Learning to Program

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Learning to program involves learning not only the syntax and rules of a new language, but also a new way of thinking. Not only do we want to write code that is understandable by the computer, we must also understand what the computer will do when it tries to run that code.

A key part of that is learning to think like a computer thinks. We sometimes call this our “mental model” of a computer. If we have a good “mental model” of a computer, then we can learn to read code and mentally predict what it will do, even without ever running it on an actual computer. This ability is crucial for programmers to develop.

So, as we work through this lab, we’ll need to constantly pay attention to how a real computer works, and make sure that our “mental model” is accurate. If not, we’ll have to adapt our understanding of a computer to match the real world. This process of adaptation and accommodation is an important part of learning to program - we have to have a good understanding of what a computer actually does when it runs our code, or else we won’t be able to write code that will do what we want it to do.

We’ve chosen Python because it is very easy to learn, easy to use, and it is used in many different places, from scientific computing and data analysis to web servers and even artificial intelligence. It is a very useful language to learn, and it makes a great first programming language.

As we learn to write code in a real programming language, it helps to refer to the actual documentation from time to time. So, we recommend bookmarking the official Python Documentation as a great place to start. Throughout this course, we may also include links to additional resources, and those are also worth bookmarking. One of the best parts about programming is that nearly all of the documentation is online and easily accessible, and learning how to quickly search for a particular solution or reference is just as useful as knowing how to do it from memory. In fact, most programmers really only know the basics of the language and a few handy tricks, and the rest of it is just reading documentation and learning when to use it. So, don’t worry about remembering it all right from the start - instead, learn to read the documentation and use the tools that are available, and focus on understanding the basics of the language’s syntax and rules. Let’s get started!

Working in the Terminal

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First, let’s start with the basics of writing Python code in a file and running those files. This is the first major step toward actually writing a real program, but it can definitely be difficult the first time without prior experience to rely on. So, let’s go through it step by step and make sure we know how to run our programs in Python.

At this point, we should already have Python installed on our system. For students using Codio, this is taken care of already. For students using their own computers, refer to an earlier lab to find instructions for installing Python on your system, or contact the instructors for assistance.

To make sure that Python is installed and working, we’ll need to open a terminal in our operating system. In Codio, this can be found by clicking the Tools menu at the top, and then choosing Terminal. More information can be found in the Codio Documentation. There may also already be one open for you if you are reading this content from within Codio.

If you are working on your own computer, you’ll need to open a terminal on your operating system. This should have been covered in the previous lab when you installed Python. On Windows, look for Windows Terminal or Windows PowerShell (not the old Command Prompt, which requires different commands). On Mac or Linux, look for an application called Terminal. Throughout this course, we’ll call these windows the terminal, even though they may have slightly different names in each operating system.

Once we have the terminal open, we should see something like one of these examples:

At this point, we should see a place with a blinking cursor, where we can type our commands. This is called the command prompt in the terminal. The first thing we can do is check to make sure Python is properly installed, and we can also confirm that it is the correct version. To do this, we’ll enter the following command and press enter to execute it:

python3 --version

Hopefully, that command should produce some output that looks like this:

Here, we see that the currently installed Python version is 3.10.6. As long as your Python version number begins with a 3, you have correctly installed Python and are able to run it from the terminal. So, we can continue to the next part of this lab.

If you aren’t able to run Python or aren’t sure that you have the correct version, contact the instructor for assistance!

Navigating in the Terminal

When we open a terminal, it will usually start in our user’s home folder. This may mean different locations for different operating systems. For example, if our current user’s name is <username>, the terminal will usually start in this location for each operating system:

• Windows: C:\Users\<username>
• Linux: /home/<username>
• Mac: /Users/<username>
• Codio: /home/codio/workspace

The directory that is open in the terminal is known as the working directory. We can use the pwd command to determine what our current directory is:

In this example, we are looking at the Codio terminal, so our working directory is /home/codio/workspace.

Next, we can see the files and directories contained in that directory using the ls command. Here’s the output of running this command in Codio:

In the output, we can see that there is a file named README.txt and a directory named python. In Codio, we’ll place all of our files in the python directory, so we can open that using the cd python command:

Notice how the python directory is now included in the command prompt in the terminal. Most terminals will show the working directory in the command prompt in some way.

That’s the basics of navigating in the terminal, but there is much more to learn. If you’d like to know more, consider checking out some of the resources linked below!

Resources

• Basic Linux Navigation & File Management from DigitalOcean
• Beginner’s Guide to the Bash Terminal by Joe Collins on YouTube

Print Statement

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Before we learn to write our first program, let’s start by introducing some important vocabulary terms:

• string: A string in programming is any text that is stored as a value. We typically represent strings by placing them inside double quotes "" in our code and elsewhere.
• value: A value is a piece of data that our program is storing and manipulating. In our pseudocode, values consist of either numbers or strings.
• keyword: A keyword is a reserved word in a programming language that defines a particular statement, expression, structure, or other use. As we’ll learn later, we cannot use these keywords as variable or procedure names.
• statement: A statement refers to a piece of code that performs an action, but doesn’t result in any value. Most complete lines of code are considered statements.
• expression: An expression, on the other hand, is a piece of code that, when evaluated, will result in a value that can be used or stored. An expression can even contain multiple expressions inside of it!

We’ll use these terms to help us understand different parts of the code in our programs. Now that we’ve covered them them, we can start to discuss the various statements in Python.

The first statement that we’ll cover in the Python programming language is the print(expression) statement. This statement is used to display output to the user via the terminal. So, when Python runs this statement, it will evaluate the expression to a single value, and then print that value to the terminal.

For example, the simplest Python code would be a simple Hello World program, where we use the print(expression) statement to display the text "Hello World" to the user:

print("Hello World")

Notice that the expression part of the statement contains "Hello World" in quotation marks? That is because "Hello World" is text, so we should put it in quotes and make it into a string in our code. Also, since the string "Hello World" can be treated like a value, we can also say it is an expression, and therefore we can use it in the expression part of the statement. This may seem pretty straightforward now, but as our programs become more complex it is important to think about what pieces of code can be treated as values, expressions, and statements.

When we run that program in Python, we’ll see the following output:

Hello World

If you look at the output, you might notice something strange - the text on our user interface doesn’t include the quotation marks "" that the expression "Hello World" contained. When we display text to the user, Python will remove the quotation marks from the beginning and the end of the string, and just display the text inside. Pretty handy!

Let’s go through the full process of writing and running that program in Python!

Writing a Python Program

The first step to create a program in Python is to create a text file to store the code. This file should have the file extension .py to indicate that it is a Python program. So, we’ll need to create that file either on our computers or in Codio or another tool if we are using one. For example, in Codio we can create the file in the python folder by right-clicking on it and selecting the New File option. We’ll name the file hello.py:

Once we’ve created that file, we can then open it by clicking on it. In Codio and in other online tools, it will open in the built-in editor. On a computer, we’ll need to open it in a text editor specifically designed for programming. We recommend Visual Studio Code, which is available on all platforms. Tools like the built-in Notepad tool on Windows, or a word processor like Word or Pages do not work for this task.

In that file, we’ll simply place the code shown above, like this:

That’s all there is to it!

Running a Python Program

Once we’ve written the code, we can open the Terminal and navigate to where the code is stored in order to run the program. On Codio, we’ll just use the cd python command to enter the python directory. On a computer, we’ll need to navigate the file system to find the location where we placed our code. We highly recommend creating a folder directly within the home directory and placing all of our code there - that will make it easy to find!

Once we are in the correct directory, we can use the ls command to see the contents of that directory. If we see our hello.py file, we are in the correct location:

If we don’t see our file, we should make sure we’ve saved it and that our current working directory is the same location as where the file is stored.

Finally, we can execute the file in Python using the python3 command, followed by the name of the file:

python3 hello.py

If everything works correctly, we should see output similar to this:

There we go! We’ve just run our first program in Python! That’s a great first step to take.

Of course, there are lots of ways that this could go wrong. So, if you run into any issues getting this to work, please take the time to contact an instructor and ask for assistance. This process can be daunting the first time, since there are so many things to learn and so many intricacies we simply don’t have time to cover up front. Don’t be afraid to ask for help!

Using Print

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One important key to remember: by default, the print(expression) statement in Python will add a newline to the end of the output by default. This means that multiple print(expression) statements will print on multiple lines. Let’s look at some examples!

Throughout this course, we’ll show many different code examples and their output here in the lab. To test them out, feel free to copy the code examples to a Python file and run it yourself. You can even tweak them to do something new and see how Python interprets different pieces of code. In the end, the best way to learn programming is to explore, and running these examples on your own is a great way to get started!

Example 1 - Multiple Statements

First, let’s write a simple program that prints 4 letters separated by spaces:

print("a b c d")

Just like our “Hello World” program, when we run this program, we’ll see that string printed in the user interface:

a b c d

Ok, that makes sense based on what we’ve previously seen. The print(expression) statement will simply display any string expression in our user interface.

Of course, programs can consist of multiple statements or lines of code. So, what if we write a program that contains multiple print(expression) statements, like this one:

print("one")
print("two")
print("three")
print("four")

What do you think will happen when we try to execute this program on our “mental model?” Have we learned a rule that tells us what should happen yet? Recall on the previous page we learned that it will print the value on the user interface, but that’s it. So, when we execute this program, we’ll see the following output:

one
two
three
four

That’s a very interesting result! We can see that four separate print(expression) statements will generate four lines of output by default. Each statement is printed on its own line. So, in Python we can print multiple lines of output simply by using multiple print(expression) statements!

Example 2 - Multiple Lines from Single Statement

What if we want to print output on multiple lines using a single print(expression) statement? How can we do that? In this case, we need to introduce a special symbol, the newline symbol. In most programming languages, the newline symbol is represented by a backslash followed by the letter “n”, like \n, in a string. When our user interface sees a newline symbol, it will move to the next line before printing the rest of the string. The newline symbol itself won’t appear in our output.

For example, we can update our previous program to contain newline symbols between each letter:

print("a\nb\nc\nd")

This might be a bit difficult to read at first, but as we become more and more familiar with reading code, we’ll start to see special symbols like the newline symbol just like any other letter. For now, we’ll just have to read closely and make sure we are on the lookout for special symbols in our text.

When we run this program, we should see the following output on our user interface:

a
b
c
d

There we go! We’ve now figured out how to print text on multiple lines using a single print(expression) statement.

Example 3 - Multiple Statements on Same Line

What if we want to display multiple print(expression) statements on the same line? To do that, we must add an additional option to the print(expression) statement - the end option.

For example, the following code will produce output all on the same line:

print("Hello ", end="")
print("World!")

In this example, we set end to be an empty string "". When we run this program, we’ll get the following output:

Hello World!

In fact, in Python, the print(expression) statement is an example of a function in Python. Functions are just repeatable procedures in our code - when we use them, we write the name of the function, followed by a set of parentheses and then inputs, or “arguments,” separated by commas within the parentheses. This is known as “calling” a function. So, in actuality, the expression in the print(expression) statement is just the first argument when we call the print function.

Therefore, the end option that we showed above is just a second argument that is optional - it simply lets us choose what to put at the end of the output. By default, the end parameter is set to the newline symbol \n, so if we don’t provide an argument for end it will just add a newline at the end of the value.

We can set the value of end to be any string. If we want to include a space at the end of the output, we can add end=" " to the print function call.

In this course, we won’t spend much time talking about optional parameters and default values in Python functions, but it is important to understand that statements like print are actually just Python functions behind the scenes!

Print Practice

Let’s try some simple practice problems. These problems are not graded - they are just for you to practice before doing the real exercises in the lab itself. You can find the answers below each question by clicking the button below each question.

1.1 Reading Code

What is the output that is displayed to the user after running the following Python code:

print("Kansas State University")
print("Manhattan ", end="")
print("KS", end=" ")
print("66506")
print("KSU!\nGo Cats!")

Kansas State University
Manhattan KS 66506
KSU!
Go Cats!

1.2 Constructing Code

We want to write a program that produces the following output:

*
* *
* * *

Rearrange the following Python statements to create a program that produces that output. You may not use all of these statements in your answer.

print("*")
print("*")
print("*")
print("*", end=" ")
print("*", end=" ")
print("*", end=" ")

print("*")
print("*", end=" ")
print("*")
print("*", end=" ")
print("*", end=" ")
print("*")

1.3 Writing Code

Write a Python program that will display the last five letters of the alphabet, one per line.

print("v")
print("w")
print("x")
print("y")
print("z")

Variables

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Now that we’ve learned how to use the print(expression) statement, let’s focus on the next major concept in Python, as well as any other programming language: variables.

The word variable is traditionally defined as a value that can change. We’ve seen variables like $ x $ used in Algebraic equations like $ x + 4 = 7 $ to represent unknown values that we can try to work out. In programming a variable is defined as a way to store a value in a computer’s memory so we can retrieve it later. One common way to think of variables is like a box in the real world. We can put something in the box, representing our value. Likewise, we can write a name on the side of the box, corresponding to our variable’s name. When we want to use the variable, we can get the value that it currently stores, and even change it to a different value. It’s a pretty handy mental metaphor to keep in mind!

In a later lab, we’ll learn how to use operators to manipulate the values stored in variables, but for right now we’re just going to focus on storing and retrieving data using variables.

Creating Variables

To use a variable, we must first create one. In Python, we create a variable in a special type of statement called an assignment statement. The basic structure for an assignment statement is a = expression. When our Python interpreter runs this statement, it will first evaluate expression into a single value. Then, it will store that value in the variable named a.

For example, let’s consider the Python statement:

x = "Hello World"

In that statement, we are storing the string value "Hello World" in the variable named x. Pretty handy!

Now, let’s cover some important rules related to assignment statements:

1. Assignment statements are always written with the variable on the left, and an expression on the right. We cannot reverse the statement and say expression = x in programming like we can in math. In mathematical terms, this means an assignment statement is not commutative.
2. The left side of an assignment statement must be a location where a value can be stored. For now, we’re just going to work with single variables, so we don’t have to worry about this yet. In a later lab, we’ll introduce lists as another way to store data, and we’ll revisit this rule.
3. The right side of an assignment statement must be an expression that evaluates to a value that can be stored in the location given on the left side. Right now we’re only working with string values, so we don’t have to worry about this rule yet. We’ll come back to it in a future lab.

Using Variables

Once we’ve created a variable, we can use it in any expression to recall the current value stored in the variable. So, we can extend our previous example to store a value in a variable, and then use the print(expression) statement to display it’s value. Here’s what that would look like in Python:

x = "Hello World"
print(x)

Notice that we don’t put quotation marks " around the variable x in the print(expression) statement. This is because we want to display the value stored in the variable x, not the string value "x". So, when we run this code, we should get this output:

Hello World

To confirm, feel free to try it yourself! Copy the code above into a Python file, then use the python3 command in the terminal to run the file and see what it does. Running these examples is a great way to learn how a computer actually executes code, and it helps to confirm that your “mental model” of a computer matches how a real computer operates.

Updating Variable Values

Python also allows us to change the value stored in a variable using another assignment statement. For example, we can write some Python code that uses the same variable to print multiple outputs:

a = "Output 1"
print(a)
a = "Output 2"
print(a)

When we run this code, we’ll see the following output:

Output 1
Ouptut 2

So, just like we learned above, when we evaluate a variable in code, it will result in the value currently stored in that variable at the time it is evaluated. So, even though we are printing the same variable twice, each time it is storing a different value. Recall that this is why we call items like a a variable - their value can change!

Variable Names

Finally, Python has a few simple rules that determine what names can be used for variables in our code. Let’s quickly review those rules, as well as some conventions that most Python developers follow when naming variables.

First, the rules that must be followed:

1. Variable names must begin with either a letter or an underscore _.
2. Variable names may only contain letters, numbers, and underscores _.
3. Variable names are case sensitive.

Next, here are the conventions that most Python developers follow for variable names, which we will also follow in this course:

1. Variable names beginning with an underscore _ have a special meaning. So, we won’t create any variables beginning with an underscore right now, but later we’ll learn about what they mean and start using them.
2. Variables should have a descriptive name, like total or average, that makes it clear what the variable is used for.
3. Variables should be named using Snake Case. This means that spaces are represented by underscores _, as in number_of_inputs
4. Try to use traditional variable names only for their specific uses. Some examples of traditional variable names:
   1. tmp or temp are temporary variables.
   2. i, j, and k are iterator variables (we’ll learn about those later).
   3. x, y, and z are coordinates in a coordinate plane.
   4. r, g, b, a are colors in an RGB color system.
5. Variables should not have the same name as keywords or any built-in statements or expressions in the language.
   1. For example, Python has a print statement, so we should not name a variable print in our language.
6. In general, longer variable names are more useful than short ones, even if they are more difficult to type.

These conventions are not strict requirements enforced by the Python language itself, but they are general rules to help us write code that is meaningful and easy to read.

Finally, don’t forget that some of the code examples in this course will NOT follow these conventions, mainly because long, descriptive variable names might give away the purpose of the code itself. We’ll still follow the rules that are required, but in many cases we’ll use simple variable names so that the focus is learning to read the structure of the code, not inferring what it does based solely on the names of the variables.

Code Tracing

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As we learn to write more complex programs in Python, it is important to make sure we can still mentally execute the code we are writing in our “mental model” of a computer before we actually run it on a computer. After all, if we don’t have at least an idea of what the code actually does before we write it, we really haven’t learned much about programming!

Thankfully, when working in a real programming language such as Python, there are many tools to help us visualize how the code works when we run it. This helps us continue to develop our “mental model” of a computer by looking behind the scenes a bit to see what is happening when we run our code.

One such tool is Python Tutor, a website that can quickly run short pieces of Python code to help us visualize what each line does and how it works. This tool is also integrated directly into Codio!

Python Tutor Example

Let’s look at an example of how Python Tutor compares to the manual code traces we performed in a previous lab. For this example, we’re going to use the following code:

x = "Hello"
y = x
print(y, end=" ")
x = "World"
print(x, end=", ")
print(y)

This visualization can be found by clicking this Python Tutor Link to open Python Tutor on the web.

Stepping Through Code

The initial setup for Python Tutor is shown in the image below:

This looks similar to the setup we used when performing code tracing with pseudocode. We have an arrow next to our code that is keeping track of the next line to be executed, and we have areas to the side to record variables and outputs. In Python Tutor, the variables are stored in the Frames section. We’ll learn why that is important later in this lab when we start looking at Python functions.

So, let’s click the Next > button once to execute the first line of code. After we do that, we should see the following setup in Python Tutor:

That line is an assignment statement, so Python Tutor added an entry in the Frames section for the variable x, showing that it now contains the string value "Hello". It placed that variable in a frame it is calling the “Global frame,” which simply contains variables that are created outside of a function in Python.

When we click the Next > button again, we should see this:

Once again, the line that was just executed is an assignment statement, so Python Tutor will add a new variable entry for y to the list of variables. It will also store the string value "Hello". Just like before, notice that the variable y is storing the same value as x, but it is a copy of that value. The variables are not connected in any other way.

We can click Next > again to execute the next line of code:

Once this line is executed, we’ll see that it prints the value of the variable y to the output. Python Tutor will look up the value of y in the Frames section and print it in the output, but it won’t evaluate the expression in the code like we did when we performed code tracing in pseudocode. It’s a subtle difference, but it is worth noting.

Once again, we can click Next > to execute the next assignment statement:

This statement will update the value stored in the variable x to be the string value "World". After that, we can run the next statement:

That statement prints the value of x to the output, followed by a comma , and a space as shown in the end argument provided to the print function. Finally, we can click Next > one more time to execute the last line of code:

This will print the value of y to the output. Once the entire program has been executed, we should see the output Hello World, Hello printed to the screen.

The full process is shown in the animation below:

Using tools like Python Tutor to step through small pieces of code and understand how the computer interprets them is a very helpful way to make sure our “mental model” of a computer accurately reflects what is going on behind the scenes when we run a piece of Python code on a real computer. So, as we continue to show and discuss examples in this course, feel free to use tools such as Python Tutor, as well as just running the code yourself, as a great way to make sure you understand what the code is actually doing.

Variables Practice

Let’s try some simple practice problems. These problems are not graded - they are just for you to practice before doing the real exercises in the lab itself. You can find the answers below each question by clicking the button below each question.

1.4 Reading Code

Write the output that is displayed to the user after running the following Python code:

a = "two"
b = a
c = a
print(c, end=" ")
c = "one"
b = c
a = "three"
print(a, end=" ")
print(c)

two three one

1.5 Constructing Code

We want to write a program that produces the following output:

and a 1
and a 2
and a 3

Rearrange the following Python statements to create a program that produces that output. You may not use all of these statements in your answer.

x = "1"
x = "2"
x = "3"
print(x)
print(x)
print(x)
print("and a", end=" ")
print("and a", end=" ")
print("and a", end=" ")

x = "1"
print("and a", end=" ")
print(x)
x = "2"
print("and a", end=" ")
print(x)
x = "3"
print("and a", end=" ")
print(x)

1.6 Writing Code

We wish to write a Python program that displays the following output:

abba banana bandana

We currently have the following code:

a = "abba"
b = "banana"
c = "bandana"

# more code goes here

print(c, end=" ")
print(b, end=" ")
print(a)

Complete the Python program by writing code where the # more code goes here comment is found so that the desired output is produced. See if you can do it by only using assignment statements and variables, without using any new string values. (Hint: you’ll need to create a least one new variable!)

a = "abba"
b = "banana"
c = "bandana"

# more code goes here
temp = a
a = c
c = temp

print(c, end=" ")
print(b, end=" ")
print(a)

Summary

Python

We introduced some basic statements and structures in the Python programming language. Let’s quickly review them!

Print Statement

The print(expression) statement is used to print output on the terminal.

1. It will evaluate expression to a value, then display it to the screen.
2. By default, it will add a newline to the end of the output.
3. We can change that using the end parameter, such as print(expression, end="") to remove the newline.

Assignment Statement

The assignment statement, like a = expression is used to create variables and store values in the variables.

1. The variable must be on the left side, and the right side must be an expression that evaluates to a single value.
2. If the variable does not exist, it is created. Otherwise, the current value of the variable is replaced by the new value.
3. Variable names must begin with a letter or underscore, and may only contain letters, numbers, and underscores.
4. Variable names beginning with an underscore have a special meaning, so we won’t use them right now.

Integers

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So far, we’ve only worked with string values in Python. Strings are a very useful data type in programming languages such as Python, but they are very limited in their use. Recall that a data type simply defines how a particular value is stored in a computer. The str data type is used to store string values in Python.

Python supports many different data types for handling various data that we’d like to store and manipulate in our programs. In this lab, we’re going to cover the two basic types used for storing numbers in Python, the int or integer type, and the float or floating-point type.

Integers

In mathematics, an integer is a whole number, such as $ 3 $, $ -5 $, or even $ 0 $. Basically, any positive or negative number that doesn’t include a fractional or decimal portion is a whole number, and therefore it is an integer. In Python, those numbers can be stored in the int data type.

In Python, we can store an integer value in a variable using an assignment statement:

x = 5

That statement will store the integer value $ 5 $ in the variable x. Notice that the value $ 5 $ does not have quotation marks around it. This is because we want to store the integer value $ 5 $ and not the string value "5" in the variable. Also, as we learned earlier, this is why we cannot create variable names that begin with a number - since numerical values start with a number, this is how Python can tell the difference between a numerical value and a variable.

We can also store negative numbers in a variable by placing a negative symbol - in front of the numerical value:

y = -8

We’ll need to be careful and make sure that there is a space after the equals sign =, but no space between the negative symbol - and the number after it. Otherwise, the negative symbol could be confused for the minus symbol, which is an operator that we’ll learn about later in this lab.

In Python, there is effectively no maximum size for an integer, so we can store any arbitrarily large whole number (either positive or negative) in an int variable.

Floats

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The other type of number we can store in Python is a floating-point number. We won’t go into too much detail about floating-point values here, since you’ll learn about them elsewhere in this class. For the purposes of programming, the only thing to know about floating-point numbers is that they are used to represent numbers that include a fractional or decimal portion. In Python, these values are stored in the float data type.

To create a variable that stores a floating-point value in Python, we can use an assignment statement that includes a value with a decimal point, like this:

a = 5.8

We can also create negative values using the negative symbol -:

b = -7.987

Finally, it is possible to store a whole number in a floating-point value by simply adding a decimal point and a 0 at the end of the value, as in this example:

c = 42.0

Later in this lab, we’ll see a couple of situations where that may be useful.

For now, we’re just going to assume that Python can easily handle any reasonable number we want it to store in a float variable, but there are some limits to the size and accuracy of those numbers. To reach these limits, we usually have to be dealing with numbers that have $ 100 $ or more digits, either before or after the decimal place. So, for the purposes of this class, those limits really won’t apply to what we’re doing. You’ll learn about these limits in detail in later programming classes.

Types

Resources

• Slides

Determining Variable Type

One thing that is very useful to know how to do in Python is determining the type of data stored in a variable. Python is very flexible, and we can store any type of data in any variable. In fact, a variable’s data type can even change in Python, which is something that many other programming languages won’t allow. Technically speaking, we would say that Python uses strong typing, which means that each variable has a known data type that we can find, and dynamic typing, meaning that the type of the variable can change while the program is running.

To determine the type of a variable, we can use the type(expression) function in Python. We can simply place any variable or expression in the expression argument, and then it will tell us the type of the value that results from evaluating that expression. Then, we can simply use the print() function to print it to the screen. We won’t use this in our programs themselves, but it can be helpful for debugging purposes or to just better understand what is going on with data types.

Here’s a quick example program showing the type() function in Python:

x = "Hello"
y = 5
z = 6.7
print(type(x))
print(type(y))
print(type(z))

When we execute this code in Python, we should see the following output:

<class 'str'>
<class 'int'>
<class 'float'>

Based on that output, we can assume that the variable x is the str data type for strings, y is the int data type for whole numbers, and z is the float data type for decimal numbers. The type() function is pretty handy!

Converting Between Data Types

We can also convert values between the various data types in Python. To do this, there are special functions that match the name of the data types themselves. So, to convert any value to a string, we can use the str() function. Likewise, to convert anything to an integer, we can use the int() function. And finally, to convert anything to a floating-point value, we can use the float() function.

So, we can extend the previous example a bit by showing how we can convert values between different data types:

x = "5.7"
print(x)
print(type(x))
print()
y = float(x)
print(y)
print(type(y))
print()
z = int(y)
print(z)
print(type(z))

When we run this program, we’ll get this output:

5.7
<class 'str'>

5.7
<class 'float'>

5
<class 'int'>

In this program, we’re starting with the string value "5.7" stored in variable x. So, the first two print() statements will print that string value, and show that x is indeed storing a str data type. Then, we’ll use the float() function to convert the string value "5.7" stored in x to the floating-point value $ 5.7 $ and store that in y. The next two print statements will print that value, and show that y is storing a float data type. Notice that the value printed for both variables x and y looks identical, but the data type of each variable is different!

Finally, we can use the int() function to convert the floating-point value $ 5.7 $ to an integer. In math, when we are asked to convert the number $ 5.7 $ to a whole number, our first instinct is probably to just round up to $ 6 $, since that is the closest value. However, in Python, as in most other programming languages, this function will simply truncate the value instead. Truncating a value simply means we take off the end of the value, so to convert $ 5.7 $ to an integer we just remove the decimal portion, and we’re left with the value $ 5 $. So, in the output above, we see that z stores the integer value $ 5 $, and it is the int data type.

Notice that we are careful to say that the int() function will truncate the value, and not that it will round down. This is due to how Python handles negative numbers like $ -5.7 $. When converting that to an integer, it will also truncate it to $ -5 $ instead of rounding down to $ -6 $. So, we use the word truncate as the best way to describe the int() function.

Exceptions

Since we are running our Python programs on a real computer, we have to be a bit careful about how we use these functions. Specifically, if we try to convert a value to a different data type and Python can’t figure out how to do that, we’ll cause an exception to occur. An exception in programming is any error that happens when the computer tries to run our code.

For example, what if we try to convert the string value "5.7" directly to an int data type, as in this example:

a = "5.7"
print(a)
print(type(a))
print()
b = int(a)
print(b)
print(type(b))

When we try to run this code in a file, such as the tutor.py file shown here, we’ll see this output printed on the terminal:

5.7
<class 'str'>

Traceback (most recent call last):
  File "tutor.py", line 5, in <module>
    b = int(a)
ValueError: invalid literal for int() with base 10: '5.7'

Uh oh! That’s not good. In the output, we can see that we’ve caused a ValueError, which is an exception that happens when we try to use a value in an incorrect way. So, we’ll need to carefully look at our code to see if we can find and fix the error.

Thankfully, in the output, it will tell us that the error occurred on line 5 of the file tutor.py, so we can open that file and scroll to that line of code:

b = int(a)

This is where the error occurred. There are several ways we can fix it. The easiest would be to simply convert a to a floating-point value using the float() function instead.

Learning how to find and fix these exceptions is a key part of learning how to program. We’ll inevitably run into a few exceptions as we start to build larger and more complex programs. In this course, most exceptions can be easily handled simply by working carefully through the code, but every once in a while we may run into an exception that is truly difficult to solve. That’s one of the important things to remember when learning how to program - it is sometimes much easier to cause an exception than it is to figure out how to fix it, and sometimes you may need to reach out for help to get past a particularly tricky exception. So, don’t be afraid to ask the instructors or TAs for help if you get stuck on an exception. Many times, it’s a great chance for you to learn some new programming skills.

Basic Operators

Resources

• Slides

Now that we have the ability to store numerical data in variables in Python, we should also learn how to manipulate that data into something new. To do that, let’s learn about operators. An operator in programming is a special symbol that can be used in an expression to manipulate the data in some way.

Most operators are binary operators, which means they perform an operation that uses two values as input and produces a single value as output. In fact, in some programming languages, the operators themselves are implemented as functions in the language! Finally, we have to be careful not to confuse these with bitwise operators, which are operators perform operations on the binary value stored in a variable. We won’t cover those operators in this class, but the terminology is a bit confusing.

An expression containing a binary operator typically follows this format:

<expression> <operator> <expression>

As before, the <expression> parts can be any valid expression in the language that can be reduced to a single value, and the <operator> part is typically a single symbol, but it can also be a short keyword as well.

Thankfully, these operators should all be very familiar to us from mathematics already, so this is just a quick discussion of how they can be used in programming.

Addition and Subtraction

For starters, we can use the plus + and minus - symbols as operators to perform addition and subtraction in Python, just like in math. For example, we can add two variables together to create a third variable as shown in this example:

a = 5
b = 7
c = a + b
print(c)

When we run this code in the Python interpreter, we should get this result:

12

Likewise, we can subtract two variables using the minus symbol - as shown here:

x = 24
y = 10
z = x - y
print(z)

This code should produce this output:

14

Multiplication and Division

In Python, we use the asterisk *, sometimes referred to as the star symbol, to multiply two values together. For example, we can find the product of two values as shown in this Python block:

a = 6
b = 7
c = a * b
print(c)

When we run this code, we should see the following result displayed to the user:

42

Division is performed using the slash / symbol. A great way to think of division in programming is just like a fraction, since it uses the same symbol between the two numbers. For example, if we execute this code:

x = 27
y = 3
z = x / y
print(z)

we would see this output:

9.0

What if the division would result in a remainder? In that case, we’ll simply use decimal values in Python so that the result is exactly correct. For example, if we try to divide $ 19 $ by $ 5 $, as in this example:

a = 19
b = 5
c = a / b
print(c)

When we run this code, the Python interpreter will produce the following output:

3.8

So, as we can see, all of these operators in Python work exactly like their counterparts in math. So, we can easily use them in a way that should be very familiar to us.

New Operators

Resources

• Slides

Modulo Operator

The Python programming language also includes a few math operators that are not commonly used outside of programming.

The first one is the modulo operator. The modulo operator is used to find the remainder of a division operation. If we think back to math again, we’ve probably learned how to perform long division when dividing two values. At the end, we might be left with a remainder, or a portion of the first number that is left over after the operation is complete. In many computer programs, that value is very useful, so we have a special operator we can use to find that value. In Python, we use the percent symbol % as the modulo operator.

For example, if we want to find the remainder after dividing $ 19 $ by $ 5 $, we would use the following code:

x = 19
y = 5
z = x % y
print(z)

When we run this code, we would get the following output:

4

This is because the value $ 5 $ will fit into the value $ 19 $ only $ 3 $ times, and then we’ll have the value $ 4 $ left over. Mathematically, we are saying that $ 19 / 5 = (3 * 5) + 4 $. Since $ 4 $ is the leftover portion, it is the resulting value when we use the modulo operator.

In this course, we’ll only worry about how the modulo operator works when applied to positive whole numbers. In practice, it can be applied to any numerical value, including decimal values and negative numbers, but those values are not really useful in most cases. So, to keep things simple, we’ll only use positive whole numbers with this operator.

Exponentiation Operator

The next operator introduced by Python is the exponentiation operator. The double star ** operator is used to represent exponentiation, sometimes referred to the power operator. In Python, the expression 2 ** 3 would be written mathematically as $ 2^3 $, which is the operation of taking $ 2 $ to the power of $ 3 $, or multiplying $ 2 $ by itself $ 3 $ times.

Here’s a quick example of using the ** operator in code:

x = 5 ** 3
print(x)
print(type(x))

When this code is run, we see the following output:

125
<class 'int'>

||| growthhack

Don’t Use the Carat ^!

Many other programming languages and tools use the carat ^ character to represent the exponentiation operation. However, in Python, the carat ^ character is used to represent the bitwise XOR operation. So, we must be careful not to accidentally use the ^ operator when we actually mean to use the ** operator for exponentiation.

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Integer Division Operator

Finally, Python also includes the integer division operator, represented by two forward slashes //, which is used to perform division that truncates the result to an integer. However, as we’ll see on the next page, if either of the values in the operation is a float value, it will return a value that is a float data type, even though the result is an integer value.

Let’s look at an example with that operator in code:

a = 17.5 // 4.5
print(a)
print(type(a))

As we expect, when we run this program, we’ll get the following output:

3.0
<class 'float'>

So, we see that this operator will return a float value, even though it is truncating the result to an integer, simply because the input values contained a float.

Resulting Types

Resources

• Slides

Since Python has multiple numeric data types, there are some rules that govern which data type is produced as a result of various math operations. Thankfully, the rules themselves are pretty straightforward once they are explained.

Resulting Data Types - Same Type

The basic rule to remember, if a mathematical operator is applied to two variables of the same data type, the result will also be that data type.

Let’s see what that means in practice. Here’s a quick example in Python using the multiplication operator on two integer values:

x = 5
y = 10
z = x * y
print(z)
print(type(z))

When we run this code, we should see the following output:

50
<class 'int'>

Since both x and y are the int data type, the result of x * y will also be an int value, so the variable z will have that data type, as shown in this example.

However, there is one exception to this rule, which is the division operator /. In Python, the division operator will always return a float value, even if it is a whole number. Here’s an example that demonstrates that:

a = 9
b = 3
c = 4
x = a / b
print(x)
print(type(x))
print()
y = a / c
print(y)
print(type(y))

When we run this program, we’ll see the following output:

3.0
<class 'float'>

2.25
<class 'float'>

So, as we can see, even though we are dividing two int values, we’ll get a float value as a result each time we use the division operator.

Following the rule above, if we perform a mathematical operation between two float values, the resulting value will always be a float as well:

a = 2.5
b = 4.5
c = a + b
print(c)
print(type(c))

Running this code will produce this output:

7.0
<class 'float'>

So, even though the result is a whole number, the value that is stored is the float data type.

Resulting Data Types - Different Type

The other rule to remember is anytime an operation involves a float value and an int value, the result will be a float. So, if there are mixed types, Python will default to the float data type.

This can be seen in the following example:

a = 5
b = 2.0
c = a - b
print(c)
print(type(c))

When this code is executed, the output should look like this:

3.0
<class 'float'>

Once again, even though the result is a whole number, because the variable b is a float value, the entire result will also be a float value.

Learning the data types that are returned by a mathematical operator can be tricky, but most programmers slowly develop an intuition of how each operator works and what to expect. So, don’t worry too much if this is confusing right now, since it will become much clearer with practice! Also, don’t forget that we can always create a simple test program like the examples shown above to confirm the result for any operation.

Order of Operations

Resources

• Slides

Finally, just like in mathematics, we must also be aware of the order that these operators are applied, especially if they are combined into a single expression. Thankfully, the same rules we learned in mathematics apply in programming as well. Specifically, operators in Python are applied in this order:

1. Operations in parentheses are resolved first, moving from left to right.
2. ** is resolved second, moving from left to right
3. *, /, // and % are resolved third, moving from left to right.
4. + and - are resolved fourth, moving from left to right.

You might recall the “PEMDAS” acronym for remembering the order of operations in math, and thankfully it still applies here. Of course, this means that there are now 4 operators that all fit in the “multiplication and division” portion, so we have to carefully make sure they are all taken care of in the correct way.

Also, as you’ve probably already learned in math, it is always best to add extra parentheses to any expression to make the intent very clear instead of relying on the order of operations. So, when in doubt, use extra parentheses wherever needed!

Let’s work through a quick example just to see the order of operations in practice. Here’s a complex expression in Python that we can try to evaluate:

x = 8 / 4 + 5 * (3 + 1) - 7 % 4

Looking at our order of operations, the first step is to handle any expressions inside of parentheses. So, we’ll first start with the expression (3 + 1) and evaluate it to 4.

x = 8 / 4 + 5 * 4 - 7 % 4

Then, we’ll go left to right and perform any multiplication, division, and modulo operations. This means we’ll evaluate 8 / 4, 5 * 4 and 7 % 4 and replace them with the resulting values:

x = 2.0 + 20 - 3

Notice that 8 / 4 was reduced to 2.0 instead of just 2. This is because the division operator is the one exception to the rule where an operator applied to two integers will result in an integer. The division operator always produces a floating-point value.

Finally, we’ll perform addition and subtraction from left to right. So, we’ll evaluate 2.0 + 20 first, and then subtract 3 from the result of that operation. At the end, we’ll have this statement:

x = 19.0

So, we were able to use our knowledge of the order of operations to evaluate that complex expression to a single value, $ 19.0 $, which will be stored in the variable x.

Math Practice

Let’s try some simple practice problems. These problems are not graded - they are just for you to practice before doing the real exercises in the lab itself. You can find the answers below each question by clicking the button below each question.

2.1 Reading Code

Write the output that is displayed to the user after running the following Python code:

x = 13
z = "5"
y = int(z)
var = (x + y) % y * y - x
var = var / 2
print(var)

Pay special attention to data types! Make sure the answer is presented as the correct type.

1.0

(x + y) % y * y - x
(13 + 5) % 5 * 5 - 13
18 % 5 * 5 - 13

18 % 5 * 5 - 13
3 * 5 - 13
15 - 13

15 - 13
2

2.2 Reading Code

Write the output that is displayed to the user after running the following Python code:

x = 9
y = 3.0
z = 5
ans = (x - z) % 2 // y + z ** (y - 1)
print(ans)

Pay special attention to data types! Make sure the answer is presented as the correct type.

25.0

(x - z) % 2 // y + z ** (y - 1)
(9 - 5) % 2 // 3.0 + 5 ** (3.0 - 1)
4 % 2 // 3.0 + 5 ** 2.0

4 % 2 // 3.0 + 5 ** 2.0
0 // 3.0 + 5 ** 2.0
0.0 + 5 ** 2.0
0.0 + 25.0

2.3 Writing Code

You are teaching a class and would like to put your students into a number of groups. You know how many students are in the class and the number of groups to create, but you aren’t sure how many students should be in each group.

We’ll assume that these values are stored in the students and groups variables, respectively.

Write a Python program to compute the ideal group size for each group in the class. When divided, the groups in the class should have no fewer than size people, and no more than size+1 people, and there should be exactly groups groups total. For example, if there are $ 15 $ people and the desired number of groups is $ 4 $, then code should start with the following two variable assignments.

students = 15
groups = 4

# more code goes here

Your code should produce the following output for these values:

groups of 3 or 4

This is because the ideal group size for $ 4 $ groups out of $ 15 $ people is $ 3 $, and all groups should have either $ 3 $ or $ 4 $ members (in this case, one group of $ 3 $ and the rest groups of $ 4 $).

Write the rest of this Python program. Try different values for the students and groups variables to make sure that your answers are correct!

students = 15
groups = 4

# more code goes here
size = students // groups
print("groups of ", end="")
print(size, end="")
print(" or ", end="")
size = size + 1
print(size)

Summary

In this lab, we covered several major important topics. Let’s quickly review them.

Data Types in Python

• str data type stores text (strings). Use str(expression) to convert an expression to a string if possible.
• int data type stores whole numbers (integers). Use int(expression) to convert an expression to an integer if possible.
• float data type stored decimal numbers (floating-point). Use float(expression) to convert an expression to a floating-point value, if possible.

Math Operators in Python

• + Addition
• - Subtraction
• * Multiplication
• ** Exponentiation (Power)
• / Division
• // Integer Division
• % Modulo (the remainder of division)

Python Order of operations

1. Parentheses
2. Exponentiation
3. Multiplication, Division, Integer Division, and Modulo from left to right
4. Addition and Subtraction from left to right

Input

Resources

• Slides

The Python programs we’ve written up to this point are very static - each time we run the program, it will perform the same exact operations. Since we’re running these programs on a real computer, it might be helpful to build programs that can read and respond to input from the user, making them much more useful overall. Python includes many different ways to handle user input, but in this lab we’ll just focus on the simple input() function.

Input in Python

The input() function is used to display a prompt the user and then record input. Let’s look at a quick example:

name = input("Enter your name: ")
print("Hello ", end="")
print(name)

Here, we see that the input() function actually accepts a message as an argument, which will be displayed to the user. After the message is printed, the user will be given a cursor to enter text immediately after it. Once the user presses the ENTER key, the input() function will read the input that was entered and store it as a string value or str data type in the variable called name.

For example, if the user inputs Willie Wildcat at the prompt, this program’s output will look like this:

Enter your name: Willie Wildcat
Hello Willie Wildcat

We can see this even more clearly in the terminal. When we first run the program, we’ll see the prompt "Enter your name:" printed, followed by a cursor prompting the user to enter something:

Once the user types the input and presses ENTER, the rest of the program will run:

Now we see the cursor is at the next command prompt, ready to run another program.

Numerical Input

Resources

• Slides

Of course, we can also read numerical input in Python using the input() function. To do this, we must simply use either the int() or float() function to convert the input received as a string to the correct data type.

Here’s a quick example program that requires two inputs for the price and quantity of an item being purchased:

text_one = input("Enter the price of one item: ")
price = float(text_one)
text_two = input("Enter the quantity of items: ")
quantity = int(text_two)
cost = price * quantity
print("The total cost is $", end="")
print(cost)

If the user wishes to purchase $ 3 $ items at the price of $ 2.75 $ per item, then the program’s output would look like this:

Enter the price of one item: 2.75
Enter the quantity of items: 3
The total cost is $8.25

In the program, we simply read each input from the user as a string value, then use the appropriate conversion function to convert it to the correct data type. For numbers including a decimal point, we use float(), but for whole numbers we use the int() function.

This works well if the user enters values that make sense. But, what if the user wishes to purchase a fraction of an item? Will this program work? Unfortunately, if the user enters a value with a decimal point for the second input, it can no longer be converted to an integer and we’ll get an error:

Enter the price of one item: 2.75
Enter the quantity of items: 3.5
Traceback (most recent call last):
  File "tutor.py", line 4, in <module>
    quantity = int(text_two)
ValueError: invalid literal for int() with base 10: '3.5'

For right now, there’s not much we can do about that error other than write programs that clearly tell the user what type of data is expected, but later we’ll learn how to handle errors like this and prompt the user for input again.

String Operators

Resources

• Slides

Concatenate

Python includes an operator that can be used to concatenate, or combine, two strings together, one after another. We can use the plus symbol + between two strings to concatenate them together into a single string, making it much simpler to build more complex strings and outputs.

A simple example is shown below:

first = "Hello"
second = "World"
print(first + second)

When executed, this code will display this output:

HelloWorld

As we can see, using the + operator in Python to concatenate two strings together is a quick and easy way to simplify our print statements.

Concatenating Numbers

Python also requires both sides of the + operator to be strings in order for concatenation to work. So, if we want to concatenate a string with a numeric value, we’ll have to convert it to a string using the special str() function. Here’s an example:

text = "Your total is $"
value = 2.75
print(text + str(value))

When we run this program, we’ll receive the following output:

Your total is $2.75

However, if we forget to convert the value variable to a string, as in this example:

text = "Your total is $"
value = 2.75
print(text + value)

we’ll receive an error instead:

Traceback (most recent call last):
  File "tutor.py", line 3, in <module>
    print(text + value)
TypeError: must be str, not float

So, we’ll have to be careful and make sure that we convert all of our numbers to strings before trying to concatenate them together. Of course, if both sides of the + operator are numbers, then it will perform addition instead of concatenation! There are better ways to concatenate strings though, but we will cover this in another section. For now, the + operator is a quick an easy way to get the job done!

x = 10
print(x)
# python will implicitly do the following instead
# print(str(x))

Repeat

Python also includes an operator that allows us to quickly repeat strings. We can use the asterisk * operator to effectively “multiply” a string. Consider this example:

word = "repeat"
print(word * 2)
print(word * 4)

When we run this program, we’ll see the following output:

repeatrepeat
repeatrepeatrepeatrepeat

This allows us to build outputs that consist of repeated strings. We can also use this to quickly format our output into various shapes and structures!

Escape

Finally, we can also use the escape operator \ to handle some certain special characters in Python strings. We’ve already seen this used in the newline character, which is simply the escape operator \ followed by the letter n, as in \n.

However, we can also use it to include quotation marks in our string itself. Consider the following example:

sentence = "Hello from "the little apple!""
print(sentence)

When we try to run this code, we’ll get the following error:

  File test.py, line 1
    sentence = "Hello from "the little apple!""
                            ^
SyntaxError: invalid syntax

This is because the double quotation mark " is used to show which part of the code is a string value, but the Python interpreter gets confused when we try to include quotation marks inside of our strings. So, we can use the escape operator \ to tell it to treat those quotation marks as part of the string value itself, and not part of the Python code:

sentence = "Hello from \"the little apple!\""
print(sentence)

This program will execute properly and produce the following output:

Hello from "the little apple!"

Notice that the quotation marks now appear correctly in the output, but the escape operators are not shown. There are many other special ways to use the escape operator in Python, all of which can be found in the Python Documentation.

sentence = 'Hello from "the little apple!"'
print(sentence)

F-Strings

Resources

• Slides

Python also includes another method of building strings, which are known as “F-strings”. F-strings allow us to put placeholders in strings that are later replaced with values from variables, effectively creating a way to build “templates” that can be used throughout our program.

The easiest way to see how this works is looking at a few examples. Let’s start with a simple one:

name = input("Enter your name: ")
print(f"Hello {name}")

If the user inputs "Willie Wildcat" when prompted, then this code will produce this output:

Enter your name: Willie Wildcat
Hello Willie Wildcat

There are many important parts to this example, so let’s look at each one individually. First, in the print() statement, we see the string "Hello {name}". This is an example of a template string, which includes a set of curly braces {} as placeholders for data to be inserted. Each template string can have unlimited sets of curly braces. Inside of each set of curly braces, we can place the variable or expression that will be substituted at that location as a string.

Also, we notice that in front of the string, we see the character f. Preceding a string with f outside of the quotation marks will denote the string as an f-string (hence the name), which allows the values inside to be interpolated. Interpolation is the term used when formatting marks in a string, such as the curly braces in an f-string, are interpreted and replaced with the correct values they represent.

Formatting Numbers

The most powerful use of f-strings is to insert numerical values directly into strings without having to convert each value directly to the str data type - the interpolation process handles this for us.

For example, we can update our previous program to use f-strings to display the output in a single print() statement, and we can also add additional information with ease:

text_one = input("Enter the price of one item: ")
price = float(text_one)
text_two = input("Enter the quantity of items: ")
quantity = int(text_two)
cost = price * quantity
print(f"{quantity} items at ${price} each is ${cost} total")

When we execute this program, we’ll see output that looks like this:

Enter the price of one item: 2.75
Enter the quantity of items: 3
3 items at $2.75 each is $8.25 total

This example shows how easy it is to build complex output strings using f-strings. We can expand the template syntax to even round numbers. f"{.2f}" allows us to format a number to two decimal places. The 2 represents the number of places you want to round to and the ‘f’ inside the curly brackets indicates that you are trying to format the number as a floating point number.

text_one = input("Enter Numerator:")
numerator = float(text_one)
text_one = input("Enter Denominator:")
denominator = float(text_one)
result = numerator / denominator
print(f"{numerator} divided by {denominator}, rounded to two decimal places is {result:.2f}")

Enter Numerator: 2
Enter Numerator: 3
2.0 divided by 3.0, rounded to two decimal places is 0.67

If we add a comma into the template string f"{,.2f}" , we can automatically format in commas for thousands, millions, etc. For example:

text_one = input("Enter the price of one item: ")
price = float(text_one)
text_two = input("Enter the quantity of items: ")
quantity = int(text_two)
cost = price * quantity
print(f"{quantity} items at ${price:,.2f} each is ${cost:,.2f} total")

300 items at $2,500.45 each is $750,135.00 total

print("{} items at ${} each is ${} total".format(quantity, price, cost))

Complex Statements

Resources

• Slides

Finally, let’s look at how we can rewrite some of our previous Python programs by combining expressions into more complex statements. Python allows us to perform multiple actions on a single line of code, provided they can all be combined in some way to create a single statement.

Let’s consider the example on the previous page, shown here:

text_one = input("Enter the first number: ")
one = int(text_one)
text_two = input("Enter the second number: ")
two = int(text_two)
one = one * one
two = two * two
total = one + two
print(f"The sum of squares of {one} and {two} is {total}")

There are many ways we can write this program to perform the same work. For example, the process of computing the sum of squares itself can actually be reduced to a single line of code as seen below:

total = (one * one) + (two * two)

We can perform multiple mathematical operations in a single expression, and as long as we either use parentheses or pay attention to the order of operations, we’ll get the expected answer. We don’t have to store the intermediate values in variables, since Python will do that for us when it evaluates the expression.

Likewise, we can put the input() function inside of the int() function, allowing us to read input as a string and convert it to an integer in a single line:

one = int(input("Enter the first number: "))
two = int(input("Enter the second number: "))
total = (one * one) + (two * two)
print(f"The sum of squares of {one} and {two} is {total}")

Finally, we can also move the computation of the sum of squares directly into the f-string. When Python tries to print the f-string, the interpolation process must compute that value before it can print the output.

one = int(input("Enter the first number: "))
two = int(input("Enter the second number: "))
print(f"The sum of squares of {one} and {two} is {(one * one) + (two * two)}")

Functionally, this code will create the exact same output as the previous code, but it will do so using fewer variables and statements. Each statement is simply more complex, consisting of multiple expressions.

Concise and Readable

The real question is: which of these two examples is best? That is, which one is the preferred coding style to learn? The answer is that it really depends - both have their merits, and functionally they will work nearly identically on most modern computers and programming languages.

It really is a question of style - is it better to have more lines of code and variables, clearly spelling out what each step of the process is, or is it better to have shorter programs with more complex lines of code but maybe fewer variables? For beginning programmers, it is usually recommended to follow the first style, using as many variables as needed and focusing on short, concise lines of code over large, complex statements.

However, as we become more accustomed to programming, we’ll find that many times it is easier to read and understand complex statements, and our code can be written in a way that better reflects what it is actually doing.

This is very similar to learning how to read, write, and speak in a new language. We must start with short, concise sentences, and slowly build up our knowledge of more complex statements and grammar rules until we become a fluent speaker.

Overall, the best advice to follow is to make your code readable to both yourself and anyone else who may have to read and maintain the code. As long as it is clear what the code is doing based on what it says, it is probably a good style to follow. As we continue to learn more, we’ll slowly refine our coding style to be one that is easy to follow and understand for everyone.

Input Practice

Let’s try some simple practice problems. These problems are not graded - they are just for you to practice before doing the real exercises in the lab itself. You can find the answers below each question by clicking the button below each question.

3.1 Reading Code

Write the output that is displayed to the user after running the following Python code:

x = input("Enter a number: ")
y = x + "00"
z = int(y)
a = int(z + (z / 100))
print(f"The result is {a}")

Assume the user inputs the string "4" when prompted.

The result is 404

3.2 Reading Code

Write the output that is displayed to the user after running the following Python code:

x = int(input("Enter a number: "))
x = x // 100 * 100 + x % 10
print(f"The answer twice is {x * 2}")

Assume the user inputs the string "121" when prompted.

The answer twice is 202

3.3 Writing Code

Write a Python program that prompts the user to input a verb and a noun that will be stored in separate variables. Then, use those two words to create a simple compliment for the user and print it to the terminal. The compliment should be in the form of:

You "<verb>" my "<noun>"? That's awesome!

For example, if the user inputs "love" and "food", then the string printed by the program should be:

You "love" my "food"? That's awesome!

Hint: Notice that the output includes quotation marks around the two words provided by the user, and an apostrophe (single quote) as well. You’ll need to account for this in your code!

You can expand on this program to prompt the user for additional inputs, and use f-strings to create a Mad Libs style game!

verb = input("Enter a verb: ")
noun = input("Enter a noun: ")
print(f"You \"{verb}\" my \"{noun}\"? That's awesome!")

Summary

Python input

• input(expression) will display the expression to the user as a prompt, then return what was typed by the user as a string value.
• input(expression) is terminated by the user pressing ENTER, so next output starts on a new line.

Python String Operators

• + Concatenation (join strings together). May only be applied to two strings.
• * Repeat (duplicate strings)
• \ Escape Operator for special characters
• f-string are used to format output. Template strings include curly braces as placeholders {} and inside the placeholder is any variable or expression to be printed in that location in the template string.

Complex Statements

Expressions can be combined in many ways within a statement. Expressions can be used as arguments to functions, and more!

Introduction

Resources

• Slides

In this lab, we’re going to introduce another data type in Python, the Boolean data type. Working with Boolean values in programming is a very important skill to learn.

Elsewhere in this course, we’ve already learned about Boolean values and how they work, so we won’t cover that information again here. Feel free to refer to other areas in this course for more background on Booleans, Boolean Logic, Boolean Algebra, and other related topics.

In this lab, we’ll learn how to create Boolean values in Python, as well as the various Boolean operators and comparators that can we can use in our code. Then, we’ll explore how to use conditional statements to affect the control flow of our programs. This allows us to build programs that will perform different tasks based on the input we receive from the user, which is a very key concept in programming. Let’s get to it!

Booleans

Resources

• Slides

In Python, Boolean values are stored in the bool data type. Variables of the bool data type can only store one of two values, True or False. So, a Boolean value is really the simplest data value we can imagine - it is a single binary bit representing a value of True or False.

To create a Boolean variable in Python, we can simply assign those values to a variable in an assignment statement:

a = True
b = False
print(a)
print(type(b))

When we execute that code, we’ll see the following output:

True
<class 'bool'>

In Python, we use the keywords True and False to represent Boolean values. In Python, it is very important to make sure these values are capitalized, otherwise the program will not work properly. Also, since these are keywords and not strings, we don’t need to put them in quotation marks.

Converting Between Data Types

Python includes the special bool() function, which can be used to convert any data type into a Boolean value. The bool() function follows these rules to determine what to return:

1. If the input is the value False, the value 0, the value None, or anything with 0 length, including the empty string, it will return False.
2. Otherwise, for all other values it will return True.

Let’s look at a couple of quick examples. First, let’s try to convert the strings "True" and "False" to their Boolean equivalents:

print(bool("True"))
print(bool("False"))

When this code is executed, we’ll see this output:

True
True

This seems a bit strange, since the string "False" ended up creating the Boolean value True. However, if we look at the rules above, since the string "False" has a length greater than 1, and it is not any of the special values listed above, it should always result in the Boolean value True.

In this example, we can check a couple of special values using the bool() function:

print(bool(0))
print(bool(1))
print(bool(""))

In this case, we’ll see the following output:

False
True
False

Here, we see that the value 0, as well as the empty string "", both result in a value of False. However, the value 1 is True, since it is a non-zero value.

In practice, we won’t use the bool() function directly very often. Instead, if we want to determine if a user inputs a True or False value, we can just use one of the Boolean comparators that we’ll see later in this lab.

Boolean Operators

Resources

• Slides

Python also includes several operators that can be applied to one or two Boolean values. These operators make up the basis of Boolean logic, and allow us to construct complex expressions of Boolean values. Let’s quickly review the three basic Boolean operators present in Python.

And Operator

In Python, we use the keyword and to perform the Boolean and operation. This operator will return True if both input values are also True, otherwise it will return False.

This corresponds to the following truth table:

Here’s a quick example of the and operator in Python:

x = True
y = False
print(x and y)

When we run this Python code, we should see this output:

False

Since the variable y is False, the resulting value is also False.

Or Operator

Likewise, the keyword or is used in Python for the Boolean or operation. This operator will return True if either of the input values is True, but it will return False if neither of them are True.

This corresponds to the following truth table:

Let’s look at an example:

a = False
b = True
print(a or b)

When we execute this code, we’ll get this output:

True

Since b is True, we know that at least one input is True and the result of a or b is also True.

Not Operator

Finally, Python uses the keyword not to represent the Boolean not operation, which simply inverts a Boolean value from True to False and vice-versa.

This corresponds to the following truth table:

Here’s an example:

x = True
print(not x)
print(not not x)

When we run this code, we’ll see this printed to the terminal:

False
True

Since x is True, we know that not x is False. We can then perform the not operation again, on that result, as shown in not not x, which will result in the original value of x, which is True.

Boolean Comparators

Resources

• Slides

Python also uses various comparators to allow us to compare values of many different data types to produce a Boolean value. We can compare numbers, strings, and many other data types in Python using these comparators.

The basic comparators in Python are:

• == equal
• != not equal
• < less than
• <= less than or equal to
• > greater than
• >= greater than or equal to

Notice that the equal comparator in Python now uses two equals signs == instead of a single one. This is because the single equals sign = is used in the assignment statement, and we don’t want to confuse a Boolean comparator for an assignment. So, in Python, as in most other programming languages, we use two equals signs == when comparing values, and one equals sign = when we are storing a value in a variable.

Comparing values

We usually only want to compare values of the same data type using comparators. It really only makes sense to compare strings to strings, and Booleans to Booleans. However, for the numeric data types int and float, we can easily compare values of either data type together in Python.

Consider the following example:

x = 5
y = 5.0
print(x == y)

When we execute this code, we’ll get this output:

True

Even though x is the int data type and y is the float data type, they both store the same numerical value, and our comparators will work just like we expect. So, we can easily compare both integers and floating-point values in our code.

Strings in Python can also be compared using the various comparator operators. When two strings are compared using any type of “less than” or “greater than” comparator, they will be compared according to their lexicographic order. In general, this means that each letter will be ordered based on its value in the ASCII encoding standard.

Let’s look at a quick example:

a = "First"
b = "fir"
print(a < b)

When we run this code, we’ll see this output:

True

This may seem surprising, since we’d expect the word "fir" to come before the word "First" in a dictionary, since it has fewer letters. However, in Python, the letters "f" and "F" are not treated identically. According to the ASCII encoding standard, capital letters have a lower value than lower-case letters, so all words starting with a capital "F" will come before any words starting with a lower-case "f".

This can be a bit confusing, so it is always important to remember that we can always write a quick sample program in Python to test how a particular operator will work for a given set of values. If nothing else, we can always count on our computer to produce the same result for the same operation, no matter if it is part of a larger program or a small test program.

Order of Operations

Finally, Python’s order of operations can be updated to include these Boolean comparators and operators. So, when we see an expression that combines mathematical operators with Boolean operators and comparators, we’ll follow this order:

1. Math operators (according to their order of operations)
2. Boolean comparators
3. not operator
4. and operator
5. or operator

As always, it is considered good practice to include parentheses in any complex expressions to make sure that the intent is clear, regardless of the order of operations.

Boolean Practice

Let’s try some simple practice problems. These problems are not graded - they are just for you to practice before doing the real exercises in the lab itself. You can find the answers below each question by clicking the button below each question.

4.1 Reading Code

What is printed to the terminal when the following Python program is run?

a = 5
b = 10
c = 15
x = b > a and c < b or (not (a + b > c))
y = b + a >= c or c / a > b / a
z = c - b != a and not c % a == 0
print(x)
print(y)
print(z)

Try to work out the solution yourself first before running the code.

True
True
False

4.2 Reading Code

What is printed to the terminal when the following Python program is run?

x = 7
y = 42
z = 6
a = x >= z and y <= x * z or not y % z == 0
b = not (y // x <= z or x - z > 0 or y % x != 1)
c = y // (z + x) < z / 2 and not bool(z)
print(a)
print(b)
print(c)

Try to work out the solution yourself first before running the code.

True
False
False

4.3 Writing Code

Write a Python program that performs the following operations:

1. Prompts the user for four integer inputs and stores them in variables
2. If the first input is strictly smaller than all other inputs, and the last input is strictly larger than all other inputs, print True. If not, print False.

Your program may not use any conditional statements (if statements).

Hint: what comparisons must be made, and what comparisons do not need to be made, to answer these questions definitively?

a = int(input("Enter an integer: "))
b = int(input("Enter an integer: "))
c = int(input("Enter an integer: "))
d = int(input("Enter an integer: "))
x = a < b and a < c and a < d and b < d and c < d
print(x)

4.4 Writing Code

Write a complete program in Python that performs the following operations:

1. Prompts the user for three integer inputs and stores them in variables.
2. If the second and third inputs are multiples of the first, and the third input is a multiple of the second, print true. If not, print false.

Your program may not use any conditional statements (if statements).

Hint: you can use the modulo % operator to determine if one number is evenly divisible by another number. Can this also tell you which number is a multiple of another number?

x = int(input("Enter an integer: "))
y = int(input("Enter an integer: "))
z = int(input("Enter an integer: "))
a = y % x == 0 and z % y == 0
print(a)

If

Resources

• Slides

Now that we understand how to use Boolean values in our programs, it’s time to put those values to use. One way to think of the result of a Boolean expression is that it helps us make a decision in our programs. For example, if we want to do something special in our program when the user inputs the value $ 42 $ into the variable x, then we can write the Boolean expression x == 42 to help us decide if the user input the correct value.

Once we have that decision made, we need some way to tell our program to run a different piece of code based on the outcome of that decision. In Python, as well as most other programming languages, we can use a special construct called an if statement to do this. If statements are one example of a conditional statement in programming.

These conditional statements allow us to affect the control flow of our programs. Effectively, we can use a Boolean expression to determine whether a particular piece of code should be executed or not. In an if statement, we include a Boolean expression and a block of statements. If the Boolean expression evaluates to True, then we execute the code in the block of statements. If it is False, then we skip the block and continue with the rest of the program.

The structure of an if statement in Python is shown below:

if <boolean expression>:
    <block of statements>

In this structure, we have a <boolean expression> that is evaluated. After the Boolean expression is a colon :.

Then, the <block of statements> is included below the if statement’s first line, and it must be indented one level. In Python, the <block of statements> must include at least one line of code, otherwise Python won’t be able to understand it.

Indentation in Python

Let’s briefly discuss indentation in Python, since it is very important and is usually something that trips up new Python developers. Most programming languages use symbols such as curly braces {} to surround blocks of statements in code, making it easy to tell where one block ends and another begins. Similarly, those languages also use symbols such as semicolons ; at the end of each line of code, indicating the end of a particular statement.

These programming languages use those symbols to make it clear to both the developer and the computer where a particular line or block of code begins and ends, and it makes it very easy for tools to understand and run the code. As an interesting side effect, it also means that the code doesn’t need to follow any particular structure beyond the use of those symbols - many of the languages allow developers to place multiple statements, or even entire programs, on a single line of code. Likewise, indentation is completely optional, and only done to help make the code more readable.

Python takes a different approach. Instead of using symbols like semicolons ; and curly braces {} to show the structure of the code, Python uses newlines and indentation for this purpose. By doing so, Python is simultaneously simpler (since it has fewer symbols to learn) and more complex (the indentation has meaning) than other languages. It’s really a tradeoff, though most Python programmers will admit that not having to deal with special symbols in Python is well worth the effort of making sure that the indentation is correct, especially since most other languages follow the same conventions anyway, even if it isn’t strictly required.

So, how can we indent code in Python? Typically, we use four consecutive spaces for each level of indentation. So, below the conditional statement if <boolean expression>: shown above, we would place four spaces before the first line of the <block of statements>, and then continue to do so for each line below that should be included in the block of code.

Thankfully, most text editors used for programming, such as Codio, Atom, Visual Studio Code, and more, are all configured to convert tabs to spaces. So, we can simply press the Tab key on the keyboard to insert the correct amount of spaces for one level of indentation. Likewise, we can usually use Shift+Tab to remove four spaces.

Finally, it is worth noting that there is a special symbol that actually is a tab in text, which is represented as \t in a string. Like the newline symbol, we can’t see it in our UI in most cases, but it is there. Some non-programming text editors, such as Notepad in Windows, will insert that symbol instead of four spaces when we press the Tab key. If we try to run a program that contains those symbols, the Python interpreter may not be able to read our program and will give us an error about inconsistent use of tabs and spaces. If that happens, we’ll need to make sure our program is consistently using only tabs or spaces for indentation. Most editors used for programming have a special function for converting tabs to spaces, and there are lots of online tools available for this as well.

Code Tracing Example - False

Let’s go through a couple of code tracing examples in Python Tutor to see how an if statement works in code.

Consider this program in Python:

x = int(input("Enter a number: "))
if x == 7:
    print("That's a lucky number!")
print("Thanks for playing!")

We can see this example by clicking on this Python Tutor link. At first, our window should look something like this:

When we step through the program, the first line of code will ask the user to input a number, so Python Tutor will show an input box at the bottom of the window:

For this first time through the program, let’s assume the user inputs the string "42" as input:

So, when we click Submit, we’ll see the integer value $ 42 $ stored in the variable x in the Global frame:

At this point, we’ve reached the if statement. The first step is to evaluate the Boolean expression x == 7. Since x is actually storing the value $ 42 $, this statement will evaluate to False. So, when we click the Next button on the state below:

We’ll see that the program arrow jumps past the block of statements in the if statement. So, the next line will simply print the goodbye message:

The entire process is shown in the animation below:

As we can see, when the Boolean expression evaluates to False, we’ll just skip the block of statements inside of the if statement.

Code Tracing Example - True

Now let’s see what happens when the Boolean expression evaluates to True instead. To see that, we can go back to the point where our program is asking for input, as shown below:

This time, we’ll assume the user inputs the string "7" as input.

So, when we click Submit, we’ll see the integer value $ 7 $ stored in the variable x in the Global frame:

This time, when we reach the if statement, we’ll see that the Boolean expression x == 7 will evaluate to True. So, when we click the Next button, we’ll be taken to the block of statements inside of the if statement:

Here, we’ll print the special message for finding a lucky number:

Then, we’ll print the program’s goodbye message:

The entire process can be seen in this animation:

So, when the Boolean expression evaluates to True, we’ll run the code inside the if statement. If it is False, then we’ll just skip past the if statement and continue with the rest of our program.

If Statement Flowchart

Another way to visualize an if statement is using a flowchart. Consider the following Python code:

x = int(input("Input: "))
if x > 0:
  print(x)
print("Goodbye")

This Python code can also be represented as the flowchart shown below:

When we read flowcharts like this, we typically go from top to bottom, starting with the circle labeled “START”. The first step is to read input from the user, and store that value in the variable x.

Then, we reach a decision node, which uses a diamond shape. This node asks if the value in the variable x is greater than 0. Notice that there are two lines coming from the decision node: one labeled “True” and another labeled “False”. They correspond to the possible values that can result from evaluating the Boolean expression x > 0. So, if the result of that expression is True, then we’ll follow the path that exits the diamond from the side labeled “True”, and we’ll output the value x to the terminal. Once that is done, we can follow that path around to the next box that will output the string "Goodbye".

If that result is False, then we’ll follow the downward path labeled “False” and skip the path to the side. Instead, we’ll just reach the line that prints "Goodbye" and then end the program.

As we learn how to use if statements in our code, we might find it easier to use flowcharts or other tools to help understand exactly what our program will do. The path that a program takes through a flowchart like this is called the control flow of the program. We’ll discuss that topic a bit more toward the end of this lab.

If-Else

Resources

• Slides

Python also supports another kind of conditional statement, the if-else statement. An if-else statement contains a single Boolean expression, but two blocks of code. If the Boolean expression evaluates to True, then one block of statements is executed. If it is False, then the other block will be executed.

The general structure of an if-else statement in Python is shown below:

if <boolean expression>:
    <block of statements 1>
else:
    <block of statements 2>

Notice that the else keyword is followed by a colon, just like the first line of the if-else statement. As we’d expect, the first block of statements is executed if the <boolean expression> portion is True and the second block is executed if it is False.

Once again, let’s go through a couple of code traces using Python Tutor to explore how an if-else statement works in Python.

Code Tracing Example - True

Here’s a simple Python program that contains an if-else statement:

x = int(input("Enter a number: "))
if x >= 0:
    print("Your number is positive!")
else:
    print("Your number is negative")
print("Thanks for playing!")

This program accepts an input from the user, converts it to an integer, and then determines if the integer is a positive or negative number. Let’s go through this program a couple of times in Python Tutor to see how it works. As always, you can copy and paste this code into Python Tutor, or click this Python Tutor link to follow along!

We’ll start an input prompt as shown here:

Let’s assume that the user inputs the string "5" here.

That means that the integer value $ 5 $ will be stored in the variable named x:

Now we’ve reached the if-else statement. So, we’ll need to evaluate the Boolean expression x >= 0. Since x is currently storing the value $ 5 $, this expression will evaluate to True. Therefore, we’ll move into the first block of statements in the if-else statement:

From here, we’ll print the message that the user’s input was a positive number, as well as the goodbye message at the end of the program. It will entirely skip the second block of statements in the if-else statement, as we can see here:

A full execution of this program is shown in the animation below:

Code Tracing Example - False

What if the user inputs a negative value, such as $ -7 $? In that case, we’ll be at this state in our program:

From here, we’ll evaluate the Boolean expression x >= 0 again. This time, however, we’ll see that it evaluates to False, so we’ll jump down to the second block of statements inside the if-else statement:

This will print a message to the user that the input was a negative number, and then it will print the goodbye message. We completely skipped the first block of statements:

A complete run through this program is shown in this animation:

So, as we expect, an if-else statement allows us to run one block of statements or the other, based on the value of the Boolean expression. It is a very powerful piece of code, and it allows us to write programs that perform different actions based on the input provided by the user or the values of other variables.

If-Else Statement Flowchart

Another way to visualize an if-else statement is using a flowchart. Consider the following Python code:

x = int(input("Input: " ))
if x >= 0:
  print(x)
else:
  print(-1 * x)
print("Goodbye")

This code can also be represented as the flowchart shown below:

Once again, we start at the top of the flowchart at the circle labeled “START”. From there, we read an input from the user and store it in the variable x. At this point, we reach our if-else statement’s decision node, represented by the diamond. Here, we are using the Boolean expression x >= 0 to determine which branch to follow. So, if the user inputs a positive value, then we’ll follow the path to the right that is labeled “True”, which will simply print the value of x to the screen.

However, if the user inputs a negative value for x, then the Boolean expression will be False and we’ll follow the path to the left labeled “False”. In this branch, we’ll print the value of (-1 * x) to the screen, which simply removes the negative sign from the value in x.

Finally, after each path, we’ll merge back together to run the last piece of code, which prints the "Goodbye" message to the screen.

Notice that there is no way to print both x and -1 * x in the same execution of this program. If we follow the arrows through the flowchart, we see that we can only follow one branch or the other, and not both. This is an important concept to remember when working with if-else statements! The control flow of the program can only pass through one of the two blocks of statements, but not both.

Testing Branches & Paths

Resources

• Slides

One important concept to understand when writing if statements and if-else statements is the control flow of the program. Before we learned about conditional statements, our programs had a linear control flow - there was exactly one pathway through the program, no matter what. Each time we ran the program, the same code would be executed each time in the same order. However, with the introduction of conditional statements, this is no longer the case.

Because of this, testing our programs becomes much more complicated. We cannot simply test it once or twice, but instead we should plan on testing our programs multiple times to make sure they work exactly the way we want. So, let’s go through some of the various ways we can test our programs that include conditional statements.

Simple Example

First, let’s consider this example program:

x = int(input("Enter a number: "))
y = int(input("Enter a number: "))
if x + y > 10:
  print("Branch 1")
else:
  print("Branch 2")
if x - y > 10:
  print("Branch 3")
else:
  print("Branch 4")

We can represent the two if-else statements in this program in the following flowchart as well:

Branch Coverage

First, let’s consider how we can achieve branch coverage by executing all of the branches in this program. This means that we should test the program using different sets of inputs that will run different branches in the code. Our goal is to execute the code in each branch at least once. In this example, we see that there are four branches, helpfully labeled with the numbers one through four in the print() statements included in each branch.

So, let’s start with a simple set of inputs, which will store $ 6 $ in both x and y. Which branches will be executed in this case? Before reading ahead, see if you can work through the code above and determine which branches are executed?

In the first if-else statement, we’ll see the x + y is equal to $ 12 $, which is greater than $ 10 $, making that Boolean statement True. So, we’ll execute the code in branch 1 this time through. When we reach the second if-else statement, we’ll compute the value of x - y to be $ 0 $, which is less than $ 10 $. In this case, that makes the Boolean statement False, so we’ll execute branch 4. Therefore, by providing the inputs 6 and 6, we’ve executed the code in branches 1 and 4.

So, can we think of a second set of inputs that will execute the code in branches 2 and 3? That can be a bit tricky - we want to come up with a set of numbers that add to a value less than $ 10 $, but with a difference that is greater than $ 10 $. However, we can easily do this using negative numbers! So, let’s assume that the user inputs $ 6 $ for x and $ -6 $ for y. When we reach the first if-else statement, we can compute the result of x + y, which is $ 0 $. That is less than $ 10 $, so the Boolean statement is False and we’ll execute the code in branch 2. So far, so good!

Next, we’ll reach the second if-else statement. Here, we’ll compute the result of x - y. This time, we know that $ 6 - -6 $ is actually $ 12 $, which is greater than $ 10 $. So, since that Boolean expression is True, we’ll run the code in branch 3. That’s exactly what we wanted!

Therefore, if we want to test this program and try to execute all the branches, we only have to provide two pairs of inputs:

• $ 6 $ and $ 6 $
• $ 6 $ and $ -6 $

However, that’s only one way we can test our program. There are many other methods we can follow!

Path Coverage

A more thorough way to test our programs is to achieve path coverage. In this method, our goal is to execute all possible paths through the program. This means that we want to execute every possible ordering of branches! So, since our program has 4 branches in two different if-else statements, the possible orderings of branches are listed below:

1. Branch 1 -> Branch 3
2. Branch 1 -> Branch 4
3. Branch 2 -> Branch 3
4. Branch 2 -> Branch 4

As we can see, there are twice as many paths as there are branches in this case! We’ve already covered ordering 2 and 3 from this list, so let’s see if we can find inputs that will cover the other two orderings.

First, we want to find a set of inputs that will execute branch 1 followed by branch 3. So, we’ll need two numbers that add to a value greater than 10, but their difference is also greater than 10. So, let’s try the value $ 12 $ for x and $ 0 $ for y. In that case, their sum is greater than $ 10 $, so we’ll execute branch 1 in the first if-else statement. When we reach the second if-else statement, we can evaluate x - y and find that it is also $ 12 $, which is greater than $ 10 $ and we’ll execute branch 3. So, we’ve covered the first ordering!

The last ordering can be done in a similar way - we need a pair of inputs with a sum less than $ 10 $ and also a difference less than $ 10 $. A simple answer would be to input $ 4 $ for both x and y. In this case, their sum is $ 8 $ and their difference is $ 0 $, so we’ll end up executing branch 2 of the first if-else statement, and branch 4 of the second if-else statement. So, that covers the last ordering.

As we can see, path coverage will also include branch coverage, but it is a bit more difficult to achieve. If we want to cover all possible paths, we should test our program with these 4 sets of inputs now:

• $ 6 $ and $ 6 $
• $ 6 $ and $ -6 $
• $ 12 $ and $ 0 $
• $ 4 $ and $ 4 $

There’s still one more way we can test our program, so let’s try that out as well.

Edge Cases

Finally, we should also use a set of inputs that test the “edges” of the Boolean expressions used in each if-else statement. An edge is a value that is usually the smallest or largest value before the Boolean expression’s output would change. So, an edge case is simply a set of input values that are specifically created to be edges for the Boolean expressions in our code.

For example, let’s look at the first Boolean logic statement, x + y > 10. If we are simply considering whole numbers, then the possible edge cases for this Boolean expression are when the sum of x + y is exactly $ 10 $ and $ 11 $. When it is $ 10 $ or below, the result of the Boolean expression is False, but when the result is $ 11 $ or higher, then the Boolean expression is true. So, $ 10 $ and $ 11 $ represent the edge between True values and False values. The same applies to the Boolean expression in the second if-else statement.

So, to truly test the edge cases in this example, we should come up with a set of values with the sum and difference of exactly $ 10 $ and exactly $ 11 $. For the first one, a very easy solution would be to set x to $ 10 $ and y to $ 0 $. In this case, both the sum and the difference is $ 10 $, so we’re on the False side of the edge. Our program will correctly execute branches 2 and 4.

For the other edge, we can use a similar set of inputs where x is $ 11 $ and y is still $ 0 $. In this case, both the sum and difference is $ 11 $, so each Boolean expression will evaluate to True and we’ll execute branches 1 and 3.

Why is this important? To understand that, we must think a bit about what was intended by this program. What if the program should execute branches 1 and 3 if the value is greater than or equal to $ 10 $? In that case, both of our chosen edge cases should execute branches 1 and 3, but instead we saw that the edge case $ 10 $ executed branches 2 and 4 instead. This is a simple logic error that happens all the time in programming - we simply forgot to use the greater than or equal to symbol >= instead of the simple greater than symbol > in our code. So, a minor typo like that can change the entire execution of our program, and we wouldn’t have noticed it in any of our previous tests. This is why it is important to test the edge cases along with other inputs.

In fact, it would probably be a good idea to add a third edge case, where the sum and difference equal $ 9 $, just to be safe. That way, we can clearly see the difference between True and False values in this program.

So, the final set of values we may want to test this program with are listed below:

• $ 6 $ and $ 6 $ (branch coverage 1 and 4)
• $ 6 $ and $ -6 $ (branch coverage 2 and 3)
• $ 12 $ and $ 0 $ (path coverage 1 and 3)
• $ 4 $ and $ 4 $ (path coverage 2 and 4)
• $ 9 $ and $ 0 $ (edge case 2 and 4)
• $ 10 $ and $ 0 $ (edge case 2 and 4)
• $ 11 $ and $ 0 $ (edge case 1 and 3)

As we can see, even a simple program with just a few lines of code can require substantial testing to really be sure it works correctly! This doesn’t even include other types of testing, where we make sure it works properly with invalid input values, decimal numbers, and other situations. That type of testing is really outside of the scope of this lab, but we’ll learn more about some of those topics later in this course.

Complex Example

Let’s go through one more example to get some more practice with creating test inputs for conditional statements. For this example, let’s consider the following program in Python:

a = int(input("Enter a number: "))
b = int(input("Enter a number: "))
if a // b >= 5:
    # Branch 1
    print(f"{b} goes into {a} at least 5 times")
else:
    # Branch 2
    print(f"{a} is less than 5 times {b}")
if a % b == 0:
    # Branch 3
    print(f"{b} evenly divides {a}")
else:
    # Branch 4
    print(f"{a} / {b} has a remainder")

This program is a bit trickier to evaluate. First, it accepts two numbers as input. Then, it will check to see if the first number is at least 5 times the second number. It will print an appropriate message in either case. Then, it will check to see if the first number is evenly divided by the second number, and once again it will print a message in either case.

Branch Coverage

To achieve branch coverage, we need to come up with a set of inputs that will cause each print statement to be executed. Thankfully, the branches are numbered using comments in the Python code, so it is easy to keep track of them.

Let’s start with an easy input - we’ll set a to $ 25 $ and b to $ 5 $. This means that a // b is equal to $ 5 $, so we’ll go into branch 1 in the first if statement. The second if statement will go into branch 3, since a % b is exactly $ 0 $ because $ 5 $ stored in b will evenly divide the $ 25 $ stored in a without any remainder. So, we’ve covered branches 1 and 3 with this input.

For another input, we can choose $ 21 $ for a and $ 5 $ for b. In the first if statement, we’ll see that a // b is now $ 4 $, so we’ll go into branch 2 instead. Likewise, in the second if statement we’ll execute branch 4, since a % b is equal to $ 1 $ with these inputs. That means we’ve covered branches 2 and 4 with these inputs.

Therefore, we can achieve branch coverage with just two inputs:

• $ 25 $ and $ 5 $
• $ 21 $ and $ 5 $

Path Coverage

Recall that there are four paths through a program that consists of two if statements in a row:

1. Branch 1 -> Branch 3
2. Branch 1 -> Branch 4
3. Branch 2 -> Branch 3
4. Branch 2 -> Branch 4

We’ve already covered the first and last of these paths, so we must simply find inputs for the other two.

One input we can try is $ 31 $ and $ 5 $. In the first if statement, we’ll go to branch 1 since a // b will be $ 6 $ this time. However, when we get to the second if statement, we’ll find that there is a remainder of $ 1 $ after computing a % b, so we’ll go to branch 4 instead. This covers the second path.

The third path can be covered by inputs $ 20 $ and $ 5 $. In the first if statement, we’ll go to branch 2 because a // b is now $ 4 $, but we’ll end up in branch 3 in the second if statement since a % b is exactly 0.

Therefore, we can achieve path coverage with these four inputs:

• $ 25 $ and $ 5 $
• $ 21 $ and $ 5 $
• $ 31 $ and $ 5 $
• $ 20 $ and $ 5 $

Edge Cases

What about edge cases? For the first if statement, we see the statement a // b >= 5, so that clues us into the fact that we’ll probably want to try values that result in the numbers $ 4 $, $ 5 $, and $ 6 $ for this statement. Thankfully, if we look at the inputs we’ve already tried, we can see that this is already covered! By being a bit deliberate with the inputs we’ve been choosing, we can not only achieve branch and path coverage, but we can also choose values that test the edge cases for various Boolean expressions as well.

Likewise, the Boolean statement in the second if statement is a % b == 0, so we’ll want to try situations where a % b is exactly $ 0 $, and when it is some other value. Once again, we’ve already covered both of these instances in our sample inputs!

So, with a bit of thinking ahead, we can come up with a set of just 4 inputs that not only achieve full path and branch coverage, but also properly test the various edge cases that are present in the program!

Conditionals Practice

Let’s try some simple practice problems. These problems are not graded - they are just for you to practice before doing the real exercises in the lab itself. You can find the answers below each question by clicking the button below each question.

4.5 Reading Code

Consider the following Python program:

a = int(input("Enter a whole number: "))
b = int(input("Enter a whole number: "))
if (a + b) % 3 == 0:
    print("Branch 1")
else:
    print("Branch 2")
a = a * 2
b = b * 3
if (a + b) % 3 == 0:
    print("Branch 3")
else:
    print("Branch 4")

What output is printed when the user inputs the values $ 2 $ and $ 4 $?

Branch 1
Branch 4

4.6 Testing Code

Consider the program in the previous question. Write a set of possible inputs to the program that will achieve path coverage. For each set of inputs, identify which branches will be executed when that input is provided to the program.

Think carefully! The same if statement is executed twice with different values, but those values are related to each other.

3 and 3 - Branch 1 & 3
2 and 4 - Branch 1 & 4
3 and 2 - Branch 2 & 3
2 and 2 - Branch 2 & 4

4.7 Reading Code

Consider the following Python program:

x = int(input("Enter an integer: "))
y = int(input("Enter an integer: "))
if x * 5 > y:
  print("Branch 1")
else:
  print("Branch 2")
if y % 5 == x:
  print("Branch 3")
else:
  print("Branch 4")

What output is displayed when the user inputs the values $ 4 $ and $ 29 $?

Branch 2
Branch 3

4.8 Testing Code

Consider the program in the previous question. Write a set of possible inputs to the program that will achieve path coverage. For each set of inputs, identify which branches will be executed when that input is provided to the program.

3 and 13 - Branches 1 & 3
4 and 13 - Branches 1 & 4
4 and 29 - Branches 2 & 3
4 and 30 - Branches 2 & 4

4.9 Writing Code

Write a complete Python that performs the following actions:

1. Accept a single input from the user and convert it to an integer
2. If the number is even, display “That is an even number”. If it is odd, display “That is an odd number”.
3. If the number is negative, display “That is a negative number”. If it is zero or positive, display “That is a positive number”.

Your program should include at least two conditional statements.

x = int(input("Enter an integer: "))
if x % 2 == 0:
  print("That is an even number")
else:
  print("that is an odd number")
if x < 0:
  print("That is a negative number")
else:
  print("That is a positive number")

4.10 Writing Code

Write a complete Python program in that file that performs the following actions:

1. Accept a single string as input from the user
2. If the string comes before the string “First” in lexicographic order, print “That comes before First”. If not, print “That comes after First”.
3. If the string comes after the string “last” in lexicographic order, print “That comes after last”. If not, print “That comes before last”.

Recall that you can use Boolean comparators such as < and > to compare two strings in Python. Also, pay special attention to the capitalization of the words “First” and “last” in the description above.

Your program should include at least two conditional statements.

Bonus food for thought: look at your completed answer - is there a path that is not possible to actually test? Why is that?

s = input("Enter a string: ")
if s < "First":
  print("That comes before First")
else:
  print("That comes after First")
if s > "last":
  print("That comes after last")
else:
  print("That comes before last")

Linear Conditionals

Resources

• Slides

Conditional statements are a very powerful tool for programmers to use. So far in this lab, we’ve explored simple conditional statements, including the if statement and if-else statement in Python. However, up to this point we’ve only looked at how we can use a single conditional statement at a time in a program, which can be very limiting. Now we’re going to explore how we can combine conditional statements in a variety of different ways to build even more complex programs. These combined, or nested conditional statements, are commonly used across all programming languages, but learning how to build and debug them takes time and practice. We’ll work through several examples, and then you’ll get a chance to try it yourself.

Linear Conditional Statements

To explore the various ways we can use conditional statements in code, let’s start by examining the same problem using three different techniques. We’ll use the classic Rock Paper Scissors game. In this game, two players simultaneously make one of three hand gestures, and then determine a winner based on the following diagram:

¹

For example, if the first player displays a balled fist, representing rock and the second player displays a flat hand, representing paper, then the second player wins because “paper covers rock” according to the rules. If the players both display the same symbol, the game is considered a tie.

So, to make this work, we’re going to write a Python program that reads two inputs representing the symbols, and then we’ll use some conditional statements to determine which player wins. A basic skeleton of this program is shown below:

p1 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 1: ")
p2 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 2: ")

# determine the winner

Our goal, therefore, is to replace the comment # determine the winner with a set of conditional statements and print statements to accomplish this goal.

Linear Conditional Statements

First, let’s try to write this program using what we already know. We’ve already learned how to use conditional statements, and it is completely possible to write this program using just a set of linear conditional statements and nothing else. So, to do this, we need to determine each possible combination of inputs, and then write an if statement for each one. Since there are $ 3 $ possible inputs for each player, we know there are $ 3 * 3 = 9 $ possible combinations:

We also have to deal with a final option where one player or the other inputs an invalid value. So, in total, we’ll have $ 10 $ conditional statements in our program!

Let’s first try to make a quick flowchart of this program. Since we need $ 10 $ conditional statements, our flowchart will have $ 10 $ of the diamond-shaped decision nodes. Unfortunately, that would make a very large flowchart, so we’ll only include the first three decision nodes in the flowchart shown here:

As we can see, this flowchart is very tall, and just consists of one decision node after another. Thankfully, we should know how to implement this in code based on what we’ve previously learned. Below is a full implementation of this program in Python, using just linear conditional statements:

p1 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 1: ")
p2 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 2: ")

if p1 == "rock" and p2 == "rock":
    print("tie")
if p1 == "rock" and p2 == "paper":
    print("player 2 wins")
if p1 == "rock" and p2 == "scissors":
    print("player 1 wins")
if p1 == "paper" and p2 == "rock":
    print("player 1 wins")
if p1 == "paper" and p2 == "paper":
    print("tie")
if p1 == "paper" and p2 == "scissors":
    print("player 2 wins")
if p1 == "scissors" and p2 == "rock":
    print("player 2 wins")
if p1 == "scissors" and p2 == "paper":
    print("player 1 wins")
if p1 == "scissors" and p2 == "scissors":
    print("tie")
if not (p1 == "rock" or p1 == "paper" or p1 == "scissors") or not (p2 == "rock" or p2 == "paper" or p2 == "scissors"):
    print("error")

This program seems a bit complex, but if we step through it one step at a time it should be easy to follow. For example, if the user inputs "scissors" for player 1 and "rock" for player 2, we just have to find the conditional statement that matches that input and print the correct output. Since we were careful about how we wrote the Boolean expressions for these conditional statements, we know that it is only possible for one of them to evaluate to true. So, we’d say that those Boolean expressions are mutually exclusive, since it is impossible for two of them to be true at the same time.

Things get a bit more difficult in the last conditional statement. Here, we want to make sure the user has not input an invalid value for either player. Unfortunately, the only way to do that using linear conditional statements is to explicitly check each possible value and make sure that the user did not input that value. This can seem pretty redundant, and it indeed is! As we’ll see later in this lab, there are better ways we can structure this program to avoid having to explicitly list all possible inputs when checking for an invalid one.

Before moving on, it is always a good idea to run this code yourself, either directly in Python or using Python Tutor, to make sure you understand how it works and what it does.

Alternative Version

There is an alternative way we can write this program using linear if statements. Instead of having an if statement for each possible combination of inputs, we can instead have a single if statement for each possible output, and construct more complex Boolean expressions that are used in each if statement. Here’s what that would look like in Python:

p1 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 1: ")
p2 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 2: ")

if (p1 == "rock" and p2 == "rock") or (p1 == "paper" and p2 == "paper") or (p1 == "scissors" and p2 == "scissors"):
    print("tie")
if (p1 == "rock" and p2 == "paper") or (p1 == "paper" and p2 == "scissors") or (p1 == "scissors" and p2 == "rock"):
    print("player 2 wins")
if (p1 == "rock" and p2 == "scissors") or (p1 == "paper" and p2 == "rock") or (p1 == "scissors" and p2 == "paper"):
    print("player 1 wins")
if not (p1 == "rock" or p1 == "paper" or p1 == "scissors") or not (p2 == "rock" or p2 == "paper" or p2 == "scissors"):
    print("error")

In this example, we now have just four if statements, but each one now requires a Boolean expression that includes multiple parts. So, this program is simultaneously more and less complex than the previous example. It has fewer lines of code, but each line can be much trickier to understand and debug.

Again, feel free to run this code in either Python Tutor or directly in Python to confirm that it works and make sure you understand it before continuing.

In terms of style, both of these options are pretty much equivalent - there’s no reason to choose one over the other. However, we’ll see later in this lab, there are much better ways we can write this program using chaining and nesting with conditional statements.

Chaining Conditionals

Resources

• Slides

In the previous example, we saw a set of linear if statements to represent a Rock Paper Scissors game. As we discussed on that page, the Boolean expressions are meant to be mutually exclusive, meaning that only one of the Boolean expressions will be true no matter what input the user provides.

When we have mutually exclusive Boolean expressions like this, we can instead use if-else statements to make the mutually exclusive structure of the program clearer to the user. Let’s see how we can do that.

Chaining Conditional Statements

To chain conditional statements, we can simply place the next conditional statement on the False branch of the first statement. This means that, if the first Boolean expression is True, we’ll execute the True branch, and then jump to the end of the entire statement. If it is False, then we’ll go to the False branch and try the next conditional statement. Here’s what this would look like in a flowchart:

This flowchart is indeed very similar to the previous one, but with one major change. Now, if any one of the Boolean expressions evaluates to True, that branch will be executed and then the control flow will immediately drop all the way to the end of the program, without ever testing any of the other Boolean expressions. This means that, overall, this program will be a bit more efficient than the one with linear conditional statements, because on average it will only have to try half of them before the program ends.

Now let’s take a look at what this program would look like in Python:

p1 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 1: ")
p2 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 2: ")

if p1 == "rock" and p2 == "rock":
    print("tie")
else:
    if p1 == "rock" and p2 == "paper":
        print("player 2 wins")
    else: 
        if p1 == "rock" and p2 == "scissors":
            print("player 1 wins")
        else:
            if p1 == "paper" and p2 == "rock":
                print("player 1 wins")
            else:
                if p1 == "paper" and p2 == "paper":
                    print("tie")
                else:
                    if p1 == "paper" and p2 == "scissors":
                        print("player 2 wins")
                    else:
                        if p1 == "scissors" and p2 == "rock":
                            print("player 2 wins")
                        else:
                            if p1 == "scissors" and p2 == "paper":
                                print("player 1 wins")
                            else:
                                if p1 == "scissors" and p2 == "scissors":
                                    print("tie")
                                else:
                                    if not (p1 == "rock" or p1 == "paper" or p1 == "scissors") or not (p2 == "rock" or p2 == "paper" or p2 == "scissors"):
                                        print("error")

As we can see, this program is basically the same code as the previous program, but with the addition of a number of else keywords to place each subsequent conditional statement into the False branch of the previous one. In addition, since Python requires us to add a level of indentation for each conditional statement, we see that this program very quickly becomes difficult to read. In fact, the last Boolean expression is so long that it doesn’t even fit well on the screen!

We can make a similar change to the other example on the previous page:

p1 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 1: ")
p2 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 2: ")

if (p1 == "rock" and p2 == "rock") or (p1 == "paper" and p2 == "paper") or (p1 == "scissors" and p2 == "scissors"):
    print("tie")
else:
    if (p1 == "rock" and p2 == "paper") or (p1 == "paper" and p2 == "scissors") or (p1 == "scissors" and p2 == "rock"):
        print("player 2 wins")
    else:
        if (p1 == "rock" and p2 == "scissors") or (p1 == "paper" and p2 == "rock") or (p1 == "scissors" and p2 == "paper"):
            print("player 1 wins")
        else:
            if not (p1 == "rock" or p1 == "paper" or p1 == "scissors") or not (p2 == "rock" or p2 == "paper" or p2 == "scissors"):
                print("error")

Again, this results in very long lines of code, but it still makes it easy to see that the program is built in the style of mutual exclusion, and only one of the True branches will be executed. As before, feel free to run these programs directly in Python or using Python Tutor to confirm they work and that you understand how they work before continuing.

The Final Case

Now that we’ve built a program structure that enforces mutual exclusion, we’ll might notice something really interesting - the final if statement is no longer required! This is because we’ve already exhausted all other possible situations, so the only possible case is the last one. In that case, we can remove that entire statement and just replace it with the code from the True branch. Here’s what that would look like in Python:

p1 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 1: ")
p2 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 2: ")

if p1 == "rock" and p2 == "rock":
    print("tie")
else:
    if p1 == "rock" and p2 == "paper":
        print("player 2 wins")
    else: 
        if p1 == "rock" and p2 == "scissors":
            print("player 1 wins")
        else:
            if p1 == "paper" and p2 == "rock":
                print("player 1 wins")
            else:
                if p1 == "paper" and p2 == "paper":
                    print("tie")
                else:
                    if p1 == "paper" and p2 == "scissors":
                        print("player 2 wins")
                    else:
                        if p1 == "scissors" and p2 == "rock":
                            print("player 2 wins")
                        else:
                            if p1 == "scissors" and p2 == "paper":
                                print("player 1 wins")
                            else:
                                if p1 == "scissors" and p2 == "scissors":
                                    print("tie")
                                else:
                                    # All other options have been checked
                                    print("error")

In this code, there is a comment showing where the previous if statement was placed, and now it simply prints an error. Again, this is possible because we’ve tried every possible valid combination of inputs in the previous Boolean expressions, so all that is left is invalid input. Try it yourself! See if you can come up with any valid input that isn’t already handled by the Boolean expressions - there shouldn’t be any of them.

Elif Keyword

To help build programs that include chaining conditional statements, Python includes a special keyword elif for this exact situation. The elif keyword is a shortened version of else if, and it means to replace the situation where an if statement is directly placed inside of an else branch. So, when we are chaining conditional statements, we can now do so without adding additional levels of indentation.

Here’s what the previous example looks like when we use the elif keyword in Python:

p1 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 1: ")
p2 = input("Enter \"rock\", \"paper\", or \"scissors\" for player 2: ")

if p1 == "rock" and p2 == "rock":
    print("tie")
elif p1 == "rock" and p2 == "paper":
    print("player 2 wins")
elif p1 == "rock" and p2 == "scissors":
    print("player 1 wins")
elif p1 == "paper" and p2 == "rock":
    print("player 1 wins")
elif p1 == "paper" and p2 == "paper":
    print("tie")
elif p1 == "paper" and p2 == "scissors":
    print("player 2 wins")
elif p1 == "scissors" and p2 == "rock":
    print("player 2 wins")
elif p1 == "scissors" and p2 == "paper":
    print("player 1 wins")
elif p1 == "scissors" and p2 == "scissors":
    print("tie")
else:
    # All other options have been checked
    print("error")