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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 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
  • 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
  • NumPy - Citing NumPy
    If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper:
  • numpy. reshape — NumPy v2. 3 Manual
    NumPy reference Routines and objects by topic Array manipulation routines numpy reshape


















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