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- NumPy
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
- NumPy - Installing NumPy
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
- NumPy - Learn
Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community
- What is NumPy? — NumPy v2. 3 Manual
What is NumPy? # NumPy is the fundamental package for scientific computing in Python
- NumPy quickstart — NumPy v2. 5. dev0 Manual
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
- NumPy user guide — NumPy v2. 3 Manual
NumPy user guide # This guide is an overview and explains the important features; details are found in NumPy reference
- Data types — NumPy v2. 3 Manual
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
- Constants — NumPy v2. 3 Manual
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!
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