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NumPy is an open-source project aimed at enabling digital computing using Python. It was founded in 2005 on the foundational work of the libraries Numeric and numarray.

NumPy is always free to use and distributed under the terms of the Modified BSD License, which is 100 percent open source. NumPy was created publicly by the NumPy consensus on GitHub and the larger Python scientific community.

The NumPy Steering Board’s role is to assure, work with, and support the greater NumPy community, as well as the project’s long-term well-being, both technically and as a community. The NumPy Steering Board now includes numerous members from all across the world.

• Strong array object with N dimensions
• C/C++ and Fortran code integration tools
• The ability to employ linear algebra, Fourier analysis, and random number transform
• Aside from its obvious scientific applications, it may also be utilized as a powerful multidimensional container for publicly available data.
• It is possible to specify arbitrary data types.
• QuillBot will rewrite the file. Begin by writing or copying something, then click the button to paraphrase it.
• At https://docs.scipy.org, you can get a comprehensive archive of documentation for all versions of NumPy (Numerical Python) (minor versions; bugfix releases do not contain substantial changes to the documentation) from 2009.

### What is NumPy for Python?

NumPy is Python’s core scientific computing package. It is a Python library that provides a multidimensional array object, various derived objects (such as matrices and masked matrices), and a wide range of methods for fast matrix operations, such as math, selection, input/output, discrete Fourier transforms, linear algebra and basic statistical operations. More simulation and randomness.

A ndarray object is at the heart of the NumPy package. For performance, this wraps n-dimensional arrays of homogenous data types, with numerous operations executed in the produced code. There are some significant differences between NumPy arrays and traditional Python sequences:

Unlike Python lists, NumPy arrays have a fixed size when they are generated (which can grow dynamically). The array resizing function will generate a new array and remove the old one. To have the same memory size, all NumPy array elements must be of the same data type. Exception: object arrays (including Python) allow for arrays of objects of variable sizes.

##### NumPy arrays provide complex arithmetic and data processing. Such operations are frequently carried out more efficiently and with less code than the integrated Python sequences.

NumPy arrays are used by many Python-based scientific and mathematical programs. Although it often accepts Python sequence input, it transforms it to NumPy arrays before processing and frequently outputs NumPy arrays.

In other words, learning how to use Python’s built-in sequence types is not enough to successfully use many (or perhaps most) scientific/mathematical applications based on Python; one must additionally understand how to use NumPy arrays.

Scientific computing is very important in terms of sequence size and performance. Consider the case when each element in a single-dimensional sequence is multiplied by the appropriate element in another sequence of the same length. If the data is held in two Python lists, a, and b, we can iterate over the entries. Download NumPy for Python Free for Windows, Mac & Linux.

## Install the Numpy Python library

Before we start dealing with arrays in the  Numpy library, we must make sure that they are among the libraries installed in the Python environment. We have several options that we can choose to download from the library, as follows:

##### First: using Python Wheels
• Make sure that the Python environment is installed on your computer, and if not, you can install Python from here.
• Make sure that pip is running and Python is added in the PATH environment variable.
• On the cmd screen, run pip install –upgrade pip to update pip.
• Open the Python command interpreter and import the library, and if you do not see any error messages, you have successfully installed the library, and you can then start working with it.

##### Second: using the Anaconda Python distribution

To get the  Numpy library easily and quickly, you can install the Anaconda distribution from here.

### You may ask, realistically, what makes this option easier?

The good thing about getting Python with Anaconda is that you don’t have to worry about installing the  Numpy library separately or any other data analysis library like  Pandas or  Scikit-learn.

If you are new to the world of Python or programming in general, it is very convenient to use the Anaconda Python distribution, which contains more than 100 ready-made libraries specialized in the field of data science, and it is also approved by many data analysis scientists.

Also, Anaconda includes several open-source development tools and environments, such as Jupyter and Spyder.

In order to save time, I advise you to install Anaconda to deal with  Numpy and other data analysis libraries and dive into the wonderful data science.

#### Why NumPy Fast?

Vectorization refers to the absence of explicit looping, indexing, and so on in the code; these things, of course, occur only “behind the scenes” in precompiled, optimized C code. There are several advantages to using vector code, including:

Vector code is shorter and easy to read.
In general, fewer lines of code indicate fewer mistakes.
The sign is quite similar to normal mathematical notation (which makes accurately notifying mathematical calculations easy).

The command produces more “Pythonic” code. Without the directive, the code would be riddled with useless loops and difficult to comprehend.

The word “broadcast” refers to the underlying process behavior of each piece. In general, all operations in NumPy, not only arithmetic, but also logical, bitwise, functional, and so on, operate in this implicit fashion, i.e. they are broadcast.

#### Who else makes use of NumPy?

Starting with ndarray, NumPy fully supports the object-oriented approach. The array, for example, is a class with several methods and attributes. Many of its methods are mirrored by functions in the external NumPy namespace, enabling the programmer to write code in any format they desire. Download NumPy for Python Free

Because of its adaptability, the NumPy array dialect and the NumPy ndarray class have become the de facto language for multidimensional data sharing in Python.

📌 Note: Requires Python.

#### Technical Details

• Program name: NumPy
• Category: Utility Tools
• Version: latest
• File size: 9.8 MB
• Core: 32/64-bits
• Operating systems: all Windows, Mac, Linux, etc
• Languages: Multilingual
• Developed by: Jarrod Millman
• Official website: numpy.org