Expanding window operations in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the expanding sum of the 'sales' column.

Pandas

df['expanding_sum'] = df['sales'].[1]().sum()

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

Acumsum

Brolling

Cexpanding

Dgroupby

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling() instead of expanding() causes a fixed window sum.

Using cumsum() directly does not use expanding window object.

The expanding() method creates an expanding window object that includes all data up to the current row. Using sum() on it calculates the cumulative sum over this expanding window.

2fill in blank

medium

Complete the code to calculate the expanding mean of the 'temperature' column.

Pandas

mean_temp = df['temperature'].[1]().mean()

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

Acumsum

Bexpanding

Crolling

Dgroupby

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling() instead of expanding() gives a fixed window mean.

Using cumsum() does not calculate mean.

The expanding() method creates a window that grows with each row, and mean() calculates the average over this window.

3fill in blank

hard

Fix the error in the code to calculate the expanding maximum of the 'price' column.

Pandas

max_price = df['price'].[1]().max()

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

Aexpanding

Bgroupby

Ccumsum

Drolling

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling() gives max over fixed window, not expanding.

Using cumsum() is for sums, not max.

The expanding() method is needed to create an expanding window before applying max(). Using cumsum() or groupby() here is incorrect.

4fill in blank

hard

Fill both blanks to create a dictionary of expanding minimum values for words longer than 4 characters.

Pandas

expanding_min = {word: df['value'].[1]().min() for word in words if len(word) [2] 4}

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

Aexpanding

B>

C<

Drolling

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling instead of expanding for the window.

Using < instead of > for filtering word length.

The expanding() method creates the expanding window for the 'value' column. The condition len(word) > 4 filters words longer than 4 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary with keys as uppercase words and values as expanding mean of 'score' for scores greater than 50.

Pandas

result = [1]: df['score'].[2]().mean() for word in words if df['score'] [3] 50

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

Aword.upper()

Bexpanding

C>

Drolling

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling instead of expanding for the window.

Using < instead of > for filtering scores.

Not converting words to uppercase for keys.

The key is the uppercase version of each word using word.upper(). The expanding window is created with expanding(), and the filter uses > 50 to select scores greater than 50.

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