MISSION 68

Working with Jupyter Console

In this mission, you will learn how to work with the Jupyter console, an enhanced Python interpreter. You will learn about things like using Jupyter magics, pasting in code, and more. 

From our earlier mission, you may recall that by typing Python on the command line, you get access to an interactive shell that lets you write and execute Python code. Jupyter console enhances this shell and adds several niceties that make working with data easier.

Generally, it's useful to use the shell in situations where you need to quickly test some code you're writing. This happens frequently when you're writing data analysis scripts. It can also be used to quickly explore datasets and do basic analysis. Another use case is prototyping code before later saving it to a script file.

The main difference between Jupyter console and Jupyter notebooks is that the console functions in interactive mode. Whenever you type a line of code, it is immediately executed, and you can see the results. If you want to write medium-length pieces of code, do a deep exploration of a dataset to tell a story, the notebook is better. If you want to test out code you're writing, or run quick commands, the console is better. Each has their uses, and a good data scientist will be able to use both.

As you work through each concept, you’ll get to apply what you’ve learned from within your browser so that there's no need to use your own machine to do the exercises. The Jupyter console environment inside of this course includes answer checking so you can ensure that you've fully mastered each concept before learning the next concept.

Objectives

  • Learn how to work with the Jupyter console
  • Learn how to use Jupyter magics to iteratively develop scripts.

Mission Outline

1. Jupyter console
2. Getting help
3. Persistent sessions
4. Jupyter magics
5. Autocompletion
6. Accessing the shell
7. Pasting in code
8. Next steps
9. Takeaways

command-line-intermediate

Course Info:

Intermediate

The median completion time for this course is 6 hours. View Details

This course requires a basic subscription and includes four missions and one guided project. It is the ninth course in the Data Analyst in Python path and Data Scientist in Python path.

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