6 Tips to write BETTER For Loops in Python 

I agree, but thanks Patrick, very useful to know.

The assumption is that you can read the manual to find out what things mean.

Problem here is more deep, than you might think. This guy said "functions are optimized", but that's not a complete true. Functions can allocate extra stuff during calculations what makes such ones not optimized. Allocations could be avoided using just loops. Even if function is fully optimized you may have another flaw, because sometimes more than one operation is needed and then output from one function goes to input of another function, which now do extra calculations and may be not optimized. This is why you should not rely on those functions on 100% if you don't understand concepts of memory management and optimization

@@alexmiller3260 well the easy answer here is that partially D'Oliviero already answered you and I can say that seems you are referring to iterative and recursive functions. Of course if you are programming you have to know of how it works regarding memory management etc... It is a choice you will make at the moment and maybe refactor it along the way...

For loops in Python are terribly slow, you should always avoid them.

It is not equivalent, the slicing syntax copy the slice while islice() is a generator that doesn't copy the array. For memory use and sometimes execution speed, islice will be better.

@@chaddaifouche536 Didn't know that, thanks

@@Migoyan Additionally, islice will also work on generators while the slicing syntax is specific to lists.

@Bebtelovimab I see you don't need tips on being unfunny.

@Bebtelovimab Ok bro, we understand you're a kid. Now you can resume your day.

I was also thinking the same thing.

I think the main situation you would use islice is when you want to slice a generator. For a plain list, regular slicing in the index (as you've written) is easily understandable and also probably the most efficient. But generators don't support indexing and islice supports any iterable including generators. A small caveat that I discovered after checking the docs is that islice doesn't support negative indices but regular slicing does (e.g. last 3 elements lines[-3:])

islice() creates an iterator over the initial array content while list slicing creates a copy (with Python lists, slicing on numpy arrays just creates alias, which sometimes results in strange behaviour, probably the reason why basic Python doesn't do it this way). On small slices this is not important but if you use big slices, in a nested loop, slicing may give a lot of work to your garbage collector.

You can do that with dictionaries as well: **dict will place (key, value) pairs in the function call as optional arguments in a key=value form. However this video is about for loops and not about arrays or other data structures, so I guess that's why function calls not mentioned.

You might want to look at DictCursor which is part of the python database API and does this for you.

@@glennmglazer I can’t see that in the standard library anywhere.

@@lawrencedoliveiro9104 he meant psycopg2 functionality. Don’t you use it to call db queries from your python code? I don’t believe you do it using standard library

@@baturin_m Not part of the “Python database API” that I can see. I have used APSW, pymysql and of course Oracle’s mysql.connector classes.

@@lawrencedoliveiro9104 got it. It’s strange that all of these connectors don’t provide this feature like psycopg2 does. It really helpes a lot in case you cannot use ORM for higher abstraction level.

python's strength is definetly not speed so i dont think it matters that much

(also mentioned this in detail in another answer) islice supports generators and you can't do regular slicing in generators because you can't index into a generator

islice, like a generator, does not store all the values in memory at once, so if your list is very large and you don't want to store a copy of some of it, then you should use islice. This can help save memory. However, islice should only be used when your list is big; otherwise it can actually be slower than normal slice notation as using islice requires calling a function, and in Python calling a function is very slow. In the example in the video, on my computer using Python 3.10.7, the code actually ran faster using normal slice notation than using islice.

It is not the same thing. *lst[start:end:step]* is another list than *lst* that can be modified later separately form *lst* . It is created when you use that syntax, and each item belonging to the slicing will be copied into it. If the slice is huge, you will be consuming much memory and using much copying operations just by doing *lst[start:end:step]* But if you use *slc=islice(lst,start,end,step)* nothing will be copied or calculated. When you iterate through *slc* , python will retrieve values from the original list *lst* using the indices of that slicing. You can try it yourself. l1=[1,2,3,4] l2=l1[:] then modify *l1* by reassigning values, appending, popping etc... then iterate trough *l2* , it will contain the original 1, 2, 3, 4 However if you do like this, lst=[1,2,3,4] s=islice(lst,4) then modify *lst* then iterate trough *s* , you will find that it yields the modified values.

Suppose I write a generator which never terminates. Then I can use islice to extract any initial part of the infinite sequence.

@@lawrencedoliveiro9104 That's an amazing idea.

The more you do it, the more adept you will get at it, and when you write a truly elegant and performant piece of code (be it a function, a class, or whatever), it will feel like poetry. Same with elegant mathematical proofs. I love Python, but I prefer Kotlin because it has so much elegant, built in functional programming constructs, and functional programming can take your code and shrink its length dramatically. Python has some functional programming concepts built in, but they're a bit clunky. It's still a great language, though, if you're not worried about performance (and if you are, just make sure you use built-ins and numpy, since they're implemented in C so they run much faster than loops).

@@vorpal22 Guess I gotta learn Kotlin next. But trying to get into coding, I feel like I should at least spend some time with Python more before learning another language. Although the job market requires multiple languages for us to land a job, I just feel like becoming advanced in one language could perhaps be beneficial. Any thoughts?

@@md.masumomarjashim I agree that you should focus on one language for awhile before moving to the next... if you really want to become a developer, you should have a strong working knowledge of at least one language, and then have some exposure to other languages so you cover your bases. You probably want to know some of each of the following: 1. Object-oriented programming 2. Procedural programming 3. Functional programming Python is - according to many surveys - the language that is in highest demand now (followed by Java, but it is declining), so it's a good choice. There's a book called "Fluent Python" that is the best I have ever looked at: it is a hefty one, but you don't have to read it cover-to-cover and it'll teach you a lot of Python than you probably didn't even know existed. O'Reilly just updated it for Python 3.10, so it's very up-to-date, and you can just skip around the chapters and learn what you need or what interests you. No Python developer should be without it. To learn Kotlin is a bit harder, because you do need to have some working knowledge of Java since Kotlin compiles into Java bytecode (same with Scala and Clojure, which are also both hybrid object-oriented and functional programming languages... Clojure is really messy, in my opinion, but it's interesting to dabble in because it makes you think about programming very differently). Keep in mind that if you know a couple languages, many companies aren't too concerned about what languages you know: you can learn one pretty quickly and it's a small investment on their part. The best thing to do is show that you have problem solving skills. There is a website called "daily coding problem" that is really valuable and covers lots of questions you'd encounter in an interview. You can sign up for free to get the problems delivered to your inbox daily (they are ranked by easy, medium, and hard), and if you pay a small fee, you can also get the best solutions to the problems the next day. Another thing that is becoming more and more popular in the hiring process is having a GitHub account with a bunch of repositories that you're working on: either personal projects, or contributions to open-source projects (I find that a bit more intimidating), school assignments, etc. It's like a programmer's equivalent of an artist's portfolio. If you don't know GitHub yet, you should take a few days to learn the basics. You usually don't need more than the basics, and if you do, you can google the answer, but it's what virtually everyone is using these days for source code repositories. Some people will probably disagree with me because we all have different opinions. How are you learning? By studying Python on your own? Do you have a reasonable background in math? You might not need to use, say, algebra much unless you get into image processing or manipulation or gaming, but math really teaches you fundamentals of thinking logically. Anyway, if you have any more questions, feel free to hit me up. Always happy to help. It was easy for me, because I got a Commodore 64 when I was five and I've been programming since then, so the huge mountain of things to know was never that insurmountable since I've been doing it for 40 years, but one thing I will warn you about: if you want to be a developer, it has to be your career as well as your hobby, because if you don't spend some of your free time keeping up with tech changes, you'll fall behind. It's kind of exhausting and stressful that way.

@@vorpal22 Wow! You have such an elegant and meticulous way of writing!! I just have a single question. What should I do to be a full stack developer? What things should I learn?(not only languages but frameworks and stuff like that) I'm kinda new to programming. I have started with python before like 2 months and I think I know the basics. Does python really help me with being a full stack developer? Let me know your thoughts. Thanks!

@@vorpal22 this is such a well thought and written comment. Trurly remarkable sir.

It will almost always make it faster on the expense of more memory consumption and binary dependency on the import.

consider using math.fsum() numpy is typically 3rd party, while math is built-in

Thanks for the feedback! Yes that's on my list for 2023

@@patloeber Thank you.

one dark pro i guess

Yes, VS Code w/ the "shades of purple" theme

yes but it is implemented in C which is way faster

@@DendrocnideMoroides thanks bro, could you please explain why python is slow as compared to c, is it because python use interpreter ?

​@@sleeplessmidnight779 there are 2 main reasons why python is slower than C (and most other programming languages) (yes that fact that python is interpreted also slows it down but that is not one of the main reasons) Biggest Reason: it is dynamically-typed meaning that if you create a variable that is of a certain type you can easily change its type afterward (For example if you write a=5 (which is an integer) and then you write a="Hello" (which is a string) python will accept that instead of throwing an error like statically-typed languages) this means python will always need to check the type of the variable before any operation this issue can be overcome by using CPython where you can specify the types variables, CPython is not quite as fast as pure C but still a good option because its syntax is very similar to that of python's, and in fact you do not need to specify the type of every variable but the more you specify the faster the code will become 2nd Biggest Reason: it has a "Global Interpreter Lock" which means python can only use one of the threads/cores of your computer while executing code (this is for safety reasons and exists because Python is dynamically-typed) this issue can be overcome by using multithreading but using multithreading is less safe and you should only do it if you know what you are doing

There is no magical way to sum a list without enumerating it. However, not much can beat compiled C code, performance wise, which is what the built-in sum() function uses under-the-hood. It's generally at least 5x faster than a standard Python for loop.

No you shouldn't. This is exactly what Python's built-ins do for you. Use them.

shades of purple

1:00 I assume he means "enumerate" is more intuitive to use, since it avoids having to pass indices to arrays to retrieve values. I think he's right in this case. A lot of bugs live in for loops - especially near boundaries.

What general purpose language(s) do you prefer?

@@djst3rling863 C#, rust, go, dart, kotlin, swift.

What you're looking at is just syntactic sugar. Recall that Python is a high level language which generally sits on top of C. Thus, it's all the same under-the-hood. Compile your Python to C and disassemble it. You'll see all of your familiar goto, cmp, jmp, etc. - statements.

It’s not only about run-time efficiency, but also code readability, maintainability and reduce the risk of introducing bugs. If you want to compute the sum of all values of a list, it is better to state exactly that on a single line that to create a custom loop, with local variables with no other purpose, and letting to the reader to identify the pattern.

There is a massive difference in performance between a loop of Python bytecode instructions (which a Python for loop will result in) and pure C loop which, for example, the sum() function will use.

@@adrianbool4568 i agree with what you say, however, you are still missing my point.

@@juanignaciobruzzone2896 If your argument is that you want people to learn more; how can you argue against teaching people alternative (and often more efficient) ways to implement algoirthms beyond blindly repeating for loops all over the place?