Multi-Category Charts | How to become an Expert of MS Excel - Class 6 PDF Download

Introduction

Data visualization is a powerful tool that helps us understand complex information quickly and effectively. Among the various types of charts, multi-category charts stand out for their ability to display multiple categories or variables in a single chart. In this article, we will explore different types of multi-category charts, provide simple code examples, and explain their applications. By the end, you'll have a solid understanding of how to create and interpret these charts.

What are Multi-Category Charts?

Multi-category charts, also known as multi-variable charts or combination charts, are graphical representations that enable us to compare and analyze multiple categories or variables simultaneously. These charts are particularly useful when we want to observe relationships, patterns, or trends among different data points.
There are several types of multi-category charts, including bar charts, stacked bar charts, grouped bar charts, pie charts, and doughnut charts. Let's explore each of these with examples and accompanying code.

Bar Charts

A bar chart is a simple yet effective way to visualize categorical data. It displays data using rectangular bars, where the length of each bar represents the quantity or value of a particular category. Let's look at a code example to create a bar chart using Python's Matplotlib library:

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']

values = [10, 15, 7, 12]

plt.bar(categories, values)

plt.xlabel('Categories')

plt.ylabel('Values')

plt.title('Bar Chart Example')

plt.show()

Code Explanation:

• We import the 'matplotlib.pyplot' module, which provides the necessary functions to create visualizations.
• We define the categories and their corresponding values as lists.
• We use the 'plt.bar()' function to create the bar chart by passing the categories and values as arguments.
• We add labels to the x-axis, y-axis, and a title to provide context and improve readability.
• Finally, we use 'plt.show()' to display the chart.

Stacked Bar Charts

A stacked bar chart is an extension of the bar chart, where multiple categories are stacked on top of each other to show the total value for each category. This chart is useful for comparing the composition of different categories within a larger category. Here's an example using Matplotlib:

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']

values1 = [10, 15, 7, 12]

values2 = [5, 8, 3, 10]

plt.bar(categories, values1, label='Group 1')

plt.bar(categories, values2, bottom=values1, label='Group 2')

plt.xlabel('Categories')

plt.ylabel('Values')

plt.title('Stacked Bar Chart Example')

plt.legend()

plt.show()

Code Explanation:

• We define two sets of values ('values1' and 'values2') representing different groups within each category.
• By using the 'bottom' parameter in the second 'plt.bar()' function, we stack the bars of the second group on top of the first group.
• We add a legend to distinguish between the two groups and provide labels for the x-axis, y-axis, and the chart's title.

Grouped Bar Charts

A grouped bar chart allows for a side-by-side comparison of different categories. It is helpful when you want to compare multiple variables across distinct categories. Here's a code example using Matplotlib:

import numpy as np

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']

values1 = [10, 15, 7, 12]

values2 = [5, 8, 3, 10]

bar_width = 0.35

x = np.arange(len(categories))

plt.bar(x - bar_width/2, values1, width=bar_width, label='Group 1')

plt.bar(x + bar_width/2, values2, width=bar_width, label='Group 2')

plt.xlabel('Categories')

plt.ylabel('Values')

plt.title('Grouped Bar Chart Example')

plt.xticks(x, categories)

plt.legend()

plt.show()

Code Explanation:

• We import the 'numpy' module as 'np' to create evenly spaced values for the x-axis.
• The x array is created using 'np.arange()' with the length of the categories.
• We set the 'bar_width' to control the width of each group.
• Using 'plt.bar()' twice with adjusted x-coordinates, we create the grouped bar chart.
• We add x-tick labels using 'plt.xticks()' to display the categories on the x-axis.

Pie Charts

A pie chart is a circular chart divided into slices, where each slice represents a category or proportion of a whole. Pie charts are useful for illustrating the distribution of a dataset or comparing parts of a whole. Here's an example using Matplotlib:

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']

values = [30, 15, 25, 30]

plt.pie(values, labels=categories, autopct='%1.1f%%')

plt.title('Pie Chart Example')

plt.show()

Code Explanation:

• We use 'plt.pie()' to create the pie chart, passing the values and categories as arguments.
• The 'labels' parameter assigns labels to each slice.
• The 'autopct' parameter specifies the format for displaying the percentage values in each slice.
• We include a title using 'plt.title()' to provide additional context.

Doughnut Charts

A doughnut chart is similar to a pie chart but with a hole in the center. It allows for the representation of multiple categories and the comparison of proportions in a visually appealing way. Here's an example using Matplotlib:

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']

values = [30, 15, 25, 30]

plt.pie(values, labels=categories, autopct='%1.1f%%', wedgeprops=dict(width=0.4))

plt.title('Doughnut Chart Example')

plt.show()

Code Explanation:

• Similar to the pie chart, we use 'plt.pie()' with values and categories.
• The 'wedgeprops' parameter is used to specify the width of the hole in the center of the chart.
• We add a title to provide context.

Sample Problems and Solutions

Problem 1: Create a stacked bar chart to compare the sales performance of different products (Product A, B, C) in two regions (Region 1, Region 2). The sales data is given as follows:

Product A: Region 1 - 100 units, Region 2 - 150 units

Product B: Region 1 - 80 units, Region 2 - 120 units

Product C: Region 1 - 70 units, Region 2 - 90 units

import matplotlib.pyplot as plt

products = ['Product A', 'Product B', 'Product C']

region1_sales = [100, 80, 70]

region2_sales = [150, 120, 90]

plt.bar(products, region1_sales, label='Region 1')

plt.bar(products, region2_sales, bottom=region1_sales, label='Region 2')

plt.xlabel('Products')

plt.ylabel('Sales')

plt.title('Sales Performance Comparison')

plt.legend()

plt.show()

Problem 2: Create a pie chart to visualize the market share of different smartphone brands. The market shares are as follows:

Apple: 45%

Samsung: 30%

Xiaomi: 15%

Others: 10%

import matplotlib.pyplot as plt

brands = ['Apple', 'Samsung', 'Xiaomi', 'Others']

market_shares = [45, 30, 15, 10]

plt.pie(market_shares, labels=brands, autopct='%1.1f%%')

plt.title('Smartphone Market Share')

plt.show()

Conclusion

Multi-category charts are versatile tools for visualizing and comparing multiple categories or variables simultaneously. In this article, we explored various types of multi-category charts, including bar charts, stacked bar charts, grouped bar charts, pie charts, and doughnut charts. We provided simple code examples and explanations to help you get started with creating these charts. Remember, data visualization is a powerful way to gain insights and communicate information effectively.