Axes vs pyplot interface comparison in Matplotlib - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a line plot using pyplot interface.

Matplotlib

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.[1]()

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

Adraw

Bshow

Cplot

Dfigure

Attempts:

3 left

💡 Hint

Common Mistakes

Using plt.draw() instead of plt.show() which does not display the plot window.

Calling plt.figure() which creates a new figure but does not display the plot.

The plt.show() function displays the plot window when using pyplot interface.

2fill in blank

medium

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

Matplotlib

import matplotlib.pyplot as plt
fig, [1] = plt.subplots()
[1].plot([1, 2, 3], [3, 2, 1])
plt.show()

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

Aaxis

Baxes

Cax

Dfigure

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'figure' instead of 'axes' which is the wrong object for plotting.

Using 'axis' which is not the correct variable name returned by plt.subplots().

plt.subplots() returns a figure and axes object; the axes object is commonly named 'ax'.

3fill in blank

hard

Fix the error in the code by choosing the correct method to set the title using the axes object.

Matplotlib

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [3, 2, 1])
ax.[1]('My Plot')
plt.show()

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

Alabel

Btitle

Csetlabel

Dset_title

Attempts:

3 left

💡 Hint

Common Mistakes

Using ax.title() which does not exist for axes objects.

Using ax.label() which is unrelated to titles.

The axes object uses set_title() method to set the plot title.

4fill in blank

hard

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

Matplotlib

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.[1]([1, 2, 3], [4, 5, 6])
ax.[2]('X axis')
ax.set_ylabel('Y axis')
plt.show()

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() for scatter plot.

Using ax.xlabel() which is not a method.

Use ax.scatter() to create scatter plot and ax.set_xlabel() to set x-axis label.

5fill in blank

hard

Fill all three blanks to create a bar chart using pyplot, set title and y label.

Matplotlib

import matplotlib.pyplot as plt
plt.[1](['A', 'B', 'C'], [5, 7, 3])
plt.[2]('Category Counts')
plt.[3]('Count')
plt.show()

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

Abar

Btitle

Cylabel

Dplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using plt.plot() instead of plt.bar() for bar chart.

Using plt.xlabel() instead of plt.ylabel() for y-axis label.

Use plt.bar() for bar chart, plt.title() for title, and plt.ylabel() for y-axis label.

Practice

1. Which statement best describes the difference between the pyplot interface and the Axes interface in matplotlib?

easy

A. Axes is simpler and good for quick plots, while pyplot offers more control for complex figures.

B. pyplot is simpler and good for quick plots, while Axes offers more control for complex figures.

C. Both pyplot and Axes interfaces are equally simple and offer the same control.

D. pyplot is only used for 3D plots, while Axes is for 2D plots.

Solution

1. Step 1: Understand pyplot interface

   The pyplot interface is designed for quick and easy plotting with simple commands.

2. Step 2: Understand Axes interface

   The Axes interface provides detailed control over plot elements, useful for complex figures.

3. Final Answer:

   pyplot is simpler and good for quick plots, while Axes offers more control for complex figures. -> Option B

4. Quick Check:

   Interface complexity = pyplot simple, Axes detailed [OK]

Hint: Remember: pyplot = quick, Axes = control [OK]

Common Mistakes:

• Confusing which interface is simpler
• Thinking both interfaces have same control level
• Assuming pyplot is only for 3D plots

2. Which of the following is the correct way to create a figure and axes using the Axes interface?

easy

A. fig, ax = plt.subplots()

B. ax = plt.figure()

C. fig = plt.axes()

D. plt.plot(), ax = plt.figure()

Solution

1. Step 1: Recall syntax for creating figure and axes

   The correct syntax to create a figure and axes object is fig, ax = plt.subplots().

2. Step 2: Check other options

   plt.figure() creates only a figure, plt.axes() creates axes but not figure, and plt.plot() is for plotting, not creating axes.

3. Final Answer:

   fig, ax = plt.subplots() -> Option A

4. Quick Check:

   Correct Axes creation = plt.subplots() [OK]

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

Common Mistakes:

• Using plt.figure() expecting axes returned
• Confusing plt.axes() with plt.figure()
• Trying to assign plt.plot() to axes

3. What will be the output of this code snippet?

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

medium

A. A plot without title because plt.title() affects only pyplot interface.

B. An error because plt.title() cannot be used with Axes interface.

C. A blank figure with no plot or title.

D. A plot with line connecting points (1,4), (2,5), (3,6) and title 'My Plot'.

Solution

1. Step 1: Understand plotting with Axes

   The ax.plot() draws the line on the axes object.

2. Step 2: Understand plt.title() effect

   plt.title() sets the title on the current axes, so it works here.

3. Final Answer:

   A plot with line connecting points (1,4), (2,5), (3,6) and title 'My Plot'. -> Option D

4. Quick Check:

   Axes plot + plt.title() = plot with title [OK]

Hint: plt.title() sets title on current axes even with Axes interface [OK]

Common Mistakes:

• Thinking plt.title() won't work with Axes interface
• Expecting error due to mixing interfaces
• Assuming no plot will show

4. Identify the error in this code using the Axes interface:

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

medium

A. fig, ax = plt.subplots() is incorrect syntax.

B. ax.set_title() is invalid; should use plt.title() instead.

C. Using plt.plot() instead of ax.plot() causes the plot to appear on a different axes.

D. plt.show() is missing and causes no output.

Solution

1. Step 1: Check plotting method

   plt.plot() plots on the current pyplot axes, not the ax created by plt.subplots().

2. Step 2: Check title setting

   ax.set_title() correctly sets the title on the ax axes.

3. Final Answer:

   Using plt.plot() instead of ax.plot() causes the plot to appear on a different axes. -> Option C

4. Quick Check:

   Plotting on wrong axes = plt.plot() vs ax.plot() [OK]

Hint: Use ax.plot() to plot on specific axes, not plt.plot() [OK]

Common Mistakes:

• Confusing plt.plot() and ax.plot()
• Thinking ax.set_title() is invalid
• Missing plt.show() causing no output

5. You want to create a figure with two side-by-side plots sharing the same y-axis. Which approach using the Axes interface is best?

hard

A. Use fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True) and plot on ax1 and ax2.

B. Call plt.plot() twice and then set plt.sharey(True).

C. Create two separate figures with plt.figure() and plot independently.

D. Use fig, ax = plt.subplots() and plot both lines on the same axes.

Solution

1. Step 1: Understand subplot creation with shared axis

   plt.subplots(1, 2, sharey=True) creates two axes side-by-side sharing the y-axis.

2. Step 2: Plot on each axes separately

   Plotting on ax1 and ax2 allows independent x-data but shared y-axis.

3. Final Answer:

   Use fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True) and plot on ax1 and ax2. -> Option A

4. Quick Check:

   Shared y-axis subplots = plt.subplots with sharey=True [OK]

Hint: Use plt.subplots with sharey=True for shared y-axis plots [OK]

Common Mistakes:

• Trying to share axis with plt.plot() only
• Creating separate figures instead of subplots
• Plotting both lines on one axes when separate plots needed