Pearson correlation in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function for Pearson correlation from scipy.stats.

SciPy

from scipy.stats import [1]

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

Apearson

Bpearson_corr

Ccorrelation

Dpearsonr

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'pearson' instead of 'pearsonr'.

Trying to import 'correlation' which does not exist.

Using 'pearson_corr' which is not a valid function.

The function to calculate Pearson correlation in scipy.stats is pearsonr.

2fill in blank

medium

Complete the code to calculate the Pearson correlation coefficient between lists x and y.

SciPy

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

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

Adata_x

Blist_x

Cx

Dvalues_x

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a variable that does not exist like 'data_x'.

Passing the second list 'y' twice.

Using a wrong variable name.

The variable holding the first list is named x, so it should be passed to pearsonr.

3fill in blank

hard

Fix the error in the code to correctly calculate the Pearson correlation coefficient.

SciPy

corr, p_value = pearsonr(x, [1])

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

Ax

By

Cdata

Dz

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the same list twice.

Passing a variable that does not exist.

Swapping the order of arguments incorrectly.

The second argument to pearsonr should be the second data list y.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 3 characters.

SciPy

{word: [1] for word in words if [2]

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the word variable in the condition instead of its length.

Using incorrect comparison operators.

The dictionary comprehension uses len(word) as the value and filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 4 characters.

SciPy

{ [1]: [2] for word in words if [3] }

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

Aword.upper()

Blen(word)

Clen(word) > 4

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Using the word itself instead of its length for values.

Incorrect filter condition.

The dictionary keys are uppercase words using word.upper(), values are lengths with len(word), and the filter keeps words longer than 4 characters with len(word) > 4.

Practice

1. What does the Pearson correlation coefficient measure between two variables?

easy

A. The frequency of values in each variable

B. The difference in means between two groups

C. The total sum of values in each variable

D. The strength and direction of a linear relationship

Solution

1. Step 1: Understand Pearson correlation purpose

   Pearson correlation measures how two variables move together in a straight line.

2. Step 2: Identify what it quantifies

   It quantifies both strength (how close points are to a line) and direction (positive or negative slope).

3. Final Answer:

   The strength and direction of a linear relationship -> Option D

4. Quick Check:

   Pearson correlation = strength and direction [OK]

Hint: Pearson = linear relationship strength and direction [OK]

Common Mistakes:

• Confusing correlation with difference in means
• Thinking it measures frequency counts
• Assuming it measures total sums

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

easy

A. from scipy.stats import pearsonr

B. import scipy.pearsonr

C. from scipy import pearsonr

D. import pearsonr from scipy.stats

Solution

1. Step 1: Recall scipy.stats module structure

   The pearsonr function is inside scipy.stats, so import must reflect that.

2. Step 2: Check correct import syntax

   Python import syntax for functions is 'from module import function', so 'from scipy.stats import pearsonr' is correct.

3. Final Answer:

   from scipy.stats import pearsonr -> Option A

4. Quick Check:

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

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

Common Mistakes:

• Using 'import scipy.pearsonr' which is invalid
• Trying 'from scipy import pearsonr' missing stats
• Wrong syntax like 'import pearsonr from scipy.stats'

3. What is the output of this code?

from scipy.stats import pearsonr
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
corr, p = pearsonr(x, y)
print("{:.2f}".format(corr))

medium

A. 0.00

B. -1.00

C. 1.00

D. 0.50

Solution

1. Step 1: Analyze the data relationship

   y is exactly 2 times x, so they have a perfect positive linear relationship.

2. Step 2: Understand Pearson correlation output

   Perfect positive linear relationship means correlation coefficient is 1.0.

3. Final Answer:

   1.00 -> Option C

4. Quick Check:

   Perfect positive linear correlation = 1.00 [OK]

Hint: Perfect linear scaling means correlation = 1.00 [OK]

Common Mistakes:

• Confusing correlation with p-value
• Expecting 0.5 for proportional data
• Ignoring perfect linear relationship

4. Identify the error in this code snippet:

from scipy.stats import pearsonr
x = [1, 2, 3]
y = [4, 5]
corr, p = pearsonr(x, y)
print(corr)

medium

A. Lists x and y have different lengths

B. pearsonr function is not imported correctly

C. Missing parentheses in print statement

D. Variables x and y must be numpy arrays

Solution

1. Step 1: Check input data lengths

   pearsonr requires x and y to have the same length. Here, x has 3 elements, y has 2.

2. Step 2: Understand error cause

   Different lengths cause a ValueError when calling pearsonr.

3. Final Answer:

   Lists x and y have different lengths -> Option A

4. Quick Check:

   Input length mismatch causes error [OK]

Hint: Ensure x and y lists have equal length [OK]

Common Mistakes:

• Assuming pearsonr accepts different length inputs
• Thinking print syntax is wrong in Python 3
• Believing inputs must be numpy arrays

5. You have two variables: temperature (in Celsius) and ice cream sales. You want to check if higher temperature relates to higher sales. Which code correctly computes the Pearson correlation and interprets a strong positive relationship?

hard

A. from scipy.stats import pearsonr
corr, p = pearsonr(temp, sales)
if p > 0.7:
print('Strong positive correlation')

B. from scipy.stats import pearsonr
corr, p = pearsonr(temp, sales)
if corr > 0.7:
print('Strong positive correlation')

C. from scipy.stats import pearsonr
corr, p = pearsonr(temp, sales)
if corr < -0.7:
print('Strong positive correlation')

D. from scipy.stats import pearsonr
corr, p = pearsonr(temp, sales)
if abs(corr) < 0.3:
print('Strong positive correlation')

Solution

1. Step 1: Calculate Pearson correlation

   Use pearsonr to get correlation coefficient and p-value between temperature and sales.

2. Step 2: Interpret correlation coefficient

   A strong positive correlation means corr > 0.7. Checking p-value > 0.7 or negative corr is incorrect.

3. Final Answer:

   from scipy.stats import pearsonr corr, p = pearsonr(temp, sales) if corr > 0.7: print('Strong positive correlation') -> Option B

4. Quick Check:

   Strong positive correlation if corr > 0.7 [OK]

Hint: Check corr > 0.7 for strong positive relation [OK]

Common Mistakes:

• Using p-value instead of correlation coefficient
• Checking for negative correlation for positive relation
• Using absolute value less than threshold incorrectly