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.

Practice

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

easy

A. Calculates the average of the current and previous 2 values for each position.

B. Calculates the sum of all values in the Series.

C. Calculates the difference between consecutive values.

D. Sorts the Series in ascending order.

Solution

1. Step 1: Understand rolling window size

   The window=3 means we look at 3 values at a time: the current and previous 2.

2. Step 2: Apply mean function on each window

   For each position, the mean is calculated over these 3 values, smoothing the data.

3. Final Answer:

   Calculates the average of the current and previous 2 values for each position. -> Option A

4. Quick Check:

   Rolling mean = average over window [OK]

Hint: Rolling mean averages over the window size including current [OK]

Common Mistakes:

• Thinking it sums all values, not just window
• Confusing mean with difference
• Assuming it sorts data

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 named 'sales'?

easy

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

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

C. df['sales'].rolling(4).sum()

D. df['sales'].sum().rolling(4)

Solution

1. Step 1: Identify correct method chaining

   In pandas, rolling is called on the Series, then sum() is applied.

2. Step 2: Check syntax correctness

   df['sales'].rolling(4).sum() correctly uses df['sales'].rolling(4).sum() which is valid syntax.

3. Final Answer:

   df['sales'].rolling(4).sum() -> Option C

4. Quick Check:

   Correct rolling sum syntax = df['sales'].rolling(4).sum() [OK]

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

Common Mistakes:

• Passing column name as string to rolling()
• Calling sum() before rolling()
• Using undefined rolling() function

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

medium

A. [2, 6, 12, 18, 24]

B. [NaN, 6, 12, 18, 24]

C. [2, 4, 6, 8, 10]

D. [NaN, NaN, 12, 18, 24]

Solution

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

   For the first two positions, there are not enough values to fill the window, so result is NaN.

2. Step 2: Calculate sums for positions 3 to 5

   Position 3 sum: 2+4+6=12, position 4 sum: 4+6+8=18, position 5 sum: 6+8+10=24.

3. Final Answer:

   [NaN, NaN, 12, 18, 24] -> Option D

4. Quick Check:

   Rolling sum with window=3 pads start with NaN [OK]

Hint: Rolling sum outputs NaN until window fills [OK]

Common Mistakes:

• Expecting sums from first element
• Confusing rolling sum with cumulative sum
• Ignoring NaN for incomplete windows

4. What is wrong with this code snippet to calculate rolling mean on column 'temp' with window 2?

df = pd.DataFrame({'temp': [10, 20, 30]})
df['rolling_mean'] = df.rolling(2).mean()['temp']

medium

A. The code is correct and will run without errors.

B. The window size must be 3 or more.

C. mean() cannot be used after rolling().

D. The rolling() should be called on the column, not the whole DataFrame.

Solution

1. Step 1: Analyze the code execution

   df.rolling(2).mean() applies the rolling mean to the 'temp' column, producing a DataFrame with values [NaN, 15.0, 25.0].

2. Step 2: Column access and assignment

   df.rolling(2).mean()['temp'] extracts the Series correctly, and assignment to df['rolling_mean'] succeeds without errors.

3. Final Answer:

   The code is correct and will run without errors. -> Option A

4. Quick Check:

   df.rolling(2).mean()['temp'] works for column rolling mean [OK]

Hint: df.rolling(window).mean()['col'] is valid for single-column rolling [OK]

Common Mistakes:

• Believing rolling() must only be called on Series
• Thinking window size must be 3 or more
• Assuming mean() cannot be used after rolling()

5. You have daily sales data in a pandas DataFrame df with column 'sales'. You want to create a new column 'weekly_avg' that shows the average sales over the last 7 days, but only if at least 5 days of data are available in the window. Which code correctly achieves this?

hard

A. df['weekly_avg'] = df['sales'].rolling(7).mean(min_periods=5)

B. df['weekly_avg'] = df['sales'].rolling(window=7, min_periods=5).mean()

C. df['weekly_avg'] = df['sales'].rolling(window=5).mean()

D. df['weekly_avg'] = df['sales'].rolling(window=7).sum() / 5

Solution

1. Step 1: Understand rolling with min_periods

   min_periods=5 means the rolling function will compute mean only if at least 5 values exist in the window.

2. Step 2: Check correct syntax for rolling mean with conditions

   df['weekly_avg'] = df['sales'].rolling(window=7, min_periods=5).mean() correctly uses rolling(window=7, min_periods=5).mean() to get average over 7 days with minimum 5 days data.

3. Final Answer:

   df['weekly_avg'] = df['sales'].rolling(window=7, min_periods=5).mean() -> Option B

4. Quick Check:

   Use min_periods to require minimum data in rolling [OK]

Hint: Use min_periods in rolling() to set minimum data points [OK]

Common Mistakes:

• Passing min_periods to mean() instead of rolling()
• Using wrong window size
• Dividing sum by fixed number ignoring data count