Matplotlib.md

Matplotlib

In this module, we will explore the Matplotlib library, which is widely used for plotting and data visualization in Python. We will cover the basics of plotting and creating visualizations using Matplotlib.

Plotting and Data Visualization:

Matplotlib is a powerful library for creating various types of plots, including line plots, scatter plots, bar plots, histograms, and more. It provides a flexible and customizable interface for visualizing data in Python.

Let's see an example of creating a line plot using Matplotlib:

import matplotlib.pyplot as plt

# Data for the plot
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Create a line plot
plt.plot(x, y)

# Add labels and title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot')

# Display the plot
plt.show()

In this example, we import the Matplotlib library and create two lists, x and y, representing the data points. We then use the plot() function to create a line plot by passing x and y as arguments.

We can customize the plot by adding labels to the x-axis and y-axis using xlabel() and ylabel() functions, and giving the plot a title using title() function.

Finally, we display the plot using the show() function.

Matplotlib provides a wide range of functions and options for customizing plots, including changing line colors, adding legends, setting axis limits, and more.

Customizing Plots:

Matplotlib allows for extensive customization of plots to make them more informative and visually appealing. You can change line styles, marker types, colors, add legends, annotations, and much more.

Let's see an example of customizing a scatter plot using Matplotlib:

import matplotlib.pyplot as plt

# Data for the plot
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Create a scatter plot
plt.scatter(x, y, color='red', marker='o', label='Data Points')

# Add labels and title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot')

# Add a legend
plt.legend()

# Display the plot
plt.show()

In this example, we create a scatter plot using the scatter() function by passing x and y as arguments. We customize the scatter plot by specifying the color as 'red' and the marker type as 'o' (circle). We also add a label to the scatter plot using the label parameter.

We can add a legend to the plot using the legend() function. This will display the label we provided earlier.

Again, we use xlabel(), ylabel(), and title() to add labels and a title to the plot.