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NumPy provides fast numerical computing capabilities from "summary" of Python for Data Analysis by Wes McKinney
NumPy is a fundamental package for numerical computing in Python. It provides comprehensive support for efficient array operations, particularly concerning mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and so forth. NumPy excels at providing an easy-to-use and flexible interface for manipulating large arrays and matrices of numeric data. It is not surprising that NumPy is at the core of many other scientific computing packages in Python. NumPy's primary object is the homogeneous multidimensional array, a table of elements (usually numbers), all of the same type, indexed by a tuple of nonnegative integers. In NumPy, dimensions are called axes. The number of axes is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each axis. NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original. The elements in a NumPy array are all required to be of the same data type, and thus will be the same size in memory. NumPy data types map directly onto an underlying machine representation, which makes it easy to read and write binary streams of data to disk and also to connect to code written in a low-level language like C or Fortran. NumPy provides a flexible way of storing data in memory, and it enables users to perform complex mathematical operations on large datasets with ease. The performance of NumPy is optimized for large arrays, making it the go-to choice for numerical computing tasks that require speed and efficiency. By leveraging NumPy's capabilities, users can analyze and manipulate large datasets with ease, paving the way for faster and more efficient data analysis workflows.
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