Numpy

Numpy

• NumPy, short for Numerical Python, is the core library for scientific computing in Python. It has been designed specifically for performing basic and advanced array operations. It primarily supports multi-dimensional arrays and vectors for complex arithmetic operations.

•A library is a collection of files (called modules) that contains functions for use by other programs. A Python library is a reusable chunk of code that you may want to include in your programs.

• Many popular Python libraries are NumPy, SciPy, Pandas and Scikit-Learn. Python visualization libraries are matplotlib and Seaborn.

• NumPy has risen to become one of the most popular Python science libraries and just secured a round of grant funding.

• NumPy's multidimensional array can perform very large calculations much more easily and efficiently than using the Python standard data types.

• To get started, NumPy has many resources on their website, including documentation and tutorials.

• NumPy (Numerical Python) is a perfect tool for scientific computing and performing basic and advanced array operations.

• The library offers many handy features performing operations on a n-arrays and matrices in Python. It helps to process arrays that store values of the same data type and makes performing math operations on arrays easier. In fact, the vectorization of mathematical operations on the NumPy array type increases performance and accelerates the execution time.

• Numpy is the core library for scientific computing in Python. It provides a high performance multidimensional array object and tools for working with these arrays.

• NumPy is the fundamental package needed for scientific computing with Python. It contains:

a) A powerful N-dimensional array object

b) Basic linear algebra functions

c) Basic Fourier transforms

d) Sophisticated random number capabilities

e) Tools for integrating Fortran code

f) Tools for integrating C/C++ code.

• NumPy is an extension package to Python for array programming. It provides "closer to the hardware" optimization, which in Python means C implementation.