Why patterns solve common tasks in Matplotlib - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib plotting library.

Matplotlib

import [1] as plt

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

Amatplotlib.pyplot

Bseaborn

Cpandas

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong library like pandas or numpy.

Forgetting to import pyplot specifically.

We import matplotlib.pyplot as plt to create plots easily.

2fill in blank

medium

Complete the code to create a simple line plot of y versus x.

Matplotlib

plt.[1](x, y)
plt.show()

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

Aplot

Bscatter

Chist

Dbar

Attempts:

3 left

💡 Hint

Common Mistakes

Using scatter which draws points but no lines.

Using hist which draws histograms.

The plot function draws a line plot connecting points.

3fill in blank

hard

Fix the error in the code to label the x-axis correctly.

Matplotlib

plt.xlabel([1])

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

Ax_label

B'x label'

Cxlabel

Dx label

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a variable name that is not defined.

Forgetting to put quotes around the label text.

The label text must be a string, so it needs quotes.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths if length is greater than 3.

Matplotlib

{word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong condition in the if clause.

Mapping to the word itself instead of its length.

The dictionary maps each word to its length, but only if the length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts if count is greater than 1.

Matplotlib

{ [1]: [2] for [3], [2] in word_counts.items() if [2] > 1 }

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

Aword.upper()

Bcount

Cword

Dword_counts

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable names in the loop.

Not converting the word to uppercase for the key.

The comprehension maps each word in uppercase to its count, but only if the count is more than 1.

Practice

1. Why do common plotting patterns help when using matplotlib?

easy

A. They make charts harder to read

B. They make plots slower to create

C. They increase the chance of errors

D. They save time by reusing common plotting steps

Solution

1. Step 1: Understand the purpose of patterns

   Patterns are repeated ways to do tasks that save time and effort.

2. Step 2: Connect patterns to plotting

   Using patterns in plotting means reusing steps, which speeds up work and keeps charts clear.

3. Final Answer:

   They save time by reusing common plotting steps -> Option D

4. Quick Check:

   Patterns save time = A [OK]

Hint: Patterns reuse steps to save time and reduce errors [OK]

Common Mistakes:

• Thinking patterns slow down plotting
• Believing patterns cause more errors
• Assuming patterns make charts confusing

2. Which of these is the correct way to create a simple line plot using matplotlib?

easy

A. plt.plot([1, 2, 3], [4, 5, 6])

B. plt.line([1, 2, 3], [4, 5, 6])

C. plt.draw_line([1, 2, 3], [4, 5, 6])

D. plt.graph([1, 2, 3], [4, 5, 6])

Solution

1. Step 1: Recall the basic plotting function

   The main function to plot lines in matplotlib is plt.plot().

2. Step 2: Check the options

   Only plt.plot([1, 2, 3], [4, 5, 6]) uses plt.plot() correctly with two lists for x and y values.

3. Final Answer:

   plt.plot([1, 2, 3], [4, 5, 6]) -> Option A

4. Quick Check:

   Correct function is plt.plot() = C [OK]

Hint: Use plt.plot() for line plots in matplotlib [OK]

Common Mistakes:

• Using non-existent functions like plt.line()
• Confusing function names with plt.draw_line()
• Trying plt.graph() which is not a matplotlib function

3. What will the following code output?

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.title('My Plot')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.show()

medium

A. An error because plt.show() is missing arguments

B. A scatter plot with no labels

C. A line plot with title 'My Plot' and labeled axes

D. A bar chart with default labels

Solution

1. Step 1: Analyze the plot commands

   The code uses plt.plot() which creates a line plot. It sets title and axis labels.

2. Step 2: Understand plt.show()

   plt.show() displays the plot with all settings applied.

3. Final Answer:

   A line plot with title 'My Plot' and labeled axes -> Option C

4. Quick Check:

   plt.plot() + labels + plt.show() = A [OK]

Hint: plt.plot() + plt.show() displays labeled line plot [OK]

Common Mistakes:

• Confusing line plot with scatter plot
• Thinking plt.show() needs arguments
• Assuming default labels appear without setting them

4. Identify the error in this code snippet:

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

medium

A. The x and y lists have different lengths

B. plt.plot() is missing parentheses

C. plt.show() should be called before plt.plot()

D. The import statement is incorrect

Solution

1. Step 1: Check the data lengths

   The x list has 3 elements, but the y list has only 2 elements.

2. Step 2: Understand matplotlib requirements

   For plotting, x and y must have the same length to pair points correctly.

3. Final Answer:

   The x and y lists have different lengths -> Option A

4. Quick Check:

   Unequal list lengths cause error = D [OK]

Hint: Ensure x and y lists have same length for plt.plot() [OK]

Common Mistakes:

• Thinking plt.plot() needs no parentheses
• Calling plt.show() before plotting
• Misunderstanding import syntax

5. You want to create multiple line plots with the same style and labels quickly. Which pattern helps you do this efficiently in matplotlib?

hard

A. Writing separate full code blocks for each plot

B. Using a function to wrap common plotting steps

C. Copy-pasting code and changing only data

D. Plotting without labels to save time

Solution

1. Step 1: Identify the goal

   You want to reuse the same style and labels for many plots quickly.

2. Step 2: Choose the best pattern

   Wrapping common steps in a function lets you reuse code easily and keep consistency.

3. Step 3: Compare other options

   Copy-pasting or writing separate code is slower and error-prone; skipping labels reduces clarity.

4. Final Answer:

   Using a function to wrap common plotting steps -> Option B

5. Quick Check:

   Functions reuse code and keep style = B [OK]

Hint: Wrap repeated plotting steps in a function for reuse [OK]

Common Mistakes:

• Copy-pasting code instead of using functions
• Skipping labels to save time
• Writing full code blocks repeatedly