Python Data Structures and Algorithms
Project description
Python Data Structures and Algorithms
Install
pip3 install gopy
or
pip install gopy
Usage
You can test this by making a python file test.py
Example: Bubble Sort
from gopy.sorting import bubble
print(bubble.sort([5,4,3,2,1]))
Output:
[1,2,3,4,5]
Example: Linear Search
from gopy.search import lsearch
print(lsearch.search(3,[5,4,3,2,1]))
Output:
2
Example: Binary Search
from gopy.search import bsearch
print(bsearch.search(30,[5,4,3,2,1]))
Output:
Not Found
For Education
This library can be used for production software as well as for educational purposes.
Example: Learn Quick Sort
import gopy.sorting.quick as quick
print(quick.__doc__)
Output:
Quick sort is a highly efficient sorting algorithm and is based on partitioning of array of data
into smaller arrays. A large array is partitioned into two arrays one of which holds values
smaller than the specified value, say pivot, based on which the partition is made and another
array holds values greater than the pivot value.
Quick sort partitions an array and then calls itself recursively twice to sort the two resulting
subarrays. This algorithm is quite efficient for large-sized data sets as its average and worst
case complexity are of Ο(n2), where n is the number of items.
### Quick Sort Pivot Algorithm
Based on our understanding of partitioning in quick sort, we will now try to write an algorithm for
it, which is as follows.
Step 1 − Choose the highest index value has pivot
Step 2 − Take two variables to point left and right of the list excluding pivot
Step 3 − left points to the low index
Step 4 − right points to the high
Step 5 − while value at left is less than pivot move right
Step 6 − while value at right is greater than pivot move left
Step 7 − if both step 5 and step 6 does not match swap left and right
Step 8 − if left ≥ right, the point where they met is new pivot
### Quick Sort Pivot Pseudocode
The pseudocode for the above algorithm can be derived as −
```python
function partitionFunc(left, right, pivot)
leftPointer = left
rightPointer = right - 1
while True do
while A[++leftPointer] < pivot do
//do-nothing
end while
while rightPointer > 0 && A[--rightPointer] > pivot do
//do-nothing
end while
if leftPointer >= rightPointer
break
else
swap leftPointer,rightPointer
end if
end while
swap leftPointer,right
return leftPointer
end function
Quick Sort Algorithm
Using pivot algorithm recursively, we end up with smaller possible partitions. Each partition is then processed for quick sort. We define recursive algorithm for quicksort as follows −
Step 1 − Make the right-most index value pivot Step 2 − partition the array using pivot value Step 3 − quicksort left partition recursively Step 4 − quicksort right partition recursively
Quick Sort Pseudocode
To get more into it, let see the pseudocode for quick sort algorithm −
procedure quickSort(left, right)
if right-left <= 0
return
else
pivot = A[right]
partition = partitionFunc(left, right, pivot)
quickSort(left,partition-1)
quickSort(partition+1,right)
end if
end procedure
### For Analysis
You can see profiling of all algorithms
**Example:** Analyse ternary search
```python
from gopy.search.ternary import *
print(profile())
Output:
7 function calls (6 primitive calls) in 0.000 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.000 0.000 <string>:1(<module>)
2/1 0.000 0.000 0.000 0.000 ternary.py:14(ternary_search)
1 0.000 0.000 0.000 0.000 ternary.py:31(search)
1 0.000 0.000 0.000 0.000 {built-in method builtins.exec}
1 0.000 0.000 0.000 0.000 {built-in method builtins.len}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
Check input data for profiling
from gopy.search.ternary import *
print(profile.__doc__)
Output:
profiling input
search(10,[0,1,2,3,4,5,6,7,8,9,10])
List of implementations
Contributing
Any form of contribution is welcome :smile:
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