# How do you sort a list by value in Python?

## How do you sort a list by value in Python?

The **sort**() method sorts the elements of a given **list** in a specific ascending or descending **order**. The syntax of the **sort**() method is: **list**. **sort**(key=..., reverse=...)

## How does Python sort work?

It was invented by Tim Peters in 2002 for use in the **Python** programming language. The algorithm finds subsets of the data that are already ordered, and uses the subsets to **sort** the data more efficiently. This is done by merging an identified subset, called a run, with existing runs until certain criteria are fulfilled./span>

## What is the advantage of quick sort?

The quick sort is regarded as the best sorting algorithm. This is because of its significant advantage in terms of **efficiency** because it is able to deal well with a huge list of items. Because it sorts in place, no additional storage is required as well./span>

## What is Big O used for?

**Big O** notation is **used in** Computer Science to describe the performance or complexity of an algorithm. **Big O** specifically describes the worst-case scenario, and can be **used to** describe the execution time required or the space **used** (e.g. in memory or on disk) by an algorithm.

## Is Big O the worst case?

But **Big O** notation focuses on the **worst**-**case** scenario, which is 0(n) for simple search. It's a reassurance that simple search will never be slower than **O**(n) time./span>

## What is big O runtime?

**Big O** Notation is the language we use to describe the complexity of an algorithm. In other words, **Big O** Notation is the language we use for talking about how long an algorithm takes to run. ... With **Big O** Notation we express the **runtime** in terms of — how quickly it grows relative to the input, as the input gets larger ./span>

## How is Big O runtime calculated?

**To calculate Big O, there are five steps you should follow:**

- Break your algorithm/function into individual operations.
**Calculate**the**Big O**of each operation.- Add up the
**Big O**of each operation together. - Remove the constants.
- Find the highest order term — this will be what we consider the
**Big O**of our algorithm/function.

## Which is better O N or O Nlogn?

Yes constant time i.e. **O**(1) is **better** than linear time **O**(**n**) because the former is not depending **on** the input-size of the problem. The order is **O**(1) > **O** (logn) > **O** (**n**) > **O** (**nlogn**)./span>

## Which time complexity is fastest?

**Types of Big O Notations:**

- Constant-
**Time**Algorithm - O (1) - Order 1: This is the**fastest time complexity**since the**time**it takes to execute a program is always the same. ... - Linear-
**Time**Algorithm - O(n) - Order N: Linear**Time complexity**completely depends on the input size i.e directly proportional.

## Which time complexity is faster?

In general cases, we mainly used to measure and compare the worst-case theoretical running **time complexities** of algorithms for the performance analysis. The **fastest** possible running **time** for any algorithm is O(1), commonly referred to as Constant Running **Time**.

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