Contenido del Curso
Getting into NumPy Basics
Introduction
What is NumPy?
NumPy is a library for the Python programming language, designed for handling large, multi-dimensional arrays and matrices of numerical data. It plays a crucial role in scientific computing within Python and finds extensive use across various fields, including machine learning, data science, and scientific computing.
The library offers a vast array of functions and tools specifically for managing large volumes of numerical data. Among these capabilities are functions for executing mathematical operations on arrays, including computations of the mean, median, standard deviation, and beyond.
Additionally, NumPy equips users with numerous utilities for array manipulation, such as sorting, reshaping, and indexing, enhancing its indispensability in numerical and scientific computation.
One of the key advantages of NumPy is its capability for fast, efficient computations on large data arrays. This efficiency is due to the underlying optimized C and Fortran code, allowing NumPy to execute computations much faster than if using pure Python code.
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What is NumPy?
NumPy is a library for the Python programming language, designed for handling large, multi-dimensional arrays and matrices of numerical data. It plays a crucial role in scientific computing within Python and finds extensive use across various fields, including machine learning, data science, and scientific computing.
The library offers a vast array of functions and tools specifically for managing large volumes of numerical data. Among these capabilities are functions for executing mathematical operations on arrays, including computations of the mean, median, standard deviation, and beyond.
Additionally, NumPy equips users with numerous utilities for array manipulation, such as sorting, reshaping, and indexing, enhancing its indispensability in numerical and scientific computation.
One of the key advantages of NumPy is its capability for fast, efficient computations on large data arrays. This efficiency is due to the underlying optimized C and Fortran code, allowing NumPy to execute computations much faster than if using pure Python code.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones