5 Best Ways to Add Multiple Text Labels from DataFrame Columns in Python Plotly

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💡 Problem Formulation: When visualizing data using Plotly in Python, we often want to display multiple pieces of text information on a graph to enhance data comprehension. For instance, given a DataFrame with columns for sales, profit, and product names, how can we create a plot that displays all these attributes as text labels on respective data points? The desired output is a Plotly graph with each point annotated with customizable text drawn from multiple DataFrame columns.

Method 1: Using Plotly Graph Objects Scatter

Plotly’s Graph Objects module allows a high degree of customization, including setting multiple text labels. By accessing the text parameter and specifying a list of strings to be displayed, we can achieve complex annotations.

Here’s an example:

import plotly.graph_objects as go
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'Sales': [150, 200, 300], 'Profit': [50, 80, 120], 'Product': ['Widget', 'Gadget', 'Doodad']})
fig = go.Figure(data=[go.Scatter(
    x=df['Sales'],
    y=df['Profit'],
    text=df['Product'] + ': Sales ' + df['Sales'].astype(str) + ', Profit ' + df['Profit'].astype(str),
    mode='markers+text',
    textposition='top center'
)])

fig.show()

Output: A scatter plot with each point labeled by the product name, sales, and profit, positioned above the marker.

This code snippet creates a scatter plot where each data point is annotated with text that combines product name, sales, and profit information. The text parameter is set to a Pandas Series of concatenated strings, which Plotly then places on the plot at specified positions.

Method 2: Using Plotly Express

Plotly Express is a simpler interface for creating figures in Plotly. It can automatically infer data labels from DataFrame columns by assigning a list to the text parameter within the update_traces method.

Here’s an example:

import plotly.express as px
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'Sales': [150, 200, 300], 'Profit': [50, 80, 120], 'Product': ['Widget', 'Gadget', 'Doodad']})
fig = px.scatter(df, x='Sales', y='Profit')
fig.update_traces(text=df['Product'] + '<br>' + 'Sales: ' + df['Sales'].astype(str) + '<br>' + 'Profit: ' + df['Profit'].astype(str), textposition='top center')

fig.show()

Output: A scatter plot with each point displaying a multi-line label with product, sales, and profit data.

In this method, the Plotly Express scatter plot is created first, and then the update_traces method is called to add a list of strings as text labels. Here, HTML line breaks are used to align the text in multiple lines.

Method 3: Custom Data Hover

While not strictly a ‘label’, adding custom hover information can also act like multiple text labels on demand. Using the hover_data attribute in Plotly Express, various columns can be displayed as hover-over text.

Here’s an example:

import plotly.express as px
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'Sales': [150, 200, 300], 'Profit': [50, 80, 120], 'Product': ['Widget', 'Gadget', 'Doodad']})
fig = px.scatter(df, x='Sales', y='Profit', hover_data=['Product', 'Sales', 'Profit'])

fig.show()

Output: Scatter plot with points that, on hover, show a tooltip with the product name, sales, and profit.

This snippet generates a scatter plot where each point displays data from the ‘Product’, ‘Sales’, and ‘Profit’ columns as hover text, providing a clean look while still making detailed information accessible.

Method 4: Annotating with plotly.graph_objects.Layout

The go.Layout class in Plotly Graph Objects allows for explicit annotation of each data point with its own styling and positioning settings for advanced control.

Here’s an example:

import plotly.graph_objects as go
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'Sales': [150, 200, 300], 'Profit': [50, 80, 120}, 'Product': ['Widget', 'Gadget', 'Doodad']})
fig = go.Figure(data=[go.Scatter(x=df['Sales'], y=df['Profit'], mode='markers')])
annotations = [dict(
    x=row['Sales'],
    y=row['Profit'],
    xref='x',
    yref='y',
    text=row['Product'],
    showarrow=True,
    arrowhead=7,
    ax=0,
    ay=-40
) for index, row in df.iterrows()]
fig.update_layout(annotations=annotations)

fig.show()

Output: A scatter plot with annotated text labels showing the product name at each point.

This method demonstrates how to create customized annotations with precise control over text positioning and style, suitable for sophisticated plotting requirements.

Bonus One-Liner Method 5: Plotly Express with Custom Data

For a quick and easy multi-label solution, you can pass custom data directly to the hover_data parameter in Plotly Express, with some HTML styling for good measure.

Here’s an example:

import plotly.express as px
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'Sales': [150, 200, 300], 'Profit': [50, 80, 120], 'Product': ['Widget', 'Gadget', 'Doodad']})
fig = px.scatter(df, x='Sales', y='Profit', hover_data=[df['Product'] + '<br>' + 'Sales: ' + df['Sales'].astype(str) + '<br>' + 'Profit: ' + df['Profit'].astype(str)])

fig.show()

Output: A succinct setup resulting in hover labels displaying multi-line styled text.

A quick one-liner that enriches the hover labels feature with HTML line breaks to separate different text pieces, giving a more readable format for the end-user.

Summary/Discussion

  • Method 1: Custom Scatter Labels. Provides precise control for data point annotations. May require more verbose coding for complex label setups.
  • Method 2: Plotly Express Updates. Convenient for moderately complex labels. Limited customization options compared to Graph Objects.
  • Method 3: Custom Data Hover. Offers an interactive way to display data without cluttering the plot. Labels are not always visible but on demand.
  • Method 4: Advanced Annotations. Best for detailed and highly customized text labels. Can be more complex to iterate and manage for large datasets.
  • Bonus Method 5: Express One-Liner. Quick and practical solution for hover labels with stylized HTML content. Might not be suitable for static text display needs.