Automate the Boring Stuff with Python: Practical Programming for Total Beginnerso (2015)

Part II. Automating Tasks

Chapter 10. Debugging

Now that you know enough to write more complicated programs, you may start finding not-so-simple bugs in them. This chapter covers some tools and techniques for finding the root cause of bugs in your program to help you fix bugs faster and with less effort.

To paraphrase an old joke among programmers, “Writing code accounts for 90 percent of programming. Debugging code accounts for the other 90 percent.”

Your computer will do only what you tell it to do; it won’t read your mind and do what you intended it to do. Even professional programmers create bugs all the time, so don’t feel discouraged if your program has a problem.

Fortunately, there are a few tools and techniques to identify what exactly your code is doing and where it’s going wrong. First, you will look at logging and assertions, two features that can help you detect bugs early. In general, the earlier you catch bugs, the easier they will be to fix.

Second, you will look at how to use the debugger. The debugger is a feature of IDLE that executes a program one instruction at a time, giving you a chance to inspect the values in variables while your code runs, and track how the values change over the course of your program. This is much slower than running the program at full speed, but it is helpful to see the actual values in a program while it runs, rather than deducing what the values might be from the source code.

Raising Exceptions

Python raises an exception whenever it tries to execute invalid code. In Chapter 3, you read about how to handle Python’s exceptions with try and except statements so that your program can recover from exceptions that you anticipated. But you can also raise your own exceptions in your code. Raising an exception is a way of saying, “Stop running the code in this function and move the program execution to the except statement.”

Exceptions are raised with a raise statement. In code, a raise statement consists of the following:

§ The raise keyword

§ A call to the Exception() function

§ A string with a helpful error message passed to the Exception() function

For example, enter the following into the interactive shell:

>>> raise Exception('This is the error message.')

Traceback (most recent call last):

File "<pyshell#191>", line 1, in <module>

raise Exception('This is the error message.')

Exception: This is the error message.

If there are no try and except statements covering the raise statement that raised the exception, the program simply crashes and displays the exception’s error message.

Often it’s the code that calls the function, not the fuction itself, that knows how to handle an expection. So you will commonly see a raise statement inside a function and the try and except statements in the code calling the function. For example, open a new file editor window, enter the following code, and save the program as boxPrint.py:

def boxPrint(symbol, width, height):

if len(symbol) != 1:

➊ raise Exception('Symbol must be a single character string.')

if width <= 2:

➋ raise Exception('Width must be greater than 2.')

if height <= 2:

➌ raise Exception('Height must be greater than 2.')

print(symbol * width)

for i in range(height - 2):

print(symbol + (' ' * (width - 2)) + symbol)

print(symbol * width)

for sym, w, h in (('*', 4, 4), ('O', 20, 5), ('x', 1, 3), ('ZZ', 3, 3)):

try:

boxPrint(sym, w, h)

➍ except Exception as err:

➎ print('An exception happened: ' + str(err))

Here we’ve defined a boxPrint() function that takes a character, a width, and a height, and uses the character to make a little picture of a box with that width and height. This box shape is printed to the console.

Say we want the character to be a single character, and the width and height to be greater than 2. We add if statements to raise exceptions if these requirements aren’t satisfied. Later, when we call boxPrint() with various arguments, our try/except will handle invalid arguments.

This program uses the except Exception as err form of the except statement ➍. If an Exception object is returned from boxPrint() ➊➋➌, this except statement will store it in a variable named err. The Exception object can then be converted to a string by passing it to str() to produce a user-friendly error message ➎. When you run this boxPrint.py, the output will look like this:

****

* *

* *

****

OOOOOOOOOOOOOOOOOOOO

O O

O O

O O

OOOOOOOOOOOOOOOOOOOO

An exception happened: Width must be greater than 2.

An exception happened: Symbol must be a single character string.

Using the try and except statements, you can handle errors more gracefully instead of letting the entire program crash.

Getting the Traceback as a String

When Python encounters an error, it produces a treasure trove of error information called the traceback. The traceback includes the error message, the line number of the line that caused the error, and the sequence of the function calls that led to the error. This sequence of calls is called thecall stack.

Open a new file editor window in IDLE, enter the following program, and save it as errorExample.py:

def spam():

bacon()

def bacon():

raise Exception('This is the error message.')

spam()

When you run errorExample.py, the output will look like this:

Traceback (most recent call last):

File "errorExample.py", line 7, in <module>

spam()

File "errorExample.py", line 2, in spam

bacon()

File "errorExample.py", line 5, in bacon

raise Exception('This is the error message.')

Exception: This is the error message.

From the traceback, you can see that the error happened on line 5, in the bacon() function. This particular call to bacon() came from line 2, in the spam() function, which in turn was called on line 7. In programs where functions can be called from multiple places, the call stack can help you determine which call led to the error.

The traceback is displayed by Python whenever a raised exception goes unhandled. But you can also obtain it as a string by calling traceback.format_exc(). This function is useful if you want the information from an exception’s traceback but also want an except statement to gracefully handle the exception. You will need to import Python’s traceback module before calling this function.

For example, instead of crashing your program right when an exception occurs, you can write the traceback information to a log file and keep your program running. You can look at the log file later, when you’re ready to debug your program. Enter the following into the interactive shell:

>>> import traceback

>>> try:

raise Exception('This is the error message.')

except:

errorFile = open('errorInfo.txt', 'w')

errorFile.write(traceback.format_exc())

errorFile.close()

print('The traceback info was written to errorInfo.txt.')

116

The traceback info was written to errorInfo.txt.

The 116 is the return value from the write() method, since 116 characters were written to the file. The traceback text was written to errorInfo.txt.

Traceback (most recent call last):

File "<pyshell#28>", line 2, in <module>

Exception: This is the error message.

Assertions

An assertion is a sanity check to make sure your code isn’t doing something obviously wrong. These sanity checks are performed by assert statements. If the sanity check fails, then an AssertionError exception is raised. In code, an assert statement consists of the following:

§ The assert keyword

§ A condition (that is, an expression that evaluates to True or False)

§ A comma

§ A string to display when the condition is False

For example, enter the following into the interactive shell:

>>> podBayDoorStatus = 'open'

>>> assert podBayDoorStatus == 'open', 'The pod bay doors need to be "open".'

>>> podBayDoorStatus = 'I\'m sorry, Dave. I\'m afraid I can't do that.''

>>> assert podBayDoorStatus == 'open', 'The pod bay doors need to be "open".'

Traceback (most recent call last):

File "<pyshell#10>", line 1, in <module>

assert podBayDoorStatus == 'open', 'The pod bay doors need to be "open".'

AssertionError: The pod bay doors need to be "open".

Here we’ve set podBayDoorStatus to 'open', so from now on, we fully expect the value of this variable to be 'open'. In a program that uses this variable, we might have written a lot of code under the assumption that the value is 'open' — code that depends on its being 'open' in order to work as we expect. So we add an assertion to make sure we’re right to assume podBayDoorStatus is 'open'. Here, we include the message 'The pod bay doors need to be "open".' so it’ll be easy to see what’s wrong if the assertion fails.

Later, say we make the obvious mistake of assigning podBayDoorStatus another value, but don’t notice it among many lines of code. The assertion catches this mistake and clearly tells us what’s wrong.

In plain English, an assert statement says, “I assert that this condition holds true, and if not, there is a bug somewhere in the program.” Unlike exceptions, your code should not handle assert statements with try and except; if an assert fails, your program should crash. By failing fast like this, you shorten the time between the original cause of the bug and when you first notice the bug. This will reduce the amount of code you will have to check before finding the code that’s causing the bug.

Assertions are for programmer errors, not user errors. For errors that can be recovered from (such as a file not being found or the user entering invalid data), raise an exception instead of detecting it with an assert statement.

Using an Assertion in a Traffic Light Simulation

Say you’re building a traffic light simulation program. The data structure representing the stoplights at an intersection is a dictionary with keys 'ns' and 'ew', for the stoplights facing north-south and east-west, respectively. The values at these keys will be one of the strings 'green', ' yellow', or 'red'. The code would look something like this:

market_2nd = {'ns': 'green', 'ew': 'red'}

mission_16th = {'ns': 'red', 'ew': 'green'}

These two variables will be for the intersections of Market Street and 2nd Street, and Mission Street and 16th Street. To start the project, you want to write a switchLights() function, which will take an intersection dictionary as an argument and switch the lights.

At first, you might think that switchLights() should simply switch each light to the next color in the sequence: Any 'green' values should change to 'yellow', 'yellow' values should change to 'red', and 'red' values should change to 'green'. The code to implement this idea might look like this:

def switchLights(stoplight):

for key in stoplight.keys():

if stoplight[key] == 'green':

stoplight[key] = 'yellow'

elif stoplight[key] == 'yellow':

stoplight[key] = 'red'

elif stoplight[key] == 'red':

stoplight[key] = 'green'

switchLights(market_2nd)

You may already see the problem with this code, but let’s pretend you wrote the rest of the simulation code, thousands of lines long, without noticing it. When you finally do run the simulation, the program doesn’t crash — but your virtual cars do!

Since you’ve already written the rest of the program, you have no idea where the bug could be. Maybe it’s in the code simulating the cars or in the code simulating the virtual drivers. It could take hours to trace the bug back to the switchLights() function.

But if while writing switchLights() you had added an assertion to check that at least one of the lights is always red, you might have included the following at the bottom of the function:

assert 'red' in stoplight.values(), 'Neither light is red! ' + str(stoplight)

With this assertion in place, your program would crash with this error message:

Traceback (most recent call last):

File "carSim.py", line 14, in <module>

switchLights(market_2nd)

File "carSim.py", line 13, in switchLights

assert 'red' in stoplight.values(), 'Neither light is red! ' + str(stoplight)

➊ AssertionError: Neither light is red! {'ns': 'yellow', 'ew': 'green'}

The important line here is the AssertionError ➊. While your program crashing is not ideal, it immediately points out that a sanity check failed: Neither direction of traffic has a red light, meaning that traffic could be going both ways. By failing fast early in the program’s execution, you can save yourself a lot of future debugging effort.

Disabling Assertions

Assertions can be disabled by passing the -O option when running Python. This is good for when you have finished writing and testing your program and don’t want it to be slowed down by performing sanity checks (although most of the time assert statements do not cause a noticeable speed difference). Assertions are for development, not the final product. By the time you hand off your program to someone else to run, it should be free of bugs and not require the sanity checks. See Appendix B for details about how to launch your probably-not-insane programs with the -Ooption.

Logging

If you’ve ever put a print() statement in your code to output some variable’s value while your program is running, you’ve used a form of logging to debug your code. Logging is a great way to understand what’s happening in your program and in what order its happening. Python’s loggingmodule makes it easy to create a record of custom messages that you write. These log messages will describe when the program execution has reached the logging function call and list any variables you have specified at that point in time. On the other hand, a missing log message indicates a part of the code was skipped and never executed.

Using the logging Module

To enable the logging module to display log messages on your screen as your program runs, copy the following to the top of your program (but under the #! python shebang line):

import logging

logging.basicConfig(level=logging.DEBUG, format=' %(asctime)s - %(levelname)s

- %(message)s')

You don’t need to worry too much about how this works, but basically, when Python logs an event, it creates a LogRecord object that holds information about that event. The logging module’s basicConfig() function lets you specify what details about the LogRecord object you want to see and how you want those details displayed.

Say you wrote a function to calculate the factorial of a number. In mathematics, factorial 4 is 1 × 2 × 3 × 4, or 24. Factorial 7 is 1 × 2 × 3 × 4 × 5 × 6 × 7, or 5,040. Open a new file editor window and enter the following code. It has a bug in it, but you will also enter several log messages to help yourself figure out what is going wrong. Save the program as factorialLog.py.

import logging

logging.basicConfig(level=logging.DEBUG, format=' %(asctime)s - %(levelname)s

- %(message)s')

logging.debug('Start of program')

def factorial(n):

logging.debug('Start of factorial( %)' % (n))

total = 1

for i in range(n + 1):

total *= i

logging.debug('i is ' + str(i) + ', total is ' + str(total))

logging.debug('End of factorial( %)' % (n))

return total

print(factorial(5))

logging.debug('End of program')

Here, we use the logging.debug() function when we want to print log information. This debug() function will call basicConfig(), and a line of information will be printed. This information will be in the format we specified in basicConfig() and will include the messages we passed todebug(). The print(factorial(5)) call is part of the original program, so the result is displayed even if logging messages are disabled.

The output of this program looks like this:

2015-05-23 16:20:12,664 - DEBUG - Start of program

2015-05-23 16:20:12,664 - DEBUG - Start of factorial(5)

2015-05-23 16:20:12,665 - DEBUG - i is 0, total is 0

2015-05-23 16:20:12,668 - DEBUG - i is 1, total is 0

2015-05-23 16:20:12,670 - DEBUG - i is 2, total is 0

2015-05-23 16:20:12,673 - DEBUG - i is 3, total is 0

2015-05-23 16:20:12,675 - DEBUG - i is 4, total is 0

2015-05-23 16:20:12,678 - DEBUG - i is 5, total is 0

2015-05-23 16:20:12,680 - DEBUG - End of factorial(5)

0

2015-05-23 16:20:12,684 - DEBUG - End of program

The factorial() function is returning 0 as the factorial of 5, which isn’t right. The for loop should be multiplying the value in total by the numbers from 1 to 5. But the log messages displayed by logging.debug() show that the i variable is starting at 0 instead of 1. Since zero times anything is zero, the rest of the iterations also have the wrong value for total. Logging messages provide a trail of breadcrumbs that can help you figure out when things started to go wrong.

Change the for i in range(n + 1): line to for i in range(1, n + 1):, and run the program again. The output will look like this:

2015-05-23 17:13:40,650 - DEBUG - Start of program

2015-05-23 17:13:40,651 - DEBUG - Start of factorial(5)

2015-05-23 17:13:40,651 - DEBUG - i is 1, total is 1

2015-05-23 17:13:40,654 - DEBUG - i is 2, total is 2

2015-05-23 17:13:40,656 - DEBUG - i is 3, total is 6

2015-05-23 17:13:40,659 - DEBUG - i is 4, total is 24

2015-05-23 17:13:40,661 - DEBUG - i is 5, total is 120

2015-05-23 17:13:40,661 - DEBUG - End of factorial(5)

120

2015-05-23 17:13:40,666 - DEBUG - End of program

The factorial(5) call correctly returns 120. The log messages showed what was going on inside the loop, which led straight to the bug.

You can see that the logging.debug() calls printed out not just the strings passed to them but also a timestamp and the word DEBUG.

Don’t Debug with print()

Typing import logging and logging.basicConfig(level=logging.DEBUG, format= '%(asctime)s - %(levelname)s - %(message)s') is somewhat unwieldy. You may want to use print() calls instead, but don’t give in to this temptation! Once you’re done debugging, you’ll end up spending a lot of time removing print() calls from your code for each log message. You might even accidentally remove some print() calls that were being used for nonlog messages. The nice thing about log messages is that you’re free to fill your program with as many as you like, and you can always disable them later by adding a single logging.disable(logging.CRITICAL) call. Unlike print(), the logging module makes it easy to switch between showing and hiding log messages.

Log messages are intended for the programmer, not the user. The user won’t care about the contents of some dictionary value you need to see to help with debugging; use a log message for something like that. For messages that the user will want to see, like File not found or Invalid input, please enter a number, you should use a print() call. You don’t want to deprive the user of useful information after you’ve disabled log messages.

Logging Levels

Logging levels provide a way to categorize your log messages by importance. There are five logging levels, described in Table 10-1 from least to most important. Messages can be logged at each level using a different logging function.

Table 10-1. Logging Levels in Python

Your logging message is passed as a string to these functions. The logging levels are suggestions. Ultimately, it is up to you to decide which category your log message falls into. Enter the following into the interactive shell:

>>> import logging

>>> logging.basicConfig(level=logging.DEBUG, format=' %(asctime)s -

%(levelname)s - %(message)s')

>>> logging.debug('Some debugging details.')

2015-05-18 19:04:26,901 - DEBUG - Some debugging details.

>>> logging.info('The logging module is working.')

2015-05-18 19:04:35,569 - INFO - The logging module is working.

>>> logging.warning('An error message is about to be logged.')

2015-05-18 19:04:56,843 - WARNING - An error message is about to be logged.

>>> logging.error('An error has occurred.')

2015-05-18 19:05:07,737 - ERROR - An error has occurred.

>>> logging.critical('The program is unable to recover!')

2015-05-18 19:05:45,794 - CRITICAL - The program is unable to recover!

The benefit of logging levels is that you can change what priority of logging message you want to see. Passing logging.DEBUG to the basicConfig() function’s level keyword argument will show messages from all the logging levels (DEBUG being the lowest level). But after developing your program some more, you may be interested only in errors. In that case, you can set basicConfig()’s level argument to logging.ERROR. This will show only ERROR and CRITICAL messages and skip the DEBUG, INFO, and WARNING messages.

Disabling Logging

After you’ve debugged your program, you probably don’t want all these log messages cluttering the screen. The logging.disable() function disables these so that you don’t have to go into your program and remove all the logging calls by hand. You simply pass logging.disable() a logging level, and it will suppress all log messages at that level or lower. So if you want to disable logging entirely, just add logging.disable(logging.CRITICAL) to your program. For example, enter the following into the interactive shell:

>>> import logging

>>> logging.basicConfig(level=logging.INFO, format=' %(asctime)s -

%(levelname)s - %(message)s')

>>> logging.critical('Critical error! Critical error!')

2015-05-22 11:10:48,054 - CRITICAL - Critical error! Critical error!

>>> logging.disable(logging.CRITICAL)

>>> logging.critical('Critical error! Critical error!')

>>> logging.error('Error! Error!')

Since logging.disable() will disable all messages after it, you will probably want to add it near the import logging line of code in your program. This way, you can easily find it to comment out or uncomment that call to enable or disable logging messages as needed.

Logging to a File

Instead of displaying the log messages to the screen, you can write them to a text file. The logging.basicConfig() function takes a filename keyword argument, like so:

import logging

logging.basicConfig(filename='myProgramLog.txt', level=logging.DEBUG, format='

%(asctime)s - %(levelname)s - %(message)s')

The log messages will be saved to myProgramLog.txt. While logging messages are helpful, they can clutter your screen and make it hard to read the program’s output. Writing the logging messages to a file will keep your screen clear and store the messages so you can read them after running the program. You can open this text file in any text editor, such as Notepad or TextEdit.

IDLE’s Debugger

The debugger is a feature of IDLE that allows you to execute your program one line at a time. The debugger will run a single line of code and then wait for you to tell it to continue. By running your program “under the debugger” like this, you can take as much time as you want to examine the values in the variables at any given point during the program’s lifetime. This is a valuable tool for tracking down bugs.

To enable IDLE’s debugger, click Debug▸Debugger in the interactive shell window. This will bring up the Debug Control window, which looks like Figure 10-1.

When the Debug Control window appears, select all four of the Stack, Locals, Source, and Globals checkboxes so that the window shows the full set of debug information. While the Debug Control window is displayed, any time you run a program from the file editor, the debugger will pause execution before the first instruction and display the following:

§ The line of code that is about to be executed

§ A list of all local variables and their values

§ A list of all global variables and their values

Figure 10-1. The Debug Control window

You’ll notice that in the list of global variables there are several variables you haven’t defined, such as __builtins__, __doc__, __file__, and so on. These are variables that Python automatically sets whenever it runs a program. The meaning of these variables is beyond the scope of this book, and you can comfortably ignore them.

The program will stay paused until you press one of the five buttons in the Debug Control window: Go, Step, Over, Out, or Quit.

Go

Clicking the Go button will cause the program to execute normally until it terminates or reaches a breakpoint. (Breakpoints are described later in this chapter.) If you are done debugging and want the program to continue normally, click the Go button.

Step

Clicking the Step button will cause the debugger to execute the next line of code and then pause again. The Debug Control window’s list of global and local variables will be updated if their values change. If the next line of code is a function call, the debugger will “step into” that function and jump to the first line of code of that function.

Over

Clicking the Over button will execute the next line of code, similar to the Step button. However, if the next line of code is a function call, the Over button will “step over” the code in the function. The function’s code will be executed at full speed, and the debugger will pause as soon as the function call returns. For example, if the next line of code is a print() call, you don’t really care about code inside the built-in print() function; you just want the string you pass it printed to the screen. For this reason, using the Over button is more common than the Step button.

Out

Clicking the Out button will cause the debugger to execute lines of code at full speed until it returns from the current function. If you have stepped into a function call with the Step button and now simply want to keep executing instructions until you get back out, click the Out button to “step out” of the current function call.

Quit

If you want to stop debugging entirely and not bother to continue executing the rest of the program, click the Quit button. The Quit button will immediately terminate the program. If you want to run your program normally again, select Debug▸Debugger again to disable the debugger.

Debugging a Number Adding Program

Open a new file editor window and enter the following code:

print('Enter the first number to add:')

first = input()

print('Enter the second number to add:')

second = input()

print('Enter the third number to add:')

third = input()

print('The sum is ' + first + second + third)

Save it as buggyAddingProgram.py and run it first without the debugger enabled. The program will output something like this:

Enter the first number to add:

5

Enter the second number to add:

3

Enter the third number to add:

42

The sum is 5342

The program hasn’t crashed, but the sum is obviously wrong. Let’s enable the Debug Control window and run it again, this time under the debugger.

When you press F5 or select Run▸Run Module (with Debug▸Debugger enabled and all four checkboxes on the Debug Control window checked), the program starts in a paused state on line 1. The debugger will always pause on the line of code it is about to execute. The Debug Control window will look like Figure 10-2.

Figure 10-2. The Debug Control window when the program first starts under the debugger

Click the Over button once to execute the first print() call. You should use Over instead of Step here, since you don’t want to step into the code for the print() function. The Debug Control window will update to line 2, and line 2 in the file editor window will be highlighted, as shown inFigure 10-3. This shows you where the program execution currently is.

Figure 10-3. The Debug Control window after clicking Over

Click Over again to execute the input() function call, and the buttons in the Debug Control window will disable themselves while IDLE waits for you to type something for the input() call into the interactive shell window. Enter 5 and press Return. The Debug Control window buttons will be reenabled.

Keep clicking Over, entering 3 and 42 as the next two numbers, until the debugger is on line 7, the final print() call in the program. The Debug Control window should look like Figure 10-4. You can see in the Globals section that the first, second, and third variables are set to string values'5', '3', and '42' instead of integer values 5, 3, and 42. When the last line is executed, these strings are concatenated instead of added together, causing the bug.

Figure 10-4. The Debug Control window on the last line. The variables are set to strings, causing the bug.

Stepping through the program with the debugger is helpful but can also be slow. Often you’ll want the program to run normally until it reaches a certain line of code. You can configure the debugger to do this with breakpoints.

Breakpoints

A breakpoint can be set on a specific line of code and forces the debugger to pause whenever the program execution reaches that line. Open a new file editor window and enter the following program, which simulates flipping a coin 1,000 times. Save it as coinFlip.py.

import random

heads = 0

for i in range(1, 1001):

➊ if random.randint(0, 1) == 1:

heads = heads + 1

if i == 500:

➋ print('Halfway done!')

print('Heads came up ' + str(heads) + ' times.')

The random.randint(0, 1) call ➊ will return 0 half of the time and 1 the other half of the time. This can be used to simulate a 50/50 coin flip where 1 represents heads. When you run this program without the debugger, it quickly outputs something like the following:

Halfway done!

Heads came up 490 times.

If you ran this program under the debugger, you would have to click the Over button thousands of times before the program terminated. If you were interested in the value of heads at the halfway point of the program’s execution, when 500 of 1000 coin flips have been completed, you could instead just set a breakpoint on the line print('Halfway done!') ➋. To set a breakpoint, right-click the line in the file editor and select Set Breakpoint, as shown in Figure 10-5.

Figure 10-5. Setting a breakpoint

You don’t want to set a breakpoint on the if statement line, since the if statement is executed on every single iteration through the loop. By setting the breakpoint on the code in the if statement, the debugger breaks only when the execution enters the if clause.

The line with the breakpoint will be highlighted in yellow in the file editor. When you run the program under the debugger, it will start in a paused state at the first line, as usual. But if you click Go, the program will run at full speed until it reaches the line with the breakpoint set on it. You can then click Go, Over, Step, or Out to continue as normal.

If you want to remove a breakpoint, right-click the line in the file editor and select Clear Breakpoint from the menu. The yellow highlighting will go away, and the debugger will not break on that line in the future.

Summary

Assertions, exceptions, logging, and the debugger are all valuable tools to find and prevent bugs in your program. Assertions with the Python assert statement are a good way to implement “sanity checks” that give you an early warning when a necessary condition doesn’t hold true. Assertions are only for errors that the program shouldn’t try to recover from and should fail fast. Otherwise, you should raise an exception.

An exception can be caught and handled by the try and except statements. The logging module is a good way to look into your code while it’s running and is much more convenient to use than the print() function because of its different logging levels and ability to log to a text file.

The debugger lets you step through your program one line at a time. Alternatively, you can run your program at normal speed and have the debugger pause execution whenever it reaches a line with a breakpoint set. Using the debugger, you can see the state of any variable’s value at any point during the program’s lifetime.

These debugging tools and techniques will help you write programs that work. Accidentally introducing bugs into your code is a fact of life, no matter how many years of coding experience you have.

Practice Questions

Practice Project

For practice, write a program that does the following.

Debugging Coin Toss

The following program is meant to be a simple coin toss guessing game. The player gets two guesses (it’s an easy game). However, the program has several bugs in it. Run through the program a few times to find the bugs that keep the program from working correctly.

import random

guess = ''

while guess not in ('heads', 'tails'):

print('Guess the coin toss! Enter heads or tails:')

guess = input()

toss = random.randint(0, 1) # 0 is tails, 1 is heads

if toss == guess:

print('You got it!')

else:

print('Nope! Guess again!')

guesss = input()

if toss == guess:

print('You got it!')

else:

print('Nope. You are really bad at this game.')