Rolling mean and sum 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 [nan, nan, 2.0, 3.0, 4.0]

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

C [nan, nan, 3.0, 4.0, 5.0]

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

💡 Hint

Remember that rolling mean with window=3 needs 3 values to compute the first mean.

The rolling mean with window size 3 computes the mean of the current and previous two values. The first two positions have less than 3 values, so result is NaN there.

❓ data_output

intermediate

Sum of rolling window with min_periods

What is the output of the rolling sum with window=3 and min_periods=1 on this series?

Pandas

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

A [nan, nan, 6.0, 9.0, 12.0]

B [1.0, 3.0, 6.0, 9.0, 12.0]

C [1, 2, 3, 4, 5]

D [1.0, 2.0, 3.0, 4.0, 5.0]

💡 Hint

min_periods=1 allows calculation even if the window is not full.

With min_periods=1, the rolling sum starts calculating from the first element, summing available values up to the window size.

🔧 Debug

advanced

Identify the error in rolling sum code

What error does this code raise?

Pandas

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

A AttributeError: 'str' object has no attribute 'sum'

B ValueError: window size must be positive

C TypeError: window must be an integer

D No error, outputs rolling sum

💡 Hint

Check the type of the window parameter.

The window parameter must be an integer. Passing a string causes a TypeError.

❓ visualization

advanced

Plot rolling mean and sum

Which option correctly plots both rolling mean and rolling sum with window=2 for the series?

Pandas

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series([1, 3, 5, 7, 9])
rm = s.rolling(window=2).mean()
rs = s.rolling(window=2).sum()
plt.plot(s, label='Original')
plt.plot(rm, label='Rolling Mean')
plt.plot(rs, label='Rolling Sum')
plt.legend()
plt.show()

A Plots three lines: original, rolling mean (smooth), rolling sum (higher values)

B Plots only original and rolling mean, rolling sum missing

C Plots original and rolling sum, rolling mean missing

D Plots original twice, no rolling calculations

💡 Hint

Check which lines are plotted with plt.plot calls.

The code plots original series, rolling mean, and rolling sum lines with labels and legend.

🚀 Application

expert

Calculate rolling weighted mean with custom weights

Given the series s = pd.Series([2, 4, 6, 8, 10]) and weights [0.1, 0.3, 0.6], which code correctly calculates the rolling weighted mean with window=3?

A s.rolling(window=3).apply(lambda x: (x * [0.1, 0.3, 0.6]).mean())

B s.rolling(window=3).apply(lambda x: (x * [0.6, 0.3, 0.1]).sum())

C s.rolling(window=3).apply(lambda x: (x * [0.1, 0.3]).sum())

D s.rolling(window=3).apply(lambda x: (x * [0.1, 0.3, 0.6]).sum())

💡 Hint

Weights should align with the order of values in the window from oldest to newest.

Rolling windows provide values from oldest to newest. The weights must match this order to calculate weighted mean correctly.