Quicksort In Python Explained (With Example And Code)
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- čas přidán 9. 07. 2024
- Quicksort is an efficient sorting algorithm with O(n*logn) average running time. In this video I show you a quick example and how to implement this algorithm in Python step by step.
This video is part of the basic algorithms in Python playlist. The goal is to get an understanding of basic computer science algorithms and their implementation in Python.
For more coding videos subscribe to my youtube channel. - Věda a technologie
i've been searching for hours for a video that could explain exactly what happens when there is only 2 elements, and i finally got it! thanks a lot
Of all the videos I watched on youtube for quick sort, I understood this one a lot better in a much more intuitive way.
this entire playlist saved me great deal of time. thank you very much!
Best explanation on CZcams. You should continue making these types of videos. You do such a great job at it!
one of the finest explanations found on youtube.
Very best explanation of the quick sort. Thank you. Please ignore the Dislikes and Keep it up bro. You did an amazing job to clear the understanding of this algorithm. Thank you again!
Bro you gave me full understanding of quick sort! Thank you! Please continue to make such videos with other sortings in Python!
This is indeed a great explanation.Thanks a ton.
Thanks bro, really great visual. You saved me a lot of time
LOVE THE SWAP ANIMATION!
Masha"Allah Felix, This took me a day to understand, you video helped me alot
Thank you for so comprehensive explanation
nice smoothing voice, thanks for the explanation FelixTechTips!!
The graphic explanation is perfect.
Thank you sir. That was very good explanation.
you saved my time thank you
Thank you, this helps me ✨
Very good explanation
Could we also make the right endpoint just the very end of the array and the left at index 0? Or how would we do it if a different pivot was used?
I got it thank you
Nice animations!
In your visual explanation, you mentioned a scenario where j < i. That will never happen in your code because both while loops break when i=j. Anyway, it's a good vid nonetheless. thanks
I don't think that's right. Only the outer loop will break when i=j
when i does not get incremented if the while loop does not run at all initially, that then creates a negative -1 right parameter in the quicksort(arr, left, partition_pos-1) ,where partition_pos=0, function later on. Why isn't this a problem?
good works
Cool!
Incredible explanation!
Thank you :)
what ever your reason was, that made you stop making videos. 1. Thanks for making them, really well explained! Hut ab!
2. The market for tutorial creators might seem "overcrowded" but really sad that you stopped, I think you have great potential!
Have a good life, mate! 🤘
Vielen vielen Dank für die nette Worte. Ich hatte wenig Zeit in den letzten Jahren, aber ich plane ab 2024 weiterzumachen :) Grüße nach Indonesien :)
@@FelixTechTips dann wünsche ich dir einen starken Start im neuen Jahr! 💪🏻🤘🏻🤘🏻🤘🏻🤘🏻😁
Hey !your explanation is great.one quick question
Python has line by line interpreter
So,when we call partition function won't that cause declaration error because it should be declared first before calling...
Instead of passing list as input, if tuple, dictionary, set is passed. So will the time complexity of the algorithm remains same or will differ?
Thanks,
Now this won't work on tuple i suppose because tuples are immutable and in this algorithm we swap and make changes in place itself. So.. Nope it won't work for tuples.it would give us error... So it's time complexity might not be there with tuples.
cual es la condicion de parada para la recursion?
Super.....but ur code should be zoomed it will look better anyway nice ❤️
At 11:08, you say "the j that defines the point right of the pivot," but if the pivot is always the last element in the array, nothing can be to its right. Did you mean "the point left of the pivot"?
i think he wanted to say "right next to the pivot"
Thank you! Your explanations were very good. How would I change the code for parallel quickSort? Is dynamic parallel quickSort the same as just parallel quickSort?
I’m not sure what this means but this sounds like you could know the answer to this. Could we also make the right endpoint just the very end of the array and the left at index 0? Or how would we do it if a different pivot was used?
For me, i found another way to do the partitioning using a for loop in python
def partition(arr, high, low=0):
if not arr and not high:
return 'No array and no upperbound'
pivot = arr[high]
partion_at = low
for j in range(low, high):
if arr[j]
No
@@atharvachouhan474 no what?
@@samuelvalentine7846 yes
ur awesome
Why don't we use "if array[i] > pivot and i > j "# indicating that they have crossed each other instead of "if array[i] > pivot:" since our objective is to handle the scenario where they cross each other?
I'm having trouble understanding one thing. After i and j meet, shouldn't we just unconditionally exchange arr[i] and the pivot? Why do we check that arr[i] > arr[right] at that point? Shouldn't the pivot be placed at that point that i and j meet in order to be properly sorted?
Same doubt bro
i think the check is considering the case that only TWO elements were left. we always pick up the last element as our pivot, and the remaining one will become the one=i=j automatically. so we should check it first before we swap them. Like in the video 8:27, what if we have 77 88 left already inplace instead of 88 77?
Thank you so much for the explanation!
By the way, how should this algorithm be implemented with random element chosen as the pivot?
we could use any element as pivot in this case he has taken the last one
@@anirudhsoni6529would we put the right pointer at the very end of the array instead of how he has it here, where it is just left of the last element?
Kalander op
i faced a problem in it that quick sort isn't quick with me at all
idk why it doesn't work i guess my laptop is kinda weak
j can be less than i???
13:36 my own video mark
Dadu pashu 😍😍😍😍
What if there are duplicates?
Thanks for the wonderful video. The code was not worked for me. It went infinite loop. I fixed the issue by adding i += 1 and j -= 1 after arr[i], arr[j] = arr[j], arr[i]
Can you please upload the quick sort for worst case scenario with O(n^2) ?
That happena when the pivot element is either really big or really small not sure which it was, maybe both
Could you please explain me what will the space complexity be ?
I may be late but Quicksort has O(logn) space complexity. This is due to the algorithm recursively calling itself which takes up space in memory because it must remember where it was in the previous levels of recursion. Because Quicksort on average has log base (4/3) of n recursion levels as that is the average partition for Quicksort and log base (4/3) of n is in O(logn), that is its space complexity.
Sirr where r urrr Videos... it's been a yearr...Did u quit???? 😭😭😭
Why return i?
Qulandar
Please why is J right -1?
right means size of array i.e, 8
here index will start from 0th so array[last_ment] =>array[8]
it means we dont have 8th element in array
thats why right -1
I think it's ' j= right '
unbound local partition error
I got an error when I use 100 elements of array
I suppose it's stack overflow cuz it hits stack size of your system
how about this:
def quicksort(arr):
if len(arr) pivot]
return quicksort(left) + middle + quicksort(right)
Dadu
ok, I posted a comment before that was incorrect. I was missing a piece of code - Works wonderfully - Here is another example in python of quickSort application
Check my function below and works for any example of an array.
def quickSort(dataset, first, last):
if first < last:
pivotIdx = partition(dataset, first, last)
# now sort the two partitions
quickSort(dataset, first, pivotIdx-1)
quickSort(dataset, pivotIdx+1, last)
def partition(datavalues, first, last):
# choose the first item as the pivot value
pivotvalue = datavalues[first]
# establish the upper and lower indexes
lower = first + 1
upper = last
# start searching for the crossing point
done = False
while not done:
# TODO: advance the lower index
while lower = lower:
upper -= 1
# TODO: if the two indexes cross, we have found the split point
if upper < lower:
done = True
else: # if they haven't cross each other
temp = datavalues[lower]
datavalues[lower] = datavalues[upper]
datavalues[upper] = temp
temp = datavalues[first]
datavalues[first] = datavalues[upper]
datavalues[upper] = temp
# return the split point index
return upper
# test the merge sort with data
print(items)
quickSort(items, 0, len(items)-1)
print(items)
If you run the sorting function, the result is None. This is because the sorting function does not Return anything.
You may want to edit your code.
works fine for me. It isn't returning anything because this algorithm is designed to be in-place and it is just modifying the original array itself. print your array, call quick_sort(), then print your same array again and you will see it sorted. If you want, you can add return arr at the end of your quick_sort function, then in your main call it using sorted_array = quick_sort(), but it's not necessary.
it will work just fine cause he is sending list as a parameter and that is passed by reference
May you pillow be always cold on both sides