Descriptive statistics (describe) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that calculates descriptive statistics.

SciPy

from scipy.stats import [1]
data = [1, 2, 3, 4, 5]
result = describe(data)
print(result)

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

Adescribe

Bmode

Cmedian

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Importing mean or median instead of describe.

Forgetting to import from scipy.stats.

The describe function from scipy.stats calculates descriptive statistics like mean, variance, min, max, and more.

2fill in blank

medium

Complete the code to calculate descriptive statistics for the given data list.

SciPy

from scipy.stats import describe
data = [10, 20, 30, 40, 50]
result = describe([1])
print(result)

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

A[1, 2, 3]

Brange(5)

Clist

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a range object instead of the data list.

Passing the word 'list' instead of the variable.

You need to pass the actual data list data to the describe function to get statistics.

3fill in blank

hard

Fix the error in the code to correctly calculate descriptive statistics.

SciPy

from scipy.stats import describe
data = [5, 10, 15, 20]
result = describe([1])
print(result)

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

Adata

Bdata.values

Cdata.tolist()

Ddata.items()

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to use values or items on a list.

Passing a method call instead of the list variable.

The describe function expects a list or array. Since data is already a list, pass it directly.

4fill in blank

hard

Fill both blanks to create a dictionary of descriptive statistics for words longer than 3 letters.

SciPy

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using less than instead of greater than in the condition.

We want the length of each word as the value, so use len(word). The condition filters words longer than 3 letters, so use >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 2.

SciPy

data = {'a': 1, 'b': 3, 'c': 5}
filtered = {{ [1]: [2] for k, v in data.items() if v [3] 2 }}
print(filtered)

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys instead of uppercase.

Using wrong comparison operator in filter.

Swapping keys and values in dictionary comprehension.

Keys are converted to uppercase with k.upper(). Values are kept as v. The filter keeps values greater than 2 using >.

Practice

1. What does the scipy.stats.describe function provide when applied to a dataset?

easy

A. Summary statistics like count, mean, variance, min, max, skewness, and kurtosis

B. Only the mean and median of the dataset

C. A plot showing data distribution

D. A list of unique values in the dataset

Solution

1. Step 1: Understand the purpose of scipy.stats.describe

   This function is designed to give a quick summary of key statistics about the data.

2. Step 2: Identify the statistics returned

   It returns count, mean, variance, minimum, maximum, skewness, and kurtosis, which describe data shape and spread.

3. Final Answer:

   Summary statistics like count, mean, variance, min, max, skewness, and kurtosis -> Option A

4. Quick Check:

   Descriptive stats = count, mean, variance, min, max, skewness, kurtosis [OK]

Hint: Remember: describe summarizes data with key stats [OK]

Common Mistakes:

• Thinking it returns only mean and median
• Confusing it with plotting functions
• Expecting a list of unique values

2. Which of the following is the correct way to import and use describe from scipy.stats?

easy

A. from scipy.stats import describe; result = describe(data)

B. import scipy.stats; result = describe(data)

C. from scipy import describe; result = describe(data)

D. import describe from scipy.stats; result = describe(data)

Solution

1. Step 1: Check correct import syntax for a function

   Functions are imported using from module import function syntax in Python.

2. Step 2: Verify usage of describe after import

   After importing, call describe(data) directly.

3. Final Answer:

   from scipy.stats import describe; result = describe(data) -> Option A

4. Quick Check:

   Correct import and call = from scipy.stats import describe; result = describe(data) [OK]

Hint: Use 'from scipy.stats import describe' to call directly [OK]

Common Mistakes:

• Using wrong import syntax like 'import describe from ...'
• Importing scipy.stats but not calling with module prefix
• Importing from wrong module

3. Given the code below, what will be the output for the mean and min values?

import numpy as np
from scipy.stats import describe

data = np.array([2, 4, 6, 8, 10])
result = describe(data)
print(result.mean, result.minmax[0])

medium

A. 6.0 0

B. 5.0 2

C. 6.0 2

D. 5.0 0

Solution

1. Step 1: Calculate the mean of the data array

   The data is [2, 4, 6, 8, 10]. Mean = (2+4+6+8+10)/5 = 30/5 = 6.0.

2. Step 2: Identify the minimum value from minmax

   minmax returns (min, max). The minimum is 2.

3. Final Answer:

   6.0 2 -> Option C

4. Quick Check:

   Mean = 6.0, Min = 2 [OK]

Hint: Mean is average; min is smallest value [OK]

Common Mistakes:

• Calculating mean incorrectly as 5.0
• Confusing minmax tuple order
• Assuming min is 0 without checking data

4. What is wrong with the following code snippet?

from scipy.stats import describe
data = [1, 2, 3, 4, 5]
result = describe(data, axis=1)
print(result)

medium

A. The print statement is missing parentheses

B. The data list should be converted to a numpy array first

C. The describe function requires a second argument for axis

D. Axis=1 is invalid for 1D data, causing an error

Solution

1. Step 1: Understand the data shape and axis parameter

   The data is a 1D list. Axis=1 means operate along columns of 2D or higher arrays.

2. Step 2: Identify the error caused by axis=1 on 1D data

   Using axis=1 on 1D data causes an AxisError because axis 1 does not exist.

3. Final Answer:

   Axis=1 is invalid for 1D data, causing an error -> Option D

4. Quick Check:

   Axis must exist in data shape [OK]

Hint: Check data dimensions before setting axis [OK]

Common Mistakes:

• Assuming axis=1 works for 1D data
• Thinking data must be numpy array for describe
• Ignoring Python 3 print syntax

5. You have a dataset with some zero and negative values: data = [0, -1, 2, 3, 4]. Using scipy.stats.describe, which statistic helps you understand if the data distribution is skewed?

hard

A. Variance

B. Skewness

C. Count

D. Minimum

Solution

1. Step 1: Recall what skewness measures

   Skewness shows if data is symmetric or leans left/right (skewed).

2. Step 2: Identify which statistic indicates skewness

   Among the options, only skewness directly measures data asymmetry.

3. Final Answer:

   Skewness -> Option B

4. Quick Check:

   Skewness = data asymmetry [OK]

Hint: Skewness shows data leaning direction [OK]

Common Mistakes:

• Confusing variance with skewness
• Thinking count or minimum show skewness
• Ignoring negative and zero values impact