Spearman correlation in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function to calculate Spearman correlation.

SciPy

from scipy.stats import [1]

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

Akendalltau

Bspearmanr

Cpearsonr

Dttest_ind

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'pearsonr' instead of 'spearmanr'.

Using unrelated functions like 'ttest_ind'.

The spearmanr function from scipy.stats calculates Spearman correlation.

2fill in blank

medium

Complete the code to calculate Spearman correlation between two lists x and y.

SciPy

corr, p_value = spearmanr([1], y)

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

Ax

Brange(10)

C[1, 2, 3]

Dy

Attempts:

3 left

💡 Hint

Common Mistakes

Passing 'y' twice.

Passing a range instead of the variable.

You need to pass the first data list x to the spearmanr function.

3fill in blank

hard

Fix the error in the code to correctly calculate Spearman correlation for lists a and b.

SciPy

result = spearmanr(a, [1])

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

Ab

Bc

Ca

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing 'a' twice.

Passing an undefined variable like 'c'.

The second argument must be the second data list b to compute correlation with a.

4fill in blank

hard

Fill both blanks to create a dictionary of Spearman correlations for each pair of columns in data.

SciPy

correlations = {col1: {col2: spearmanr(data[col1], data[1])[0] for col2 in data if col1 [2] col2} for col1 in data}

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

A[col2]

B(col2)

C!= col2

D== col2

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses instead of square brackets for column access.

Using '==' instead of '!=' in the condition.

Use square brackets to access the column col2 in the DataFrame. The condition excludes pairs where columns are the same using !=.

5fill in blank

hard

Fill all three blanks to filter pairs with correlation above 0.5 and create a result dictionary.

SciPy

result = [1]: corr for col1, corr in correlations.items() for col2, corr in corr.items() if corr [2] 0.5 and col1 [3] 'target'}

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

Acol1

B>

C!=

Dcol2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'col1' as the key instead of 'col2'.

Using '<' instead of '>' for filtering.

Using '==' instead of '!=' to exclude 'target'.

The dictionary key should be col2, correlation must be greater than 0.5, and exclude the 'target' column using '!='.

Practice

1. What does the Spearman correlation measure between two variables?

easy

A. The variance of a single variable

B. The strength and direction of a monotonic relationship based on ranks

C. The difference in means of two variables

D. The linear relationship between two variables

Solution

1. Step 1: Understand correlation types

   Spearman correlation measures how two variables move together based on their rank order, not their actual values.

2. Step 2: Compare with other correlations

   Unlike Pearson correlation, which measures linear relationships, Spearman focuses on monotonic relationships using ranks.

3. Final Answer:

   The strength and direction of a monotonic relationship based on ranks -> Option B

4. Quick Check:

   Spearman = monotonic rank correlation [OK]

Hint: Spearman uses ranks, not raw values, to find monotonic links [OK]

Common Mistakes:

• Confusing Spearman with Pearson correlation
• Thinking it measures linear relationships only
• Assuming it compares means or variances

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

easy

A. from scipy.stats import spearmanr

B. import scipy.spearmanr

C. from scipy import spearmanr

D. import spearmanr from scipy.stats

Solution

1. Step 1: Recall scipy.stats module structure

   The Spearman correlation function is inside the stats submodule of scipy, so it must be imported from scipy.stats.

2. Step 2: Check correct import syntax

   The correct Python syntax to import a function is 'from module import function', so 'from scipy.stats import spearmanr' is correct.

3. Final Answer:

   from scipy.stats import spearmanr -> Option A

4. Quick Check:

   Correct import syntax = from scipy.stats import spearmanr [OK]

Hint: Use 'from scipy.stats import spearmanr' to import correctly [OK]

Common Mistakes:

• Trying to import spearmanr directly from scipy
• Using incorrect import syntax like 'import spearmanr from ...'
• Missing the stats submodule in import

3. What is the output of this code snippet?

from scipy.stats import spearmanr
x = [1, 2, 3, 4, 5]
y = [5, 6, 7, 8, 7]
result = spearmanr(x, y)
print(round(result.correlation, 2))

medium

A. 0.5

B. 0.9

C. 1.0

D. 0.82

Solution

1. Step 1: Understand the data ranks

   x is increasing from 1 to 5, y mostly increases but has a slight drop at the end (7 instead of 9), so ranks are similar but not perfect.

2. Step 2: Calculate Spearman correlation

   Spearman correlation measures rank similarity; here it is less than 1 but still strong, about 0.82 after rounding.

3. Final Answer:

   0.82 -> Option D

4. Quick Check:

   Spearman correlation ≈ 0.82 [OK]

Hint: Spearman close to 1 means strong rank similarity [OK]

Common Mistakes:

• Assuming perfect correlation (1.0) without checking data
• Confusing Pearson and Spearman correlation values
• Ignoring the slight rank difference in y

4. Identify the error in this code that calculates Spearman correlation:

from scipy.stats import spearmanr
x = [1, 2, 3]
y = [4, 5]
result = spearmanr(x, y)
print(result.correlation)

medium

A. Missing import for numpy

B. spearmanr function is misspelled

C. Lists x and y have different lengths

D. result.statistic attribute does not exist

Solution

1. Step 1: Check input data lengths

   Spearman correlation requires both input lists to have the same length. Here, x has 3 elements, y has 2.

2. Step 2: Understand function requirements

   Because lengths differ, spearmanr will raise an error. Other options are incorrect as spelling and attribute usage are correct.

3. Final Answer:

   Lists x and y have different lengths -> Option C

4. Quick Check:

   Input length mismatch causes error [OK]

Hint: Ensure input lists have equal length before correlation [OK]

Common Mistakes:

• Ignoring length mismatch of input lists
• Thinking spearmanr needs numpy import explicitly
• Assuming result.statistic is invalid

5. You have two variables with many tied ranks and some missing values (NaNs). Which approach using scipy.stats.spearmanr is best to get a valid Spearman correlation?

hard

A. Use spearmanr with nan_policy='omit' to ignore NaNs and handle ties automatically

B. Replace NaNs with zeros and then use spearmanr without extra parameters

C. Remove all tied values manually before using spearmanr

D. Use spearmanr with nan_policy='raise' to get an error for NaNs

Solution

1. Step 1: Handle missing values properly

   Spearman correlation in scipy can handle NaNs if nan_policy='omit' is set, which ignores NaNs safely.

2. Step 2: Understand tied ranks handling

   Spearman correlation automatically manages tied ranks, so no manual removal is needed.

3. Step 3: Evaluate other options

   Replacing NaNs with zeros or removing tied values manually can bias results; raising errors is not helpful for analysis.

4. Final Answer:

   Use spearmanr with nan_policy='omit' to ignore NaNs and handle ties automatically -> Option A

5. Quick Check:

   nan_policy='omit' + automatic tie handling = Use spearmanr with nan_policy='omit' to ignore NaNs and handle ties automatically [OK]

Hint: Use nan_policy='omit' to skip NaNs safely in spearmanr [OK]

Common Mistakes:

• Replacing NaNs with zeros causing bias
• Trying to remove tied ranks manually
• Not setting nan_policy causing errors