Top 9 Most Essential Python Libraries For Beginners Top 9 Most Essential Python Libraries For Beginners
People worldwide know Python as the most used programming language to date. Major tech companies like Google, Amazon, Meta, Instagram, and... Top 9 Most Essential Python Libraries For Beginners

People worldwide know Python as the most used programming language to date. Major tech companies like Google, Amazon, Meta, Instagram, and Uber use Python for various applications. From web development to machine learning projects, Python is an essential tool in a data scientist’s kit.

Many understand Python is well-suited for beginners. Learning it is a good starting point for novice programmers because of its simplicity. It uses natural language as its base and does not require syntax technicalities. It’s also reasonably easy to download and install, and has hundreds of thousands of libraries to make programming simpler and more convenient.

If you’re interested in programming or just starting with Python, continue reading to learn essential Python libraries you should be aware of.

A Brief Overview of Python Libraries

Python libraries are collections of related modules — or bundles of code — that programmers can use across different programs. Using libraries allows you to avoid writing the same code repeatedly when using various programs.

Many developers rely on Python because of its extensive collection of libraries. There are an estimated 137,000 libraries and more than 198,000 packages. Programmers can use all of these modules for a variety of modern solutions depending on the project.

Because there are so many libraries available, it may be overwhelming for beginners to choose the most important ones. Below is a list of some of the top Python libraries that beginners should know.


1. Keras

Keras is one of the most essential Python libraries there is. It’s considered a deep learning API that was specifically designed for human use rather than machine use. Keras minimizes the number of user actions for some of the most common use cases in programming.

One unique feature of Keras is that it alerts users with clear, actionable error messages to increase productivity and reduce time-consuming troubleshooting. This library is one of the most frequently used deep learning frameworks for Python, as it enables users to run new experiments efficiently. Scientific organizations around the world, such as CERN, NASA and NIH, use Keras for their operations.

2. PyTorch

This essential library is an open-source ML framework capable of speeding up research prototyping, allowing companies to enter the production deployment phase.

Key PyTorch features include robust cloud support, a rich ecosystem of tools, distributed training and native ONNX (Open Neural Network Exchange) support. PyTorch also has TorchServe, an easy-to-use tool that helps deploy PyTorch models at scale. Another advantage is that PyTorch is environment- and cloud-agnostic, which can save time and money and enable collaboration.

3. NumPy

NumPy — short for Numerical Python — is fundamental for numerical computation in Python and contains a powerful N-dimensional array object. It’s a general-purpose array-processing package that provides multidimensional objects.

Some other NumPy features include:

  • Object-oriented approach
  • Compact and fast computations for vectorization
  • Fast, precompiled functions for efficient numerical routines

You can often use NumPy in data analysis, to form the base for other libraries like SciPy and scikit-learn, and to replace MATLAB if you’re using SciPy and matplotlib. It’s essential for beginners because ML applications use it often, and it’s vital for full-stack developers.

4. Pandas

Anyone interested in data science knows that learning Pandas is a must. It’s the most popular and widely used library in Python, often for data cleaning and analysis.

With Pandas, you can create your own function, run it across data to achieve high-level abstraction, and easily work with high-level data structures. It’s also common for professionals to use Pandas for extract, transform, load (ETL) jobs and statistics, finance, and neuroscience applications.

5. TensorFlow

The TensorFlow open-source library for Python is another popular one beginners should learn more about. Moving ML projects in the cloud on any device using TensorFlow becomes very easy, so programmers highly recommend it for businesses. Research estimates that 97% of enterprises used the cloud in 2021, so it’s no surprise that developers use TensorFlow so widely.

TensorFlow has a community of around 1,500 contributors, and various scientific fields often utilize it. It’s optimized for speed, using techniques like XLA for any linear algebra operations.

6. scikit-learn

scikit-learn is an ML library for Python that will help you access just about any ML algorithm you may need for the project you’re working on. It’s worth noting that programmers specifically designed scikit-learn for use in tandem with SciPy and NumPy.

Typical applications include model selection, regression, dimensionality reduction, clustering, and classification.

7. Pillow

Pillow is considered a fork of the Python Image Library and is essentially a modern version of the PIL. With Pillow, you can open any image, save it, and also influence an image’s environment. Pillow supports many types of files you may work with, including:

  • PDF
  • JPEG
  • GIF
  • PSD
  • PCX
  • PNG
  • IM
  • EPS
  • And more

Pillow offers excellent community support. As a beginner, you can use the community as a valuable resource.

8. Matplotlib

The main benefit of Matplotlib is its stunning visualizations. It’s a plotting library with a vibrant community of around 700 contributors. Programmers most frequently utilize Matplotlib for data visualization projects. The data visualization market could reach approximately $7.76 billion by 2023, with a compound annual growth rate of 9.47%.

Matplotlib is highly useful and has low memory consumption. Some professionals will use Matplotlib to replace MATLAB, considering it’s free and open source. You can also use this library with toolkits like Python scripts, IPython Shells, Jupyter Notebook, and more.

9. OpenCV Python

OpenCV — also called Open Computer Vision — is a useful package for image processing. It can monitor basic functions, but it does not have adequate documentation. Some experts suggest that OpenCV is one of the hardest libraries to learn. However, it can assist beginners with learning the ins and outs of computer vision.

With OpenCV, you can reconstruct, stop, and understand 3D environments from their 2D environment. You can also diagnose any special objects in a video or image, including things like trees, eyes, or faces. OpenCV is compatible with many operating systems, including Windows, macOS, and OpenBSD.

Other popular Python Libraries:

Some other popular, well-known Python libraries include:

  • Keras
  • BeautifulSoup
  • Scrapy
  • PyTorch
  • Theano

The more libraries you are familiar with, the more capabilities you’ll have as a programming professional. Understand that it may take time to learn these, but it will help you in the long run.

Learning Python Libraries for Programming Roles

As a data science or programming beginner, deciding which Python libraries are worth your time and effort can be daunting. Consider following the suggestions above to get started with Python libraries, and you’ll be ready to build on that foundation in no time.

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April Miller

April Miller

April Miller is a staff writer at ReHack Magazine who specializes in AI, machine learning while writing on topics across the technology sphere. You can find her work on ReHack.com and by following ReHack's Twitter page.