How to add multiple text labels from DataFrame columns in Python Plotly?

Plotly is a powerful Python library for creating interactive visualizations. You can add multiple text labels from DataFrame columns to enhance your charts with additional information that appears on hover or as annotations.

In this tutorial, we'll learn how to create a scatter plot with text labels from different DataFrame columns using Plotly's graph_objects module.

Required Libraries

We'll use the following Python libraries ?

• plotly.graph_objects For creating interactive plots

• pandas For DataFrame operations

• plotly.offline For generating HTML output

Creating the DataFrame

First, let's create a sample DataFrame with student data ?

import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py

# Create sample DataFrame
df = pd.DataFrame({
    'Age': [20, 22, 23, 24, 21],
    'Mark': [80, 85, 90, 95, 99],
}, index=['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'])

# Set index name
df.index.name = 'Student'

# Sort by Age for better visualization
df = df.sort_values('Age')
print(df)

         Age  Mark
Student            
Alice     20    80
Eve       21    99
Bob       22    85
Charlie   23    90
Diana     24    95

Adding Text Labels from Multiple Columns

Now we'll create a scatter plot where hover text shows information from multiple DataFrame columns ?

import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py

# Create DataFrame
df = pd.DataFrame({
    'Age': [20, 22, 23, 24, 21],
    'Mark': [80, 85, 90, 95, 99],
}, index=['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'])

df.index.name = 'Student'
df = df.sort_values('Age')

# Create scatter plot with text labels
trace = go.Scatter(
    x=df.index,
    y=df['Mark'],
    name='Student Marks',
    text=df['Age'],  # Age values as hover text
    hovertemplate='<b>%{x}</b><br>Mark: %{y}<br>Age: %{text}<extra></extra>',
    mode='lines+markers+text',
    textposition='top center',
    line=dict(color='green', width=3),
    marker=dict(size=10, color='darkgreen')
)

# Set layout
layout = dict(
    title='Student Marks with Age Labels',
    xaxis=dict(title='Students'),
    yaxis=dict(title='Marks'),
    showlegend=True
)

# Create and display the plot
data = [trace]
fig = dict(data=data, layout=layout)
py.offline.plot(fig, filename='student_marks.html')

Advanced Example with Custom Text Labels

For more complex text labels combining multiple columns, you can create custom hover text ?

import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py

# Create DataFrame with more columns
df = pd.DataFrame({
    'Age': [20, 22, 23, 24, 21],
    'Mark': [80, 85, 90, 95, 99],
    'Subject': ['Math', 'Physics', 'Chemistry', 'Biology', 'English']
}, index=['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'])

df.index.name = 'Student'

# Create custom hover text combining multiple columns
df['hover_text'] = df.apply(lambda row: f"Age: {row['Age']}, Subject: {row['Subject']}", axis=1)

trace = go.Scatter(
    x=df.index,
    y=df['Mark'],
    name='Student Performance',
    text=df['hover_text'],
    hovertemplate='<b>%{x}</b><br>Mark: %{y}<br>%{text}<extra></extra>',
    mode='markers+lines',
    line=dict(color='blue', width=2),
    marker=dict(size=12, color='lightblue', line=dict(color='darkblue', width=2))
)

layout = dict(
    title='Student Performance with Multi-Column Labels',
    xaxis=dict(title='Students'),
    yaxis=dict(title='Marks'),
    template='plotly_white'
)

fig = dict(data=[trace], layout=layout)
py.offline.plot(fig, filename='advanced_labels.html')
print("Plot saved as 'advanced_labels.html'")

Plot saved as 'advanced_labels.html'

Key Parameters for Text Labels

Conclusion

Adding multiple text labels from DataFrame columns in Plotly enhances data visualization by providing additional context on hover or as annotations. Use the text parameter for simple labels or create custom hovertemplate for complex multi-column information display.