Expanding window operations in Pandas - Mini Project: Build & Apply

Expanding Window Operations with pandas

📖 Scenario: You work as a data analyst for a small online store. You have daily sales data and want to understand how the total sales grow over time. Instead of just looking at daily sales, you want to see the running total of sales up to each day.

🎯 Goal: Build a pandas DataFrame with daily sales data, then use expanding window operations to calculate the running total of sales for each day.

📋 What You'll Learn

Create a pandas DataFrame with exact daily sales data

Create a variable to hold the sales column name

Use pandas expanding() method to calculate running total sales

Print the resulting DataFrame with the running total column

💡 Why This Matters

🌍 Real World

Expanding window operations help analyze cumulative trends in sales, stock prices, or any time series data.

💼 Career

Data analysts and scientists use expanding windows to summarize data over time and detect patterns or growth.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with two columns: 'day' and 'sales'. Use these exact values: days from 1 to 5, and sales as 100, 150, 200, 130, 170.

Pandas

# Create the sales_data DataFrame with 'day' and 'sales' columns
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the exact lists for 'day' and 'sales'.

2

Set the sales column name

Create a variable called sales_column and set it to the string 'sales'.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5],
    'sales': [100, 150, 200, 130, 170]
})

# Create the sales_column variable
# Your code here

Hint

Just assign the string 'sales' to the variable sales_column.

3

Calculate running total sales using expanding window

Create a new column in sales_data called 'running_total' by applying the pandas expanding() method on the sales_column and then calling sum().

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5],
    'sales': [100, 150, 200, 130, 170]
})

sales_column = 'sales'

# Calculate running total sales using expanding sum
# Your code here

Hint

Use sales_data[sales_column].expanding().sum() to get the running total and assign it to a new column.

4

Print the DataFrame with running totals

Write a print() statement to display the sales_data DataFrame with the new 'running_total' column.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'day': [1, 2, 3, 4, 5],
    'sales': [100, 150, 200, 130, 170]
})

sales_column = 'sales'

sales_data['running_total'] = sales_data[sales_column].expanding().sum()

# Print the sales_data DataFrame
# Your code here

Hint

Use print(sales_data) to show the DataFrame with the running totals.

Practice

1. What does an expanding window operation in pandas do?

easy

A. Sorts data in ascending order

B. Calculates values only for a fixed-size sliding window

C. Filters data based on a condition

D. Calculates cumulative values including all previous data points

Solution

1. Step 1: Understand expanding window concept

   Expanding window operations include all data points from the start up to the current point.

2. Step 2: Compare with other window types

   Unlike rolling windows, expanding windows grow with each step, accumulating more data.

3. Final Answer:

   Calculates cumulative values including all previous data points -> Option D

4. Quick Check:

   Expanding window = cumulative calculation [OK]

Hint: Expanding means cumulative from start to current [OK]

Common Mistakes:

• Confusing expanding with rolling window
• Thinking it filters data
• Assuming it sorts data

2. Which of the following is the correct syntax to create an expanding window object on a pandas Series s?

easy

A. s.window()

B. s.expanding()

C. s.cumulative()

D. s.rolling()

Solution

1. Step 1: Recall pandas expanding syntax

   The method to create an expanding window object is expanding() called on a Series or DataFrame.

2. Step 2: Eliminate incorrect options

   rolling() is for rolling windows, cumulative() and window() do not exist as methods.

3. Final Answer:

   s.expanding() -> Option B

4. Quick Check:

   Use .expanding() for expanding windows [OK]

Hint: Use .expanding() method on Series/DataFrame [OK]

Common Mistakes:

• Using .rolling() instead of .expanding()
• Trying non-existent methods like .cumulative()
• Confusing window types

3. Given the pandas Series s = pd.Series([2, 4, 6, 8]), what is the output of s.expanding(min_periods=2).mean()?

medium

A. [NaN, 3.0, 4.0, 5.0]

B. [2.0, 3.0, 4.0, 5.0]

C. [NaN, 4.0, 6.0, 8.0]

D. [2.0, 4.0, 6.0, 8.0]

Solution

1. Step 1: Understand min_periods effect

   With min_periods=2, results start from the second element; first is NaN.

2. Step 2: Calculate expanding mean values

   Means are: index 1: (2+4)/2=3.0, index 2: (2+4+6)/3=4.0, index 3: (2+4+6+8)/4=5.0.

3. Final Answer:

   [NaN, 3.0, 4.0, 5.0] -> Option A

4. Quick Check:

   Expanding mean with min_periods=2 starts NaN then cumulative means [OK]

Hint: min_periods controls when output starts (NaN before) [OK]

Common Mistakes:

• Ignoring min_periods and showing first value
• Calculating rolling mean instead of expanding mean
• Miscomputing cumulative averages

4. What is wrong with this code snippet?

import pandas as pd
s = pd.Series([1, 2, 3])
result = s.expanding(min_periods=-1).sum()

medium

A. sum() cannot be used after expanding()

B. expanding() method does not exist for Series

C. min_periods cannot be negative; it must be >= 0

D. The Series must be converted to DataFrame first

Solution

1. Step 1: Check min_periods parameter rules

   min_periods must be >= 0; negative is invalid and causes an error.

2. Step 2: Verify method usage

   expanding() exists for Series, and sum() is valid after expanding().

3. Final Answer:

   min_periods cannot be negative; it must be >= 0 -> Option C

4. Quick Check:

   min_periods ≥ 0 required [OK]

Hint: min_periods must be >= 0, never negative [OK]

Common Mistakes:

• Setting min_periods to a negative value causing errors
• Thinking expanding() is unavailable for Series
• Misusing sum() after expanding()

5. You have daily sales data in a pandas DataFrame df with a column 'sales'. You want to calculate a running average of sales but only start showing results after at least 3 days of data. Which code correctly achieves this using expanding window operations?

hard

A. df['sales'].expanding(min_periods=3).mean()

B. df['sales'].rolling(window=3).mean()

C. df['sales'].expanding(min_periods=1).mean()

D. df['sales'].cumsum() / 3

Solution

1. Step 1: Understand requirement for running average with minimum data

   Expanding with min_periods=3 starts calculating mean only after 3 data points.

2. Step 2: Evaluate options

   df['sales'].expanding(min_periods=3).mean() uses expanding with min_periods=3 correctly. df['sales'].rolling(window=3).mean() uses rolling window, which is different. df['sales'].expanding(min_periods=1).mean() starts from 1 data point, not 3. df['sales'].cumsum() / 3 divides cumulative sum by 3 always, which is incorrect.

3. Final Answer:

   df['sales'].expanding(min_periods=3).mean() -> Option A

4. Quick Check:

   Expanding with min_periods=3 for running average [OK]

Hint: Use expanding(min_periods=3) for running average after 3 days [OK]

Common Mistakes:

• Using rolling instead of expanding
• Setting min_periods too low
• Dividing cumulative sum by fixed number