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Learn Streamlit Python

Jesse E. Agbe

Are you having difficulties trying to build web applications for your data science projects? Do you spend more time trying to create a simple MVP app with your data to show your clients and others? Then let me introduce you to Streamlit - a python framework for building web apps.

Welcome to the coolest  online resource for learning how to create Data Science Apps and Machine Learning Web Apps using the

awesome Streamlit Framework and Python.

This course will teach you Streamlit - the python framework that saves you from spending days and weeks in creating

Read more

Are you having difficulties trying to build web applications for your data science projects? Do you spend more time trying to create a simple MVP app with your data to show your clients and others? Then let me introduce you to Streamlit - a python framework for building web apps.

Welcome to the coolest  online resource for learning how to create Data Science Apps and Machine Learning Web Apps using the

awesome Streamlit Framework and Python.

This course will teach you Streamlit - the python framework that saves you from spending days and weeks in creating

data science and machine learning web applications.

In this course we will cover everything you need to know concerning streamlit such as

  • Fundamentals and the Basics of Streamlit ;

- Working with Text

- Working with Widgets (Buttons,Sliders,

- Displaying Data

- Displaying Charts and Plots

 - Working with Media Files (Audio,Images,Video)

- Streamlit Layouts

- File Uploads

- Streamlit Static Components

  • Creating cool data visualization apps

  • How to Build A Full Web Application with Streamlit

By the end of this exciting course you will be able to

  • Build data science apps in hours not days

  • Productionized your machine learning models into web apps using streamlit

  • Build some cools and fun data apps

  • Deploy your streamlit apps using Docker,Heroku,Streamlit Share and more

Join us as we explore the world of building Data and ML Apps.

See you in the Course,Stay blessed.

Tips for getting through the course

  • Please write or code along with us do not just watch,this will enhance your understanding.

  • You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you.

  • Suggested Prerequisites is understanding of Python

  • This course is about Streamlit an ML Framework to create data apps in hours not weeks. We  will try our best to cover some concepts for the beginner and the pro .

Enroll now

What's inside

Learning objectives

  • Learn the basics of streamlit framework
  • Use streamlit to create machine learning web apps and data apps
  • Deploying streamlit python web applications

Syllabus

Introduction
Where to Get Help and Quick Course Guide and Materials
What is Streamlit?
Why Learn Streamlit?
Read more
Overview of Streamlit Framework API
Setup & Installation In Virutal Environment
Exploring Streamlit
Displaying Text In Streamlit
Behind the Source Code - Inspecting the Text
Working with Colorful Bootstap-Like Text
Displaying Results with St.write() "Superfunction"
Displaying Pandas DataFrame,Tables and JSON
Working with Streamlit Widgets - Buttons,Radio Buttons and Checkbox
Working with Streamlit Widgets - Select, Multi-select,Sliders and Select Slider
Displaying and Working with Media Files -Images,Audio and Video
Working with Text Input - Receiving Input From User
How to Configure Streamlit Page
How to Update Streamlit & How to work with Beta Changes
Plotting In Streamlit : Using Plotly
Working with File Uploads - Indepth Tutorial
Saving Uploaded File into A Directory In Streamlit
Working with Multiple File Uploads
Structuring Streamlit Apps
Tracking Visited Sections of Streamlit App Via Logging
How to Add File Downloads to Streamlit Apps
Working with Streamlit Forms
Streamlit-Forms - How to Reset Forms
Memory Profiling Streamlit Apps
Streamlit Data Editor (New Feature)
Streamlit Chat Input Widget (New Feature)

⏲️===TimeStamps===⏲️

0:01 Introduction

01:30 Streamlit CLI

02:30 Text Elements

06:12 st.write, markdown

09:35 Error Elements

11:02 Input Widgets

13:15 Date & Number Input

14:57 Radio & Checkbox, Toggle

16:17 Sliders & Selectors

22:08 Data Elements

27:20 Media Elements(Img,Audio,Video)

29:35 Camera Input

32:49 File Upload & Download

35:20 Status Elements (spinner,progress)

37:40 St.toast

38:15 Chat Elements for LLM

42:20 Streaming Text- Typewriter Effect

46:27 Layout

47:04 st.tabs

48:37 st.columns

51:30 Containers in Streamlit

53:20 Expander to hide or show

53:50 Popover & Dialog

55:10 Plotting in Streamlit

58:10 Utils

59:10 St Forms

1:00:20 Streamlit Components

1:01:00 Link Button

1:02:01 Streamlit Session State

1:02:40 Streamlit cloud

Module 02 - Data Visualization In Streamlit
Plotting In Streamlit-Introduction
Plotting In Streamlit : Using St.pyplot For Matplotlib and Others
Plotting In Streamlit : Bar Charts, Area Charts and Altair Charts
Understand and Explore Streamlit Component - Static Components ,Bi-Directional,etc Streamlit Themes
Introduction to Streamlit Components
Working with Static Streamlit Components - HTML and IFrame

Note: Streamlit Themes are available from version 0.79 and upwards so you will have to upgrade or update to get this feature

Streamlit Multi-Pages (Native)
Streamlit Navigation Pages
Streamlitflow - ReactFlow Components
Build several types of Data Apps using Streamlit From End to End
Project - NLP & Summarization App
Project - Summarization App - Structuring the App
Project - Summarization App -Adding the Summary Process (LexRank and TextRank)
Project - Summarization App - Evaluating the Extractive Summary with Rouge
Project - Text Analysis & NLP App
Project -Text Analysis & Spacy App - Structuring the App
Project - Text Analysis & Spacy App - Adding the Text Analysis Process
Project -Text Analysis & Spacy App - Word Statistics and Sentiment Analysis
Project - Text Analysis & Spacy App - Adding the Plots and Visualizations
Project - Text Analysis & Spacy App - File Download of Results
Project - Text Analysis & Spacy App - File Upload (PDF,Txt and Docx)
Project - Text Analysis & Spacy App - Refactoring and Modularize The App
Project - Text Analysis & Spacy App - Fixing Insufficient Data For Plot
Module 03 - Project Section - Building Streamlit Apps - Text Analysis Apps
Project 03 - Text Analysis App -Demo
Project 03 - Text Analysis App -Building the App (Full Length)
Project - Building Streamlit Apps -Data Apps
Project 01 - MetaData Extracton App - Demo
Project 01 - MetaData Extraction App - Setting Up and Structuring the App
Project 01 - MetaData Extraction App -Home Section
Project 01 - Building the File Upload Section
Project 01 - MetaData Extraction App - Extraction Process
Project 01 - MetaData Extraction App - Adding Result Download
Project 01 - MetaData Extracton App - Extracting MetaData From Audio files
Project 01 - MetaData Extraction App - Extracting MetaData From PDF Section
Project 01 - MetaData Extraction App - Analytics and Monitor Section
Static Code Analysis & Refactoring Streamlit App
Build ML Web apps using Streamlit and Scikit Learn
Project - Machine Learning Web App - Diabetes Prediction App -Demo
Project - Diabetes Prediction App - Structuring the App
Project -Diabetes Prediction App - Exploratory Data Analysis Section
Project - Diabetes Prediction App - Plotting and Data Visualization
Project - Diabetes Prediction App - Machine Learning Section
Project - Diabetes Prediction App - Applying the Models For Prediction
Building the ML Model For Diabetes Prediction -Full Length
Project - Building Streamlit Apps - CRUD Apps (Create Read Update Delete)
ToDo App in Streamlit - Full Length (CRUD)
ToDo App In Streamlit- Deploying with Streamlit Sharing
Be able to build a CRUD app.
Simple CRUD App in Streamlit - Demo
TaskList CRUD App - Structuring the App
TaskList CRUD App - Create (Adding Data To Database)
TaskList CRUD App - Update(Editing From the Front End)
TaskList CRUD App - Update the Database
TaskList CRUD App - Deleting Data
TaskList CRUD App - Reading Data
TaskList CRUD App - Analytics & Plots
Learn how to build apps from idea,to deployment using GitHub,Streamlit,etc
StreamBible App - Demo
StreamBible App - Intro & App Structure,Single Verse Section
StreamBible App - Multiple Verses & Test Analysis Section
Refactoring Streamlit Apps From Monolithic to Modular App (Modulith)
Email Extractor App - Demo

This is a full length video on building a  streamlit app for Extracting Emails,URLs and Phonenumbers

Email Extractor App - Adding Emails to Database (Sqlite3)

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Focuses on Streamlit, a Python framework, which enables rapid development and deployment of data science and machine learning web applications, saving valuable time and resources
Covers a wide range of Streamlit functionalities, including working with text, widgets, data display, media files, layouts, and file uploads, providing a comprehensive understanding of the framework
Includes practical projects such as building NLP and text analysis apps, as well as machine learning web apps, offering hands-on experience in applying Streamlit to real-world scenarios
Assumes a foundational understanding of Python, which may require beginners to acquire additional knowledge before fully grasping the concepts taught in the course
Explores deployment options like Docker, Heroku, and Streamlit Share, which equips learners with the skills to make their Streamlit applications accessible to a wider audience
Features new Streamlit features like the Data Editor and Chat Input Widget, which keeps learners up-to-date with the latest advancements in the framework and its capabilities

Save this course

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Learn Streamlit Python with these activities:
Review Python Fundamentals
Solidify your understanding of Python fundamentals, which are essential for effectively using Streamlit.
Browse courses on Python Basics
Show steps
  • Review data types, control flow, and functions in Python.
  • Practice writing simple Python scripts.
  • Familiarize yourself with Python's package management system (pip).
Review 'Python Crash Course'
Strengthen your Python skills with a comprehensive guide that covers the basics and beyond.
View Python Crash Course, 2nd Edition: A Hands-On,... on Amazon
Show steps
  • Read the chapters covering fundamental Python concepts.
  • Complete the exercises and projects in the book.
  • Focus on topics like data structures, functions, and object-oriented programming.
Follow Streamlit Tutorials
Gain hands-on experience with Streamlit by following online tutorials and building simple applications.
Show steps
  • Search for Streamlit tutorials on the Streamlit website or platforms like YouTube.
  • Choose tutorials that cover topics relevant to the course syllabus.
  • Follow the tutorials step-by-step, writing the code yourself.
  • Experiment with the code and try to modify it to create your own variations.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Streamlit Widgets
Reinforce your understanding of Streamlit widgets by creating small applications that utilize different widget types.
Show steps
  • Create a simple app with a text input widget and display the input text.
  • Build an app with a slider widget and use the slider value to control a plot.
  • Implement an app with a select widget to choose from a list of options and display the selected option.
Build a Simple Data Visualization App
Apply your Streamlit knowledge by building a data visualization app that displays data from a CSV file or API.
Show steps
  • Choose a dataset or API to use for your app.
  • Design the layout of your app and choose appropriate Streamlit widgets.
  • Write the code to load the data, perform calculations, and display visualizations.
  • Deploy your app to Streamlit Cloud or another hosting platform.
Write a Streamlit Blog Post
Deepen your understanding of Streamlit by writing a blog post about a specific feature or application.
Show steps
  • Choose a topic related to Streamlit that you find interesting.
  • Research the topic and gather information from the Streamlit documentation and other resources.
  • Write a clear and concise blog post that explains the topic and provides examples.
  • Publish your blog post on a platform like Medium or your own website.
Contribute to Streamlit Open Source
Enhance your Streamlit skills by contributing to the Streamlit open-source project.
Show steps
  • Explore the Streamlit GitHub repository and identify areas where you can contribute.
  • Report bugs, suggest new features, or contribute code to fix existing issues.
  • Follow the Streamlit contribution guidelines and submit your changes for review.

Career center

Learners who complete Learn Streamlit Python will develop knowledge and skills that may be useful to these careers:
Data Scientist
A data scientist uses programming and statistics to analyze data, build models, and extract insights. This role often requires creating data visualizations and presenting findings to stakeholders. This course is useful for data scientists, particularly because it teaches how to build interactive web applications for displaying data and machine learning models using Streamlit. The course covers displaying various data formats, charts, and plots, all of which are central to the work of a data scientist. This will help a data scientist communicate findings more effectively and present work in a user friendly way.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
A machine learning engineer develops and deploys machine learning models. This includes building pipelines for data processing, model training, and model deployment. This course may be useful for a machine learning engineer as it teaches how to use Streamlit to quickly create and deploy web applications for machine learning models. The course's focus on deploying machine learning models into web apps by using Streamlit is helpful for those whose job includes making models accessible, interactive, and presentable to end users. Knowing how to use Streamlit in this context will allow a machine learning engineer to focus on model performance while swiftly building an interface.
See salaries and explore the career path for Machine Learning Engineer
Data Analyst
A data analyst gathers, cleans, and analyzes data to identify trends and provide insights. They often use data visualization to communicate findings. This course may be useful for data analysts to learn how to create interactive dashboards using Streamlit to better showcase their findings and allow stakeholders to explore data. The course covers how to display data, charts, and plots within web apps using Streamlit, which is a valuable skill for any data analyst who wants to give end users a more interactive experience for data exploration. The emphasis on file uploads, media files and text analysis is also helpful in this field.
See salaries and explore the career path for Data Analyst
Data Science Consultant
A data science consultant advises clients on data-driven strategies and solutions. They often analyze data, build models, and present findings to clients. This course will be useful for a data science consultant as it teaches how to build interactive web applications using Streamlit to present data science projects and results to clients. The course covers skills such as working with text, widgets, data, charts, and media files which are all important in any project demonstration. The ability to quickly build web apps in this way will help a consultant better communicate results to a client.
See salaries and explore the career path for Data Science Consultant
Business Intelligence Analyst
A business intelligence analyst uses data to identify trends, develop insights, and improve business processes. They use data visualization to communicate their findings. This course may be useful for a business intelligence analyst to create interactive dashboards that allow end users to explore data and gain insights. The course covers specific topics like file uploads, displaying data, charts and plots, which are helpful tools for anyone working as an analyst. The course will help a business intelligence analyst create more dynamic and usable reports.
See salaries and explore the career path for Business Intelligence Analyst
Research Scientist
A research scientist conducts experiments and analyzes data to advance knowledge in their field. Sharing findings through presentations and publications is a regular activity. This course may be useful for a research scientist as it provides skills to create web applications for visualizing data and results. The course covers key aspects of using Streamlit, such as displaying data, charts, plots, media files, file uploads and more. Any research scientist would benefit from the ability to rapidly create interactive web apps for demonstrating models or research progress. This would make that research more assessable to other researchers or the general public.
See salaries and explore the career path for Research Scientist
Analytics Manager
An analytics manager leads a team of analysts to extract insights from data and improve business performance. They build reports, summaries, and dashboards to present data. This course may help an analytics manager better understand the tools and technologies used by their team, and the process of creating useful web applications for data exploration and presentation. By taking this course, an analytics manager will gain familiarity with Streamlit and be able to provide informed guidance to their team on the best approach for creating data-driven solutions. The course will teach how to structure apps and work with various data types within the Streamlit framework.
See salaries and explore the career path for Analytics Manager
Data Visualization Specialist
A data visualization specialist focuses on creating graphical representations of data for better understanding. They design dashboards and reports to communicate insights from data. This course may be useful for someone in this role because it teaches how to use Streamlit to create web-based visualizations. The course specifically covers displaying diverse data formats, charts, and plots in a user-friendly way. Using Streamlit to quickly develop interactive web apps will enable a data visualization specialist to more effectively share their work with others.
See salaries and explore the career path for Data Visualization Specialist
Quantitative Analyst
A quantitative analyst, also known as a 'quant,' develops and implements mathematical and statistical models for financial applications. Quants analyze data to make predictions related to pricing, risk, and investment. This course may be useful to a quantitative analyst as it teaches how to build web applications to present data and models in an interactive, user-friendly way. This would be helpful for quants who need to present their findings and models to clients or stakeholders. The course specifically includes ways to display data, and working with plots, which would help a quantitative analyst make their work more accessible.
See salaries and explore the career path for Quantitative Analyst
Software Developer
A software developer writes code to create software applications. This role also involves testing, debugging, and maintaining software. While this course focuses on building web apps rather than general software, the techniques of using a Python framework will give any software developer a useful skillset to rapidly construct custom web interfaces. The specific instruction on how to use Streamlit for data visualization and file handling can give a software developer an added efficiency for creating specific types of applications. This is why a software developer should consider taking a course on Streamlit, a python web app framework.
See salaries and explore the career path for Software Developer
Financial Analyst
A financial analyst provides insights and recommendations based on the analysis of financial data. This role often requires creating reports and presentations for stakeholders. This course may be useful for a financial analyst because it teaches how to quickly build interactive web applications to display financial data and models using Streamlit. The course covers how to display various data formats, charts, and plots, which are all key for communicating financial information. The ability to create web apps with Streamlit will help a financial analyst present their findings more effectively and allow others to explore the data intuitively.
See salaries and explore the career path for Financial Analyst
Research Analyst
A research analyst conducts research to gather information and insights, often using both qualitative and quantitative methods. They summarize their findings and create reports, white papers, or presentations. This course may help a research analyst as it provides skills to create web applications for displaying data and results. The course covers key aspects of using Streamlit, such as displaying data, charts, plots, text, media files, and file uploads. Such expertise would allow a research analyst to present their findings in a dynamic and engaging way. This would enable other researchers or the public to personally explore the data through interactive web applications.
See salaries and explore the career path for Research Analyst
Statistician
A statistician uses statistical methods and mathematical models to collect, analyze, and interpret data. They often present findings through reports and visualizations. This course may be helpful for statisticians who need to create interactive visualizations and applications for exploring and understanding data. The course focuses on Streamlit, a Python framework for building web apps, and includes material on displaying different data types, plots, and charts. A statistician can use Streamlit skills to create user-friendly tools for colleagues and other stakeholders to explore statistical analyses.
See salaries and explore the career path for Statistician
Bioinformatician
A bioinformatician studies biological data using computation. The work involves data analysis, visualization, and the development of algorithms. This course may be useful for bioinformaticians who wish to present data findings in an easily understood way through web apps. The course's focus on using Streamlit to create data apps, including displaying various data formats, charts, and plots, would help a bioinformatician communicate their insights to both experts and non-experts. Furthermore, learning Streamlit's file upload features may streamline the bioinformatician's workflow for analyzing research data, such as genomic sequences.
See salaries and explore the career path for Bioinformatician
Information Architect
An information architect designs and organizes digital information to improve usability and findability. This includes creating sitemaps, wireframes, and navigation systems. This role typically requires a master's degree. This course may be useful for an information architect as it offers practical experience in organizing various types of content within a user interface. The course focuses on using Streamlit, a Python framework for building web apps, and includes how to structure apps, implement layouts, and work with forms and file uploads, all relevant to the planning and design of digital information architecture. The experience gained through this course may allow an information architect to better understand user needs and improve the user experience of digital products.
See salaries and explore the career path for Information Architect

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Learn Streamlit Python.
Cover image
Python Crash Course, 2nd Edition
Save
Provides a solid foundation in Python programming, covering essential concepts and syntax. It's particularly useful for beginners or those looking to refresh their Python skills before diving into Streamlit. The project-based approach helps solidify understanding through practical application. While not specific to Streamlit, it provides the necessary Python knowledge to succeed in the course.
Python Crash Course, 2nd Edition: A Hands-On,...
Paperback
$$
Python Crash Course, 2nd Edition: A Hands-On,...
Kindle Edition
$$

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Effort
37.5 total hours
Via
Udemy
Instructor
Jesse E. Agbe
Language
English
Good to know
Focuses on Streamlit, a Python framework, which enables rapid development and deployment of data science and machine learning web applications, saving valuable time and resources
Covers a wide range of Streamlit functionalities, including working with text, widgets, data display, media files, layouts, and file uploads, providing a comprehensive understanding of the framework
Includes practical projects such as building NLP and text analysis apps, as well as machine learning web apps, offering hands-on experience in applying Streamlit to real-world scenarios
Assumes a foundational understanding of Python, which may require beginners to acquire additional knowledge before fully grasping the concepts taught in the course
Explores deployment options like Docker, Heroku, and Streamlit Share, which equips learners with the skills to make their Streamlit applications accessible to a wider audience
Features new Streamlit features like the Data Editor and Chat Input Widget, which keeps learners up-to-date with the latest advancements in the framework and its capabilities
Share and help others discover this course.
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