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Table of contents
1.
Introduction
2.
Python Libraries used in Data Science and Visualisations 
2.1.
NumPy
2.2.
Pandas
2.3.
Matplotlib
3.
Python Libraries used in AI 
3.1.
Keras
3.2.
Tensorflow
3.3.
PyTorch
4.
Python Libraries used in Web and Mobile Applications Development 
4.1.
FastAPI
4.2.
Flask
4.3.
Django
5.
FAQs
6.
Key Takeaways
Last Updated: Mar 27, 2024

Popular Python Libraries

Author dhruv sharma
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Basics of Python
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Introduction

Python is renowned for its state of the art set of out-of-the-box tools and capabilities, its support for multi-paradigm programming features and styles and robust set of libraries and frameworks, which are maintained frequently and reliably by many independent organisations and active contributors in the open-source community. Among such popular Python Libraries and frameworks actively used by developers, this article briefly covers a few of such standard libraries in a variety of areas like Artificial intelligence, Data Science, Big Data, IoT, Desktop, Web and Mobile Applications development etc.

Also Read, Divmod in Python, Swapcase in Python

Python Libraries used in Data Science and Visualisations 

Python has an extensive set of libraries and tools that one could use to solve data science problems and perform analysis, prepare, clean, organise, and visualise data. Some such libraries are

Also Read About, Python for Data Science

NumPy

Travis Oliphant created the NumPy library, which is one of those python libraries widely used to perform basic to advanced scientific and mathematical calculations on multi-dimensional arrays for efficient operations on matrices and vectors. The features present in the library are performant as they have a lot of their base implementations in C. In addition to all these capabilities, NumPy is also being used in libraries such as Seaborn, Matplotlib, SciPy, Tensorflow etc.

Run the following command using pip to install NumPy:

pip install numpy

Pandas

The Pandas library, which Wes McKinney contributed, is one such python library that is used popularly as a data structure for solving problems in data science where time-series creation or data structuring is required. Pandas has inherent features that label and categories data into relational, tabular data arranged in rows and columns. This is achieved by utilising the following two basic structures: 

“Series” (1d list of objects/dictionary items) and “Dataframes” (2d lists of objects/dictionary items arranged in a tabular manner with multiple rows and columns). These data structures can be used for data filtering, wrangling, manipulating etc.

Run the following command using pip to install Pandas:

pip install pandas

Matplotlib

The Matplotlib library, which has similar features compared to the popular scientific tool Matlab and was contributed by John D. Hunter as one of those python libraries that one could use to visualise data anywhere from a minimalistic requirement of 2d graphs such as histograms, distribution graphs, joint plots, bar charts, scatterplots etc. one can also use it to extend on top of classes in an objected-oriented project setup for accommodating plots in the applications.

Run the following command using pip to install Matplotlib:

pip install matplotlib
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Python Libraries used in AI 

Python has an extensive set of libraries and tools that one could use in various Artificial intelligence applications such as machine learning, deep learning, computer vision etc. Some such libraries are:\

Keras

Francois Chollet designed the Keras library as one of those python libraries with flexible and powerful capabilities that can be utilised while solving problems involving machine learning, deep learning, and neural networks. It has a simplistic core design that is robust and readily extensible. One can use it in various other libraries such as Tensorflow, Microsoft integrated CNTK (Microsoft Cognitive Toolkit), etc. It can be deployed on multiple webs, mobile, and embedded devices.

Run the following command using pip to install Keras:

pip install Keras

Tensorflow

The Tensorflow library has been a well maintained and widely used open-source library/framework by the Google Brain Team as one of those powerful python libraries that considered as the best tool for solving problems such as speech recognition, object identification, artificial neural networks that require multiple data sets and is heavily used for training and developing efficient machine learning and deep learning models on CPUs, GPUs and TPUs(tensor processing units).

Run the following command using pip to install Tensorflow:

pip install TensorFlow

PyTorch

The PyTorch library provides a robust set of tools and capabilities for developing high-end machine learning models on GPUs for advanced use-cases such as Natural Language Processing and Computer Vision. It is also widely used to build and train performant artificial neural networks. It is efficient and fast as the implementation is based on an open-source deep-learning library, 'Torch', in C. 

Run the following command using pip to install PyTorch:

pip install torch

Also read,  Python filename extensions

Python Libraries used in Web and Mobile Applications Development 

Python has also become a popular choice for comprehensively building applications for the web, desktop and mobile, which have extensive community support and a robust set and range of libraries and tools that One could use for all types of project requirements.

FastAPI

The FastAPI library is an advanced and modern web framework/library that one can use to build highly scalable and fast APIs with Python, which is on par with tools like NodeJS, Spring, Golang etc. It has a lightweight design with inherent capabilities offering asynchronous APIs with flexible and standard JSON schemas and serialisation.

Run the following command using pip to install FastAPI:


pip install fastapi uvicorn

Flask

The Flask library is a micro-framework with an out-of-the-box set of tools available such as form-validations, database ORM integration and abstraction, jinja2 templating, routing etc. It is used for developing lightweight web apps to host a range of features such as web servers, APIs, machine learning or deep learning models etc.

Run the following command using pip to install flask:

pip install flask

Django

The Django library has been a well maintained and widely used open-source library/framework. It is a popular, high-level Python Web framework that supports the development of web applications. It is free and open-source, and extremely popular among developers. Django is a server-side framework with all supported features. It procures fast development of websites, offers excellent security features, and supports maintenance. Some of its features to look out for -: a variety of database support, database schema migration, ORM implementation, an authentication mechanism, admin dashboard. 

Run the following command using pip to install Django:

pip install Django

Also read, Convert String to List Python

FAQs

  1. Are there libraries that support mobile app development in Python?
    Yes, In Python, one can use Kivy, Python-for-Android, Plyer etc., for developing mobile applications.
     
  2. How is desktop and other cross-platform application development done in Python?
    Tkinter is one of the most widely-used Python libraries for developing desktop applications. One can use other libraries like PyGame for game development for multiple platforms such as macOS, Windows, Linux GTK, Android,tvOS, iOS etc., similar environments.

Key Takeaways

In this article, we learned about various types of open-source python libraries for different use-cases, domains such as Web Applications development, writing, training and building machine learning, deep learning models, etc., or solving data science visualisation problems. To learn about more such unique python libraries, refer and refer

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