Learn & Deploy Data Science Web Apps with Streamlit

Learn to build a data science web app with Streamlit using minimal code, even with no streaming knowledge, with automatic graph updates and a progress bar in the browser.

Set up your project folder, write and run a simple Streamlit app from the terminal, and view live updates in a browser at port 8501 with automatic reruns.

Learn to build a Streamlit visualization app in 24 lines with a sidebar, progress bar, and dynamic line chart that updates from random data.

Explore how Streamlit's st.write API can display almost anything—from strings and dataframes to arrays, dictionaries, and even machine learning models—enabling visualization and organization of data.

Learn to build interactive data apps with streamlit by using st.write to display text, pandas data frames, and numpy arrays, and run a local streamlit app to iterate.

Discover how magic commands in Streamlit let you display data, charts, and variables with a single line of code, enabling seamless, immediate app visuals.

Explore Streamlit text elements to format text beyond plain text, including titles and subtitles. See how text elements present text in varied styles and preview upcoming options.

Discover markdown, a lightweight language for formatting plain text with headings and emphasis via st.markdown in streamlit. Run streamlit run to view the app at localhost:8501 and see header styling.

Explore how to structure text with markdown in a streamlit app, using header levels with hashtags, paragraphs, horizontal rules, and emphasis with bold, italic, and code blocks.

Demonstrate using st.markdown to create ordered and unordered lists, headers, and subheaders, and to add links, images, and emails with proper indentation and block code.

Explore how to format text in streamlit using text elements, including title, header, subheader, captions, and code blocks. Learn to render latex for mathematical expressions within a live app.

Learn to display data in Streamlit using dataframe, static and interactive tables, and json to visualize data quickly from multiple angles with adjustable width and height.

Explore status elements in Streamlit to display dynamic progress with a progress bar and status bar animations, including updating progress values from 0 to 100 and looping with time delays.

Learn to use a spinner in Streamlit to display a temporary message while code runs, and pair it with a progress bar that updates to 100.

Explore how to display status messages using error, info, success, and warning elements in Streamlit, and add celebratory balloons after completion.

Learn to display images, videos, and audio in a Streamlit data science web app by loading media from a resources folder and using st.image, st.video, and st.audio.

Learn to build dynamic layouts in Streamlit by using sidebars, columns, expanders, containers, and empty placeholders, then bind widgets to a dataframe and visualize it.

Create and populate three side-by-side columns in Streamlit to arrange images, dataframes, and dynamic widgets using column one, column two, and column three.

Learn how the expander layout in Streamlit groups multiple elements into a collapsible container. Build expanders with labels, insert code blocks, and display explanations in a data science web app.

Master how containers in streamlit hold and display multiple elements, using headers and nested layouts to organize content, including expander-style behavior.

Explore how to use the empty container in Streamlit to hold a single element, update its content dynamically, and replace items over time to create responsive, time-based displays.

Explore input widgets in a Streamlit app, including buttons, checkboxes, radios, selects, sliders, and text inputs, and trigger actions like sampling and displaying random rows from a data frame.

Learn how to implement single and multiple checkboxes in Streamlit, group them in containers, capture selections into a dictionary, and display results with a submit button.

Learn to implement a radio button in a Streamlit app to let users pick exactly one option from a list, such as a favorite color, and read the selected value.

Explore using a select box with radio buttons to create a dropdown menu in a Streamlit app, and learn how selecting an option returns a value and updates the app.

Use the multi select widget to pick multiple movie options and return the selections as a list. See how selecting comedy and sci fi updates the display in your app.

Use st.slider to configure a slider for the loan amount by setting min, max, steps, and a default value, then display the selected amount and rerun the app for updates.

Learn to create interactive user input forms in Streamlit using text_input, number_input, text_area, and date_input, with labels, defaults, and a submit button that collects and displays a data dictionary.

Learn to implement a streamlit file uploader to upload any file type and save it to a folder named assay_folder, with drag-and-drop or browse, and success or warning messages.

Create a registration form in a data science web app, collecting name, title, gender, designation, date of birth, and age, then display a submit success message with the entered details.

Explore static visualizations using Matplotlib and Seaborn within a Streamlit app, load a restaurant dataset, and perform exploratory data analysis to answer two questions.

Learn to compute the male–female distribution from a sex column using value_counts, and visualize it in a Streamlit app with a matplotlib pie chart and labeled slices.

Learn to display a Matplotlib bar chart and a pie chart side by side in Streamlit using a two-column layout, showcasing male and female distribution with value counts.

Learn to build a dynamic Streamlit app that uses input widgets to select a categorical feature (sex, smoker, time) and display corresponding pie or bar charts using value counts.

visualize the distribution of total spend by gender using a seaborn box plot in a streamlit app, comparing male and female spending, with related visuals like violin plots and histograms.

Create interactive Seaborn figures with Streamlit widgets, selecting histogram, violin, KDE plots, or box plots to dynamically visualize distributions and gender-based data using a single display chart.

Compute the average total bill by day and gender using pandas group by, and visualize it in streamlit with stacked bar area and line plots built via a widget.

Learn how to build dynamic, widget-controlled plots in streamlit by selecting categorical features and chart types (bar, area, line, stacked), and impute missing values with zeros.

Create a scatterplot of total bill versus tips and render it in a Streamlit app, then add a widget with a select box to choose categorical columns for visualization.

Explore interactive visualizations in streamlit, using line, area, and bar charts where each data column becomes a category. Hovering reveals details, with streaming and support for libraries like bokeh.

Build interactive visualizations in streamlit by creating a sample dataframe and rendering bar, area, and line charts for categories A, B, and C.

Discover how Plotly enables interactive charts and visualization in Python, including scatterplots, regressions, box plots, bar plots, and parallel coordinates, and learn to display them in a streamlit app.

Learn to visualize data in Streamlit using Plotly Express by building and displaying an interactive histogram of total_bill, colored by sex.

Learn how to build interactive data visualizations in Streamlit using select boxes to filter by category, create histograms and scatter plots, and color by smoker, day, and time.

visualize hierarchical categorical data with sunburst charts in streamlit using plotly, selecting features and defining the path to reveal patterns across sex, day, and time.

Install Bokeh with pip and manage version compatibility with Streamlit, downgrading to Bokeh 2.4.1 when 2.4.2 isn’t supported.

Explore creating and displaying interactive Bokeh charts in Streamlit, including a simple line chart and scatterplot with customizable axis labels and line width.

Create a Bokeh scatterplot in Streamlit showing total bill versus tips, with color by category and interactive controls to select categories.

Set up a data profiling project by creating and activating a virtual environment, install required libraries via a requirements file, including numpy, pandas, matplotlib, streamlit, and pandas-profiling.

Build a streamlit app with a drag-and-drop sidebar file uploader to load csv or excel data, then display the top rows and generate pandas profiling reports.

Generate and display a data profile report in Streamlit using pandas profiling, showing overview, variable details, statistics, distributions, and missing values.

Learn to add input widgets in a Streamlit app to toggle minimal or detailed reports, switch dark, orange, or primary display modes, and control data uploads with validation.

Implement file type validation in a streamlit data science app to accept only csv and excel files, with clear error messages and dynamic excel sheet selection.

Implement a file size validation in a data profiler, compute the file size in bytes, convert to megabytes, and enforce a 10 megabyte limit with an error when exceeded.

Learn to deploy a data science web app to the Streamlit cloud by pushing to GitHub and connecting the repository, and install Git on Windows.

sign up for GitHub, create a public repository named Streamlit data, initialize it, and push app.py and requirements.txt with excel-support libraries; set remote and branch, then push.

Launch the streamlit app, test file upload and report analysis, then deploy by choosing the deploy option, linking a GitHub repository, and monitoring logs for 10 to 15 minutes.

Learn how to view and interpret logs for data science web apps built with Streamlit, with no audio narration.

Deploy and use a Streamlit app to upload Excel data, generate reports, view variables, interactions, and correlations, and deploy the project with a complete data profiling workflow.

Develop a chat-like LM application in Streamlit using the OpenAI API with GPT-4, enabling interactive prompts and streaming chunked responses in the UI.

Build an lm based app in Streamlit using the OpenAI chat completion model, including obtaining api keys, managing billing, and implementing code examples.

Learn how to build conversational apps in Streamlit by using chat_input and chat_message, display user prompts with avatars, and store chat history with session_state for a running conversation.

Learn to build a ChatGPT app in Streamlit using OpenAI API, manage secrets securely, implement session state and chat completion, and utilize the stream API for real-time responses.

Learn how to enable and display streaming responses from OpenAI in Streamlit by setting stream=true and rendering chunked messages with write_stream, creating a chat-like app.