Rolling mean and sum in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

df['rolling_mean'] = df['sales'].[1](3).mean()

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Acumsum

Bgroupby

Crolling

Dsort_values

Attempts:

3 left

💡 Hint

Common Mistakes

Using groupby instead of rolling

Using cumsum which calculates cumulative sum, not rolling mean

The rolling function creates a rolling window to calculate statistics like mean.

2fill in blank

medium

Complete the code to calculate the rolling sum of the 'temperature' column with a window size of 5.

Pandas

df['rolling_sum'] = df['temperature'].[1](5).sum()

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Acumsum

Bgroupby

Cexpanding

Drolling

Attempts:

3 left

💡 Hint

Common Mistakes

Using cumsum which sums all previous values cumulatively

Using expanding which grows the window size instead of fixed size

The rolling function is used to create a moving window, then sum() calculates the sum within each window.

3fill in blank

hard

Fix the error in the code to correctly calculate the rolling mean with a window size of 4.

Pandas

rolling_avg = df['value'].rolling([1]).mean()

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A4

B'4'

Cwindow=4

Dsize=4

Attempts:

3 left

💡 Hint

Common Mistakes

Passing window size as a string

Using incorrect keyword arguments like window=4

The rolling function expects an integer for the window size, not a string or keyword argument.

4fill in blank

hard

Fill both blanks to create a rolling sum with window size 3 and minimum periods 2.

Pandas

rolling_sum = df['data'].[1](3, [2]=2).sum()

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Arolling

Bmin_periods

Cwindow

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using cumsum instead of rolling

Using window instead of min_periods for minimum data points

rolling creates the rolling window. min_periods sets the minimum number of observations required to have a value.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its rolling mean length if length is greater than 3.

Pandas

result = {word: df['lengths'].rolling([1]).mean().iloc[i] for i, word in enumerate(words) if [2](word) [3] 3}

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A3

Blen

C>

Drolling

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling instead of window size number

Using incorrect comparison operators

Not using len() to get word length

The window size is 3, len gets the word length, and > checks if length is greater than 3.