Python still stands as one of the three most popular programming languages because it’s incredibly useful for data scraping, data engineering, and data analysis — meaning non-programmers that are handy with numbers, such as accountants or Economics B.A., can get hold of it and bring home a pay cheque.

Fortunately for beginners and experts alike, Python sports many libraries that can help ease up the setup process. Programmers who want to crack their neighbour’s Wi-Fi password should have it easy building up a script with some libraries, but here we’re focusing on use cases that can bring genuine help to work projects or to job searches. 

We’ve put together the most helpful Python libraries that developers should know about — and rely on — in 2024. These are the 13 top Python libraries for developers:

1. OS and Shutil 

The OS and Shutil modules keep being brought up by online users as some of the most easy-to-work with libraries for Python — even if they’re not strictly libraries. The OS module lets you interact with your computer's operating system by providing tools to get information, work with file paths, and perform file and directory operations. The Shutil module focuses on file operations and offers functions for tasks like recursive copying and removing, finding the path of an executable file, and changing file ownership.

Shutil and OS — not libraries, exactly — can help you work your way around in Python if you want to:

• Get the current working directory
• Change the current working directory
• Work with paths
• Work with files and directories

Some of the documentation relative to the OS module dates from 2003. Since they couldn’t have known Python would blow up like it did, you almost cannot blame them for actually naming a function like an acronym for an operating system, making searching out info about it nigh impossible. But they could’ve definitely called it something else and eased our web searches—remember when a band named themselves The Band and when the internet started to be a thing, no one could get their hits on their PCs?

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2. NumPy

NumPy stands for “Numerical Python”. It is a commonly used open-source library that is used in almost all fields of science and engineering because of its speciality in numerical computation and data analysis, especially in large volumes. It incorporates into its system new objects called “arrays,” where collections of data of the same type are represented in several dimensions. Its API is used in Pandas, SciPy, Matplotlib and most Python data science packages.

The advantage of NumPy is that array processing is much faster in this library, which makes Numpy ideal for processing large vectors and arrays.

NumPy also provides "ndarray". It adds data structures to Python that guarantee efficient computations with arrays. All in all, Python users suggest that NumPy is the ideal library for large amounts of data. It is so fast because most of it is written in C, although its drawback is that since the arrays are stored entirely in memory, it is easy to run out of it.

3. Matplotlib

Matplotlib is a Python library for visualising two-dimensional graphics (plotting). It is the most widely used Python package for viewing 2D plots. It is built on top of the NumPy library.

It provides a very fast way to visualise numerical data and figures, making it a staple of data analysis. It is open source and has the following main functions:

• Graph creation
• Axis and label control, including scale and limits.
• Customisation of styles.
• Export of graphs created in various formats, such as PNG, PDF, SVG and others, making it easy to incorporate the graphs into presentations and reports

Hell, we could’ve even made custom PNGs for this blog using Matplotlib, but our clanking desktop PC can’t run anything above MS Paint.

4. SciPy

SciPy, shorthand for “Scientific Python,” is an open-source library for high-level scientific computations that can’t be done with your brave, little abacus 🧮. This library is built over an extension of NumPy that provides modules for optimisation, linear algebra, integration, interpolation, and other scientific and engineering tasks. 

One of the most called-out features of SciPy is optimisation. It’s one of the few times that word will mean something, so, lo and behold, it’s the process of finding a mathematical function's minimum or maximum value.

5. Pandas

Pandas is an open-source Python library that is mainly used for data analysis and machine-learning tasks. It is built on top of NumPy and provides flexible high-level data structures and analysis tools, including DataFrame structures, which work like spreadsheets. Pandas facilitate data analysis, manipulation, and cleanup. Cleaning data up refers to quickly getting rid of faulty data from your otherwise stellar dataset, and Pandas excel in that.

Pandas aim to be the fundamental high-level building block for performing data analysis in Python, and users don’t play this mission down. Python programmers call it out as being the best way to analyse data on the language, which is a bold claim since Python and data are slowly becoming a single entity. 

Here is a list of things that we can do using Pandas:

• Cleaning, merging, and joining datasets.
• Data visualisation.
• Easy handling of missing data (represented as NaN).
• It can manipulate and delete DataFrame columns and objects with higher dimensions.
• It has a very good function in application operations, splitting and merging of datasets.

Users highlight that Pandas is used along with other libraries when data science calls for it. Since it’s built on top of NumPy, many of the Numpy structures are used in Pandas. The data produced in Pandas could be used as input for Matplotlib's plotting and statistical analysis functions in SciPy.

6. Scikit-learn

Scikit-Learn is another free Python library for machine learning. It’s handy for anyone starting to program on Python because of its easy use in everything related to the structuring of data analysis and statistical modelling. Scikit-Learn has different algorithms for classification, clustering, and dimensionality reduction, and it is compatible with ScyPy and other Python libraries such as Pandas and Matplotlib. 

Users bring it out as a great Python library because of the various algorithms and modules that help perform data analysis in the early processing stages. It is constantly updated.

The library also stands out for supporting more novel algorithms such as KNN, XGBoost, SVM and so on. Users agree that Scikit-learn also has one of the best documentation bases of all open-source libraries.

7. Requests

The Python Requests library — pitched as “HTTP for humans” — works specifically on executing HTTP requests and operations to a specific web server. Due to this, programmers can now aggregate different content — such as forms and files split into several parts via other libraries — by sending and receiving requests from an HTTP server.

Python Requests will be your great ally if you want to make web requests — either to consume an API, extract information from a site or send the content of a form in an automated way. Some users say that also, if you have this kind of request, you could work with Urllib, but the reality is that Requests is easier to use and has more features which ends up making its use wider in functions since:

• Its syntax is easier to use since requests can be made with few lines of code.
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• As for errors, it handles them automatically, unlike Urllib, which requires more code to handle HTTP errors manually. Same story with cookies: Requests handles them automatically and Urllib requires more code.
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• It provides built-in methods for working with JSON data, unlike Urllib which requires additional steps to work with JSON data.

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8. Scrapy

Scrapy is an open-source tool mainly aimed at extracting information from specific websites. Scrapy web will be useful for developing web crawlers and spiders 🕸️🕷. It provides web crawling that stands out for its speed and high-level screen scraping. That is why programmers say that Scrapy is not just a library, but a complete web scraping framework for Python. It is also often used for data mining and automated data checking. Some facts that pull Scrapy into a category of its own are:

• It handles a cross-platform language compatible with Windows, Linux, Mac and BSD.
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• It has Scrapy Cloud, a specific tool for hosting web crawlers in the cloud.
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• It can scan the web to check out if a new meaning for “scrappy” shows up in a business pep talk
  
  

9. JSON on Python

This is yet another case of a module that’s mentioned along with libraries. Python supports JSON natively, so you can run it from the get-go. The JSON module allows developers to parse JSON into a Python dictionary, and then write a Python dictionary into JSON. 

Since JSON contains structured data and is used to transfer information between different systems, you’ll see it on APIs all the time, and even non-programmers might interact with it when a web app gives back a structured JSON message containing, let’s say, a YouTube video transcript. This means that, for example, in the case of developers who need to transfer data structures from one language to formats recognisable by other languages, JSON is an ideal tool for them. Yes, that’s why an API is so helpful in a nutshell.

The JSON text format is entirely independent of the programming language but uses terms commonly known to programmers like C++, Java, or Python. That's why, being an independent format, the services that share information by this method do not need to “speak” the same language.

You need to be aware of the following if you want to use JSON:

• It is just a data format.
• A misplaced comma or colon can prevent a JSON file from working.
• A string or a single number can be valid JSON objects.
• Claiming you’re a JSON expert will earn you some eye-rolls.
  
  

10. Flask

Flask, a tool for building the backend of web apps on Python, is rigorously classified as a “web microframework” (yes) but Python programmers still name it along their favourite libraries. Flask is still for web development beginners who are good at Python and are interested in backend web development. It’s lightweight, it’s easy to learn, and gets the job done when you need. It's a great starting point before moving to more scalable frameworks like Django, which should handle bigger projects with bigger databases.

11. re for Regular Expressions

Python devs who admit to liking regular expressions consider themselves insane. If you’re part of that Suicide Squad, you can chime in to admit your love for re. The Re module, which is built-in into Python and thus is a module and not a library, is recommended for pattern matching and text processing. Regular expressions can be complex to put together — reading through one could get you thinking that Captchas are actually marvellous — but are highly convenient for specific text manipulation tasks. Re users also suggest checking out RegEx, which adds some functionalities.

12. PyQt5

GUI applications often consist of a main window and several dialogues. If you want to design these graphical components effectively and make them user-friendly, then Qt Designer will save you some time. PyQt5 is integrated to Qt Designer and users give it props when it comes to creating GUIs. PyQt5 offers plenty of widgets and ease in handling tasks like multithreading.

13. Rich

Rich is appreciated because it can make errors look nice. It works with produce visually attention-getting console outputs, including well-formatted errors, markdown rendering, progress bars, and more. Rich supports various colour schemes (4-bit or 8-bit) and terminal types, so it’s not your poor man’s error screen if you were thinking that. If you’re going to botch your code, better make it look pleasant.

Ready to supercharge Python and land a job? 

Since Large Language Models dropped, the prospect of “supercharging” your life appears to be ubiquitous. Now, none of those tools seems to live up to that promise as Python and its library and module set. Now that you’ve explored what fellow Python developers are calling out as the most convenient tools, are you ready to master Python and get a job?

At WeAreDevelopers, we’re vetting the best opportunities for developers in Europe who want to work for German, Swiss, British (and more) organizations. To put into practice everything you have learned, so check job boards and work where you always wanted. You can also level up your Java game by checking our list of the best Java libraries. Good luck!