How to create a scatter plot with Plotly Graph Objects in Python

Plotly Graph Objects is a Python library that provides a flexible and powerful way to create interactive data visualizations. It is part of the larger Plotly ecosystem, which includes Plotly Express and Plotly.py. Plotly Graph Objects allows you to create and customize various types of charts, plots, and graphs with full control over the visual aspects and interactivity.

Features of the scatter plot in Plotly Graph Objects

The following are some key features of scatter plots using Plotly Graph Objects:

• Data visualization: Scatter plots allow you to display individual data points as markers on a two-dimensional plane. They effectively visualize the distribution, relationships, and patterns within your data.

• Customization options: You can customize various aspects of the scatter plot, including marker colors, sizes, shapes, and opacity. This enables you to differentiate data points and highlight specific trends.

• Hover text: Scatter plots can include hover text that provides additional information about each data point when users hover over them. This feature enhances the interactivity and data exploration experience.

• Subplots: Plotly Graph Objects support the creation of subplots, allowing you to arrange multiple scatter plots (or other types of plots) within a single figure. This is useful for comparing different datasets or visualizing multiple variables simultaneously.

• Trend lines and regression: You can add trend lines, regression lines, or best-fit lines to scatter plots to visualize trends and relationships more clearly.

• Annotations: Scatter plots support annotations, which enable you to add text labels, arrows, or shapes to highlight specific points or regions of interest in the plot.

• Interactive features: Plotly scatter plots are interactive by default. Users can zoom, pan, and hover over data points for detailed information. You can also customize interactivity by adding click events to data points.

• Axis customization: Customize the appearance of axes, including labels, titles, and scales, to provide context and clarity to your scatter plot.

• Layout control: Plotly Graph Objects allow you to control the layout of the entire figure, including titles, legends, and grid settings.

• Export and sharing: You can export scatter plots as static images or interactive web-based visualizations. Plotly provides options for saving plots in various formats and sharing them online.

• Integration: Scatter plots created with Plotly Graph Objects can be easily integrated into web applications, notebooks, dashboards, and reports.

Syntax

The scatter plot syntax typically follows this structure:

Parameters

Following are the key parameters for creating a scatter plot using Plotly Graph Objects:

• x: X-axis data (list or array)

• y: Y-axis data (list or array)

• mode: Determines how data points are displayed ('markers', 'lines', 'lines+markers', 'markers+lines', or 'text')

• name: Name of the trace for legends

• marker: Dictionary for specifying marker style (e.g., color, size, symbol)

• line: Dictionary for specifying line style (e.g., color, width)

• text: Text labels associated with each data point

• hoverinfo: Specifies what information appears on hover ('x', 'y', 'text', 'name', or combinations)

• hovertext: Hover text labels for each data point

• hovertemplate: Template for formatting hover text

• showlegend: Whether to show this trace in legends

• opacity: Opacity of the trace (0 to 1)

• fill: Fill type between points ('none', 'tozeroy', 'tozerox', 'tonexty', 'tonextx', 'tonext')

• fillcolor: Fill color if fill is set

• textposition: Position of text labels relative to markers ('top', 'bottom', 'middle', 'inside', or 'none')

• line_shape: Line shape between points ('linear', 'spline', 'hv', 'vh', 'hvh', 'vhv', or 'linear spline')

Return type

The return type of creating a scatter plot using Plotly Graph Objects in Python is an instance of the plotly.graph_objs._figure.Figure class. This instance represents the complete figure containing the scatter plot, along with any additional traces, layouts, annotations, and configurations you’ve applied to the plot. This return type allows you to further customize and manipulate the plot before displaying or saving it.

Implementation

In the following playground, we create a scatter plot using a sample dataset called Iris, which is provided by Plotly Express. Used attributes (sepal_width and sepal_length) defined as follows:

sepal_length: It represents the length of the sepal, which is the outer part of the flower that protects the petals. It’s typically measured in centimeters.

sepal_width: It represents the width of the sepal, measured in centimeters. It’s the measurement taken at the widest part of the sepal.

Note: The above code generates a Plotly scatter plot illustrating the correlation between sepal width and sepal length in the Iris dataset.

Explanation

The code above is explained in detail below:

• Lines 1–3: Import the necessary modules: plotly.graph_objects for creating custom plots, plotly.express for simplified plotting, and pandas for data manipulation.

• Line 6: Load the Iris dataset using Plotly Express’s built-in sample dataset.

• Line 9: Print the first five rows of the loaded dataset using the head() method to inspect the data.

• Lines 12–17: Create a scatter trace using go.Scatter, specifying the x and y data from the dataset columns sepal_width and sepal_length. The mode is set to 'markers', indicating that individual data points will be plotted as markers. The name is set to Sepal for use in legends.

• Line 20: Create a new figure (go.Figure) and add the previously created scatter trace to it.

• Line 23: Update the layout of the figure by setting its title.

• Line 26: Display the finalized scatter plot figure using the show() method.

Conclusion

Plotly Graph Objects provides a streamlined pathway to crafting interactive scatter plots in Python. Its flexibility and customization options empower users to succinctly visualize data relationships and patterns. The dynamic capabilities of Plotly Graph Objects streamline the process of creating impactful scatter plots, enhancing data exploration and communication.