💡 Problem Formulation: When visualizing multiple datasets in a single figure using Plotly in Python, it’s often necessary to differentiate between subplots with unique titles. This enhances readability and provides context. The challenge lies in assigning individual titles to a grid of subplots, where each subplot represents different data. Users aim to output a multi-plot figure where each subplot has a distinct title reflecting its content.
Method 1: Using the make_subplots() Function
This method involves using the make_subplots() function provided by Plotly, which allows for the creation of a subplot layout, where specific titles can be given to each subplot during the initial setup. This function is particularly useful when you want to pre-define the grid and title each subplot as you go.
Here’s an example:
import plotly.graph_objs as go
from plotly.subplots import make_subplots
fig = make_subplots(rows=2, cols=2, subplot_titles=("Plot 1", "Plot 2", "Plot 3", "Plot 4"))
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[6, 5, 4]), row=1, col=2)
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[5, 6, 4]), row=2, col=1)
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 6, 5]), row=2, col=2)
fig.show()
The output is a 2×2 grid of subplots, each labeled with a different title as specified in the subplot_titles argument.
In this example, we create a 2-by-2 grid of subplots with unique titles using the make_subplots() function. We then add individual traces to specified rows and columns, and the resulting figure contains a neatly organized set of titled subplots.
Method 2: Annotating Each Subplot After Creation
Another approach is to annotate each subplot with a title after the subplots have been created. This allows for dynamic title assignment which may depend on data-driven conditions or subsequent analysis results.
Here’s an example:
import plotly.graph_objs as go
from plotly.subplots import make_subplots
fig = make_subplots(rows=2, cols=2)
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[6, 5, 4]), row=1, col=2)
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[5, 6, 4]), row=2, col=1)
fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 6, 5]), row=2, col=2)
titles = ["Plot 1", "Plot 2", "Plot 3", "Plot 4"]
for i, title in enumerate(titles):
fig.add_annotation(text=title, xref="paper", yref="paper", x=(i%2)/2, xanchor="center", y=1-((i//2)/2 + 0.1), yanchor="bottom", showarrow=False)
fig.show()
The output is a 2×2 grid of subplots, with annotations acting as titles for each subplot. Each title is placed above its corresponding subplot.
In the provided code snippet, we add annotations to an already created grid of subplots. The coordinates for each annotation’s position are calculated so that the titles appear correctly above their respective subplots.
Method 3: Using the Update Layout Function
Plotly’s layout update function provides a means of customizing the appearance of subplots, including the addition of titles post-creation. Titles can be updated or added to each subplot individually, offering flexibility in the plotting process.
Here’s an example:
import plotly.graph_objs as go
from plotly.subplots import make_subplots
fig = make_subplots(rows=2, cols=2)
fig.update_layout(
title_text='Main Title',
subplot_titles=['Plot 1', 'Plot 2', 'Plot 3', 'Plot 4']
)
fig.show()
The output is a 2×2 grid of subplots with each subplot having a unique title, in addition to a main title for the entire figure.
This code updates an existing figure’s layout to add or change the titles of the subplots. It’s especially useful for applying changes globally to the entire layout, such as setting a main title in addition to individual subplot titles.
Method 4: Customizing Annotations for Each Subplot
By customizing annotations for each subplot, titles can be made more versatile, incorporating a variety of styles and positions relative to each subplot. This gives a high level of control over the final visualization’s appearance.
Here’s an example:
import plotly.graph_objs as go from plotly.subplots import make_subplots fig = make_subplots(rows=2, cols=2) # Adding custom annotations fig.add_annotation(text="Plot 1", x=0.25, y=1, showarrow=False, font=dict(size=16), xref="paper", yref="paper") fig.add_annotation(text="Plot 2", x=0.75, y=1, showarrow=False, font=dict(size=16), xref="paper", yref="paper") fig.add_annotation(text="Plot 3", x=0.25, y=0.5, showarrow=False, font=dict(size=16), xref="paper", yref="paper") fig.add_annotation(text="Plot 4", x=0.75, y=0.5, showarrow=False, font=dict(size=16), xref="paper", yref="paper") fig.show()
The output displays four subplots, each with a customized title annotation positioned according to the x and y coordinates specified.
This example showcases how to add highly customized title annotations for each subplot. By adjusting the coordinates (x, y), font size, and other presentation details, the titles can be aligned and designed precisely as desired.
Bonus One-Liner Method 5: Using List Comprehension
A quick one-liner method to set titles for a grid of subplots can be achieved using list comprehension to generate the subplot_titles argument for the make_subplots function, which can be useful for large or dynamic datasets.
Here’s an example:
import plotly.graph_objs as go
from plotly.subplots import make_subplots
fig = make_subplots(rows=2, cols=2, subplot_titles=[f"Plot {i+1}" for i in range(4)])
# Your plotting code ...
fig.show()
Using the one-liner technique, the output is similar to previous methods—a 2×2 grid of subplots, each with a title ‘Plot 1’, ‘Plot 2’, etc.
This efficient code uses list comprehension to create a list of titles that is passed directly to the make_subplots function, automating the title generation process based on the number of plots.
Summary/Discussion
- Method 1: Using the
make_subplots()function. Strengths are that titles can be set during initial setup, creating a clear, pre-defined structure for your plots. Weaknesses include less flexibility for dynamic title changes after the fact. - Method 2: Annotating each subplot after creation. This approach allows dynamic placement and styling of titles, and is especially useful when titles are based on computed results. However, positioning annotations can become complex for intricate layouts.
- Method 3: Using the update layout function. This method is great for setting or updating titles easily for each subplot and gives the added ability to set a main title. The weakness may be less direct control over individual title placement compared to annotations.
- Method 4: Customizing annotations for each subplot. The strength lies in the high degree of personalization available for subplot titles. However, this method requires more code and can become cumbersome for larger subplot grids.
- Bonus Method 5: Using list comprehension for one-liner title setting. This is a strength in terms of concise code and scalability, but may not offer the same customization level as other methods for individual titles.