# How do you sort elements in descending order in Python?

## How do you sort elements in descending order in Python?

**Python** list. **sort**() is a inbuilt method in **Python**, it is used to **sort the elements**/objects of the list in **Ascending** and **Descending Order**. To **sort** a list in **descending order**, we pass reverse=True as an argument with **sort**() method.

## How do you sort a list alphabetically backwards in Python?

Use **sort**() to change your **list** so it's stored in **alphabetical order**. Print the **list** to show that its **order** has been changed. Use **sort**() to change your **list** so it's stored in **reverse alphabetical order**. Print the **list** to show that its **order** has changed.

## How do you sort in reverse order in Python?

If you want to **sort** in a **descending order**, all you have to do is add the parameter **reverse** = True to either the **sort** or **sorted** functions. They both accept it!

## What does sorted () do in Python?

**Python sorted()** Function The **sorted()** function returns a **sorted** list of the specified iterable object. You **can** specify ascending or descending order. Strings are **sorted** alphabetically, and numbers are **sorted** numerically. Note: You cannot **sort** a list that contains BOTH string values AND numeric values.

## What is O N K?

**O**(**nk**) means the time it takes is proportional to **n** * **k** . **O**(**n**+**k**) means the time it takes is proportional to **n** + **k** . It's exactly what it seems like. ... In your case, the algorithm's runtime is **O**(**nk**) because the inner loop runs a total of **n** * **k** times.

## What is Big O of N?

**O**(**n**) represents the complexity of a function that increases linearly and in direct proportion to the number of inputs. This is a good example of how **Big O** Notation describes the worst case scenario as the function could return the true after reading the first element or false after reading all **n** elements.

## Which time complexity is best?

Sorting algorithms

Algorithm | Data structure | Time complexity:Best |
---|---|---|

Merge sort | Array | O(n log(n)) |

Heap sort | Array | O(n log(n)) |

Smooth sort | Array | O(n) |

Bubble sort | Array | O(n) |

## How can we reduce time complexity?

First of all make it clear that **time** taken by program depends upon the language you choose and the algorithm you apply. You can not change the **time** taken by the language compiler but you can certainly **reduce** the **time complexity** of your program.

## What is O n in Python?

Linear Time — **O**(**n**) An algorithm is said to have a linear time complexity when the running time increases at most linearly with the size of the input data. This is the best possible time complexity when the algorithm must examine all values in the input data.

## Why do we use == in Python?

The **==** operator compares the value or equality of two objects, whereas the **Python is** operator checks whether two variables point to the same object in memory. In the vast majority of cases, this means you **should use** the equality operators **==** and != , except when you're comparing to None .

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