💡 Problem Formulation: When working in a Jupyter Notebook, running a cell that contains a Matplotlib plot often results in unwanted text output above the plot, typically information like <matplotlib.figure.Figure at 0x...>. In a professional context, we want our notebooks to be clean and only show the plots without this extra text clutter. Let’s explore how to suppress such output.
Method 1: Using the Semicolon
One of the simplest ways to prevent Matplotlib printouts is by adding a semicolon at the end of the plotting command. This is a shorthand method that tells the Jupyter notebook to suppress the output of the last line in the cell.
Here’s an example:
import matplotlib.pyplot as plt plt.plot([0, 1, 2], [0, 1, 4]);
The output is the plot itself with no additional text output.
This code block imports Matplotlib and plots a simple line graph. By ending the plot command with a semicolon, we prevent the Jupyter Notebook from displaying the text output that normally accompanies the plot.
Method 2: Using plt.show()
The plt.show() function is designed to display all figures and block until the figures have been closed. When used at the end of plotting commands in a Jupyter Notebook cell, it can also prevent the display of the unwanted textual output.
Here’s an example:
import matplotlib.pyplot as plt plt.plot([0, 1, 2], [0, 1, 4]) plt.show()
This generates a plot without any additional text output.
The plt.plot() command generates a plot while plt.show() ensures that the plot is displayed cleanly without any accompanying text output.
Method 3: Using IPython’s Display Function
IPython’s display function, when paired with clear_output, allows fine control over output and can effectively suppress unwanted text when plotting.
Here’s an example:
import matplotlib.pyplot as plt from IPython.display import display, clear_output plt.plot([0, 1, 2], [0, 1, 4]) clear_output(wait=True) display(plt.gcf())
The plot appears without any additional text output.
The code generates a plot and then uses clear_output() to remove any previous output, followed by display() with plt.gcf() to present the current figure.
Method 4: Capturing the Output
By capturing the output of the plot command with the IPython’s %%capture magic command, you can prevent the display of any output, including textual information from Matplotlib.
Here’s an example:
%%capture capt import matplotlib.pyplot as plt plt.plot([0, 1, 2], [0, 1, 4]) plt.show()
No output is shown at all, and everything is captured in the capt object.
This approach uses one of the built-in ‘magic’ commands in Jupyter Notebook, %%capture, which suppresses all output from the cell and optionally stores it in a variable for later use.
Bonus One-Liner Method 5: Using the Matplotlib Inline Backend
By using the %matplotlib inline magic command, Matplotlib is configured to work with Jupyter Notebook, often eliminating extraneous textual output from plots.
Here’s an example:
%matplotlib inline import matplotlib.pyplot as plt plt.plot([0, 1, 2], [0, 1, 4])
The figure is displayed without any additional text output.
This one-liner instructs Jupyter to set up Matplotlib for use in the notebook environment, streamlining the plotting process to avoid unwanted text.
Summary/Discussion
- Method 1: Semicolon. Simple and straightforward. May be unintuitive for those unfamiliar with the tactic.
- Method 2:
plt.show(). Official way to display plots. Ensures that the output is formatted correctly, but requires an additional function call. - Method 3: IPython’s Display Function. Offers fine control over what is displayed. Slightly more complex but very powerful.
- Method 4: Capturing Output. Completely suppresses output and is useful for batch processing of plots. The downside is it may suppress outputs that you want to see.
- Bonus Method 5: Matplotlib Inline Backend. Integrates Matplotlib more tightly with Jupyter. Doesn’t capture the output but usually helps in showing the plot without extra text.