Working with Streamlit is simple. First you sprinkle a few Streamlit commands into a normal Python script, then you run it with streamlit run:

streamlit run your_script.py [-- script args]

As soon as you run the script as shown above, a local Streamlit server will spin up and your app will open in a new tab your default web browser. The app is your canvas, where you'll draw charts, text, widgets, tables, and more.

What gets drawn in the app is up to you. For example st.text writes raw text to your app, and st.line_chart draws — you guessed it — a line chart. Refer to our API documentation to see all commands that are available to you.

.. note::
   When passing your script some custom arguments, they must be passed after
   two dashes. Otherwise the arguments get interpreted as arguments to
   Streamlit itself.

.. tip::
   You can also pass a URL to `streamlit run`! This is great when combined with
   Github Gists. For example:

   `$ streamlit run https://raw.githubusercontent.com/streamlit/demo-uber-nyc-pickups/master/streamlit_app.py`

Every time you want to update your app, save the source file. When you do that, Streamlit detects if there is a change and asks you whether you want to rerun your app. Choose "Always rerun" at the top-right of your screen to automatically update your app every time you change its source code.

This allows you to work in a fast interactive loop: you type some code, save it, try it out live, then type some more code, save it, try it out, and so on until you're happy with the results. This tight loop between coding and viewing results live is one of the ways Streamlit makes your life easier.

.. tip::
  While developing a Streamlit app, it's recommended to lay out your editor and
  browser windows side by side, so the code and the app can be seen at the same
  time. Give it a try!

Streamlit's architecture allows you to write apps the same way you write plain Python scripts. To unlock this, Streamlit apps have a unique data flow: any time something must be updated on the screen, Streamlit reruns your entire Python script from top to bottom.

This can happen in two situations:

• Whenever you modify your app's source code.

• Whenever a user interacts with widgets in the app. For example, when dragging a slider, entering text in an input box, or clicking a button.

And to make all of this fast and seamless, Streamlit does some heavy lifting for you behind the scenes. A big player in this story is the @st.cache decorator, which allows developers to skip certain costly computations when their apps rerun. We'll cover caching later in this page.

There are a few ways to display data (tables, arrays, data frames) in Streamlit apps. In getting started, you were introduced to magic and st.write(), which can be used to write anything from text to tables. Now let's take a look at methods designed specifically for visualizing data.

You might be asking yourself, "why wouldn't I always use st.write()?" There are a few reasons:

1. Magic and st.write() inspect the type of data that you've passed in, and then decide how to best render it in the app. Sometimes you want to draw it another way. For example, instead of drawing a dataframe as an interactive table, you may want to draw it as a static table by using st.table(df).
2. The second reason is that other methods return an object that can be used and modified, either by adding data to it or replacing it.
3. Finally, if you use a more specific Streamlit method you can pass additional arguments to customize its behavior.

For example, let's create a data frame and change its formatting with a Pandas Styler object. In this example, you'll use Numpy to generate a random sample, and the st.dataframe() method to draw an interactive table.

.. note::
   This example uses Numpy to generate a random sample, but you can use Pandas
   DataFrames, Numpy arrays, or plain Python arrays.

dataframe = np.random.randn(10, 20)
st.dataframe(dataframe)

Let's expand on the first example using the Pandas Styler object to highlight some elements in the interactive table.

.. note::
   If you used PIP to install Streamlit, you'll need to install Jinja2 to use
   the Styler object. To install Jinja2, run: pip install jinja2.

dataframe = pd.DataFrame(
    np.random.randn(10, 20),
    columns=('col %d' % i for i in range(20)))

st.dataframe(dataframe.style.highlight_max(axis=0))

Streamlit also has a method for static table generation: st.table().

dataframe = pd.DataFrame(
    np.random.randn(10, 20),
    columns=('col %d' % i for i in range(20)))
st.table(dataframe)

When you've got the data or model into the state that you want to explore, you can add in widgets like st.slider(), st.button() or st.selectbox(). It's really straightforward — treat widgets as variables:

import streamlit as st
x = st.slider('x')  # 👈 this is a widget
st.write(x, 'squared is', x * x)

On first run, the app above should output the text "0 squared is 0". Then every time a user interacts with a widget, Streamlit simply reruns your script from top to bottom, assigning the current state of the widget to your variable in the process.

For example, if the user moves the slider to position 10, Streamlit will rerun the code above and set x to 10 accordingly. So now you should see the text "10 squared is 100".

Streamlit makes it easy to organize your widgets in a left panel sidebar with st.sidebar. Each element that's passed to st.sidebar is pinned to the left, allowing users to focus on the content in your app while still having access to UI controls.

For example, if you want to add a selectbox and a slider to a sidebar, use st.sidebar.slider and st.siderbar.selectbox instead of st.slider and st.selectbox:

import streamlit as st

# Add a selectbox to the sidebar:
add_selectbox = st.sidebar.selectbox(
    'How would you like to be contacted?',
    ('Email', 'Home phone', 'Mobile phone')
)

# Add a slider to the sidebar:
add_slider = st.sidebar.slider(
    'Select a range of values',
    0.0, 100.0, (25.0, 75.0)
)

Beyond the sidebar, Streamlit offers several other ways to control the layout of your app. st.beta_columns lets you place widgets side-by-side, and st.beta_expander lets you conserve space by hiding away large content.

import streamlit as st

left_column, right_column = st.beta_columns(2)
# You can use a column just like st.sidebar:
left_column.button('Press me!')

# Or even better, call Streamlit functions inside a "with" block:
with right_column:
    chosen = st.radio(
        'Sorting hat',
        ("Gryffindor", "Ravenclaw", "Hufflepuff", "Slytherin"))
    st.write(f"You are in {chosen} house!")

.. note::
  `st.echo` and `st.spinner` are not currently supported inside the sidebar
  or layout options.

Streamlit supports Light and Dark themes out of the box. Streamlit will first check if the user viewing an app has a Light or Dark mode preference set by their operating system and browser. If so, then that preference will be used. Otherwise, the Light theme is applied by default.

You can also change the active theme from "☰" → "Settings".

Want to add your own theme to an app? The "Settings" menu has a theme editor accessible by clicking on "Edit active theme". You can use this editor to try out different colors and see your app update live.

When you're happy with your work, themes can be saved by setting config options in the [theme] config section. After you've defined a theme for your app, it will appear as "Custom Theme" in the theme selector and will be applied by default instead of the included Light and Dark themes.

More information about the options available when defining a theme can be found in the theme option documentation.

.. note::
   The theme editor menu is available only in local development. If you've
   deployed your app using Streamlit Sharing, the "Edit active theme" button
   will no longer be displayed in the "Settings" menu.

.. tip::
   Another way to experiment with different theme colors is to turn on the
   "Run on save" option, edit your config.toml file, and watch as your app
   reruns with the new theme colors applied.

The Streamlit cache allows your app to execute quickly even when loading data from the web, manipulating large datasets, or performing expensive computations.

To use the cache, wrap functions with the @st.cache decorator:

@st.cache  # 👈 This function will be cached
def my_slow_function(arg1, arg2):
    # Do something really slow in here!
    return the_output

When you mark a function with the @st.cache decorator, it tells Streamlit that whenever the function is called it needs to check a few things:

1. The input parameters that you called the function with
2. The value of any external variable used in the function
3. The body of the function
4. The body of any function used inside the cached function

If this is the first time Streamlit has seen these four components with these exact values and in this exact combination and order, it runs the function and stores the result in a local cache. Then, next time the cached function is called, if none of these components changed, Streamlit will skip executing the function altogether and, instead, return the output previously stored in the cache.

For more information about the Streamlit cache, its configuration parameters, and its limitations, see Caching.

Now that you know a little more about all the individual pieces, let's close the loop and review how it works together:

1. Streamlit apps are Python scripts that run from top to bottom
2. Every time a user opens a browser tab pointing to your app, the script is re-executed
3. As the script executes, Streamlit draws its output live in a browser
4. Scripts use the Streamlit cache to avoid recomputing expensive functions, so updates happen very fast
5. Every time a user interacts with a widget, your script is re-executed and the output value of that widget is set to the new value during that run.