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• Matplotlib: Matplotlib is a widely used Python library for data visualization. It allows the creation of static,
animated, and interactive visualizations such as line charts, bar graphs, and scatter plots. It serves as a foundation
for other visualization libraries like Seaborn and is widely used in data analysis and scientific research.
• SciPy: It used for scientific and technical computing. It builds on NumPy and provides a wide range of functions
for optimisation, integration, interpolation, eigenvalue problems, and other advanced mathematical tasks. It is
commonly used in fields like physics, engineering, and data science.
• NLTK: NLTK (Natural Language Toolkit) is a Python library designed for Natural Language Processing (NLP). It
provides tools for tasks such as tokenization, stemming, and sentiment analysis. It is extensively used in natural
language processing (NLP) research and educational purposes due to its comprehensive features and datasets.
• OpenCV: OpenCV (Open-Source Computer Vision Library) is a powerful library for computer vision tasks. It
enables image processing, video analysis, and object detection. It supports multiple programming languages
and is extensively used in real-time applications, including facial recognition and augmented reality.
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.
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