Tip
Static tables with st.table are the most basic way to display dataframes. For the majority of cases, we recommend using st.dataframe to display interactive dataframes, and st.data_editor to let users edit dataframes.
st.table
Display a static table.
While st.dataframe is geared towards large datasets and interactive data exploration, st.table is useful for displaying small, styled tables without sorting or scrolling. For example, st.table may be the preferred way to display a confusion matrix or leaderboard. Additionally, st.table supports Markdown.
| Function signature[source] | |
|---|---|
st.table(data=None, *, border=True) | |
| Parameters | |
data (Anything supported by st.dataframe) | The table data. All cells including the index and column headers can optionally contain GitHub-flavored Markdown. Syntax information can be found at: https://github.github.com/gfm. See the body parameter of st.markdown for additional, supported Markdown directives. |
border (bool or "horizontal") | Whether to show borders around the table and between cells. This can be one of the following:
|
Examples
Example 1: Display a confusion matrix as a static table
import pandas as pd
import streamlit as st
confusion_matrix = pd.DataFrame(
{
"Predicted Cat": [85, 3, 2, 1],
"Predicted Dog": [2, 78, 4, 0],
"Predicted Bird": [1, 5, 72, 3],
"Predicted Fish": [0, 2, 1, 89],
},
index=["Actual Cat", "Actual Dog", "Actual Bird", "Actual Fish"],
)
st.table(confusion_matrix)
Example 2: Display a product leaderboard with Markdown and horizontal borders
import streamlit as st
product_data = {
"Product": [
":material/devices: Widget Pro",
":material/smart_toy: Smart Device",
":material/inventory: Premium Kit",
],
"Category": [":blue[Electronics]", ":green[IoT]", ":violet[Bundle]"],
"Stock": ["🟢 Full", "🟡 Low", "🔴 Empty"],
"Units sold": [1247, 892, 654],
"Revenue": [125000, 89000, 98000],
}
st.table(product_data, border="horizontal")
Concatenate a dataframe to the bottom of the current one.
Important
add_rows is deprecated and might be removed in a future version. If you have a specific use-case that requires the add_rows functionality, please tell us via this [issue on Github](https://github.com/streamlit/streamlit/issues/13063).
| Function signature[source] | |
|---|---|
element.add_rows(data=None, **kwargs) | |
| Parameters | |
data (pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.dataframe.DataFrame, Iterable, dict, or None) | Table to concat. Optional. |
**kwargs (pandas.DataFrame, numpy.ndarray, Iterable, dict, or None) | The named dataset to concat. Optional. You can only pass in 1 dataset (including the one in the data parameter). |
Example
import time
import pandas as pd
import streamlit as st
from numpy.random import default_rng as rng
df1 = pd.DataFrame(
rng(0).standard_normal(size=(50, 20)), columns=("col %d" % i for i in range(20))
)
df2 = pd.DataFrame(
rng(1).standard_normal(size=(50, 20)), columns=("col %d" % i for i in range(20))
)
my_table = st.table(df1)
time.sleep(1)
my_table.add_rows(df2)
You can do the same thing with plots. For example, if you want to add more data to a line chart:
# Assuming df1 and df2 from the example above still exist... my_chart = st.line_chart(df1) time.sleep(1) my_chart.add_rows(df2)
And for plots whose datasets are named, you can pass the data with a keyword argument where the key is the name:
my_chart = st.vega_lite_chart(
{
"mark": "line",
"encoding": {"x": "a", "y": "b"},
"datasets": {
"some_fancy_name": df1, # <-- named dataset
},
"data": {"name": "some_fancy_name"},
}
)
my_chart.add_rows(some_fancy_name=df2) # <-- name used as keyword
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