Rolling mean and sum in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a rolling mean in pandas?

A rolling mean calculates the average of values in a moving window over a series. It helps smooth out short-term fluctuations and highlight longer-term trends.

beginner

How do you calculate a rolling sum in pandas?

Use the rolling(window).sum() method on a pandas Series or DataFrame to get the sum of values within a moving window of specified size.

beginner

What does the window parameter specify in rolling calculations?

The window parameter sets the size of the moving window, i.e., how many consecutive values are included in each calculation.

intermediate

What happens to the first few values when using rolling mean with a window size of 3?

The first two values will be NaN because there are not enough data points to fill the window of size 3.

intermediate

How can you include the current row in the rolling window calculation?

By default, the rolling window includes the current row and previous rows up to the window size. You can adjust this with parameters like min_periods or center.

Which pandas method calculates the rolling mean?

A mean(window)

B rolling(window).sum()

C rolling(window).mean()

D sum(window)

The method rolling(window).mean() computes the rolling mean over the specified window.

If you use rolling(4).sum() on a Series, what does the number 4 represent?

A The size of the moving window

B The number of rows to skip

C The number of columns to sum

D The total sum of the Series

The number 4 is the window size, meaning the sum is calculated over 4 consecutive values.

What will be the output for the first value when using rolling(3).mean()?

A The mean of first 3 values

B The first value itself

C Zero

D NaN

The first value is NaN because there are not enough values to fill the window of size 3.

How can you change the rolling window to be centered on the current row?

A Set <code>min_periods=0</code>

B Set <code>center=True</code> in rolling()

C Use <code>shift()</code> method

D Use <code>sum()</code> instead of <code>mean()</code>

Setting center=True centers the window on the current row.

What does min_periods control in rolling calculations?

A Minimum number of observations in window to have a value

B Maximum window size

C Number of columns to include

D Number of rows to skip

min_periods sets the minimum number of data points required in the window to compute a result instead of returning NaN.

Explain how rolling mean and rolling sum work in pandas and why they are useful.

Think about how you might smooth noisy data or find running totals.

Describe how to use the rolling() method with parameters to customize the window behavior.

Consider how to adjust window size and alignment.

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