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NumPy: AI tool designed to speed up numerical computing

by Attune World Wide / /


NumPy (short for Numerical Python) provides an efficient interface to store and operate on dense data buffers. This interface or library is the most essential and powerful package for working with data in Python. A solid understanding of NumPy is necessary to work on data analysis or machine learning projects because all data analysis packages, including Pandas, are based on and built on top of NumPy. This apart, the Scikit-Learn package — which is used to build machine learning applications – also relies heavily on NumPy.

At its core, the main data structure in NumPy is a ‘NumPy array’. All data is stored in arrays. An ‘ndarray’ object (short for n-dimensional arrays) allows storing of multiple items of the same data type. NumPy provides excellent ‘ndarray’ objects. The facilities around the array object makes NumPy very convenient for performing mathematical and data manipulations.

NumPy Features

NumPy is an open source numerical library which contains multidimensional array and matrix data structures. It can be utilised to perform a number of mathematical operations on arrays such as trigonometric, statistical and algebraic routines. In some ways, NumPy arrays are like Python’s built-in list type, but its arrays provide much more efficient storage and data operations as the arrays grow larger in size. NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python.


NumPy arrays have several advantages over Python lists. These benefits are focused on providing high-performance manipulation of sequences of homogeneous data items. Several of these benefits are as follows:

Contiguous allocation in memory: Provides benefits in performance by ensuring that all elements of an array are directly accessible at a fixed offset from the beginning of the array.

– Vectorized operations: Hardware designed to support vector instructions generally has hardware that is capable of performing multiple ALU operations in general when vector instructions are used.

Boolean selection: NumPy automatically creates a boolean array when comparisons are made between arrays and scalars or between arrays of the same shape.

Sliceability: A subsequence of the structure can be indexed and retrieved.

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