NumPy is a Python library designed for efficient array manipulation, and it includes functions for linear algebra, Fourier transforms, and matrix operations. Developed by Travis Oliphant in 2005, NumPy is an open-source project available for free use.
The name NumPy stands for Numerical Python.
In Python, while lists can function as arrays, they are relatively slow for processing. NumPy addresses this by offering an array object, ndarray, which is up to 50 times faster than standard Python lists.
The ndarray comes with numerous built-in functions that simplify working with arrays. Arrays are essential in data science, where performance and efficiency are critical.
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NumPy is faster than Python lists because it uses fixed-type arrays, which allow for more efficient memory usage and operations. Unlike lists, which store objects individually, NumPy arrays store data in contiguous memory blocks, enabling faster access and processing.
Additionally, NumPy leverages optimized C and Fortran libraries for performance-critical operations.
NumPy is a Python library, but while some of it is written in Python, the performance-critical components are implemented in C or C++ to ensure faster computations.
The source code for NumPy can be found in its GitHub repository at https://github.com/numpy/numpy.
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