Learn Adding Interactivity: Hover, Zoom, and Selection

Interactive features are a core strength of Plotly charts, making data exploration more intuitive and engaging. With Plotly, you can add hover tooltips to reveal details about each point, zoom in to examine specific data regions, and select subsets of data directly on the chart. These interactive elements are especially useful when you need to explore complex datasets, identify trends, or share insights with others who can interact with your visualizations. By default, Plotly Express charts include many interactive capabilities, but you can further customize them to highlight the information most relevant to your analysis.


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import plotly.express as px
import pandas as pd
from IPython.display import display, HTML

# Sample data
df = pd.DataFrame({
    "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
    "population": [8398748, 3990456, 2705994, 2325502, 1660272],
    "area": [783.8, 1214.9, 589.6, 1651.1, 1340.6]
})

# Create scatter plot with custom hover data
fig = px.scatter(
    df,
    x="area",
    y="population",
    text="city",
    hover_data={
        "city": True,
        "population": ":,",
        "area": ":.1f"
    },
    labels={"area": "City Area (sq km)", "population": "Population"},
    title="City Population vs. Area"
)
fig.update_traces(marker=dict(size=14, color='skyblue'), textposition="top center")

html = fig.to_html(full_html=False, include_plotlyjs="cdn")
display(HTML(html))

In this example, you see how to create a scatter plot that displays custom information in the hover tooltip. The hover_data parameter allows you to specify exactly which columns appear in the tooltip and how they are formatted. Here, the city name, population (with thousands separators), and area (with one decimal place) are shown. You can also use the text parameter to display labels directly on the chart points, making it easier to identify each city at a glance. This level of customization helps you present the most relevant details to your audience without cluttering the chart.


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import plotly.express as px
import pandas as pd
from IPython.display import display, HTML

# Sample data
df = pd.DataFrame({
    "category": ["A", "B", "C", "D", "E"],
    "value1": [10, 15, 13, 17, 12],
    "value2": [23, 11, 18, 10, 15]
})

# Create a scatter plot to demonstrate zoom and selection
fig = px.scatter(
    df,
    x="value1",
    y="value2",
    color="category",
    title="Zoom and Selection Example"
)

# By default, Plotly Express enables zoom and selection tools
# You can configure the dragmode (e.g., 'zoom', 'select', 'lasso') as needed
fig.update_layout(dragmode='select')  # Try 'zoom', 'pan', or 'lasso' for different behaviors

html = fig.to_html(full_html=False, include_plotlyjs="cdn")
display(HTML(html))

Interactivity transforms static charts into powerful data exploration tools. With Plotly Express, features like hover tooltips, zooming, and selection are enabled by default, letting you and your audience investigate data more deeply. Customizing hover information makes it easy to surface key details, while zoom and selection controls help you focus on specific patterns or outliers. These interactive features not only enhance your analysis but also make your visualizations more engaging and informative for others.

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Section 1. Chapter 6

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              1234567891011121314151617181920212223242526272829
            
import plotly.express as px
import pandas as pd
from IPython.display import display, HTML

# Sample data
df = pd.DataFrame({
    "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
    "population": [8398748, 3990456, 2705994, 2325502, 1660272],
    "area": [783.8, 1214.9, 589.6, 1651.1, 1340.6]
})

# Create scatter plot with custom hover data
fig = px.scatter(
    df,
    x="area",
    y="population",
    text="city",
    hover_data={
        "city": True,
        "population": ":,",
        "area": ":.1f"
    },
    labels={"area": "City Area (sq km)", "population": "Population"},
    title="City Population vs. Area"
)
fig.update_traces(marker=dict(size=14, color='skyblue'), textposition="top center")

html = fig.to_html(full_html=False, include_plotlyjs="cdn")
display(HTML(html))


              1234567891011121314151617181920212223242526
            
import plotly.express as px
import pandas as pd
from IPython.display import display, HTML

# Sample data
df = pd.DataFrame({
    "category": ["A", "B", "C", "D", "E"],
    "value1": [10, 15, 13, 17, 12],
    "value2": [23, 11, 18, 10, 15]
})

# Create a scatter plot to demonstrate zoom and selection
fig = px.scatter(
    df,
    x="value1",
    y="value2",
    color="category",
    title="Zoom and Selection Example"
)

# By default, Plotly Express enables zoom and selection tools
# You can configure the dragmode (e.g., 'zoom', 'select', 'lasso') as needed
fig.update_layout(dragmode='select')  # Try 'zoom', 'pan', or 'lasso' for different behaviors

html = fig.to_html(full_html=False, include_plotlyjs="cdn")
display(HTML(html))

Everything was clear?

Thanks for your feedback!

Section 1. Chapter 6