## Creating Multiple Plots in Matplotlib

Data visualization is an essential aspect of data analysis, and Matplotlib is a popular library used for creating plots in Python. As you explore larger and more complex datasets, you may find that you need to create multiple plots to display your data comprehensively.

Matplotlib allows you to create a grid of plots to display multiple plots simultaneously, making it easier for you to analyze your data from different angles. In this article, we’ll explore how to create multiple plots in Matplotlib.

## Stack Plots Vertically

Stacking plots vertically allows you to display plots on top of one another, providing a clear view of their differences and similarities. To create a stack of plots in Matplotlib, you will break down the data into different subplots that you will later stack vertically.

Here’s a code snippet to create a stack of plots vertically:

```
#Importing the required libraries
import matplotlib.pyplot as plt
import numpy as np
#Creating data
x = np.arange(1, 11)
y1 = np.random.randint(1, 10, size=10)
y2 = np.random.randint(1, 10, size=10)
#Creating two subplots stacked vertically
figure, axes = plt.subplots(nrows=2, ncols=1)
figure.suptitle('Vertically stacked subplots')
axes[0].plot(x, y1)
axes[1].plot(x, y2)
#Showing the plot
plt.show()
```

In the above code, we create two subplots that each contain a random set of integers between 1 to 10. We then stack them vertically using the.rows parameter and add a title using the figure.suptitle() function.

Lastly, we display the plot using plt.show().

## Stack Plots Horizontally

Stacking plots horizontally works similarly to the vertical stack, with plots arranged next to each other. This method is useful in situations where the differences between the plots’ x-axes are apparent.

Here’s a code snippet to create plots that are stacked horizontally:

```
#Importing the required libraries
import matplotlib.pyplot as plt
import numpy as np
#Creating data
x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)
y1 = np.cos(2*np.pi*x1)
y2 = np.cos(2*np.pi*x2)*np.exp(-x2)
#Creating two subplots stacked horizontally
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(x1, y1, 'o-')
ax1.set_xlabel('x1')
ax1.set_ylabel('Amplitude')
ax2.plot(x2, y2, '.-')
ax2.set_xlabel('x2')
ax2.set_ylabel('Amplitude')
fig.suptitle('Horizontally stacked subplots')
#Showing the plot
plt.show()
```

In the above code, we create two subplots, each with different sets of cosine and exponential functions. We stack these plots horizontally to display them side by side.

## Create a Grid of Plots

Creating a grid of plots allows you to display multiple plots in a grid formation. The row and column parameters in the subplots() function allow you to create grids of plots that can vary in size and layout.

### The following code snippet shows you how to create a grid of subplots:

```
#Importing the required libraries
import matplotlib.pyplot as plt
import numpy as np
#Creating data
x = np.arange(1, 11)
y1 = np.random.randint(1, 10, size=10)
y2 = np.random.randint(1, 10, size=10)
y3 = np.random.randint(1, 10, size=10)
y4 = np.random.randint(1, 10, size=10)
#Creating a grid of plots
fig, axs = plt.subplots(nrows=2, ncols=2)
axs[0, 0].plot(x, y1)
axs[0, 0].set_title('Plot 1')
axs[0, 1].plot(x, y2)
axs[0, 1].set_title('Plot 2')
axs[1, 0].plot(x, y3)
axs[1, 0].set_title('Plot 3')
axs[1, 1].plot(x, y4)
axs[1, 1].set_title('Plot 4')
fig.suptitle('Grid of subplots')
#Showing the plot
plt.show()
```

In the above code, we create a grid of plots containing four randomly generated sets of data. We then use subplots to divide the figure into 2 rows and 2 columns, where each subplot contains a set of data.

The parameter nrows=2 and ncols=2 signify the number of rows and columns in the subplot.

## Share Axes Between Plots

Sharing axes between plots simplifies visual comparisons between multiple plots. Matplotlib allows you to share the x and y-axes across different subplots, making it easier for viewers to compare data between them.

Here’s a code snippet to share the x-axis between two plots with different data:

```
#Importing the required libraries
import matplotlib.pyplot as plt
import numpy as np
#Creating data
x = np.arange(1, 11)
y1 = np.random.randint(1, 10, size=10)
y2 = np.random.randint(1, 10, size=10)
#Creating a grid of plots that share the x-axis
fig, axs = plt.subplots(nrows=2, ncols=1, sharex=True)
fig.suptitle('Sharing X axes between subplots')
axs[0].plot(x, y1)
axs[1].plot(x, y2)
#Showing the plot
plt.show()
```

In the above code, we create two plots that share the same x-axis by using sharex parameter while creating the subplots. This feature facilitates visual comparison between the plots.

## Additional Resources

As with any new library, learning its functionality can be challenging, but having a community of supportive people can help ease the process. Here are some additional resources and tips for learning Matplotlib:

- The Matplotlib official documentation: This resource provides specific information on using the library.
- Practice regularly: Matplotlib has many features, so it’s important to practice regularly to master its ins and outs. Consider working on some small projects to hone your skills.
- Join Matplotlib communities: Communities such as Stack Overflow and Reddit are full of people willing to help answer your questions.
- Use examples: Don’t hesitate to use examples from other sources.

You can find many examples on the Matplotlib website or a simple Google search.

## Conclusion

In summary, Matplotlib is an extremely versatile library that allows users to create amazing visualizations by creating multiple plots. By stacking plots vertically or horizontally, creating grids, and sharing axes, you can explore your data from different perspectives, revealing hidden insights that otherwise would be missed.

With the right resources and consistent practice, you can become an expert Matplotlib user in no time. In conclusion, using Matplotlib to create multiple plots is essential for proper data visualization.

By stacking plots vertically or horizontally, creating grids, and sharing axes, you can explore data from multiple angles and reveal new insight. The article outlined each method, providing examples and Python code.

With these tools, data analysts can create more informative and impactful visualizations. By staying curious and practicing regularly, mastering the Matplotlib library is well within reach.