Beginning Programming with Python For Dummies
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You might be able to divine the potential sources of error in your Python application by reading tea leaves, but that’s hardly an efficient way to do things. Errors actually fall into well-defined categories that help you predict (to some degree) when and where they’ll occur. By thinking about these categories as you work through your application, you’re far more likely to discover potential errors sources before they occur and cause potential damage. The two principle categories are
  • Errors that occur at a specific time
  • Errors that are of a specific type
The overall concept is that you need to think about error classifications in order to start finding and fixing potential errors in your application before they become a problem.

Classifying when errors occur

Errors occur at specific times. The two major time frames are
  • Compile time
  • Runtime
  • No matter when an error occurs, it causes your application to misbehave.

Compile time

A compile time error occurs when you ask Python to run the application. Before Python can run the application, it must interpret the code and put it into a form that the computer can understand. A computer relies on machine code that is specific to that processor and architecture. If the instructions you write are malformed or lack needed information, Python can’t perform the required conversion. It presents an error that you must fix before the application can run.

Fortunately, compile-time errors are the easiest to spot and fix. Because the application won’t run with a compile-time error in place, user never sees this error category. You fix this sort of error as you write your code.

The appearance of a compile-time error should tell you that other typos or omissions could exist in the code. It always pays to check the surrounding code to ensure that no other potential problems exist that might not show up as part of the compile cycle.


A runtime error occurs after Python compiles the code you write and the computer begins to execute it. Runtime errors come in several different types, and some are harder to find than others. You know you have a runtime error when the application suddenly stops running and displays an exception dialog box or when the user complains about erroneous output (or at least instability).

Not all runtime errors produce an exception. Some runtime errors cause instability (the application freezes), errant output, or data damage. Runtime errors can affect other applications or create unforeseen damage to the platform on which the application is running. In short, runtime errors can cause you quite a bit of grief, depending on precisely the kind of error you’re dealing with at the time.

Many runtime errors are caused by errant code. For example, you can misspell the name of a variable, preventing Python from placing information in the correct variable during execution. Leaving out an optional but necessary argument when calling a method can also cause problems. These are examples of errors of commission, which are specific errors associated with your code. In general, you can find these kinds of errors by using a debugger or by simply reading your code line by line to check for errors.

Runtime errors can also be caused by external sources not associated with your code. For example, the user can input incorrect information that the application isn’t expecting, causing an exception. A network error can make a required resource inaccessible. Sometimes even the computer hardware has a glitch that causes a nonrepeatable application error. These are all examples of errors of omission, from which the application might recover if your application has error-trapping code in place. It’s important that you consider both kinds of runtime errors — errors of commission and omission — when building your application.

Distinguishing error types

You can distinguish errors by type, that is, by how they’re made. Knowing the error types helps you understand where to look in an application for potential problems. Exceptions work like many other things in life. For example, you know that electronic devices don’t work without power. So, when you try to turn your television on and it doesn’t do anything, you might look to ensure that the power cord is firmly seated in the socket.

Understanding the error types helps you locate errors faster, earlier, and more consistently, resulting in fewer misdiagnoses. The best developers know that fixing errors while an application is in development is always easier than fixing it when the application is in production because users are inherently impatient and want errors fixed immediately and correctly. In addition, fixing an error earlier in the development cycle is always easier than fixing it when the application nears completion because less code exists to review.

The trick is to know where to look. With this in mind, Python (and most other programming languages) breaks errors into the following types:
  • Syntactical
  • Semantic
  • Logical
A syntactical error is generally the easiest; a logical error is generally the hardest.


Whenever you make a typo of some sort, you create a syntactical error. Some Python syntactical errors are quite easy to find because the application simply doesn’t run. The interpreter may even point out the error for you by highlighting the errant code and displaying an error message. However, some syntactical errors are quite hard to find. Python is case sensitive, so you may use the wrong case for a variable in one place and find that the variable isn’t quite working as you thought it would. Finding the one place where you used the wrong capitalization can be quite challenging.

Most syntactical errors occur at compile time and the interpreter points them out for you. Fixing the error is made easy because the interpreter generally tells you what to fix, and with considerable accuracy. Even when the interpreter doesn’t find the problem, syntactical errors prevent the application from running correctly, so any errors the interpreter doesn’t find show up during the testing phase. Few syntactical errors should make it into production as long as you perform adequate application testing.


When you create a loop that executes one too many times, you don’t generally receive any sort of error information from the application. The application will happily run because it thinks that it’s doing everything correctly, but that one additional loop can cause all sorts of data errors. When you create an error of this sort in your code, it’s called a semantic error.

Semantic errors occur because the meaning behind a series of steps used to perform a task is wrong — the result is incorrect even though the code apparently runs precisely as it should. Semantic errors are tough to find, and you sometimes need some sort of debugger to find them. (You can find blog posts about debugging.)


Some developers don’t create a division between semantic and logical errors, but they are different. A semantic error occurs when the code is essentially correct but the implementation is wrong (such as having a loop execute once too often). Logical errors occur when the developer’s thinking is faulty. In many cases, this sort of error happens when the developer uses a relational or logical operator incorrectly. However, logical errors can happen in all sorts of other ways, too. For example, a developer might think that data is always stored on the local hard drive, which means that the application may behave in an unusual manner when it attempts to load data from a network drive instead.

Logical errors are quite hard to fix because the problem isn’t with the actual code, yet the code itself is incorrectly defined. The thought process that went into creating the code is faulty; therefore, the developer who created the error is less likely to find it. Smart developers use a second pair of eyes to help spot logical errors. Having a formal application specification also helps because the logic behind the tasks the application performs is usually given a formal review.

About This Article

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John Paul Mueller is a freelance author and technical editor with more than 107 books and 600 articles to his credit. His subjects range from networking and artificial intelligence to database management and heads-down programming. He also consults and writes certification exams. Visit his website at

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