Selection Sort with Python
Selection Sort
The Selection Sort algorithm finds the lowest value in an array and moves it to the front of the array.
{{ msgDone }}
The algorithm looks through the array again and again, moving the next lowest values to the front, until the array is sorted.
How it works:
- Go through the array to find the lowest value.
- Move the lowest value to the front of the unsorted part of the array.
- Go through the array again as many times as there are values in the array.
Manual Run Through
Before we implement the Selection Sort algorithm in Python program, let's manually run through a short array only one time, just to get the idea.
Step 1: We start with an unsorted array.
[ 7, 12, 9, 11, 3]Step 2: Go through the array, one value at a time. Which value is the lowest? 3, right?
[ 7, 12, 9, 11, 3]Step 3: Move the lowest value 3 to the front of the array.
[ 3, 7, 12, 9, 11]Step 4: Look through the rest of the values, starting with 7. 7 is the lowest value, and already at the front of the array, so we don't need to move it.
[ 3, 7, 12, 9, 11]Step 5: Look through the rest of the array: 12, 9 and 11. 9 is the lowest value.
[ 3, 7, 12, 9, 11]Step 6: Move 9 to the front.
[ 3, 7, 9, 12, 11]Step 7: Looking at 12 and 11, 11 is the lowest.
[ 3, 7, 9, 12, 11]Step 8: Move it to the front.
[ 3, 7, 9, 11, 12]Finally, the array is sorted.
Run the simulation below to see the steps above animated:
Implement Selection Sort in Python
To implement the Selection Sort algorithm in Python, we need:
- An array with values to sort.
- An inner loop that goes through the array, finds the lowest value, and moves it to the front of the array. This loop must loop through one less value each time it runs.
- An outer loop that controls how many times the inner loop must run. For an array with \(n\) values, this outer loop must run \(n-1\) times.
The resulting code looks like this:
Example
Using the Selection sort on a Python list:
n = len(mylist)
for i in range(n-1):
min_index = i
for j in range(i+1, n):
if mylist[j]< mylist[min_index]:
min_index = j
min_value = mylist.pop(min_index)
mylist.insert(i, min_value)
print(mylist)
Selection Sort Shifting Problem
The Selection Sort algorithm can be improved a little bit more.
In the code above, the lowest value element is removed, and then inserted in front of the array.
Each time the next lowest value array element is removed, all following elements must be shifted one place down to make up for the removal.
These shifting operation takes a lot of time, and we are not even done yet! After the lowest value (5) is found and removed, it is inserted at the start of the array, causing all following values to shift one position up to make space for the new value, like the image below shows.
Note:You will not see these shifting operations happening in the code if you are using a high level programming language such as Python or Java, but the shifting operations are still happening in the background. Such shifting operations require extra time for the computer to do, which can be a problem.
Solution: Swap Values!
Instead of all the shifting, swap the lowest value (5) with the first value (64) like below.
We can swap values like the image above shows because the lowest value ends up in the correct position, and it does not matter where we put the other value we are swapping with, because it is not sorted yet.
Here is a simulation that shows how this improved Selection Sort with swapping works:
{{ msgDone }}
We will insert the improvement in the Selection Sort algorithm:
Example
The improved Selection Sort algorithm, including swapping values:
n = len(mylist)
for i in range(n):
min_index = i
for j in range(i+1, n):
if mylist[j]< mylist[min_index]:
min_index = j
mylist[i], mylist[min_index] = mylist[min_index], mylist[i]
print(mylist)
Selection Sort Time Complexity
Selection Sort sorts an array of \(n\) values.
On average, about \(\frac{n}{2}\) elements are compared to find the lowest value in each loop.
And Selection Sort must run the loop to find the lowest value approximately \(n\) times.
We get time complexity: \( O( \frac{n}{2} \cdot n) = {O(n^2)} \)
The time complexity for the Selection Sort algorithm can be displayed in a graph like this:
As you can see, the run time is the same as for Bubble Sort: The run time increases really fast when the size of the array is increased.
