Multi-Line Tooltip

This example shows how you can use selections and layers to create a tooltip-like behavior tied to the x position of the cursor. If you are looking for more standard tooltips, see the Multi-Line Tooltip (Standard) example.

In this example, we’ll employ a little trick to isolate the x-position of the cursor: we add some transparent points with only an x encoding (no y encoding) and tie a nearest selection to these, tied to the “x” field.

Method syntax

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

np.random.seed(42)
columns = ["A", "B", "C"]
source = pd.DataFrame(
    np.cumsum(np.random.randn(100, 3), 0).round(2),
    columns=columns, index=pd.RangeIndex(100, name="x")
)
source = source.reset_index().melt("x", var_name="category", value_name="y")

# Create a selection that chooses the nearest point & selects based on x-value
nearest = alt.selection_point(nearest=True, on="pointerover",
                              fields=["x"], empty=False)

# The basic line
line = alt.Chart(source).mark_line(interpolate="basis").encode(
    x="x:Q",
    y="y:Q",
    color="category:N"
)

# Transparent selectors across the chart. This is what tells us
# the x-value of the cursor
selectors = alt.Chart(source).mark_point().encode(
    x="x:Q",
    opacity=alt.value(0),
).add_params(
    nearest
)

# Draw points on the line, and highlight based on selection
points = line.mark_point().encode(
    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)

# Draw text labels near the points, and highlight based on selection
text = line.mark_text(align="left", dx=5, dy=-5).encode(
    text=alt.condition(nearest, "y:Q", alt.value(" "))
)

# Draw a rule at the location of the selection
rules = alt.Chart(source).mark_rule(color="gray").encode(
    x="x:Q",
).transform_filter(
    nearest
)

# Put the five layers into a chart and bind the data
alt.layer(
    line, selectors, points, rules, text
).properties(
    width=600, height=300
)

Attribute syntax

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

np.random.seed(42)
columns = ["A", "B", "C"]
source = pd.DataFrame(
    np.cumsum(np.random.randn(100, 3), 0).round(2),
    columns=columns, index=pd.RangeIndex(100, name="x")
)
source = source.reset_index().melt("x", var_name="category", value_name="y")

# Create a selection that chooses the nearest point & selects based on x-value
nearest = alt.selection_point(nearest=True, on="pointerover",
                              fields=["x"], empty=False)

# The basic line
line = alt.Chart(source).mark_line(interpolate="basis").encode(
    x="x:Q",
    y="y:Q",
    color="category:N"
)

# Transparent selectors across the chart. This is what tells us
# the x-value of the cursor
selectors = alt.Chart(source).mark_point().encode(
    x="x:Q",
    opacity=alt.value(0),
).add_params(
    nearest
)

# Draw points on the line, and highlight based on selection
points = line.mark_point().encode(
    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
)

# Draw text labels near the points, and highlight based on selection
text = line.mark_text(align="left", dx=5, dy=-5).encode(
    text=alt.condition(nearest, "y:Q", alt.value(" "))
)

# Draw a rule at the location of the selection
rules = alt.Chart(source).mark_rule(color="gray").encode(
    x="x:Q",
).transform_filter(
    nearest
)

# Put the five layers into a chart and bind the data
alt.layer(
    line, selectors, points, rules, text
).properties(
    width=600, height=300
)