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

chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])

c = (
   alt.Chart(chart_data)
   .mark_circle()
   .encode(x="a", y="b", size="c", color="c", tooltip=["a", "b", "c"])
)

st.altair_chart(c, use_container_width=True)

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The following two examples have equivalent definitions. Each one has a point and interval selection parameter include in the chart definition. The point seleciton parameter is named "point_selection". The interval or box selection parameter is named "interval_selection".

The follow example uses st.altair_chart:

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

if "data" not in st.session_state:
    st.session_state.data = pd.DataFrame(
        np.random.randn(20, 3), columns=["a", "b", "c"]
    )
df = st.session_state.data

point_selector = alt.selection_point("point_selection")
interval_selector = alt.selection_interval("interval_selection")
chart = (
    alt.Chart(df)
    .mark_circle()
    .encode(
        x="a",
        y="b",
        size="c",
        color="c",
        tooltip=["a", "b", "c"],
        fillOpacity=alt.condition(point_selector, alt.value(1), alt.value(0.3)),
    )
    .add_params(point_selector, interval_selector)
)

event = st.altair_chart(chart, key="alt_chart", on_select="rerun")

event

The following example uses st.vega_lite_chart:

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

if "data" not in st.session_state:
    st.session_state.data = pd.DataFrame(
        np.random.randn(20, 3), columns=["a", "b", "c"]
    )

spec = {
    "mark": {"type": "circle", "tooltip": True},
    "params": [
        {"name": "interval_selection", "select": "interval"},
        {"name": "point_selection", "select": "point"},
    ],
    "encoding": {
        "x": {"field": "a", "type": "quantitative"},
        "y": {"field": "b", "type": "quantitative"},
        "size": {"field": "c", "type": "quantitative"},
        "color": {"field": "c", "type": "quantitative"},
        "fillOpacity": {
            "condition": {"param": "point_selection", "value": 1},
            "value": 0.3,
        },
    },
}

event = st.vega_lite_chart(
    st.session_state.data, spec, key="vega_chart", on_select="rerun"
)

event

Try selecting points in this interactive example. When you click a point, the selection will appear under the attribute, "point_selection", which is the name given to the point selection parameter. Similarly, when you make an interval selection, it will appear under the attribute "interval_selection". You can give your selection parameters other names if desired.

If you hold Shift while selecting points, existing point selections will be preserved. Interval selections are not preserved when making additional selections.

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