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

df = pd.DataFrame(
    np.random.randn(1000, 2) / [50, 50] + [37.76, -122.4],
    columns=['lat', 'lon'])

st.map(df)

You can also customize the size and color of the datapoints:

st.map(df, size=20, color='#0044ff')

And finally, you can choose different columns to use for the latitude and longitude components, as well as set size and color of each datapoint dynamically based on other columns:

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

df = pd.DataFrame({
    "col1": np.random.randn(1000) / 50 + 37.76,
    "col2": np.random.randn(1000) / 50 + -122.4,
    "col3": np.random.randn(1000) * 100,
    "col4": np.random.rand(1000, 4).tolist(),
})

st.map(df,
    latitude='col1',
    longitude='col2',
    size='col3',
    color='col4')

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