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NumPy (Numerical Python)
NumPy is a powerful open-source scientific package that stands for 'Numerical Python'. It uses mathematical
and logical operations for handling large datasets through powerful data structure-n-dimensional arrays that
also speeds up data processing. NumPy is the first step in learning, to become a Python data scientist in the
future. Various other libraries like Pandas, Matplotlib, and Scikit-learn are built on using some concepts of this
magical library. It can also be easily interfaced with other Python packages and provides tools for integrating
with other programming languages like C, C++ etc.
If you are using basic Python installed through https://www.python.org website then the NumPy package is not
included by default. You need to install it separately.
NumPy can be installed by typing following command:
pip install NumPy
Once it is installed, it can be readily used in any Python code by using import keyword as shown below:
Numpy can also be imported into the Jupyter Notebook by using the given statement:
import numpy # this will import the complete numpy package
OR
import numpy as npy # this will import numpy and referred as npy
OR
from numpy import array #this will import ONLY arrays from whole numy package
OR
from numpy import array as ary #this will import ONLY arrays and referred as ary
Arrays
Arrays are an ordered collection of values of the same data type that can be arranged in one or more dimensions.
They can store numbers, characters, Boolean values, etc. The elements are referred to using index numbers
(positions) that start from 0. Almost all programming languages support arrays in one form or another.
• A one-dimensional array is called a Vector.
• A two-dimensional array is called a Matrix.
• An array with multiple dimensions is called an n-dimensional array.
In NumPy, we can create n-dimensional arrays, which are considered an alternative to Python lists because
they allow faster access to reading and writing elements efficiently. The NumPy library provides a large set of built-
in functions in the form of modules and packages for creating, manipulating, and transforming NumPy arrays.
NumPy Arrays vs Python Lists
The difference between NumPy Arrays vs Python Lists is shown in the below table:
Feature NumPy Array Python List
Data Type Homogeneous (stores elements of the Heterogeneous (can store elements of
same type) different types)
Data Type Does not support mixed types within the Supports mixed types and allows implicit
Conversion same array conversion
Memory Efficiency More memory-efficient Occupies more memory
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