Kolmogorov-Smirnov test in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Kolmogorov-Smirnov test function from scipy.stats.

SciPy

from scipy.stats import [1]

# Now you can use the function for the KS test

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

Aks_test

Bks_2samp

Ckolmogorov_smirnov

Dkstest

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ks_2samp' which is for two-sample KS test, not one-sample.

Trying 'ks_test' which is not a valid function name.

Using 'kolmogorov_smirnov' which is not a function in scipy.stats.

The function kstest is the correct name for the Kolmogorov-Smirnov test in scipy.stats.

2fill in blank

medium

Complete the code to perform a one-sample Kolmogorov-Smirnov test comparing data to a normal distribution.

SciPy

from scipy.stats import kstest, norm

result = kstest(data, [1])
print(result)

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

A'norm'

B'gaussian'

C'normal'

D'stdnorm'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'normal' which is not recognized by kstest.

Using 'gaussian' which is not a valid distribution name here.

Using 'stdnorm' which is not a valid string for this function.

The string 'norm' tells kstest to compare the data to a normal distribution.

3fill in blank

hard

Fix the error in the code to correctly perform a two-sample Kolmogorov-Smirnov test.

SciPy

from scipy.stats import [1]

result = ks_2samp(sample1, sample2)
print(result)

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

Aks_2samp

Bks_2sample

Cks2samp

Dkstest

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'kstest' which is for one-sample test.

Using 'ks2samp' without underscore which causes import error.

Using 'ks_2sample' which is not a valid function name.

The correct function name for the two-sample KS test is ks_2samp.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

SciPy

lengths = {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.

Checking if the word string is greater than 3, which is invalid.

Using the length without a comparison in the condition.

The dictionary maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each uppercase word to its frequency only if frequency is greater than 1.

SciPy

freq_dict = [1]: [2] for [3] in word_counts if word_counts[[3]] > 1

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

Aword.upper()

Bword_counts[word]

Cword

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as key without uppercasing.

Using the wrong variable name in the loop.

Using lowercase instead of uppercase for keys.

The dictionary comprehension maps each word in word_counts to its frequency. The key is the uppercase version of the word (word.upper()), the value is the frequency (word_counts[word]), and the loop variable is word. The condition filters frequencies greater than 1.

Practice

1. What is the main purpose of the scipy.stats.ks_2samp function in data analysis?

easy

A. To generate random numbers from a normal distribution

B. To calculate the mean of a dataset

C. To perform linear regression between two variables

D. To compare two samples and check if they come from the same distribution

Solution

1. Step 1: Understand the function purpose

   ks_2samp is designed to compare two independent samples.

2. Step 2: Identify what it tests

   It tests if the two samples come from the same continuous distribution.

3. Final Answer:

   To compare two samples and check if they come from the same distribution -> Option D

4. Quick Check:

   Kolmogorov-Smirnov test compares distributions [OK]

Hint: Remember KS test compares distributions, not means or regressions [OK]

Common Mistakes:

• Confusing KS test with mean calculation
• Thinking KS test performs regression
• Assuming KS test generates random data

2. Which of the following is the correct way to import the Kolmogorov-Smirnov two-sample test from scipy?

easy

A. import ks_2samp from scipy.stats

B. import scipy.ks_2samp

C. from scipy.stats import ks_2samp

D. from scipy import ks_2samp

Solution

1. Step 1: Recall correct import syntax

   Functions inside scipy.stats are imported using from scipy.stats import function_name.

2. Step 2: Match the function name

   The function for KS two-sample test is ks_2samp, so the correct import is from scipy.stats import ks_2samp.

3. Final Answer:

   from scipy.stats import ks_2samp -> Option C

4. Quick Check:

   Correct import uses 'from scipy.stats import' [OK]

Hint: Import functions from scipy.stats using 'from scipy.stats import' [OK]

Common Mistakes:

• Using 'import scipy.ks_2samp' which is invalid
• Trying 'from scipy import ks_2samp' which misses stats
• Using wrong import order like 'import ks_2samp from scipy.stats'

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

from scipy.stats import ks_2samp
sample1 = [1, 2, 3, 4, 5]
sample2 = [2, 3, 4, 5, 6]
result = ks_2samp(sample1, sample2)
print(round(result.pvalue, 3))

medium

A. 0.999

B. 0.500

C. 0.317

D. 0.050

Solution

1. Step 1: Understand the samples and test

   Samples are close but not identical; KS test compares their distributions.

2. Step 2: Run the KS test and check p-value

   Running the code shows p-value approximately 0.999, indicating no strong evidence to reject similarity.

3. Final Answer:

   0.999 -> Option A

4. Quick Check:

   Close samples give very high p-value ~0.999 [OK]

Hint: Close samples usually yield high p-values, not moderate or low [OK]

Common Mistakes:

• Assuming p-value is always very small for different samples
• Confusing p-value with test statistic
• Rounding errors leading to wrong choice

4. Identify the error in this code snippet that attempts to perform a KS test:

from scipy.stats import ks_2samp
sample1 = [1, 2, 3]
sample2 = [4, 5, 6]
result = ks_2samp(sample1)
print(result.statistic)

medium

A. Incorrect import statement for ks_2samp

B. Missing second sample argument in ks_2samp call

C. Using print instead of return to show result

D. Samples contain integers instead of floats

Solution

1. Step 1: Check function call parameters

   ks_2samp requires two sample arguments, but only one is given.

2. Step 2: Identify the error cause

   Missing second sample causes a TypeError because the function expects two inputs.

3. Final Answer:

   Missing second sample argument in ks_2samp call -> Option B

4. Quick Check:

   ks_2samp needs two samples [OK]

Hint: ks_2samp always needs two sample inputs [OK]

Common Mistakes:

• Forgetting the second sample argument
• Thinking print causes error
• Assuming integer samples cause error

5. You have two datasets representing customer wait times from two stores. You want to check if their wait times differ significantly. Which approach using scipy is best?

hard

A. Use ks_2samp to compare the two samples' distributions

B. Calculate the mean wait time of each and compare with subtraction

C. Use linear regression to predict wait times from store ID

D. Use ttest_ind to compare the two samples assuming normality

Solution

1. Step 1: Understand the data and goal

   We want to check if two samples (wait times) differ in distribution, not just means.

2. Step 2: Choose appropriate test

   ks_2samp compares full distributions without assuming normality, ideal for wait times.

3. Step 3: Evaluate other options

   Mean subtraction ignores distribution shape; regression is unrelated; ttest_ind assumes normality which may not hold.

4. Final Answer:

   Use ks_2samp to compare the two samples' distributions -> Option A

5. Quick Check:

   KS test compares distributions best for this case [OK]

Hint: KS test compares distributions without normality assumptions [OK]

Common Mistakes:

• Using mean difference which ignores distribution shape
• Applying t-test without checking normality
• Using regression for distribution comparison