NumPy Library

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A NumPy Library is a Python-based numerical analysis library.





    • NumPy is the fundamental package for scientific computing with Python. It contains among other things:
    • Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

      Numpy is licensed under the BSD license, enabling reuse with few restrictions.


    • In NumPy the basic type is a multidimensional array. Operations on these arrays in all dimensionalities including 2D are elementwise operations. However, there is a special matrix type for doing linear algebra, which is just a subclass of the array class. Operations on matrix-class arrays are linear algebra operations.
    • Python uses 0 (zero) based indexing. The initial element of a sequence is found using a[0].
    • NumPy is based on Python, which was designed from the outset to be an excellent general-purpose programming language. While Matlab's syntax for some array manipulations is more compact than NumPy's, NumPy (by virtue of being an add-on to Python) can do many things that Matlab just cannot, for instance subclassing the main array type to do both array and matrix math cleanly.
    • In NumPy arrays have pass-by-reference semantics. Slice operations are views into an array.
    • NumPy code is Python code, so it has no such restrictions. You can define functions wherever you like.
    • NumPy does not have one standard IDE. However, the IPython environment provides a sophisticated command prompt with full completion, help, and debugging support, and interfaces with the Matplotlib library for plotting and the Emacs/XEmacs editors.