9:46 You forgot to say "- 1" after "right will be the length of the array" (since left starts at 0 and not 1). The explanation is great! I forgot about how this works after several years of no practice, but now I remembered everything!
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!
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
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! 🤘
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]
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...
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?
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]
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?
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'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?
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! 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?
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)
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.
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.
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
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.
Of all the videos I watched on youtube for quick sort, I understood this one a lot better in a much more intuitive way.
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
9:46 You forgot to say "- 1" after "right will be the length of the array" (since left starts at 0 and not 1).
The explanation is great! I forgot about how this works after several years of no practice, but now I remembered everything!
this entire playlist saved me great deal of time. thank you very much!
Best explanation on TH-cam. You should continue making these types of videos. You do such a great job at it!
Thank you sir. That was very good explanation.
Thanks a million for making this algorithm easy to understand.
Bro you gave me full understanding of quick sort! Thank you! Please continue to make such videos with other sortings in Python!
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!
one of the finest explanations found on youtube.
The graphic explanation is perfect.
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
Yes, I also found this discrepancy. Probably just an omission on author's side. But in general - wonderful explanation, thanks a lot!
Thanks bro, really great visual. You saved me a lot of time
I can't like this video enough!!! Danke schön! You explain so well -- I've finally understood Quicksort!
LOVE THE SWAP ANIMATION!
man thanks a lot even though i don't get it well yet but i will rewatch again
This is indeed a great explanation.Thanks a ton.
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! 💪🏻🤘🏻🤘🏻🤘🏻🤘🏻😁
@@FelixTechTips Really eager to get your videos more and more on algorithms and coding.
That was an amazing way of explaining quick sort thanks
thank you for explanation but I'm looking for in-place implementation for the quick sort because it's an in-place one
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]
nice smoothing voice, thanks for the explanation FelixTechTips!!
Incredible explanation!
Thank you :)
Masha"Allah Felix, This took me a day to understand, you video helped me alot
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...
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?
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?
Thank you for so comprehensive explanation
Very good explanation
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
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?
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"
Super.....but ur code should be zoomed it will look better anyway nice ❤️
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?
Sirr where r urrr Videos... it's been a yearr...Did u quit???? 😭😭😭
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?
In your example there was no scene where you swapped the elements at I and j position then why did you do so in code?
Thank you!
you saved my time thank you
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
Thank you so much
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?
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
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)
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.
Thank you, this helps me ✨
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.
good works
I got it thank you
Danke bro!
cual es la condicion de parada para la recursion?
Nice animations!
13:36 my own video mark
j can be less than i???
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
how about this:
def quicksort(arr):
if len(arr) pivot]
return quicksort(left) + middle + quicksort(right)
Cool!
What if there are duplicates?
Kalander op
Dadu pashu 😍😍😍😍
ur awesome
I think it's ' j= right '
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
Why return i?
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
Bro this could have been done with a single while loop, why increase time complexity. Nice vdo but you lost me there
Qulandar
May you pillow be always cold on both sides
Dadu