5 Best Ways to Set the Font Style to Bold in Python Plotly : Emily Rosemary Collins

5 Best Ways to Set the Font Style to Bold in Python Plotly
by: Emily Rosemary Collins
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💡 Problem Formulation: When visualizing data in Python using Plotly, you may want to emphasize certain text elements by making them bold. This could pertain to titles, annotations, axis labels, or legend text. This article presents various methods to achieve bold font styles in different aspects of a Plotly chart, enhancing the readability and aesthetic appeal of your data presentation.

Method 1: Setting the Title Font to Bold

Plotly provides a straightforward option to set the font style of the chart title to bold using the title_font parameter under the layout object. This parameter is capable of taking a dictionary where one can specify the font properties such as family, size, and whether it should be bold.

Here’s an example:

import plotly.graph_objects as go

fig = go.Figure()
fig.update_layout(title_text="Bold Title",
                  title_font=dict(size=20, family='Arial', color='black', weight='bold'))

The output will be a Plotly chart displaying the title “Bold Title” in 20pt Arial font with bold text.

In this code snippet, we create a simple Plotly chart and update the layout to include a title with the desired bold font characteristics.

Method 2: Bold Axis Labels

To make axis labels bold in Plotly, you can utilize the title_font attribute within the xaxis and yaxis properties. This method enhances the clarity of axis labeling, which can be particularly useful when dealing with complex datasets.

Here’s an example:

import plotly.graph_objects as go

fig = go.Figure(data=go.Bar(y=[2, 3, 1]))
fig.update_layout(xaxis_title="X Axis",
                  yaxis_title="Y Axis",

This code produces a bar chart with both X and Y axis labels set in bold font.

In this example, while creating the bar chart, we specifically direct Plotly to use bold font for the x-axis and y-axis titles using the xaxis_title_font and yaxis_title_font settings.

Method 3: Bold Legend Text

Emphasizing the legend text in bold can be achieved by modifying the legend_title_font and legend_font parameters in Plotly. This improves the distinction between legend entries for better interpretation of the chart.

Here’s an example:

import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6],
                    name='Sample Trace'))
fig.update_layout(legend_title_text='Legend Title',

The resulting visualization will display both the legend entries and the legend title in bold text.

In this snippet, we add a scatter plot trace to our figure, and then specifically set the legend title and entries to bold within the layout parameters.

Method 4: Bold Annotation Text

Bold annotations within a Plotly chart draw attention to specific data points or provide crucial information. By setting the font attribute inside each annotation dictionary, we can manipulate the font weight to be bold.

Here’s an example:

import plotly.graph_objects as go

fig = go.Figure(data=go.Bar(y=[3, 1, 2]))

The output is a bar chart with a bold annotation “Highlight” at the specified coordinates on the chart.

This snippet adds an annotation to the bar chart with the text “Highlight” in bold, emphasizing this particular text over other elements in the chart.

Bonus One-Liner Method 5: Quick Inline Text Styling

For rapid inline stylings such as bolding text within the traces themselves, Plotly lets you use basic HTML tags like <b></b> directly in the text strings.

Here’s an example:

import plotly.graph_objects as go

fig = go.Figure(data=go.Scatter(x=[1, 2, 3], y=[4, 5, 6],
                    text=['One', '<b>Two</b>', 'Three'],
                    textposition="top center"))

The output displays scatter plot points with the text labels “One”, “Two”, and “Three”, where “Two” appears in bold because it is enclosed in the <b> tag.

This method demonstrates how to quickly include HTML-like styling directly in data points, which in our case makes the text “Two” appear bold.


  • Method 1: Title Font. Easy to implement. Only applies to the title.
  • Method 2: Axis Labels. Enhances axis readability. Limited to axis titles.
  • Method 3: Legend Text. Improves legend clarity. Only affects the legend.
  • Method 4: Annotation Text. Draws focus to specific chart aspects. May require positioning adjustments.
  • Method 5: Inline Text Styling. Quick and straightforward for data point labels. Limited HTML tag support.

February 28, 2024 at 06:28PM
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