Important Python Libraries

When we are learning Python, one of the important features we come across is its wide range of libraries. These libraries help when we are building with different applications and projects.

You would be wondering which libraries to learn from? This answer differs based on the domain you choose. In this article, we will see some of the important libraries. Before this, let us see what is a Python library?

Introduction to Python Libraries

A Python library is a collection of a chunk of code that can be reused iteratively. These provide pre-written codes that we can use in our program, instead of writing them from scratch every time. This saves time and also reduces the length of the code.

Often people get confused between a module, a library, and a package. A module is a collection of modules and global variables. A library is a collection of modules. And a package is a library that can be installed using a package manager like rubygems or npm or pip.

Standard Libraries are a collection of exact syntax, token, and semantics of Python. They get installed along with the core Python distribution. There are more than 200 core modules in the standard library. This library comes with Python. We can also access a lot of other collections from the Python Package Index (PyPI).

Now let us use different Python libraries.

1. Matplotlib

Matplotlib is a library used for data visualization and analysis. It is an open-source and also a mathematical extension of NumPy. It is widely used for the plotting of quality figures and sharing them in a variety of hard copy formats and interactive environments across platforms. We just need to write a few lines of code and we can design charts, graphs, pie charts, bar plots, scatterplots, histograms, etc.

To go deep into the visualization, one can also learn Seaborn.

2. Pandas

It is an open-source library that provides tools for easy representation and analysis of large sets of data. One need not learn a specialized data-processing language like R.

The data can be structured (tabular, multidimensional, potentially heterogeneous) and time-series data. One can use simple syntaxes to add, manipulate and delete data into the data structures.

3. Requests

Writing a code that sends HTTP requests is hard as it is not in a human readable format. For this purpose, Python provides the Requests that let one send HTTP/1.1 requests, add headers, form data, multipart files, and parameters with the help of dictionaries.

It keeps connections with HTTP servers alive automatically, eliminating the need to write a bunch of code to form the connection again. In addition, it also lets you access the response data in the same way.

4. NumPy

We all know mathematics includes complex computations. We can do basic operations without any special functions. However, if we want to do complex math, the NumPy package is a good choice.

It provides tools to build multi-dimensional arrays and matrices. It also contains methods to perform calculations on these arrays. In addition, one can solve algebraic formulas, perform statistical operations, etc. To get deeper, one can also learn the SciPy library which is an extension of NumPy.

5. BeautifulSoup

It is a web scraping library that is used to get data from HTML and XML files. It is a great tool for beginners interested in extracting data as it represents the data. In the information in the form of a parse tree which is human readable. It is also easy to navigate, search and modify the parse tree.

Other modules which we can refer to for web scraping are Scrapy and Selenium.

6. PyGame

PyGame is an open source library used for developing 2D games in Python. It includes libraries for adding computer graphics and sound. It is an easy-to-understand and beginner-friendly platform that can help you develop games quickly.

Another module that one can learn for game development is Pyglet which is an object oriented programming interface.

7. Tkinter

This is a library that helps in developing an app with a Graphical User Interface (GUI). This is one of the most commonly used frameworks by developers for creating GUIs. It connects Python to the TK GUI toolkit, which virtually works on every modern operating system.

Another module that can be used for this purpose is PyQt that binds Python with a cross platform toolkit named Qt.

8. Django

Django is a high level Python web framework used for building almost any website. It allied with content management and wikis. It is a great start to expertise in web development. Django can work with any client-side framework. It also delivers content in almost any format which includes HTML, RSS feeds, JSON, XML, etc. In addition, one does not need to write everything from scratch, so it makes the development process easier and faster.

Another framework used for this purpose is Pyramid. Other micro web frameworks include Flask and Bottle.

9. Pillow

If you are interested in image processing then PIL or Pillow is a must-have Python library. It provides simple and easy-to-use commands to open, manipulate, and save different image file formats. It is a good place to start for a beginner.

To go deep into it, one can choose OpenCv that helps us to perform complex image processing algorithms.

10. SQLAlchemy

SQLAlchemy is a library designed for accessing databases. It allows us to form a connection between Python and the database. It provides easy-to-understand commands that can be used by beginners too. This library of Python supports most platforms which include Python 2.5, Jython, and Pypy.

Other libraries that can be used are SQLite and mysql-connector.

11. PyWin32

For the programmer using Windows, Pywin32 is a package that they must know. It provides methods and classes for interacting with Windows.

PyWin32 provides access to many of the native Windows API functions. It allows you to interact with the Windows registry, use the Windows clipboard, and so on.

12. Scikit-learn

It is a free library that is used for building machine learning models using Python. It contains many algorithms including classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and also preprocessing tools.

Other similar libraries used for deep learning are TensorFlow, PyTorch, and Keras.

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of web development, so you can focus on writing your app without needing to reinvent the wheel.

Conclusion

We are at the end of the article. We saw different libraries used for different applications. Hope you could find the library of your interest. Happy learning!