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Interactive Dashboards and Data Apps with Plotly and Dash

Creating Multiple Scatter Traces in a Single Plot

Explore how to build multiple scatter traces in a single Plotly plot using both graph_objects and Plotly Express. Learn to filter data subsets and customize line and marker styles for clearer comparisons across categories. This lesson helps you decide between ease of use and customization when creating interactive scatterplots in data apps.

We'll cover the following...

We will mostly be focusing on using Plotly Express as much as possible because of its convenience. It’s still very important to know how to work with Figure objects since we will encounter many situations in which we’ll need to work with them, especially when we have a lot of customizations to make. Also, keep in mind that, although the most important chart types are supported by Plotly Express, not all of them are.

Creating traces using the graph_objects module

Let’s extend the preceding chart with traces of other countries and compare the two approaches. We start with the graph_objects module’s Figure object.

C++
# create list of countries to fiter
countries = ['Argentina', 'Mexico', 'Brazil']
# create a subset of poverty
df = (poverty
    [poverty['Country Name'].isin(countries)]
    [['year','Country Name', perc_pov_19]]
    .dropna())
# create Figure object
fig = go.Figure()
# Add a trace for each country
for country in countries:
    df_country = df[df['Country Name']==country]
# Add another trace using add_scater
fig.add_scatter(x=df_country['year'],
    y=df_country[perc_pov_19],
    name=country,
    mode='markers+lines')
# Add a title for y-axis
fig.layout.yaxis.title = perc_pov_19
fig.show()
  • Line 2: We create a countries list to filter with.
  • Lines 5–8: We create a subset of poverty, which we will call df, in which the values of the Country Name column are in the countries list (using the isin method). We then extract the year, Country Name, and
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