Streamlit : Deploy your Data & ML app on the web with Python

Presentation of the preparation of your working environment.

You will learn how to create a virtual execution environment in order to program in a closed environment and avoid dependency conflicts. Very used in the Data domain!

(If you are a windows user, download the pdf in attachment.)

That's it, let's get into the code!

You will download the code locally on your computer from Github and I will present you the different parts of the training directory!

Configuration of your virtual environment with the requirements file.

With this, no bug on python dependencies !

Welcome to this new section!

Here we will learn the basics of Streamlit: how to run an application, display text, display data, create an attractive layout etc!

First part of the exercises for the fundamentals of Streamlit.

We will finally code our first methods with Streamlit! We start slowly with the basics :)

Second part of the exercises for the fundamentals of Streamlit. We learn how to create an attractive layout in order to build an application with a great UX!

That's it, this is the first session on the development of our final application!

But don't panic, for now we will only implement what we have seen in the previous videos. We're going to start little by little. We will define the structure of the final application with the sidebar then display the different titles and then display the table with the characteristics of the companies of the S&P500.

Welcome to this new section.

This time we'll see together how it's possible to interact with a user. I will introduce you to the famous Streamlit widgets!

First part of the exercises of the section on interaction. We are going to code our first widgets!

Second part of the exercises of the section on interaction. We continue with other widgets!

First part of the project of the interaction part!

We take our application project that we started to develop in the previous section and we add the interaction part with the user! So we integrate the widgets in the application to be able to interact with the user!

Second part of the project of the interaction part.

We finish the interaction part! We will see in particular in this session how to filter the table of the companies of the S&P500 according to the parameters defined by the user!

Welcome to this new section dedicated to visualization.

We will learn how to code visuals in a Streamlit application! For this we will use Python libraries commonly used by Data Analysts nowadays.

Let's go for the exercises on visualization with Streamlit. On the agenda: the use of Python libraries to create interactive graphics!

Adding the "visualization" component in the final application of the project.

We will see how to display the stock price of the share selected by the user in our final application.

This is also the last video of the project. At the end of the video, your application will be fully developed! Congratulations!

Presentation of the advanced features offered by Streamlit. You will learn how to optimize your application. Let's go to the next level ;)

Part of Streamlit's "Forms". This feature will allow you to create a more pleasant user experience!

Part on the "Session" proposed by Streamlit. We will see how it is possible to keep in memory of the application some values in your Python script.

Part on the "Cache" proposed by Streamlit. We will avoid to reexecute a function if it has not changed from one run to another. Your application will run much faster!

Welcome to this penultimate section!

Section dedicated to the production part of your application with Streamlit Cloud.

This will allow us to go from a locally executed application to a web accessible application!

Last video of this training.

You now know how to develop your own data applications with Streamlit!

Congratulations!