Rolling standard deviation in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the rolling standard deviation with a window size of 3.

Pandas

import pandas as pd

data = pd.Series([1, 2, 3, 4, 5])
rolling_std = data.[1](3).std()
print(rolling_std)

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

Arolling

Bmean

Csum

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of rolling().

Trying to call std() directly on the Series without rolling.

The rolling method creates a rolling window object. Then std() calculates the standard deviation for each window.

2fill in blank

medium

Complete the code to calculate the rolling standard deviation with a window size of 4 and minimum periods of 2.

Pandas

import pandas as pd

data = pd.Series([10, 20, 30, 40, 50])
rolling_std = data.rolling(window=[1], min_periods=2).std()
print(rolling_std)

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

A2

B4

C3

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong window size like 2 or 3.

Confusing min_periods with window.

The window size is set to 4 to calculate the rolling standard deviation over 4 values.

3fill in blank

hard

Fix the error in the code to correctly calculate the rolling standard deviation with a window size of 3.

Pandas

import pandas as pd

data = pd.Series([5, 10, 15, 20, 25])
rolling_std = data.rolling(3).[1]()
print(rolling_std)

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

Asum

Bmean

Cvar

Dstd

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of std().

Using var() which calculates variance, not standard deviation.

The std() method calculates the standard deviation for each rolling window.

4fill in blank

hard

Fill both blanks to create a dictionary of words and their rolling standard deviation of lengths with window size 2, filtering lengths greater than 3.

Pandas

import pandas as pd

words = ['apple', 'bat', 'cat', 'dog', 'elephant']
lengths = {word: len(word) for word in words}
rolling_std = pd.Series(list(lengths.values())).rolling([1]).std()
filtered = {word: rolling_std[i] for i, word in enumerate(words) if lengths[word] [2] 3}
print(filtered)

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

A2

B>

C<

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong window size like 3 or 4.

Using '<' instead of '>' in the filter condition.

The rolling window size is 2, and we filter words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their rolling standard deviation of lengths with window size 2, filtering lengths less than or equal to 5.

Pandas

import pandas as pd

words = ['apple', 'bat', 'cat', 'dog', 'elephant']
lengths = {word: len(word) for word in words}
rolling_std = pd.Series(list(lengths.values())).rolling([1]).std()
filtered = {word.[2](): rolling_std[i] for i, word in enumerate(words) if lengths[word] [3] 5}
print(filtered)

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

A2

Bupper

C<=

Dlower

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong window size like 3.

Using lower() instead of upper().

Using '<' or '>' instead of '<=' in the filter.

The rolling window size is 2, words are converted to uppercase, and filtered for lengths less than or equal to 5.

Practice

1. What does the rolling(window).std() function calculate in pandas?

easy

A. The standard deviation of values within a moving window

B. The sum of values within a moving window

C. The average of values within a moving window

D. The maximum value within a moving window

Solution

1. Step 1: Understand rolling window concept

   The rolling function creates a moving window over data points.

2. Step 2: Apply std() to rolling window

   The std() function calculates the standard deviation of values inside each window.

3. Final Answer:

   The standard deviation of values within a moving window -> Option A

4. Quick Check:

   rolling(window).std() = standard deviation [OK]

Hint: Remember std() means standard deviation, not sum or mean [OK]

Common Mistakes:

• Confusing std() with sum() or mean()
• Thinking rolling() alone calculates std
• Assuming it returns a single value, not a series

2. Which of the following is the correct syntax to calculate a rolling standard deviation with a window size of 3 on a pandas Series named data?

easy

A. data.std().rolling(3)

B. data.rolling_std(3)

C. rolling(data, 3).std()

D. data.rolling(3).std()

Solution

1. Step 1: Identify correct method chaining

   The rolling() method is called on the Series, then std() is applied.

2. Step 2: Check syntax correctness

   data.rolling(3).std() uses correct pandas syntax: data.rolling(3).std()

3. Final Answer:

   data.rolling(3).std() -> Option D

4. Quick Check:

   Correct method chaining = data.rolling(3).std() [OK]

Hint: Call rolling() first, then std() to get rolling std dev [OK]

Common Mistakes:

• Calling std() before rolling()
• Using non-existent rolling_std() method
• Passing window size inside std()

3. Given the pandas Series data = pd.Series([2, 4, 6, 8, 10]), what is the output of data.rolling(3).std()?

medium

A. [NaN, 1.0, 2.0, 3.0, 4.0]

B. [NaN, NaN, 2.0, 2.0, 2.0]

C. [2.0, 2.0, 2.0, 2.0, 2.0]

D. [NaN, NaN, NaN, NaN, NaN]

Solution

1. Step 1: Understand rolling std with window=3

   For the first two values, window size 3 is not met, so result is NaN.

2. Step 2: Calculate std for windows

   Windows: [2,4,6], [4,6,8], [6,8,10] each have std deviation 2.0.

3. Final Answer:

   [NaN, NaN, 2.0, 2.0, 2.0] -> Option B

4. Quick Check:

   Rolling std with window 3 = [NaN, NaN, 2.0, 2.0, 2.0] [OK]

Hint: First (window-1) results are NaN, then std calculated [OK]

Common Mistakes:

• Expecting values for first two positions
• Confusing std with mean or sum
• Miscomputing standard deviation values

4. What is wrong with this code snippet to calculate rolling standard deviation on a DataFrame column df['values'] with window size 4?

df['rolling_std'] = df['values'].rolling.std(4)

medium

A. The code is correct and will run without error

B. The std() function should be called before rolling()

C. The rolling method is missing parentheses before std()

D. Window size should be passed to std(), not rolling()

Solution

1. Step 1: Check method chaining syntax

   rolling() requires parentheses with window size: rolling(4)

2. Step 2: Identify error in code

   Code uses rolling.std(4) which is invalid; should be rolling(4).std()

3. Final Answer:

   The rolling method is missing parentheses before std() -> Option C

4. Quick Check:

   rolling() needs parentheses before std() [OK]

Hint: Always use rolling(window).std(), not rolling.std(window) [OK]

Common Mistakes:

• Calling std() before rolling()
• Passing window size to std() instead of rolling()
• Omitting parentheses after rolling

5. You have daily sales data in a pandas DataFrame df with a column 'sales'. You want to detect days where sales variability over the last 5 days was unusually high. Which code correctly adds a column 'high_volatility' that is true when the rolling standard deviation exceeds 100?

hard

A. df['high_volatility'] = df['sales'].rolling(5).std() > 100

B. df['high_volatility'] = df['sales'].std().rolling(5) > 100

C. df['high_volatility'] = df['sales'].rolling(window=5).mean() > 100

D. df['high_volatility'] = df['sales'].rolling(5).sum() > 100

Solution

1. Step 1: Calculate rolling standard deviation over 5 days

   Use rolling(5).std() on 'sales' to get variability in last 5 days.

2. Step 2: Compare rolling std to threshold 100

   Create boolean column where rolling std is greater than 100.

3. Final Answer:

   df['high_volatility'] = df['sales'].rolling(5).std() > 100 -> Option A

4. Quick Check:

   rolling(5).std() > 100 detects high variability [OK]

Hint: Use rolling(5).std() then compare to threshold for volatility [OK]

Common Mistakes:

• Using std() before rolling()
• Using mean() or sum() instead of std()
• Comparing rolling sum or mean to threshold