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Table of contents
1.
Introduction
2.
What is Matplotlib Inline in Python?
3.
Magic Function
4.
Why is matplotlib inline used?
5.
How to use matplotlib plot inline?
5.1.
Using Matplotlib with Inline
5.2.
Using Matplotlib without Inline
6.
Uses of  Matplotlib Inline in Popular IDE
6.1.
matplotlib inline Jupyter
6.2.
matplotlib inline in Google Colab
6.3.
matplotlib inline in PyCharm
6.4.
matplotlib inline in Spyder
6.5.
matplotlib inline in VSCode
7.
matplotlib inline vs matplotlib notebook
8.
Advantages of Matplotlib Inline in Python
9.
Disadvantages of Matplotlib Inline in Python
10.
Frequently Asked Questions
10.1.
Is matplotlib inline necessary?
10.2.
What is inline backend in matplotlib?
10.3.
What is a better alternative to matplotlib?
10.4.
What is the difference between %Matplotlib widget and inline?
11.
Conclusion
Last Updated: Mar 27, 2024
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What is Matplotlib Inline in Python?

Author Nikunj Goel
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Introduction

Jupyter Notebooks provide a powerful environment for interactive data analysis and visualization in Python. One key feature is the ability to display plots directly within the notebook interface. The %matplotlib inline command is a magic command that is used to display plots directly inline, which comes under the Matplotlib Inline package.

matplotlib inline

In this blog post, we will explore how to use the "matplotlib inline" command and provide some examples to demonstrate its functionality. So without further ado, let's start!  

What is Matplotlib Inline in Python?

Matplotlib is a popular data visualization library in Python that allows you to create a wide range of charts and graphs. When working with Matplotlib, you may come across the term "inline", which refers to a setting that allows you to display Matplotlib plots directly in a Jupyter Notebook or IPython shell.

Matplotlib plots are displayed in a separate window or as image files by default. However, when you use the "inline" setting, the plots are embedded directly in the Jupyter Notebook or IPython shell output.

To use "Matplotlib inline" in a Jupyter Notebook, you must include the following line at the beginning of your notebook:

%matplotlib inline

 

This line tells Jupyter to display Matplotlib plots directly in the output of the notebook. One of the major benefits of using "Matplotlib inline" is that it allows you to quickly and easily visualize your data without having to save plots as image files or open them in a separate window. This can save you time and make your data analysis workflow more efficient.

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Magic Function

%matplotlib is a magic function in the Python programming language that is used to enable plotting within Jupyter notebooks or IPython shells. This function is used to display and manipulate graphs, charts, and other visualizations directly within the notebook or shell environment.

Magic functions are a special type of function in IPython that allow for advanced functionality and convenient shortcuts within the interactive Python environment. These functions are preceded by a "%" sign, which tells IPython to treat them as special commands rather than regular Python functions. 

There are two types of magic functions in IPython: 

  1. Line magics 
    Line magics are magic functions that operate on a single line of code. For example, %timeit is a line magic that can be used to time the execution of a single line of code.
     
  2. Cell magics
    Cell magics, on the other hand, are magic functions that operate on entire cells of code. For example, %%timeit is cell magic that can be used to time the execution of an entire cell of code.

 

Here is an example of using "Matplotlib inline" to display a simple line plot:

import matplotlib.pyplot as plt
%matplotlib inline
 
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

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

plt.show()

 

Output

 

Output of Matplotlib inline

 

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 see that the plot is displayed just below the code(shell output) and not in a separate window.

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:
 

  • 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.
     
  • Interactivity: 
    "Matplotlib inline" allows you to interact with your plots directly in the notebook or shell output, such as zooming, panning, and saving.
     
  • Ease of use: 
    Using "Matplotlib inline" is straightforward and requires only a single line of code to enable.
     
  • 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

Using Matplotlib with Inline 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

Using Matplotlib without Inline 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:

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:

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:

adding 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:

error in the 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:

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.

installing jupyter in vscode

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:

output

matplotlib inline vs matplotlib notebook

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

i%matplotlib notebook

%matplotlib inline

Renders plots as interactive widgets within the notebook Renders plots as static images in the notebook
Supports zooming, panning, and other interactive features Does not support interactive features
Can be used to create animations or interactive visualizations Best suited for basic visualizations or inclusion in reports
Can be slower and use more resources than inline plotting Faster and more lightweight than notebook plotting
Can display multiple plots in the same cell Each plot must be displayed in a separate output cell
Requires the user to manually close the plot when finished Plots are automatically displayed and do not require any extra steps to close

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 Matplotlib Inline in Python along with code, advantages, and disadvantages.

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

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