Why image handling matters in Matplotlib - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for image plotting.

Matplotlib

import [1] as plt

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

Amatplotlib.pyplot

Bpandas

Cseaborn

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy or pandas instead of matplotlib.pyplot.

Using seaborn which is for statistical plots, not basic image plotting.

We use matplotlib.pyplot to plot images and graphs in Python.

2fill in blank

medium

Complete the code to load an image file named 'photo.png' using matplotlib.

Matplotlib

image = plt.[1]('photo.png')

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

Aimread

Bshow

Cplot

Dimshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow which displays but does not load the image.

Using plot which is for line plots, not images.

The imread function reads an image file into an array for processing.

3fill in blank

hard

Fix the error in the code to display the loaded image correctly.

Matplotlib

plt.[1](image)
plt.show()

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

Aplot

Bread

Cimshow

Dload

Attempts:

3 left

💡 Hint

Common Mistakes

Using plot which is for line graphs, not images.

Using read or load which are not matplotlib functions.

The imshow function displays image data on the plot.

4fill in blank

hard

Fill both blanks to create a dictionary with image filenames as keys and their sizes as values.

Matplotlib

sizes = {file: image.[1] for file in files if image.[2] > 1000}

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

Ashape

Bsize

Cndim

Ddtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using ndim which only gives the number of dimensions, not size.

Using dtype which gives data type, not size.

shape gives the dimensions of the image array, and size gives the total number of pixels.

5fill in blank

hard

Fill all three blanks to filter images by width and create a dictionary of their heights.

Matplotlib

heights = {file: image.shape[[1]] for file, image in images.items() if image.shape[[2]] [3] 500}

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

A0

B1

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up height and width indices.

Using less than instead of greater than for filtering.

Index 1 of shape is width, index 0 is height. We filter images with width greater than 500 pixels.

Practice

1. Why is handling images important in data science when using matplotlib?

easy

A. Because images are always small files and easy to process

B. Because images contain visual data that can reveal patterns and insights

C. Because matplotlib can only display images, not analyze them

D. Because images do not require any preprocessing before analysis

Solution

1. Step 1: Understand the role of images in data science

   Images hold visual information that can be analyzed to find patterns, trends, or anomalies.

2. Step 2: Recognize matplotlib's role

   matplotlib helps load and display images, making it easier to explore visual data.

3. Final Answer:

   Because images contain visual data that can reveal patterns and insights -> Option B

4. Quick Check:

   Images = Visual data insights [OK]

Hint: Images hold visual clues; matplotlib helps show them [OK]

Common Mistakes:

• Thinking images are always small and easy to process
• Believing matplotlib only displays but cannot help analyze
• Assuming images need no preprocessing

2. Which of the following is the correct way to load and display an image using matplotlib?

easy

A. import matplotlib.pyplot as plt
img = plt.imread('image.png')
plt.imshow(img)
plt.show()

B. import matplotlib.image as mpimg
img = mpimg.load('image.png')
plt.show(img)

C. import matplotlib.pyplot as plt
img = plt.load_image('image.png')
plt.display(img)

D. import matplotlib.pyplot as plt
img = plt.read('image.png')
plt.plot(img)

Solution

1. Step 1: Identify the correct functions to load and display images

   plt.imread() loads the image, plt.imshow() displays it, and plt.show() renders the plot.

2. Step 2: Check each option's syntax

   import matplotlib.pyplot as plt img = plt.imread('image.png') plt.imshow(img) plt.show() uses the correct functions and order. Others use incorrect or non-existent functions.

3. Final Answer:

   import matplotlib.pyplot as plt img = plt.imread('image.png') plt.imshow(img) plt.show() -> Option A

4. Quick Check:

   Use imread + imshow + show [OK]

Hint: Remember: imread loads, imshow displays, show renders [OK]

Common Mistakes:

• Using non-existent functions like plt.load_image or plt.read
• Confusing plt.show() with plt.display()
• Trying to plot image data with plt.plot()

3. What will be the output type of the variable img after running this code?

import matplotlib.pyplot as plt
img = plt.imread('sample.png')

medium

A. A NumPy array representing the image pixels

B. A file path string to the image

C. A matplotlib figure object

D. A Python list of image filenames

Solution

1. Step 1: Understand what plt.imread() returns

   This function reads an image file and returns its pixel data as a NumPy array.

2. Step 2: Eliminate other options

   The variable is not a string, figure, or list but an array of pixel values.

3. Final Answer:

   A NumPy array representing the image pixels -> Option A

4. Quick Check:

   imread output = NumPy array [OK]

Hint: imread returns pixel data as NumPy array [OK]

Common Mistakes:

• Thinking it returns a file path or string
• Confusing image data with plot objects
• Assuming it returns a list instead of array

4. Identify the error in this code snippet that tries to display an image:

import matplotlib.pyplot as plt
img = plt.imread('photo.jpg')
plt.imshow(img)
plt.show

medium

A. plt.imshow cannot display JPG images

B. Incorrect function to read the image, should use plt.load()

C. Missing parentheses after plt.show to display the image

D. The image file path must be absolute

Solution

1. Step 1: Check the function calls for displaying the image

   plt.show is missing parentheses, so the image will not display.

2. Step 2: Verify other parts of the code

   plt.imread is correct for reading images, plt.imshow works with JPG, and relative paths are allowed if correct.

3. Final Answer:

   Missing parentheses after plt.show to display the image -> Option C

4. Quick Check:

   Always call plt.show() with parentheses [OK]

Hint: plt.show needs () to run and display [OK]

Common Mistakes:

• Forgetting parentheses on plt.show
• Using non-existent plt.load() function
• Thinking JPG images can't be shown
• Assuming file path must be absolute always

5. You want to analyze a set of images for brightness using matplotlib. Which approach correctly prepares the images for analysis?

hard

A. Save images as PNG, then open them in an external editor for brightness analysis

B. Load images with plt.imshow() and directly calculate brightness from the plot

C. Use plt.show() to display images and estimate brightness visually

D. Load images with plt.imread(), convert to grayscale arrays, then calculate average pixel values

Solution

1. Step 1: Understand image data preparation for brightness analysis

   Images must be loaded as arrays, converted to grayscale to simplify brightness calculation.

2. Step 2: Evaluate each option's method

   Load images with plt.imread(), convert to grayscale arrays, then calculate average pixel values correctly loads and processes images for numeric analysis. Others rely on visualization or external tools, not suitable for data science tasks.

3. Final Answer:

   Load images with plt.imread(), convert to grayscale arrays, then calculate average pixel values -> Option D

4. Quick Check:

   Load -> grayscale -> numeric analysis [OK]

Hint: Convert images to grayscale arrays before analysis [OK]

Common Mistakes:

• Trying to analyze brightness from plots or visuals
• Skipping grayscale conversion before calculations
• Relying on external editors instead of code