Python Libraries

Aditya Raj

3 months ago

Table of Content
What are Python Libraries?
How to Install Libraries in Python?
  • How to Install Libraries in Python by downloading them?
  • How to Install Libraries in Python using PIP?
How to Import Libraries in Python?
What are some of the best Python libraries for beginners?
  • Numpy
  • Pandas
  • Matplotlib
  • OpenCV
  • Scipy
  • SQLAlchemy
  • wxPython
  • BeautifulSoup
  • Requests
  • TextBlob
Best Python Libraries for Machine Learning and Data Science
  • Natural Language Toolkit (NLTK)
  • Scikit-Learn
  • Seaborn
  • Keras
  • Pytorch
  • TensorFlow
  • Spark MLib
  • Theano
  • MXNet
  • Plotly


Python is a feature-rich programming language. Thanks to one of the most significant open source communities, Python has many libraries designed for various fields in programming. Python has dedicated libraries for machine learning, data science, data analytics, image processing, web scraping, web development, natural language processing, etc. 
In this article, we will see how to install and import different libraries in Python for different tasks. We will also look at some of the best Python libraries designed for beginners and will see some examples of their usage. 

What are Python Libraries?

In simplest terms, Python libraries are collections of reusable source code written in Python. We can describe the Python libraries as collections of different modules. Modules are files containing source codes for specific tasks. Python has an inbuilt standard library that is automatically installed when we install Python on our machines. The standard Python library contains more than 200 modules for basic operations like handling I/O, file handling, arithmetic operations, coercions, and much more. 

How to Install Libraries in Python?

There are various ways to install libraries in Python. To successfully install any Python library, you should first read the installation requirements of specific Python libraries. After fulfilling the installation requirements, you can install the required Python libraries.
There are two main ways to install any Python library. First, we can download the particular libraries from the official website and then install them. The second way is to install Python libraries using a package manager such as PIP. We will discuss both ways to install the Python libraries. 

How to Install Libraries in Python by downloading them?

We can download and install Python libraries when we have superuser access on any computer. 
  • To install any Python library, we will need to download the file from the official website. After downloading, we will extract the files into a folder in a local directory. 
  • After successfully extracting the files, we will go to the folder in which the files were extracted. Here we will look for a file named is the file containing the installation requirements and is used to install the Python library on the computer. 
  • After satisfying requirements for the Python library to be installed, open the command prompt in the same directory where is located and type Python install for Linux systems. You can use py install on windows; this will install the particular Python library on the computer.
  • For Python version 3.x, you can use Python3 install for completing the installation on Linux systems.
Manually installing Python libraries after downloading them is a tedious process.  To manually install python libraries, we need to manually install the packages required to fulfill the installation requirements. To avoid all these, we can use package installers such as PIP to download the Python libraries and packages.

How to Install Libraries in Python using PIP?

Using PIP is the easiest way to install Python packages and libraries. When you install any Python package or library using PIP, it automatically downloads the required Python libraries and packages to fulfill the installation requirements for the package we want to install. We can install any package in Python using PIP by simply typing pip install library_name in the command prompt. For example, if we need to install the matplotlib library on our machine, we can install it simply by running the command pip install matplotlib in the command prompt. For Python version 3.x, you can use pip3 install library_name. 
To open the command prompt in windows, you can simply type “command prompt” in the windows search box and open the command prompt. In Linux systems, you can press Ctrl+Alt+T to open the command prompt.
You may get an error “pip is not recognized as an internal or external command, operable program or a batch file” while installing Python libraries using pip. this means that you do not have PIP installed on your system. To install PIP, you can download the installation file using the curl command in the command prompt. 
To download the file run: curl -o
After that, run py  on windows and Python on Linux systems to complete PIP installation.
To upgrade the PIP, run py -m pip install -U pip on windows and Python -m pip install -U pip on Linux systems in the command prompt. 
After downloading PIP, you can easily install any Python library or package by executing pip install library_name or pip3 install library_name in the command prompt according to your PIP version.

How to Import Libraries in Python?

To import a library in a Python program, you can use the import library_name statement at the start of the program. Make sure the library is installed on your computer; otherwise an error will occur. To import a specific module from a Python library, you can use from library_name import module_name. 

What are some of the best Python libraries for beginners?

Python has a large number of open-source libraries that can be imported and used by any programmer. In this section, we will look at some of the best Python libraries for beginners. 


Numpy is first in the list of best Python libraries for beginners. It has been developed for numerical calculations and supports array processing. Numpy provides various functionalities for processing multidimensional arrays and matrices. Due to its efficiency in numerical calculations, various other Python libraries like Pandas and Scipy are built using Numpy.  
Numpy offers efficient implementation of many mathematical functions and matrix operations. With data structures like 1-d arrays, 2-d arrays, and masked arrays, Numpy also provides support for complex mathematical operations like integration which makes it a good substitute for MATLAB. Moreover, Python is open source and MATLAB is licensed, making Numpy a popular choice for programmers.


Pandas, equipped with its two popular data structures, namely Series and DataFrame, is one of the best tools to process tabular or time-series data. Having tools for easily organizing, exploring, representing, and manipulating data, Pandas is a must to learn library for data science. It has special features for indexing, alignment, data labelling, and handling missing values. 
We can import data from Excel spreadsheets, JSON files, HDF5, and CSV files directly into pandas Dataframes and process them with so ease that even a non-programmer can understand the code. For its ability to handle data efficiently and being so easy to learn, Pandas finds its place in the list of the best Python libraries for beginners.


Matplotlib has been developed for plotting graphs in Python. We can use matplotlib to create high-quality graphs and various types of other plots from tabular or linear data. We can also create multiple plots in a single image for comparisons.
Matplotlib library can also be integrated with other libraries such as ggplot, seaborn, and basemap for mapping different types of graphs and plots.


OpenCV is a Python library designed for image processing. It is an ideal tool for image processing as it allows users to read and write images at the same time. We can diagnose and manipulate images and videos for better detailing using functions available in OpenCV. It uses Numpy arrays to process images and is very efficient in terms of cost of execution. 
A major drawback with OpenCv is that it has no proper documentation. Despite this, you can learn it using other sources and perform image processing very easily in Python. 


Scipy is built on Numpy and is designed for technical and scientific computations. Scipy includes different modules designed for tasks like integration, optimization, statistics, and linear algebra. It also provides support for image processing as it uses Numpy arrays, and images are represented using arrays. Being an open-source library, it also has good online forums where we can discuss any issues we face while programming.


SQLAlchemy is a database abstraction library designed for beginners. With an easy-to-understand user interface and adjustable system, SQLAlchemy provides users an easy way to access and manipulate databases. It supports a wide range of databases and comes with many consistent patterns and layouts, which have been designed for efficiency. 
With SQLAlchemy, we can also perform CRUD operations in batches. It also comes with functionalities, which enable us to create database schemas and object models from scratch.  All these features make SQLAlchemy a good choice for database manipulation tools for beginners.


wxPython is an alternative to Tkinter. It is a graphical user interface toolkit, which can be used to manage and customize layout designs. With cross-platform support and simple installation and usage, wxPython is a good tool for beginners to start with GUI design and development.  


BeautifulSoup is a library designed for parsing HTML and XML documents. We can directly perform web scraping and extract data from web pages using this library. Being an open-source library, it has a large developer community and proper documentation that comes with the library. It makes BeautifulSoup an easy-to-use tool because we can read the documentation to look for usage of the functionalities. Moreover, we can also ask queries on online forums when we encounter any issue while using it.


Requests is a Python library used for handling HTTP requests. It allows us to inspect, customize, authorize and configure HTTP requests using common HTTP methods. We can work with custom headers, SSL certificates, Cookies, connection pooling, thread security, and authentication with the help of functions and methods provided by the Requests library. With the Requests library, we can also work easily with files as it allows us to upload multiple files in a single attempt. Moreover, it provides automatic unzipping, which allows us to restore and easily recover compressed files. 


TextBlob is a library that provides natural language processing functionalities with utmost ease. With less than 10 lines of code, we can perform natural language processing on any text data with TextBlob which makes it the first choice for beginners who are going to perform natural language processing in Python. TextBlob offers complex functionalities like tokenization, lemmatization, translation, N-gram detection, and part of speech marking which makes it one of the best Python libraries for beginners.

Best Python Libraries for Machine Learning and Data Science

Natural Language Toolkit (NLTK) 

NLTK is one of the most popular libraries for natural language processing. We can perform intensive processing of text data using NLTK with functionalities such as tokenization, tagging, classification, semantic reasoning, and many more. More than fifty open-source text corpora such as SentiWordNet, SemCor, and  Stopwords Corpus are available for use in NLTK, making natural language processing an easy task for us.
With NLTK, we also get a handbook for using the library. It also has many online discussion forums to discuss any issues we face while using this library. The features of NLTK and its community forums make it one of the best Python libraries for machine learning and data science. 


Written in Python, C, and C++, Python is one of the simplest and useful libraries for machine learning.  Scikit-Learn has a very clean and neat API, and it can be used with other libraries such as Numpy, Scipy, and Pandas. It has different machine learning algorithms for clustering, classification, and regression. It also supports advanced machine learning algorithms like random forests, DBSCAN, gradient boosting, and many more. 
With useful documentation for beginners, increased adaptability, and excellent functionalities for data representation, Scikit-Learn earns a place in the list of best Python libraries for machine learning and data science. 


Seaborn is a library built on the top Python libraries like pandas and matplotlib. We can create graphs and plots with more features than matplotlib as Seaborn has many functionalities in addition to matplotlib. It also has some built-in plots, which is not a feature of matplotlib.
Seaborn is more abstract and we can visualize data using very little code compared to matplotlib. It makes Seaborn one of the best Python libraries for machine learning and data science. 


Keras is a good choice for learning deep neural networks in Python. It is open-source and entirely written in Python. With a user-friendly modular structure, Keras allows you to work with a variety of blocks such as functions, layers, objectives, and optimizers to form neural networks. Keras also supports convolutional and recurrent neural networks and can also be used for working with images and texts. Due to its capability of running on different platforms such as TensorFlow, PaidML, and Microsoft cognitive toolkit, Keras finds its place in the list of best Python libraries for machine learning and data science.


It is an open-source library designed for machine learning. Pytorch offers support for a wide range of applications such as computer vision and natural language processing along with traditional machine learning tasks. With C++ runtime libraries, it provides faster execution and optimizations.


TensorFlow is an easy-to-learn machine learning library with many useful features. With an easy-to-learn architecture, we can easily create machine learning models and deploy them on any machine. TensorFlow features an immediate iteration of machine learning models, which allows us to create and manipulate machine learning models very easily. 

Spark MLib 

Spark MLib was developed by Apache as a scalable machine learning library. With functionalities such as featurization, pipelines, and machine learning algorithms like regression, clustering, dimensional reduction, classification, feature extraction, etc., it soon became one of the best Python libraries for machine learning.


Theano is an optimizing compiler. With Theano, we can analyze, optimize and describe the implementations of mathematical statements in our code. Working with Theano gives us the freedom not to care about the efficiency of programs as it is handled by the compiler itself.
Theano uses Numpy arrays and makes good use of multi-dimensional arrays. It also analyzes the source code and can detect bugs in the program. This allows the users to focus on the logic of the program without caring about silly bugs. With its optimizing capabilities, Theano can make the computations more than 100 times faster.


MXNet is a Python library used for deep learning. It is a popular library for training and deploying neural networks as it supports very fast model training and is highly scalable. Notably, Amazon AWS prefers MXNet as its choice for deep learning frameworks.


Evident from its name, Plotly is used to plot and visualize data in Python. Created using Python and Django, it is one of the best data visualization tools available. This is due to Plotly’s ability to create high-quality interactive plots with different types of charts such as heatmaps, boxplots, and bubble charts. If you have been using matplotlib in the past and have complained about its features, you should consider using Plotly in your projects once.


Python is one of the richest programming languages in terms of libraries and features. In this article, We have discussed Python libraries, their installation, and usage. We also looked at some of the best Python libraries for beginners as well as for machine learning and data science. To dive deeper into their usage, you can try hands-on projects, which will help you understand the features of Python programming and its libraries in a better way. You can also try machine learning and data science courses on InsideAIML to gain a deep understanding of concepts along with hands-on experience.
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