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  1. Contents
  2. st.table
  3. element.add_rows
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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)

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.

Examples

Example 1: Display a simple dataframe as a static table

import streamlit as st
import pandas as pd
import numpy as np

df = pd.DataFrame(
    np.random.randn(10, 5), columns=("col %d" % i for i in range(5))
)

st.table(df)
Built with Streamlit 🎈

Example 2: Display a table of Markdown strings

import streamlit as st
import pandas as pd

df = pd.DataFrame(
    {
        "Command": ["**st.table**", "*st.dataframe*"],
        "Type": ["`static`", "`interactive`"],
        "Docs": [
            "[:rainbow[docs]](https://docs.streamlit.io/develop/api-reference/data/st.dataframe)",
            "[:book:](https://docs.streamlit.io/develop/api-reference/data/st.table)",
        ],
    }
)
st.table(df)
Built with Streamlit 🎈

element.add_rows

Concatenate a dataframe to the bottom of the current one.

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 streamlit as st
import pandas as pd
import numpy as np

df1 = pd.DataFrame(
    np.random.randn(50, 20), columns=("col %d" % i for i in range(20))
)

my_table = st.table(df1)

df2 = pd.DataFrame(
    np.random.randn(50, 20), columns=("col %d" % i for i in range(20))
)

my_table.add_rows(df2)
# Now the table shown in the Streamlit app contains the data for
# df1 followed by the data for 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)
my_chart.add_rows(df2)
# Now the chart shown in the Streamlit app contains the data for
# df1 followed by the data for 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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