rolling() for moving windows in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

import pandas as pd

data = pd.Series([1, 2, 3, 4, 5])
rolling_mean = data.[1](3).mean()
print(rolling_mean.tolist())

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

Acumsum

Brolling

Csort_values

Dgroupby

Attempts:

3 left

💡 Hint

Common Mistakes

Using groupby instead of rolling causes an error.

Using cumsum calculates cumulative sum, not rolling mean.

The rolling() function creates a moving window of the specified size (3 here). Then mean() calculates the average within each window.

2fill in blank

medium

Complete the code to calculate the rolling sum with a window size of 4.

Pandas

import pandas as pd

values = pd.Series([10, 20, 30, 40, 50])
rolling_sum = values.[1](4).sum()
print(rolling_sum.tolist())

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

Arolling

Bexpanding

Ccumsum

Ddiff

Attempts:

3 left

💡 Hint

Common Mistakes

Using cumsum sums all values up to current, not in a fixed window.

Using diff calculates differences, not sums.

The rolling() function with window 4 creates windows of 4 elements. Then sum() adds values in each window.

3fill in blank

hard

Fix the error in the code to calculate the rolling max with a window size of 2.

Pandas

import pandas as pd

scores = pd.Series([5, 3, 8, 7])
rolling_max = scores.[1](window=2).max()
print(rolling_max.tolist())

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

Arolling

Brolling_window

Cwindow_rolling

Drollingmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent functions like rolling_window or rollingmean.

Misspelling the function name.

The correct pandas function is rolling(). Other options are invalid and cause errors.

4fill in blank

hard

Fill both blanks to create a rolling window of size 3 and calculate the rolling standard deviation.

Pandas

import pandas as pd

values = pd.Series([2, 4, 6, 8, 10])
rolling_std = values.[1]([2]).std()
print(rolling_std.tolist())

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

Arolling

B3

C5

Dwindow

Attempts:

3 left

💡 Hint

Common Mistakes

Using window=5 instead of 3 changes the window size.

Using 'window' as a parameter instead of a number.

rolling(3) creates a moving window of size 3. Then std() calculates the standard deviation in each window.

5fill in blank

hard

Fill all three blanks to create a rolling window of size 2, calculate the rolling median, and assign it to a new column in a DataFrame.

Pandas

import pandas as pd

data = pd.DataFrame({'values': [1, 3, 5, 7, 9]})
data['rolling_median'] = data['values'].[1]([2]).[3]()
print(data)

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

Arolling

B2

Cmedian

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of median() changes the result.

Forgetting to assign the result to a new column.

rolling(2) creates windows of size 2, and median() calculates the median in each window. The result is assigned to a new column.

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()