10 Useful Python Modules You NEED to Know

The true power of Python lies in its extensive libraries and modules. That's why I've created this list of ten Python modules that you need to know. If you really want to take advantage of the language. Now, each of these modules has a different use case, but I can guarantee you they're very useful and most of you will encounter almost all of them throughout your Python career. So with that said, let's get into the first Python module that you need to know. The first module I have is the simplest and this is the request module. Now this is used to send Http requests. So thing get requests, post requests, put patch delete, you name it. If you want to interact with some kind of web service or API, you can use the request module. That it takes just a few minutes to learn how to use this. And it's very effective because regardless of what you do with Python, chances are you'll be interacting with some kind of API and you need to send a network or Http request. So learn the request module. Well worth it. And like I said, just takes a few minutes. The next module on my list is flask. Now, flask is great for building a lightweight web applications. You can build full websites or APIs. Now I find myself using flask all the time, especially for hobby projects where I need to quickly spin up some kind of backend service. I don't want to deal with a more complicated framework like Django. Flask is relatively fast. It's very easy to get started with, and again, it's really good for building simple web services. For example, I built an application where I had a simple front end built in JavaScript, and rather than doing something in Node.js or using something like Django or a more complicated language, I simply use flask and set up a very basic back end that connected to a database to save the data from my front end. I use flask all the time. It's a great module to have under your belt, and it's really good for prototyping hobby projects or something simple that you'll push into production. It's not something I would use for a huge scale application, but it's very flexible. It has all kinds of plugins and extensions, and although I typically use it for simple things, you can build more complicated applications with flask due to its extensibility. Regardless, learn flask I don't think you'll regret it. Now, the next thing I think you should learn is actually not a Python module, but a free resource created by myself and a good friend of the channel, HubSpot. Now this resource breaks down how to land a developer role in the world of AI, and it's authored by me. This is a summary of my over ten years of experience as a developer, where I discuss the best programing languages to learn and how to learn them effectively. I also go through best practices for crafting your portfolio and resume, and a list of YouTube channels and other resources to check out to level up your development skills. I put a link in the description where you can check it out completely for free. Now this is all centered around standing out in the world of AI. It gives you key insights and tips that you can take advantage of to be competitive in this new market. Now, a massive thank you to HubSpot for sponsoring this video and teaming up with me to create this resource. Check it out from the link in the description. And now let's move on to the next module. Moving on we have pedantic. Now this is a really powerful Python module that provides data validation and settings management. Now this is typically used in collaboration with other Python modules. For example fast API uses this and even some modules like Lang chain take advantage of pedantic types. Now what this allows you to do is have a more comprehensive Python type in system, so that you can safeguard yourself and make sure you have the correct input data. It can do automatic data validation and it can handle settings management through things like environment variables. Pedantic is a great module. It has a lot of different features. Again, most of this is related to making your Python code safer and allowing you to provide type hints that people understand how to utilize your functions or the code that you're writing. Definitely check it out because you'll probably see it in a lot of other Python modules that you might be using. The next module on my list is fast API. Now this is pretty straightforward, but if you want to create an API in Python, I definitely recommend using fast API. Like I said, it integrates with pedantic. It can do automatic data validation, has a lot of built in features, and it's extremely, extremely fast. The reason for that is it has asynchronous programing built into the framework, and also provides automatic documentation for all of the different endpoints that you write. If you're trying to get an API up quickly and you want it to be very performant, definitely turn to fast API. You can use flask as well, but fast API is a little bit more professional. It's something I would lean to more in production, and because of the self-documenting feature of it, it's really nice when other people are going to or using your API. It has a few other features as well, but overall it comes with a lot of things in the box. You can also install plugins and extensions to give it that much more capability. Regardless, fast API should be on your list, so definitely check it out. Next on my list I have Django. Now, Django is really meant for when you're building a more professional, serious web application. I prefer using Django when I'm building out an entire website because of the number of features that come built in. Now it is a little bit more difficult to get set up with and to learn. There is a lot of boilerplate code that you need to go through, but when you're building something a bit larger and you want to have more organized code that fits a certain standard, Django is definitely what to go with. Now it contains features like an LRM. This is an object relational mapping that allows you to have your database map to Python objects that make it really easy to work with, and also has authentication, built in automatic routing, and things like an admin panel, which make it really nice for user management, especially when you're just starting with your site. I've built lots of very large websites with Django. A lot of big companies have as well. It's definitely something you'd want to learn. If you're looking to be a Python web developer as a career, or for some kind of developer role. Django is constantly in demand, and it's a great framework when you want to build a full website that's a little bit more professional than something you'd work with in Flask or Fast API. Moving on, we have selenium now. Selenium is one of my favorite modules, and that's because it performs web automation. Selenium allows you to control browsers like Firefox and Chrome, and to interact with various web pages like a human would do it as selenium is really good when you're trying to automate some task on the web. It's actually really designed for web testing. So doing end to end testing of websites and seeing the website exactly like the user would. But personally, I use selenium a lot for doing web scraping, so grabbing data from a more dynamic website and I use it for creating bots. So if there's some kind of task on a website that I need to automate, maybe I want to do it every day or I need to do it hundreds of times and I don't want to do it manually. I can write a simple selenium script that will go to the website, interact with the page, and do the tasks that I need. I have all kinds of tutorials on this channel on how to use selenium, so definitely check it out if web automation interests you. Now we get into numpy. Now numpy is really the math module. Whenever you're going to be doing any serious math in Python, you're going to be leaning on numpy. That's because of its support for n dimensional arrays and the performance it has over using traditional Python objects. Almost all of your major scientific computing or machine learning libraries have numpy as a dependency. And even though you might not know it, it's using numpy. Behind the scenes, a lot of numpy functions are actually written in C or C plus. Plus. It gives it a much higher performance than working with standard Python types. And it's just a module you use when you want to do a lot of math. I can go on and on about numpy, but overall, if you're doing any kind of scientific computing, machine learning, or you're doing a lot of math, statistics, anything mathy in Python, you're going to be using numpy, so you need to learn it. Now a close relative of numpy is pandas. Now pandas is typically used in combination with numpy or other libraries, and it's used for data manipulation and analysis. Pandas provides a DataFrame object that allows you to manipulate data easily and to do data validation and preprocessing or cleaning before something like data analysis or machine learning. Pandas is great because it provides all kinds of tools to read and data from different structures in memory, or to put it back into those structures. So going from in-memory to real physical objects like Json, CSV, text files, whatever it might be. Pandas is great. It's definitely something you need to know. And again, it will be used a lot in combination with various other libraries. Think a lot of your machine learning libraries, data analysis, scientific computing. A lot of them will lean on pandas as well as numpy. Moving on. We have another very closely related module and this is matplotlib. Now this is really used for data visualization. It's very customizable. You can create bar charts, line charts, scatter plots you name it. You can probably make it in matplotlib. It can get very complicated. And you can create some really great visualizations. Or you can do some really simple visualizations just to look at what a data set looks like. Really, when you're doing data visualization in Python, you lean on matplotlib. It integrates with pandas and numpy. And again, very customizable. And you can do some really cool stuff with it. Even if you don't become an expert, I recommend learning a little bit about it. It's very helpful, especially if you're doing scientific computing, data science, or any machine learning. To quickly have a look at a data set before you actually start going and manipulating it or feeding it to a machine learning model. And just the final thing here, it's worth noting that you can actually create static, interactive or animated plots. So it has a lot of different features. And it's not just a static image. You can actually interact with the data set that you're visualizing. So now we're moving on to the final module on my list. But stick around because I do have one surprise bonus for all of you that made it to the end of the video. Now this module is TensorFlow. Now this is the deep learning module. And this is what you're going to use if you want to create neural networks or advanced machine learning or AI applications. Now TensorFlow is great because it actually has a high level API called Keras, which makes it really easy to build models even if you're just starting out. But it also gives you the full flexibility to really customize what you're doing and to scale what you're doing into production. It's very good. Most people use it. It's supported by Google. It's actually an open source platform and TensorFlow is something I've used a ton and had a lot of fun messing around with. If you want to do something like image recognition, speech recognition, something like a convolutional neural network, TensorFlow is the way to go. There are a few other deep learning modules, say like PyTorch, but usually I recommend TensorFlow and that's the one I like the best. So now we move on to my bonus module for all of you that stuck around. That's why you watch until the end of the video in this module is Lang chain. Now Lang chain is a relatively new module that acts as a way to more easily create complex AI applications or AI agents. I have all kinds of tutorials on this channel where I utilize Lang flow, and it's really cool what you're able to do using this module. Lang chain is great because it provides a high level API that makes it really easy to interact with open source models. So think of it. Things like GPT four, things like Lambda two models. You can use all of those from Lang chain, and then you can add all kinds of different features that are really simplified by the Lang Chain library. Again, I've got a bunch of tutorials. Definitely check it out. Something that I think is going to be on the rise over the next few years, and I definitely recommend you look at. So there you go. That wraps up my ten Python modules that you need to know. Did I miss any? Let me know in the comments down below and I look forward to seeing you in another video.