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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
- NumPy Documentation
NumPy 1 20 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1 19 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1 18 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1 17 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1 16 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy
- 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
- NumPy quickstart — NumPy v2. 4. 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. where — NumPy v2. 3 Manual
numpy where # numpy where(condition, [x, y, ] ) # Return elements chosen from x or y depending on condition
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