Before-after comparison plots in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the plotting library needed for before-after comparison plots.

Matplotlib

import [1] as plt

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

Anumpy

Bseaborn

Cpandas

Dmatplotlib.pyplot

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy or pandas instead of matplotlib.pyplot.

Using seaborn without importing matplotlib.pyplot.

We use matplotlib.pyplot to create plots including before-after comparison plots.

2fill in blank

medium

Complete the code to create a figure and axis for plotting before-after data.

Matplotlib

fig, [1] = plt.subplots()

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

Afigure

Baxis

Cplot

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'figure' as the second variable instead of 'axis'.

Using 'plot' or 'data' which are not returned by subplots.

The subplots() function returns a figure and an axis object. The axis is used to draw plots.

3fill in blank

hard

Fix the error in the code to plot before and after values as connected points.

Matplotlib

axis.plot(['Before', 'After'], [before_value, after_value], marker=[1])

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

Ao

B'x'

C'o'

D'marker'

Attempts:

3 left

💡 Hint

Common Mistakes

Using marker without quotes causing a NameError.

Using an invalid marker string.

The marker style must be a string like 'o' to show points on the plot.

4fill in blank

hard

Fill both blanks to add labels and a title to the before-after plot.

Matplotlib

axis.set_xlabel([1])
axis.set_title([2])

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

A'Time Point'

B'Value Change'

C'Before and After Comparison'

D'Measurement'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the label and title texts.

Using unrelated labels or titles.

The x-axis label should describe the time points, and the title should describe the plot purpose.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each time point to its value if the value increased.

Matplotlib

result = [1]: [2] for [3] in ['Before', 'After'] if values[[3]] > values['Before']

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

A'time'

Bvalues[time]

Ctime

D'value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using string literals instead of variables for keys or values.

Using incorrect variable names.

The comprehension creates a dictionary with keys as time points and values as their corresponding values if they increased from 'Before'.

Practice

1. What is the main purpose of a before-after comparison plot in matplotlib?

easy

A. To visually compare data from two different time points

B. To show the distribution of a single dataset

C. To display the correlation between two variables

D. To create a 3D surface plot

Solution

1. Step 1: Understand the concept of before-after plots

   Before-after plots are used to compare data points from two different times or conditions to see changes.

2. Step 2: Identify the correct purpose

   Among the options, only To visually compare data from two different time points describes comparing data from two time points, which matches the before-after plot purpose.

3. Final Answer:

   To visually compare data from two different time points -> Option A

4. Quick Check:

   Before-after plots = compare two time points [OK]

Hint: Before-after plots compare two sets of data visually [OK]

Common Mistakes:

• Confusing before-after plots with distribution plots
• Thinking they show correlation instead of change
• Assuming they create 3D plots

2. Which of the following is the correct way to plot two sets of data side-by-side for before-after comparison using matplotlib?

easy

A. plt.plot(before_data); plt.plot(after_data)

B. plt.bar([0,1], before_data); plt.bar([0,1], after_data)

C. plt.bar([0,1], before_data); plt.bar([1,2], after_data)

D. plt.bar([1,2], [before_data, after_data])

Solution

1. Step 1: Understand bar plot positioning

   To show before and after side-by-side, bars must not overlap. Using different x positions for before and after data avoids overlap.

2. Step 2: Analyze options for correct bar positions

   plt.bar([0,1], before_data); plt.bar([1,2], after_data) places before_data at positions 0 and 1, and after_data at 1 and 2, so bars for the same category are side-by-side without overlap.

3. Final Answer:

   plt.bar([0,1], before_data); plt.bar([1,2], after_data) -> Option C

4. Quick Check:

   Side-by-side bars need different x positions [OK]

Hint: Use different x positions to avoid bar overlap [OK]

Common Mistakes:

• Plotting bars at same x positions causing overlap
• Using plt.plot instead of plt.bar for categorical data
• Passing data incorrectly as list of lists

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

import matplotlib.pyplot as plt
before = [5, 7]
after = [8, 6]
plt.plot([1, 2], before, label='Before')
plt.plot([1, 2], after, label='After')
plt.legend()
plt.show()

medium

A. An error because plt.plot cannot take two lists

B. A bar chart comparing before and after data

C. A scatter plot with points at (1,5), (2,7), (1,8), (2,6)

D. Two overlapping line plots showing before and after data

Solution

1. Step 1: Understand plt.plot with x and y lists

   plt.plot([1, 2], before) plots points (1,5) and (2,7) connected by a line. Similarly for after data.

2. Step 2: Identify plot type and legend

   Two line plots will appear overlapping on the same axes with labels 'Before' and 'After'. No error occurs.

3. Final Answer:

   Two overlapping line plots showing before and after data -> Option D

4. Quick Check:

   plt.plot with x,y lists = line plot [OK]

Hint: plt.plot(x, y) draws lines connecting points [OK]

Common Mistakes:

• Thinking plt.plot creates bar charts
• Expecting scatter plot without plt.scatter
• Assuming plt.plot with two lists causes error

4. Identify the error in this code for before-after bar plot:

import matplotlib.pyplot as plt
before = [3, 4]
after = [5, 6]
plt.bar([0, 1], before)
plt.bar([0, 1], after)
plt.show()

medium

A. plt.show() is missing

B. Bars for before and after overlap at same positions

C. before and after lists must be same length

D. plt.bar requires three arguments

Solution

1. Step 1: Check bar positions

   Both before and after bars are plotted at positions 0 and 1, causing them to overlap and hide one another.

2. Step 2: Identify correct fix

   To avoid overlap, after bars should be shifted to different x positions, e.g., [0.3, 1.3].

3. Final Answer:

   Bars for before and after overlap at same positions -> Option B

4. Quick Check:

   Same x positions cause bar overlap [OK]

Hint: Shift bars on x-axis to avoid overlap [OK]

Common Mistakes:

• Thinking plt.bar needs 3 arguments
• Ignoring bar overlap issue
• Assuming plt.show() is missing

5. You have sales data before and after a marketing campaign for 3 products: before = [100, 150, 200], after = [120, 180, 210]. How would you create a clear before-after bar plot with labels and legend in matplotlib?

hard

A. Use plt.bar with shifted x positions for before and after, add labels and legend

B. Plot before and after using plt.plot without labels

C. Use plt.scatter for both datasets on same x positions

D. Plot only after data as a bar chart

Solution

1. Step 1: Plan bar positions and labels

   To compare before and after clearly, plot bars side-by-side with shifted x positions, e.g., before at [0,1,2], after at [0.3,1.3,2.3]. Add x-axis labels for products.

2. Step 2: Add legend and labels for clarity

   Use plt.legend() to distinguish before and after bars, and plt.xlabel/plt.ylabel for axis labels.

3. Final Answer:

   Use plt.bar with shifted x positions for before and after, add labels and legend -> Option A

4. Quick Check:

   Shift bars + labels + legend = clear before-after plot [OK]

Hint: Shift bars and add legend for clear comparison [OK]

Common Mistakes:

• Plotting bars at same positions causing confusion
• Skipping labels and legend
• Using scatter plot instead of bar plot