Hello, world. It's me your brother AS. So, in this post we will know about 20+ Best Python Libraries. Before getting further, let us know what a python Library is.
What is Python Library?
A library is a collection of modules (Library either contains built-in modules written in C + modules written in Python), whereas a module is a set of lines of code that are used for a specific purpose and can be used in other programmes as is, to avoid DRY (Don't Repeat Yourself) as a team and focusing on the main requirement. Each of a group of standardised parts or self-contained units can be used to build a more complex structure.
We know that a module is a file containing Python code and that a package is a directory containing sub-packages and sub-modules of a package. There is, however, some confusion between a Python library and a package. You may want to include a Python library in your programs/projects because it contains reusable code. In contrast to languages such as C++ or C, Python libraries aren't tied to any particular context. A 'library' in this context refers to a group of core modules.
So, a library, in essence, is a grouping of modules. One way to install packages is through the use of a package manager such as Rubygems or npm. In python, PIP is the most commonly used library (Package Index of Python).
What is Python Standard Library?
The Python Standard Library is a collection of Python's exact syntax, tokens, and semantics. It is pre-installed with the core Python distribution. When we started with an introduction, we mentioned this. The standard library is built around more than 200 core modules. Python includes this library.
It is written in C and handles features such as I/O and other core modules. Python is the language it is because of all of its functionality. However, in addition to this library, the Python Package Index provides access to a growing collection of several thousand components (PyPI). It was mentioned in the previous blog.
I hope you got it, now let's move to the best python libraries, Here we go:
- PyGame
- Pillow
- Matplotlib
- Pandas
- iPython
- Flask
- Django
- PyGTK
- Tkinter
- Tensorflow
- Numpy
- PyTorch
- Requests
- Beautifulsoup
- SQLAlchemy
- SciPy
- Pyttsx3
- Pyinstaller
- Keras
- Scikitlearn
Now, I am going to explain all:
1) PyGame:
Pygame is a collection of Python modules for creating video games. Pygame extends the excellent SDL library with functionality. This enables you to write full-featured games and multimedia programmes in Python. Millions of people have downloaded Pygame. Pygame is available for free. It is licenced under the LGPL and can be used to create open-source, freeware, shareware, and commercial games. For more information, please see the licence. Pygame is extremely portable and can be run on almost any platform or operating system.
Consider the chimp tutorial and the introduction for python programmers for an introduction to pygame. other backends, including an ASCII art backend. For this reason, professional games use a variety of backends on both Linux and Windows systems.
Features:
- C and Assembly code optimised for core functions.
- CPUs with multiple cores can be used with ease. Comes with a number of different operating systems.
- Simply put, it's a breeze to work with.
2) Pillow:
Pillow or PIL (Python Imaging Library) provides support for opening, manipulating, and saving images in the Python programming language (Python). A large number of formats can be identified and read by the current version of the software. However, only the most common interchange and presentation formats are supported. In the core image library, data is stored in a handful of basic pixel formats that can be accessed quickly.
As a general-purpose image processing tool, it should be a good starting point. Open-source Python Imaging Library adds support for opening, manipulating, and saving a wide variety of image file formats to the Python programming language. It's available for Windows, Mac OS X, and Linux users to download. It is free and open-source.
3) Matplotlib:
For the Python programming language and its NumPy numerical mathematics extension, Matplotlib is a library for plotting graphs. For applications that use GUI toolkits such as Tkinter, wxPython, Qt, or GTK it provides an object-oriented API for embedding plots into the application code. Although its use is discouraged, there is also a procedural "pylab" interface based on a state machine (like OpenGL) that is designed to closely resemble MATLAB's. Matplotlib is used by SciPy to create plots and graphs. John D. Hunter was the original author of Matplotlib, which is a data visualisation library. An active development community exists since then and it's licenced in BSD-style.
For 2D plots of arrays, Matplotlib is a fantastic Python visualisation library for 2D plots. Data visualisation library Matplotlib is based on NumPy arrays and designed to work with the SciPy framework. John Hunter introduced it in the year 2002, and it has been around ever since.
As a result of visualisation, we have visual access to vast amounts of data in easily digestible visuals. Many different types of plots are available in Matplotlib, such as line, bar, scatter, histogram, and more.
4) Pandas:
A Python package called pandas provides fast, flexible and expressive data structures for working with "relational" or "labelled" data. As such, it aims to serve as a high-level building block for doing practical, real-world data analysis in Python. Additional goals include becoming the most powerful and flexible open-source data analysis/manipulation tool available in any language. This goal has already been achieved.
pandas is a software library for data manipulation and analysis written in the Python programming language. As a result, it provides data structures as well as operations for manipulating numerical tables and time series. It's free software released under the BSD licence, which has three clauses.
5) iPython:
A command shell originally developed for Python, IPython (Interactive Python) allows interactive computing in a variety of programming languages. It includes introspection, rich media, shell syntax, tab completion, and history features. Interactive data visualisation and the use of GUI toolkits are supported by the software. Embedded interpreters that can be loaded into one's own projects are available. Code, text, mathematical expressions, inline plots and other media are supported in the browser-based notebook interface. A parallel computing tool.
Interacting with Python is made easier with IPython's rich toolkit. It's a powerful Python shell that can be used interactively. To work with Python code in Jupyter notebooks and other interactive front-ends, you'll need a Jupyter kernel (see below).
Following are the main features of the enhanced Python shells and kernels for interactive use:
Introspection of the entire object.
Past entries are stored across sessions and are accessible.
With the help of automatically generated references, output results can be cached during a session and retrieved later.
Extensions for tab-completion include python variables and keywords, as well as filenames and function names.
6) Flask:
Flask is a Python-based micro-web framework that allows you to create simple web applications. As a microframework, it does not require any specific tools or libraries to be installed on the computer system. You won't find any pre-existing third-party libraries for database abstraction, form validation, or anything else. There are however extensions available that can add application features as if they were built into Flask. It is possible to add extensions for object-relational mapping, form validation, upload handling, various open authentication technologies, and several common framework-related tools. Pinterest and LinkedIn are two examples of applications that use the Flask framework.
As a result of the efforts of Armin Ronacher and Pocco, a group of Python enthusiasts from around the world, it was developed. In addition to Werkzeug, Flask includes the Jinja2 template engine and the Werkzeug WSGI toolkit. Pocco was responsible for both of them. When it was first released in 2010, it was a huge hit.
7) Django:
Django is a Python web framework that promotes rapid development and clean, pragmatic design. It was created by experienced developers to take care of much of the hassle of web development, allowing you to focus on writing your app instead of reinventing the wheel. It is both free and open source. It is maintained by the Django Software Foundation (DSF), a 501(c)(3) non-profit organisation based in the United States. It is a web framework that adheres to the architectural pattern of a model–template–views (MTV). Django was created to assist developers in moving applications from concept to completion as quickly as possible.
Django prioritises security and assists, developers, in avoiding many common security blunders. Some of the busiest websites on the internet make use of Django's ability to scale quickly and flexibly. Django's main goal is to make it easier to create complex, database-driven websites. The framework emphasises component reusability and "pluggability," less code, low coupling, rapid development, and the don't repeat yourself principle. Django also includes an optional administrative create, read, update, and delete interface, which is generated dynamically via introspection and configured via admin models. Instagram, Mozilla, Dropbox, and other well-known websites use Django.
8) PyGTK:
PyGTK is a Python wrapper library for the GTK graphical user interface library. PyGTK is open source software licenced under the LGPL. It is similar to PyQt/PySide and wxPython, which are Python wrappers for Qt and wxWidgets, respectively. James Henstridge, a GNOME developer, is the original author. The core development team consists of six people, with numerous others submitting patches and bug reports. PyGTK has been chosen as the preferred environment for applications running on One Laptop Per Child systems.
9) Tkinter:
Tkinter is a Python wrapper around the Tk GUI toolkit. It is Python's de-facto standard GUI and the standard Python interface to the Tk GUI toolkit. Tkinter is included with standard Python installations on Linux, Microsoft Windows, and Mac OS X. Python provides numerous options for developing graphical user interfaces (GUIs) (Graphical User Interface). Tkinter is the most commonly used of all the GUI methods. It is a standard Python interface to the Python-supplied Tk GUI toolkit. Python with tkinter is the quickest and easiest way to create graphical user interface (GUI) applications. Using tkinter to create a GUI is a simple task.
Tkinter also provides access to the geometric configuration of widgets, which can be used to organise widgets in parent windows.
10) Tensorflow:
TensorFlow is a free and open-source library for numerical computation. Google created and maintains it, and it is licenced under the Apache 2.0 open source licence. The API is ostensibly for the Python programming language, but it also provides access to the underlying C++ API. Unlike other numerical libraries for Deep Learning, such as Theano, TensorFlow was designed for use in both research and development and production systems, including RankBrain in Google search and the entertaining DeepDream project. It is capable of running on single CPU systems, GPUs, mobile devices, and large scale distributed systems with hundreds of machines.
11) Numpy:
NumPy is a Python-based array-processing library. It includes a high-performance multidimensional array object as well as tools for manipulating these arrays. It is a Python library that is used to work with arrays.
It also includes functions for working with linear algebra, Fourier transforms, and matrices. Travis Oliphant created NumPy in 2005. It is an open-source project that you are free to use. NumPy is an abbreviation for Numerical Python. Apart from its obvious scientific applications, it can also be used as an effective multi-dimensional container of generic data. Numpy can define arbitrary data types, allowing NumPy to seamlessly and quickly integrate with a wide range of databases.
12) PyTorch:
PyTorch is an open-source machine learning library based on the Torch library, primarily developed by Facebook's AI Research lab for applications such as computer vision and natural language processing. It is open-source software distributed under the Modified BSD licence. Despite the fact that the Python interface is more polished and the primary focus of development, PyTorch also includes a C++ interface. Deep learning software such as Tesla Autopilot, Uber's Pyro, HuggingFace's Transformers PyTorch Lightning, and Catalyst is built on top of PyTorch.
PyTorch includes features such as:
Tensor computation (similar to NumPy) with strong acceleration is provided by graphics processing units (GPU).
Deep neural networks are based on an automatic differentiation system based on the type.
13) Requests:
Python's requests module allows you to exchange web requests. It's a very useful library with a lot of useful methods and features for sending HTTP requests. As previously stated, HTTP functions as a request-response system between a server and a client. The client could be your web browser, and the server could be the system that hosts the site you want to visit. The Python requests module provides a simple API for handling all of these requests. It has a lot of cool features, like passing parameters within URLs, adding headers, sending custom headers, and so on.
14) Beautifulsoup:
BeautifulSoup is a Python library used for web scraping to extract data from HTML and XML files. It generates a parse tree from page source code, which can be used to extract data in a more hierarchical and readable manner. It was first introduced by Leonard Richardson, who continues to contribute to this project, and it is also supported by Tidelift (a paid subscription tool for open-source maintenance). Beautifulsoup3 was officially released in May 2006. The most recent version released by BeautifulSoup is 4.9.2, which supports both Python 3 and Python 2.4.
15) SQLAlchemy:
SQLAlchemy is a library that allows Python programmes to communicate with databases. This library is typically used as an Object Relational Mapper (ORM) tool, converting Python classes to tables in relational databases and automatically converting function calls to SQL statements. SQLAlchemy provides a standard interface through which developers can write database-agnostic code that communicates with a wide range of database engines. SQLAlchemy uses common design patterns (such as Object Pools) to make it simple for developers to create and ship enterprise-grade, production-ready applications. Aside from that, SQLAlchemy abstracts away boilerplate code for tasks like database connections, allowing developers to focus on business logic.
16) Scipy:
SciPy is a Python library that is free and open-source that is used for scientific and technical computing. It is a collection of mathematical algorithms and utility functions based on Python's NumPy extension. It significantly increases the power of an interactive Python session by providing the user with high-level commands and classes for manipulating and visualising data. As previously stated, SciPy is built on NumPy, so there is no need to import NumPy if you import SciPy. SciPy has optimised and expanded on functions found in NumPy and Data Science.
17) Pyttsx3:
pyttsx3 is a Python text-to-speech conversion library. It works offline, unlike other libraries, and is compatible with Python 2 and 3. To obtain a reference to a pyttsx3. Engine instance, an application calls the pyttsx3.init() factory function. It is a simple tool that converts the entered text into speech. The pyttsx3 module supports two voices, one female and one male, which are provided by “sapi5” for Windows.
18) Pyinstaller:
PyInstaller is a Python package that can be installed using pip. PyInstaller can be installed in your default Python installation, but it is preferable to create a virtual environment for the project you want to package and install PyInstaller there instead.
PyInstaller reads your Python programme, analyses all of the imports it makes, and bundles copies of those imports with your programme. PyInstaller reads your programme from the beginning. For example, if your program's entry point is myapp.py, you would run pyinstaller myapp.py to analyse it. Many common Python packages, such as NumPy, can be detected and packaged automatically by PyInstaller, but you may need to provide hints in some cases.
19) Keras:
Keras is built on open-source machine libraries such as TensorFlow, Theano, and Cognitive Toolkit (CNTK). Theano is a Python library for performing fast numerical computations. TensorFlow is the most well-known symbolic math library, and it is used to build neural networks and deep learning models. TensorFlow is extremely adaptable, with the primary advantage being distributed computing. Microsoft's CNTK is a deep learning framework. It makes use of libraries such as Python, C#, and C++, as well as standalone machine learning toolkits. Theano and TensorFlow are powerful libraries for building neural networks, but they are difficult to grasp.
Keras has a simple structure that allows for the creation of deep learning models based on TensorFlow or Theano. Keras is intended to be used to quickly define deep learning models. Keras, on the other hand, is an excellent choice for deep learning applications.
20) ScikitLearn:
Scikit-learn is probably the most useful Python machine learning library. The sklearn library includes many efficient tools for machine learning and statistical modelling, such as classification, regression, clustering, and dimensionality reduction.
Please keep in mind that sklearn is used to create machine learning models. It should not be used to read data, manipulate it, or summarise it. There are better libraries available for that (e.g. NumPy, Pandas etc.)
And, that's it you want to know more, just comment below, thanks for reading the post, see ya in the next one.