Introduction to NumPy
NumPy (Numerical Python) is a fundamental library
for scientific computing in Python. It provides support for handling large,
multi-dimensional arrays and matrices, along with a collection of mathematical
functions to operate on these arrays. It's widely used in data analysis,
machine learning, and engineering applications.
Travis Oliphant created NumPy package in 2005 by
injecting the features of the ancestor module Numeric into another module
Numarray.
It is an extension module of Python which is mostly
written in C. It provides various functions which are capable of performing the
numeric computations with a high speed.
NumPy provides various powerful data structures,
implementing multi-dimensional arrays and matrices. These data structures are
used for the optimal computations regarding arrays and matrices.
With the revolution of data science, data analysis
libraries like NumPy, SciPy, Pandas, etc. have seen a lot of growth. With a
much easier syntax than other programming languages, python is the first choice
language for the data scientist.
NumPy provides a convenient and efficient way to
handle the vast amount of data. NumPy is also very convenient with Matrix
multiplication and data reshaping. NumPy is fast which makes it reasonable to
work with a large set of data.