Annotate an Altair chart

Altair allows you to annotate your charts with text, images, and emojis. You can do this by overlaying two charts to create a layered chart.

Applied concepts

Use layered charts in Altair to create annotations.

Prerequisites

This tutorial requires the following Python libraries:

streamlit altair>=4.0.0 vega_datasets
This tutorial assumes you have a clean working directory called your-repository.
You should have a basic understanding of the Vega-Altair charting library.

Summary

In this example, you will create a time-series chart to track the evolution of stock prices. The chart will have two layers: a data layer and an annotation layer. Each layer is an altair.Chart object. You will combine the two charts with the + opterator to create a layered chart.

Within the data layer, you'll add a multi-line tooltip to show information about datapoints. To learn more about multi-line tooltips, see this example in Vega-Altair's documentation. You'll add another tooltip to the annotation layer.

Here's a look at what you'll build:

Complete codeexpand_more

import streamlit as st
import altair as alt
import pandas as pd
from vega_datasets import data


@st.cache_data
def get_data():
    source = data.stocks()
    source = source[source.date.gt("2004-01-01")]
    return source


stock_data = get_data()

hover = alt.selection_single(
    fields=["date"],
    nearest=True,
    on="mouseover",
    empty="none",
)

lines = (
    alt.Chart(stock_data, title="Evolution of stock prices")
    .mark_line()
    .encode(
        x="date",
        y="price",
        color="symbol",
    )
)

points = lines.transform_filter(hover).mark_circle(size=65)

tooltips = (
    alt.Chart(stock_data)
    .mark_rule()
    .encode(
        x="yearmonthdate(date)",
        y="price",
        opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),
        tooltip=[
            alt.Tooltip("date", title="Date"),
            alt.Tooltip("price", title="Price (USD)"),
        ],
    )
    .add_selection(hover)
)

data_layer = lines + points + tooltips

ANNOTATIONS = [
    ("Sep 01, 2007", 450, "🙂", "Something's going well for GOOG & AAPL."),
    ("Nov 01, 2008", 220, "🙂", "The market is recovering."),
    ("Dec 01, 2007", 750, "😱", "Something's going wrong for GOOG & AAPL."),
    ("Dec 01, 2009", 680, "😱", "A hiccup for GOOG."),
]
annotations_df = pd.DataFrame(
    ANNOTATIONS, columns=["date", "price", "marker", "description"]
)
annotations_df.date = pd.to_datetime(annotations_df.date)

annotation_layer = (
    alt.Chart(annotations_df)
    .mark_text(size=20, dx=-10, dy=0, align="left")
    .encode(x="date:T", y=alt.Y("price:Q"), text="marker", tooltip="description")
)

combined_chart = data_layer + annotation_layer
st.altair_chart(combined_chart, use_container_width=True)

Built with Streamlit 🎈

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Build the example

Initialize your app

In your_repository, create a file named app.py.
In a terminal, change directories to your_repository and start your app.

streamlit run app.py

Your app will be blank since you still need to add code.
In app.py, write the following:

import streamlit as st import altair as alt import pandas as pd from vega_datasets import data

You'll be using these libraries as follows:
- You'll download a dataset using vega_datasets.
- You'll maniputate the data using pandas.
- You'll define a chart using altair.
Save your app.py file and view your running app.
Click "Always rerun" or hit your "A" key in your running app.

Your running preview will automatically update as you save changes to app.py. Your preview will still be blank. Return to your code.

Build the data layer

You'll build an interactive time-series chart of the stock prices with a multi-line tooltip. The x-axis represents the date, and the y-axis represents the stock price.

Import data from vega_datasets.

@st.cache_data def get_data(): source = data.stocks() source = source[source.date.gt("2004-01-01")] return source stock_data = get_data()

The @st.cache_data decorator turns get_data() into a cahced function. Streamlit will only download the data once since the data will be saved in a cache. For more information about caching, see Caching overview.
Define a mouseover selection event in Altair.

hover = alt.selection_single( fields=["date"], nearest=True, on="mouseover", empty="none", )

This defines a mouseover selection for points. fields=["date"] allows Altair to identify other points with the same date. You will use this to create a vertical line highlight when a user hovers over a point.
Define a basic line chart to graph the five series in your data set.

lines = ( alt.Chart(stock_data, title="Evolution of stock prices") .mark_line() .encode( x="date", y="price", color="symbol", ) )
Draw points on the lines and highlight them based on the mouseover selection.

points = lines.transform_filter(hover).mark_circle(size=65)

Since the mouseover selection includes fields=["date"], Altair will draw circles on each series at the same date.
Draw a vertical rule at the location of the mouseover selection.

tooltips = ( alt.Chart(stock_data) .mark_rule() .encode( x="yearmonthdate(date)", y="price", opacity=alt.condition(hover, alt.value(0.3), alt.value(0)), tooltip=[ alt.Tooltip("date", title="Date"), alt.Tooltip("price", title="Price (USD)"), ], ) .add_selection(hover) )

The opacity parameter ensures each vertical line is only visible when it's part of a mouseover selection. Each alt.Tooltip adds a line to your multi-line tooltip.
Combine the lines, points, and tooltips into a single chart.

data_layer = lines + points + tooltips
Optional: Test out your code by rendering your data layer.

st.altair_chart(data_layer, use_container_width=True)

Save your file and examine the chart in your app. Use your mouse to hover over points. Observe the circle marks, vertical line, and tooltip as you hover over a point. Delete the line or keep it at the end of your app to be updated as you continue.

Build the annotation layer

Now that you have the first chart that shows the data, you can annotate it with text and an emoji. In this section, you'll add some emojis and tooltips to mark specifc points of interest.

Create a list of annotations.

ANNOTATIONS = [ ("Sep 01, 2007", 450, "🙂", "Something's going well for GOOG & AAPL."), ("Nov 01, 2008", 220, "🙂", "The market is recovering."), ("Dec 01, 2007", 750, "😱", "Something's going wrong for GOOG & AAPL."), ("Dec 01, 2009", 680, "😱", "A hiccup for GOOG."), ] annotations_df = pd.DataFrame( ANNOTATIONS, columns=["date", "price", "marker", "description"] ) annotations_df.date = pd.to_datetime(annotations_df.date)

The first two columns ("date" and "price") determine where Altair will place the marker. The third column ("marker") determines what icon Altair will place. The last column ("description") will fill in the associated tooltip.
Create a scatter plot with the x-axis representing the date and the y-axis representing the height ("price") of each annotation.

annotation_layer = ( alt.Chart(annotations_df) .mark_text(size=20, dx=-10, dy=0, align="left") .encode(x="date:T", y=alt.Y("price:Q"), text="marker", tooltip="description") )

The dx=-10, dy=0 inside of .mark_text() offsets the icons so they are centered at the coordinate in your annotations dataframe. The four columns are passed to the chart through the .encode() method. If you want to use the same marker for all points, you can remove text="marker" from the .encode() method and add the marker to .mark_text(). For example, .mark_text(text="🥳") would make all the icons be "🥳". For more information about .mark_text(), see Altair's documentation.

Combine the chart layers

Define the combined chart.

combined_chart = data_layer + annotation_layer
Display the chart in Streamlit.

st.altair_chart(combined_chart, use_container_width=True)

Next steps

Play around with your new app.

If you want to use custom images instead of text or emojis to annotation your chart, you can replace the line containing .mark_text() with .mark_image(). For some URL string stored in a variable IMAGE_URL, you could do something like this:

.mark_image( width=12, height=12, url=IMAGE_URL, )
If you want to enable panning and zooming for your chart, add .interactive() when you define your combined chart:

combined_chart = (data_layer + annotation_layer).interactive()

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