Highlighting date ranges in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib plotting module.

Matplotlib

import [1].pyplot as plt

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

Apandas

Bnumpy

Cseaborn

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy or pandas instead of matplotlib.

Forgetting to import pyplot submodule.

We import matplotlib.pyplot as plt to create plots.

2fill in blank

medium

Complete the code to create a date range from January 1, 2023 to January 10, 2023 using pandas.

Matplotlib

dates = pd.date_range(start='2023-01-01', end=[1])

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

A'2023-01-10'

B'2023-01-15'

C'2023-01-05'

D'2023-02-01'

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a date outside the desired range.

Using wrong date format.

The end parameter defines the last date in the range. We want January 10, 2023.

3fill in blank

hard

Fix the error in the code to highlight the date range between '2023-01-03' and '2023-01-06' on the plot.

Matplotlib

plt.axvspan(pd.to_datetime('2023-01-03'), [1], color='yellow', alpha=0.3)

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

A'2023-01-06'

Bpd.to_datetime('2023-01-06')

Cdatetime('2023-01-06')

Dpd.date_range('2023-01-06')

Attempts:

3 left

💡 Hint

Common Mistakes

Passing date strings directly without conversion.

Using wrong datetime conversion functions.

The axvspan function needs datetime objects for the start and end. We convert the end date string to datetime with pd.to_datetime.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each date to its value only if the value is greater than 50.

Matplotlib

highlighted = {date: value for date, value in data.items() if value [1] 50 and date [2] pd.to_datetime('2023-01-05')}

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

A>

B<

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators.

Mixing up date and value comparisons.

We want values greater than 50 and dates before January 5, 2023, so use > and <.

5fill in blank

hard

Fill all three blanks to create a plot with highlighted date ranges and labels.

Matplotlib

plt.plot(dates, values)
plt.axvspan([1], [2], color='green', alpha=0.2)
plt.text([3], max(values), 'Highlight', verticalalignment='bottom')
plt.show()

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

Apd.to_datetime('2023-01-02')

Bpd.to_datetime('2023-01-07')

Cpd.to_datetime('2023-01-03')

Dpd.to_datetime('2023-01-05')

Attempts:

3 left

💡 Hint

Common Mistakes

Using dates outside the data range.

Placing label outside the plot area.

The highlighted range is from January 5 to January 7, and the label is placed at January 3.

Practice

1. What does the plt.axvspan(start_date, end_date) function do in matplotlib?

easy

A. It draws a horizontal line at start_date.

B. It highlights a vertical span (date range) on the plot between start_date and end_date.

C. It creates a scatter plot of dates between start_date and end_date.

D. It sets the x-axis limits to start_date and end_date.

Solution

1. Step 1: Understand the function purpose

   plt.axvspan is used to highlight a vertical area on the plot, usually to mark a range on the x-axis.

2. Step 2: Match parameters to behavior

   The parameters start_date and end_date define the start and end of this vertical span on the x-axis, highlighting that date range.

3. Final Answer:

   It highlights a vertical span (date range) on the plot between start_date and end_date. -> Option B

4. Quick Check:

   Highlight date range = plt.axvspan(start_date, end_date) [OK]

Hint: Remember axvspan highlights vertical areas between two x-values [OK]

Common Mistakes:

• Confusing axvspan with line drawing functions
• Thinking it changes axis limits
• Assuming it plots points instead of highlighting

2. Which of the following is the correct syntax to highlight the date range from '2023-01-01' to '2023-01-10' with a semi-transparent red color in matplotlib?

easy

A. plt.axvspan(start='2023-01-01', end='2023-01-10', color='red', alpha=0.5)

B. plt.axvspan('2023-01-01', '2023-01-10', color='red', transparency=0.5)

C. plt.axvspan('2023-01-01', '2023-01-10', color='red', alpha=0.5)

D. plt.axvspan('2023-01-01', '2023-01-10', alpha=50, color='red')

Solution

1. Step 1: Check parameter names and order

   plt.axvspan takes start and end positions as first two arguments, then keyword arguments like color and alpha.

2. Step 2: Validate correct keyword for transparency

   alpha controls transparency and should be between 0 and 1. The value 0.5 means 50% transparent.

3. Final Answer:

   plt.axvspan('2023-01-01', '2023-01-10', color='red', alpha=0.5) -> Option C

4. Quick Check:

   Use alpha for transparency, not transparency keyword [OK]

Hint: Use alpha (0-1) for transparency, not transparency keyword [OK]

Common Mistakes:

• Using 'transparency' instead of 'alpha'
• Passing alpha as 50 instead of 0.5
• Using named parameters start= and end= which are invalid

3. Given the following code, what will be the color and transparency of the highlighted date range?

import matplotlib.pyplot as plt
import pandas as pd

dates = pd.date_range('2023-01-01', periods=5)
values = [10, 20, 15, 25, 30]
plt.plot(dates, values)
plt.axvspan(pd.Timestamp('2023-01-02'), pd.Timestamp('2023-01-04'), color='green', alpha=0.3)
plt.show()

medium

A. A green shaded area with 30% opacity between Jan 2 and Jan 4

B. A green shaded area with 70% opacity between Jan 2 and Jan 4

C. A fully opaque green shaded area between Jan 2 and Jan 4

D. No shaded area will appear because alpha is too low

Solution

1. Step 1: Understand alpha parameter meaning

   Alpha=0.3 means 30% opacity (or 70% transparent), so the color is faint but visible.

2. Step 2: Confirm color and date range

   The color is green, and the highlighted range is from Jan 2 to Jan 4 as specified.

3. Final Answer:

   A green shaded area with 30% opacity between Jan 2 and Jan 4 -> Option A

4. Quick Check:

   alpha=0.3 means 30% opacity, color='green' [OK]

Hint: Alpha 0.3 means 30% opacity, not 70% [OK]

Common Mistakes:

• Confusing alpha as transparency percentage
• Thinking alpha=0.3 means 70% opacity
• Assuming no highlight appears if alpha is low

4. Identify the error in the following code snippet that tries to highlight a date range:

import matplotlib.pyplot as plt
plt.axvspan('2023-01-01', '2023-01-05', color='blue', alpha=0.4)
plt.show()

medium

A. Dates are passed as strings; they should be converted to datetime objects.

B. The alpha value must be an integer, not a float.

C. The color parameter should be 'colour' instead of 'color'.

D. plt.show() is missing and must be added.

Solution

1. Step 1: Check parameter types for axvspan

   plt.axvspan expects numeric or datetime values for start and end. Passing strings directly causes errors.

2. Step 2: Correct usage for dates

   Convert strings to datetime objects using pd.Timestamp or datetime before passing.

3. Final Answer:

   Dates are passed as strings; they should be converted to datetime objects. -> Option A

4. Quick Check:

   axvspan needs datetime, not string dates [OK]

Hint: Convert date strings to datetime objects before axvspan [OK]

Common Mistakes:

• Passing date strings directly without conversion
• Misusing alpha as integer
• Wrong color parameter spelling

5. You have a time series plot of daily sales data from '2023-01-01' to '2023-01-31'. You want to highlight weekends (Saturdays and Sundays) with a light gray transparent background. Which approach correctly applies this using matplotlib?

hard

A. Use plt.fill_between with weekend dates and color='gray', alpha=0.2.

B. Use plt.axvspan once with start='2023-01-01' and end='2023-01-31' and color='gray', alpha=0.2.

C. Plot vertical lines on weekends using plt.axvline with color='gray' and alpha=0.2.

D. Loop through dates, check if weekday is Saturday or Sunday, then use plt.axvspan for each weekend with color='gray' and alpha=0.2.

Solution

1. Step 1: Identify weekend dates in the range

   Loop through each date, check if it is Saturday (weekday=5) or Sunday (weekday=6).

2. Step 2: Highlight each weekend separately

   Use plt.axvspan for each weekend day or range with desired color and transparency.

3. Step 3: Understand why other options fail

   Use plt.axvspan once with start='2023-01-01' and end='2023-01-31' and color='gray', alpha=0.2. highlights entire month, not weekends. Plot vertical lines on weekends using plt.axvline with color='gray' and alpha=0.2. draws lines, not spans. Use plt.fill_between with weekend dates and color='gray', alpha=0.2. is for y-axis fills, not x-axis spans.

4. Final Answer:

   Loop through dates, check if weekday is Saturday or Sunday, then use plt.axvspan for each weekend with color='gray' and alpha=0.2. -> Option D

5. Quick Check:

   Highlight weekends individually with axvspan [OK]

Hint: Highlight each weekend separately with axvspan in a loop [OK]

Common Mistakes:

• Highlighting entire date range instead of weekends
• Using axvline instead of axvspan for spans
• Trying to fill y-axis area instead of x-axis date range