Pie Chart Creation

Pie Chart

A pie chart is a circular statistical graphic that is divided into slices to illustrate numerical proportions. Each slice represents a category, and its size is proportional to the percentage or fraction of the whole it represents. The entire circle represents 100% of the data. Pie charts are commonly used in business, statistics, and data analysis to compare parts of a dataset in relation to the overall total.

Pie Charts with Matplotlib

Matplotlib, a powerful data visualization library in Python, offers a straightforward pie() function for creating pie charts. This function allows for extensive customization through various parameters.

Syntax of matplotlib.pyplot.pie()

matplotlib.pyplot.pie(x, explode=None, labels=None, colors=None, autopct=None, shadow=False, startangle=0)

Key Parameters

• x: A list or array of numerical values representing the size or proportion of each slice.

• explode (optional): A tuple or list of the same length as x. Each element specifies the fraction of the radius by which to offset the corresponding wedge (slice) from the center. This is useful for highlighting specific slices.

• labels (optional): A sequence of strings that will be used to label each slice.

• colors (optional): A list of colors to assign to each slice. If not provided, Matplotlib will use its default color cycle.

• autopct (optional): A format string that dictates how the percentage value for each slice is displayed directly on the slice. For example, '%1.1f%%' will display the percentage with one decimal place followed by a percent sign.

• shadow (optional): A boolean value. If True, a shadow will be cast beneath the pie chart, giving it a 3D appearance.

• startangle (optional): An integer or float representing the angle in degrees to rotate the start of the pie chart counterclockwise from the x-axis. By default, the first slice starts at the 0-degree (right-hand) position.

Customizing Pie Charts

Matplotlib provides extensive options to customize pie charts for better visualization and emphasis.

Exploded Pie Chart

An "exploded" pie chart is a variation where one or more slices are detached from the center of the pie. This technique is effective for drawing attention to specific categories.

Example:

import matplotlib.pyplot as plt

## Data for the slices
sizes = [20, 35, 25, 20]
## Explode the second slice (index 1)
explode = (0, 0.1, 0, 0)
labels = ['Category A', 'Category B', 'Category C', 'Category D']

## Create the pie chart with one exploded slice
plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%')
plt.title('Exploded Pie Chart')
plt.show()

Custom Colored Pie Chart

You can assign specific colors to each slice to improve readability and aesthetic appeal.

Example:

import matplotlib.pyplot as plt

## Data for the slices
sizes = [30, 20, 25, 25]
labels = ['Category A', 'Category B', 'Category C', 'Category D']
colors = ['gold', 'lightskyblue', 'lightcoral', 'lightgreen']

## Create the pie chart with custom colors
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%')
plt.title('Custom Colored Pie Chart')
plt.show()

Generating a Pie Chart with Random Colors

For charts with many slices, generating random colors can be useful. This example uses hexadecimal color codes generated randomly.

Example:

import matplotlib.pyplot as plt
import random
import numpy as np

## Configure figure size
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

## Generate data and random colors
n_slices = 40
colors = ["#" + ''.join(random.choices('0123456789ABCDEF', k=6)) for _ in range(n_slices)]
data = np.random.random(n_slices)

## Create the pie chart with random colors
plt.pie(data, colors=colors)
plt.title('Pie Chart with Many Random Colors')
plt.show()

Percentage-Labeled Pie Chart

Displaying percentages directly on the slices is crucial for understanding the precise proportions each category represents.

Example:

import matplotlib.pyplot as plt

## Data for the slices
sizes = [15, 30, 45, 10]
labels = ['Category A', 'Category B', 'Category C', 'Category D']

## Create the pie chart with percentage labels
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title('Percentage-Labeled Pie Chart')
plt.show()

Shadowed Pie Chart

Adding a shadow effect can give the pie chart a subtle 3D appearance, enhancing its visual depth.

Example:

import matplotlib.pyplot as plt

## Data for the slices
sizes = [25, 20, 30, 25]
labels = ['Category A', 'Category B', 'Category C', 'Category D']

## Create a shadowed pie chart
plt.pie(sizes, labels=labels, shadow=True, autopct='%1.1f%%')
plt.title('Shadowed Pie Chart')
plt.show()

Categorical Pie Chart (Real-world Example)

This example combines several customization options to visualize time distribution across different daily activities.

Example:

import matplotlib.pyplot as plt

## Data representing time spent on activities
slices = [7, 2, 3, 13]
activities = ['Sleeping', 'Eating', 'Working', 'Playing']
colors = ['c', 'm', 'r', 'b'] # Cyan, Magenta, Red, Blue

## Create a pie chart with multiple customizations
plt.pie(slices,
        labels=activities,
        colors=colors,
        startangle=90,  # Start the first slice at the top
        shadow=True,    # Add a shadow
        explode=(0, 0.1, 0, 0), # Explode 'Eating' slice
        autopct='%1.1f%%') # Display percentages

plt.title('Daily Activities Pie Chart')
plt.show()

Conclusion

Pie charts are a valuable tool for visually representing the distribution of categorical data and the relative proportions of its components. Matplotlib's pie() function provides a flexible and powerful way to create and customize these charts, allowing you to effectively control:

• Labels: Clearly identify each category.

• Colors: Enhance visual appeal and differentiate slices.

• Shadows: Add depth and a 3D effect.

• Percentages: Display precise proportional values.

• Slice Explosion: Highlight specific segments of the data.

• Rotation Angles: Control the starting position of slices for better layout.