NumPy is the fundamental package needed for scientific computing with Python. This package contains:
NumPy alternatives and similar packages
Based on the "Science and Data Analysis" category.
Alternatively, view NumPy alternatives based on common mentions on social networks and blogs.
Pandas9.9 10.0 L2 NumPy VS PandasFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
SymPy9.4 9.9 L2 NumPy VS SymPyA computer algebra system written in pure Python
SciPy9.4 9.9 L2 NumPy VS SciPySciPy library main repository
NetworkX9.4 8.6 L3 NumPy VS NetworkXNetwork Analysis in Python
Dask9.2 9.8 L2 NumPy VS DaskParallel computing with task scheduling
statsmodels9.1 9.6 L3 NumPy VS statsmodelsStatsmodels: statistical modeling and econometrics in Python
PyMC8.9 9.8 L4 NumPy VS PyMCBayesian Modeling in Python
Numba8.8 9.9 L3 NumPy VS NumbaNumPy aware dynamic Python compiler using LLVM
astropy8.3 9.9 L2 NumPy VS astropyAstronomy and astrophysics core library
Biopython8.2 9.5 L2 NumPy VS BiopythonOfficial git repository for Biopython (originally converted from CVS)
orange8.1 9.7 L2 NumPy VS orange🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Interactive Parallel Computing with IPythonIPython Parallel: Interactive Parallel Computing in Python
blaze7.4 0.0 L4 NumPy VS blazeNumPy and Pandas interface to Big Data
RDKit7.0 7.6 L1 NumPy VS RDKitThe official sources for the RDKit library
Cubes6.0 0.0 L3 NumPy VS CubesLight-weight Python OLAP framework for multi-dimensional data analysis
Open Mining5.7 0.0 L3 NumPy VS Open MiningBusiness Intelligence (BI) in Python, OLAP
bcbio-nextgen5.4 2.1 L3 NumPy VS bcbio-nextgenValidated, scalable, community developed variant calling, RNA-seq and small RNA analysis
NIPY5.3 9.0 L3 NumPy VS NIPYWorkflows and interfaces for neuroimaging packages
bcolz4.8 0.0 NumPy VS bcolzA columnar data container that can be compressed.
bccb4.5 0.0 L4 NumPy VS bccbIncubator for useful bioinformatics code, primarily in Python and R
Neupy4.4 0.0 L5 NumPy VS NeupyNeuPy is a Tensorflow based python library for prototyping and building neural networks
Bubbles3.7 0.0 L5 NumPy VS Bubbles[NOT MAINTAINED] Bubbles – Python ETL framework
PyDy3.5 0.0 L3 NumPy VS PyDyMultibody dynamics tool kit.
signac2.4 6.6 NumPy VS signacManage large and heterogeneous data spaces on the file system.
harold2.3 0.0 L2 NumPy VS haroldAn open-source systems and controls toolbox for Python3
LynxKite2.1 9.7 NumPy VS LynxKiteThe complete graph data science platform
PatZilla2.0 2.1 NumPy VS PatZillaPatZilla is a modular patent information research platform and data integration toolkit with a modern user interface and access to multiple data sources.
Kotori1.9 1.3 NumPy VS KotoriA flexible data historian based on InfluxDB, Grafana, MQTT, and more. Free, open, simple.
Terkin1.8 0.0 NumPy VS TerkinTerkin-Datalogger for MicroPython and CPython
cclib0.9 NumPy VS cclibA library for parsing and interpreting the results of computational chemistry packages.
ElasticBatch0.8 0.0 NumPy VS ElasticBatchElasticsearch tool for easily collecting and batch inserting Python data and pandas DataFrames
dask-memusage0.8 0.0 NumPy VS dask-memusageA low-impact profiler to figure out how much memory each task in Dask is using
Open BabelA chemical toolbox designed to speak the many languages of chemical data.
Access the most powerful time series database as a service
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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NumPy is the fundamental package for scientific computing with Python.
- Website: https://www.numpy.org
- Documentation: https://numpy.org/doc
- Mailing list: https://mail.python.org/mailman/listinfo/numpy-discussion
- Source code: https://github.com/numpy/numpy
- Contributing: https://www.numpy.org/devdocs/dev/index.html
- Bug reports: https://github.com/numpy/numpy/issues
- Report a security vulnerability: https://tidelift.com/docs/security
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
hypothesis. Tests can then be run after installation with:
python -c 'import numpy; numpy.test()'
Code of Conduct
NumPy is a community-driven open source project developed by a diverse group of contributors. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the NumPy Code of Conduct for guidance on how to interact with others in a way that makes our community thrive.
Call for Contributions
The NumPy project welcomes your expertise and enthusiasm!
Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact us through the mailing list first.
Writing code isn’t the only way to contribute to NumPy. You can also:
- review pull requests
- help us stay on top of new and old issues
- develop tutorials, presentations, and other educational materials
- maintain and improve our website
- develop graphic design for our brand assets and promotional materials
- translate website content
- help with outreach and onboard new contributors
- write grant proposals and help with other fundraising efforts
For more information about the ways you can contribute to NumPy, visit our website. If you’re unsure where to start or how your skills fit in, reach out! You can ask on the mailing list or here, on GitHub, by opening a new issue or leaving a comment on a relevant issue that is already open.
Our preferred channels of communication are all public, but if you’d like to speak to us in private first, contact our community coordinators at [email protected] or on Slack (write [email protected] for an invitation).
We also have a biweekly community call, details of which are announced on the mailing list. You are very welcome to join.
If you are new to contributing to open source, this guide helps explain why, what, and how to successfully get involved.