Why the OO interface matters in Matplotlib - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a figure and axes using the object-oriented interface.

Matplotlib

fig, ax = plt.[1]()

Drag options to blanks, or click blank then click option'

Afigure

Bshow

Cplot

Dsubplots

Attempts:

3 left

💡 Hint

Common Mistakes

Using plt.figure() returns only the figure, not axes.

Using plt.plot() creates a plot but does not return figure and axes objects.

Using plt.show() just displays the plot and does not create objects.

The plt.subplots() function creates a figure and axes objects for plotting using the OO interface.

2fill in blank

medium

Complete the code to plot data using the axes object in the OO interface.

Matplotlib

ax.[1](x, y)

Drag options to blanks, or click blank then click option'

Aplot

Bfigure

Cshow

Dsubplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using ax.figure() does not plot data.

Using ax.show() only displays the plot but does not plot data.

Using ax.subplot() is not a valid method.

Using ax.plot() plots the data on the axes object in the OO interface.

3fill in blank

hard

Fix the error in setting the title using the OO interface.

Matplotlib

ax.[1]('My Plot Title')

Drag options to blanks, or click blank then click option'

Atitle

Blabel

Cset_title

Dsetlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Using ax.title() causes an error because it does not exist.

Using ax.setlabel() or ax.label() are invalid for titles.

The correct method to set the title in the OO interface is set_title().

4fill in blank

hard

Fill both blanks to create a scatter plot and set x and y labels using the OO interface.

Matplotlib

ax.[1](x, y)
ax.[2]('X axis')
ax.set_ylabel('Y axis')

Drag options to blanks, or click blank then click option'

Ascatter

Bset_xlabel

Cplot

Dxlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Using ax.plot() instead of ax.scatter() changes the plot type.

Using ax.xlabel() instead of ax.set_xlabel() causes an error.

Use ax.scatter() to create a scatter plot and ax.set_xlabel() to set the x-axis label in the OO interface.

5fill in blank

hard

Fill all three blanks to create a bar chart, set the title, and display the plot using the OO interface.

Matplotlib

fig, ax = plt.subplots()
ax.[1](categories, values)
ax.[2]('Category Counts')
plt.[3]()

Drag options to blanks, or click blank then click option'

Abar

Bset_title

Cshow

Dplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using ax.plot() instead of ax.bar() changes the chart type.

Using ax.title() instead of ax.set_title() causes an error.

Forgetting plt.show() means the plot won't display.

Use ax.bar() to create a bar chart, ax.set_title() to set the title, and plt.show() to display the plot.

Practice

1.

Why is the Object-Oriented (OO) interface in matplotlib preferred for creating multiple plots?

easy

A. It uses less memory than the pyplot interface.

B. It allows better control and customization of each plot separately.

C. It automatically creates plots without any code.

D. It requires fewer lines of code for simple plots.

Solution

1. Step 1: Understand the OO interface purpose

   The OO interface lets you create and manage plot objects like figures and axes directly.

2. Step 2: Compare with pyplot interface

   Unlike pyplot, OO interface lets you control each plot independently, which is useful for multiple plots.

3. Final Answer:

   It allows better control and customization of each plot separately. -> Option B

4. Quick Check:

   OO interface = better control [OK]

Hint: OO interface = control each plot object separately [OK]

Common Mistakes:

• Thinking OO interface is only for fewer lines of code
• Believing it automatically creates plots
• Confusing memory use with interface choice

2.

Which of the following is the correct way to create a figure and axes using the OO interface in matplotlib?

import matplotlib.pyplot as plt
# Fill in the blank
fig, ax = _____

easy

A. plt.plot()

B. plt.figure()

C. plt.subplots()

D. plt.show()

Solution

1. Step 1: Recall OO interface syntax

   The OO interface uses plt.subplots() to create a figure and axes objects together.

2. Step 2: Check other options

   plt.figure() creates only a figure, plt.plot() plots data, and plt.show() displays plots.

3. Final Answer:

   plt.subplots() -> Option C

4. Quick Check:

   OO interface creation = plt.subplots() [OK]

Hint: Use plt.subplots() to get figure and axes [OK]

Common Mistakes:

• Using plt.figure() expecting axes returned
• Confusing plt.plot() with creating axes
• Calling plt.show() before creating plots

3.

What will be the output of the following code snippet?

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
ax.set_title('My Plot')
print(type(ax))

medium

A. <class 'matplotlib.axes._subplots.AxesSubplot'>

B. <class 'matplotlib.figure.Figure'>

C. <class 'list'>

D. SyntaxError

Solution

1. Step 1: Identify the object returned by plt.subplots()

   It returns a tuple: a Figure object and an AxesSubplot object.

2. Step 2: Check the type of ax

   ax is the axes object, specifically an instance of AxesSubplot.

3. Final Answer:

   <class 'matplotlib.axes._subplots.AxesSubplot'> -> Option A

4. Quick Check:

   ax type = AxesSubplot [OK]

Hint: ax from plt.subplots() is AxesSubplot object [OK]

Common Mistakes:

• Thinking ax is a Figure object
• Expecting a list type
• Confusing syntax error with correct code

4.

Identify the error in this code using the OO interface:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
plt.title('Wrong Title')
plt.show()

medium

A. Missing plt.show() to display the plot.

B. The plt.subplots() call is incorrect and should be plt.figure().

C. The ax.plot() method does not exist in the OO interface.

D. Using plt.title() instead of ax.set_title() is not the proper OO interface usage.

Solution

1. Step 1: Understand title setting in OO interface

   In OO interface, titles must be set on the axes object using ax.set_title().

2. Step 2: Identify misuse of pyplot title

   Using plt.title() relies on pyplot's state tracking, which mixes interfaces and is not recommended for OO usage.

3. Final Answer:

   Using plt.title() instead of ax.set_title() is not the proper OO interface usage. -> Option D

4. Quick Check:

   OO interface title = ax.set_title() [OK]

Hint: Set title on ax, not plt, in OO interface [OK]

Common Mistakes:

• Using plt.title() with OO interface axes
• Confusing plt.subplots() with plt.figure()
• Forgetting plt.show() to display plot

5.

You want to create two side-by-side plots with different data and titles using the OO interface. Which code snippet correctly achieves this?

import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2)
axs[0].plot([1, 2, 3], [4, 5, 6])
axs[0].set_title('First Plot')
axs[1].plot([10, 20, 30], [40, 50, 60])
axs[1].set_title('Second Plot')
plt.show()

hard

A. This code correctly creates two separate plots with individual titles.

B. The code will raise an error because axs is not iterable.

C. The titles will not appear because set_title() is not valid for axs elements.

D. plt.show() is missing, so plots will not display.

Solution

1. Step 1: Understand subplots with multiple axes

   plt.subplots(1, 2) returns a figure and an array of two axes objects.

2. Step 2: Check plotting and title setting on each axes

   Each axes in axs can be used to plot data and set titles independently.

3. Step 3: Confirm plt.show() usage

   plt.show() is called to display the figure with both plots.

4. Final Answer:

   This code correctly creates two separate plots with individual titles. -> Option A

5. Quick Check:

   Multiple axes = individual control [OK]

Hint: Use axs[index] to control each subplot separately [OK]

Common Mistakes:

• Assuming axs is not iterable
• Trying to set title on figure instead of axes
• Forgetting plt.show() to display plots