        plt.title('Deaths per Country\n' + str(df1.index[iv].strftime('%y-%m-%d')))
    else:
        plt.barh(y_pos, performance, align='center',
                 color=['red','green','blue','orange'])
        plt.yticks(y_pos, objects)
        plt.xlabel('Deaths')
        plt.ylabel('Countries')
animator = ani.FuncAnimation(fig, buildmebarchart, interval=100)
plt.show()
</code>

Saving the animation

After the animation is created, it can be saved to a GIF file with a single line of code:

<code>animator.save(r'C:\temp\myfirstAnimation.gif')
</code>

By following these steps, readers can produce animated visualizations for line, bar, and pie charts, making data stories more engaging and easier to understand.