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.