Subplots in Plotly

Plotly is an open source interactive data visualization library that allows for easy creation of plots and charts in Python. It supports a wide range of chart types including scatter plots, line plots, bar graphs, histograms, heat maps, and more. One of the unique features of Plotly is its ability to create subplots, which can be used to display multiple related plots on a single page. In this article, we will explore how to create subplots in Plotly and use them to visualize data.

Creating Subplots in Plotly

To create subplots in Plotly, you can use the fig.add_trace() method to add multiple traces to a single figure object. Each trace is represented as a dictionary with attributes such as x, y, and type. The type attribute specifies the type of chart (e.g., scatter plot, line plot, etc.) that you want to create for each trace. For example, the following code creates two subplots:

import plotly.graph_objs as go
import plotly.io as pio

fig = go.Figure()

# Add a scatter plot to the first subplot
fig.add_trace(go.Scatter(x=x1, y=y1, mode='markers', marker=dict(size=20)))

# Add a line plot to the second subplot
fig.add_trace(go.Line(x=x2, y=y2))

pio.show(fig)

In this example, we create a Figure object and add two traces to it: a scatter plot with x1 and y1 data and a line plot with x2 and y2 data. The resulting subplots are displayed using the show() method from the plotly.io module.

Customizing Subplots in Plotly

Once you have created subplots, you can customize them by adding titles, axis labels, legends, and other elements to each plot. For example, the following code adds a title and axis labels to both subplots:

import plotly.graph_objs as go
import plotly.io as pio

fig = go.Figure()

# Add a scatter plot with a title and axis labels
fig.add_trace(go.Scatter(x=x1, y=y1, mode='markers', marker=dict(size=20)))
fig.update_layout(title="Scatter Plot", xaxis_title="X-Axis Label", yaxis_title="Y-Axis Label")

# Add a line plot with a title and axis labels
fig.add_trace(go.Line(x=x2, y=y2))
fig.update_layout(title="Line Plot", xaxis_title="X-Axis Label", yaxis_title="Y-Axis Label")

pio.show(fig)

In this example, we add a title and axis labels to each subplot using the update_layout() method with the appropriate attributes (e.g., title, xaxis_title, etc.). The resulting plots are displayed using the show() method from the plotly.io module.

Combining Multiple Subplots in Plotly

You can also combine multiple subplots into a single plot by using the subplot() method to create a grid layout of subplots. For example, the following code creates three subplots arranged in a 3x1 grid:

import plotly.graph_objs as go
import plotly.io as pio

fig = go.Figure()

# Add a scatter plot to the first subplot
fig.add_trace(go.Scatter(x=x1, y=y1, mode='markers', marker=dict(size=20)))

# Add a line plot to the second subplot
fig.add_trace(go.Line(x=x2, y=y2))

# Add a bar chart to the third subplot
fig.add_trace(go.Bar(x=categories, y=values))

# Create a grid layout of three subplots
fig.update_layout(title="Multiple Subplots", showlegend=False)
fig.update_layout(row_width=[0.35, 0.35, 0.2], row_heights=[None] * 3)

pio.show(fig)

In this example, we use the subplot() method to create a grid layout of three subplots with equal widths and heights using the row_width and row_heights attributes. The resulting plot is displayed using the show() method from the plotly.io module.

Conclusion

Plotly provides an easy way to create interactive data visualizations in Python. With its support for subplots, you can create complex plots with multiple related charts on a single page. By customizing each subplot and combining them into a grid layout, you can easily present your data in a clear and organized manner.