This is Day 15 of the #100DaysOfPython challenge.

This post will look at home exceptions are rescued and will demonstrate how to handle different exceptions raised.

Prerequisites

1. Familiarity with Pipenv. See here for my post on Pipenv.
2. Familiarity with JupyterLab. See here for my post on JupyterLab.

Getting started

Let’s create the hello-python-exceptions directory and install Pillow.

Now we can start up the notebook server.

The server will now be up and running.

Creating the notebook

Once on http://localhost:8888/lab, select to create a new Python 3 notebook from the launcher.

Ensure that this notebook is saved in hello-python-exceptions/docs/<your-file-name>.

We will create four cells to handle four parts of this mini project:

1. A demonstration on how try/except works.
2. Looking at raising an error from a function.
3. Creating and using custom errors.

Try/Except

We can use the try/except statement to handle errors. You raise an Exception whenever you want to throw an error from a code block.

The Exception is self can then be delegated higher up the code chain to be handled.

For example, if we execute this in our first JupyterLab code block:

We notice the output is There was an issue. We do not make it passed the raised exception.

We can also capture exceptions by their type. For example, if we execute this in our second JupyterLab code block:

You will notice that 'Did not make it to final catch-all block' was captured and printed, where as the final except code block is used a capture all.

Raising an error from a function

Defining a function that raises an error will traverse up the code block to the top level.

This also prints out 'There was an issue'.

Creating and using custom errors

We can create custom errors simply by defining a class that extends Exception.

We can see this in action working randomly when we use the randrange function as a helper to determine which error to raise:

Running this code in a block will print out 'You shall not pass' or 'There was an error' based on the random value.

That means that our CustomError is being handled in the except CustomError as e block.

Summary

Today’s post demonstrated how to create custom errors by extending Exception and demonstrating how errors are raised in a simple manner.

Managing errors in Python is a necessity when working with more complex code that requires more fine-grained control over the possible error outcomes.

Resources and further reading

1. The ABCs of Pipenv
2. Hello, JupyterLab
3. Pipenv
4. Python Exceptions — An Introduction
5. User Defined Exceptions in Python

Photo credit: freestocks

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