Why the OO interface matters in Matplotlib - See It in Action

Why the OO interface matters

📖 Scenario: You are working on a small project to visualize sales data for a local store. You want to create a clear and neat line chart showing sales over a week. Using the Object-Oriented (OO) interface of matplotlib helps you control the plot better and avoid confusion when you add more plots later.

🎯 Goal: Build a simple line chart using matplotlib's OO interface to plot sales data over 7 days. Learn how to create a figure and axes, plot data on the axes, and label the chart properly.

📋 What You'll Learn

Create a list called sales with exact values for 7 days

Create a list called days with exact day names

Create a figure and axes using plt.subplots()

Plot the sales data on the axes using ax.plot()

Set the title of the plot using ax.set_title()

Label the x-axis and y-axis using ax.set_xlabel() and ax.set_ylabel()

Display the plot using plt.show()

💡 Why This Matters

🌍 Real World

Visualizing sales data helps store owners understand trends and make better decisions.

💼 Career

Data scientists and analysts often use matplotlib's OO interface to create clear, customizable charts for reports and presentations.

1

Create the sales and days data

Create a list called sales with these exact values: [150, 200, 170, 220, 180, 210, 190]. Also create a list called days with these exact strings: ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"].

Matplotlib

# Create the sales and days lists
# Your code here

Hint

Use square brackets to create lists. Make sure the values and strings match exactly.

2

Create the figure and axes

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

Matplotlib

sales = [150, 200, 170, 220, 180, 210, 190]
days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]

# Import matplotlib.pyplot as plt
# Create figure and axes with plt.subplots()
# Your code here

Hint

Use import matplotlib.pyplot as plt to import. Then call plt.subplots() to get figure and axes.

3

Plot the sales data on the axes

Use ax.plot() to plot days on the x-axis and sales on the y-axis.

Matplotlib

import matplotlib.pyplot as plt

sales = [150, 200, 170, 220, 180, 210, 190]
days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]

fig, ax = plt.subplots()

# Plot days vs sales on ax
# Your code here

Hint

Use ax.plot(x, y) where x is days and y is sales.

4

Add labels, title, and show the plot

Use ax.set_title() to set the title to "Weekly Sales". Use ax.set_xlabel() to label the x-axis as "Day". Use ax.set_ylabel() to label the y-axis as "Sales". Finally, display the plot using plt.show().

Matplotlib

import matplotlib.pyplot as plt

sales = [150, 200, 170, 220, 180, 210, 190]
days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]

fig, ax = plt.subplots()
ax.plot(days, sales)

# Set title, x-label, y-label
# Show the plot
# Your code here

Hint

Use ax.set_title(), ax.set_xlabel(), and ax.set_ylabel() to add labels. Use 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