A Pie Chart is a circular statistical plot that can display only one series of data. The area of the chart is the total percentage of the given data.  Pie charts in Python are widely used in business presentations, reports, and dashboards due to their simplicity and effectiveness in displaying data distributions. In this article, we will explore how to create apie chart in Python using theMatplotlib library, one of the most widely used libraries for data visualization in Python.

Why Use Pie Charts?

Pie charts provide a visual representation of data that makes it easy to compare parts of a whole. They are particularly useful when:

• Displaying relative proportions or percentages.
• Summarizing categorical data.
• Highlighting significant differences between categories.

However, while pie charts are useful, they also have limitations. They can become cluttered with too many categories or lead to misinterpretation if not designed thoughtfully. Despite this, a well-craftedpie chart using Matplotlib can significantly enhance the presentation of your data.

Basic Structure of a Pie Chart

A pie chart consists of slices that represent different categories. The size of each slice is proportional to the quantity it represents. The following components are essential when creating apie chart in Matplotlib:

• Data: The values or counts for each category.
• Labels: The names of each category, which will be displayed alongside the slices.
• Colors: Optional, but colors can be used to differentiate between slices effectively.

Matplotlib API has pie() function in its pyplot module which create a pie chart representing the data in an array.  let's create pie chart in python.

Syntax: matplotlib.pyplot.pie(data, explode=None, labels=None, colors=None, autopct=None, shadow=False)

Parameters:

• data represents the array of data values to be plotted, the fractional area of each slice is represented bydata/sum(data)
• labels is a list of sequence of strings which sets the label of each wedge.
• color attribute is used to provide color to the wedges.
• autopct is a string used to label the wedge with their numerical value.
• shadow is used to create shadow of wedge.
  

Plotting a Pie Chart in Matplotlib

Let's create a simplepie chart using the pie() function in Matplotlib.This function is a powerful and easy way to visualize the distribution of categorical data.

# Import librariesfrommatplotlibimportpyplotaspltimportnumpyasnp# Creating datasetcars=['AUDI','BMW','FORD','TESLA','JAGUAR','MERCEDES']data=[23,17,35,29,12,41]# Creating plotfig=plt.figure(figsize=(10,7))plt.pie(data,labels=cars)# show plotplt.show()

Output:

Customizing Pie Charts

Once you are familiar with the basics ofpie charts in Matplotlib, you can start customizing them to fit your needs. A pie chart can be customized on the basis several aspects:

• startangle: This attribute allows you to rotate thepie chart in Python counterclockwise around the x-axis by the specified degrees.. By adjusting this angle, you can change the starting position of the first wedge, which can improve the overall presentation of the chart.
• shadow: This boolean attribute adds a shadow effect below the rim of the pie. Setting this toTrue can make your chart stand out and give it a more three-dimensional appearance, enhancing the overall look of yourpie chart in Matplotlib.
• wedgeprops: This parameter accepts a Python dictionary to customize the properties of each wedge in the pie chart. You can specify various attributes such aslinewidth,edgecolor, andfacecolor. This level of customization allows you to enhance the visual distinction between wedges, making yourmatplotlib pie chart more informative.
• frame: When set toTrue, this attribute draws a frame around thepie chart. This can help emphasize the chart's boundaries and improve its visibility, making it clearer when presenting data.
• autopct: This attribute controls how the percentages are displayed on the wedges. You can customize the format string to define the appearance of the percentage labels on each slice.

The explode parameter separates a portion of the chart, and colors define each wedge's color. The autopct function customizes text display, and legend and title functions enhance chart readability and aesthetics.

# Import librariesimportnumpyasnpimportmatplotlib.pyplotasplt# Creating datasetcars=['AUDI','BMW','FORD','TESLA','JAGUAR','MERCEDES']data=[23,17,35,29,12,41]# Creating explode dataexplode=(0.1,0.0,0.2,0.3,0.0,0.0)# Creating color parameterscolors=("orange","cyan","brown","grey","indigo","beige")# Wedge propertieswp={'linewidth':1,'edgecolor':"green"}# Creating autocpt argumentsdeffunc(pct,allvalues):absolute=int(pct/100.*np.sum(allvalues))return"{:.1f}%\n({:d} g)".format(pct,absolute)# Creating plotfig,ax=plt.subplots(figsize=(10,7))wedges,texts,autotexts=ax.pie(data,autopct=lambdapct:func(pct,data),explode=explode,labels=cars,shadow=True,colors=colors,startangle=90,wedgeprops=wp,textprops=dict(color="magenta"))# Adding legendax.legend(wedges,cars,title="Cars",loc="center left",bbox_to_anchor=(1,0,0.5,1))plt.setp(autotexts,size=8,weight="bold")ax.set_title("Customizing pie chart")# show plotplt.show()

Output:

By leveraging the capabilities of theplt.pie() function inMatplotlib, we can create informative and visually appealingpie charts that help to communicate with data effectively. Whether you are presenting data to stakeholders or creating visual aids for your reports, mastering the art of plottingpie charts in Python is a valuable skill.

Creating a Nested Pie Chart in Python

Anested pie chart is an effective way to represent hierarchical data, allowing you to visualize multiple categories and subcategories in a single view. InMatplotlib, you can create anested pie chart by overlaying multiple pie charts with different radii. Below, we’ll explore how to create this type of chart in Python.

Here’s a simple example of how to create anested pie chart usingMatplotlib:

# Import librariesfrommatplotlibimportpyplotaspltimportnumpyasnp# Creating datasetsize=6cars=['AUDI','BMW','FORD','TESLA','JAGUAR','MERCEDES']data=np.array([[23,16],[17,23],[35,11],[29,33],[12,27],[41,42]])# normalizing data to 2 pinorm=data/np.sum(data)*2*np.pi# obtaining ordinates of bar edgesleft=np.cumsum(np.append(0,norm.flatten()[:-1])).reshape(data.shape)# Creating color scalecmap=plt.get_cmap("tab20c")outer_colors=cmap(np.arange(6)*4)inner_colors=cmap(np.array([1,2,5,6,9,10,12,13,15,17,18,20]))# Creating plotfig,ax=plt.subplots(figsize=(10,7),subplot_kw=dict(polar=True))ax.bar(x=left[:,0],width=norm.sum(axis=1),bottom=1-size,height=size,color=outer_colors,edgecolor='w',linewidth=1,align="edge")ax.bar(x=left.flatten(),width=norm.flatten(),bottom=1-2*size,height=size,color=inner_colors,edgecolor='w',linewidth=1,align="edge")ax.set(title="Nested pie chart")ax.set_axis_off()# show plotplt.show()

Output:

• The outer pie chart represents the main categories, while the inner pie chart represents subcategories related to one of those main categories. This structure is particularly useful for showing proportions within proportions, helping viewers quickly grasp the relationships within the data.
• Center Circle: Thecentre_circle is added to create the donut effect, providing a clean visual separation between the outer and inner pie charts.

As with a regularpie chart in Python, you can customize various attributes, such asstartangle,shadow,autopct, andwedgeprops, to enhance the overall aesthetics of yournested pie chart.

Creating 3D Pie Charts

To create a proper 3D pie chart inMatplotlib, you can use the following code snippet. Note thatMatplotlib does not have a direct function for 3D pie charts, but we can simulate it with a 3D surface plot or use a workaround with 2D pie charts:

Conclusion

In this article, we explored the fundamentals of creating and customizingpie charts in Python using theMatplotlib library. From constructing a simplepie chart in Matplotlib to visualizing more complex datasets with 2D and3D pie charts in Python, we have covered various aspects that can enhance the effectiveness of our visualizations.

By utilizingplt.pie in Python, we learned how to present categorical data clearly, making it easier to convey insights to stakeholders.

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