The problem: You are given an unordered array consisting of consecutive integers [1, 2, 3, …, n] without any duplicates. You are allowed to swap any two elements. You need to find the minimum number of swaps required to sort the array in ascending order. My solution/issue: My code does what it is supposed to; however, for large arrays it
Tag: sorting
Python custom function for sorting a list of objects
A list of Point class objects is given. The Point class is as follows: The goal is to sort the list by the value attribute provided that if two objects have the same value attributes, the object whose is_start value is True should go before the object whose is_start value is False. An example of input: I’ve tried using array.sort(key=lambda
pandas df – sort on index but exclude first column from sort
I want to sort this df on rows (‘bad job’) but I want to exclude the first column from the sort so it remains where it is: expected output: I don’t know to edit my code below to exclude the 1st column from the sort: Answer Use argsort with add 1 for possible add first value 0 by reindex for
Getting indices of ascending order of list
I know that this question has been asked a hundred times, but the answer always seems to be “use numpy’s argsort”. But either I am misinterpreting what most people are asking, or the answers are not correct for the question. Whatever be the case, I wish to get indices of a list’s ascending order. The phrasing is confusing, so as
Nested lambda statements when sorting lists
I wish to sort the below list first by the number, then by the text. Attempt 1 I was not happy with this since it required splitting a string twice, to extract the relevant components. Attempt 2 I came up with the below solution. But I am hoping there is a more succinct solution via Pythonic lambda statements. I looked
Sort a pandas dataframe series by month name
I have a Series object that has: Problem statement: I want to make it appear by month and compute the mean price for each month and present it with a sorted manner by month. Desired Output: I thought of making a list and passing it in a sort function: but the sort_values doesn’t support that for series. One big problem
Pandas sort_values does not sort numbers correctly
I’m new to pandas and working with tabular data in a programming environment. I have sorted a dataframe by a specific column but the answer that panda spits out is not exactly correct. Here is the code I have used: The result that the sort method yields values in column ‘overall league position’ are not sorted in ascending or order
how to sort pandas dataframe from one column
I have a data frame like this: As you can see, months are not in calendar order. So I created a second column to get the month number corresponding to each month (1-12). From there, how can I sort this data frame according to calendar months’ order? Answer Use sort_values to sort the df by a specific column’s values: If
How to sort by value efficiently in PySpark?
I want to sort my K,V tuples by V, i.e. by the value. I know that TakeOrdered is good for this if you know how many you need: Using TakeOrdered: Using Lambda I’ve checked out the question here, which suggests the latter. I find it hard to believe that takeOrdered is so succinct and yet it requires the same number
How does the functools cmp_to_key function work?
In Python, both list.sort method and sorted built-in function accepts an optional parameter named key, which is a function that, given an element from the list returns its sorting key. Older Python versions used a different approach using the cmp parameter instead, which is a function that, given two elements from the list returns a negative number if the first