The Most Popular Python Libraries

What programming language is simple to learn and has enormous potential? Python! Its power comes from the amazing libraries that are used by programmers on a daily basis to solve problems. Let’s see which Python libraries are the most popular and what they are used for.

Python is a programming language that has numerous applications across a variety of industries. It has gained tremendous popularity in recent years. Compared to other popular programming languages, Python offers a shallow learning curve, which attracts people who want to take their first steps into programming.

Python is an easy-to-learn language, but it is also a highly competitive one. In terms of the performance, it keeps pace with its major competitors. In fact, Python is used by many tech giants (such as Google, Netflix, and IBM), which is a strong indicator for the performance.

Why Learn Python?

Considering the rise of software products, data science, and machine learning – and the dominance of Python in these domains – learning Python is a great investment. There are a huge number of jobs for people with Python skills. Moreover, you can use Python for improving your daily work. If you would like to learn about the Python job market, check out this article on Python Jobs and Salaries in 2022.

You can start learning Python with the interactive online courses on LearnPython.com. Our Python Basics: Part 1 course will help you enter the world of programming and write Python code that actually performs simple tasks. Thanks to our online console, you can immediately practice what you learn with 95 interactive exercises. Another great feature of this course is that LearnPython.com offers it for free.

Another advantage for learning Python is that it has a rich selection of libraries. A Python library is a collection of modules and packages that can be used for doing a particular set of tasks without writing your own code. It is important to note that the term “library” is used interchangeably with “package” in Python. Thus, you might see people calling a library a package and vice versa.

The Most Popular Python Libraries

There are over 100,000 Python libraries that you can install and use in your scripts. Thus, whatever you are building with Python, it is highly likely that there is a library that expedites your job. You can install a library using pip, the standard package manager for Python.

In this article, we’ll go over the most popular Python libraries and what they are used for. Once you obtain a basic level of Python skills, you will definitely want to check these libraries out and use them in your projects. But before you can use libraries effectively, you need to know the essentials of Python – which you can learn from the Python Basics track.

1.   pandas

The pandas package is a data analysis and manipulation library. Considering Python’s dominance in the data science ecosystem, pandas might be the most-widely used Python library.

When creating a data-based product or a machine learning model, a significant amount of time is spent on data cleaning and preprocessing. Up to 80-90% of the time might be spent on data prep. And pandas has lots of functions that simplify the process of preparing the data.

In addition to cleaning, processing, and manipulating data, pandas can also be used for data analysis. Once the raw data is processed and becomes ready for analysis, pandas helps us perform an exploratory data analysis.

It’s good to know that pandas is compatible with other popular Python data science libraries. For instance, pandas data structures can be used with scikit-learn, a popular machine learning library. This is important because machine learning systems or data-based products are created as an entire process, from data collection to the end product.

2.   Scikit-learn

If you’re looking into machine learning capabilities, Scikit-learn is a go-to Python library. It covers a broad range of machine learning algorithms, from linear regression to complex neural networks. It also provides functions for data preprocessing and feature engineering.

You can use Scikit-learn for different kinds of learning problems, including:

• Classification: A supervised learning task where the target variable is discrete. Typical use cases are spam email detection and image classification.
• Regression: A supervised learning task where the target variable is continuous. Demand forecasting and stock price prediction are examples of regression problems.
• Clustering: An unsupervised learning task for grouping unlabeled data. Customer segmentation is a typical example of clustering.

The Scikit-learn library also provides several metrics to evaluate the performance and accuracy of machine learning models.

3.   Matplotlib

Data visualization is a key component in data science. It helps us discover, interpret, and analyze data. Data visualization is also a highly efficient tool for delivering results and storytelling – which is quite difficult to do using plain numbers!

Matplotlib is Python’s most popular data visualization library. You can use it for creating several different types of plots, such as line plots, scatter plots, and histograms. You can also do three-dimensional plotting with Matplotlib. It can be considered as a low-level library (i.e. you need to write relatively longer code to create the plots), but it gives you full control over them. Thus, you can create highly customized visualizations with Matplotlib.

4.   Beautiful Soup

Today’s largest data source is the web. If you are working in Natural Language Processing (NLP) or Natural Language Understanding (NLU), the most practical way of collecting data is through web pages. However, this is not an easy task if you’re doing it manually!

Beautiful Soup is a Python web scraping library with functions and methods that pull data out of HTML and XML files. You can also perform an analysis on the textual data.

If you would like to learn more about web scraping with Python, I strongly recommend reading our article Web Scraping with Python Libraries.

5.   Requests

Requests is one of the most downloaded Python libraries, with approximately 30 million downloads per week. It is used for sending HTTP requests in very simple and efficient ways.

A request returns a response object that contains all the relevant data, such as content, encoding, status, etc. Here is a simple demonstration:

>>> import requests
>>> resp = requests.get("https://learnpython.com")
>>> type(resp)
requests.models.Response
>>> resp.encoding
'Utf-8'
>>> resp.content
returns the HTML content of the page

6.   Pygame

According to its documentation, this library is a set of Python modules designed for writing video games. It can also be used for creating multimedia programs in Python. Pygame is built on the SDL library.

7.   Random

The Random module can be used for creating random number generators based on different distributions. It is also used for making random choices and selections. Let’s see a few examples of how it works, starting with importing it:

>>> import random

• Return a random integer between the given lower and upper limits:

	>>> random.randint(5, 20)
	15

• Make a random selection from a collection:

	>>> names = ["James", "Jane", "John", "Emily", "Ashley"]
	>>> random.choice(names)
	‘Emily’

• Shuffle a collection:

	>>> mylist = [1, 2, 3, 4]
	>>> random.shuffle(mylist)
	>>> mylist
	[1, 4, 3, 2]

Tips for Learning Python Libraries

You have learned about some of the most popular Python libraries. I picked them from different domains to demonstrate how wide-spread Python is. You can use Python in data science, IoT, artificial intelligence, web development, and game development. Does this make you want to learn Python? Great!

Once you decide to learn Python, you should choose your learning materials carefully. There are a ton of resources available for learning Python, which can be an advantage or a disadvantage. Well-structured and organized material is key.

Another very important point is to practice – and do lots of it. It is the fundamental requirement of learning. LearnPython.com offers carefully designed courses and tracks with an interactive online console that lets you practice while you learn. Our Learn Programming with Python track will  help you gain a comprehensive understanding of the fundamentals of Python and computer programming.

Another very important thing to keep in mind is consistency. When learning Python, or any other programming language, try not to take long breaks. Code every day, even if it is just for a few minutes. You’ll retain more than you would studying for long hours and then taking a long pause. Here is a list of 5 great tips for learning Python from scratch.

Happy learning!