> ## Content Index > Fetch the complete content index at: https://datascientyst.com/llms.txt > Use this file to discover other available public pages before exploring further. # Streamlit Cheatsheet: Build Dashboards Faster with AI - URL: https://datascientyst.com/streamlit-cheatsheet-build-dashboards-faster-with-ai/ - Published: 2026-09-30T11:30:42.000Z - Updated: 2026-09-30T11:30:42.000Z - Author: John D K - Tags: Cheat Sheet Over the past year I migrated my team's dashboards from GUI-based BI tools to a fully code-driven workflow with Python + Streamlit, using AI coding assistants like Claude Code and Cursor to speed up iteration. Right now I'm migrating dashboards off Looker (Google Data Studio) and aggregating information from various tools into Streamlit dashboards. The reason is simple: it saves time to build a POC with AI, and then I can continue the updates myself. No waiting on a BI license, no drag-and-drop ceiling — just Python. ## Sample prompt The example below gives **OK results for a POC** — enough to see something on screen, not enough to ship: ```md create me Streamlit dashboard about compare multiple sites: * section onsite analysis * serp analysis * ranking * search results - google, bing, duckduckgo * keyword comparison - home page: title, headings etc * pricing * features files: * dashboard.py - UI * data_processor.py - data preprocessing * visualization.py - data plots and figures * scraper.py - data collector * config.yaml - configuration add more if needed to make it better ``` **But to get better results, smaller steps are needed.** Instead of one big prompt, I iterate file by file with concrete instructions, then move to the next one. For example: `dashboard.py` - concise layout - no tabs - sections - onsite — basic info about titles, pages - serp — compare serp data So far the results are mixed. Still experimenting. ## What actually drives good results In my experience, the output depends mostly on: - **The initial idea** — I prefer to outline the idea in my head first, then on paper. The clearer the shape before prompting, the better. - **How the idea is shared with the model** — smaller steps beat one giant prompt. Deterministic tests and validation keep the agent honest. - **Inspecting the code yourself** — always verify accuracy rather than trusting the first output. ## Dashboard design principles Good code is only half the battle. A dashboard that actually works needs to respect a few principles: - **Show only important information** — minimize or avoid scrolling so key metrics land in the first glance. - **Organize info in grouped levels** — use containers, columns, and tabs to create hierarchy rather than dumping everything on one page. - **Use color deliberately** — make changes and indicators visible with color, but reserve it for what actually needs attention. - **Adopt a narrative approach** — tell a story with your data, not just raw numbers and graphs. - **Make it self-explanatory** — design so viewers need minimal additional questions to understand what they're seeing. - **Structure as a master dashboard plus details** — personally, I prefer one master dashboard with top-level info, supported by a few others for deeper detail. The full cheatsheet is based on [cheat-sheet.streamlit.app](https://cheat-sheet.streamlit.app/?ref=datascientyst.com). If you're already fluent in Python, this should get you from idea to working dashboard in minutes rather than hours. ## Checking Examples and Asking the Model to Update Sometimes I browse the examples from [streamlit.io/gallery?category=favorites](https://streamlit.io/gallery?category=favorites&ref=datascientyst.com) to get inspiration, then ask the model to update my dashboard based on what I see. Below are some Streamlit dashboard examples from the web that I've collected — useful as reference points when prompting an AI agent. | Link | Description | | ----------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | | [Cheat Sheet](https://share.streamlit.io/daniellewisdl/streamlit-cheat-sheet/master/app.py?ref=datascientyst.com) | A cheat sheet for Streamlit by @daniellewisDL | | [Goodreads App](https://share.streamlit.io/tylerjrichards/streamlit%5Fgoodreads%5Fapp/books.py?ref=datascientyst.com) | Analyzing your Goodreads reading habits by @tylerjrichards | | [Arup Social Data](https://share.streamlit.io/arup-group/eviction-data/run.py?ref=datascientyst.com) | Code and data for eviction and housing analysis in the US by @arup-group | | [Sales Dashboard](https://www.salesdashboard.pythonandvba.com/?ref=datascientyst.com) | Interactive dashboard visualizing sales data from Excel with dynamic filters and key KPIs | | [Health Analytics](https://kats-health-analytics.streamlit.app/?ref=datascientyst.com) | Modern health analytics dashboard visualizing recovery trends, sleep patterns, movement, and calories burned | | [AI Data Analysis Agent](https://ai-data-analysis-agent-cykfmmzzrpjqrjgvkguanq.streamlit.app/?ref=datascientyst.com) | AI-powered data analysis dashboard for CSV exploration, visualization, and insights | | [COVID-19 Report Dashboard](https://github.com/miteshgupta07/Covid-19-Report-Dashboard-Using-Streamlit?ref=datascientyst.com) | Streamlit dashboard for COVID-19 reporting with real-time updates and visualizations | So far the results from this approach are mixed — some prompts produce a solid layout out of the gate, others need several rounds of iteration. Still experimenting. Note: that often dashboards can disappear. That's why I'm creating a local library of good dashboard examples. For this I'm using Obsidian as a master organizer for: - dashboards - Jupyter notebooks - Python scripts - datasets ## Cheatsheet ## Setup Install and import Streamlit ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_basics.png) `pip install streamlit` Install streamlit `import streamlit as st` Import convention `a = st.sidebar.radio('Choose:',[1,2])` Add widgets to sidebar (just add it after st.sidebar) `'_This_ is some __Markdown__' a=3 'dataframe:', data` Magic commands implicitly st.write() `streamlit --help streamlit run your_script.py streamlit hello streamlit config show streamlit cache clear streamlit docs streamlit --version` Command line `pip uninstall streamlit pip install streamlit-nightly --upgrade` Pre-release features ## Display text Display text ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_basics.png) `st.text('Fixed width text')` Fixed width text `st.markdown('_Markdown_') # optional kwarg unsafe_allow_html = True` Markdown `st.caption('Balloons. Hundreds of them...')` Caption `st.latex(r''' e^{i\pi} + 1 = 0 ''')` LaTeX `st.write('Most objects') # df, err, func, keras!` Write most objects `st.write(['st', 'is <', 3])` Write a list `st.title('My title')` Title `st.header('My header')` Header `st.subheader('My sub')` Subheader `st.code('for i in range(8): foo()')` Code block ## Display data Display data ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_read.png) `st.dataframe(my_dataframe)` DataFrame as an interactive table `st.table(data.iloc[0:10])` Static table `st.json({'foo':'bar','fu':'ba'})` JSON object `st.metric(label='Temp', value='273 K', delta='1.2 K')` Metric with delta ## Display media Display media ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_write.png) `st.image('./header.png')` Image `st.audio(data)` Audio `st.video(data)` Video ## Columns Columns ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_info.png) `col1, col2 = st.columns(2) col1.write('Column 1') col2.write('Column 2')` Create two columns and write into them `col1, col2, col3 = st.columns([3,1,1])` Three columns with different widths (col1 is wider) `with col1: st.write('This is column 1')` Using 'with' notation ## Tabs Tabs ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_select.png) `tab1, tab2 = st.tabs(['Tab 1', 'Tab2']) tab1.write('this is tab 1') tab2.write('this is tab 2')` Insert containers separated into tabs `with tab1: st.radio('Select one:', [1, 2])` Tabs using 'with' notation ## Control flow Control flow ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_add.png) `st.stop()` Stop execution immediately `st.experimental_rerun()` Rerun script immediately `with st.form(key='my_form'): username = st.text_input('Username') password = st.text_input('Password') st.form_submit_button('Login')` Group multiple widgets in a form ## Personalize apps for users Personalize apps for users ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_drop.png) `if st.user.email == 'jane@email.com': display_jane_content() elif st.user.email == 'adam@foocorp.io': display_adam_content() else: st.write('Please contact us to get access!')` Show different content based on the user's email address ## Display interactive widgets Display interactive widgets ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_sort.png) `st.button('Hit me')` Button `st.data_editor('Edit data', data)` Data editor `st.checkbox('Check me out')` Checkbox `st.radio('Pick one:', ['nose','ear'])` Radio `st.selectbox('Select', [1,2,3])` Selectbox `st.multiselect('Multiselect', [1,2,3])` Multiselect `st.slider('Slide me', min_value=0, max_value=10)` Slider `st.select_slider('Slide to select', options=[1,'2'])` Select slider `st.text_input('Enter some text')` Text input `st.number_input('Enter a number')` Number input `st.text_area('Area for textual entry')` Text area `st.date_input('Date input')` Date input `st.time_input('Time entry')` Time input `st.file_uploader('File uploader')` File uploader `st.download_button('On the dl', data)` Download button `st.camera_input('一二三,茄子!')` Camera input `st.color_picker('Pick a color')` Color picker `for i in range(int(st.number_input('Num:'))): foo()` Use widgets' returned values in variables `st.slider('Pick a number', 0, 100, disabled=True)` Disable widgets to remove interactivity ## Build chat-based apps Build chat-based apps ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_filter.png) `with st.chat_message('user'): st.write('Hello 👋') st.line_chart(np.random.randn(30, 3))` Insert a chat message container `st.chat_input('Say something')` Display a chat input widget ## Mutate data Mutate data ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_groupby.png) `element = st.dataframe(df1) element.add_rows(df2)` Add rows to a dataframe after showing it `element = st.line_chart(df1) element.add_rows(df2)` Add rows to a chart after showing it ## Display code Display code ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_convert.png) `with st.echo(): st.write('Code will be executed and printed')` Echo code ## Placeholders, help, and options Placeholders, help, and options ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_basics.png) `element = st.empty() element.line_chart(...) element.text_input(...) # Replaces previous.` Replace any single element `elements = st.container() elements.line_chart(...) st.write('Hello') elements.text_input(...) # Appears above 'Hello'.` Insert out of order `st.help(pandas.DataFrame)` Help `st.get_option(key) st.set_option(key, value)` Get and set options `st.set_page_config(layout='wide')` Page config `st.experimental_show(objects)` Show objects `st.experimental_get_query_params() st.experimental_set_query_params(**params)` Query params ## Connect to data sources Connect to data sources ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_basics.png) `st.experimental_connection('pets_db', type='sql')` Connection with type `conn = st.experimental_connection('sql')` SQL connection `conn = st.experimental_connection('snowpark')` Snowpark connection `class MyConnection(ExperimentalBaseConnection[myconn.MyConnection]): def _connect(self, **kwargs) -> MyConnection: return myconn.connect(**self._secrets, **kwargs) def query(self, query): return self._instance.query(query)` Create your own connection class ## Optimize performance Optimize performance ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_read.png) `@st.cache_data def foo(bar): # Do something expensive and return data return data d1 = foo(ref1) d2 = foo(ref1) # cached, d1 == d2 d3 = foo(ref2) # different arg, executes foo.clear() st.cache_data.clear()` Cache data objects (cached by value) `@st.cache_resource def foo(bar): # Create and return a non-data object return session s1 = foo(ref1) s2 = foo(ref1) # cached, s1 == s2 s3 = foo(ref2) # different arg, executes foo.clear() st.cache_resource.clear()` Cache global resources (cached by reference) `@st.cache def foo(bar): # Do something expensive in here... return data d1 = foo(ref1) d2 = foo(ref1) # cached, d1 == d2 d3 = foo(ref2)` Deprecated caching (@st.cache) ## Display progress and status Display progress and status ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_write.png) `with st.spinner(text='In progress'): time.sleep(3) st.success('Done')` Show a spinner during a process `bar = st.progress(50) time.sleep(3) bar.progress(100)` Show and update progress bar `st.balloons()` Balloons `st.snow()` Snow `st.toast('Mr Stay-Puft')` Toast `st.error('Error message')` Error message `st.warning('Warning message')` Warning message `st.info('Info message')` Info message `st.success('Success message')` Success message `st.exception(e)` Exception