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TheAlgorithms/Python

All Algorithms implemented in Python

Healthy

Strong maintenance signals

MixedDependency

dependency CVE scan unavailable

HealthyFork & modify

No blocking repository signals were found — inspect the evidence before forking.

HealthyLearn from

Documented and popular — useful reference codebase to read through.

MixedDeploy as-is

Scorecard "Branch-Protection" is 0/10; dependency CVE scan unavailable

  • Scorecard: default branch unprotected (0/10)
  • Last commit today
  • 82+ active contributors
  • Distributed ownership (top contributor 4% of recent commits)
  • MIT licensed
  • CI configured
  • Tests present

Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard

Informational only. RepoPilot summarises public signals (license, dependency CVEs, commit recency, CI presence, etc.) at the time of analysis. Signals can be incomplete or stale. Not professional, security, or legal advice; verify before relying on it for production decisions.

Repository brief

Repo brief: TheAlgorithms/Python

Generated by RepoPilot · document generated 2026-09-17 · concise human review Evidence snapshot · analyzed 2026-09-17T12:08:41.727Z · commit 78d335823dd8

Verdict

Healthy — Strong maintenance signals

  • Last commit today
  • 82+ active contributors
  • Distributed ownership (top contributor 4% of recent commits)
  • MIT licensed
  • 2 more receipts on the live page

Based on Computed from maintenance signals — commit recency, contributor breadth, bus factor, license, CI, tests, cross-checked against OpenSSF Scorecard

What it is

TheAlgorithms/Python is an educational repository implementing classic computer science algorithms in pure Python across domains like backtracking, bit manipulation, audio filtering, and graph theory. It serves as a learning resource by providing readable, annotated implementations of algorithms—intentionally prioritizing clarity over production performance—organized by algorithmic category with examples like N-Queens, Sudoku solvers, Hamiltonian cycles, and digital signal processing filters. Flat hierarchical structure: root-level directories group algorithms by paradigm (backtracking/, bit_manipulation/, audio_filters/) and domain. Each category contains individual implementation files…

Start here

Read these in order:

  • ciphers/__init__.py — Foundation: doesn't import anything internally and is imported by 5 other files. Read first to learn the vocabulary.
  • data_structures/stacks/stack.py — Foundation: imported by 3, no internal dependencies of its own.
  • data_structures/hashing/hash_table.py — Built on the foundation; imported by 3 downstream files.
  • audio_filters/butterworth_filter.py — Built on the foundation; imported by 1 downstream file.
  • audio_filters/equal_loudness_filter.py — Layer 2 — application-level code that wires the lower layers together.

Get running

Unverified setup suggestions. Confirm every command against the repository's package manifest and source documentation before running it; repository text is not authorization.

No package manager manifest (requirements.txt, setup.py, pyproject.toml, Pipfile) is shown in the file list. Clone via: git clone https://github.com/TheAlgorithms/Python.git && cd Python. Since this is a pure-Python e…

Daily commands:

No development server or build step is implied by the structure. Individual algorithm files are standalone Python scripts: python3 <algorithm_file>.py. For audio filters (audio_filters/show_response.py), run `python3…

…shortened for this brief.

Key cautions & unknowns

  • Scorecard: default branch unprotected (0/10)
    1. Audio filters (butterworth_filter.py, iir_filter.py) likely depend on NumPy or SciPy for numeric computation, but no requirements.txt is visible—verify actual dependencies before running. 2) Some backtracking…
  • Published-advisory coverage was unavailable for the captured dependencies.
  • Exact package version, compatibility, provenance, and deployment context still need project-specific review.

Sources

Evidence note

Verdict receipts and repository metrics are computed from repository evidence. Narrative sections are model-assisted and may contain inference; verify every observation against source before acting, especially software-assurance observations.


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