NumPy is a powerful scientific research toolbox. Numpy is an Open Source library for the Python programming code, providing support for multi-dimensional arrays, multi-type matrices and high-degree mathematical operations to run on those arrays. NumPy also helps in solving the scientific equations and is an excellent package for scientific computation, predictive analysis and data processing. It is used for all kinds of scientific computing including computing statistics, probability or chi-square analysis, numerical analyses, and even general data processing. NumPy makes it easy to work on financial and accounting programs, optimization or simulation and scientific computation.

Data structures and algorithms for scientific computing have traditionally been the domain of the mathematician. Working in the dimensions of individual variables or even large multiple-dimensional arrays proved difficult for the average scientist. The recent developments in the field of computer science and mathematics have paved the way to allow scientists to exploit the power of computers and numerical analysis for their work. Numpy arrays are just two of the many libraries that make numerical work easier. Other libraries such as Sci Python, Pandas, and Lotus come with extensive support for other types of data structures and algorithms for scientific computing.

The Numpy library is based on the numpy package of Python which was originally developed by Peter Norvig for use in the NLP and intelligence research community. The Numpy object is a Python script or function that generates and navigates numpy arrays and simplifies complex numerical computations for scientific computation. The Numpy package provides functions for loading, slicing and protecting numpy arrays. Besides Numpy, there are other similar Python libraries like the Discrete Mathematics Core Library, the Pandas Package, and the Sci Python Toolkit that are used for scientific computation and data analysis.

A major advantage of Numpy and other Numpy libraries is that they are strongly typed and allow for automatic memory management. This allows for automatic memory optimization that makes it useful for scientific computing. In addition, Numpy is built-in with C libraries that make it easy to work with scientific programming languages that use C. Some of the other libraries provided with the distribution include the Pygments language tool, the curses-based graphing library, and the library containing various card formats for representing numeric data. The Num Py package also comes with a Data visualization tool called NumPy visualizer that can be used for visualizing data sets produced using Num Py.

There are differences between the Numpy Python libraries but basically, Numpy works on all platforms and works well with any scientific programming language. On the downside, it does not support Windows and has a Graphical interface that is not as powerful as the ones supported by numpy. However, the Numpy package comes with its own documentation that lists all its capabilities. The Numpy package can be installed with easy one-step scripts for beginners. It is also included in the Python archive.

The Numpy package comes along with NumPy users guide that contains complete details about the installation and operation of Numpy and it also comes with sample scripts to get you started immediately. The Numpy package also comes along with a number of useful libraries including the Pandas and Sci Python libraries that make numerical analysis much easier. There are more advanced statistical packages like the SAS and Python Dataframes that are available as add-ons. You should check whether your statistical operations need any Numpy modules before you begin using Numpy.