Heaps & Priority Queues in Python

Today we learn how to use heaps in Python. Heaps are efficient implementations of priority queues.
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[@dhbroad2660]

Thank you for this! So many other videos on heaps just speak about the theory, but don't show any implementation or manipulation.

[@Gooloso98]

nice video, remind me of data structures classes. min heap its great and efficient. Usually its implemented on top of arrays, although its best viewed as graphs. min heap always keep a min height state since the height of the tree generated will be log2(numbers inside).

[@rafaelsartoricavalcante4253]

Best video on subject, thanks

[@vikasjha1064]

Brilliant mate, ,Please make these type of explanation for other Data Structure also. Thanks

[@takshdhabalia1476]

Great video Loved the content Keep it up

[@adarshsasidharan254]

Brilliant video

[@lzdps]

awesome!

[@servantofthelord8147]

Thank you!

[@tcgvsocg1458]

can you do a video on screen recorder but no import with vanilla pyhton please

[@anthonyrojas9989]

Your videos are awesome, but the music is loud af in the beginning and then the voice audio is way lower. I think that could be better

[@soniahandle]

hi, can someone explain what "maintaining heap invariant" means?

[@serratedhumor8468]

invariant = unchanging, immutable.

[@abhishekpardeshi5701]

you did not mention priority queue code

[@disrael2101]

What for / where it's used for mainly

[@jerrylin4980]

When you need to insert and retrieve multiple times while the order of the list needs to be maintain, I think it's good to use a heap.

[@Pickolzi]

I am still new to this topic but I will try to explain why I believe it is better to use heaps. Say you have a list of 10,000 elements and that you always need the lowest or highest number. If you are constantly adding and removing elements from the array, after each append() or pop(), you will need to sort the list to maintain the order of the list. 1. Using list.sort() will at worst case run in O(n*logn) run time 2. iterating through each item of the list to find the proper position to place the new element will at worst case run in O(n) run time 3. inserting an element in a heap will at worst case run in O(logn) run time So to insert a new element into a 10,000 element array and maintain order would take 1. ~130,000 iterations for list.sort() 2. ~10,000 iterations for iterating the array 3. ~14 iterations for a heappush() So imagine you have 10,000 people trying to buy tickets online, each person has a rank depending on previous activity. You can store their data in a tuple (rank, user_id). So when tickets start to become available for sale and the website wants to prioritize higher ranked customers, using a heap will allow the website to run a lot faster especially with a large audience.

[@disrael2101]

cool thanks for the explanation, it makes sense only the math formula i didn't understand as you didn't explain what each parameter means.. also what's the equalivent of heap in js? as i work with js for webapps@@Pickolzi

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[@user-vm9hl3gl5h]

valid heap: 2:12 of kzbin.info/www/bejne/npzcq5aCoNKieLs => parent should be smaller than children (min-heap).

[@digvijaynadimetla9934]

devudivi sami

[@niteshanthony2324]

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[@dheerajsharma355]

Hare Krishna!!

[@Mr_Clumsee]

Jai Shree Ram!!

[@mrsteth993]

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