Matplotlib Pie Charts

A pie chart is a circular statistical graphic that is divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, and the size of each slice is proportional to the quantity it represents.

WHEN TO USE PIE CHARTS

Pie charts are commonly used in the following scenarios:

1. Relative Proportions: Pie charts are effective for displaying the relative proportions or percentages of different categories within a dataset. They provide a visual representation of how the parts contribute to the whole, making it easy to compare the sizes of individual categories at a glance.

2. Comparing Categories: Pie charts allow for quick comparison between categories based on their relative sizes. This is particularly useful when you want to highlight the distribution of a categorical variable or compare the prevalence of different groups within a population.

3. Simple Visualization: Pie charts are simple and intuitive visualizations that are easy to understand, even for non-technical audiences. They provide a clear and concise way to communicate the composition of a dataset without overwhelming viewers with complex information.

4. Limited Categories: Pie charts work best when visualizing data with a small number of categories (typically less than seven) to avoid overcrowding and ensure clarity. They are ideal for representing nominal or ordinal data with distinct categories that do not overlap or have a natural order.

5. Showing Percentages: Pie charts are well-suited for displaying percentages or proportions associated with each category. The size of each slice corresponds to the relative frequency or proportion of the corresponding category, making it easy to see the distribution of values in relation to the total.

6. Highlighting Dominant Categories: Pie charts are useful for highlighting dominant or significant categories within a dataset. Larger slices attract attention and emphasize the relative importance of certain categories compared to others.

7. Explaining Market Share: Pie charts are commonly used in business and marketing to visualize market share, sales distribution, or customer demographics. They provide a concise overview of how different products, services, or market segments contribute to overall sales or revenue.

While pie charts are useful for certain types of data visualization, it’s important to consider their limitations, such as difficulty in comparing slices with similar sizes, potential misinterpretation of small differences, and challenges in representing complex or overlapping categories. As such, pie charts should be used judiciously and in situations where they provide clear and meaningful insights into the data.

Matplotlib provides the `pie` function to create pie charts easily. Here’s an example of how to create a pie chart using Matplotlib:

“`python
import matplotlib.pyplot as plt

# Sample data
sizes = [20, 30, 25, 15, 10]
labels = [‘A’, ‘B’, ‘C’, ‘D’, ‘E’]

# Plotting the pie chart
plt.pie(sizes, labels=labels, autopct=’%1.1f%%’, startangle=140, colors=[‘gold’, ‘yellowgreen’, ‘lightcoral’, ‘lightskyblue’, ‘lightpink’])

# Equal aspect ratio ensures that pie is drawn as a circle
plt.axis(‘equal’)

# Adding title
plt.title(‘Pie Chart of Sample Data’)

# Display the plot
plt.show()
“`

In this example:
– The `sizes` list contains the numerical values for each slice of the pie chart.
– The `labels` list contains the corresponding labels for each slice.
– The `plt.pie()` function is used to create the pie chart. We specify the sizes of the slices using the `sizes` parameter and the labels using the `labels` parameter.
– The `autopct` parameter is used to display the percentage of each slice, formatted to one decimal place.
– The `startangle` parameter specifies the angle at which the first slice starts. Here, we set it to 140 degrees to rotate the pie chart.
– The `colors` parameter specifies the colors for each slice of the pie chart.
– We ensure that the aspect ratio is equal using `plt.axis(‘equal’)`, which ensures that the pie chart is drawn as a circle.
– Finally, we add a title to the plot using `plt.title()`.

Running this code will display a pie chart showing the distribution of the sample data. Each slice represents a proportionate part of the whole, and the size of each slice is proportional to the quantity it represents. The labels and percentages are displayed inside each slice.