The great thing about writing code that is memory-efficient is that it does not necessarily require you to write more lines of code. More available RAM would generally mean more room for cache, which will help speed up disk access. It leads to faster processing and less need for resources, namely random access memory (RAM) usage.So what do we get out of writing memory-efficient code? Ultimately, you can enforce it as a habit that can potentially be adopted in other programming languages that you know. However, having a good understanding of Python memory management is a great start that will enable you to write more efficient code. Due to its simplicity, however, Python does not provide you much freedom in managing memory usage, unlike in languages like C++ where you can manually allocate and free memory. Ever heard of the Python memory manager? It is the manager keeping Python's memory in check, thus enabling you to focus on your code instead of having to worry about memory management. How so? Python memory management is implemented in a way that makes our life easier. Python's memory management plays a role in its popularity, too. While Python is not the fastest language out there, its great readability coupled with unrivaled community support and library availability has made it extremely attractive for getting things done with code. This is largely due to its super friendly syntax and its applicability for just about any purpose. However, according to the 2020 Stack Overflow Developer Survey results, Python is the 2nd most popular programming language behind JavaScript (as you may have guessed). Step 2 − Move the last element of last level to root.Python is not known to be a "fast" programming language. Deletion in Max (or Min) Heap always happens at the root to remove the Maximum (or minimum) value. Let us derive an algorithm to delete from max heap. We consider the same input sample that we used earlier. Let's understand Max Heap construction by an animated illustration. Note − In Min Heap construction algorithm, we expect the value of the parent node to be less than that of the child node. Step 5 − Repeat step 3 & 4 until Heap property holds. Step 4 − If value of parent is less than child, then swap them. Step 3 − Compare the value of this child node with its parent. Step 1 − Create a new node at the end of heap. While insertion, we also assume that we are inserting a node in an already heapified tree. At any point of time, heap must maintain its property. We are going to derive an algorithm for max heap by inserting one element at a time. The procedure to create Min Heap is similar but we go for min values instead of max values. We shall use the same example to demonstrate how a Max Heap is created. Max-Heap − Where the value of the root node is greater than or equal to either of its children.īoth trees are constructed using the same input and order of arrival. Min-Heap − Where the value of the root node is less than or equal to either of its children. Based on this criteria, a heap can be of two types −įor Input → 35 33 42 10 14 19 27 44 26 31 If α has child node β then −Īs the value of parent is greater than that of child, this property generates Max Heap. Heap is a special case of balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly.
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