Dendrogram visualization in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function needed to create a dendrogram.

SciPy

from scipy.cluster.hierarchy import [1]

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

Adendrogram

Blinkage

Cfcluster

Dcophenet

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'linkage' instead of 'dendrogram'.

Trying to import from the wrong module.

The dendrogram function is used to visualize hierarchical clustering results as a tree diagram.

2fill in blank

medium

Complete the code to perform hierarchical clustering on data using the linkage method.

SciPy

Z = linkage(data, method=[1])

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

A'average'

B'complete'

C'single'

D'ward'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'single' which can produce chaining effect.

Using 'average' which is less common for dendrograms.

The 'ward' method minimizes the variance within clusters and is commonly used for dendrograms.

3fill in blank

hard

Fix the error in the code to plot the dendrogram correctly.

SciPy

plt.figure(figsize=(10, 5))
dendrogram([1])
plt.show()

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

Adata

BZ

Clinkage

Ddendrogram

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw data instead of linkage matrix.

Passing the dendrogram function itself.

The dendrogram function requires the linkage matrix Z as input, not the original data.

4fill in blank

hard

Fill both blanks to create a dendrogram with labels and a color threshold.

SciPy

dendrogram([1], labels=[2], color_threshold=1.5)

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

AZ

Blabels

Cdata

Dlinkage

Attempts:

3 left

💡 Hint

Common Mistakes

Passing raw data instead of linkage matrix.

Passing data instead of labels for the labels argument.

The first argument is the linkage matrix Z, and labels is the list of labels for the leaves.

5fill in blank

hard

Fill all three blanks to create and plot a dendrogram with orientation and truncate mode.

SciPy

plt.figure(figsize=(8, 6))
dendrogram([1], orientation=[2], truncate_mode=[3])
plt.show()

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

AZ

B'top'

C'lastp'

D'right'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong orientation like 'right' which changes the dendrogram direction.

Not using truncate_mode or using an invalid value.

The linkage matrix Z is plotted with orientation 'top' and truncate_mode 'lastp' to show the last p merged clusters.

Practice

1. What is the main purpose of a dendrogram in data science?

easy

A. To visualize hierarchical clustering as a tree

B. To perform linear regression analysis

C. To calculate the mean of a dataset

D. To create a scatter plot of two variables

Solution

1. Step 1: Understand dendrogram function

   A dendrogram is used to show hierarchical clustering results visually as a tree structure.

2. Step 2: Compare with other options

   The other options describe different data analysis or visualization methods unrelated to dendrograms.

3. Final Answer:

   To visualize hierarchical clustering as a tree -> Option A

4. Quick Check:

   Dendrogram = hierarchical clustering tree [OK]

Hint: Dendrograms always show clusters as tree diagrams [OK]

Common Mistakes:

• Confusing dendrogram with scatter plot
• Thinking dendrogram calculates statistics
• Mixing dendrogram with regression plots

2. Which of the following is the correct way to import the dendrogram function from scipy?

easy

A. from scipy.visualization import dendrogram

B. import scipy.dendrogram

C. import dendrogram from scipy.cluster

D. from scipy.cluster.hierarchy import dendrogram

Solution

1. Step 1: Recall correct import syntax

   The dendrogram function is located in scipy.cluster.hierarchy, so the correct import is from scipy.cluster.hierarchy import dendrogram.

2. Step 2: Check other options for syntax errors

   The other options use incorrect module paths or invalid import syntax.

3. Final Answer:

   from scipy.cluster.hierarchy import dendrogram -> Option D

4. Quick Check:

   Correct import path = from scipy.cluster.hierarchy import dendrogram [OK]

Hint: Remember dendrogram is in scipy.cluster.hierarchy [OK]

Common Mistakes:

• Using wrong module path
• Incorrect import syntax
• Assuming dendrogram is in scipy.visualization

3. Given the following code, what will be the output type of dn?

from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np

X = np.array([[1, 2], [3, 4], [5, 6]])
Z = linkage(X, 'single')
dn = dendrogram(Z)

medium

A. A NumPy array of cluster labels

B. A dictionary containing dendrogram data

C. A matplotlib figure object

D. A list of linkage distances

Solution

1. Step 1: Understand dendrogram return value

   The dendrogram function returns a dictionary with keys like 'icoord', 'dcoord', 'leaves', and 'color_list' describing the dendrogram structure.

2. Step 2: Check other options

   A NumPy array of cluster labels is incorrect because cluster labels are not returned by dendrogram. A matplotlib figure object is wrong because dendrogram does not return a figure object. A list of linkage distances is incorrect as linkage distances are part of the linkage matrix, not dendrogram output.

3. Final Answer:

   A dictionary containing dendrogram data -> Option B

4. Quick Check:

   dendrogram() returns dict = A dictionary containing dendrogram data [OK]

Hint: dendrogram() returns a dict with plotting info [OK]

Common Mistakes:

• Expecting dendrogram to return a plot object
• Confusing dendrogram output with linkage matrix
• Thinking dendrogram returns cluster labels

4. Identify the error in this code snippet for plotting a dendrogram:

from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt

X = [[1, 2], [3, 4], [5, 6]]
Z = linkage(X, 'ward')
dendrogram(Z)
plt.show()

medium

A. Linkage method 'ward' is invalid

B. Missing import for numpy

C. No error; code runs and plots dendrogram correctly

D. X should be a NumPy array, not a list

Solution

1. Step 1: Check data input type

   Linkage accepts array-like input, so a Python list of lists is valid for X.

2. Step 2: Verify linkage method and plotting

   'ward' is a valid linkage method. The code imports matplotlib.pyplot as plt and calls plt.show(), so the dendrogram will plot correctly.

3. Final Answer:

   No error; code runs and plots dendrogram correctly -> Option C

4. Quick Check:

   List input and 'ward' method are valid [OK]

Hint: Linkage accepts lists; 'ward' is valid method [OK]

Common Mistakes:

• Assuming input must be NumPy array
• Thinking 'ward' is invalid linkage method
• Forgetting plt.show() to display plot

5. You want to visualize clusters with different colors in a dendrogram using scipy.cluster.hierarchy.dendrogram. Which parameter should you set to control the color threshold for cluster coloring?

hard

A. color_threshold

B. linkage_method

C. leaf_rotation

D. distance_metric

Solution

1. Step 1: Identify parameter for cluster color control

   The parameter color_threshold in dendrogram controls the threshold distance to color clusters differently.

2. Step 2: Eliminate unrelated parameters

   linkage_method and distance_metric relate to clustering, not coloring. leaf_rotation controls label rotation, not colors.

3. Final Answer:

   color_threshold -> Option A

4. Quick Check:

   Cluster colors controlled by color_threshold [OK]

Hint: Use color_threshold to set cluster color boundaries [OK]

Common Mistakes:

• Confusing color_threshold with linkage method
• Using leaf_rotation to change colors
• Assuming distance_metric affects dendrogram colors