Now that you’ve mastered Streamlit’s main concepts, let’s take a look at some advanced functionality, like styling data, adjusting the order of elements in a report, and adding animations.
We’re adding these sections as quickly as we can, but please let us know what’s important to you. Ping us in the community forum.
Display and style data¶
There are a few ways to display data (tables, arrays, data frames) in Streamlit
apps. In getting started, you were introduced to magic
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:
- 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).
- 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.
- 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,
st.dataframe() method to draw an
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.
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:
dataframe = pd.DataFrame( np.random.randn(10, 20), columns=('col %d' % i for i in range(20))) st.table(dataframe)
Insert elements out of order¶
You can use the
st.empty method as a placeholder,
to “save” a slot in your app that you can use later.
st.text('This will appear first') # Appends some text to the app. my_slot1 = st.empty() # Appends an empty slot to the app. We'll use this later. my_slot2 = st.empty() # Appends another empty slot. st.text('This will appear last') # Appends some more text to the app. my_slot1.text('This will appear second') # Replaces the first empty slot with a text string. my_slot2.line_chart(np.random.randn(20, 2)) # Replaces the second empty slot with a chart.
Let’s combine some of the things you’ve learned to create compelling animations in your app.
progress_bar = st.progress(0) status_text = st.empty() chart = st.line_chart(np.random.randn(10, 2)) for i in range(100): # Update progress bar. progress_bar.progress(i) new_rows = np.random.randn(10, 2) # Update status text. status_text.text( 'The latest random number is: %s' % new_rows[-1, 1]) # Append data to the chart. chart.add_rows(new_rows) # Pretend we're doing some computation that takes time. time.sleep(0.1) status_text.text('Done!') st.balloons()
Append data to a table or chart¶
In Streamlit, you can not only replace entire elements in your app, but also modify the data behind those elements. Here is how:
import numpy as np import time # Get some data. data = np.random.randn(10, 2) # Show the data as a chart. chart = st.line_chart(data) # Wait 1 second, so the change is clearer. time.sleep(1) # Grab some more data. data2 = np.random.randn(10, 2) # Append the new data to the existing chart. chart.add_rows(data2)
Return the value of a Streamlit call¶
Coming soon! Ping us in the community forum if you just can’t wait and have to have this info immediately.