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()

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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}

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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.