st.dataframe(data=None, width=None, height=None)
data (pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, Iterable, dict, or None)
The data to display.
If 'data' is a pandas.Styler, it will be used to style its underyling DataFrame. Streamlit supports custom cell values and colors. (It does not support some of the more exotic pandas styling features, like bar charts, hovering, and captions.) Styler support is experimental! Pyarrow tables are not supported by Streamlit's legacy DataFrame serialization (i.e. with config.dataFrameSerialization = "legacy"). To use pyarrow tables, please enable pyarrow by changing the config setting, config.dataFrameSerialization = "arrow".
width (int or None)
Desired width of the UI element expressed in pixels. If None, a default width based on the page width is used.
height (int or None)
Desired height of the UI element expressed in pixels. If None, a default height is used.
df = pd.DataFrame( np.random.randn(50, 20), columns=('col %d' % i for i in range(20))) st.dataframe(df) # Same as st.write(df)(view standalone Streamlit app)st.dataframe(df, 200, 100)
You can also pass a Pandas Styler object to change the style of the rendered DataFrame:df = pd.DataFrame( np.random.randn(10, 20), columns=('col %d' % i for i in range(20))) st.dataframe(df.style.highlight_max(axis=0))(view standalone Streamlit app)
Dataframes displayed as interactive tables with
st.dataframe have the following interactive features:
- Column sorting: sort columns by clicking on their headers.
- Column resizing: resize columns by dragging and dropping column header borders.
- Table (height, width) resizing: resize tables by dragging and dropping the bottom right corner of tables.
- Search: search through data by clicking a table, using hotkeys (
⌘ Cmd + For
Ctrl + F) to bring up the search bar, and using the search bar to filter data.
- Copy to clipboard: select one or multiple cells, copy them to clipboard, and paste them into your favorite spreadsheet software.
Copy to clipboard does not currently work on Streamlit Cloud.