Creating Dynamic Charts in Python with Matplotlib FuncAnimation

When reading technical blogs, many authors use animated charts to convey data more intuitively; this article shows a straightforward way to create such animations in Python with Matplotlib.

Matplotlib is a widely used open‑source plotting library, and its FuncAnimation class (part of matplotlib.animation ) can turn static figures into dynamic ones by repeatedly redrawing the canvas.

Basic usage starts with two lines of code:

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

The function chartfunc is called for each frame; the interval argument sets the delay between frames in milliseconds.

Data preparation – the example loads COVID‑19 death data, filters a few countries, removes unnecessary columns, drops rows with all zeros, and converts the index to dates:

<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')
df_interest = df.loc[
    df['Country/Region'].isin(['United Kingdom','US','Italy','Germany']) &
    df['Province/State'].isna()]
df_interest.rename(index=lambda x: df_interest.at[x, 'Country/Region'], inplace=True)
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 – after setting figure aesthetics, a function updates the plot up to the current frame index:

<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>

<code>def buildmebarchart(i=int):
    plt.legend(df1.columns)
    p = plt.plot(df1[:i].index, df1[:i].values)  # only data up to i
    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, but each frame recomputes a pie slice based on the latest cumulative values:

<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 code demonstrates both vertical and horizontal orientations, selecting the appropriate Matplotlib bar function based on a variable:

<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>

Finally, the animation can be saved as a GIF file with a single command:

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

These examples demonstrate that creating dynamic visualizations in Python is accessible with only a few lines of code, making it easier for data scientists and analysts to present time‑series information interactively.