Data display elements

When you're working with data, it is extremely valuable to visualize that data quickly, interactively, and from multiple different angles. That's what Streamlit is actually built and optimized for.

You can display data via charts, and you can display it in raw form. These are the Streamlit commands you can use to display raw data.

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Dataframes

Display a dataframe as an interactive table.

st.dataframe(my_data_frame)
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Static tables

Display a static table.

st.table(my_data_frame)
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Metrics

Display a metric in big bold font, with an optional indicator of how the metric changed.

st.metric("My metric", 42, 2)
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Dicts and JSON

Display object or string as a pretty-printed JSON string.

st.json(my_dict)

Third-party components

These are featured components created by our lovely community. If you don't see what you're looking for, check out our Components Hub app and Streamlit Extras for more examples and inspiration!

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Streamlit Aggrid

Implementation of Ag-Grid component for Streamlit. Created by @PablocFonseca.

df = pd.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
grid_return = AgGrid(df, editable=True)

new_df = grid_return['data']
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Streamlit Folium

Streamlit Component for rendering Folium maps. Created by @randyzwitch.

m = folium.Map(location=[39.949610, -75.150282], zoom_start=16)
folium.Marker([39.949610, -75.150282], popup="Liberty Bell", tooltip="Liberty Bell").add_to(m)

st_data = st_folium(m, width=725)
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Pandas Profiling

Pandas profiling component for Streamlit. Created by @okld.

df = pd.read_csv("https://storage.googleapis.com/tf-datasets/titanic/train.csv")
pr = df.profile_report()

st_profile_report(pr)

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