Useful Jupyter Notebook Shortcuts

Jenny Palomino, Leah Wasser

Modified Aug 30, 2023

Learn how to…

• List useful keyboard shortcuts in Jupyter Notebook.

• Be able to access the list of keyboard shortcuts in Jupyter Notebook.

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn useful shortcuts in Jupyter Notebook that can help you complete your tasks quickly and efficiently.

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List of Useful Jupyter Notebook Shortcuts

Menu Tools vs. Keyboard Shortcuts

As you have seen in this chapter, you can manipulate your Jupyter Notebook using the drop-down tools from the menu, with keyboard shortcuts, or using both.

The table below lists common tasks in Jupyter Notebook and how to do them using keyboard shortcuts or the menu tool.

Function	Keyboard Shortcut	Menu Tools
Save notebook	Esc + s	File → Save and Checkpoint
Create new cell	Esc + a (above), Esc + b (below)	Insert→ cell above Insert → cell below
Run Cell	Ctrl + enter	Cell → Run Cell
Copy Cell	c	Copy Key
Paste Cell	v	Paste Key
Interrupt Kernel	Esc + i i	Kernel → Interrupt
Restart Kernel	Esc + 0 0	Kernel → Restart
Find and replace on your code but not the outputs	Esc + f	N/A
merge multiple cells	Shift + M	N/A
When placed before a function Information about a function from its documentation	?	N/A

For a full list of keyboard shortcuts, click the help button, then the keyboard shortcuts button.

Help menu of Jupyter Notebook.

Note

Additional Resources

• Why Jupyter Notebook and Python

• Jupyter Notebook Basics

• Dataquest tips and tricks for Jupyter Notebook.

Exercise 1

Test your Jupyter Notebook skills to:

1. Launch Jupyter Notebook from your earth-analytics directory.

2. Create a new Jupyter Notebook file called jupyter-notebook-interface.ipynb.

3. Add a Code cell and copy/paste the following Python code to determine which day had the most precipitation (i.e. the day of the greatest flooding) during the Fall 2013 flood in Boulder, CO, U.S.A.

# Import necessary packages
import matplotlib.pyplot as plt
import pandas as pd

# Create data
boulder_precip = pd.DataFrame(columns=["date", "precip"], 
                              data=[
                                  ["2013-09-09", 0.1], ["2013-09-10", 1.0], 
                                  ["2013-09-11", 2.3], ["2013-09-12", 9.8], ["2013-09-13", 1.9],
                                  ["2013-09-14", 0.01], ["2013-09-15", 1.4], ["2013-09-16", 0.4]])      
# Create plot
fig, ax = plt.subplots()
ax.bar(boulder_precip['date'].values, boulder_precip['precip'].values)
ax.set(title="Daily Precipitation (inches)\nBoulder, Colorado 2013", 
       xlabel="Date", ylabel="Precipitation (Inches)")
plt.setp(ax.get_xticklabels(), rotation=45);

4. Run the Python cell.

You have now experienced the benefits of using Jupyter Notebook for open reproducible science!

Without writing your own code, you were able to easily replicate this analysis because this code block can be shared with and run by anyone using Python in Jupyter Notebook.

5. Add a Code cell and run each of the following Python calculations:

   • 16 - 4

   • 24 / 4

   • 2 * 4

   • 2 ** 4

     • What do you notice about the output of 24 / 4 compared to the others?

     • What operation does ** execute?

6. Create a new directory called chap-3 in your earth-analytics directory.

7. Create a new directory called test in your earth-analytics directory and move it into in the newly created directory called chap-3.

8. Delete the test directory - do you recall how to find the test directory in its new location?

9. Rename the Jupyter Notebook file that you created in step 2 (e.g. jupyter-notebook-interface.ipynb) using your first initial and last name (e.g. jpalomino-jupyter-notebook-interface.ipynb).

10. Create a new folder called chap-3 in your earth-analytics directory.

11. Move your renamed Jupyter Notebook file (e.g. jpalomino-jupyter-notebook-interface.ipynb) into the new chap-3 directory.