Pick events for data interaction in Matplotlib - Mini Project: Build & Apply

Pick Events for Data Interaction with Matplotlib

📖 Scenario: You are working with a simple scatter plot showing sales data points for different products. You want to make the plot interactive so that when you click on a point, it shows the product name and sales value.

🎯 Goal: Create a scatter plot with clickable points using Matplotlib's pick events. When a point is clicked, display the product name and sales value in the console.

📋 What You'll Learn

Create a dictionary called sales_data with product names as keys and sales numbers as values

Create a variable called fig, ax to set up the Matplotlib figure and axes

Plot the sales data as a scatter plot with picker=True to enable picking

Define a function called on_pick that prints the product name and sales value when a point is clicked

Connect the pick event to the on_pick function

Display the plot with plt.show()

💡 Why This Matters

🌍 Real World

Interactive plots help users explore data visually by clicking on points to get more details, useful in sales analysis, scientific data, and dashboards.

💼 Career

Data scientists and analysts often create interactive visualizations to communicate insights clearly and allow stakeholders to explore data dynamically.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Apples': 50, 'Bananas': 75, 'Cherries': 30, 'Dates': 90, 'Elderberries': 45.

Matplotlib

# Create the sales_data dictionary with product names and sales values
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with the exact keys and values.

2

Set up the Matplotlib figure and axes

Import matplotlib.pyplot as plt. Create a figure and axes using fig, ax = plt.subplots().

Matplotlib

sales_data = {'Apples': 50, 'Bananas': 75, 'Cherries': 30, 'Dates': 90, 'Elderberries': 45}

# Import matplotlib.pyplot as plt and create fig, ax
# Your code here

Need a hint?

Use import matplotlib.pyplot as plt and then plt.subplots() to create the figure and axes.

3

Plot the scatter plot with picker enabled

Plot the sales data as a scatter plot on ax using ax.scatter(). Use the product indices as x-values and sales values as y-values. Set picker=True to enable picking.

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'Apples': 50, 'Bananas': 75, 'Cherries': 30, 'Dates': 90, 'Elderberries': 45}

fig, ax = plt.subplots()

# Plot scatter with picker=True
# Your code here

Need a hint?

Convert the dictionary keys and values to lists. Use range(len(products)) for x-values and sales for y-values. Enable picking with picker=True.

4

Add pick event handler and show plot

Define a function called on_pick(event) that prints the product name and sales value of the clicked point. Connect this function to the figure's pick event using fig.canvas.mpl_connect('pick_event', on_pick). Finally, call plt.show() to display the plot.

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'Apples': 50, 'Bananas': 75, 'Cherries': 30, 'Dates': 90, 'Elderberries': 45}

fig, ax = plt.subplots()

products = list(sales_data.keys())
sales = list(sales_data.values())
scatter = ax.scatter(range(len(products)), sales, picker=True)
ax.set_xticks(range(len(products)))
ax.set_xticklabels(products)

# Define on_pick function and connect pick event
# Your code here

Need a hint?

Use event.ind[0] to get the index of the clicked point. Print the product and sales using that index. Connect the event with fig.canvas.mpl_connect.