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.