List comprehensions, break, continue, pass and exception handling in Python (2023)

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List comprehensions, break-continue, exception handling in Python

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As we have learned for loop to walk through a sequence, and do something with each item, at least read some value from it.

There is a scenario, similar to what we saw with the last example in the for-loop introduction, that involves making a new list from the result of doing an operation on each item in that list.

We call this construct a list comprehension, and it builds on the syntax of a list. An example of a list comprehension is:

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Another example is:

List comprehensions, break, continue, pass and exception handling in Python (3)

And list comprehensions also allow us to use conditions. We can have an if clause to test a condition and only add a result to the list if condition is true. For example:

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We can also iterate this on multiple items for example:

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With list comprehension we can also do above operation in one line of code:

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Iterable

Theiterablecan be any iterable object, like a list, tuple, set etc.

(Video) Python Part 8 - List Comprehensions

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Same example with condition:

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Expression

Theexpressionis the current item in the iteration, but it is also the outcome, which you can manipulate before it ends up like a list item in the new list:

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Theexpressioncan also contain conditions, not like a filter, but as a way to manipulate the outcome:

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The break statement

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We have mentioned the break statement when discussing an infinite loop. The break statement is used only inside a loop, to stop the execution of the looping statement when needed, even if the loop condition is still true (in the case of while loop) or the last member of the sequence has not been reached.

Here is an example where we break out of the loop that iterates over the list when we reach the ‘Banglore’ city:

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Note the subtle difference in the output, if you place the print(i) after the break statement, as follows:

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In the case, Bangalore is not in the output because; the break happens before the iteration variable takes that value.

Let’s see another example, where we break out of the loop while searching for a number in a sequence. We break when the number to search is found, as there is no point in iterating over the remaining list.

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Using Break in Nested Loops

(Video) Python List Comprehensions Magic Tricks Tutorial #26

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In the above example, you saw two lists with four integer values in each. It then used two for loops to iterate through the lists and multiply the integers. Next, it defined the break conditions. When the break condition was met, the innermost loop was terminated, and the control went back to the outer loop.

The continue statement

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Let’s see an example to illustrate what the continue statement does.

Sometimes, in a loop, we may have an if statement to test a condition for the current step of the loop, and if the condition is true, we want to do nothing. So basically, we would write code as follows:

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Now, that was just a start. What we want, instead of printing we do nothing, which is like actually doing something, is to have no visible effect at all.

Here is where the continue statement is useful. We can use it instead of the line that was doing the printing, and the only other changes are: we remove the else line while keeping the instruction it was branching, and do an-indentation so that the block is aligned with the entire if block.

The previous code example becomes:

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Some other examples:

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The pass statement

The pass statement is an interesting feature of Python: a statement that tells the interpreter to do nothing. Ok, but what’s the point? Well, again, the typical use is in an if block where you do not want to produce any result yet.

So, at least semantically, we could use this trick for the example we previously discussed, before choosing to use the continue trick. The code would be as follow:

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(Video) Python tutorial - Exception Handling in List Comprehensions (Try/Excepts and Handler Functions)

But wait, there in more to Remember, earlier I said you do not want to add code that produces result yet. And yes, you use pass instead of opting for using continue, if you are still writing/testing your code and you anticipate that, in the near term you would add some specific code in that if block. So, it is like, you are making progress structuring your code, putting skeleton in place, which you want to already be testable and partially producing the target functionality,

It is common for a developer to have several if…else blocks where the only instruction is a pass statement, preparing things for later branching the required those conditions. This makes agile and productive.

So yes, the pass statement could look not a big deal, but it is very useful productive programmer.

Error handling

Now, we will cover the set of tools python offers us to handle errors: Exceptions.

Python uses exceptions to handle exceptional/unexpected situations while running your script and will throw them at you when they happen. This allows you to get a specific error message automatically, allowing you to fix the problem right away and move forward.

And when an error occurs, the reason behind can be very specific. That’s why a specific exception is raised. Using this mechanism, the programmer can catch the execution and output of a user-friendly error message or even continue the program flow. Depending on the type of execution.

There is a set of built-in or standard exceptions and a programmer can define a custom exception using Python’s object-oriented programming techniques.

Python standard exceptions

Python has Exception class which is the base class for all built-in exceptions. All of the built-in exceptions are derived from this class.

Let’s present a few common exception types in python.

  • The SyntaxError exception is raised when the interpreter finds a syntax error while parsing the code.
  • The ZeroDivisionError exception is raised when the divider in a division or modulo operation is 0, a situation that is not allowed.
  • The ValueError exception is raised when a built-in operation or function receives an argument that has an inappropriate value.
  • The KeyboardInterrupt exception is interesting. It is raised when the user hits the interrupt key (using Ctrl + c).
  • The NameError exception is raised when a local or global name is not found.

Raising an exception

Though by default an exception is raised when an error has occurred. You can explicitly raise an exception in you code by using the raise statement (followed by exception object)

Here is an example:

When executing the code, we got the above output.

Raising an exception is an important technique. When dealing with complex use cases, the programmer can use it to make things more explicit, which is better to avoid bugs and help for future debug if needed. A developer writing code that would call the piece of code, could catch the exception using a special statement we will see in a minute, and limit the impact of the error on the user (showing a more user-friendly error message and/or doing something else when that error occurs).

Handling exception

When writing code that can be fragile, depending on what a line or a block of code.

is doing, we can try…except syntax to catch an exception that could be raised in some situations, depending on some input or our code’s execution environment. That is what exception handling is about.

(Video) Beyond the List Comprehension - A Python Short by Michael Kennedy

We encapsulate the block or the line of code where the error might happen, with the try…except technique, as follows:

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Here what we are doing is use the base Exception class, so we will catch any type of exception. If we wanted to catch only ValueError exceptions for example, we would do:

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Let’s see how it goes. With rea code, building on a previous example. We catch the ValueError exception that is raised inside the code block; you guessed it, using the ValueError type. That way our exception handling is precise, and that is better to write solid code.

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As we can see, the exception was handled, and as a user of the program. We did not notice the effect of the error condition.

That small example what exceptions and exception handling offer. Of course, instead of doing nothing when the exception is raised, using the pass statement, you can choose to write code that does something useful in that situation.

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FAQs

Are Python list comprehensions fast? ›

List comprehensions are faster than for loops to create lists. But, this is because we are creating a list by appending new elements to it at each iteration. This is slow. Side note: It would even be worse if it was a Numpy Array and not a list.

What is list comprehension method in Python? ›

A Python list comprehension consists of brackets containing the expression, which is executed for each element along with the for loop to iterate over each element in the Python list. Python List comprehension provides a much more short syntax for creating a new list based on the values of an existing list.

What are the advantages of using list comprehensions in Python? ›

One main benefit of using a list comprehension in Python is that it's a single tool that you can use in many different situations. In addition to standard list creation, list comprehensions can also be used for mapping and filtering. You don't have to use a different approach for each scenario.

Are list comprehensions loops? ›

Every list comprehension can be rewritten as a for loop but not every for loop can be rewritten as a list comprehension. The key to understanding when to use list comprehensions is to practice identifying problems that smell like list comprehensions.

Are Python list comprehensions lazy? ›

'Lazy lists' are called generators in Python. Just use (...) Instead of [...] in your example. I'm aware of generator expressions, but the fact that list comprehensions are not lazy can trip people if they are used to other languages with this feature, since they are usually lazy.

Are list comprehensions good practice? ›

List comprehensions are great because they require less lines of code, are easier to comprehend, and are generally faster than a for loop. While list comprehensions are not the most difficult concept to grasp, it can definitely seem unintuitive at first (it certainly was for me!).

What are list comprehensions used for? ›

List comprehension offers a shorter syntax when you want to create a new list based on the values of an existing list. Example: Based on a list of fruits, you want a new list, containing only the fruits with the letter "a" in the name.

What is a list used for? ›

A list is any information displayed or organized in a logical or linear formation. Below is an example of a numerical list, often used to show several steps that need to be performed to accomplish something.

What is list and its uses? ›

Explanation: List:- A list may be used for a variety of purposes, such as storing objects or removing and adding objects. So in order for the programmer to perform the various tasks for the list, the software should have enough storage to stay up with the changes made to the list.

How much faster are list comprehensions? ›

From the timed cells below, you can see that the list comprehension runs almost twice as fast as the for loop for this calculation. This is one of the primary benefits of using list comprehension.

Why is it called list comprehension Python? ›

Because it's a very comprehensive way to describe a sequence (a set in math and other languages, and a list/sequence in Python).

What are 3 benefits of Python? ›

Top Reasons to Learn Python
  • Data science.
  • Scientific and mathematical computing.
  • Web development.
  • Finance and trading.
  • System automation and administration.
  • Computer graphics.
  • Basic game development.
  • Security and penetration testing.
8 Sept 2022

What are the 3 types of loops? ›

Loops are control structures used to repeat a given section of code a certain number of times or until a particular condition is met. Visual Basic has three main types of loops: for.. next loops, do loops and while loops.

Are list comprehensions functional? ›

List comprehensions are a concise notation borrowed from the functional programming language Haskell. We can think of them like a syntactic sugar for the filter and map functions. We have seen that list comprehensions can be a good alternative to for loops because they are more compact and faster.

Does list comprehension use less memory? ›

So while list comprehensions use less memory, they're probably significantly slower because of all the resizes that occur. These will often have to copy the list backbone to a new memory area.

Is list comprehension faster than lambda? ›

Graphical representation of list comprehension vs lambda + filter. As we can see from the graph that overall list comprehension is much faster than the filter function.

Is list comprehension good or bad? ›

List comprehensions are unarguably the most powerful feature of Python. They are pythonic, powerful, and provide readability.

Can list comprehension return two lists? ›

The question was: 'is it possible to return two lists from a list comprehension? '. I answer that it is possible, but in my opinion is better to iterate with a loop and collect the results in two separate lists.

How many types of comprehensions are there? ›

The first two types of comprehension – literal and inferential – we think of as 'reading comprehension. ' The third type of comprehension – analytical – we think of as 'writing comprehension.

What is the use of list () in Python? ›

List. Lists are used to store multiple items in a single variable. Lists are one of 4 built-in data types in Python used to store collections of data, the other 3 are Tuple, Set, and Dictionary, all with different qualities and usage.

What is comprehension example? ›

Any kind of mental grasping of an idea or a subject is a kind of comprehension. You might attempt comprehension of a curious situation, like the fact that your goofy roommate always manages to date models. Sometimes, such mysteries are beyond comprehension!

What are the three parts of a list comprehension? ›

List comprehension is considerably faster than processing a list using the for loop. As per the above syntax, the list comprehension syntax contains three parts: an expression, one or more for loop, and optionally, one or more if conditions. The list comprehension must be in the square brackets [] .

Is list a data type? ›

The LIST data type is a collection type that can store ordered non-NULL elements of the same SQL data type. The LIST data type supports, but does not require, duplicate element values. The elements of a LIST data type have ordinal positions.

What is called as list? ›

a series of names or other items written or printed together in a meaningful grouping or sequence so as to constitute a record. a list of members.

What are lists explain? ›

A list refers to any information displayed in a logical or linear form. It is a series of items written together in a meaningful group or sequence and marked by bullet points, numbers, etc. In HTML, there are three list types, each with a specific purpose and tag.

What are the three types of list? ›

An example of the three list types, which are unordered list, ordered list, and definition list.

How data is stored in list? ›

Lists, then, are stored in distinct chunks of memory which are linked together with pointers, which enables efficient use of memory generally and doesn't require resizing. It also allows for the easy and quick manipulation of pointers when transforming the list.

Why is a list important? ›

By keeping such a list, you make sure that your tasks are written down all in one place so you don't forget anything important. And by prioritizing tasks, you plan the order in which you'll do them, so that you can tell what needs your immediate attention, and what you can leave until later.

Which is faster list or set Python? ›

Generally the lists are faster than sets. But in the case of searching for an element in a collection, sets are faster because sets have been implemented using hash tables. So basically Python does not have to search the full set, which means that the time complexity in average is O(1).

Which is faster filter or list comprehension? ›

For large lists with one million elements, filtering lists with list comprehension is 40% faster than the built-in filter() method. What is this? The reason is the efficient implementation of the list comprehension statement.

Which is the fastest loop in Python? ›

A faster way to loop in Python is using built-in functions. In our example, we could replace the for loop with the sum function. This function will sum the values inside the range of numbers.

Can we convert list to string in Python? ›

To convert a list to a string, use Python List Comprehension and the join() function. The list comprehension will traverse the elements one by one, and the join() method will concatenate the list's elements into a new string and return it as output.

What is the difference between a string and a list? ›

A string is a sequence of characters between single or double quotes. A list is a sequence of items, where each item could be anything (an integer, a float, a string, etc).

How do you compare two lists in Python? ›

We can club the Python sort() method with the == operator to compare two lists. Python sort() method is used to sort the input lists with a purpose that if the two input lists are equal, then the elements would reside at the same index positions.

What are the 4 main uses of Python? ›

Python is commonly used for developing websites and software, task automation, data analysis, and data visualization. Since it's relatively easy to learn, Python has been adopted by many non-programmers such as accountants and scientists, for a variety of everyday tasks, like organizing finances.

What is pass in Python? ›

Python pass Statement

The pass statement is used as a placeholder for future code. When the pass statement is executed, nothing happens, but you avoid getting an error when empty code is not allowed. Empty code is not allowed in loops, function definitions, class definitions, or in if statements.

What are the 3 operators in Python? ›

Python divides the operators in the following groups: Arithmetic operators. Assignment operators. Comparison operators.

What are the 3 data types Python uses? ›

Following are the standard or built-in data type of Python: Numeric. Sequence Type. Boolean.

What are 2 types of loops? ›

Two major types of loops are FOR LOOPS and WHILE LOOPS. A For loop will run a preset number of times whereas a While loop will run a variable number of times. For loops are used when you know how many times you want to run an algorithm before stopping.

What is loop syntax? ›

Syntax. The syntax of a for loop in C programming language is − for ( init; condition; increment ) { statement(s); } Here is the flow of control in a 'for' loop − The init step is executed first, and only once.

What are the three 3 types of loop control statements? ›

Types of Loop Control Statements in C

These are: goto statement. continue statement. break statement.

What are the 2 main types of loops in Python? ›

There are two types of loops in Python, for and while.

What are the 4 parts of a for loop? ›

Loop statements usually have four components: initialization (usually of a loop control variable), continuation test on whether to do another iteration, an update step, and a loop body.

How do you repeat 3 times in Python? ›

How do you repeat multiple times in Python? In Python, we utilize the asterisk operator to repeat a string. This operator is indicated by a “*” sign. This operator iterates the string n (number) of times.

What are the 3 functions in for loop? ›

Initialization statement, which describes the starting point of the loop, where the loop variable is initialized with a starting value. The test expression, which is the condition until when the loop is repeated. The update statement, which is usually the number by which the loop variable is incremented.

Is a list or set faster Python? ›

Generally the lists are faster than sets. But in the case of searching for an element in a collection, sets are faster because sets have been implemented using hash tables.

Are comprehensions faster? ›

List comprehensions are often not only more readable but also faster than using "for loops." They can simplify your code, but if you put too much logic inside, they will instead become harder to read and understand.

How do list comprehensions work? ›

List comprehensions provide us with a simple way to create a list based on some sequence or another list that we can loop over. In python terminology, anything that we can loop over is called iterable. At its most basic level, list comprehension is a syntactic construct for creating lists from existing lists.

Is list comprehension more memory efficient? ›

Measuring performance

To measure the performance and memory footprint differences between list comprehensions and generator expressions, we will use the script below. As you can see, list comprehensions are a bit faster but allocate more memory.

Does set allow duplicates in Python? ›

Sets cannot contain duplicates. Duplicates are discarded when initializing a set. If adding an element to a set, and that element is already contained in the set, then the set will not change.

Which is faster array or list? ›

The array is faster in case of access to an element while List is faster in case of adding/deleting an element from the collection.

Which is faster queue or list? ›

Queue is significantly faster than List , where memory accesses are 1 vs. n for List in this use case. I have a similar use case but I have hundreds of values and I will use Queue because it is an order of magnitude faster. A note about Queue being implemented on top of List : the key word is "implemented".

Which loops is faster? ›

The for loop is the fastest but poorly readable. The foreach is fast, and iteration is controllable. The for…of takes time, but it's sweeter. The for…in takes time, hence less convenient.

Which is faster filter () or list comprehension? ›

For large lists with one million elements, filtering lists with list comprehension is 40% faster than the built-in filter() method. What is this? The reason is the efficient implementation of the list comprehension statement.

What is faster than lists in Python? ›

Lists are allocated in two blocks: the fixed one with all the Python object information and a variable sized block for the data. It is the reason creating a tuple is faster than List. It also explains the slight difference in indexing speed is faster than lists, because in tuples for indexing it follows fewer pointers.

Which is faster list comprehension or map? ›

Map function is faster than list comprehension when the formula is already defined as a function earlier. So, that map function is used without lambda expression.

Why do we do comprehensions? ›

Comprehension adds meaning to what is read. Reading comprehension occurs when words on a page are not just mere words but thoughts and ideas. Comprehension makes reading enjoyable, fun, and informative. It is needed to succeed in school, work, and life in general.

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