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()

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

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()

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

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()

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

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()

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

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}

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

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.

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