Statistical plot enhancements in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Effect of adding a KDE curve to a histogram

What will be the output of this code snippet that plots a histogram with a KDE curve using matplotlib and seaborn?

Matplotlib

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

data = np.random.normal(loc=0, scale=1, size=1000)

sns.histplot(data, kde=True, color='skyblue')
plt.title('Histogram with KDE curve')
plt.show()

A A histogram with blue bars only, no KDE curve

B A KDE curve only, no histogram bars

C A histogram with blue bars and a smooth KDE curve overlayed in black

D A scatter plot of the data points

💡 Hint

The 'kde=True' parameter adds a smooth curve over the histogram.

The sns.histplot function with kde=True draws both the histogram bars and a smooth kernel density estimate curve on top. The bars are colored skyblue, and the KDE curve is black by default.

❓ data_output

intermediate

Number of bins in a histogram with automatic binning

Given this code, how many bins will the histogram have approximately?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = np.random.uniform(0, 10, 500)
plt.hist(data, bins='auto')
plt.show()

A About 50 bins

B About 20 bins

C About 30 bins

D About 10 bins

💡 Hint

The 'auto' binning method tries to find a good number of bins based on data size and distribution.

The 'auto' option in matplotlib's hist function usually selects a bin count around the square root of the number of data points or uses other heuristics. For 500 points, this is roughly 20 bins.

❓ visualization

advanced

Customizing boxplot whiskers to show 5th and 95th percentiles

Which code snippet correctly creates a boxplot where whiskers extend to the 5th and 95th percentiles instead of the default 1.5 IQR?

A plt.boxplot(data, whis=(5, 95))

B plt.boxplot(data, whis=[0.05, 0.95])

C plt.boxplot(data, whis=(0.05, 0.95))

D plt.boxplot(data, whis=[5, 95])

💡 Hint

The 'whis' parameter accepts percentiles (0-100) when given as a tuple.

To set whiskers at the 5th and 95th percentiles, use whis=(5, 95). Passing values between 0 and 100 in a tuple tells matplotlib to use those percentiles. Passing small fractions like 0.05 will not work as intended.

🔧 Debug

advanced

Identify the error in this violin plot code

What error will this code produce when run?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

data = [np.random.normal(size=100), np.random.normal(loc=2, size=100)]
plt.violinplot(data, positions=[1, 2, 3])
plt.show()

A ValueError: positions length does not match data length

B TypeError: data must be a 1D array

C No error, plot shows correctly

D IndexError: list index out of range

💡 Hint

Check if the number of positions matches the number of datasets.

The violinplot function expects the positions list length to match the number of datasets. Here, data has length 2 but positions has length 3, causing a ValueError.

🚀 Application

expert

Enhancing a scatter plot with marginal histograms

Which code snippet correctly creates a scatter plot with histograms on the top and right margins using matplotlib's gridspec?

A Use plt.subplots with gridspec_kw={'width_ratios':[1,4],'height_ratios':[1,4]} and plot scatter on top-left, histograms on bottom-right and top-right

B Use plt.subplots with gridspec_kw={'width_ratios':[1,4],'height_ratios':[4,1]} and plot scatter on top-right, histograms on bottom-left and top-left

C Use plt.subplots with gridspec_kw={'width_ratios':[4,1],'height_ratios':[4,1]} and plot scatter on bottom-right, histograms on top-right and bottom-left

D Use plt.subplots with gridspec_kw={'width_ratios':[4,1],'height_ratios':[1,4]} and plot scatter on bottom-left, histograms on top-left and bottom-right

💡 Hint

The scatter plot should occupy the larger bottom-left area, histograms on top and right margins.

To create marginal histograms, the figure is divided into a 2x2 grid with width_ratios and height_ratios set so the scatter plot is larger (bottom-left). Histograms go on top-left (top margin) and bottom-right (right margin).

Practice

1. What is the main purpose of adding a legend to a matplotlib plot?

easy

A. To explain what different colors or markers represent

B. To change the plot's background color

C. To save the plot as an image file

D. To remove grid lines from the plot

Solution

1. Step 1: Understand what a legend does

   A legend shows labels for different plot elements like colors or markers.

2. Step 2: Match legend purpose to options

   Only To explain what different colors or markers represent describes explaining plot elements, which is the legend's role.

3. Final Answer:

   To explain what different colors or markers represent -> Option A

4. Quick Check:

   Legend = Explain plot elements [OK]

Hint: Legend explains plot symbols and colors [OK]

Common Mistakes:

• Confusing legend with grid or background settings
• Thinking legend saves the plot
• Assuming legend removes plot elements

2. Which of the following is the correct way to add a title to a matplotlib plot?

easy

A. plt.set_title('My Plot')

B. plt.add_title('My Plot')

C. plt.title('My Plot')

D. plt.plot_title('My Plot')

Solution

1. Step 1: Recall matplotlib title syntax

   The correct function to add a title is plt.title().

2. Step 2: Check options for correct function name

   Only plt.title('My Plot') uses plt.title('My Plot'), which is correct syntax.

3. Final Answer:

   plt.title('My Plot') -> Option C

4. Quick Check:

   Title function = plt.title() [OK]

Hint: Use plt.title() to add plot titles [OK]

Common Mistakes:

• Using incorrect function names like set_title or add_title
• Confusing title with label functions
• Missing parentheses in function call

3. What will the following code display?

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6], marker='o', color='red')
plt.grid(True)
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Sample Plot')
plt.show()

medium

A. A red line plot with circle markers, grid lines, and labeled axes with a title

B. A blue scatter plot without grid lines or labels

C. A red bar chart with no markers or grid

D. An empty plot with only axis labels

Solution

1. Step 1: Analyze plot function and parameters

   The code plots points [1,2,3] vs [4,5,6] with red color and circle markers.

2. Step 2: Check enhancements added

   Grid is enabled, x and y axes are labeled, and a title is set.

3. Final Answer:

   A red line plot with circle markers, grid lines, and labeled axes with a title -> Option A

4. Quick Check:

   Plot with markers, grid, labels, title = A red line plot with circle markers, grid lines, and labeled axes with a title [OK]

Hint: Look for markers, colors, grid, labels in code [OK]

Common Mistakes:

• Confusing plot type (line vs scatter vs bar)
• Ignoring grid or label commands
• Assuming default colors or no markers

4. Identify the error in this code snippet that tries to add a legend:

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], label='Line 1')
plt.legend()
plt.show()

medium

A. The plot function is missing y-values

B. The legend function is called before plot

C. The label parameter is invalid in plot

D. There is no error; the code runs correctly

Solution

1. Step 1: Check plot function parameters

   The plot call has only one list, so it treats it as y-values with x as indices 0,1,2.

2. Step 2: Understand matplotlib behavior

   This is valid syntax; it plots y-values against default x-values. So no error here.

3. Step 3: Re-examine options carefully

   The plot function is missing y-values says missing y-values, but y-values are given. The legend function is called before plot is wrong order. The label parameter is invalid in plot label is valid. There is no error; the code runs correctly says no error.

4. Final Answer:

   There is no error; the code runs correctly -> Option D

5. Quick Check:

   Code runs fine with legend after plot [OK]

Hint: Check if plot syntax matches matplotlib docs [OK]

Common Mistakes:

• Assuming single list plot is invalid
• Thinking legend must come before plot
• Believing label is not accepted in plot

5. You want to create a scatter plot with blue triangles, add grid lines, and label axes as 'Height' and 'Weight'. Which code snippet correctly does this?

hard

A. plt.scatter(x, y, marker='s', color='red')
plt.grid(True)
plt.xlabel('Weight')
plt.ylabel('Height')

B. plt.scatter(x, y, marker='^', color='blue')
plt.grid(True)
plt.xlabel('Height')
plt.ylabel('Weight')

C. plt.plot(x, y, marker='o', color='green')
plt.grid(False)
plt.xlabel('Weight')
plt.ylabel('Height')

D. plt.plot(x, y, marker='^', color='blue')
plt.grid(True)
plt.xlabel('Height')
plt.ylabel('Weight')

Solution

1. Step 1: Identify scatter plot with blue triangles

   Use plt.scatter() with marker='^' and color='blue'.

2. Step 2: Check grid and axis labels

   Grid must be enabled with plt.grid(True), and axes labeled 'Height' and 'Weight' correctly.

3. Step 3: Match code snippet to requirements

   plt.scatter(x, y, marker='^', color='blue') plt.grid(True) plt.xlabel('Height') plt.ylabel('Weight') matches all requirements exactly.

4. Final Answer:

   plt.scatter(x, y, marker='^', color='blue') plt.grid(True) plt.xlabel('Height') plt.ylabel('Weight') -> Option B

5. Quick Check:

   Scatter + blue triangles + grid + correct labels = plt.scatter(x, y, marker='^', color='blue') plt.grid(True) plt.xlabel('Height') plt.ylabel('Weight') [OK]

Hint: Scatter uses plt.scatter(), triangles marker='^', grid True [OK]

Common Mistakes:

• Using plt.plot instead of plt.scatter for scatter plot
• Wrong marker symbol for triangles
• Swapping x and y axis labels
• Forgetting to enable grid lines