Guide · Open source
How to use algorithms: Python data structures and examples
Updated · written with AI from the project's own documentation and checked against our catalogue data
algorithms is an MIT-licensed Python 3 collection of data structure and algorithm implementations. Each file is self-contained, with docstrings, type hints and complexity notes, and it is designed to be read and learned from. This guide shows you how to install it, import functions and classes, and run the included examples.
algorithmskeon/algorithms · ★ 25.6k · MIT Install options & downloads Quick start
- 1Check Python 3. Confirm you have Python 3 installed, since that is the stated prerequisite.
- 2Install the package. Run pip install algorithms to install the package from PyPI.
- 3Import a function. Import merge_sort from algorithms.sorting and call it on a list of numbers.
- 4Try a data structure. Create a BinaryHeap, insert values and call remove_min to see the smallest value first.
- 5Find your topic. Use the project structure and data structures table to locate the module you want to read.
- 6Read the source. Open the module file, since each one includes docstrings, type hints and complexity notes.
Install algorithms with pip
The only prerequisite named in the project is Python 3. The package is published on PyPI, so you can install it with pip.
pip install algorithmsImport and call a function
Import a function from the topic package and call it with a list. The README's sorting example uses merge_sort, which returns the sorted list.
from algorithms.sorting import merge_sort
print(merge_sort([38, 27, 43, 3, 9, 82, 10]))
# [3, 9, 10, 27, 38, 43, 82]Use data structures such as heaps and tries
Data structures live in algorithms.data_structures. The binary heap example inserts values and then removes the minimum each time, so the output comes out in ascending order.
- Import BinaryHeap, Trie or BST from algorithms.data_structures
- Call insert on a BinaryHeap, then remove_min to take the smallest value
from algorithms.data_structures import BinaryHeap
heap = BinaryHeap()
for val in [5, 3, 8, 1, 9]:
heap.insert(val)
print(heap.remove_min()) # 1
print(heap.remove_min()) # 3Run a graph or string example
Graph functions take an adjacency dictionary and a start and end node. The README's Dijkstra example returns the total cost and the path. The string example uses Knuth-Morris-Pratt to return the starting index of a match.
from algorithms.graph import dijkstra
graph = {
"s": {"a": 2, "b": 1},
"a": {"s": 3, "b": 4, "c": 8},
"b": {"s": 4, "a": 2, "d": 2},
"c": {"a": 2, "d": 7, "t": 4},
"d": {"b": 1, "c": 11, "t": 5},
"t": {"c": 3, "d": 5},
}
print(dijkstra(graph, "s", "t"))
# (8, ['s', 'b', 'd', 't'])Find the topic you need
The project is organised into topic folders such as sorting, searching, tree, string, graph and dynamic_programming. The data structures table maps each structure to its module file and key class, for example Red-Black Tree to red_black_tree.py and RBTree.
- Look up the topic folder in the project structure
- Check the module name and key class in the data structures table
Run the tests
The repository includes a tests/ folder with one test file per topic. Run them from the project root with pytest.
python -m pytest tests/What people use it for
- Studying how merge_sort or binary_search works by reading the code
- Checking the output of Dijkstra's shortest path on a small graph
- Looking up a reference Trie or red-black tree implementation
- Generating permutations or subsets with the backtracking functions
- Practising coding problems such as two_sum or three_sum
Frequently asked questions
How do I install algorithms?
Run pip install algorithms. The project lists Python 3 as the prerequisite and publishes the package on PyPI.
Is algorithms free to use?
The project is licensed under MIT. Check the repository's license file for the full terms.
What do I need to run algorithms?
You need Python 3. The README does not list other requirements.
Can I use algorithms in production code?
The README says the code emphasizes clarity over optimization. For production systems that need heavily tested, performance-tuned libraries, the project is not designed as the right choice.
Are there alternatives to algorithms?
The analysis for this listing did not identify comparable projects, so no alternatives are named here.
How do I run the tests?
From the project root, run python -m pytest tests/. The tests folder contains one test file per topic.
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Our verdict
Use algorithms if you are a student, a developer reviewing a classic algorithm in Python, or someone preparing for interviews or competitive programming. Pick a performance-focused, well-tested library if you need production-grade code, or a non-Python project if you need implementations in another language.
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