%matplotlib inline is an essential command for anyone working with Jupyter Notebooks and plotting libraries such as Matplotlib. In this article, we will delve into the details of what
%matplotlib inline does, why it is necessary, and how to use it effectively with various examples.
Introduction to Matplotlib
Before we dive into the usage of
%matplotlib inline, let’s understand what Matplotlib is. Matplotlib is a powerful and widely-used plotting library for Python. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, or Qt, as well as an interface for generating plots as images or displaying them interactively.
Matplotlib is specifically good for creating static, animated, and interactive visualizations in Python. It’s highly customizable and allows you to create a wide range of plots, including line plots, scatter plots, bar plots, error bars, histograms, contour plots, quiver plots, etc.
Jupyter Notebooks and Matplotlib
Jupyter Notebook is an open-source web application that allows for the creation and sharing of documents that contain live code, equations, visualizations, and narrative text. It’s widely used for data cleaning, transformation, numerical simulations, statistical modeling, and machine learning.
When using Matplotlib within Jupyter Notebooks, you need a way to display the plots you create. This is where
%matplotlib inline comes into play.
%matplotlib inline is a magic command in Jupyter that enables the inline backend for usage with the IPython kernel. This backend is essentially an interface for rendering plots within the notebook itself. Without this command, your plots may be displayed in a new window. In environments such as Jupyter Notebooks, however, inline display is often more convenient.
It’s important to note that this command is specific to the Jupyter Notebook and Jupyter QtConsole. It doesn’t affect scripts, or the use of Matplotlib in other IDEs or applications.
%matplotlib inline has several benefits:
- Convenience: Your plots will be displayed inline, below the code cells that produced them. This makes it easier to view and understand the data and the corresponding visual representation.
- Notebook Compatibility: When sharing your Jupyter Notebook, the plots will be automatically included. This is especially useful if you are using Jupyter for reports or presentations.
- Customization: You can customize the plots even after they are rendered by simply tweaking the plotting code and running the cell again.
Now that we’ve covered the fundamentals, let’s delve into the practical aspect.
Let’s get started by using
%matplotlib inline in a Jupyter Notebook.
Step 1: Starting Jupyter Notebook
First, make sure you have both Jupyter Notebook and Matplotlib installed. You can install them using pip if you don’t have them:
pip install jupyter matplotlib
After the installation, run Jupyter Notebook in terminal or command prompt.
This will open the Jupyter Notebook interface in your web browser.
Step 2: Importing Matplotlib
In your Jupyter Notebook, import Matplotlib. Typically, the pyplot module is imported under the alias
import matplotlib.pyplot as plt
Step 3: Using
Now, simply input the magic command
%matplotlib inline in a code cell and run the cell:
That’s it! Any plot you create will now be displayed inline, below the code cell that created it.
Step 4: Creating Plots
Now you can start creating plots. Here is an example where we create a simple line plot.
import numpy as np # Data for plotting x = np.linspace(0, 10, 100) y = np.sin(x) # Creating the plot plt.plot(x, y, label='sin(x)') plt.xlabel('X Axis') plt.ylabel('Y Axis') plt.title('A Simple Line Plot') plt.legend() # Display the plot plt.show()
This will display the plot directly below the cell.
%matplotlib inline is very useful, there is another magic command that can be even more handy:
%matplotlib notebook. This command enables the nbagg backend, which provides an interactive, zoom-able version of your plot, within the Jupyter Notebook.
Use it in the same way as
Fine-Tuning Your Plots
You can further customize and fine-tune your plots using various functions available in Matplotlib. For example, you can add labels, titles, and legends; change colors and styles; and even combine several plots.
Here’s an example that shows a more complex plot:
%matplotlib inline import matplotlib.pyplot as plt import numpy as np # Data for plotting x = np.linspace(0, 10, 100) y1 = np.sin(x) y2 = np.cos(x) # Creating the first plot plt.figure(figsize=(10,5)) plt.plot(x, y1, label='sin(x)', color='blue', linestyle='dashed') plt.plot(x, y2, label='cos(x)', color='red', linestyle='dotted') # Adding labels, title, and legend plt.xlabel('X Axis') plt.ylabel('Y Axis') plt.title('Multiple Plots with Customizations') plt.legend() # Display the plot plt.show()
%matplotlib inline is an incredibly useful magic command that makes working with Matplotlib in Jupyter Notebooks much more convenient. As we have seen, it’s simple to use and essential for displaying plots inline. Whether you are just getting started with data visualization or are a seasoned veteran,
%matplotlib inline is likely to be an integral part of your workflow in Jupyter Notebooks.