Nearly every scientist working in Python draws on the power of NumPy. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use.
The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science.
The NumPy library contains multidimensional array data structures, such as the homogeneous, N-dimensional ndarray, and a large library of functions that operate efficiently on these data structures.
Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community.
NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers.
The reference guide contains a detailed description of the functions, modules, and objects included in NumPy. The reference describes how the methods work and which parameters can be used.
Notes NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Also that positive infinity is not equivalent to negative infinity. But infinity is equivalent to positive infinity. Examples Try it in your browser!
NumPy numerical types are instances of numpy.dtype (data-type) objects, each having unique characteristics. Once you have imported NumPy using import numpy as np you can create arrays with a specified dtype using the scalar types in the numpy top-level API, e.g. numpy.bool, numpy.float32, etc.
