Plotly.express.scatter

3D plots are very useful when you need to introduce interactivity to your figures and provide more visual accuracy.

In this article, you will learn how to create 3-dimensional scatter plots using the plotly express module.

Plotly.Express.Scatter_3d

We use the scatter_3d function to create 3D scatter plots in Plotly. The function syntax is as shown below:

plotly.express.scatter_3d(data_frame=None, x=None, y=None, z=None, color=None, symbol=None, size=None, text=None, hover_name=None, hover_data=None, custom_data=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None, error_z=None, error_z_minus=None, animation_frame=None, animation_group=None, category_orders=None, labels=None, size_max=None, color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, log_x=False, log_y=False, log_z=False, range_x=None, range_y=None, range_z=None, title=None, template=None, width=None, height=None)

The function parameters are as shown:

data_frame – specifies the column names to be used in the plot. You can pass the values a DataFrame, a dictionary, or array_like object.
x, y, z – specifies the values used to mark positions along the x, y, and z axis, respectively.
color – specifies the values used to assign color to marks. You can pass either a column within a DataFrame, array_like object.
symbol – specifies the values used to assign symbols to marks.
size – defines the values used to assign mark sizes.
title – represents the figure title.
Width/height – sets the figure width and height in pixels, respectively.

Example 1

We can create a 3d scatter plot using the scatter_3d function and pass the x, y, and z parameters.

For example, the code below uses the gapminder() data from plotly express to create a simple 3d scatter plot.

import plotly.express as px
df = px.data.gapminder().query("continent=='Europe'")
fig = px.scatter_3d(df, x='year', y='lifeExp', z='gdpPercap')
fig.show()

In this example, we create a data frame using the gapminder() data. This should return a 3D scatter plot as shown:

Example 2

We can assign a unique color to each bubble by setting the color parameter as the country column within the data frame.

An example is as shown:

import plotly.express as px
df = px.data.gapminder().query("continent=='Europe'")
fig = px.scatter_3d(df, x='year', y='lifeExp', z='gdpPercap', color='country')
fig.show()

Here, we assign the color column as country. Plotly will assign a unique color for each plot based on the country.

An example resulting figure is as shown:

Example 3

We can also specify the symbol markers using the symbol parameter as shown:

import plotly.express as px
df = px.data.gapminder().query("continent=='Europe'")
fig = px.scatter_3d(df, x='year', y='lifeExp', z='gdpPercap', color='country', symbol='country')
fig.show()

Setting the symbol parameter allows plotly to assign unique symbols to the values of the plot. For example, in this case, plotly will set a different symbol for each country plot as shown:

Example 4

To set the width and height of the plot, we can set the values as:

import plotly.express as px
df = px.data.gapminder().query("continent=='Europe'")
fig = px.scatter_3d(df, x='year', y='lifeExp', z='gdpPercap', color='country', symbol='country', width=1200, height=800)
fig.show()

Setting custom width and height values allows us to improve visibility of the plots as you have control of the working area.

The resulting figure from the above plot:

You can see we are able to see more data due to the bigger working real-estate.

Conclusion

In this article, you learned how to create 3-dimensional scatter plots by using the scatter_3d function.

Plotly.express.scatter_3d