Pick events for data interaction in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output when clicking on a scatter point?

Consider the following matplotlib code that plots points and prints the index of the clicked point.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
points = ax.scatter([1, 2, 3], [4, 5, 6], picker=True)

clicked_indices = []

def on_pick(event):
    ind = event.ind[0]
    clicked_indices.append(ind)
    print(f"Clicked point index: {ind}")

fig.canvas.mpl_connect('pick_event', on_pick)
plt.show()

If the user clicks on the point at (2, 5), what will be printed?

Matplotlib

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
points = ax.scatter([1, 2, 3], [4, 5, 6], picker=True)

clicked_indices = []

def on_pick(event):
    ind = event.ind[0]
    clicked_indices.append(ind)
    print(f"Clicked point index: {ind}")

fig.canvas.mpl_connect('pick_event', on_pick)
plt.show()

A Clicked point index: 0

B Clicked point index: 1

C Clicked point index: 2

D No output, because picker=True is not enough

💡 Hint

Remember that the points are indexed in the order they are given in the scatter call.

The points are plotted in order: (1,4) index 0, (2,5) index 1, (3,6) index 2. Clicking on (2,5) triggers the pick event with index 1.

❓ data_output

intermediate

What data is available in the pick event?

Given a matplotlib pick event handler:

def on_pick(event):
    print(event.artist)
    print(event.mouseevent.xdata, event.mouseevent.ydata)
    print(event.ind)

What will event.ind contain when picking a scatter plot?

Matplotlib

def on_pick(event):
    print(event.artist)
    print(event.mouseevent.xdata, event.mouseevent.ydata)
    print(event.ind)

A The matplotlib figure object

B The x and y coordinates of the picked point

C A list of indices of the picked points

D The color of the picked point

💡 Hint

Check the documentation for pick_event attributes.

For scatter plots, event.ind is a list of indices of the points that were picked.

❓ visualization

advanced

Which code produces a plot that highlights picked points?

Which option correctly updates the color of a picked scatter point to red when clicked?

Matplotlib

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
sc = ax.scatter([1, 2, 3], [4, 5, 6], picker=True)

colors = ['blue', 'blue', 'blue']
sc.set_facecolors(colors)

def on_pick(event):
    ind = event.ind[0]
    colors[ind] = 'red'
    sc.set_facecolors(colors)
    fig.canvas.draw()

fig.canvas.mpl_connect('pick_event', on_pick)
plt.show()

A Update colors but do not assign back to sc.set_facecolors(colors)

B Change colors[ind] to 'green' but forget to call fig.canvas.draw()

C Use sc.set_color(colors) instead of sc.set_facecolors(colors)

D The code above

💡 Hint

Remember to update the scatter plot's facecolors and redraw the canvas.

Only option D correctly updates the facecolors list, assigns it back, and redraws the canvas to show the change.

🔧 Debug

advanced

Why does this pick event handler not work?

Consider this code snippet:

fig, ax = plt.subplots()
points = ax.scatter([1, 2, 3], [4, 5, 6])

def on_pick(event):
    print(event.ind)

fig.canvas.mpl_connect('pick_event', on_pick)
plt.show()

Why does clicking on points not print anything?

A Because picker=True is missing in scatter, so points are not pickable

B Because the event handler function is not connected properly

C Because event.ind does not exist for scatter plots

D Because plt.show() must be called before connecting the event

💡 Hint

Check the scatter call parameters.

Without picker=True, matplotlib does not generate pick events for the scatter points.

🚀 Application

expert

How to implement a pick event that toggles point visibility?

You want to click on scatter points to toggle their visibility on the plot. Which code snippet correctly implements this behavior?

Matplotlib

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
sc = ax.scatter([1, 2, 3], [4, 5, 6], picker=True)

visible = [True, True, True]

colors = ['blue', 'blue', 'blue']
sc.set_facecolors(colors)

def on_pick(event):
    ind = event.ind[0]
    visible[ind] = not visible[ind]
    colors[ind] = 'blue' if visible[ind] else 'none'
    sc.set_facecolors(colors)
    fig.canvas.draw()

fig.canvas.mpl_connect('pick_event', on_pick)
plt.show()

A The code above toggles visibility by changing facecolors to 'none' and back

B Change the alpha value of the point to 0 or 1 but forget to redraw the canvas

C Remove the point from the scatter data arrays and redraw

D Toggle visibility by setting the point size to 0 or original size but do not update scatter

💡 Hint

Changing facecolors to 'none' hides points without changing data arrays.

Option A correctly toggles visibility by changing facecolors and redrawing the canvas, which is efficient and simple.