Welcome to the absolute beginner’s guide to NumPy! NumPy (Num erical Py thon) is an open source Python library that’s widely used in science and engineering.

Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. Beginners # There’s a ton of information about NumPy out there. If you are just starting, we’d strongly recommend the following: Tutorials NumPy Quickstart Tutorial

NumPy (Numerical Python) is an open source Python library that’s used in almost every field of science and engineering. It’s the universal standard for working with numerical data in Python, and it’s at the core of the scientific Python and PyData ecosystems.

NumPy Quickstart Tutorial (Tutorial de Início Rápido) NumPy Tutorials Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy.

NumPy arrays facilitate advanced mathematical and other types of operations on large numbers of data. Typi-cally, such operations are executed more efficiently and with less code than is possible using Python’s built-in sequences.

The basics # 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. In NumPy dimensions are called axes. For example, the array for the coordinates of a point in 3D space, [1,2,1], has one axis.

Tutoriales de NumPy Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy.

May 24, 2020 · The Basics ¶ 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. In NumPy dimensions are called axes. For example, the coordinates of a point in 3D space [1, 2, 1] has one axis.

These documents clarify concepts, design decisions, and technical constraints in NumPy. This is a great place to understand the fundamental NumPy ideas and philosophy.

Learner profile ¶ This tutorial is for people who have a basic understanding of NumPy and want to understand how masked arrays and the numpy.ma module can be used in practice.