Numpy- Also known as numerical Python, is a library used for working with arrays. It is also a general-purpose array-processing package that provides comprehensive mathematical functions, linear algebra routines, Fourier transforms, and more.
NumPy aims to provide less memory to store the data compared to python list and also helps in creating n-dimensional arrays. This is the reason why NumPy is used in Python.
NumPy in python is defined as a fundamental package for scientific computing that helps in facilitating advanced mathematical and other types of operations on large numbers of data.
NumPy is a python library mainly used for working with arrays(a data structure, which can store a fixed-size collection of elements of the same data type) and to perform a wide variety of mathematical operations on arrays. NumPy guarantees efficient calculations with arrays and matrices on high-level mathematical functions that operate on these arrays and matrices.
Go through the below points and decide whether to use NumPy or Pandas, here we go:
Numpy array is formed by all the computations performed by the NumPy library. This is a powerful N-dimensional array object with a central data structure and is a collection of elements that have the same data types.
NumPy is a Python library that is partially written in Python and most of the parts are written in C or C++. And it also supports extensions in other languages, commonly C++ and Fortran.
NumPy is an open-source Python library that is mainly used for data manipulation and processing in the form of arrays. NumPy is easy to learn as it works fast, works well with other libraries, has lots of built-in functions, and lets you do matrix operations.
Numpy in Jupyter notebook
Clink on the link to get access to my Jupyter notebook on Numpy(how to call and work with it in Jupyter notebook) or https://drive.google.com/drive/folders/1kTsaJD434pzEO-RCGK7KxiKemXGcUu6t?usp=sharing