Rolling window calculations in Data Analysis Python - Mini Project: Build & Apply

Rolling Window Calculations

📖 Scenario: You work as a data analyst for a small store. You have daily sales data for a week. You want to understand how sales change over time by calculating the average sales over a rolling window of 3 days.

🎯 Goal: Build a small program that calculates the 3-day rolling average of daily sales using Python and pandas.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data for 7 days.

Set a window size variable for the rolling calculation.

Calculate the rolling average of sales using the window size.

Print the resulting rolling average values.

💡 Why This Matters

🌍 Real World

Rolling window calculations help smooth out short-term fluctuations in data, making it easier to see trends. This is useful in sales, finance, weather data, and many other fields.

💼 Career

Data analysts and scientists often use rolling calculations to prepare data for reports and decision-making, helping businesses understand patterns and make better choices.

1

Create the sales data

Import pandas as pd and create a DataFrame called sales_data with two columns: 'day' containing numbers 1 to 7, and 'sales' containing these exact values: 100, 120, 130, 90, 110, 115, 105.

Data Analysis Python

# Import pandas and create the sales_data DataFrame
# Your code here

Hint

Use pd.DataFrame with a dictionary to create the data.

2

Set the rolling window size

Create a variable called window_size and set it to the integer 3 to represent the 3-day window.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 110, 115, 105]
})
# Set the window_size variable to 3
# Your code here

Hint

Just assign the number 3 to window_size.

3

Calculate the rolling average

Create a new column in sales_data called 'rolling_avg' that contains the rolling average of the 'sales' column using the window_size. Use the pandas rolling() method followed by mean().

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 110, 115, 105]
})
window_size = 3
# Calculate the rolling average and store it in 'rolling_avg' column
# Your code here

Hint

Use sales_data['sales'].rolling(window=window_size).mean() to get the rolling average.

4

Print the rolling average

Print the 'rolling_avg' column from the sales_data DataFrame to see the rolling averages.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5, 6, 7],
    'sales': [100, 120, 130, 90, 110, 115, 105]
})
window_size = 3
sales_data['rolling_avg'] = sales_data['sales'].rolling(window=window_size).mean()
# Print the 'rolling_avg' column
# Your code here

Hint

Use print(sales_data['rolling_avg']) to display the rolling averages.

Practice

1. What does the rolling(window=3).mean() function do on a data series?

easy

A. Calculates the average of every group of 3 consecutive values in the series

B. Calculates the sum of all values in the series

C. Returns the maximum value in the entire series

D. Sorts the series in ascending order

Solution

1. Step 1: Understand rolling window

   The rolling function with window=3 looks at every group of 3 consecutive values in the series.

2. Step 2: Apply mean function

   For each group of 3 values, mean() calculates their average, producing a smoothed series.

3. Final Answer:

   Calculates the average of every group of 3 consecutive values in the series -> Option A

4. Quick Check:

   Rolling mean = average over window [OK]

Hint: Rolling mean smooths data by averaging fixed-size groups [OK]

Common Mistakes:

• Thinking it sums all values instead of averaging groups
• Confusing rolling mean with overall mean
• Assuming it sorts or finds max values

2. Which of the following is the correct syntax to calculate a rolling sum with a window size of 4 on a pandas Series named data?

easy

A. data.rolling(4).sum

B. rolling(data, window=4).sum()

C. data.rolling(window=4).sum()

D. data.rolling_sum(window=4)

Solution

1. Step 1: Recall pandas rolling syntax

   The rolling function requires the window size as a named argument: rolling(window=4).

2. Step 2: Apply sum function correctly

   After rolling, call sum() to get the rolling sum over each window.

3. Final Answer:

   data.rolling(window=4).sum() -> Option C

4. Quick Check:

   Correct rolling sum syntax uses rolling(window=) [OK]

Hint: Use rolling(window=) with function, not rolling_sum() [OK]

Common Mistakes:

• Omitting 'window=' keyword argument
• Using non-existent rolling_sum method
• Calling rolling as a standalone function

3. Given the pandas Series data = pd.Series([2, 4, 6, 8, 10]), what is the output of data.rolling(window=2).sum()?

medium

A. [2, 6, 12, 20, 30]

B. [2, 4, 6, 8, 10]

C. [NaN, 3, 5, 7, 9]

D. [NaN, 6, 10, 14, 18]

Solution

1. Step 1: Understand rolling sum with window=2

   Each value is the sum of the current and previous value. The first value has no previous, so result is NaN.

2. Step 2: Calculate sums step-by-step

   Positions: NaN (no previous), 2+4=6, 4+6=10, 6+8=14, 8+10=18.

3. Final Answer:

   [NaN, 6, 10, 14, 18] -> Option D

4. Quick Check:

   Rolling sum window=2 sums pairs [OK]

Hint: First rolling sum is NaN, then sums of pairs follow [OK]

Common Mistakes:

• Ignoring NaN for first value
• Confusing sum with mean
• Adding values cumulatively instead of rolling pairs

4. What is wrong with this code snippet for calculating a rolling mean with window size 3 on a pandas DataFrame column df['values']?

rolling_means = df.rolling(3).mean()['values']

medium

A. The window size must be passed as a string, not an integer

B. The rolling function should be applied on the column, not the whole DataFrame

C. The mean() function cannot be used after rolling()

D. The syntax is correct and will work without errors

Solution

1. Step 1: Identify rolling application target

   Rolling should be applied on the Series (column) directly, not on the entire DataFrame.

2. Step 2: Correct the code

   Use df['values'].rolling(3).mean() to get rolling mean of that column.

3. Final Answer:

   The rolling function should be applied on the column, not the whole DataFrame -> Option B

4. Quick Check:

   Apply rolling on Series, not DataFrame for single column [OK]

Hint: Apply rolling on the column Series, not the whole DataFrame [OK]

Common Mistakes:

• Applying rolling on DataFrame then selecting column
• Passing window size as string
• Thinking mean() is invalid after rolling

5. You have daily sales data in a pandas Series sales. You want to calculate a 7-day rolling average but only for days where the window is fully available (no partial windows). Which parameter should you set in rolling() to achieve this?

hard

A. min_periods=7

B. center=True

C. window=7, min_periods=1

D. window=7, center=False

Solution

1. Step 1: Understand min_periods parameter

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

2. Step 2: Set min_periods=7 for full windows only

   Setting min_periods=7 means rolling average is calculated only when all 7 days are present, avoiding partial windows.

3. Final Answer:

   min_periods=7 -> Option A

4. Quick Check:

   min_periods=window size ensures full window data [OK]

Hint: Use min_periods equal to window size for full windows only [OK]

Common Mistakes:

• Using min_periods=1 allows partial windows
• Confusing center parameter with min_periods
• Not setting min_periods at all