Python Libraries You Need to Know in 2023

If you are serious about learning Python and want to make your daily tasks easier, start using these Python libraries. Each one will save you time and effort.

Python is a programming language with a breadth of applications in data science, Artificial Intelligence, machine learning, web development, and game development. To understand what makes Python such a widely-used and beginner-friendly choice, let’s look at the goals set by Python creator Guido van Rossum. He wanted it to be:

• An easy and intuitive language that’s just as powerful as its competitors.
• Open source, so anyone can contribute to its development.
• As understandable as plain English.
• Suitable for everyday tasks.
• Quick to code, leading to shorter development times.

Considering the overwhelming popularity of Python, it is safe to say that Guido van Rossum achieved his goals.

On top of the aforementioned great features, Python has a very active community that supports the development of core Python and contributes to its many external libraries. These libraries simplify complicated tasks and expedite the development process.

In this article, we’ll talk about the most popular Python libraries. If you’re an absolute newcomer to coding, we recommend learning something about Python first. Our Learn Programming with Python track is ideal for first-time programmers.

It is important to note that the terms “library” and “package” are sometimes used interchangeably. A library refers to a reusable chunk of code that may contain several modules and packages. You can learn more about the difference between Python modules, packages, libraries, and frameworks elsewhere in our blog.

Python has so many libraries that it’s not possible to learn them all. And you don’t need to, even if you want to be an expert Python developer. These libraries usually perform a particular set of tasks, so the libraries you need will depend on your field of study. For instance, if you are learning Python to work in machine learning, your focus should be on libraries like pandas, NumPy, scikit-learn, TensorFlow, and so on.

So, let’s see what Python libraries are the most popular for this year, 2023, and what field each library supports.

The Most Popular Python Libraries in 2023

1.   pandas

pandas is a data analysis and manipulation library. Data science products and applications start with real-life data, which almost always requires preprocessing. This library helps with cleaning and preprocessing raw data so that it becomes ready-to-use for other operations.

The pandas library also provides dozens of functions for data analysis. With just a few lines of code, you can extract valuable insights from the data. It simplifies the process of turning raw data into information. Thus, this is a go-to library for data analysts and business analysts. It’s also essential for machine learning engineers, who need to prepare data before it’s fed into machine learning models. The performance and accuracy of models is directly proportional to the input data. Garbage in, garbage out! You cannot expect a model to perform well with messy, unprocessed raw data.

It is important to note that pandas was originally built on NumPy (another Python library). Versions earlier than 2.0 use NumPy to represent data. Performance was adequate in most cases, but as data size increased, things started to slow down. As a result, pandas alternatives such as Dask and Polars have been introduced. Starting from version 2.0 (which was released on April 3, 2023), Pandas allows using an Apache Pyarrow backend that significantly helps with these pain points.

2.   Polars

Polars is a DataFrame library that can be used as an alternative for pandas, especially when you’re working with very large datasets. Some of its advantages include:

• Polars utilizes all available cores on your machine; pandas uses a single CPU core to execute the operations.
• Polars is more lightweight than pandas and has no dependencies. This makes it faster to import Polars (70 ms) than pandas (520 ms).
• Polars optimizes queries to reduce unnecessary memory allocation. It can also process queries partially or entirely in a streaming fashion. As a result, Polars can handle datasets that are larger than the RAM available in your machine.

3.   scikit-learn

scikit-learn is a machine learning library that supports creating and training supervised and unsupervised machine learning models. It also provides tools for data preprocessing, model evaluation, model selection, and many other utilities.

It can be considered as an all-in-one package for those implementing machine learning in their products. The scikit-learn library provides a huge selection of machine learning algorithms, from linear regression to complex neural networks.

Hence, scikit-learn offers solutions for common machine learning tasks, including:

• Classification: A supervised learning problem with a discrete target variable. Image classification and churn prediction are examples of classification tasks.
• Regression: A supervised learning problem with a continuous target variable, such as price, temperature, or sales amounts.
• Clustering: An unsupervised learning problem that aims to group data points in clusters, so that similar data points are grouped together.

You can also use scikit-learn to perform a thorough evaluation of your models. It supports simple metrics such as accuracy, mean absolute error, and R-squared; it also provides more advanced techniques like cross-validation and grid search.

4.   TensorFlow

TensorFlow is a machine learning library created by Google. It can be considered an end-to-end machine learning platform because it includes functions and tools for preparing data, building machine learning models, deploying models, and implementing machine learning operations (i.e. MLOps).

TensorFlow also offers the high-level Keras API, which adds an extra layer of abstraction. It makes getting started with TensorFlow and machine learning easy.

5.   PyTorch

PyTorch is a machine learning library that was first introduced by Facebook in 2017. Thanks to tensor computation with GPU acceleration, it allows for quick processing and offers high performance.

It is mainly preferred for deep learning tasks, such as natural language processing (NLP) and computer vision.

6.   Requests

Requests is a commonly-used HTTP library for Python. Its easy-to-use API is aimed at making HTTP statements easier to understand and work with. It is one of the most downloaded Python libraries, with approximately 30 million downloads per week. We can use it to create HTTP requests to interact with the resources on the internet and consume data in our applications. In a sense, this library is a programmatic access to the internet.

The Requests library offers a wide range of features like customizing URLs with parameters, sending custom headers, tracking redirects, and so on. It also does SSL verification, which can be critical when communicating with secure sites over HTTP.

7.   Beautiful Soup

Today’s largest data source is the web. The tools that amaze us (such as ChatGPT and GPT-4) rely on the data found online. To make use of this freely available online data, we need a programmatic way of getting the data and converting it to informative insights.

8.   Seaborn

Seaborn is a data visualization library built on top of another famous Python library, Matplotlib. I personally prefer Seaborn over Matplotlib because it simplifies creating a variety of data visualizations.

If you prefer greater control over your plot’s details (even if it means more lines of code), Matplotlib might be a better option for you.

Tips for Learning Python Libraries

The aforementioned libraries come in quite handy for a variety of tasks. To use them efficiently, you need to have at least an introductory level of Python knowledge, which you can obtain with the tracks and courses offered by LearnPython.com.

Our Learn Programming with Python track will help you gain a comprehensive understanding of the fundamentals of Python and computer programming. Once you complete this track, you will be ready to enrich your scripts with built-in and external libraries.

Keep in mind that consistency and practice are fundamental when learning a new programming language. Hence, it is important that you practice every day, even if it is just for an hour. Interactive online courses like ours are great for practicing, as they offer lots of hands-on exercises.

Happy learning!