Rolling mean and sum in Pandas - Step-by-Step Execution

Concept Flow - Rolling mean and sum

Start with data series

↓

Choose window size

↓

Slide window over data

↓

Calculate mean or sum in window

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Store result for each position

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Output rolling mean or sum series

We take a series of numbers, pick a window size, slide it over the data, and calculate the mean or sum inside that window for each position.

Execution Sample

Pandas

import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
rolling_mean = s.rolling(window=3).mean()
rolling_sum = s.rolling(window=3).sum()

Calculate rolling mean and sum with a window size of 3 on a simple series.

Execution Table

Step	Window Position	Window Values	Rolling Mean	Rolling Sum
1	Index 0	[1]	NaN	NaN
2	Index 1	[1, 2]	NaN	NaN
3	Index 2	[1, 2, 3]	2.0	6.0
4	Index 3	[2, 3, 4]	3.0	9.0
5	Index 4	[3, 4, 5]	4.0	12.0
6	End	No more windows	Stop	Stop

💡 Window cannot slide beyond index 4 because window size 3 requires 3 values.

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4	Final
s	[1, 2, 3, 4, 5]	[1, 2, 3, 4, 5]	[1, 2, 3, 4, 5]	[1, 2, 3, 4, 5]	[1, 2, 3, 4, 5]	[1, 2, 3, 4, 5]
rolling_mean	[NaN, NaN, NaN, NaN, NaN]	[NaN, NaN, NaN, NaN, NaN]	[NaN, NaN, 2.0, NaN, NaN]	[NaN, NaN, 2.0, 3.0, NaN]	[NaN, NaN, 2.0, 3.0, 4.0]	[NaN, NaN, 2.0, 3.0, 4.0]
rolling_sum	[NaN, NaN, NaN, NaN, NaN]	[NaN, NaN, NaN, NaN, NaN]	[NaN, NaN, 6.0, NaN, NaN]	[NaN, NaN, 6.0, 9.0, NaN]	[NaN, NaN, 6.0, 9.0, 12.0]	[NaN, NaN, 6.0, 9.0, 12.0]

Key Moments - 2 Insights

Why are the first two rolling mean and sum values NaN?

Because the window size is 3, the calculation starts only when there are 3 values in the window. Rows 1 and 2 in the execution_table show windows with less than 3 values, so results are NaN.

What happens when the window slides beyond the last index?

The window cannot slide beyond index 4 because it needs 3 values. The execution_table exit_note and step 6 show that the calculation stops there.

Visual Quiz - 3 Questions

Look at the execution_table at Step 3. What is the rolling mean value?

A 3.0

B 2.0

C NaN

D 6.0

💡 Hint

Check the 'Rolling Mean' column at Step 3 in the execution_table.

At which step does the rolling sum first become a number instead of NaN?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Rolling Sum' column in the execution_table and find the first non-NaN value.

If the window size changed to 2, how would the rolling mean at Index 1 change?

A It would be 1.5

B It would be NaN

C It would be 2.0

D It would be 3.0

💡 Hint

With window 2, rolling mean at Index 1 uses values at Index 0 and 1. Check variable_tracker for values.

Concept Snapshot

Rolling mean and sum in pandas:
- Use s.rolling(window=n).mean() or .sum()
- Window slides over data series
- Results start after window fills
- Early positions show NaN
- Useful for smoothing or cumulative sums

Full Transcript

This visual execution shows how rolling mean and sum work in pandas. We start with a series of numbers and pick a window size of 3. The window slides over the data, and at each position, we calculate the mean and sum of the values inside the window. The first two positions have less than 3 values, so results are NaN. From the third position, the rolling mean and sum are calculated and stored. The window cannot slide beyond the last index where it can hold 3 values, so the process stops. This helps smooth data or find sums over moving windows.

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