rolling() for moving windows in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of rolling mean with window size 3

What is the output of the following code snippet?

Pandas

import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
result = s.rolling(window=3).mean()
print(result.tolist())

A [1.0, 1.5, 2.0, 3.0, 4.0]

B [nan, nan, nan, 2.0, 3.0]

C [nan, nan, 2.0, 3.0, 4.0]

D [nan, 2.0, 3.0, 4.0, 5.0]

💡 Hint

Remember that rolling mean with window=3 requires 3 values before it can compute the mean.

The rolling mean with window size 3 computes the mean of the current and previous two values. The first two results are NaN because there are not enough values to fill the window.

❓ data_output

intermediate

Count of rolling window sums greater than 10

Given the code below, how many rolling sums with window size 4 are greater than 10?

Pandas

import pandas as pd
s = pd.Series([2, 5, 3, 8, 7, 4, 6])
rolling_sum = s.rolling(window=4).sum()
count = (rolling_sum > 10).sum()
print(count)

A 1

B 3

C 2

D 4

💡 Hint

Calculate sums for windows: positions 3 to 6 and count how many exceed 10.

The rolling sums for windows of size 4 are: NaN, NaN, NaN, (2+5+3+8)=18, (5+3+8+7)=23, (3+8+7+4)=22, (8+7+4+6)=25. All except the first three are valid. Among these, all four sums are greater than 10, so count is 4.

🔧 Debug

advanced

Identify the error in rolling window code

What error does the following code produce?

Pandas

import pandas as pd
s = pd.Series([1, 2, 3, 4])
result = s.rolling(window=0).mean()
print(result)

A ValueError: window must be >= 1

B TypeError: window must be an integer

C AttributeError: 'Series' object has no attribute 'rolling'

D No error, prints rolling mean

💡 Hint

Check the window parameter value and its allowed range.

The rolling window size must be at least 1. Using window=0 raises a ValueError indicating the window must be >= 1.

🚀 Application

advanced

Using rolling to detect trends in stock prices

You have daily stock prices in a pandas Series. You want to create a new Series that shows the 5-day rolling average price but only for days where the average is higher than the previous day's average. Which code snippet correctly achieves this?

A

rolling_avg = prices.rolling(window=5).sum()
result = rolling_avg[rolling_avg > rolling_avg.shift(1)]

B

rolling_avg = prices.rolling(window=5).mean()
result = rolling_avg[rolling_avg > rolling_avg.shift(1)]

C

rolling_avg = prices.rolling(window=5).mean()
result = rolling_avg[rolling_avg == rolling_avg.shift(1)]

D

rolling_avg = prices.rolling(window=5).mean()
result = rolling_avg[rolling_avg < rolling_avg.shift(1)]

💡 Hint

Compare the rolling average to its previous value using shift(1).

Option B correctly computes the 5-day rolling mean and filters to keep only days where the rolling average is greater than the previous day's rolling average.

🧠 Conceptual

expert

Effect of min_periods in rolling windows

Consider a pandas Series with 6 values. You apply rolling(window=4, min_periods=2).sum(). How many values in the resulting Series will be non-NaN?

A 5

B 4

C 3

D 6

💡 Hint

min_periods=2 means the sum is computed if at least 2 values are present in the window.

With window=4 and min_periods=2, the rolling sum starts producing values from the second element onward, so positions 1 to 5 (5 values) will have sums, the first position is NaN.

Practice

1. What does the rolling(window=3).mean() function do on a pandas Series?

easy

A. Removes missing values from the Series

B. Calculates the sum of all values in the Series

C. Sorts the Series in ascending order

D. Calculates the average of every 3 consecutive values as the window moves

Solution

1. Step 1: Understand rolling window concept

   The rolling(window=3) creates a moving window of size 3 over the data.

2. Step 2: Apply mean function on each window

   The mean() calculates the average of values inside each window as it moves along the Series.

3. Final Answer:

   Calculates the average of every 3 consecutive values as the window moves -> Option D

4. Quick Check:

   rolling + mean = moving average [OK]

Hint: rolling(window).mean() = moving average over window [OK]

Common Mistakes:

• Thinking rolling sums all values in the Series
• Confusing rolling with sorting or filtering
• Ignoring the window size effect

2. Which of the following is the correct syntax to calculate a rolling sum with a window size of 4 on a pandas DataFrame column df['sales']?

easy

A. df['sales'].rolling(4,sum()).sum()

B. df['sales'].rolling(window=4).sum()

C. df.rolling('sales', 4).sum()

D. df['sales'].rolling_sum(4)

Solution

1. Step 1: Identify correct rolling syntax

   The rolling function requires the window size as a named argument: window=4.

2. Step 2: Confirm method chaining

   After rolling, the aggregation function sum() is called to compute the rolling sum.

3. Final Answer:

   df['sales'].rolling(window=4).sum() -> Option B

4. Quick Check:

   rolling(window=4).sum() syntax correct [OK]

Hint: Use rolling(window=4).sum() for rolling sums [OK]

Common Mistakes:

• Passing window size as positional argument without naming
• Using non-existent method rolling_sum
• Incorrect argument order or syntax

3. Given the following code, what is the output?

import pandas as pd
s = pd.Series([2, 4, 6, 8, 10])
result = s.rolling(window=3, min_periods=1).mean()
print(result.tolist())

medium

A. [2.0, 3.0, 4.0, 6.0, 8.0]

B. [nan, nan, 4.0, 6.0, 8.0]

C. [2.0, 3.0, 4.0, 5.0, 6.0]

D. [nan, 3.0, 4.0, 6.0, 8.0]

Solution

1. Step 1: Understand rolling with min_periods=1

   With min_periods=1, the rolling mean starts calculating from the first value, even if the window is not full.

2. Step 2: Calculate rolling means step-by-step

   Values: - Index 0: mean([2]) = 2.0 - Index 1: mean([2,4]) = 3.0 - Index 2: mean([2,4,6]) = 4.0 - Index 3: mean([4,6,8]) = 6.0 - Index 4: mean([6,8,10]) = 8.0

3. Final Answer:

   [2.0, 3.0, 4.0, 6.0, 8.0] -> Option A

4. Quick Check:

   rolling mean with min_periods=1 = [2.0, 3.0, 4.0, 6.0, 8.0] [OK]

Hint: min_periods=1 allows early rolling calculations [OK]

Common Mistakes:

• Expecting NaN for first values despite min_periods=1
• Miscomputing window slices for rolling mean
• Confusing rolling mean with cumulative mean

4. The following code throws an error. What is the problem?

import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
result = s.rolling(window=0).mean()
print(result)

medium

A. Window size cannot be zero; it must be at least 1

B. The Series contains integers; it must be floats

C. mean() cannot be used with rolling windows

D. The rolling function requires a string window size

Solution

1. Step 1: Check window parameter validity

   The window size in rolling must be a positive integer greater than zero.

2. Step 2: Identify error cause

   Using window=0 is invalid and causes a ValueError.

3. Final Answer:

   Window size cannot be zero; it must be at least 1 -> Option A

4. Quick Check:

   rolling window > 0 required [OK]

Hint: Window size must be positive integer > 0 [OK]

Common Mistakes:

• Setting window=0 by mistake
• Thinking window can be zero or negative
• Confusing data type issues with window size errors

5. You have daily sales data in a pandas DataFrame df with a column 'sales'. You want to calculate a 7-day moving average but only want results when at least 4 days of data are available in the window. Which code correctly achieves this?

hard

A. df['sales'].rolling(window=7).mean(min_periods=4)

B. df['sales'].rolling(window=4, min_periods=7).mean()

C. df['sales'].rolling(window=7, min_periods=4).mean()

D. df['sales'].rolling(min_periods=7, window=4).mean()

Solution

1. Step 1: Set window size to 7 for 7-day moving average

   The window=7 parameter defines the size of the moving window as 7 days.

2. Step 2: Use min_periods=4 to require at least 4 valid days

   The min_periods=4 ensures the mean is calculated only if 4 or more days have data in the window.

3. Step 3: Confirm correct method chaining and argument order

   The syntax rolling(window=7, min_periods=4).mean() is correct and will produce the desired result.

4. Final Answer:

   df['sales'].rolling(window=7, min_periods=4).mean() -> Option C

5. Quick Check:

   rolling(window=7, min_periods=4).mean() for partial window averages [OK]

Hint: Use min_periods to control minimum data points in window [OK]

Common Mistakes:

• Swapping window and min_periods values
• Passing min_periods to mean() instead of rolling()
• Incorrect argument order in rolling()