<strong>Originally published by </strong> <a href="https://therenegadecoder.com/author/jeremy-grifski/" target="_blank">JEREMY GRIFSKI</a><strong> </strong><em>at </em><a href="https://therenegadecoder.com/code/how-to-write-a-list-comprehension-in-python/" target="_blank">therenegadecoder.com</a>
Welcome back to yet another post in the How to Python series. This time I’m looking to step back a little bit to talk about one of Python’s builtin features called the list comprehension. While we’ve used them a few times in the series, I never thought to really explain them until now.Table of Contents
Unlike other articles in this series, there’s not exactly a concrete problem we’re trying to solve in this article. Instead, the goal is to understand the list comprehension syntax:
nums = [2, 6, 10, -4] negative_nums = [x for x in nums if x < 0]
What is this bizarre syntax, and how does it work? That’s the goal of the article today. In particular, we’ll look at a few scenarios where a list comprehension is useful such as:
If you know of anything else we can do with a list comprehension, let me know!Solutions
Before we can dive into the solutions, let’s talk about the syntax a bit. Here’s my best attempt at illustrating the concept:
output = [expression(item) for item in some_list]
At the most basic level, we can construct a list comprehension that iterates over each item in some list, performs some expression on that item, and places that new item in an output list. Or as a loop:
output =  for item in some_list: output.append(expression(item))
Of course, we can do a lot more than just create a list from some other list with a list comprehension. In the following subsections, we’ll take a look at a few examples.
Perhaps the simplest use of a list comprehension is duplicating another list:
my_list = [2, 5, -4, 6] output = [item for item in my_list] # [2, 5, -4, 6]
In this case,
output will be equivalent to
my_list. For completeness, here’s the same solution as a loop:
my_list = [2, 5, -4, 6] output =  for item in my_list: output.append(item)
As we can see, the list comprehension is significantly more concise. In either case, we will only perform a shallow copy—meaning items in the new list may point to the same items in the old list—so it’s a good idea to only use this syntax for copying lists of immutable values like numbers.
Now that we know how to duplicate a list, let’s try modifying the items before we add them to the output list:
my_list = [2, 5, -4, 6] output = [2 * item for item in my_list] # [4, 10, -8, 12]
Instead of copying the original list directly, we modified each item by multiplying it by two before storing it in the new list. As a result, we end up with a list where each term is twice as big as it was in the original list. Here’s the same concept using a loop:
my_list = [2, 5, -4, 6] output =  for item in my_list: output.append(item * 2)
To be clear, as the asterisk probably hints, we didn’t actually change the original list. Instead, we created a completely new list with the items doubled.
my_list contained objects or some other mutable data type like a list, there would be nothing stopping us from modifying them. Of course, that’s considered bad practice, so I neglected to share an example on the off chance that someone haphazardly copies it into a production system.
While duplicating and modifying lists is fun, sometimes it’s helpful to be able to filter a list:
my_list = [2, 5, -4, 6] output = [item for item in my_list if item < 0] # [-4]
In this case, we’ve added a new expression to the rightmost portion of the list comprehension that reads:
if item < 0. Of course, the loop equivalent might look something like the following:
my_list = [2, 5, -4, 6] output =  for item in my_list: if item < 0: output.append(item)
In other words, for each item in the list, only consider it if it’s less than zero. If it is, dump it to the new list. As a result, we end up with a list that only contains negative values.
Naturally, we can both modify and filter a list at the same time by combining the syntax:
my_list = [2, 5, -4, 6] output = [2 * item for item in my_list if item < 0] # [-8]
In this case, we’ve decided to double all negative values before dumping the results to a list. Once again, the same syntax as a loop might look something like:
my_list = [2, 5, -4, 6] output =  for item in my_list: if item < 0: output.append(item * 2)
As a result, the output list only contains
-8. Once again, it’s important to mention that we didn’t actually modify the original list.
Now, we’re starting to get into some of the more advanced features of list comprehensions. In particular, we’re looking to generate pairs of values between two lists:
# [(1, 2), (1, 4), (1, 6), (3, 2), (3, 4), (3, 6), (5, 2), (5, 4), (5, 6)] output = [(a, b) for a in (1, 3, 5) for b in (2, 4, 6)]
Here, we’ve created a list that contains all combinations of pairs from two lists. As usual, we can implement the same thing with the following set of loops:
output =  for a in (1, 3, 5): for b in (2, 4, 6): output.append((a, b))
If we wanted to make things more interesting, we could apply some filtering:
# [(3, 2), (5, 2), (5, 4)] output = [(a, b) for a in (1, 3, 5) for b in (2, 4, 6) if a > b]
In this case, we only generate a pair if the number from the first list is larger than the number from the second list.
With the shallow copy example mentioned earlier, we’re not able to duplicate nested lists such as two-dimensional matrices. To do that, we can leverage nested list comprehensions:
my_list = [[1, 2], [3, 4]] output = [[item for item in sub_list] for sub_list in my_list] print(output) # Prints [[1, 2], [3, 4]]
Instead of performing a surface-level copy, we retrieve each list and copy them using the same comprehension from before. As you can probably imagine, we could abstract this concept into a recursive function which performs a list comprehension on every dimension of the matrix:
def deep_copy(to_copy): if type(to_copy) is list: return [deep_copy(item) for item in to_copy] else: return to_copy
How cool is that? Of course, if you have anything other than numbers or strings at the deepest levels of your matrix, you’ll have to handle the rest of the cloning process yourself.A Little Recap
As always, here is a giant dump of all the examples covered in this article with comments briefly explaining each snippet. Feel free to grab what you need and go!
# Define a generic 1D list of constants my_list = [2, 5, -4, 6] # Duplicate a 1D list of constants [item for item in my_list] # Duplicate and scale a 1D list of constants [2 * item for item in my_list] # Duplicate and filter out non-negatives from 1D list of constants [item for item in my_list if item < 0] # Duplicate, filter, and scale a 1D list of constants [2 * item for item in my_list if item < 0] # Generate all possible pairs from two lists [(a, b) for a in (1, 3, 5) for b in (2, 4, 6)] # Redefine list of contents to be 2D my_list = [[1, 2], [3, 4]] # Duplicate a 2D list [[item for item in sub_list] for sub_list in my_list] # Duplicate an n-dimensional list def deep_copy(to_copy): if type(to_copy) is list: return [deep_copy(item) for item in to_copy] else: return to_copy
I hope you had as much fun reading through this article on list comprehensions as I did writing it. I think at this point in the series I’m going to start exploring basic concepts like this and stretching them to their limits. Do you have a Python concept that you’d like explored? Let me know!
In the meantime, why not check out some of these other awesome Python articles:
And, if you’re feeling extra generous, make your way over to the members page and take a look at your options. You’re welcome to try before you buy. That’s why there’s a free option. At any rate, thanks again for the support. Come back soon!
In this tutorial, you’ll learn all the basics of using **IDLE** to write Python programs. You'll know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.
In this tutorial, you'll learn how to use the development environment included with your Python installation. Python IDLE is a small program that packs a big punch! You'll learn how to use Python IDLE to interact with Python directly, work with Python files, and improve your development workflow.
If you’ve recently downloaded Python onto your computer, then you may have noticed a new program on your machine called IDLE. You might be wondering, “What is this program doing on my computer? I didn’t download that!” While you may not have downloaded this program on your own, IDLE comes bundled with every Python installation. It’s there to help you get started with the language right out of the box. In this tutorial, you’ll learn how to work in Python IDLE and a few cool tricks you can use on your Python journey!
In this tutorial, you’ll learn:
Table of Contents
Every Python installation comes with an Integrated Development and Learning Environment, which you’ll see shortened to IDLE or even IDE. These are a class of applications that help you write code more efficiently. While there are many IDEs for you to choose from, Python IDLE is very bare-bones, which makes it the perfect tool for a beginning programmer.
Python IDLE comes included in Python installations on Windows and Mac. If you’re a Linux user, then you should be able to find and download Python IDLE using your package manager. Once you’ve installed it, you can then use Python IDLE as an interactive interpreter or as a file editor.
The best place to experiment with Python code is in the interactive interpreter, otherwise known as a shell. The shell is a basic Read-Eval-Print Loop (REPL). It reads a Python statement, evaluates the result of that statement, and then prints the result on the screen. Then, it loops back to read the next statement.
The Python shell is an excellent place to experiment with small code snippets. You can access it through the terminal or command line app on your machine. You can simplify your workflow with Python IDLE, which will immediately start a Python shell when you open it.
Every programmer needs to be able to edit and save text files. Python programs are files with the
.py extension that contain lines of Python code. Python IDLE gives you the ability to create and edit these files with ease.
Python IDLE also provides several useful features that you’ll see in professional IDEs, like basic syntax highlighting, code completion, and auto-indentation. Professional IDEs are more robust pieces of software and they have a steep learning curve. If you’re just beginning your Python programming journey, then Python IDLE is a great alternative!How to Use the Python IDLE Shell
The shell is the default mode of operation for Python IDLE. When you click on the icon to open the program, the shell is the first thing that you see:
This is a blank Python interpreter window. You can use it to start interacting with Python immediately. You can test it out with a short line of code:
Here, you used
print() to output the string
"Hello, from IDLE!" to your screen. This is the most basic way to interact with Python IDLE. You type in commands one at a time and Python responds with the result of each command.
Next, take a look at the menu bar. You’ll see a few options for using the shell:
You can restart the shell from this menu. If you select that option, then you’ll clear the state of the shell. It will act as though you’ve started a fresh instance of Python IDLE. The shell will forget about everything from its previous state:
In the image above, you first declare a variable,
x = 5. When you call
print(x), the shell shows the correct output, which is the number
5. However, when you restart the shell and try to call
print(x) again, you can see that the shell prints a traceback. This is an error message that says the variable
x is not defined. The shell has forgotten about everything that came before it was restarted.
You can also interrupt the execution of the shell from this menu. This will stop any program or statement that’s running in the shell at the time of interruption. Take a look at what happens when you send a keyboard interrupt to the shell:
KeyboardInterrupt error message is displayed in red text at the bottom of your window. The program received the interrupt and has stopped executing.
Python IDLE offers a full-fledged file editor, which gives you the ability to write and execute Python programs from within this program. The built-in file editor also includes several features, like code completion and automatic indentation, that will speed up your coding workflow. First, let’s take a look at how to write and execute programs in Python IDLE.
To start a new Python file, select File → New File from the menu bar. This will open a blank file in the editor, like this:
From this window, you can write a brand new Python file. You can also open an existing Python file by selecting File → Open… in the menu bar. This will bring up your operating system’s file browser. Then, you can find the Python file you want to open.
If you’re interested in reading the source code for a Python module, then you can select File → Path Browser. This will let you view the modules that Python IDLE can see. When you double click on one, the file editor will open up and you’ll be able to read it.
The content of this window will be the same as the paths that are returned when you call
sys.path. If you know the name of a specific module you want to view, then you can select File → Module Browser and type in the name of the module in the box that appears.
Once you’ve opened a file in Python IDLE, you can then make changes to it. When you’re ready to edit a file, you’ll see something like this:
The contents of your file are displayed in the open window. The bar along the top of the window contains three pieces of important information:
In the image above, you’re editing the file
myFile.py, which is located in the
Documents folder. The Python version is 3.7.1, which you can see in parentheses.
There are also two numbers in the bottom right corner of the window:
It’s useful to see these numbers so that you can find errors more quickly. They also help you make sure that you’re staying within a certain line width.
There are a few visual cues in this window that will help you remember to save your work. If you look closely, then you’ll see that Python IDLE uses asterisks to let you know that your file has unsaved changes:
The file name shown in the top of the IDLE window is surrounded by asterisks. This means that there are unsaved changes in your editor. You can save these changes with your system’s standard keyboard shortcut, or you can select File → Save from the menu bar. Make sure that you save your file with the
.py extension so that syntax highlighting will be enabled.
When you want to execute a file that you’ve created in IDLE, you should first make sure that it’s saved. Remember, you can see if your file is properly saved by looking for asterisks around the filename at the top of the file editor window. Don’t worry if you forget, though! Python IDLE will remind you to save whenever you attempt to execute an unsaved file.
To execute a file in IDLE, simply press the F5 key on your keyboard. You can also select Run → Run Module from the menu bar. Either option will restart the Python interpreter and then run the code that you’ve written with a fresh interpreter. The process is the same as when you run
python3 -i [filename] in your terminal.
When your code is done executing, the interpreter will know everything about your code, including any global variables, functions, and classes. This makes Python IDLE a great place to inspect your data if something goes wrong. If you ever need to interrupt the execution of your program, then you can press Ctrl+C in the interpreter that’s running your code.How to Improve Your Workflow
Now that you’ve seen how to write, edit, and execute files in Python IDLE, it’s time to speed up your workflow! The Python IDLE editor offers a few features that you’ll see in most professional IDEs to help you code faster. These features include automatic indentation, code completion and call tips, and code context.
IDLE will automatically indent your code when it needs to start a new block. This usually happens after you type a colon (
:). When you hit the enter key after the colon, your cursor will automatically move over a certain number of spaces and begin a new code block.
You can configure how many spaces the cursor will move in the settings, but the default is the standard four spaces. The developers of Python agreed on a standard style for well-written Python code, and this includes rules on indentation, whitespace, and more. This standard style was formalized and is now known as PEP 8. To learn more about it, check out How to Write Beautiful Python Code With PEP 8.
When you’re writing code for a large project or a complicated problem, you can spend a lot of time just typing out all of the code you need. Code completion helps you save typing time by trying to finish your code for you. Python IDLE has basic code completion functionality. It can only autocomplete the names of functions and classes. To use autocompletion in the editor, just press the tab key after a sequence of text.
Python IDLE will also provide call tips. A call tip is like a hint for a certain part of your code to help you remember what that element needs. After you type the left parenthesis to begin a function call, a call tip will appear if you don’t type anything for a few seconds. For example, if you can’t quite remember how to append to a list, then you can pause after the opening parenthesis to bring up the call tip:
The call tip will display as a popup note, reminding you how to append to a list. Call tips like these provide useful information as you’re writing code.
The code context functionality is a neat feature of the Python IDLE file editor. It will show you the scope of a function, class, loop, or other construct. This is particularly useful when you’re scrolling through a lengthy file and need to keep track of where you are while reviewing code in the editor.
To turn it on, select Options → Code Context in the menu bar. You’ll see a gray bar appear at the top of the editor window:
As you scroll down through your code, the context that contains each line of code will stay inside of this gray bar. This means that the
print() functions you see in the image above are a part of a main function. When you reach a line that’s outside the scope of this function, the bar will disappear.
A bug is an unexpected problem in your program. They can appear in many forms, and some are more difficult to fix than others. Some bugs are tricky enough that you won’t be able to catch them by just reading through your program. Luckily, Python IDLE provides some basic tools that will help you debug your programs with ease!
If you want to run your code with the built-in debugger, then you’ll need to turn this feature on. To do so, select Debug → Debugger from the Python IDLE menu bar. In the interpreter, you should see
[DEBUG ON] appear just before the prompt (
>>>), which means the interpreter is ready and waiting.
When you execute your Python file, the debugger window will appear:
In this window, you can inspect the values of your local and global variables as your code executes. This gives you insight into how your data is being manipulated as your code runs.
You can also click the following buttons to move through your code:
Be careful, because there is no reverse button! You can only step forward in time through your program’s execution.
You’ll also see four checkboxes in the debug window:
When you select one of these, you’ll see the relevant information in your debug window.
A breakpoint is a line of code that you’ve identified as a place where the interpreter should pause while running your code. They will only work when DEBUG mode is turned on, so make sure that you’ve done that first.
To set a breakpoint, right-click on the line of code that you wish to pause. This will highlight the line of code in yellow as a visual indication of a set breakpoint. You can set as many breakpoints in your code as you like. To undo a breakpoint, right-click the same line again and select Clear Breakpoint.
Once you’ve set your breakpoints and turned on DEBUG mode, you can run your code as you would normally. The debugger window will pop up, and you can start stepping through your code manually.
When you see an error reported to you in the interpreter, Python IDLE lets you jump right to the offending file or line from the menu bar. All you have to do is highlight the reported line number or file name with your cursor and select Debug → Go to file/line from the menu bar. This is will open up the offending file and take you to the line that contains the error. This feature works regardless of whether or not DEBUG mode is turned on.
Python IDLE also provides a tool called a stack viewer. You can access it under the Debug option in the menu bar. This tool will show you the traceback of an error as it appears on the stack of the last error or exception that Python IDLE encountered while running your code. When an unexpected or interesting error occurs, you might find it helpful to take a look at the stack. Otherwise, this feature can be difficult to parse and likely won’t be useful to you unless you’re writing very complicated code.How to Customize Python IDLE
There are many ways that you can give Python IDLE a visual style that suits you. The default look and feel is based on the colors in the Python logo. If you don’t like how anything looks, then you can almost always change it.
To access the customization window, select Options → Configure IDLE from the menu bar. To preview the result of a change you want to make, press Apply. When you’re done customizing Python IDLE, press OK to save all of your changes. If you don’t want to save your changes, then simply press Cancel.
There are 5 areas of Python IDLE that you can customize:
Let’s take a look at each of them now.
The first tab allows you to change things like font color, font size, and font style. You can change the font to almost any style you like, depending on what’s available for your operating system. The font settings window looks like this:
You can use the scrolling window to select which font you prefer. (I recommend you select a fixed-width font like Courier New.) Pick a font size that’s large enough for you to see well. You can also click the checkbox next to Bold to toggle whether or not all text appears in bold.
This window will also let you change how many spaces are used for each indentation level. By default, this will be set to the PEP 8 standard of four spaces. You can change this to make the width of your code more or less spread out to your liking.
The second customization tab will let you change highlights. Syntax highlighting is an important feature of any IDE that highlights the syntax of the language that you’re working in. This helps you visually distinguish between the different Python constructs and the data used in your code.
Python IDLE allows you to fully customize the appearance of your Python code. It comes pre-installed with three different highlight themes:
You can select from these pre-installed themes or create your own custom theme right in this window:
Unfortunately, IDLE does not allow you to install custom themes from a file. You have to create customs theme from this window. To do so, you can simply start changing the colors for different items. Select an item, and then press Choose color for. You’ll be brought to a color picker, where you can select the exact color that you want to use.
You’ll then be prompted to save this theme as a new custom theme, and you can enter a name of your choosing. You can then continue changing the colors of different items if you’d like. Remember to press Apply to see your changes in action!
The third customization tab lets you map different key presses to actions, also known as keyboard shortcuts. These are a vital component of your productivity whenever you use an IDE. You can either come up with your own keyboard shortcuts, or you can use the ones that come with IDLE. The pre-installed shortcuts are a good place to start:
The keyboard shortcuts are listed in alphabetical order by action. They’re listed in the format Action - Shortcut, where Action is what will happen when you press the key combination in Shortcut. If you want to use a built-in key set, then select a mapping that matches your operating system. Pay close attention to the different keys and make sure your keyboard has them!
The customization of the keyboard shortcuts is very similar to the customization of syntax highlighting colors. Unfortunately, IDLE does not allow you to install custom keyboard shortcuts from a file. You must create a custom set of shortcuts from the Keys tab.
Select one pair from the list and press Get New Keys for Selection. A new window will pop up:
Here, you can use the checkboxes and scrolling menu to select the combination of keys that you want to use for this shortcut. You can select Advanced Key Binding Entry >> to manually type in a command. Note that this cannot pick up the keys you press. You have to literally type in the command as you see it displayed to you in the list of shortcuts.
The fourth tab of the customization window is a place for small, general changes. The general settings tab looks like this:
Here, you can customize things like the window size and whether the shell or the file editor opens first when you start Python IDLE. Most of the things in this window are not that exciting to change, so you probably won’t need to fiddle with them much.
The fifth tab of the customization window lets you add extensions to Python IDLE. Extensions allow you to add new, awesome features to the editor and the interpreter window. You can download them from the internet and install them to right into Python IDLE.
To view what extensions are installed, select Options → Configure IDLE -> Extensions. There are many extensions available on the internet for you to read more about. Find the ones you like and add them to Python IDLE!Conclusion
In this tutorial, you’ve learned all the basics of using IDLE to write Python programs. You know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.
You’ve learned how to:
Now you’re armed with a new tool that will let you productively write Pythonic code and save you countless hours down the road. Happy programming!
Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving...
Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving challenges. The codes of Python can easily be deployed in Data Science and Machine Learning. Due to this ease of deployment and easier syntaxes, this platform has a lot of real world problem solving applications. According to the sources the companies are eagerly hunting for the professionals with python skills along with SQL. An average python developer in the united states makes around 1 lakh U.S Dollars per annum. In some of the top IT hubs in our country like Bangalore, the demand for professionals in the domains of Data Science and Python Programming has surpassed over the past few years. As a result of which a lot of various python certification courses are available right now.
Array in Python: An array is defined as a data structure that can hold a fixed number of elements that are of the same python data type. The following are some of the basic functions of array in python:
Along with this one can easily crack any python interview by means of python interview questions
This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.
This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.
Original video source: https://www.youtube.com/watch?v=VMP1oQOxfM0