One Liners

In this problem, we'll go over how tools like list comprehensions, lambdas and builtin functions like sorted, sum max and min can be used to solve hard problems in satisfyingly few lines of code.

Comprehensions

Suppose we have some list of tuples representing statistics for how frequently a website is visited by particular browsers, as follows:

stats = [('foo.com', 'firefox', 23), ('bar.com', 'chrome', 47), ('foo.com', 'chrome', 3), ('bar.com', 'firefox', 16)]

In addition, note that the builtin sum function takes a list of numbers and returns the sum of those numbers, as follows:

          
>>> sum([1, 2, 3])
6
          
        

Armed with this list, sum, and your knowledge of list comprehensions, find one-line expressions for each of these values:

  1. Get a list of the number of visits per browser to foo.com. For example, if applied to the list above, your expression should evaluate to [23, 3].
  2. Find the total number of visits to foo.com. For example, if applied to the list above, your expression should evaluate to 26.
  3. Find the number of visits to any website from the browser firefox. For example, if applied to the list above, your expression shoudl evaluate to 39.

More List Comprehensions

Given a list of numbers lst, write one-line list comprehensions to do the following:

Now, suppose we have a list of pairs such as this one:

pairs = [('zzz', 3), ('bbb', 10), ('ccc', 4), ('aaa', 6)]

Write one-line list comprehensions to do the following:


Data Analysis Using Jupyter Notebooks

In class this week, we explored the use of Jupyter notebooks as a means of more interactively engaging with the code we write by embedding it in a narrative that also includes textual commentary, images and graphs. In this problem, we'll be employing those same tools to aid us in solving a very real problem.

To get started, make sure you have the Jupyter notebook package installed, which allows you to open and run Jupyter notebooks on your own computer as well as matplotlib, which allows you to quickly and easily draw graphs. If you haven't done this already, open your Terminal application (called 'Terminal' on a Mac and 'Command Prompt' or 'PowerShell' on Windows) and run the following command:

            
$ python3 -m pip install jupyter matplotlib
            
        

If you have a Windows computer, substitute python for python3.

Next, go ahead and download and unzip the section starter code herefrom the online version of this handout. There are two files in this starter code:

Now, you'll need to open this folder in your terminal. On a Mac, type cd into your terminal and (without pressing enter), drag the folder from Finder into the terminal window (you should see the full folder path show up) and then press enter. On a PC, open the directory containing the folder, right-click the folder and click 'Open Command Window here'.

You should now have a terminal window that is open in the Section8 folder. Now, type jupyter notebook and press enter. This command should open up your web browser to the Jupyter Notebook explorer. Open who_won_the_race.ipynb to start the project!


Experimenting with the Debugger

As you made your way through the assignments this quarter, you likely encountered bugs that required you to inspect the state of your program to assess exactly what was going wrong. Perhaps you strategically printed the values of variables at key points in the program's execution, or perhaps you traced through them by hand, or perhaps you just randomly perturbed parts of your code until it worked (as a teaching staff, we can't endorse that last strategy, but we've all been there).

It's important to have a deep understanding of how to debug your programs by yourself, but it turns out that PyCharm comes with a feature called the debugger, which allows you to specify points at which you'd like to pause their program's execution and poke around to see the values of the variables. In this problem, we'll be exploring how to use the debugger. Begin by downloading the PyCharm project here from the online version of this handout, importing it into PyCharm and opening debugger_intro.py.

This program is a nonsensical one designed entirely to allow us to explore the debugger, so begin by running the program as per normal to see what it does (it should print a number). Following that, verify to yourself that -- at a high level -- you understand what each of the functions does.

Now, we're going to set a breakpoint on a line in our code, which indicates to PyCharm that when we run our program, we would like to pause the program before that line executes, and see what our variables look like at that point. To set a breakpoint, click on the editor's gutter, directly to the right of the line number, and note that a red dot shows up in that position. Set a breakpoint on line 32, which calls the foo function:

A breakpoint

Now, click the bug button next to the run button in the top right corner to start the debugger:

Debug button

After a brief pause, you should see line 32 highlighted, since the program is paused there, and the debug pane appear in the bottom of your PyCharm window:

Debug pane

The first portion of interest in this debuging pane is the Stack Trace, which shows all the functions that are currently running. Notice the top of this trace says main, since that is the function that most recently begun execution. That said, the key portion of this pane is the variables subpane, which displays the values of the program's variables. Right now, since we've paused our program before line 32 executes, no variables have been created and so the pane doesn't show us anything. In order to step our program forward, we turn our attention to the toolbar of buttons at the top of the debug pane:

Debug pane

Far and away the most common buttons you'll be using are the leftmost two, which are called the Step Over and Step Into buttons, respectively. The Step Over button executes the current line of code in its entirety and moves on to the next one. If the current line of code contains a call to another function, the Step Into button jumps into the first function to be called on that line and pauses before it begins to execute. Click the Step Into button to step into the foo function.

Debug pane

Notice that the Stack Trace has updated to show that foo has been begun (and main hasn't yet ended), and that the variables pane has been updated to show the current value of foo's n parameter, which in this case is equal to 8, since we passed that value in when calling foo. Verify to yourself that this output makes sense, and then move the program forward a few lines by clicking the Step Over button a few times, noting that the program cycles through the for loop, and that entries for i and total are created and updated in the variables pane. As an added bonus, PyCharm displays the values for variables on the line at which they are declared.

Keep moving forward in the program (the Step Over button will automatically jump out of functions when they return) until you get to line 33, at which point you should step into the make_mystery_list function. At this point, keep stepping forward, but pay particular attention to the value of lst in the variables pane. Notice that its members list gets updated as the function progresses:

Debug pane

As you keep stepping through the program, you'll notice the same is true for dictionaries:

Debug pane

Now that you have some familiarity with the debugger, open up broken_program.py, a simple program aimed at demonstrating the differences between how integers and lists are passed as parameters. Trace through the program, and then run it, verifiying that it behaves as you expect it to. Now, armed with the debugger, step through the program, making sure that the values of the variables are what you expect them to be at key points in the program.