- 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 quickstart — NumPy v2. 3 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 - 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. polyfit — NumPy v2. 3 Manual
Since version 1 4, the new polynomial API defined in numpy polynomial is preferred A summary of the differences can be found in the transition guide Fit a polynomial p(x) = p[0] * x**deg + + p[deg] of degree deg to points (x, y) Returns a vector of coefficients p that minimises the squared error in the order deg, deg-1, … 0
- 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
- numpy. reshape — NumPy v2. 3 Manual
NumPy reference Routines and objects by topic Array manipulation routines numpy reshape
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