OSS TanbouOverview
SciPy builds on NumPy arrays to provide higher-level numerical routines for optimization, integration, linear algebra, statistics, Fourier transforms, signal and image processing, ODE solvers, and other scientific-computing tasks.
Features and best fit
Based on official documentation; not hands-on tested · Content checked:
Use a broad set of specialized numerical routines
Optimization, integration, linear algebra, statistics, FFTs, signal processing, and related algorithms are available through a consistent scientific Python stack.
Sources: [2]
Operate directly on NumPy arrays
SciPy is designed to work with NumPy arrays, making it straightforward to add specialized numerical algorithms to existing ndarray-based workflows.
Sources: [2]
For scientific and engineering computations with established algorithms
SciPy fits research, simulation, and analytical work involving optimization problems, signal analysis, statistical computation, differential equations, and similar tasks.
Choose methods with numerical assumptions in mind
Convergence, stability, precision, and performance depend on the algorithm and problem characteristics. Review method-specific assumptions rather than relying blindly on default parameters.
Sources: [4]
Official sources
- [1]scipy/scipy repository(2026-09-20)
- [2]SciPy README(2026-09-20)
- [3]SciPy BSD-3-Clause license(2026-09-20)
- [4]SciPy documentation(2026-09-20)
Supplemental curator note
If NumPy provides the array and basic numerical foundation, SciPy adds higher-level numerical algorithms over those arrays. It is often worth checking SciPy's existing optimization, integration, signal, or statistics modules before implementing numerical methods from scratch.
Try it in 3 steps
- 1
Install SciPy
Install the released SciPy package alongside NumPy in a virtual environment.
python -m pip install scipy - 2
Try numerical integration
Use a simple integral to inspect SciPy’s higher-level numerical routine API.
python -c "from scipy import integrate; print(integrate.quad(lambda x: x**2, 0, 1))" - 3
Try optimization
Pass an objective function and initial value and inspect the optimization result object.
python -c "from scipy import optimize; print(optimize.minimize(lambda x: (x[0]-3)**2, [0]))"
Growth
Growth trends · Last 30 days
15,027 Stars
Trend data is still being collected.
Development activity
Last 90 days · weekly
- Commits (last 30 days)
- 195
- Open PRs
- 405
- Issues opened
- 155
- Issues closed
- 183
- PRs opened
- 583
- PRs merged
- 404
Issues
155 / 183
Pull requests
583 / 404
Maintenance
- Median first response
- 8.2 hr
- Issue response rate
- 48.8% (41/84)
Based on up to the 100 newest issues opened by external users in the last 90 days. A first comment from an OWNER, MEMBER, or COLLABORATOR counts as a response; issues whose full comment history cannot be checked are excluded. The median and response rate update weekly.
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Categories and tags
Categories
GitHub data
GitHub dataView detailed GitHub data
GitHub Topics
- python
- scipy
- algorithms
- closember
- scientific-computing
- Stars
- 15,027
- Forks
- 5,952
- Watchers
- 347
- Open issues
- 1,436
- Contributors
- 415
- Primary language
- Python
- License
- BSD-3-Clause
- Repository last updated
- Sep 19, 2026
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