What is Matplotlib Inline in Python?

Why is Matplotlib inline used?

Matplotlib inline is used to display plots directly. When using matplotlib inline, you don't need to save plots as image files or open them in a separate window.

There are several benefits to using "Matplotlib inline" in your Python projects:

1. Efficiency: 
   By displaying plots directly in the Jupyter Notebook or IPython shell output, you can save time and make your data analysis workflow more efficient.
2. Interactivity: 
   "Matplotlib inline" allows you to interact with your plots directly in the notebook or shell output, such as zooming, panning, and saving.
3. Ease of use: 
   Using "Matplotlib inline" is straightforward and requires only a single line of code to enable.
4. Reproducibility: 
   When you use "Matplotlib inline", your plots are saved as part of the notebook or shell output, which makes it easier to reproduce your analysis and share your results with others.

How to use Matplotlib Plot Inline?

To use Matplotlib plot inline, you can include the following line of code at the beginning of your Jupyter Notebook or IPython shell:

%matplotlib inline

This line tells Jupyter to display Matplotlib plots directly in the output of the notebook or shell.

Using Matplotlib with Inline

Here is an example of using Matplotlib with inline:

import matplotlib.pyplot as plt
%matplotlib inline

x = [1, 2, 3, 4, 5]
y = [2, 5, 9, 18, 20]

plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Line Plot with Inline")
 
plt.show()

Output

In this example, we first import Matplotlib and include the inline setting. Then, we define our data points and create a simple line plot. Finally, we add labels and a title to the plot and display it using the show() function.

You can also practice with the help of Online Python Compiler

Using Matplotlib without Inline

Here is an example of Matplotlib without inline:

import matplotlib.pyplot as plt
 
x = [1, 2, 3, 4, 5]
y = [2, 5, 3, 14, 20]
 
plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Line Plot without Inline")

plt.show()

Output

In this example, we create the same line plot as before but without including the inline setting. When we run this code, the plot will be displayed in a separate window instead of in the output of the notebook or shell.

Uses of  Matplotlib Inline in Popular IDE

The %matplotlib inline magic command is commonly used in Jupyter Notebook or JupyterLab to enable the inline plotting of matplotlib figures within the notebook interface. We can also use it in the other IDEs. Let us understand how we can use it:

matplotlib inline Jupyter

We can use Jupyter Notebook to use matplotlib inline. So, we can write the following code after creating a new notebook and installing matplotlib:

import matplotlib.pyplot as plt
%matplotlib inline

x = [1, 2, 3, 4, 5]
y = [2, 5, 9, 18, 20]

plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Line Plot with Inline")
 
plt.show()

This will give the following output:

matplotlib inline in Google Colab

Google Colab is a cloud-based development environment provided by Google. It is a Jupyter Notebook-based platform that allows you to write, run, and share Python code directly in your web browser. We can use it to display the plot inline, for this, we need to write the following code in it:

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np

x=np.array([0,5])
y=np.array([0,50])

plt.plot(x,y)
plt.show()

This will give the following output:

matplotlib inline in PyCharm

PyCharm provides a set of tools and features to help Python developers. It helps to write, debug, and test their code efficiently. It offers a user-friendly interface and supports a wide range of Python frameworks, libraries, and technologies. We can not use %matplotlib inline in PyCharm. This will throw a syntax error because magic commands are available in the IPython environment. Let us try to add matplotlib:

Now we can write the code in our .py file:

import matplotlib.pyplot
%matplotlib inline
data=(3,6,9,12,15)

fig, simple_chart = matplotlib.pyplot.subplots()

simple_chart.plot(data)
matplotlib.pyplot.show()

This will show the following output:

matplotlib inline in Spyder

Spyder is an open-source IDE. It is specifically designed for scientific computing and data analysis using Python. It provides a user-friendly interface and a comprehensive set of tools tailored for scientific programming tasks. In Spyder, the %matplotlib inline command is not necessary to display plots inline, unlike in Jupyter Notebooks. Spyder has a built-in feature that automatically displays plots inline within the IDE. You can use %matplotlib inline if you save the file with the .ipy extension. You can write the following code:

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
x=np.array([0,10])
y=np.array([0,100])
plt.plot(x,y)
plt.show()

This will give you the following output:

matplotlib inline in VSCode

VSCode, or Visual Studio Code, is a free and open-source code editor developed by Microsoft. It is widely used by developers across different programming languages, including Python, JavaScript, C++, and many others. We can use matplotlib inline in VSCode, by simply installing the Jupyter extension in it.

Now we can create a notebook and start working on it. Let us write the following code:

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np

x=np.array([0,20])
y=np.array([0,300])

plt.plot(x,y)
plt.show()

This will give the following output:

Difference between matplotlib inline And matplotlib notebook

Here's a comparison of %matplotlib notebook and %matplotlib inline:

Advantages of Matplotlib Inline in Python

Here are some advantages of using Matplotlib Inline in Python:

• Allows for the direct display of Matplotlib plots in the output of the notebook or shell, making it easy to visualize and iterate on data in real-time.
• Eliminates the need to save plots to a file, reducing the number of steps needed to create and view visualizations.
• Enables the sharing of reproducible code and plots, as the output can be easily shared and viewed by others without needing to run the code.
• Facilitates the use of other popular Python libraries in conjunction with Matplotlib, such as NumPy and Pandas, to create more complex and informative visualizations.
• Helps to streamline the data exploration and analysis process, as visualizations can be created and modified more quickly and easily.

Disadvantages of Matplotlib Inline in Python

Here are some disadvantages of using Matplotlib inline in Python:

• It may not be suitable for generating high-quality plots for publications, as the inline plots can be limited in terms of resolution and formatting options.
• Inline plotting can slow down the performance of the notebook or shell, particularly when working with large datasets or complex visualizations.
• The size of the plot may be constrained by the size of the output cell in the notebook, which can limit the amount of detail that can be displayed.
• Some advanced Matplotlib features may not be supported by inline plotting, requiring the use of other Matplotlib display backends.
• Inline plotting may not be compatible with certain IDEs or environments, requiring additional configuration or workarounds to use effectively.

Frequently Asked Questions

Is matplotlib inline necessary?

No, the %matplotlib inline command is not necessary in all cases. Its purpose is to configure Jupyter Notebooks to display matplotlib plots directly within the notebook interface. 

What is inline backend in matplotlib?

The "inline" backend in matplotlib is a rendering backend option that allows you to display plots directly within the Jupyter Notebook or JupyterLab interface. It is specifically designed to work seamlessly with Jupyter environments.

What is a better alternative to matplotlib?

The choice of a better alternative to matplotlib depends on your specific requirements and the type of data visualization you want to achieve. You can choose Seaborn, Plotly, Bokeh, and Altair as better alternatives to matlplotlib.

What is the difference between %Matplotlib widget and inline?

The %matplotlib widget and inline are Jupyter Notebook magic commands. %matplotlib widget enables interactive plots in a widget-based window. On the other hand, %matplotlib inline displays static plots within the notebook interface.

Conclusion

In this article, we discussed what Matplotlib Inline in Python is. %matplotlib inline is a powerful IPython magic command that streamlines data visualization workflows in Jupyter notebooks. By automatically displaying plots within the notebook interface, it eliminates the need for separate plot windows and enhances the interactive nature of data exploration.

If you want to learn more about Python, you can also read the below-mentioned articles:

• Introduction to Matplotlib and Seaborn
• Python keywords and identifiers
• Functions in Python
• Interview Questions for Python
• Fibonacci Series in Python

If you liked our article, do upvote our article and help other ninjas grow.  You can refer to our Guided Path on Code360 to upskill yourself in Data Structures and Algorithms, Competitive Programming, System Design, and many more!