Lecture 18:- Matplotlib - Bar Graph

In Matplotlib, a bar graph (also called a bar chart) is a common way to represent categorical data using rectangular bars of varying lengths. It is useful for visualizing the comparison of different categories or the distribution of data across different groups. Here's how you can create a simple bar graph using Matplotlib:

pythonCopy code

import matplotlib.pyplot as plt # Sample data categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4', 'Category 5'] values = [10, 15, 8, 20, 12] # Create a bar graph plt.bar(categories, values, color='b', alpha=0.7) plt.xlabel('Categories') plt.ylabel('Values') plt.title('Bar Graph') plt.grid(axis='y') plt.show()

In this example, the plt.bar() function is used to create the bar graph. The first argument is the x-axis values, which represent the categories in this case. The second argument is the y-axis values, which represent the corresponding values for each category.

The color parameter allows you to specify the color of the bars. You can use various color options, such as 'b' for blue, 'g' for green, 'r' for red, and more.

The alpha parameter sets the transparency of the bars. It takes values between 0 and 1, where 0 means completely transparent, and 1 means fully opaque.

You can further customize the bar graph by adding labels, adjusting bar width, changing the bar orientation, and more.

pythonCopy code

import matplotlib.pyplot as plt # Sample data categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4', 'Category 5'] values = [10, 15, 8, 20, 12] # Create a horizontal bar graph plt.barh(categories, values, color='g', alpha=0.7) plt.xlabel('Values') plt.ylabel('Categories') plt.title('Horizontal Bar Graph') plt.grid(axis='x') plt.show()

In this example, plt.barh() creates a horizontal bar graph, and the categories are shown on the y-axis, while the values are shown on the x-axis. The axis parameter is used to add a grid either on the y-axis (axis='x') or x-axis (axis='y').

Matplotlib provides extensive customization options for bar graphs, allowing you to create visually appealing and informative visualizations for your data.