Mastering Plotly and Dash Data Visualization with Python/

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Exercise: Continuous vs. Categorical Bivariate Analysis

Exercise: Continuous vs. Categorical Bivariate Analysis

Practice with some lab exercises.

We'll cover the following...

Exercise 1

Create a bar chart in Plotly graph objects that detail the revenue per math (in thousands of Euros) for each of the top 10 leagues. We’ll use the sport_organisation_figures.csv dataset.

Python 3.5
# Prerequisite
sports = pd.read_csv('/usr/local/csvfiles/sport_organisation_figures.csv')
top_leagues = sports.iloc[:11, :].copy()

Solution

  • The below code snippet creates a bar chart using the go.Bar() function on line 2 from the plotly.graph_objs module.

  • The x-axis represents the League column of the top_leagues DataFrame, and the y-axis represents the Revenue per match (in thousands of euros) column.

  • A new figure object is created using the go.Figure() function on line 6 with the trace as its data. Finally, the show() function is called to display the generated bar chart.

Python 3.5
# Create trace
trace = go.Bar(x=top_leagues['League'], 
               y=top_leagues['Revenue per match (in thousands of euros)'])
# Add trace to figure and show
fig = go.Figure(data=[trace])
fig.show()

Exercise 2

Using Plotly graph ...