How To Reverse String In Python: 3 Techniques

After writing the post on reversing lists in python, I got a good response that I decided it would also be fine if I approach a similar concept in python: how to reverse a string in python.

 

One thing that makes people find this subject difficult is because python does not have a string method that is specifically built for reversing strings just like we have for lists. If there was one, that would have made our task easier because it would be optimized. So you have to be creative in looking for ways to reverse python strings. In this post, I outline three methods you can reverse strings in python as well as their timing characteristics.

First, using the python slice technique

The python slice technique, as I explained in the post about reversing lists through slicing, involves taking a slice of a sequence (and strings are also sequences) through definite start, stop, and step parameters. The slice notation in python is [start: stop : step] where start is where you want to start the slice, stop is at the index you want to stop the slice and step is the steps you take through the index while iterating through the items in the string. To reverse a string using the python slice technique, you just need to use this code on the string: string_name[ : : -1].

What the code above does is to copy the string, string_name, from the beginning to the end backwards.

Here is an example so you can see it for yourself.

Nice and easy not so? This method does not change the original string. In fact, as you must know, strings are immutable.

This is the method I prefer for reversing python strings.

Second, using the built-in reversed function.

I explained the reversed function on the post for reversing lists. But one more explanation would be succinct here. The syntax for the reversed function is: reversed(seq). As you can see, the function takes any sequence and reverses it. A string is also a sequence as I explained on the iterables post. When the string is reversed the function returns an iterator. What you do here is cast the iterator to a string. I think that is what gives this function an overhead, the process of casting the iterator to a string, otherwise it is very good and fast. You use the str.join(iterable) method of the string class to cast the iterator to a string.

Here is code that shows how to use the function and also cast it to a string.

This method is very succinct and readable across all levels of python skill.

Third, using a for loop and storing the characters backwards.

With this method all you need to do is use a for loop to iterate through the characters in the string, then you store the characters backwards in a new string. Very convenient and easy to understand.

Here is the code:

I hope you found the code to be fun.

Now, the question most persons ask is: Which is faster or which uses less system memory?

Let’s answer that question by timing the execution of each of the string reverse techniques in python and decide on which is faster or more optimized.

Timing all three techniques.

Try running the following code:

When you run it you will notice that the python slice technique took considerably lesser time than all the others while the for loop took more time. That is why I use the slice technique to reverse my strings.

There was a time when someone told me that during an interview question the interviewer told him that the slice technique is not optimized for reversing strings. I laughed. I have not yet seen anything better than the python slice technique for reversing strings. For now it is the fastest of all the methods I know of and I have outlined the three useful I use here. I use the for loop when I am lazy and just want to be unpythonic.

So, thanks for reading. I believe that now you have techniques you can use to reverse your strings. Leave a comment below if you have any. Also, do not fail to subscribe to my blog so you can be receiving useful updates the moment I post new articles.

Happy pythoning.

Breakthrough 3D Printing Of Heart For Treating Aortic Stenosis

When a narrowed aortic valve fails to open properly and thereby the pumping of blood from the heart to the aorta is obstructed, this might result in a condition called aortic valve stenosis. Aortic stenosis is one of the most common cardiovascular conditions in the elderly and affects about 2.7 million adults over the age of 75 in North America. If the doctors decide that the condition is severe, they may carry out a minimally invasive heart procedure to replace the valve. This procedure is called transcatheter aortic valve replacement (TAVR). But this catheterization procedure is not without some risks which might include bleeding, stroke, heart attack or even death. That is why it is important that the doctors take all care to reduce the risks. The TAVR procedure is less invasive than open heart surgery to repair the damaged valves,

In a new paper published in Science Advances, a peer-reviewed scientific journal published by the American Association for the Advancement of Science (AAAS), some researchers from the University of Minnesota along with their collaborators have been able to produce a new technique that involves 3D printing of the aortic valve along with creating lifelike models of the aortic valve and surrounding structures which models mimic the look and feel of the valve. These 3D printing would possibly help reduce the risks for doctors who want to carry out a TAVR procedure on a patient.

Precisely, they 3D printed a model of the aortic root. The aortic root is a section of the aorta that is closest to the heart and attached to the heart. Some of the components of the aortic root include the aortic valve, which is prone to aortic stenosis in the elderly, along with the openings of the coronary artery. The left ventricle muscle and the ascending aorta which are close to the aortic root are also not left out in the model.

The models include specialized 3D printing soft sensor arrays built into the structure that prints the organs for each patient. The 3D printing process is also customized. The authors believe that this organ model will be used by doctors all over the world to improve the outcomes for patients who will be subject to invasive procedures when treating aortic stenosis.

Before the models are produced CT scans of the patient’s aortic root are made so that the printing will mimic the exact shape of the patient's organ. Then specialized silicone-based inks are used to do the actual printing in order to match the exact feel of the patient's heart. These inks were specially built for this process because commercial printers in the market can print 3D shapes but they cannot be able to reflect the real feel of the heart’s organs which are soft tissues. The initial heart tissue that were used for the test of the 3D printers were obtained from the University of Minnesota's Visible Heart Laboratory. The researchers found that the specialized 3D printers produced models that they wanted, models that mimic the shape and the feel of the aortic valve at the heart.

To watch a video of how the 3D printers work, I encourage you to play the video below. You would find it interesting.

The researchers are happy with what they have achieved.

“Our goal with these 3D-printed models is to reduce medical risks and complications by providing patient-specific tools to help doctors understand the exact anatomical structure and mechanical properties of the specific patient’s heart,” said Michael McAlpine, a University of Minnesota mechanical engineering professor and senior researcher on the study. “Physicians can test and try the valve implants before the actual procedure. The models can also help patients better understand their own anatomy and the procedure itself.”

These models will surely be of help to physicians who will use them to practice on how they will carry out their catheterization procedures on the real heart. Physicians will soon have the ability to practice beforehand on the size and placement of the catheter device on patients before carrying out the real procedure thereby reducing the risks involved. One good thing about the integrated sensors that are fitted into the 3D models is that they will provide physicians with electronic pressure feedback which will guide them in determining and selecting the optimal position of the catheter when being placed into the aorta of a patient.

But the researchers do not think these are the only use cases for their findings or the models. They aim to go beyond that.

“As our 3D-printing techniques continue to improve and we discover new ways to integrate electronics to mimic organ function, the models themselves may be used as artificial replacement organs,” said McAlpine, who holds the Kuhrmeyer Family Chair Professorship in the University of Minnesota Department of Mechanical Engineering. “Someday maybe these ‘bionic’ organs can be as good as or better than their biological counterparts.”

I think these are laudable futuristic goals. If they could achieve their ambition, then McAlpine would be solving a problem that gives sleepless nights to many physicians who have to operate on elderly patients with weak aortic valves.

Because this is a problem-solving innovative solution to a challenging problem, I decided to include it in my blog. I hope you enjoyed reading about the achievements of McAlpine and his colleagues. I wish that they go further than just helping physicians have 3D models but be able to make those models replace weak natural organs.

In addition to McAlpine, the team included University of Minnesota researchers Ghazaleh Haghiashtiani, co-first author and a recent mechanical engineering Ph.D. graduate who now works at Seagate; Kaiyan Qiu, another co-first author and a former mechanical engineering postdoctoral researcher who is now an assistant professor at Washington State University; Jorge D. Zhingre Sanchez, a former biomedical engineering Ph.D. student who worked in the University of Minnesota’s Visible Heart Laboratories who is now a senior R&D engineer at Medtronic; Zachary J. Fuenning, a mechanical engineering graduate student; Paul A. Iaizzo, a professor of surgery in the Medical School and founding director of the U of M Visible Heart Laboratories; Priya Nair, senior scientist at Medtronic; and Sarah E. Ahlberg, director of research & technology at Medtronic.

This research was funded by Medtronic, the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health, and the Minnesota Discovery, Research, and InnoVation Economy (MnDRIVE) Initiative through the State of Minnesota. Additional support was provided by University of Minnesota Interdisciplinary Doctoral Fellowship and Doctoral Dissertation Fellowship awarded to Ghazaleh Haghiashtiani.

You can read the full research paper, entitled "3D printed patient-specific aortic root models with internal sensors for minimally invasive applications," at the Science Advances website.

How To Reverse List In Python: 4 Techniques

Very often I get people asking me to write a post on how to reverse a list in python. This is because this question often comes up in interviews. So, to oblige them, I have decided to write on four good and tested ways you can reverse a list in python. I also show their timing so that you can choose the best for your needs.

 

The built-in python reversed function

The syntax of the built-in python reversed function is reversed(seq). You can see that it takes in any sequence as argument. Sequences are lists, strings, tuples etc. For a refresher on sequences, see my post on iterables. The function returns an iterator. Remember that an iterator is an object that has elements but in order to extract the elements you need to cast it to list or call the next method. Most times, you cast it to a list to get out the elements. But casting afterwards to a list for this method of reversing could be an overhead cost for the method although it is easy, and uses substantially less memory unless you are casting. This method is ideal for times when you are dealing with very large lists and just want to use the elements of the reversed list when needed rather than using all at once. In this instance, you would need to use a python for loop.

Let’s take an example:

You can see from the above that I had to cast the iterator from the python reversed function to a list to get out the results. That could be an overhead as we’ll see later.

The python slice technique

Slicing a sequence is one of the ubiquitous techniques you can find with python lists and sequences. The syntax of slicing is [start:stop:step] where start is the index you want to start the slice, stop is where you want to stop the slice and step is the steps you want to take when iterating through the list to create the slice. To reverse a list using the python slice technique, you just need to use this statement: list_name[::-1], which is a shorthand for saying copy the list and then walk through it backwards.

Here is an example:

The advantage of this technique is that it is adaptable for any sequence and not just for lists. Some people claim that it is not readable but I find that argument obscure. Slicing is common in python even for the beginner. The only disadvantage I see with the python slice technique is that it uses up memory if you have a large list. This is because to create the reversed list, it needs to copy the original list and then reverse it. This sequence takes up a large chunk of memory. But when you want the original list to remain unchanged, this technique is good and recommended.

The python list reverse method

The python list reverse method is a method of the list class. The syntax is list.reverse(). In my opinion, it seems to be the easiest since it is built for lists and seems to be the fastest so far. But we will consider that in the timing section below. Unlike in the built-in python reversed function, it does not create any overhead and unlike the slicing technique, it does not require large chunk of memory even when working with large lists. It is more optimized for reversing python lists.

The advantageous fact about it is that it reverses in place. But if you want to make use of the original list after reversing, then this technique is not for you.

Here is an example:

I usually use this technique whenever I am reversing lists but if I need the original, I use the slice technique. Just to make you know.

Reverse list programmatically by swapping items

Now the last method you can use is to reverse the list programmatically by swapping items in place. You can write your own code that iterates through the elements of the list and swaps the elements in place. Here is a sample code:

This code can run fast but is not optimized for large lists. It swaps the elements in place and modifies the original list. It is worse than all the python built-in methods.

Timing the methods.

Most times when we are dealing with large lists, we want something that works very fast and doesn’t use much memory. Although with the built-in timing functions in python we cannot calculate for memory usage, but we can find out how long it takes each of the techniques above to run. We will need to import the timeit module to do this.

Here is a sample code for all three built-in methods except the programmed reverse list swapping method. The swapping technique takes a longer time for large lists that is why it is excluded.

When you run the code above, you will see that the list reverse method takes the shortest time of all three methods. Overall, its running time is 12 times lesser than the reversed method. The reversed function took longer time because of the list casting overhead. If we had a for loop, it would have taken less time. The slicing technique comes second place. So, that is why I use the python list reverse method often when reversing lists.

The list reverse method works better because it has been optimized by the developers of python for lists. I believe they look to these things that is why it was made to be a method of the list class.

So, now you have the options you can choose from while reversing lists. Use any to your heart’s desire.

Happy pythoning.