Why window functions matter in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the rolling average of sales over 3 days.

Pandas

df['rolling_avg'] = df['sales'].[1](3).mean()

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

Arolling

Bgroupby

Csort_values

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using groupby instead of rolling will group data but not create a moving window.

Using cumsum adds values cumulatively but does not average over a window.

The rolling function creates a rolling window to calculate statistics like the mean over a fixed number of rows.

2fill in blank

medium

Complete the code to calculate the cumulative sum of sales.

Pandas

df['cumulative_sales'] = df['sales'].[1]()

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

Arolling

Bcumsum

Cmean

Dshift

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling.mean() calculates a moving average, not a cumulative sum.

Using shift() moves data but does not sum values.

The cumsum function calculates the cumulative sum, adding each value to the sum of all previous values.

3fill in blank

hard

Fix the error in the code to calculate the expanding maximum of sales.

Pandas

df['expanding_max'] = df['sales'].[1]().max()

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

Arolling

Bgroupby

Cexpanding

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling().max() calculates a fixed window max, not expanding.

Using groupby() groups data but does not create expanding windows.

The expanding function creates a window that expands from the start to the current row, useful for cumulative calculations like max.

4fill in blank

hard

Fill in the blank to calculate the difference between current sales and the previous day's sales.

Pandas

df['sales_diff'] = df['sales'].[1]()

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

Adiff

Bcumsum

Cshift

Drolling

Attempts:

3 left

💡 Hint

Common Mistakes

Using shift() moves the values but does not compute the difference.

Using cumsum() computes cumulative sums, not differences.

Using rolling requires a window size and computes statistics over windows.

The diff() function calculates the difference between the current value and the previous row's value.

5fill in blank

hard

Fill all three blanks to create a dictionary of words and their lengths for words longer than 4 characters.

Pandas

lengths = { [1]: [2] for [3] in words if len([3]) > 4 }

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

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently causes errors.

Using len(word) as a key instead of value reverses the dictionary.

This dictionary comprehension uses word as the variable, maps each word to its length using len(word), and iterates over word in the list words.