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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 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 - Case Study: First Image of a Black Hole Summary # The efficient and adaptable n-dimensional array that is NumPy’s central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole A landmark moment in science, it gives stunning visual evidence of Einstein’s theory
numpy. reshape — NumPy v2. 3 Manual numpy reshape # numpy reshape(a, , shape=None, order='C', *, newshape=None, copy=None) [source] # Gives a new shape to an array without changing its data Parameters: aarray_like Array to be reshaped shapeint or tuple of ints The new shape should be compatible with the original shape If an integer, then the result will be a 1-D array of that
Random sampling — NumPy v2. 3 Manual The numpy random module implements pseudo-random number generators (PRNGs or RNGs, for short) with the ability to draw samples from a variety of probability distributions