st.map

Display a map with points on it.

This is a wrapper around st.pydeck_chart to quickly create scatterplot charts on top of a map, with auto-centering and auto-zoom.

When using this command, Mapbox provides the map tiles to render map content. Note that Mapbox is a third-party product and Streamlit accepts no responsibility or liability of any kind for Mapbox or for any content or information made available by Mapbox.

Mapbox requires users to register and provide a token before users can request map tiles. Currently, Streamlit provides this token for you, but this could change at any time. We strongly recommend all users create and use their own personal Mapbox token to avoid any disruptions to their experience. You can do this with the mapbox.token config option. The use of Mapbox is governed by Mapbox's Terms of Use.

To get a token for yourself, create an account at https://mapbox.com. For more info on how to set config options, see https://docs.streamlit.io/library/advanced-features/configuration

Streamlit in Snowflake Note

When you use the st.map, Mapbox provides the map tiles when rendering map content. Mapbox is a third-party application and is subject to Snowflake's External Offerings Terms.

Function signature[source]

st.map(data=None, zoom=None, use_container_width=True)

Parameters

data (pandas.DataFrame, pandas.Styler, pyarrow.Table, numpy.ndarray, pyspark.sql.DataFrame, snowflake.snowpark.dataframe.DataFrame, snowflake.snowpark.table.Table, Iterable, dict, or None)

The data to be plotted. Must have two columns:

  • latitude called 'lat', 'latitude', 'LAT', 'LATITUDE'
  • longitude called 'lon', 'longitude', 'LON', 'LONGITUDE'.

zoom (int)

use_container_width (bool)

No description

Example

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)
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. 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".

**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
Previous: st.line_chartNext: st.scatter_chart
forum

Still have questions?

Our forums are full of helpful information and Streamlit experts.