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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 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 Documentation
    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 1 15 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy
  • 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
  • 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!
  • 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




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