what is numpy in python

<strong>NumPy</strong> is the basic package for scientific computing in Python. It is a Python library that provides multidimensional array objects, various derived objects (such as masked arrays and matrices), and various routines for fast operations on arrays, including mathematics, logic, shape operations, sorting, selection , I/O discrete Fourier transform, basic linear algebra, basic statistical operations, stochastic simulation and more.

The core of the NumPy package is the ndarray object. This encapsulates an n-dimensional array of homogeneous data types, with many operations performed in compiled code to improve performance. There are several important differences between NumPy arrays and standard Python sequences:

1. NumPy arrays have a fixed size when created, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original array.

2. The elements in the NumPy array need to have the same data type and therefore have the same size in memory. Exception: It is possible to have arrays of (Python, including NumPy) objects, allowing arrays of elements of different sizes.

3. NumPy arrays facilitate advanced mathematical and other types of operations on large amounts of data. Generally, these operations can be performed more efficiently and with less code than using Python's built-in sequences.

4. An increasing number of Python-based scientific and mathematical packages are using NumPy arrays; although these often support Python sequence inputs, they convert these inputs to NumPy arrays before processing, and they often Output NumPy array. In other words, in order to effectively use a lot (or even most) of today's Python-based scientific/math software, it's not enough to know how to use Python's built-in sequence types - you also need to know how to use NumPy arrays.

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