NumPy is the fundamental package for scientific computing with Python.
It’s an open source Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
A wide range of scientific and mathematical Python-based packages use NumPy arrays.
NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
Key Features
- ndarray object – this encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance. It’s a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers.
- Sophisticated (broadcasting) functions. The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Broadcasting provides a means of vectorizing array operations so that looping occurs in C instead of Python.
- Supports a wide range of numerical type.
- Special matrix type for doing linear algebra, which is a subclass of the array class. Operations on matrix-class arrays are linear algebra operations.
- Fourier transforms.
- Random number capabilities:
- random_sample – uniformly distributed floats over “[0, 1)”.
- bytes – uniformly distributed random bytes.
- random_integers – uniformly distributed integers in a given range.
- permutation – randomly permute a sequence / generate a random sequence.
- shuffle – randomly permute a sequence in place.
- seed – seed the random number generator.
- choice – random sample from 1-D array.
- Includes f2py – Fortran to Python Interface Generator – offers easy-to-use mechanisms for linking C, C++, and Fortran code directly into Python.
Website: www.numpy.org
Support: Documentation, Tutorial, Mailing List, GitHub Code Repository
Developer: NumPy Developers
License: BSD license
NumPy is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Python Data Analysis | |
|---|---|
| pandas | High-level building block for doing practical, real world data analysis |
| NumPy | Core package for scientific computing with Python |
| SciPy | Ecosystem for mathematics, science, and engineering |
| Polars | DataFrame interface on top of an OLAP Query Engine |
| statsmodels | Statistical modeling and econometrics in Python |
| Dask | Advanced parallelism for analytics |
| Orange | Component-based framework for machine learning and data mining |
| Modin | Drop-in replacement for pandas |
| Vaex | Fast visualization of big data |
| AWS DW | Extends the power of pandas library |
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| datatable | Manipulate 2-dimensional tabular data structures |
| xarray | Work with labelled multi-dimensional arrays and datasets |
| pyjanitor | Extend pandas with readable data-cleaning functions |
| Optimus | Agile Data Preparation Workflows |
Read our verdict in the software roundup.
| Python Mathematics Tools | |
|---|---|
| scikit-learn | Machine learning library for Python |
| NumPy | Core package for scientific computing with Python |
| SciPy | Ecosystem for mathematics, science, and engineering. |
| statsmodels | Statistical modeling and econometrics |
| JAX | Python library for high-performance numerical computing |
| SageMath | Computer algebra system |
| SymPy | Library for symbolic mathematics |
| PyMC | Bayesian statistical modeling and probabilistic programming |
| Pyomo | Object-oriented algebraic modeling language |
| patsy | Package for describing statistical models and to build design matrices |
| mpmath | Library for arbitrary-precision floating-point arithmetic |
| SfePy | Finite element software package |
Read our verdict in the software roundup.
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